Apache HBase ™ Reference Guide

User Manual:

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Apache HBase ™ Reference Guide
Apache HBase Team
Version 3.0.0-SNAPSHOT
Contents
Preface. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê1
Getting Started. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê3
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê4
2. Quick Start - Standalone HBase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê5
Apache HBase Configuration. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê18
3. Configuration Files . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê19
4. Basic Prerequisites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê21
5. HBase run modes: Standalone and Distributed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê27
6. Running and Confirming Your Installation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê31
7. Default Configuration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê32
8. Example Configurations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê73
9. The Important Configurations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê75
10. Dynamic Configuration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê83
Upgrading. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê86
11. HBase version number and compatibility. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê87
12. Rollback . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê92
13. Upgrade Paths . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê96
The Apache HBase Shell . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê107
14. Scripting with Ruby . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê108
15. Running the Shell in Non-Interactive Mode . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê109
16. HBase Shell in OS Scripts. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê110
17. Read HBase Shell Commands from a Command File . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê112
18. Passing VM Options to the Shell. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê114
19. Overriding configuration starting the HBase Shell. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê115
20. Shell Tricks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê116
Data Model. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê122
21. Conceptual View . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê123
22. Physical View . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê125
23. Namespace . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê126
24. Table . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê128
25. Row . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê129
26. Column Family . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê130
27. Cells. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê131
28. Data Model Operations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê132
29. Versions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê134
30. Sort Order . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê139
31. Column Metadata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê140
32. Joins . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê141
33. ACID . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê142
HBase and Schema Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê143
34. Schema Creation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê144
35. Table Schema Rules Of Thumb . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê145
RegionServer Sizing Rules of Thumb . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê146
36. On the number of column families. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê147
37. Rowkey Design. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê148
38. Number of Versions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê155
39. Supported Datatypes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê156
40. Joins . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê157
41. Time To Live (TTL) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê158
42. Keeping Deleted Cells . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê159
43. Secondary Indexes and Alternate Query Paths . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê163
44. Constraints . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê165
45. Schema Design Case Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê166
46. Operational and Performance Configuration Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê176
47. Special Cases. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê179
HBase and MapReduce . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê180
48. HBase, MapReduce, and the CLASSPATH . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê181
49. MapReduce Scan Caching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê186
50. Bundled HBase MapReduce Jobs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê187
51. HBase as a MapReduce Job Data Source and Data Sink. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê188
52. Writing HFiles Directly During Bulk Import. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê189
53. RowCounter Example. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê190
54. Map-Task Splitting. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê191
55. HBase MapReduce Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê192
56. Accessing Other HBase Tables in a MapReduce Job . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê199
57. Speculative Execution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê200
58. Cascading. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê201
Securing Apache HBase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê202
59. Using Secure HTTP (HTTPS) for the Web UI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê203
60. Using SPNEGO for Kerberos authentication with Web UIs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê204
61. Secure Client Access to Apache HBase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê206
62. Simple User Access to Apache HBase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê213
63. Securing Access to HDFS and ZooKeeper . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê216
64. Securing Access To Your Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê218
65. Security Configuration Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê244
Architecture. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê247
66. Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê248
67. Catalog Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê250
68. Client. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê251
69. Client Request Filters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê255
70. Master . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê261
71. RegionServer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê264
72. Regions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê284
73. Bulk Loading. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê311
74. HDFS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê314
75. Timeline-consistent High Available Reads . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê315
76. Storing Medium-sized Objects (MOB). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê327
In-memory Compaction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê332
77. Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê333
78. Enabling. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê334
Backup and Restore . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê336
79. Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê337
80. Terminology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê338
81. Planning. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê339
82. First-time configuration steps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê341
83. Backup and Restore commands. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê343
84. Administration of Backup Images. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê349
85. Configuration keys . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê352
86. Best Practices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê353
87. Scenario: Safeguarding Application Datasets on Amazon S3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê355
88. Security of Backup Data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê358
89. Technical Details of Incremental Backup and Restore. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê359
90. A Warning on File System Growth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê360
91. Capacity Planning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê361
92. Limitations of the Backup and Restore Utility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê363
Synchronous Replication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê365
93. Background. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê366
94. Design. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê367
95. Operation and maintenance. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê368
Apache HBase APIs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê370
96. Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê371
Apache HBase External APIs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê373
97. REST . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê374
98. Thrift. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê385
99. C/C++ Apache HBase Client . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê386
100. Using Java Data Objects (JDO) with HBase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê387
101. Scala . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê390
102. Jython. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê392
Thrift API and Filter Language . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê395
103. Filter Language . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê396
HBase and Spark . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê402
104. Basic Spark . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê403
105. Spark Streaming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê406
106. Bulk Load . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê408
107. SparkSQL/DataFrames . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê412
Apache HBase Coprocessors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê419
108. Coprocessor Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê420
109. Types of Coprocessors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê421
110. Loading Coprocessors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê423
111. Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê428
112. Guidelines For Deploying A Coprocessor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê434
113. Restricting Coprocessor Usage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê436
Apache HBase Performance Tuning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê437
114. Operating System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê438
115. Network. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê439
116. Java . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê441
117. HBase Configurations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê442
118. ZooKeeper . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê446
119. Schema Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê447
120. HBase General Patterns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê451
121. Writing to HBase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê452
122. Reading from HBase. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê455
123. Deleting from HBase. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê460
124. HDFS. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê461
125. Amazon EC2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê463
126. Collocating HBase and MapReduce. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê464
127. Case Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê465
Troubleshooting and Debugging Apache HBase. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê466
128. General Guidelines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê467
129. Logs. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê468
130. Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê472
131. Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê473
132. Client . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê482
133. MapReduce . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê486
134. NameNode . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê488
135. Network. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê491
136. RegionServer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê492
137. Master . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê501
138. ZooKeeper . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê503
139. Amazon EC2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê504
140. HBase and Hadoop version issues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê505
141. HBase and HDFS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê506
142. Running unit or integration tests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê509
143. Case Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê510
144. Cryptographic Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê511
145. Operating System Specific Issues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê512
146. JDK Issues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê513
Apache HBase Case Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê514
147. Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê515
148. Schema Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê516
149. Performance/Troubleshooting. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê517
Apache HBase Operational Management. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê521
150. HBase Tools and Utilities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê522
151. Region Management. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê550
152. Node Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê551
153. HBase Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê556
154. HBase Monitoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê561
155. Cluster Replication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê567
156. Running Multiple Workloads On a Single Cluster. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê580
157. HBase Backup. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê588
158. HBase Snapshots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê590
159. Storing Snapshots in Microsoft Azure Blob Storage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê594
160. Capacity Planning and Region Sizing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê595
161. Table Rename. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê599
162. RegionServer Grouping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê600
163. Region Normalizer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê604
Building and Developing Apache HBase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê609
164. Getting Involved . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê610
165. Apache HBase Repositories. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê613
166. IDEs. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê614
167. Building Apache HBase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê617
168. Releasing Apache HBase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê621
169. Voting on Release Candidates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê629
170. Announcing Releases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê630
171. Generating the HBase Reference Guide. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê631
172. Updating hbase.apache.org. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê632
173. Tests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê633
174. Developer Guidelines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê647
Unit Testing HBase Applications. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê663
175. JUnit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê664
176. Mockito . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê666
177. MRUnit. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê668
178. Integration Testing with an HBase Mini-Cluster . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê670
Protobuf in HBase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê672
179. Protobuf. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê673
Procedure Framework (Pv2): HBASE-12439 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê675
180. Procedures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê676
181. Subprocedures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê679
182. ProcedureExecutor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê680
183. Nonces . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê681
184. Wait/Wake/Suspend/Yield . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê682
185. Locking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê683
186. Procedure Types . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê684
187. References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê685
AMv2 Description for Devs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê686
188. Background. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê687
189. New System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê688
190. Procedures Detail . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê689
191. UI. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê691
192. Logging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê692
193. Implementation Notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê693
194. New Configs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê694
195. Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê695
ZooKeeper . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê696
196. Using existing ZooKeeper ensemble. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê698
197. SASL Authentication with ZooKeeper . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê699
Community . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê706
198. Decisions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê707
199. Community Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê708
200. Commit Message format . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê709
Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê710
Appendix A: Contributing to Documentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê711
Appendix B: FAQ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê722
Appendix C: Access Control Matrix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê725
Appendix D: Compression and Data Block Encoding In HBase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê731
Appendix E: SQL over HBase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê742
Appendix F: YCSB. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê743
Appendix G: HFile format . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê744
Appendix H: Other Information About HBase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê753
Appendix I: HBase History . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê754
Appendix J: HBase and the Apache Software Foundation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê755
Appendix K: Apache HBase Orca . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê756
Appendix L: Enabling Dapper-like Tracing in HBase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê757
201. Client Modifications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê759
202. Tracing from HBase Shell . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê760
Appendix M: 0.95 RPC Specification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê761
Appendix N: Known Incompatibilities Among HBase Versions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê765
203. HBase 2.0 Incompatible Changes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ê766
Preface
This is the official reference guide for the HBase version it ships with.
Herein you will find either the definitive documentation on an HBase topic as of its standing when
the referenced HBase version shipped, or it will point to the location in Javadoc or JIRA where the
pertinent information can be found.
About This Guide
This reference guide is a work in progress. The source for this guide can be found in the
_src/main/asciidoc directory of the HBase source. This reference guide is marked up using AsciiDoc
from which the finished guide is generated as part of the 'site' build target. Run
mvn site
to generate this documentation. Amendments and improvements to the documentation are
welcomed. Click this link to file a new documentation bug against Apache HBase with some values
pre-selected.
Contributing to the Documentation
For an overview of AsciiDoc and suggestions to get started contributing to the documentation, see
the relevant section later in this documentation.
Heads-up if this is your first foray into the world of distributed computing…
If this is your first foray into the wonderful world of Distributed Computing, then you are in for
some interesting times. First off, distributed systems are hard; making a distributed system hum
requires a disparate skillset that spans systems (hardware and software) and networking.
Your cluster’s operation can hiccup because of any of a myriad set of reasons from bugs in HBase
itself through misconfigurations — misconfiguration of HBase but also operating system
misconfigurations — through to hardware problems whether it be a bug in your network card
drivers or an underprovisioned RAM bus (to mention two recent examples of hardware issues that
manifested as "HBase is slow"). You will also need to do a recalibration if up to this your computing
has been bound to a single box. Here is one good starting point: Fallacies of Distributed Computing.
That said, you are welcome.
It’s a fun place to be.
Yours, the HBase Community.
Reporting Bugs
Please use JIRA to report non-security-related bugs.
To protect existing HBase installations from new vulnerabilities, please do not use JIRA to report
security-related bugs. Instead, send your report to the mailing list private@hbase.apache.org, which
allows anyone to send messages, but restricts who can read them. Someone on that list will contact
you to follow up on your report.
1
Support and Testing Expectations
The phrases /supported/, /not supported/, /tested/, and /not tested/ occur several places throughout
this guide. In the interest of clarity, here is a brief explanation of what is generally meant by these
phrases, in the context of HBase.
Commercial technical support for Apache HBase is provided by many Hadoop
vendors. This is not the sense in which the term /support/ is used in the context of
the Apache HBase project. The Apache HBase team assumes no responsibility for
your HBase clusters, your configuration, or your data.
Supported
In the context of Apache HBase, /supported/ means that HBase is designed to work in the way
described, and deviation from the defined behavior or functionality should be reported as a bug.
Not Supported
In the context of Apache HBase, /not supported/ means that a use case or use pattern is not
expected to work and should be considered an antipattern. If you think this designation should
be reconsidered for a given feature or use pattern, file a JIRA or start a discussion on one of the
mailing lists.
Tested
In the context of Apache HBase, /tested/ means that a feature is covered by unit or integration
tests, and has been proven to work as expected.
Not Tested
In the context of Apache HBase, /not tested/ means that a feature or use pattern may or may not
work in a given way, and may or may not corrupt your data or cause operational issues. It is an
unknown, and there are no guarantees. If you can provide proof that a feature designated as
/not tested/ does work in a given way, please submit the tests and/or the metrics so that other
users can gain certainty about such features or use patterns.
2
Getting Started
3
Chapter 1. Introduction
Quickstart will get you up and running on a single-node, standalone instance of HBase.
4
Chapter 2. Quick Start - Standalone HBase
This section describes the setup of a single-node standalone HBase. A standalone instance has all
HBase daemons — the Master, RegionServers, and ZooKeeper — running in a single JVM persisting
to the local filesystem. It is our most basic deploy profile. We will show you how to create a table in
HBase using the hbase shell CLI, insert rows into the table, perform put and scan operations
against the table, enable or disable the table, and start and stop HBase.
Apart from downloading HBase, this procedure should take less than 10 minutes.
2.1. JDK Version Requirements
HBase requires that a JDK be installed. See Java for information about supported JDK versions.
2.2. Get Started with HBase
Procedure: Download, Configure, and Start HBase in Standalone Mode
1. Choose a download site from this list of Apache Download Mirrors. Click on the suggested top
link. This will take you to a mirror of HBase Releases. Click on the folder named stable and then
download the binary file that ends in .tar.gz to your local filesystem. Do not download the file
ending in src.tar.gz for now.
2. Extract the downloaded file, and change to the newly-created directory.
$ tar xzvf hbase-3.0.0-SNAPSHOT-bin.tar.gz
$ cd hbase-3.0.0-SNAPSHOT/
3. You are required to set the JAVA_HOME environment variable before starting HBase. You can set
the variable via your operating system’s usual mechanism, but HBase provides a central
mechanism, conf/hbase-env.sh. Edit this file, uncomment the line starting with JAVA_HOME, and set
it to the appropriate location for your operating system. The JAVA_HOME variable should be set to
a directory which contains the executable file bin/java. Most modern Linux operating systems
provide a mechanism, such as /usr/bin/alternatives on RHEL or CentOS, for transparently
switching between versions of executables such as Java. In this case, you can set JAVA_HOME to the
directory containing the symbolic link to bin/java, which is usually /usr.
JAVA_HOME=/usr
4. Edit conf/hbase-site.xml, which is the main HBase configuration file. At this time, you need to
specify the directory on the local filesystem where HBase and ZooKeeper write data and
acknowledge some risks. By default, a new directory is created under /tmp. Many servers are
configured to delete the contents of /tmp upon reboot, so you should store the data elsewhere.
The following configuration will store HBase’s data in the hbase directory, in the home directory
of the user called testuser. Paste the <property> tags beneath the <configuration> tags, which
should be empty in a new HBase install.
5
Example 1. Example hbase-site.xml for Standalone HBase
<configuration>
Ê <property>
Ê <name>hbase.rootdir</name>
Ê <value>file:///home/testuser/hbase</value>
Ê </property>
Ê <property>
Ê <name>hbase.zookeeper.property.dataDir</name>
Ê <value>/home/testuser/zookeeper</value>
Ê </property>
Ê <property>
Ê <name>hbase.unsafe.stream.capability.enforce</name>
Ê <value>false</value>
Ê <description>
Ê Controls whether HBase will check for stream capabilities (hflush/hsync).
Ê Disable this if you intend to run on LocalFileSystem, denoted by a
rootdir
Ê with the 'file://' scheme, but be mindful of the NOTE below.
Ê WARNING: Setting this to false blinds you to potential data loss and
Ê inconsistent system state in the event of process and/or node failures.
If
Ê HBase is complaining of an inability to use hsync or hflush it's most
Ê likely not a false positive.
Ê </description>
Ê </property>
</configuration>
You do not need to create the HBase data directory. HBase will do this for you. If you create the
directory, HBase will attempt to do a migration, which is not what you want.
The hbase.rootdir in the above example points to a directory in the local
filesystem. The 'file://' prefix is how we denote local filesystem. You should take
the WARNING present in the configuration example to heart. In standalone
mode HBase makes use of the local filesystem abstraction from the Apache
Hadoop project. That abstraction doesn’t provide the durability promises that
HBase needs to operate safely. This is fine for local development and testing
use cases where the cost of cluster failure is well contained. It is not
appropriate for production deployments; eventually you will lose data.
To home HBase on an existing instance of HDFS, set the hbase.rootdir to point at a directory up on
your instance: e.g. hdfs://namenode.example.org:8020/hbase. For more on this variant, see the
section below on Standalone HBase over HDFS.
1. The bin/start-hbase.sh script is provided as a convenient way to start HBase. Issue the command,
6
and if all goes well, a message is logged to standard output showing that HBase started
successfully. You can use the jps command to verify that you have one running process called
HMaster. In standalone mode HBase runs all daemons within this single JVM, i.e. the HMaster, a
single HRegionServer, and the ZooKeeper daemon. Go to http://localhost:16010 to view the
HBase Web UI.
Java needs to be installed and available. If you get an error indicating that Java
is not installed, but it is on your system, perhaps in a non-standard location,
edit the conf/hbase-env.sh file and modify the JAVA_HOME setting to point to the
directory that contains bin/java on your system.
Procedure: Use HBase For the First Time
1. Connect to HBase.
Connect to your running instance of HBase using the hbase shell command, located in the bin/
directory of your HBase install. In this example, some usage and version information that is
printed when you start HBase Shell has been omitted. The HBase Shell prompt ends with a >
character.
$ ./bin/hbase shell
hbase(main):001:0>
2. Display HBase Shell Help Text.
Type help and press Enter, to display some basic usage information for HBase Shell, as well as
several example commands. Notice that table names, rows, columns all must be enclosed in
quote characters.
3. Create a table.
Use the create command to create a new table. You must specify the table name and the
ColumnFamily name.
hbase(main):001:0> create 'test', 'cf'
0 row(s) in 0.4170 seconds
=> Hbase::Table - test
4. List Information About your Table
Use the list command to confirm your table exists
7
hbase(main):002:0> list 'test'
TABLE
test
1 row(s) in 0.0180 seconds
=> ["test"]
Now use the describe command to see details, including configuration defaults
hbase(main):003:0> describe 'test'
Table test is ENABLED
test
COLUMN FAMILIES DESCRIPTION
{NAME => 'cf', VERSIONS => '1', EVICT_BLOCKS_ON_CLOSE => 'false',
NEW_VERSION_BEHAVIOR => 'false', KEEP_DELETED_CELLS => 'FALSE', CACHE_DATA_ON_WRITE
=>
'false', DATA_BLOCK_ENCODING => 'NONE', TTL => 'FOREVER', MIN_VERSIONS => '0',
REPLICATION_SCOPE => '0', BLOOMFILTER => 'ROW', CACHE_INDEX_ON_WRITE => 'f
alse', IN_MEMORY => 'false', CACHE_BLOOMS_ON_WRITE => 'false',
PREFETCH_BLOCKS_ON_OPEN => 'false', COMPRESSION => 'NONE', BLOCKCACHE => 'true',
BLOCKSIZE
Ê=> '65536'}
1 row(s)
Took 0.9998 seconds
5. Put data into your table.
To put data into your table, use the put command.
hbase(main):003:0> put 'test', 'row1', 'cf:a', 'value1'
0 row(s) in 0.0850 seconds
hbase(main):004:0> put 'test', 'row2', 'cf:b', 'value2'
0 row(s) in 0.0110 seconds
hbase(main):005:0> put 'test', 'row3', 'cf:c', 'value3'
0 row(s) in 0.0100 seconds
Here, we insert three values, one at a time. The first insert is at row1, column cf:a, with a value
of value1. Columns in HBase are comprised of a column family prefix, cf in this example,
followed by a colon and then a column qualifier suffix, a in this case.
6. Scan the table for all data at once.
One of the ways to get data from HBase is to scan. Use the scan command to scan the table for
data. You can limit your scan, but for now, all data is fetched.
8
hbase(main):006:0> scan 'test'
ROW COLUMN+CELL
Êrow1 column=cf:a, timestamp=1421762485768,
value=value1
Êrow2 column=cf:b, timestamp=1421762491785,
value=value2
Êrow3 column=cf:c, timestamp=1421762496210,
value=value3
3 row(s) in 0.0230 seconds
7. Get a single row of data.
To get a single row of data at a time, use the get command.
hbase(main):007:0> get 'test', 'row1'
COLUMN CELL
Êcf:a timestamp=1421762485768, value=value1
1 row(s) in 0.0350 seconds
8. Disable a table.
If you want to delete a table or change its settings, as well as in some other situations, you need
to disable the table first, using the disable command. You can re-enable it using the enable
command.
hbase(main):008:0> disable 'test'
0 row(s) in 1.1820 seconds
hbase(main):009:0> enable 'test'
0 row(s) in 0.1770 seconds
Disable the table again if you tested the enable command above:
hbase(main):010:0> disable 'test'
0 row(s) in 1.1820 seconds
9. Drop the table.
To drop (delete) a table, use the drop command.
hbase(main):011:0> drop 'test'
0 row(s) in 0.1370 seconds
10. Exit the HBase Shell.
9
To exit the HBase Shell and disconnect from your cluster, use the quit command. HBase is still
running in the background.
Procedure: Stop HBase
1. In the same way that the bin/start-hbase.sh script is provided to conveniently start all HBase
daemons, the bin/stop-hbase.sh script stops them.
$ ./bin/stop-hbase.sh
stopping hbase....................
$
2. After issuing the command, it can take several minutes for the processes to shut down. Use the
jps to be sure that the HMaster and HRegionServer processes are shut down.
The above has shown you how to start and stop a standalone instance of HBase. In the next sections
we give a quick overview of other modes of hbase deploy.
2.3. Pseudo-Distributed Local Install
After working your way through quickstart standalone mode, you can re-configure HBase to run in
pseudo-distributed mode. Pseudo-distributed mode means that HBase still runs completely on a
single host, but each HBase daemon (HMaster, HRegionServer, and ZooKeeper) runs as a separate
process: in standalone mode all daemons ran in one jvm process/instance. By default, unless you
configure the hbase.rootdir property as described in quickstart, your data is still stored in /tmp/. In
this walk-through, we store your data in HDFS instead, assuming you have HDFS available. You can
skip the HDFS configuration to continue storing your data in the local filesystem.
Hadoop Configuration
This procedure assumes that you have configured Hadoop and HDFS on your local
system and/or a remote system, and that they are running and available. It also
assumes you are using Hadoop 2. The guide on Setting up a Single Node Cluster in
the Hadoop documentation is a good starting point.
1. Stop HBase if it is running.
If you have just finished quickstart and HBase is still running, stop it. This procedure will create
a totally new directory where HBase will store its data, so any databases you created before will
be lost.
2. Configure HBase.
Edit the hbase-site.xml configuration. First, add the following property which directs HBase to
run in distributed mode, with one JVM instance per daemon.
10
<property>
Ê <name>hbase.cluster.distributed</name>
Ê <value>true</value>
</property>
Next, change the hbase.rootdir from the local filesystem to the address of your HDFS instance,
using the hdfs://// URI syntax. In this example, HDFS is running on the localhost at port 8020.
Be sure to either remove the entry for hbase.unsafe.stream.capability.enforce or set it to true.
<property>
Ê <name>hbase.rootdir</name>
Ê <value>hdfs://localhost:8020/hbase</value>
</property>
You do not need to create the directory in HDFS. HBase will do this for you. If you create the
directory, HBase will attempt to do a migration, which is not what you want.
3. Start HBase.
Use the bin/start-hbase.sh command to start HBase. If your system is configured correctly, the
jps command should show the HMaster and HRegionServer processes running.
4. Check the HBase directory in HDFS.
If everything worked correctly, HBase created its directory in HDFS. In the configuration above,
it is stored in /hbase/ on HDFS. You can use the hadoop fs command in Hadoop’s bin/ directory to
list this directory.
$ ./bin/hadoop fs -ls /hbase
Found 7 items
drwxr-xr-x - hbase users 0 2014-06-25 18:58 /hbase/.tmp
drwxr-xr-x - hbase users 0 2014-06-25 21:49 /hbase/WALs
drwxr-xr-x - hbase users 0 2014-06-25 18:48 /hbase/corrupt
drwxr-xr-x - hbase users 0 2014-06-25 18:58 /hbase/data
-rw-r--r-- 3 hbase users 42 2014-06-25 18:41 /hbase/hbase.id
-rw-r--r-- 3 hbase users 7 2014-06-25 18:41 /hbase/hbase.version
drwxr-xr-x - hbase users 0 2014-06-25 21:49 /hbase/oldWALs
5. Create a table and populate it with data.
You can use the HBase Shell to create a table, populate it with data, scan and get values from it,
using the same procedure as in shell exercises.
6. Start and stop a backup HBase Master (HMaster) server.
11
Running multiple HMaster instances on the same hardware does not make
sense in a production environment, in the same way that running a pseudo-
distributed cluster does not make sense for production. This step is offered for
testing and learning purposes only.
The HMaster server controls the HBase cluster. You can start up to 9 backup HMaster servers,
which makes 10 total HMasters, counting the primary. To start a backup HMaster, use the local-
master-backup.sh. For each backup master you want to start, add a parameter representing the
port offset for that master. Each HMaster uses two ports (16000 and 16010 by default). The port
offset is added to these ports, so using an offset of 2, the backup HMaster would use ports 16002
and 16012. The following command starts 3 backup servers using ports 16002/16012,
16003/16013, and 16005/16015.
$ ./bin/local-master-backup.sh start 2 3 5
To kill a backup master without killing the entire cluster, you need to find its process ID (PID).
The PID is stored in a file with a name like /tmp/hbase-USER-X-master.pid. The only contents of
the file is the PID. You can use the kill -9 command to kill that PID. The following command
will kill the master with port offset 1, but leave the cluster running:
$ cat /tmp/hbase-testuser-1-master.pid |xargs kill -9
7. Start and stop additional RegionServers
The HRegionServer manages the data in its StoreFiles as directed by the HMaster. Generally, one
HRegionServer runs per node in the cluster. Running multiple HRegionServers on the same
system can be useful for testing in pseudo-distributed mode. The local-regionservers.sh
command allows you to run multiple RegionServers. It works in a similar way to the local-
master-backup.sh command, in that each parameter you provide represents the port offset for
an instance. Each RegionServer requires two ports, and the default ports are 16020 and 16030.
Since HBase version 1.1.0, HMaster doesn’t use region server ports, this leaves 10 ports (16020 to
16029 and 16030 to 16039) to be used for RegionServers. For supporting additional
RegionServers, set environment variables HBASE_RS_BASE_PORT and
HBASE_RS_INFO_BASE_PORT to appropriate values before running script local-
regionservers.sh. e.g. With values 16200 and 16300 for base ports, 99 additional RegionServers
can be supported, on a server. The following command starts four additional RegionServers,
running on sequential ports starting at 16022/16032 (base ports 16020/16030 plus 2).
$ .bin/local-regionservers.sh start 2 3 4 5
To stop a RegionServer manually, use the local-regionservers.sh command with the stop
parameter and the offset of the server to stop.
$ .bin/local-regionservers.sh stop 3
12
8. Stop HBase.
You can stop HBase the same way as in the quickstart procedure, using the bin/stop-hbase.sh
command.
2.4. Advanced - Fully Distributed
In reality, you need a fully-distributed configuration to fully test HBase and to use it in real-world
scenarios. In a distributed configuration, the cluster contains multiple nodes, each of which runs
one or more HBase daemon. These include primary and backup Master instances, multiple
ZooKeeper nodes, and multiple RegionServer nodes.
This advanced quickstart adds two more nodes to your cluster. The architecture will be as follows:
Table 1. Distributed Cluster Demo Architecture
Node Name Master ZooKeeper RegionServer
node-a.example.com yes yes no
node-b.example.com backup yes yes
node-c.example.com no yes yes
This quickstart assumes that each node is a virtual machine and that they are all on the same
network. It builds upon the previous quickstart, Pseudo-Distributed Local Install, assuming that the
system you configured in that procedure is now node-a. Stop HBase on node-a before continuing.
Be sure that all the nodes have full access to communicate, and that no firewall
rules are in place which could prevent them from talking to each other. If you see
any errors like no route to host, check your firewall.
Procedure: Configure Passwordless SSH Access
node-a needs to be able to log into node-b and node-c (and to itself) in order to start the daemons.
The easiest way to accomplish this is to use the same username on all hosts, and configure
password-less SSH login from node-a to each of the others.
1. On node-a, generate a key pair.
While logged in as the user who will run HBase, generate a SSH key pair, using the following
command:
$ ssh-keygen -t rsa
If the command succeeds, the location of the key pair is printed to standard output. The default
name of the public key is id_rsa.pub.
2. Create the directory that will hold the shared keys on the other nodes.
On node-b and node-c, log in as the HBase user and create a .ssh/ directory in the user’s home
13
directory, if it does not already exist. If it already exists, be aware that it may already contain
other keys.
3. Copy the public key to the other nodes.
Securely copy the public key from node-a to each of the nodes, by using the scp or some other
secure means. On each of the other nodes, create a new file called .ssh/authorized_keys if it does
not already exist, and append the contents of the id_rsa.pub file to the end of it. Note that you
also need to do this for node-a itself.
$ cat id_rsa.pub >> ~/.ssh/authorized_keys
4. Test password-less login.
If you performed the procedure correctly, you should not be prompted for a password when
you SSH from node-a to either of the other nodes using the same username.
5. Since node-b will run a backup Master, repeat the procedure above, substituting node-b
everywhere you see node-a. Be sure not to overwrite your existing .ssh/authorized_keys files, but
concatenate the new key onto the existing file using the >> operator rather than the > operator.
Procedure: Prepare node-a
node-a will run your primary master and ZooKeeper processes, but no RegionServers. Stop the
RegionServer from starting on node-a.
1. Edit conf/regionservers and remove the line which contains localhost. Add lines with the
hostnames or IP addresses for node-b and node-c.
Even if you did want to run a RegionServer on node-a, you should refer to it by the hostname the
other servers would use to communicate with it. In this case, that would be node-a.example.com.
This enables you to distribute the configuration to each node of your cluster any hostname
conflicts. Save the file.
2. Configure HBase to use node-b as a backup master.
Create a new file in conf/ called backup-masters, and add a new line to it with the hostname for
node-b. In this demonstration, the hostname is node-b.example.com.
3. Configure ZooKeeper
In reality, you should carefully consider your ZooKeeper configuration. You can find out more
about configuring ZooKeeper in zookeeper section. This configuration will direct HBase to start
and manage a ZooKeeper instance on each node of the cluster.
On node-a, edit conf/hbase-site.xml and add the following properties.
14
<property>
Ê <name>hbase.zookeeper.quorum</name>
Ê <value>node-a.example.com,node-b.example.com,node-c.example.com</value>
</property>
<property>
Ê <name>hbase.zookeeper.property.dataDir</name>
Ê <value>/usr/local/zookeeper</value>
</property>
4. Everywhere in your configuration that you have referred to node-a as localhost, change the
reference to point to the hostname that the other nodes will use to refer to node-a. In these
examples, the hostname is node-a.example.com.
Procedure: Prepare node-b and node-c
node-b will run a backup master server and a ZooKeeper instance.
1. Download and unpack HBase.
Download and unpack HBase to node-b, just as you did for the standalone and pseudo-
distributed quickstarts.
2. Copy the configuration files from node-a to node-b.and node-c.
Each node of your cluster needs to have the same configuration information. Copy the contents
of the conf/ directory to the conf/ directory on node-b and node-c.
Procedure: Start and Test Your Cluster
1. Be sure HBase is not running on any node.
If you forgot to stop HBase from previous testing, you will have errors. Check to see whether
HBase is running on any of your nodes by using the jps command. Look for the processes
HMaster, HRegionServer, and HQuorumPeer. If they exist, kill them.
2. Start the cluster.
On node-a, issue the start-hbase.sh command. Your output will be similar to that below.
15
$ bin/start-hbase.sh
node-c.example.com: starting zookeeper, logging to /home/hbuser/hbase-0.98.3-
hadoop2/bin/../logs/hbase-hbuser-zookeeper-node-c.example.com.out
node-a.example.com: starting zookeeper, logging to /home/hbuser/hbase-0.98.3-
hadoop2/bin/../logs/hbase-hbuser-zookeeper-node-a.example.com.out
node-b.example.com: starting zookeeper, logging to /home/hbuser/hbase-0.98.3-
hadoop2/bin/../logs/hbase-hbuser-zookeeper-node-b.example.com.out
starting master, logging to /home/hbuser/hbase-0.98.3-hadoop2/bin/../logs/hbase-
hbuser-master-node-a.example.com.out
node-c.example.com: starting regionserver, logging to /home/hbuser/hbase-0.98.3-
hadoop2/bin/../logs/hbase-hbuser-regionserver-node-c.example.com.out
node-b.example.com: starting regionserver, logging to /home/hbuser/hbase-0.98.3-
hadoop2/bin/../logs/hbase-hbuser-regionserver-node-b.example.com.out
node-b.example.com: starting master, logging to /home/hbuser/hbase-0.98.3-
hadoop2/bin/../logs/hbase-hbuser-master-nodeb.example.com.out
ZooKeeper starts first, followed by the master, then the RegionServers, and finally the backup
masters.
3. Verify that the processes are running.
On each node of the cluster, run the jps command and verify that the correct processes are
running on each server. You may see additional Java processes running on your servers as well,
if they are used for other purposes.
node-a jps Output
$ jps
20355 Jps
20071 HQuorumPeer
20137 HMaster
node-b jps Output
$ jps
15930 HRegionServer
16194 Jps
15838 HQuorumPeer
16010 HMaster
node-c jps Output
$ jps
13901 Jps
13639 HQuorumPeer
13737 HRegionServer
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ZooKeeper Process Name
The HQuorumPeer process is a ZooKeeper instance which is controlled and
started by HBase. If you use ZooKeeper this way, it is limited to one instance
per cluster node and is appropriate for testing only. If ZooKeeper is run outside
of HBase, the process is called QuorumPeer. For more about ZooKeeper
configuration, including using an external ZooKeeper instance with HBase, see
zookeeper section.
4. Browse to the Web UI.
Web UI Port Changes
Web UI Port Changes
In HBase newer than 0.98.x, the HTTP ports used by the HBase Web UI changed from 60010 for
the Master and 60030 for each RegionServer to 16010 for the Master and 16030 for the
RegionServer.
If everything is set up correctly, you should be able to connect to the UI for the Master
http://node-a.example.com:16010/ or the secondary master at http://node-b.example.com:16010/
using a web browser. If you can connect via localhost but not from another host, check your
firewall rules. You can see the web UI for each of the RegionServers at port 16030 of their IP
addresses, or by clicking their links in the web UI for the Master.
5. Test what happens when nodes or services disappear.
With a three-node cluster you have configured, things will not be very resilient. You can still test
the behavior of the primary Master or a RegionServer by killing the associated processes and
watching the logs.
2.5. Where to go next
The next chapter, configuration, gives more information about the different HBase run modes,
system requirements for running HBase, and critical configuration areas for setting up a
distributed HBase cluster.
17
Apache HBase Configuration
This chapter expands upon the Getting Started chapter to further explain configuration of Apache
HBase. Please read this chapter carefully, especially the Basic Prerequisites to ensure that your
HBase testing and deployment goes smoothly. Familiarize yourself with Support and Testing
Expectations as well.
18
Chapter 3. Configuration Files
Apache HBase uses the same configuration system as Apache Hadoop. All configuration files are
located in the conf/ directory, which needs to be kept in sync for each node on your cluster.
HBase Configuration File Descriptions
backup-masters
Not present by default. A plain-text file which lists hosts on which the Master should start a
backup Master process, one host per line.
hadoop-metrics2-hbase.properties
Used to connect HBase Hadoop’s Metrics2 framework. See the Hadoop Wiki entry for more
information on Metrics2. Contains only commented-out examples by default.
hbase-env.cmd and hbase-env.sh
Script for Windows and Linux / Unix environments to set up the working environment for
HBase, including the location of Java, Java options, and other environment variables. The file
contains many commented-out examples to provide guidance.
hbase-policy.xml
The default policy configuration file used by RPC servers to make authorization decisions on
client requests. Only used if HBase security is enabled.
hbase-site.xml
The main HBase configuration file. This file specifies configuration options which override
HBase’s default configuration. You can view (but do not edit) the default configuration file at
docs/hbase-default.xml. You can also view the entire effective configuration for your cluster
(defaults and overrides) in the HBase Configuration tab of the HBase Web UI.
log4j.properties
Configuration file for HBase logging via log4j.
regionservers
A plain-text file containing a list of hosts which should run a RegionServer in your HBase cluster.
By default this file contains the single entry localhost. It should contain a list of hostnames or IP
addresses, one per line, and should only contain localhost if each node in your cluster will run a
RegionServer on its localhost interface.
Checking XML Validity
When you edit XML, it is a good idea to use an XML-aware editor to be sure that
your syntax is correct and your XML is well-formed. You can also use the xmllint
utility to check that your XML is well-formed. By default, xmllint re-flows and
prints the XML to standard output. To check for well-formedness and only print
output if errors exist, use the command xmllint -noout filename.xml.
19
Keep Configuration In Sync Across the Cluster
When running in distributed mode, after you make an edit to an HBase
configuration, make sure you copy the contents of the conf/ directory to all nodes
of the cluster. HBase will not do this for you. Use rsync, scp, or another secure
mechanism for copying the configuration files to your nodes. For most
configurations, a restart is needed for servers to pick up changes. Dynamic
configuration is an exception to this, to be described later below.
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Chapter 4. Basic Prerequisites
This section lists required services and some required system configuration.
Java
The following table summarizes the recommendation of the HBase community wrt deploying on
various Java versions. A symbol is meant to indicate a base level of testing and willingness to
help diagnose and address issues you might run into. Similarly, an entry of or generally means
that should you run into an issue the community is likely to ask you to change the Java
environment before proceeding to help. In some cases, specific guidance on limitations (e.g.
whether compiling / unit tests work, specific operational issues, etc) will also be noted.
Long Term Support JDKs are recommended
HBase recommends downstream users rely on JDK releases that are marked as
Long Term Supported (LTS) either from the OpenJDK project or vendors. As of
March 2018 that means Java 8 is the only applicable version and that the next
likely version to see testing will be Java 11 near Q3 2018.
Table 2. Java support by release line
HBase Version JDK 7 JDK 8 JDK 9 (Non-
LTS)
JDK 10 (Non-
LTS) JDK 11
2.0+ 
HBASE-20264
HBASE-20264
HBASE-21110
1.2+ 
HBASE-20264
HBASE-20264
HBASE-21110
HBase will neither build nor run with Java 6.
You must set JAVA_HOME on each node of your cluster. hbase-env.sh provides a handy
mechanism to do this.
Operating System Utilities
ssh
HBase uses the Secure Shell (ssh) command and utilities extensively to communicate between
cluster nodes. Each server in the cluster must be running ssh so that the Hadoop and HBase
daemons can be managed. You must be able to connect to all nodes via SSH, including the local
node, from the Master as well as any backup Master, using a shared key rather than a password.
You can see the basic methodology for such a set-up in Linux or Unix systems at "Procedure:
Configure Passwordless SSH Access". If your cluster nodes use OS X, see the section, SSH: Setting
up Remote Desktop and Enabling Self-Login on the Hadoop wiki.
DNS
HBase uses the local hostname to self-report its IP address.
NTP
21
The clocks on cluster nodes should be synchronized. A small amount of variation is acceptable,
but larger amounts of skew can cause erratic and unexpected behavior. Time synchronization is
one of the first things to check if you see unexplained problems in your cluster. It is
recommended that you run a Network Time Protocol (NTP) service, or another time-
synchronization mechanism on your cluster and that all nodes look to the same service for time
synchronization. See the Basic NTP Configuration at The Linux Documentation Project (TLDP) to
set up NTP.
Limits on Number of Files and Processes (ulimit)
Apache HBase is a database. It requires the ability to open a large number of files at once. Many
Linux distributions limit the number of files a single user is allowed to open to 1024 (or 256 on
older versions of OS X). You can check this limit on your servers by running the command ulimit
-n when logged in as the user which runs HBase. See the Troubleshooting section for some of the
problems you may experience if the limit is too low. You may also notice errors such as the
following:
2010-04-06 03:04:37,542 INFO org.apache.hadoop.hdfs.DFSClient: Exception
increateBlockOutputStream java.io.EOFException
2010-04-06 03:04:37,542 INFO org.apache.hadoop.hdfs.DFSClient: Abandoning block
blk_-6935524980745310745_1391901
It is recommended to raise the ulimit to at least 10,000, but more likely 10,240, because the value
is usually expressed in multiples of 1024. Each ColumnFamily has at least one StoreFile, and
possibly more than six StoreFiles if the region is under load. The number of open files required
depends upon the number of ColumnFamilies and the number of regions. The following is a
rough formula for calculating the potential number of open files on a RegionServer.
Calculate the Potential Number of Open Files
(StoreFiles per ColumnFamily) x (regions per RegionServer)
For example, assuming that a schema had 3 ColumnFamilies per region with an average of 3
StoreFiles per ColumnFamily, and there are 100 regions per RegionServer, the JVM will open 3 *
3 * 100 = 900 file descriptors, not counting open JAR files, configuration files, and others.
Opening a file does not take many resources, and the risk of allowing a user to open too many
files is minimal.
Another related setting is the number of processes a user is allowed to run at once. In Linux and
Unix, the number of processes is set using the ulimit -u command. This should not be confused
with the nproc command, which controls the number of CPUs available to a given user. Under
load, a ulimit -u that is too low can cause OutOfMemoryError exceptions.
Configuring the maximum number of file descriptors and processes for the user who is running
the HBase process is an operating system configuration, rather than an HBase configuration. It is
also important to be sure that the settings are changed for the user that actually runs HBase. To
see which user started HBase, and that user’s ulimit configuration, look at the first line of the
HBase log for that instance.
22
Example 2. ulimit Settings on Ubuntu
To configure ulimit settings on Ubuntu, edit /etc/security/limits.conf, which is a space-
delimited file with four columns. Refer to the man page for limits.conf for details about the
format of this file. In the following example, the first line sets both soft and hard limits for
the number of open files (nofile) to 32768 for the operating system user with the username
hadoop. The second line sets the number of processes to 32000 for the same user.
hadoop - nofile 32768
hadoop - nproc 32000
The settings are only applied if the Pluggable Authentication Module (PAM) environment is
directed to use them. To configure PAM to use these limits, be sure that the
/etc/pam.d/common-session file contains the following line:
session required pam_limits.so
Linux Shell
All of the shell scripts that come with HBase rely on the GNU Bash shell.
Windows
Running production systems on Windows machines is not recommended.
4.1. Hadoop
The following table summarizes the versions of Hadoop supported with each version of HBase.
Older versions not appearing in this table are considered unsupported and likely missing necessary
features, while newer versions are untested but may be suitable.
Based on the version of HBase, you should select the most appropriate version of Hadoop. You can
use Apache Hadoop, or a vendor’s distribution of Hadoop. No distinction is made here. See the
Hadoop wiki for information about vendors of Hadoop.
Hadoop 2.x is recommended.
Hadoop 2.x is faster and includes features, such as short-circuit reads (see
Leveraging local data), which will help improve your HBase random read profile.
Hadoop 2.x also includes important bug fixes that will improve your overall HBase
experience. HBase does not support running with earlier versions of Hadoop. See
the table below for requirements specific to different HBase versions.
Hadoop 3.x is still in early access releases and has not yet been sufficiently tested
by the HBase community for production use cases.
Use the following legend to interpret this table:
23
Hadoop version support matrix
• = Tested to be fully-functional
• = Known to not be fully-functional
• = Not tested, may/may-not function
HBase-1.2.x,
HBase-1.3.x HBase-1.4.x HBase-2.0.x HBase-2.1.x
Hadoop-2.4.x 
Hadoop-2.5.x 
Hadoop-2.6.0 
Hadoop-2.6.1+ 
Hadoop-2.7.0 
Hadoop-2.7.1+ 
Hadoop-2.8.[0-1] 
Hadoop-2.8.2 
Hadoop-2.8.3+ 
Hadoop-2.9.0 
Hadoop-2.9.1+ 
Hadoop-3.0.[0-2] 
Hadoop-3.0.3+ 
Hadoop-3.1.0 
Hadoop-3.1.1+ 
Hadoop Pre-2.6.1 and JDK 1.8 Kerberos
When using pre-2.6.1 Hadoop versions and JDK 1.8 in a Kerberos environment,
HBase server can fail and abort due to Kerberos keytab relogin error. Late version
of JDK 1.7 (1.7.0_80) has the problem too. Refer to HADOOP-10786 for additional
details. Consider upgrading to Hadoop 2.6.1+ in this case.
Hadoop 2.6.x
Hadoop distributions based on the 2.6.x line must have HADOOP-11710 applied if
you plan to run HBase on top of an HDFS Encryption Zone. Failure to do so will
result in cluster failure and data loss. This patch is present in Apache Hadoop
releases 2.6.1+.
24
Hadoop 2.y.0 Releases
Starting around the time of Hadoop version 2.7.0, the Hadoop PMC got into the
habit of calling out new minor releases on their major version 2 release line as not
stable / production ready. As such, HBase expressly advises downstream users to
avoid running on top of these releases. Note that additionally the 2.8.1 release was
given the same caveat by the Hadoop PMC. For reference, see the release
announcements for Apache Hadoop 2.7.0, Apache Hadoop 2.8.0, Apache Hadoop
2.8.1, and Apache Hadoop 2.9.0.
Hadoop 3.0.x Releases
Hadoop distributions that include the Application Timeline Service feature may
cause unexpected versions of HBase classes to be present in the application
classpath. Users planning on running MapReduce applications with HBase should
make sure that YARN-7190 is present in their YARN service (currently fixed in
2.9.1+ and 3.1.0+).
Hadoop 3.1.0 Release
The Hadoop PMC called out the 3.1.0 release as not stable / production ready. As
such, HBase expressly advises downstream users to avoid running on top of this
release. For reference, see the release announcement for Hadoop 3.1.0.
Replace the Hadoop Bundled With HBase!
Because HBase depends on Hadoop, it bundles Hadoop jars under its lib directory.
The bundled jars are ONLY for use in standalone mode. In distributed mode, it is
critical that the version of Hadoop that is out on your cluster match what is under
HBase. Replace the hadoop jars found in the HBase lib directory with the
equivalent hadoop jars from the version you are running on your cluster to avoid
version mismatch issues. Make sure you replace the jars under HBase across your
whole cluster. Hadoop version mismatch issues have various manifestations.
Check for mismatch if HBase appears hung.
4.1.1. dfs.datanode.max.transfer.threads
An HDFS DataNode has an upper bound on the number of files that it will serve at any one time.
Before doing any loading, make sure you have configured Hadoop’s conf/hdfs-site.xml, setting the
dfs.datanode.max.transfer.threads value to at least the following:
<property>
Ê <name>dfs.datanode.max.transfer.threads</name>
Ê <value>4096</value>
</property>
Be sure to restart your HDFS after making the above configuration.
Not having this configuration in place makes for strange-looking failures. One manifestation is a
25
complaint about missing blocks. For example:
10/12/08 20:10:31 INFO hdfs.DFSClient: Could not obtain block
Ê blk_XXXXXXXXXXXXXXXXXXXXXX_YYYYYYYY from any node: java.io.IOException: No
live nodes
Ê contain current block. Will get new block locations from namenode and
retry...
See also casestudies.max.transfer.threads and note that this property was previously known as
dfs.datanode.max.xcievers (e.g. Hadoop HDFS: Deceived by Xciever).
4.2. ZooKeeper Requirements
ZooKeeper 3.4.x is required.
26
Chapter 5. HBase run modes: Standalone
and Distributed
HBase has two run modes: standalone and distributed. Out of the box, HBase runs in standalone
mode. Whatever your mode, you will need to configure HBase by editing files in the HBase conf
directory. At a minimum, you must edit conf/hbase-env.sh to tell HBase which java to use. In this
file you set HBase environment variables such as the heapsize and other options for the JVM, the
preferred location for log files, etc. Set JAVA_HOME to point at the root of your java install.
5.1. Standalone HBase
This is the default mode. Standalone mode is what is described in the quickstart section. In
standalone mode, HBase does not use HDFS — it uses the local filesystem instead — and it runs all
HBase daemons and a local ZooKeeper all up in the same JVM. ZooKeeper binds to a well known
port so clients may talk to HBase.
5.1.1. Standalone HBase over HDFS
A sometimes useful variation on standalone hbase has all daemons running inside the one JVM but
rather than persist to the local filesystem, instead they persist to an HDFS instance.
You might consider this profile when you are intent on a simple deploy profile, the loading is light,
but the data must persist across node comings and goings. Writing to HDFS where data is replicated
ensures the latter.
To configure this standalone variant, edit your hbase-site.xml setting hbase.rootdir to point at a
directory in your HDFS instance but then set hbase.cluster.distributed to false. For example:
<configuration>
Ê <property>
Ê <name>hbase.rootdir</name>
Ê <value>hdfs://namenode.example.org:8020/hbase</value>
Ê </property>
Ê <property>
Ê <name>hbase.cluster.distributed</name>
Ê <value>false</value>
Ê </property>
</configuration>
5.2. Distributed
Distributed mode can be subdivided into distributed but all daemons run on a single node — a.k.a.
pseudo-distributed — and fully-distributed where the daemons are spread across all nodes in the
cluster. The pseudo-distributed vs. fully-distributed nomenclature comes from Hadoop.
Pseudo-distributed mode can run against the local filesystem or it can run against an instance of
27
the Hadoop Distributed File System (HDFS). Fully-distributed mode can ONLY run on HDFS. See the
Hadoop documentation for how to set up HDFS. A good walk-through for setting up HDFS on
Hadoop 2 can be found at http://www.alexjf.net/blog/distributed-systems/hadoop-yarn-installation-
definitive-guide.
5.2.1. Pseudo-distributed
Pseudo-Distributed Quickstart
A quickstart has been added to the quickstart chapter. See quickstart-pseudo.
Some of the information that was originally in this section has been moved there.
A pseudo-distributed mode is simply a fully-distributed mode run on a single host. Use this HBase
configuration for testing and prototyping purposes only. Do not use this configuration for
production or for performance evaluation.
5.3. Fully-distributed
By default, HBase runs in standalone mode. Both standalone mode and pseudo-distributed mode
are provided for the purposes of small-scale testing. For a production environment, distributed
mode is advised. In distributed mode, multiple instances of HBase daemons run on multiple servers
in the cluster.
Just as in pseudo-distributed mode, a fully distributed configuration requires that you set the
hbase.cluster.distributed property to true. Typically, the hbase.rootdir is configured to point to a
highly-available HDFS filesystem.
In addition, the cluster is configured so that multiple cluster nodes enlist as RegionServers,
ZooKeeper QuorumPeers, and backup HMaster servers. These configuration basics are all
demonstrated in quickstart-fully-distributed.
Distributed RegionServers
Typically, your cluster will contain multiple RegionServers all running on different servers, as well
as primary and backup Master and ZooKeeper daemons. The conf/regionservers file on the master
server contains a list of hosts whose RegionServers are associated with this cluster. Each host is on
a separate line. All hosts listed in this file will have their RegionServer processes started and
stopped when the master server starts or stops.
ZooKeeper and HBase
See the ZooKeeper section for ZooKeeper setup instructions for HBase.
28
Example 3. Example Distributed HBase Cluster
This is a bare-bones conf/hbase-site.xml for a distributed HBase cluster. A cluster that is used
for real-world work would contain more custom configuration parameters. Most HBase
configuration directives have default values, which are used unless the value is overridden in
the hbase-site.xml. See "Configuration Files" for more information.
<configuration>
Ê <property>
Ê <name>hbase.rootdir</name>
Ê <value>hdfs://namenode.example.org:8020/hbase</value>
Ê </property>
Ê <property>
Ê <name>hbase.cluster.distributed</name>
Ê <value>true</value>
Ê </property>
Ê <property>
Ê <name>hbase.zookeeper.quorum</name>
Ê <value>node-a.example.com,node-b.example.com,node-c.example.com</value>
Ê </property>
</configuration>
This is an example conf/regionservers file, which contains a list of nodes that should run a
RegionServer in the cluster. These nodes need HBase installed and they need to use the same
contents of the conf/ directory as the Master server
node-a.example.com
node-b.example.com
node-c.example.com
This is an example conf/backup-masters file, which contains a list of each node that should run
a backup Master instance. The backup Master instances will sit idle unless the main Master
becomes unavailable.
node-b.example.com
node-c.example.com
Distributed HBase Quickstart
See quickstart-fully-distributed for a walk-through of a simple three-node cluster configuration
with multiple ZooKeeper, backup HMaster, and RegionServer instances.
Procedure: HDFS Client Configuration
1. Of note, if you have made HDFS client configuration changes on your Hadoop cluster, such as
configuration directives for HDFS clients, as opposed to server-side configurations, you must
use one of the following methods to enable HBase to see and use these configuration changes:
29
a. Add a pointer to your HADOOP_CONF_DIR to the HBASE_CLASSPATH environment variable in hbase-
env.sh.
b. Add a copy of hdfs-site.xml (or hadoop-site.xml) or, better, symlinks, under
${HBASE_HOME}/conf, or
c. if only a small set of HDFS client configurations, add them to hbase-site.xml.
An example of such an HDFS client configuration is dfs.replication. If for example, you want to
run with a replication factor of 5, HBase will create files with the default of 3 unless you do the
above to make the configuration available to HBase.
30
Chapter 6. Running and Confirming Your
Installation
Make sure HDFS is running first. Start and stop the Hadoop HDFS daemons by running bin/start-
hdfs.sh over in the HADOOP_HOME directory. You can ensure it started properly by testing the put and
get of files into the Hadoop filesystem. HBase does not normally use the MapReduce or YARN
daemons. These do not need to be started.
If you are managing your own ZooKeeper, start it and confirm it’s running, else HBase will start up
ZooKeeper for you as part of its start process.
Start HBase with the following command:
bin/start-hbase.sh
Run the above from the HBASE_HOME directory.
You should now have a running HBase instance. HBase logs can be found in the logs subdirectory.
Check them out especially if HBase had trouble starting.
HBase also puts up a UI listing vital attributes. By default it’s deployed on the Master host at port
16010 (HBase RegionServers listen on port 16020 by default and put up an informational HTTP
server at port 16030). If the Master is running on a host named master.example.org on the default
port, point your browser at http://master.example.org:16010 to see the web interface.
Once HBase has started, see the shell exercises section for how to create tables, add data, scan your
insertions, and finally disable and drop your tables.
To stop HBase after exiting the HBase shell enter
$ ./bin/stop-hbase.sh
stopping hbase...............
Shutdown can take a moment to complete. It can take longer if your cluster is comprised of many
machines. If you are running a distributed operation, be sure to wait until HBase has shut down
completely before stopping the Hadoop daemons.
31
Chapter 7. Default Configuration
7.1. hbase-site.xml and hbase-default.xml
Just as in Hadoop where you add site-specific HDFS configuration to the hdfs-site.xml file, for HBase,
site specific customizations go into the file conf/hbase-site.xml. For the list of configurable
properties, see hbase default configurations below or view the raw hbase-default.xml source file in
the HBase source code at src/main/resources.
Not all configuration options make it out to hbase-default.xml. Some configurations would only
appear in source code; the only way to identify these changes are through code review.
Currently, changes here will require a cluster restart for HBase to notice the change.
7.2. HBase Default Configuration
The documentation below is generated using the default hbase configuration file, hbase-default.xml,
as source.
hbase.tmp.dir
Description
Temporary directory on the local filesystem. Change this setting to point to a location more
permanent than '/tmp', the usual resolve for java.io.tmpdir, as the '/tmp' directory is cleared on
machine restart.
Default
${java.io.tmpdir}/hbase-${user.name}
hbase.rootdir
Description
The directory shared by region servers and into which HBase persists. The URL should be 'fully-
qualified' to include the filesystem scheme. For example, to specify the HDFS directory '/hbase'
where the HDFS instance’s namenode is running at namenode.example.org on port 9000, set this
value to: hdfs://namenode.example.org:9000/hbase. By default, we write to whatever
${hbase.tmp.dir} is set too — usually /tmp — so change this configuration or else all data will be
lost on machine restart.
Default
${hbase.tmp.dir}/hbase
hbase.cluster.distributed
Description
The mode the cluster will be in. Possible values are false for standalone mode and true for
distributed mode. If false, startup will run all HBase and ZooKeeper daemons together in the one
JVM.
Default
false
32
hbase.zookeeper.quorum
Description
Comma separated list of servers in the ZooKeeper ensemble (This config. should have been
named hbase.zookeeper.ensemble). For example,
"host1.mydomain.com,host2.mydomain.com,host3.mydomain.com". By default this is set to
localhost for local and pseudo-distributed modes of operation. For a fully-distributed setup, this
should be set to a full list of ZooKeeper ensemble servers. If HBASE_MANAGES_ZK is set in
hbase-env.sh this is the list of servers which hbase will start/stop ZooKeeper on as part of cluster
start/stop. Client-side, we will take this list of ensemble members and put it together with the
hbase.zookeeper.property.clientPort config. and pass it into zookeeper constructor as the
connectString parameter.
Default
localhost
zookeeper.recovery.retry.maxsleeptime
Description
Max sleep time before retry zookeeper operations in milliseconds, a max time is needed here so
that sleep time won’t grow unboundedly
Default
60000
hbase.local.dir
Description
Directory on the local filesystem to be used as a local storage.
Default
${hbase.tmp.dir}/local/
hbase.master.port
Description
The port the HBase Master should bind to.
Default
16000
hbase.master.info.port
Description
The port for the HBase Master web UI. Set to -1 if you do not want a UI instance run.
Default
16010
hbase.master.info.bindAddress
Description
The bind address for the HBase Master web UI
Default
33
0.0.0.0
hbase.master.logcleaner.plugins
Description
A comma-separated list of BaseLogCleanerDelegate invoked by the LogsCleaner service. These
WAL cleaners are called in order, so put the cleaner that prunes the most files in front. To
implement your own BaseLogCleanerDelegate, just put it in HBase’s classpath and add the fully
qualified class name here. Always add the above default log cleaners in the list.
Default
org.apache.hadoop.hbase.master.cleaner.TimeToLiveLogCleaner,org.apache.hadoop.hbase.master.c
leaner.TimeToLiveProcedureWALCleaner
hbase.master.logcleaner.ttl
Description
How long a WAL remain in the archive ({hbase.rootdir}/oldWALs) directory, after which it will
be cleaned by a Master thread. The value is in milliseconds.
Default
600000
hbase.master.procedurewalcleaner.ttl
Description
How long a Procedure WAL will remain in the archive directory, after which it will be cleaned
by a Master thread. The value is in milliseconds.
Default
604800000
hbase.master.hfilecleaner.plugins
Description
A comma-separated list of BaseHFileCleanerDelegate invoked by the HFileCleaner service. These
HFiles cleaners are called in order, so put the cleaner that prunes the most files in front. To
implement your own BaseHFileCleanerDelegate, just put it in HBase’s classpath and add the fully
qualified class name here. Always add the above default log cleaners in the list as they will be
overwritten in hbase-site.xml.
Default
org.apache.hadoop.hbase.master.cleaner.TimeToLiveHFileCleaner
hbase.master.infoserver.redirect
Description
Whether or not the Master listens to the Master web UI port (hbase.master.info.port) and
redirects requests to the web UI server shared by the Master and RegionServer. Config. makes
sense when Master is serving Regions (not the default).
Default
true
hbase.master.fileSplitTimeout
34
Description
Splitting a region, how long to wait on the file-splitting step before aborting the attempt. Default:
600000. This setting used to be known as hbase.regionserver.fileSplitTimeout in hbase-1.x. Split
is now run master-side hence the rename (If a 'hbase.master.fileSplitTimeout' setting found, will
use it to prime the current 'hbase.master.fileSplitTimeout' Configuration.
Default
600000
hbase.regionserver.port
Description
The port the HBase RegionServer binds to.
Default
16020
hbase.regionserver.info.port
Description
The port for the HBase RegionServer web UI Set to -1 if you do not want the RegionServer UI to
run.
Default
16030
hbase.regionserver.info.bindAddress
Description
The address for the HBase RegionServer web UI
Default
0.0.0.0
hbase.regionserver.info.port.auto
Description
Whether or not the Master or RegionServer UI should search for a port to bind to. Enables
automatic port search if hbase.regionserver.info.port is already in use. Useful for testing, turned
off by default.
Default
false
hbase.regionserver.handler.count
Description
Count of RPC Listener instances spun up on RegionServers. Same property is used by the Master
for count of master handlers. Too many handlers can be counter-productive. Make it a multiple
of CPU count. If mostly read-only, handlers count close to cpu count does well. Start with twice
the CPU count and tune from there.
Default
30
35
hbase.ipc.server.callqueue.handler.factor
Description
Factor to determine the number of call queues. A value of 0 means a single queue shared
between all the handlers. A value of 1 means that each handler has its own queue.
Default
0.1
hbase.ipc.server.callqueue.read.ratio
Description
Split the call queues into read and write queues. The specified interval (which should be
between 0.0 and 1.0) will be multiplied by the number of call queues. A value of 0 indicate to not
split the call queues, meaning that both read and write requests will be pushed to the same set of
queues. A value lower than 0.5 means that there will be less read queues than write queues. A
value of 0.5 means there will be the same number of read and write queues. A value greater
than 0.5 means that there will be more read queues than write queues. A value of 1.0 means that
all the queues except one are used to dispatch read requests. Example: Given the total number of
call queues being 10 a read.ratio of 0 means that: the 10 queues will contain both read/write
requests. a read.ratio of 0.3 means that: 3 queues will contain only read requests and 7 queues
will contain only write requests. a read.ratio of 0.5 means that: 5 queues will contain only read
requests and 5 queues will contain only write requests. a read.ratio of 0.8 means that: 8 queues
will contain only read requests and 2 queues will contain only write requests. a read.ratio of 1
means that: 9 queues will contain only read requests and 1 queues will contain only write
requests.
Default
0
hbase.ipc.server.callqueue.scan.ratio
Description
Given the number of read call queues, calculated from the total number of call queues
multiplied by the callqueue.read.ratio, the scan.ratio property will split the read call queues into
small-read and long-read queues. A value lower than 0.5 means that there will be less long-read
queues than short-read queues. A value of 0.5 means that there will be the same number of
short-read and long-read queues. A value greater than 0.5 means that there will be more long-
read queues than short-read queues A value of 0 or 1 indicate to use the same set of queues for
gets and scans. Example: Given the total number of read call queues being 8 a scan.ratio of 0 or 1
means that: 8 queues will contain both long and short read requests. a scan.ratio of 0.3 means
that: 2 queues will contain only long-read requests and 6 queues will contain only short-read
requests. a scan.ratio of 0.5 means that: 4 queues will contain only long-read requests and 4
queues will contain only short-read requests. a scan.ratio of 0.8 means that: 6 queues will
contain only long-read requests and 2 queues will contain only short-read requests.
Default
0
hbase.regionserver.msginterval
Description
36
Interval between messages from the RegionServer to Master in milliseconds.
Default
3000
hbase.regionserver.logroll.period
Description
Period at which we will roll the commit log regardless of how many edits it has.
Default
3600000
hbase.regionserver.logroll.errors.tolerated
Description
The number of consecutive WAL close errors we will allow before triggering a server abort. A
setting of 0 will cause the region server to abort if closing the current WAL writer fails during
log rolling. Even a small value (2 or 3) will allow a region server to ride over transient HDFS
errors.
Default
2
hbase.regionserver.hlog.reader.impl
Description
The WAL file reader implementation.
Default
org.apache.hadoop.hbase.regionserver.wal.ProtobufLogReader
hbase.regionserver.hlog.writer.impl
Description
The WAL file writer implementation.
Default
org.apache.hadoop.hbase.regionserver.wal.ProtobufLogWriter
hbase.regionserver.global.memstore.size
Description
Maximum size of all memstores in a region server before new updates are blocked and flushes
are forced. Defaults to 40% of heap (0.4). Updates are blocked and flushes are forced until size of
all memstores in a region server hits hbase.regionserver.global.memstore.size.lower.limit. The
default value in this configuration has been intentionally left empty in order to honor the old
hbase.regionserver.global.memstore.upperLimit property if present.
Default
none
hbase.regionserver.global.memstore.size.lower.limit
Description
Maximum size of all memstores in a region server before flushes are forced. Defaults to 95% of
37
hbase.regionserver.global.memstore.size (0.95). A 100% value for this value causes the minimum
possible flushing to occur when updates are blocked due to memstore limiting. The default value
in this configuration has been intentionally left empty in order to honor the old
hbase.regionserver.global.memstore.lowerLimit property if present.
Default
none
hbase.systemtables.compacting.memstore.type
Description
Determines the type of memstore to be used for system tables like META, namespace tables etc.
By default NONE is the type and hence we use the default memstore for all the system tables. If
we need to use compacting memstore for system tables then set this property to BASIC/EAGER
Default
NONE
hbase.regionserver.optionalcacheflushinterval
Description
Maximum amount of time an edit lives in memory before being automatically flushed. Default 1
hour. Set it to 0 to disable automatic flushing.
Default
3600000
hbase.regionserver.dns.interface
Description
The name of the Network Interface from which a region server should report its IP address.
Default
default
hbase.regionserver.dns.nameserver
Description
The host name or IP address of the name server (DNS) which a region server should use to
determine the host name used by the master for communication and display purposes.
Default
default
hbase.regionserver.region.split.policy
Description
A split policy determines when a region should be split. The various other split policies that are
available currently are BusyRegionSplitPolicy, ConstantSizeRegionSplitPolicy,
DisabledRegionSplitPolicy, DelimitedKeyPrefixRegionSplitPolicy, KeyPrefixRegionSplitPolicy, and
SteppingSplitPolicy. DisabledRegionSplitPolicy blocks manual region splitting.
Default
org.apache.hadoop.hbase.regionserver.SteppingSplitPolicy
38
hbase.regionserver.regionSplitLimit
Description
Limit for the number of regions after which no more region splitting should take place. This is
not hard limit for the number of regions but acts as a guideline for the regionserver to stop
splitting after a certain limit. Default is set to 1000.
Default
1000
zookeeper.session.timeout
Description
ZooKeeper session timeout in milliseconds. It is used in two different ways. First, this value is
used in the ZK client that HBase uses to connect to the ensemble. It is also used by HBase when it
starts a ZK server and it is passed as the 'maxSessionTimeout'. See http://hadoop.apache.org/
zookeeper/docs/current/zookeeperProgrammers.html#ch_zkSessions. For example, if an HBase
region server connects to a ZK ensemble that’s also managed by HBase, then the session timeout
will be the one specified by this configuration. But, a region server that connects to an ensemble
managed with a different configuration will be subjected that ensemble’s maxSessionTimeout.
So, even though HBase might propose using 90 seconds, the ensemble can have a max timeout
lower than this and it will take precedence. The current default that ZK ships with is 40 seconds,
which is lower than HBase’s.
Default
90000
zookeeper.znode.parent
Description
Root ZNode for HBase in ZooKeeper. All of HBase’s ZooKeeper files that are configured with a
relative path will go under this node. By default, all of HBase’s ZooKeeper file paths are
configured with a relative path, so they will all go under this directory unless changed.
Default
/hbase
zookeeper.znode.acl.parent
Description
Root ZNode for access control lists.
Default
acl
hbase.zookeeper.dns.interface
Description
The name of the Network Interface from which a ZooKeeper server should report its IP address.
Default
default
hbase.zookeeper.dns.nameserver
39
Description
The host name or IP address of the name server (DNS) which a ZooKeeper server should use to
determine the host name used by the master for communication and display purposes.
Default
default
hbase.zookeeper.peerport
Description
Port used by ZooKeeper peers to talk to each other. See http://hadoop.apache.org/zookeeper/docs/
r3.1.1/zookeeperStarted.html#sc_RunningReplicatedZooKeeper for more information.
Default
2888
hbase.zookeeper.leaderport
Description
Port used by ZooKeeper for leader election. See http://hadoop.apache.org/zookeeper/docs/r3.1.1/
zookeeperStarted.html#sc_RunningReplicatedZooKeeper for more information.
Default
3888
hbase.zookeeper.property.initLimit
Description
Property from ZooKeeper’s config zoo.cfg. The number of ticks that the initial synchronization
phase can take.
Default
10
hbase.zookeeper.property.syncLimit
Description
Property from ZooKeeper’s config zoo.cfg. The number of ticks that can pass between sending a
request and getting an acknowledgment.
Default
5
hbase.zookeeper.property.dataDir
Description
Property from ZooKeeper’s config zoo.cfg. The directory where the snapshot is stored.
Default
${hbase.tmp.dir}/zookeeper
hbase.zookeeper.property.clientPort
Description
Property from ZooKeeper’s config zoo.cfg. The port at which the clients will connect.
40
Default
2181
hbase.zookeeper.property.maxClientCnxns
Description
Property from ZooKeeper’s config zoo.cfg. Limit on number of concurrent connections (at the
socket level) that a single client, identified by IP address, may make to a single member of the
ZooKeeper ensemble. Set high to avoid zk connection issues running standalone and pseudo-
distributed.
Default
300
hbase.client.write.buffer
Description
Default size of the BufferedMutator write buffer in bytes. A bigger buffer takes more
memory — on both the client and server side since server instantiates the passed write buffer to
process it — but a larger buffer size reduces the number of RPCs made. For an estimate of
server-side memory-used, evaluate hbase.client.write.buffer * hbase.regionserver.handler.count
Default
2097152
hbase.client.pause
Description
General client pause value. Used mostly as value to wait before running a retry of a failed get,
region lookup, etc. See hbase.client.retries.number for description of how we backoff from this
initial pause amount and how this pause works w/ retries.
Default
100
hbase.client.pause.cqtbe
Description
Whether or not to use a special client pause for CallQueueTooBigException (cqtbe). Set this
property to a higher value than hbase.client.pause if you observe frequent CQTBE from the same
RegionServer and the call queue there keeps full
Default
none
hbase.client.retries.number
Description
Maximum retries. Used as maximum for all retryable operations such as the getting of a cell’s
value, starting a row update, etc. Retry interval is a rough function based on hbase.client.pause.
At first we retry at this interval but then with backoff, we pretty quickly reach retrying every ten
seconds. See HConstants#RETRY_BACKOFF for how the backup ramps up. Change this setting
and hbase.client.pause to suit your workload.
41
Default
15
hbase.client.max.total.tasks
Description
The maximum number of concurrent mutation tasks a single HTable instance will send to the
cluster.
Default
100
hbase.client.max.perserver.tasks
Description
The maximum number of concurrent mutation tasks a single HTable instance will send to a
single region server.
Default
2
hbase.client.max.perregion.tasks
Description
The maximum number of concurrent mutation tasks the client will maintain to a single Region.
That is, if there is already hbase.client.max.perregion.tasks writes in progress for this region,
new puts won’t be sent to this region until some writes finishes.
Default
1
hbase.client.perserver.requests.threshold
Description
The max number of concurrent pending requests for one server in all client threads (process
level). Exceeding requests will be thrown ServerTooBusyException immediately to prevent
user’s threads being occupied and blocked by only one slow region server. If you use a fix
number of threads to access HBase in a synchronous way, set this to a suitable value which is
related to the number of threads will help you. See https://issues.apache.org/jira/browse/HBASE-
16388 for details.
Default
2147483647
hbase.client.scanner.caching
Description
Number of rows that we try to fetch when calling next on a scanner if it is not served from
(local, client) memory. This configuration works together with
hbase.client.scanner.max.result.size to try and use the network efficiently. The default value is
Integer.MAX_VALUE by default so that the network will fill the chunk size defined by
hbase.client.scanner.max.result.size rather than be limited by a particular number of rows since
the size of rows varies table to table. If you know ahead of time that you will not require more
than a certain number of rows from a scan, this configuration should be set to that row limit via
42
Scan#setCaching. Higher caching values will enable faster scanners but will eat up more
memory and some calls of next may take longer and longer times when the cache is empty. Do
not set this value such that the time between invocations is greater than the scanner timeout; i.e.
hbase.client.scanner.timeout.period
Default
2147483647
hbase.client.keyvalue.maxsize
Description
Specifies the combined maximum allowed size of a KeyValue instance. This is to set an upper
boundary for a single entry saved in a storage file. Since they cannot be split it helps avoiding
that a region cannot be split any further because the data is too large. It seems wise to set this to
a fraction of the maximum region size. Setting it to zero or less disables the check.
Default
10485760
hbase.server.keyvalue.maxsize
Description
Maximum allowed size of an individual cell, inclusive of value and all key components. A value
of 0 or less disables the check. The default value is 10MB. This is a safety setting to protect the
server from OOM situations.
Default
10485760
hbase.client.scanner.timeout.period
Description
Client scanner lease period in milliseconds.
Default
60000
hbase.client.localityCheck.threadPoolSize
Default
2
hbase.bulkload.retries.number
Description
Maximum retries. This is maximum number of iterations to atomic bulk loads are attempted in
the face of splitting operations 0 means never give up.
Default
10
hbase.master.balancer.maxRitPercent
Description
The max percent of regions in transition when balancing. The default value is 1.0. So there are
no balancer throttling. If set this config to 0.01, It means that there are at most 1% regions in
43
transition when balancing. Then the cluster’s availability is at least 99% when balancing.
Default
1.0
hbase.balancer.period
Description
Period at which the region balancer runs in the Master.
Default
300000
hbase.normalizer.period
Description
Period at which the region normalizer runs in the Master.
Default
300000
hbase.normalizer.min.region.count
Description
configure the minimum number of regions
Default
3
hbase.regions.slop
Description
Rebalance if any regionserver has average + (average * slop) regions. The default value of this
parameter is 0.001 in StochasticLoadBalancer (the default load balancer), while the default is 0.2
in other load balancers (i.e., SimpleLoadBalancer).
Default
0.001
hbase.server.thread.wakefrequency
Description
Time to sleep in between searches for work (in milliseconds). Used as sleep interval by service
threads such as log roller.
Default
10000
hbase.server.versionfile.writeattempts
Description
How many times to retry attempting to write a version file before just aborting. Each attempt is
separated by the hbase.server.thread.wakefrequency milliseconds.
Default
3
44
hbase.hregion.memstore.flush.size
Description
Memstore will be flushed to disk if size of the memstore exceeds this number of bytes. Value is
checked by a thread that runs every hbase.server.thread.wakefrequency.
Default
134217728
hbase.hregion.percolumnfamilyflush.size.lower.bound.min
Description
If FlushLargeStoresPolicy is used and there are multiple column families, then every time that
we hit the total memstore limit, we find out all the column families whose memstores exceed a
"lower bound" and only flush them while retaining the others in memory. The "lower bound"
will be "hbase.hregion.memstore.flush.size / column_family_number" by default unless value of
this property is larger than that. If none of the families have their memstore size more than
lower bound, all the memstores will be flushed (just as usual).
Default
16777216
hbase.hregion.preclose.flush.size
Description
If the memstores in a region are this size or larger when we go to close, run a "pre-flush" to clear
out memstores before we put up the region closed flag and take the region offline. On close, a
flush is run under the close flag to empty memory. During this time the region is offline and we
are not taking on any writes. If the memstore content is large, this flush could take a long time to
complete. The preflush is meant to clean out the bulk of the memstore before putting up the
close flag and taking the region offline so the flush that runs under the close flag has little to do.
Default
5242880
hbase.hregion.memstore.block.multiplier
Description
Block updates if memstore has hbase.hregion.memstore.block.multiplier times
hbase.hregion.memstore.flush.size bytes. Useful preventing runaway memstore during spikes in
update traffic. Without an upper-bound, memstore fills such that when it flushes the resultant
flush files take a long time to compact or split, or worse, we OOME.
Default
4
hbase.hregion.memstore.mslab.enabled
Description
Enables the MemStore-Local Allocation Buffer, a feature which works to prevent heap
fragmentation under heavy write loads. This can reduce the frequency of stop-the-world GC
pauses on large heaps.
Default
45
true
hbase.hregion.max.filesize
Description
Maximum HFile size. If the sum of the sizes of a region’s HFiles has grown to exceed this value,
the region is split in two.
Default
10737418240
hbase.hregion.majorcompaction
Description
Time between major compactions, expressed in milliseconds. Set to 0 to disable time-based
automatic major compactions. User-requested and size-based major compactions will still run.
This value is multiplied by hbase.hregion.majorcompaction.jitter to cause compaction to start at
a somewhat-random time during a given window of time. The default value is 7 days, expressed
in milliseconds. If major compactions are causing disruption in your environment, you can
configure them to run at off-peak times for your deployment, or disable time-based major
compactions by setting this parameter to 0, and run major compactions in a cron job or by
another external mechanism.
Default
604800000
hbase.hregion.majorcompaction.jitter
Description
A multiplier applied to hbase.hregion.majorcompaction to cause compaction to occur a given
amount of time either side of hbase.hregion.majorcompaction. The smaller the number, the
closer the compactions will happen to the hbase.hregion.majorcompaction interval.
Default
0.50
hbase.hstore.compactionThreshold
Description
If more than this number of StoreFiles exist in any one Store (one StoreFile is written per flush
of MemStore), a compaction is run to rewrite all StoreFiles into a single StoreFile. Larger values
delay compaction, but when compaction does occur, it takes longer to complete.
Default
3
hbase.regionserver.compaction.enabled
Description
Enable/disable compactions on by setting true/false. We can further switch compactions
dynamically with the compaction_switch shell command.
Default
true
46
hbase.hstore.flusher.count
Description
The number of flush threads. With fewer threads, the MemStore flushes will be queued. With
more threads, the flushes will be executed in parallel, increasing the load on HDFS, and
potentially causing more compactions.
Default
2
hbase.hstore.blockingStoreFiles
Description
If more than this number of StoreFiles exist in any one Store (one StoreFile is written per flush
of MemStore), updates are blocked for this region until a compaction is completed, or until
hbase.hstore.blockingWaitTime has been exceeded.
Default
16
hbase.hstore.blockingWaitTime
Description
The time for which a region will block updates after reaching the StoreFile limit defined by
hbase.hstore.blockingStoreFiles. After this time has elapsed, the region will stop blocking
updates even if a compaction has not been completed.
Default
90000
hbase.hstore.compaction.min
Description
The minimum number of StoreFiles which must be eligible for compaction before compaction
can run. The goal of tuning hbase.hstore.compaction.min is to avoid ending up with too many
tiny StoreFiles to compact. Setting this value to 2 would cause a minor compaction each time you
have two StoreFiles in a Store, and this is probably not appropriate. If you set this value too high,
all the other values will need to be adjusted accordingly. For most cases, the default value is
appropriate. In previous versions of HBase, the parameter hbase.hstore.compaction.min was
named hbase.hstore.compactionThreshold.
Default
3
hbase.hstore.compaction.max
Description
The maximum number of StoreFiles which will be selected for a single minor compaction,
regardless of the number of eligible StoreFiles. Effectively, the value of
hbase.hstore.compaction.max controls the length of time it takes a single compaction to
complete. Setting it larger means that more StoreFiles are included in a compaction. For most
cases, the default value is appropriate.
Default
47
10
hbase.hstore.compaction.min.size
Description
A StoreFile (or a selection of StoreFiles, when using ExploringCompactionPolicy) smaller than
this size will always be eligible for minor compaction. HFiles this size or larger are evaluated by
hbase.hstore.compaction.ratio to determine if they are eligible. Because this limit represents the
"automatic include" limit for all StoreFiles smaller than this value, this value may need to be
reduced in write-heavy environments where many StoreFiles in the 1-2 MB range are being
flushed, because every StoreFile will be targeted for compaction and the resulting StoreFiles
may still be under the minimum size and require further compaction. If this parameter is
lowered, the ratio check is triggered more quickly. This addressed some issues seen in earlier
versions of HBase but changing this parameter is no longer necessary in most situations.
Default: 128 MB expressed in bytes.
Default
134217728
hbase.hstore.compaction.max.size
Description
A StoreFile (or a selection of StoreFiles, when using ExploringCompactionPolicy) larger than this
size will be excluded from compaction. The effect of raising hbase.hstore.compaction.max.size is
fewer, larger StoreFiles that do not get compacted often. If you feel that compaction is
happening too often without much benefit, you can try raising this value. Default: the value of
LONG.MAX_VALUE, expressed in bytes.
Default
9223372036854775807
hbase.hstore.compaction.ratio
Description
For minor compaction, this ratio is used to determine whether a given StoreFile which is larger
than hbase.hstore.compaction.min.size is eligible for compaction. Its effect is to limit compaction
of large StoreFiles. The value of hbase.hstore.compaction.ratio is expressed as a floating-point
decimal. A large ratio, such as 10, will produce a single giant StoreFile. Conversely, a low value,
such as .25, will produce behavior similar to the BigTable compaction algorithm, producing four
StoreFiles. A moderate value of between 1.0 and 1.4 is recommended. When tuning this value,
you are balancing write costs with read costs. Raising the value (to something like 1.4) will have
more write costs, because you will compact larger StoreFiles. However, during reads, HBase will
need to seek through fewer StoreFiles to accomplish the read. Consider this approach if you
cannot take advantage of Bloom filters. Otherwise, you can lower this value to something like 1.0
to reduce the background cost of writes, and use Bloom filters to control the number of
StoreFiles touched during reads. For most cases, the default value is appropriate.
Default
1.2F
hbase.hstore.compaction.ratio.offpeak
Description
48
Allows you to set a different (by default, more aggressive) ratio for determining whether larger
StoreFiles are included in compactions during off-peak hours. Works in the same way as
hbase.hstore.compaction.ratio. Only applies if hbase.offpeak.start.hour and
hbase.offpeak.end.hour are also enabled.
Default
5.0F
hbase.hstore.time.to.purge.deletes
Description
The amount of time to delay purging of delete markers with future timestamps. If unset, or set to
0, all delete markers, including those with future timestamps, are purged during the next major
compaction. Otherwise, a delete marker is kept until the major compaction which occurs after
the marker’s timestamp plus the value of this setting, in milliseconds.
Default
0
hbase.offpeak.start.hour
Description
The start of off-peak hours, expressed as an integer between 0 and 23, inclusive. Set to -1 to
disable off-peak.
Default
-1
hbase.offpeak.end.hour
Description
The end of off-peak hours, expressed as an integer between 0 and 23, inclusive. Set to -1 to
disable off-peak.
Default
-1
hbase.regionserver.thread.compaction.throttle
Description
There are two different thread pools for compactions, one for large compactions and the other
for small compactions. This helps to keep compaction of lean tables (such as hbase:meta) fast. If
a compaction is larger than this threshold, it goes into the large compaction pool. In most cases,
the default value is appropriate. Default: 2 x hbase.hstore.compaction.max x
hbase.hregion.memstore.flush.size (which defaults to 128MB). The value field assumes that the
value of hbase.hregion.memstore.flush.size is unchanged from the default.
Default
2684354560
hbase.regionserver.majorcompaction.pagecache.drop
Description
Specifies whether to drop pages read/written into the system page cache by major compactions.
49
Setting it to true helps prevent major compactions from polluting the page cache, which is
almost always required, especially for clusters with low/moderate memory to storage ratio.
Default
true
hbase.regionserver.minorcompaction.pagecache.drop
Description
Specifies whether to drop pages read/written into the system page cache by minor compactions.
Setting it to true helps prevent minor compactions from polluting the page cache, which is most
beneficial on clusters with low memory to storage ratio or very write heavy clusters. You may
want to set it to false under moderate to low write workload when bulk of the reads are on the
most recently written data.
Default
true
hbase.hstore.compaction.kv.max
Description
The maximum number of KeyValues to read and then write in a batch when flushing or
compacting. Set this lower if you have big KeyValues and problems with Out Of Memory
Exceptions Set this higher if you have wide, small rows.
Default
10
hbase.storescanner.parallel.seek.enable
Description
Enables StoreFileScanner parallel-seeking in StoreScanner, a feature which can reduce response
latency under special conditions.
Default
false
hbase.storescanner.parallel.seek.threads
Description
The default thread pool size if parallel-seeking feature enabled.
Default
10
hfile.block.cache.size
Description
Percentage of maximum heap (-Xmx setting) to allocate to block cache used by a StoreFile.
Default of 0.4 means allocate 40%. Set to 0 to disable but it’s not recommended; you need at least
enough cache to hold the storefile indices.
Default
0.4
50
hfile.block.index.cacheonwrite
Description
This allows to put non-root multi-level index blocks into the block cache at the time the index is
being written.
Default
false
hfile.index.block.max.size
Description
When the size of a leaf-level, intermediate-level, or root-level index block in a multi-level block
index grows to this size, the block is written out and a new block is started.
Default
131072
hbase.bucketcache.ioengine
Description
Where to store the contents of the bucketcache. One of: offheap, file, files or mmap. If a file or
files, set it to file(s):PATH_TO_FILE. mmap means the content will be in an mmaped file. Use
mmap:PATH_TO_FILE. See http://hbase.apache.org/book.html#offheap.blockcache for more
information.
Default
none
hbase.bucketcache.size
Description
A float that EITHER represents a percentage of total heap memory size to give to the cache (if <
1.0) OR, it is the total capacity in megabytes of BucketCache. Default: 0.0
Default
none
hbase.bucketcache.bucket.sizes
Description
A comma-separated list of sizes for buckets for the bucketcache. Can be multiple sizes. List block
sizes in order from smallest to largest. The sizes you use will depend on your data access
patterns. Must be a multiple of 256 else you will run into 'java.io.IOException: Invalid HFile
block magic' when you go to read from cache. If you specify no values here, then you pick up the
default bucketsizes set in code (See BucketAllocator#DEFAULT_BUCKET_SIZES).
Default
none
hfile.format.version
Description
The HFile format version to use for new files. Version 3 adds support for tags in hfiles (See
51
http://hbase.apache.org/book.html#hbase.tags). Also see the configuration
'hbase.replication.rpc.codec'.
Default
3
hfile.block.bloom.cacheonwrite
Description
Enables cache-on-write for inline blocks of a compound Bloom filter.
Default
false
io.storefile.bloom.block.size
Description
The size in bytes of a single block ("chunk") of a compound Bloom filter. This size is
approximate, because Bloom blocks can only be inserted at data block boundaries, and the
number of keys per data block varies.
Default
131072
hbase.rs.cacheblocksonwrite
Description
Whether an HFile block should be added to the block cache when the block is finished.
Default
false
hbase.rpc.timeout
Description
This is for the RPC layer to define how long (millisecond) HBase client applications take for a
remote call to time out. It uses pings to check connections but will eventually throw a
TimeoutException.
Default
60000
hbase.client.operation.timeout
Description
Operation timeout is a top-level restriction (millisecond) that makes sure a blocking operation in
Table will not be blocked more than this. In each operation, if rpc request fails because of
timeout or other reason, it will retry until success or throw RetriesExhaustedException. But if
the total time being blocking reach the operation timeout before retries exhausted, it will break
early and throw SocketTimeoutException.
Default
1200000
52
hbase.cells.scanned.per.heartbeat.check
Description
The number of cells scanned in between heartbeat checks. Heartbeat checks occur during the
processing of scans to determine whether or not the server should stop scanning in order to
send back a heartbeat message to the client. Heartbeat messages are used to keep the client-
server connection alive during long running scans. Small values mean that the heartbeat checks
will occur more often and thus will provide a tighter bound on the execution time of the scan.
Larger values mean that the heartbeat checks occur less frequently
Default
10000
hbase.rpc.shortoperation.timeout
Description
This is another version of "hbase.rpc.timeout". For those RPC operation within cluster, we rely
on this configuration to set a short timeout limitation for short operation. For example, short rpc
timeout for region server’s trying to report to active master can benefit quicker master failover
process.
Default
10000
hbase.ipc.client.tcpnodelay
Description
Set no delay on rpc socket connections. See http://docs.oracle.com/javase/1.5.0/docs/api/java/net/
Socket.html#getTcpNoDelay()
Default
true
hbase.regionserver.hostname
Description
This config is for experts: don’t set its value unless you really know what you are doing. When
set to a non-empty value, this represents the (external facing) hostname for the underlying
server. See https://issues.apache.org/jira/browse/HBASE-12954 for details.
Default
none
hbase.regionserver.hostname.disable.master.reversedns
Description
This config is for experts: don’t set its value unless you really know what you are doing. When
set to true, regionserver will use the current node hostname for the servername and HMaster
will skip reverse DNS lookup and use the hostname sent by regionserver instead. Note that this
config and hbase.regionserver.hostname are mutually exclusive. See https://issues.apache.org/
jira/browse/HBASE-18226 for more details.
Default
false
53
hbase.master.keytab.file
Description
Full path to the kerberos keytab file to use for logging in the configured HMaster server
principal.
Default
none
hbase.master.kerberos.principal
Description
Ex. "hbase/_HOST@EXAMPLE.COM". The kerberos principal name that should be used to run the
HMaster process. The principal name should be in the form: user/hostname@DOMAIN. If
"_HOST" is used as the hostname portion, it will be replaced with the actual hostname of the
running instance.
Default
none
hbase.regionserver.keytab.file
Description
Full path to the kerberos keytab file to use for logging in the configured HRegionServer server
principal.
Default
none
hbase.regionserver.kerberos.principal
Description
Ex. "hbase/_HOST@EXAMPLE.COM". The kerberos principal name that should be used to run the
HRegionServer process. The principal name should be in the form: user/hostname@DOMAIN. If
"_HOST" is used as the hostname portion, it will be replaced with the actual hostname of the
running instance. An entry for this principal must exist in the file specified in
hbase.regionserver.keytab.file
Default
none
hadoop.policy.file
Description
The policy configuration file used by RPC servers to make authorization decisions on client
requests. Only used when HBase security is enabled.
Default
hbase-policy.xml
hbase.superuser
Description
List of users or groups (comma-separated), who are allowed full privileges, regardless of stored
54
ACLs, across the cluster. Only used when HBase security is enabled.
Default
none
hbase.auth.key.update.interval
Description
The update interval for master key for authentication tokens in servers in milliseconds. Only
used when HBase security is enabled.
Default
86400000
hbase.auth.token.max.lifetime
Description
The maximum lifetime in milliseconds after which an authentication token expires. Only used
when HBase security is enabled.
Default
604800000
hbase.ipc.client.fallback-to-simple-auth-allowed
Description
When a client is configured to attempt a secure connection, but attempts to connect to an
insecure server, that server may instruct the client to switch to SASL SIMPLE (unsecure)
authentication. This setting controls whether or not the client will accept this instruction from
the server. When false (the default), the client will not allow the fallback to SIMPLE
authentication, and will abort the connection.
Default
false
hbase.ipc.server.fallback-to-simple-auth-allowed
Description
When a server is configured to require secure connections, it will reject connection attempts
from clients using SASL SIMPLE (unsecure) authentication. This setting allows secure servers to
accept SASL SIMPLE connections from clients when the client requests. When false (the default),
the server will not allow the fallback to SIMPLE authentication, and will reject the connection.
WARNING: This setting should ONLY be used as a temporary measure while converting clients
over to secure authentication. It MUST BE DISABLED for secure operation.
Default
false
hbase.display.keys
Description
When this is set to true the webUI and such will display all start/end keys as part of the table
details, region names, etc. When this is set to false, the keys are hidden.
55
Default
true
hbase.coprocessor.enabled
Description
Enables or disables coprocessor loading. If 'false' (disabled), any other coprocessor related
configuration will be ignored.
Default
true
hbase.coprocessor.user.enabled
Description
Enables or disables user (aka. table) coprocessor loading. If 'false' (disabled), any table
coprocessor attributes in table descriptors will be ignored. If "hbase.coprocessor.enabled" is
'false' this setting has no effect.
Default
true
hbase.coprocessor.region.classes
Description
A comma-separated list of region observer or endpoint coprocessors that are loaded by default
on all tables. For any override coprocessor method, these classes will be called in order. After
implementing your own Coprocessor, add it to HBase’s classpath and add the fully qualified
class name here. A coprocessor can also be loaded on demand by setting HTableDescriptor or
the HBase shell.
Default
none
hbase.coprocessor.master.classes
Description
A comma-separated list of org.apache.hadoop.hbase.coprocessor.MasterObserver coprocessors
that are loaded by default on the active HMaster process. For any implemented coprocessor
methods, the listed classes will be called in order. After implementing your own
MasterObserver, just put it in HBase’s classpath and add the fully qualified class name here.
Default
none
hbase.coprocessor.abortonerror
Description
Set to true to cause the hosting server (master or regionserver) to abort if a coprocessor fails to
load, fails to initialize, or throws an unexpected Throwable object. Setting this to false will allow
the server to continue execution but the system wide state of the coprocessor in question will
become inconsistent as it will be properly executing in only a subset of servers, so this is most
useful for debugging only.
56
Default
true
hbase.rest.port
Description
The port for the HBase REST server.
Default
8080
hbase.rest.readonly
Description
Defines the mode the REST server will be started in. Possible values are: false: All HTTP methods
are permitted - GET/PUT/POST/DELETE. true: Only the GET method is permitted.
Default
false
hbase.rest.threads.max
Description
The maximum number of threads of the REST server thread pool. Threads in the pool are reused
to process REST requests. This controls the maximum number of requests processed
concurrently. It may help to control the memory used by the REST server to avoid OOM issues. If
the thread pool is full, incoming requests will be queued up and wait for some free threads.
Default
100
hbase.rest.threads.min
Description
The minimum number of threads of the REST server thread pool. The thread pool always has at
least these number of threads so the REST server is ready to serve incoming requests.
Default
2
hbase.rest.support.proxyuser
Description
Enables running the REST server to support proxy-user mode.
Default
false
hbase.defaults.for.version.skip
Description
Set to true to skip the 'hbase.defaults.for.version' check. Setting this to true can be useful in
contexts other than the other side of a maven generation; i.e. running in an IDE. You’ll want to
set this boolean to true to avoid seeing the RuntimeException complaint: "hbase-default.xml file
seems to be for and old version of HBase (\${hbase.version}), this version is X.X.X-SNAPSHOT"
57
Default
false
hbase.table.lock.enable
Description
Set to true to enable locking the table in zookeeper for schema change operations. Table locking
from master prevents concurrent schema modifications to corrupt table state.
Default
true
hbase.table.max.rowsize
Description
Maximum size of single row in bytes (default is 1 Gb) for Get’ting or Scan’ning without in-row
scan flag set. If row size exceeds this limit RowTooBigException is thrown to client.
Default
1073741824
hbase.thrift.minWorkerThreads
Description
The "core size" of the thread pool. New threads are created on every connection until this many
threads are created.
Default
16
hbase.thrift.maxWorkerThreads
Description
The maximum size of the thread pool. When the pending request queue overflows, new threads
are created until their number reaches this number. After that, the server starts dropping
connections.
Default
1000
hbase.thrift.maxQueuedRequests
Description
The maximum number of pending Thrift connections waiting in the queue. If there are no idle
threads in the pool, the server queues requests. Only when the queue overflows, new threads
are added, up to hbase.thrift.maxQueuedRequests threads.
Default
1000
hbase.regionserver.thrift.framed
Description
Use Thrift TFramedTransport on the server side. This is the recommended transport for thrift
servers and requires a similar setting on the client side. Changing this to false will select the
default transport, vulnerable to DoS when malformed requests are issued due to THRIFT-601.
58
Default
false
hbase.regionserver.thrift.framed.max_frame_size_in_mb
Description
Default frame size when using framed transport, in MB
Default
2
hbase.regionserver.thrift.compact
Description
Use Thrift TCompactProtocol binary serialization protocol.
Default
false
hbase.rootdir.perms
Description
FS Permissions for the root data subdirectory in a secure (kerberos) setup. When master starts, it
creates the rootdir with this permissions or sets the permissions if it does not match.
Default
700
hbase.wal.dir.perms
Description
FS Permissions for the root WAL directory in a secure(kerberos) setup. When master starts, it
creates the WAL dir with this permissions or sets the permissions if it does not match.
Default
700
hbase.data.umask.enable
Description
Enable, if true, that file permissions should be assigned to the files written by the regionserver
Default
false
hbase.data.umask
Description
File permissions that should be used to write data files when hbase.data.umask.enable is true
Default
000
hbase.snapshot.enabled
Description
Set to true to allow snapshots to be taken / restored / cloned.
59
Default
true
hbase.snapshot.restore.take.failsafe.snapshot
Description
Set to true to take a snapshot before the restore operation. The snapshot taken will be used in
case of failure, to restore the previous state. At the end of the restore operation this snapshot will
be deleted
Default
true
hbase.snapshot.restore.failsafe.name
Description
Name of the failsafe snapshot taken by the restore operation. You can use the {snapshot.name},
{table.name} and {restore.timestamp} variables to create a name based on what you are
restoring.
Default
hbase-failsafe-{snapshot.name}-{restore.timestamp}
hbase.snapshot.working.dir
Description
Location where the snapshotting process will occur. The location of the completed snapshots
will not change, but the temporary directory where the snapshot process occurs will be set to
this location. This can be a separate filesystem than the root directory, for performance increase
purposes. See HBASE-21098 for more information
Default
none
hbase.server.compactchecker.interval.multiplier
Description
The number that determines how often we scan to see if compaction is necessary. Normally,
compactions are done after some events (such as memstore flush), but if region didn’t receive a
lot of writes for some time, or due to different compaction policies, it may be necessary to check
it periodically. The interval between checks is hbase.server.compactchecker.interval.multiplier
multiplied by hbase.server.thread.wakefrequency.
Default
1000
hbase.lease.recovery.timeout
Description
How long we wait on dfs lease recovery in total before giving up.
Default
900000
60
hbase.lease.recovery.dfs.timeout
Description
How long between dfs recover lease invocations. Should be larger than the sum of the time it
takes for the namenode to issue a block recovery command as part of datanode;
dfs.heartbeat.interval and the time it takes for the primary datanode, performing block recovery
to timeout on a dead datanode; usually dfs.client.socket-timeout. See the end of HBASE-8389 for
more.
Default
64000
hbase.column.max.version
Description
New column family descriptors will use this value as the default number of versions to keep.
Default
1
dfs.client.read.shortcircuit
Description
If set to true, this configuration parameter enables short-circuit local reads.
Default
false
dfs.domain.socket.path
Description
This is a path to a UNIX domain socket that will be used for communication between the
DataNode and local HDFS clients, if dfs.client.read.shortcircuit is set to true. If the string "_PORT"
is present in this path, it will be replaced by the TCP port of the DataNode. Be careful about
permissions for the directory that hosts the shared domain socket; dfsclient will complain if
open to other users than the HBase user.
Default
none
hbase.dfs.client.read.shortcircuit.buffer.size
Description
If the DFSClient configuration dfs.client.read.shortcircuit.buffer.size is unset, we will use what is
configured here as the short circuit read default direct byte buffer size. DFSClient native default
is 1MB; HBase keeps its HDFS files open so number of file blocks * 1MB soon starts to add up and
threaten OOME because of a shortage of direct memory. So, we set it down from the default.
Make it > the default hbase block size set in the HColumnDescriptor which is usually 64k.
Default
131072
hbase.regionserver.checksum.verify
Description
61
If set to true (the default), HBase verifies the checksums for hfile blocks. HBase writes
checksums inline with the data when it writes out hfiles. HDFS (as of this writing) writes
checksums to a separate file than the data file necessitating extra seeks. Setting this flag saves
some on i/o. Checksum verification by HDFS will be internally disabled on hfile streams when
this flag is set. If the hbase-checksum verification fails, we will switch back to using HDFS
checksums (so do not disable HDFS checksums! And besides this feature applies to hfiles only,
not to WALs). If this parameter is set to false, then hbase will not verify any checksums, instead
it will depend on checksum verification being done in the HDFS client.
Default
true
hbase.hstore.bytes.per.checksum
Description
Number of bytes in a newly created checksum chunk for HBase-level checksums in hfile blocks.
Default
16384
hbase.hstore.checksum.algorithm
Description
Name of an algorithm that is used to compute checksums. Possible values are NULL, CRC32,
CRC32C.
Default
CRC32C
hbase.client.scanner.max.result.size
Description
Maximum number of bytes returned when calling a scanner’s next method. Note that when a
single row is larger than this limit the row is still returned completely. The default value is 2MB,
which is good for 1ge networks. With faster and/or high latency networks this value should be
increased.
Default
2097152
hbase.server.scanner.max.result.size
Description
Maximum number of bytes returned when calling a scanner’s next method. Note that when a
single row is larger than this limit the row is still returned completely. The default value is
100MB. This is a safety setting to protect the server from OOM situations.
Default
104857600
hbase.status.published
Description
This setting activates the publication by the master of the status of the region server. When a
62
region server dies and its recovery starts, the master will push this information to the client
application, to let them cut the connection immediately instead of waiting for a timeout.
Default
false
hbase.status.publisher.class
Description
Implementation of the status publication with a multicast message.
Default
org.apache.hadoop.hbase.master.ClusterStatusPublisher$MulticastPublisher
hbase.status.listener.class
Description
Implementation of the status listener with a multicast message.
Default
org.apache.hadoop.hbase.client.ClusterStatusListener$MulticastListener
hbase.status.multicast.address.ip
Description
Multicast address to use for the status publication by multicast.
Default
226.1.1.3
hbase.status.multicast.address.port
Description
Multicast port to use for the status publication by multicast.
Default
16100
hbase.dynamic.jars.dir
Description
The directory from which the custom filter JARs can be loaded dynamically by the region server
without the need to restart. However, an already loaded filter/co-processor class would not be
un-loaded. See HBASE-1936 for more details. Does not apply to coprocessors.
Default
${hbase.rootdir}/lib
hbase.security.authentication
Description
Controls whether or not secure authentication is enabled for HBase. Possible values are 'simple'
(no authentication), and 'kerberos'.
Default
simple
63
hbase.rest.filter.classes
Description
Servlet filters for REST service.
Default
org.apache.hadoop.hbase.rest.filter.GzipFilter
hbase.master.loadbalancer.class
Description
Class used to execute the regions balancing when the period occurs. See the class comment for
more on how it works http://hbase.apache.org/devapidocs/org/apache/hadoop/hbase/master/
balancer/StochasticLoadBalancer.html It replaces the DefaultLoadBalancer as the default (since
renamed as the SimpleLoadBalancer).
Default
org.apache.hadoop.hbase.master.balancer.StochasticLoadBalancer
hbase.master.loadbalance.bytable
Description
Factor Table name when the balancer runs. Default: false.
Default
false
hbase.master.normalizer.class
Description
Class used to execute the region normalization when the period occurs. See the class comment
for more on how it works http://hbase.apache.org/devapidocs/org/apache/hadoop/hbase/master/
normalizer/SimpleRegionNormalizer.html
Default
org.apache.hadoop.hbase.master.normalizer.SimpleRegionNormalizer
hbase.rest.csrf.enabled
Description
Set to true to enable protection against cross-site request forgery (CSRF)
Default
false
hbase.rest-csrf.browser-useragents-regex
Description
A comma-separated list of regular expressions used to match against an HTTP request’s User-
Agent header when protection against cross-site request forgery (CSRF) is enabled for REST
server by setting hbase.rest.csrf.enabled to true. If the incoming User-Agent matches any of these
regular expressions, then the request is considered to be sent by a browser, and therefore CSRF
prevention is enforced. If the request’s User-Agent does not match any of these regular
expressions, then the request is considered to be sent by something other than a browser, such
as scripted automation. In this case, CSRF is not a potential attack vector, so the prevention is not
64
enforced. This helps achieve backwards-compatibility with existing automation that has not
been updated to send the CSRF prevention header.
Default
Mozilla.,Opera.
hbase.security.exec.permission.checks
Description
If this setting is enabled and ACL based access control is active (the AccessController coprocessor
is installed either as a system coprocessor or on a table as a table coprocessor) then you must
grant all relevant users EXEC privilege if they require the ability to execute coprocessor
endpoint calls. EXEC privilege, like any other permission, can be granted globally to a user, or to
a user on a per table or per namespace basis. For more information on coprocessor endpoints,
see the coprocessor section of the HBase online manual. For more information on granting or
revoking permissions using the AccessController, see the security section of the HBase online
manual.
Default
false
hbase.procedure.regionserver.classes
Description
A comma-separated list of org.apache.hadoop.hbase.procedure.RegionServerProcedureManager
procedure managers that are loaded by default on the active HRegionServer process. The
lifecycle methods (init/start/stop) will be called by the active HRegionServer process to perform
the specific globally barriered procedure. After implementing your own
RegionServerProcedureManager, just put it in HBase’s classpath and add the fully qualified class
name here.
Default
none
hbase.procedure.master.classes
Description
A comma-separated list of org.apache.hadoop.hbase.procedure.MasterProcedureManager
procedure managers that are loaded by default on the active HMaster process. A procedure is
identified by its signature and users can use the signature and an instant name to trigger an
execution of a globally barriered procedure. After implementing your own
MasterProcedureManager, just put it in HBase’s classpath and add the fully qualified class name
here.
Default
none
hbase.coordinated.state.manager.class
Description
Fully qualified name of class implementing coordinated state manager.
Default
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org.apache.hadoop.hbase.coordination.ZkCoordinatedStateManager
hbase.regionserver.storefile.refresh.period
Description
The period (in milliseconds) for refreshing the store files for the secondary regions. 0 means this
feature is disabled. Secondary regions sees new files (from flushes and compactions) from
primary once the secondary region refreshes the list of files in the region (there is no
notification mechanism). But too frequent refreshes might cause extra Namenode pressure. If
the files cannot be refreshed for longer than HFile TTL (hbase.master.hfilecleaner.ttl) the
requests are rejected. Configuring HFile TTL to a larger value is also recommended with this
setting.
Default
0
hbase.region.replica.replication.enabled
Description
Whether asynchronous WAL replication to the secondary region replicas is enabled or not. If
this is enabled, a replication peer named "region_replica_replication" will be created which will
tail the logs and replicate the mutations to region replicas for tables that have region replication
> 1. If this is enabled once, disabling this replication also requires disabling the replication peer
using shell or Admin java class. Replication to secondary region replicas works over standard
inter-cluster replication.
Default
false
hbase.http.filter.initializers
Description
A comma separated list of class names. Each class in the list must extend
org.apache.hadoop.hbase.http.FilterInitializer. The corresponding Filter will be initialized. Then,
the Filter will be applied to all user facing jsp and servlet web pages. The ordering of the list
defines the ordering of the filters. The default StaticUserWebFilter add a user principal as
defined by the hbase.http.staticuser.user property.
Default
org.apache.hadoop.hbase.http.lib.StaticUserWebFilter
hbase.security.visibility.mutations.checkauths
Description
This property if enabled, will check whether the labels in the visibility expression are associated
with the user issuing the mutation
Default
false
hbase.http.max.threads
Description
The maximum number of threads that the HTTP Server will create in its ThreadPool.
66
Default
16
hbase.replication.rpc.codec
Description
The codec that is to be used when replication is enabled so that the tags are also replicated. This
is used along with HFileV3 which supports tags in them. If tags are not used or if the hfile
version used is HFileV2 then KeyValueCodec can be used as the replication codec. Note that
using KeyValueCodecWithTags for replication when there are no tags causes no harm.
Default
org.apache.hadoop.hbase.codec.KeyValueCodecWithTags
hbase.replication.source.maxthreads
Description
The maximum number of threads any replication source will use for shipping edits to the sinks
in parallel. This also limits the number of chunks each replication batch is broken into. Larger
values can improve the replication throughput between the master and slave clusters. The
default of 10 will rarely need to be changed.
Default
10
hbase.http.staticuser.user
Description
The user name to filter as, on static web filters while rendering content. An example use is the
HDFS web UI (user to be used for browsing files).
Default
dr.stack
hbase.regionserver.handler.abort.on.error.percent
Description
The percent of region server RPC threads failed to abort RS. -1 Disable aborting; 0 Abort if even a
single handler has died; 0.x Abort only when this percent of handlers have died; 1 Abort only all
of the handers have died.
Default
0.5
hbase.mob.file.cache.size
Description
Number of opened file handlers to cache. A larger value will benefit reads by providing more
file handlers per mob file cache and would reduce frequent file opening and closing. However, if
this is set too high, this could lead to a "too many opened file handlers" The default value is 1000.
Default
1000
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hbase.mob.cache.evict.period
Description
The amount of time in seconds before the mob cache evicts cached mob files. The default value
is 3600 seconds.
Default
3600
hbase.mob.cache.evict.remain.ratio
Description
The ratio (between 0.0 and 1.0) of files that remains cached after an eviction is triggered when
the number of cached mob files exceeds the hbase.mob.file.cache.size. The default value is 0.5f.
Default
0.5f
hbase.master.mob.ttl.cleaner.period
Description
The period that ExpiredMobFileCleanerChore runs. The unit is second. The default value is one
day. The MOB file name uses only the date part of the file creation time in it. We use this time for
deciding TTL expiry of the files. So the removal of TTL expired files might be delayed. The max
delay might be 24 hrs.
Default
86400
hbase.mob.compaction.mergeable.threshold
Description
If the size of a mob file is less than this value, it’s regarded as a small file and needs to be merged
in mob compaction. The default value is 1280MB.
Default
1342177280
hbase.mob.delfile.max.count
Description
The max number of del files that is allowed in the mob compaction. In the mob compaction,
when the number of existing del files is larger than this value, they are merged until number of
del files is not larger this value. The default value is 3.
Default
3
hbase.mob.compaction.batch.size
Description
The max number of the mob files that is allowed in a batch of the mob compaction. The mob
compaction merges the small mob files to bigger ones. If the number of the small files is very
large, it could lead to a "too many opened file handlers" in the merge. And the merge has to be
split into batches. This value limits the number of mob files that are selected in a batch of the
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mob compaction. The default value is 100.
Default
100
hbase.mob.compaction.chore.period
Description
The period that MobCompactionChore runs. The unit is second. The default value is one week.
Default
604800
hbase.mob.compactor.class
Description
Implementation of mob compactor, the default one is PartitionedMobCompactor.
Default
org.apache.hadoop.hbase.mob.compactions.PartitionedMobCompactor
hbase.mob.compaction.threads.max
Description
The max number of threads used in MobCompactor.
Default
1
hbase.snapshot.master.timeout.millis
Description
Timeout for master for the snapshot procedure execution.
Default
300000
hbase.snapshot.region.timeout
Description
Timeout for regionservers to keep threads in snapshot request pool waiting.
Default
300000
hbase.rpc.rows.warning.threshold
Description
Number of rows in a batch operation above which a warning will be logged.
Default
5000
hbase.master.wait.on.service.seconds
Description
Default is 5 minutes. Make it 30 seconds for tests. See HBASE-19794 for some context.
69
Default
30
7.3. hbase-env.sh
Set HBase environment variables in this file. Examples include options to pass the JVM on start of
an HBase daemon such as heap size and garbage collector configs. You can also set configurations
for HBase configuration, log directories, niceness, ssh options, where to locate process pid files, etc.
Open the file at conf/hbase-env.sh and peruse its content. Each option is fairly well documented. Add
your own environment variables here if you want them read by HBase daemons on startup.
Changes here will require a cluster restart for HBase to notice the change.
7.4. log4j.properties
Edit this file to change rate at which HBase files are rolled and to change the level at which HBase
logs messages.
Changes here will require a cluster restart for HBase to notice the change though log levels can be
changed for particular daemons via the HBase UI.
7.5. Client configuration and dependencies connecting
to an HBase cluster
If you are running HBase in standalone mode, you don’t need to configure anything for your client
to work provided that they are all on the same machine.
Since the HBase Master may move around, clients bootstrap by looking to ZooKeeper for current
critical locations. ZooKeeper is where all these values are kept. Thus clients require the location of
the ZooKeeper ensemble before they can do anything else. Usually this ensemble location is kept
out in the hbase-site.xml and is picked up by the client from the CLASSPATH.
If you are configuring an IDE to run an HBase client, you should include the conf/ directory on your
classpath so hbase-site.xml settings can be found (or add src/test/resources to pick up the hbase-
site.xml used by tests).
For Java applications using Maven, including the hbase-shaded-client module is the recommended
dependency when connecting to a cluster:
<dependency>
Ê <groupId>org.apache.hbase</groupId>
Ê <artifactId>hbase-shaded-client</artifactId>
Ê <version>2.0.0</version>
</dependency>
A basic example hbase-site.xml for client only may look as follows:
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<?xml version="1.0"?>
<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>
<configuration>
Ê <property>
Ê <name>hbase.zookeeper.quorum</name>
Ê <value>example1,example2,example3</value>
Ê <description>The directory shared by region servers.
Ê </description>
Ê </property>
</configuration>
7.5.1. Java client configuration
The configuration used by a Java client is kept in an HBaseConfiguration instance.
The factory method on HBaseConfiguration, HBaseConfiguration.create();, on invocation, will read
in the content of the first hbase-site.xml found on the client’s CLASSPATH, if one is present (Invocation
will also factor in any hbase-default.xml found; an hbase-default.xml ships inside the
hbase.X.X.X.jar). It is also possible to specify configuration directly without having to read from a
hbase-site.xml. For example, to set the ZooKeeper ensemble for the cluster programmatically do as
follows:
Configuration config = HBaseConfiguration.create();
config.set("hbase.zookeeper.quorum", "localhost"); // Here we are running zookeeper
locally
If multiple ZooKeeper instances make up your ZooKeeper ensemble, they may be specified in a
comma-separated list (just as in the hbase-site.xml file). This populated Configuration instance can
then be passed to an Table, and so on.
7.6. Timeout settings
HBase provides a wide variety of timeout settings to limit the execution time of various remote
operations.
•hbase.rpc.timeout
•hbase.rpc.read.timeout
•hbase.rpc.write.timeout
•hbase.client.operation.timeout
•hbase.client.meta.operation.timeout
•hbase.client.scanner.timeout.period
The hbase.rpc.timeout property limits how long a single RPC call can run before timing out. To fine
tune read or write related RPC timeouts set hbase.rpc.read.timeout and hbase.rpc.write.timeout
configuration properties. In the absence of these properties hbase.rpc.timeout will be used.
71
A higher-level timeout is hbase.client.operation.timeout which is valid for each client call. When
an RPC call fails for instance for a timeout due to hbase.rpc.timeout it will be retried until
hbase.client.operation.timeout is reached. Client operation timeout for system tables can be fine
tuned by setting hbase.client.meta.operation.timeout configuration value. When this is not set its
value will use hbase.client.operation.timeout.
Timeout for scan operations is controlled differently. Use hbase.client.scanner.timeout.period
property to set this timeout.
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Chapter 8. Example Configurations
8.1. Basic Distributed HBase Install
Here is a basic configuration example for a distributed ten node cluster: * The nodes are named
example0, example1, etc., through node example9 in this example. * The HBase Master and the HDFS
NameNode are running on the node example0. * RegionServers run on nodes example1-example9. * A
3-node ZooKeeper ensemble runs on example1, example2, and example3 on the default ports. *
ZooKeeper data is persisted to the directory /export/zookeeper.
Below we show what the main configuration files — hbase-site.xml, regionservers, and hbase-
env.sh — found in the HBase conf directory might look like.
8.1.1. hbase-site.xml
<?xml version="1.0"?>
<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>
<configuration>
Ê <property>
Ê <name>hbase.zookeeper.quorum</name>
Ê <value>example1,example2,example3</value>
Ê <description>The directory shared by RegionServers.
Ê </description>
Ê </property>
Ê <property>
Ê <name>hbase.zookeeper.property.dataDir</name>
Ê <value>/export/zookeeper</value>
Ê <description>Property from ZooKeeper config zoo.cfg.
Ê The directory where the snapshot is stored.
Ê </description>
Ê </property>
Ê <property>
Ê <name>hbase.rootdir</name>
Ê <value>hdfs://example0:8020/hbase</value>
Ê <description>The directory shared by RegionServers.
Ê </description>
Ê </property>
Ê <property>
Ê <name>hbase.cluster.distributed</name>
Ê <value>true</value>
Ê <description>The mode the cluster will be in. Possible values are
Ê false: standalone and pseudo-distributed setups with managed ZooKeeper
Ê true: fully-distributed with unmanaged ZooKeeper Quorum (see hbase-env.sh)
Ê </description>
Ê </property>
</configuration>
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8.1.2. regionservers
In this file you list the nodes that will run RegionServers. In our case, these nodes are example1-
example9.
example1
example2
example3
example4
example5
example6
example7
example8
example9
8.1.3. hbase-env.sh
The following lines in the hbase-env.sh file show how to set the JAVA_HOME environment variable
(required for HBase) and set the heap to 4 GB (rather than the default value of 1 GB). If you copy
and paste this example, be sure to adjust the JAVA_HOME to suit your environment.
# The java implementation to use.
export JAVA_HOME=/usr/java/jdk1.8.0/
# The maximum amount of heap to use. Default is left to JVM default.
export HBASE_HEAPSIZE=4G
Use rsync to copy the content of the conf directory to all nodes of the cluster.
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Chapter 9. The Important Configurations
Below we list some important configurations. We’ve divided this section into required configuration
and worth-a-look recommended configs.
9.1. Required Configurations
Review the os and hadoop sections.
9.1.1. Big Cluster Configurations
If you have a cluster with a lot of regions, it is possible that a Regionserver checks in briefly after
the Master starts while all the remaining RegionServers lag behind. This first server to check in will
be assigned all regions which is not optimal. To prevent the above scenario from happening, up the
hbase.master.wait.on.regionservers.mintostart property from its default value of 1. See HBASE-
6389 Modify the conditions to ensure that Master waits for sufficient number of Region Servers
before starting region assignments for more detail.
9.2. Recommended Configurations
9.2.1. ZooKeeper Configuration
zookeeper.session.timeout
The default timeout is three minutes (specified in milliseconds). This means that if a server crashes,
it will be three minutes before the Master notices the crash and starts recovery. You might need to
tune the timeout down to a minute or even less so the Master notices failures sooner. Before
changing this value, be sure you have your JVM garbage collection configuration under control,
otherwise, a long garbage collection that lasts beyond the ZooKeeper session timeout will take out
your RegionServer. (You might be fine with this — you probably want recovery to start on the
server if a RegionServer has been in GC for a long period of time).
To change this configuration, edit hbase-site.xml, copy the changed file across the cluster and
restart.
We set this value high to save our having to field questions up on the mailing lists asking why a
RegionServer went down during a massive import. The usual cause is that their JVM is untuned and
they are running into long GC pauses. Our thinking is that while users are getting familiar with
HBase, we’d save them having to know all of its intricacies. Later when they’ve built some
confidence, then they can play with configuration such as this.
Number of ZooKeeper Instances
See zookeeper.
9.2.2. HDFS Configurations
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dfs.datanode.failed.volumes.tolerated
This is the "…number of volumes that are allowed to fail before a DataNode stops offering service.
By default any volume failure will cause a datanode to shutdown" from the hdfs-default.xml
description. You might want to set this to about half the amount of your available disks.
hbase.regionserver.handler.count
This setting defines the number of threads that are kept open to answer incoming requests to user
tables. The rule of thumb is to keep this number low when the payload per request approaches the
MB (big puts, scans using a large cache) and high when the payload is small (gets, small puts, ICVs,
deletes). The total size of the queries in progress is limited by the setting
hbase.ipc.server.max.callqueue.size.
It is safe to set that number to the maximum number of incoming clients if their payload is small,
the typical example being a cluster that serves a website since puts aren’t typically buffered and
most of the operations are gets.
The reason why it is dangerous to keep this setting high is that the aggregate size of all the puts that
are currently happening in a region server may impose too much pressure on its memory, or even
trigger an OutOfMemoryError. A RegionServer running on low memory will trigger its JVM’s
garbage collector to run more frequently up to a point where GC pauses become noticeable (the
reason being that all the memory used to keep all the requests' payloads cannot be trashed, no
matter how hard the garbage collector tries). After some time, the overall cluster throughput is
affected since every request that hits that RegionServer will take longer, which exacerbates the
problem even more.
You can get a sense of whether you have too little or too many handlers by rpc.logging on an
individual RegionServer then tailing its logs (Queued requests consume memory).
9.2.3. Configuration for large memory machines
HBase ships with a reasonable, conservative configuration that will work on nearly all machine
types that people might want to test with. If you have larger machines — HBase has 8G and larger
heap — you might find the following configuration options helpful. TODO.
9.2.4. Compression
You should consider enabling ColumnFamily compression. There are several options that are near-
frictionless and in most all cases boost performance by reducing the size of StoreFiles and thus
reducing I/O.
See compression for more information.
9.2.5. Configuring the size and number of WAL files
HBase uses wal to recover the memstore data that has not been flushed to disk in case of an RS
failure. These WAL files should be configured to be slightly smaller than HDFS block (by default a
HDFS block is 64Mb and a WAL file is ~60Mb).
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HBase also has a limit on the number of WAL files, designed to ensure there’s never too much data
that needs to be replayed during recovery. This limit needs to be set according to memstore
configuration, so that all the necessary data would fit. It is recommended to allocate enough WAL
files to store at least that much data (when all memstores are close to full). For example, with 16Gb
RS heap, default memstore settings (0.4), and default WAL file size (~60Mb), 16Gb*0.4/60, the
starting point for WAL file count is ~109. However, as all memstores are not expected to be full all
the time, less WAL files can be allocated.
9.2.6. Managed Splitting
HBase generally handles splitting of your regions based upon the settings in your hbase-default.xml
and hbase-site.xml configuration files. Important settings include
hbase.regionserver.region.split.policy, hbase.hregion.max.filesize,
hbase.regionserver.regionSplitLimit. A simplistic view of splitting is that when a region grows to
hbase.hregion.max.filesize, it is split. For most usage patterns, you should use automatic splitting.
See manual region splitting decisions for more information about manual region splitting.
Instead of allowing HBase to split your regions automatically, you can choose to manage the
splitting yourself. Manually managing splits works if you know your keyspace well, otherwise let
HBase figure where to split for you. Manual splitting can mitigate region creation and movement
under load. It also makes it so region boundaries are known and invariant (if you disable region
splitting). If you use manual splits, it is easier doing staggered, time-based major compactions to
spread out your network IO load.
Disable Automatic Splitting
To disable automatic splitting, you can set region split policy in either cluster configuration or table
configuration to be org.apache.hadoop.hbase.regionserver.DisabledRegionSplitPolicy
Automatic Splitting Is Recommended
If you disable automatic splits to diagnose a problem or during a period of fast
data growth, it is recommended to re-enable them when your situation becomes
more stable. The potential benefits of managing region splits yourself are not
undisputed.
Determine the Optimal Number of Pre-Split Regions
The optimal number of pre-split regions depends on your application and environment. A good rule
of thumb is to start with 10 pre-split regions per server and watch as data grows over time. It is
better to err on the side of too few regions and perform rolling splits later. The optimal number of
regions depends upon the largest StoreFile in your region. The size of the largest StoreFile will
increase with time if the amount of data grows. The goal is for the largest region to be just large
enough that the compaction selection algorithm only compacts it during a timed major compaction.
Otherwise, the cluster can be prone to compaction storms with a large number of regions under
compaction at the same time. It is important to understand that the data growth causes compaction
storms and not the manual split decision.
If the regions are split into too many large regions, you can increase the major compaction interval
by configuring HConstants.MAJOR_COMPACTION_PERIOD. The
org.apache.hadoop.hbase.util.RegionSplitter utility also provides a network-IO-safe rolling split of
77
all regions.
9.2.7. Managed Compactions
By default, major compactions are scheduled to run once in a 7-day period.
If you need to control exactly when and how often major compaction runs, you can disable
managed major compactions. See the entry for hbase.hregion.majorcompaction in the
compaction.parameters table for details.
Do Not Disable Major Compactions
Major compactions are absolutely necessary for StoreFile clean-up. Do not disable
them altogether. You can run major compactions manually via the HBase shell or
via the Admin API.
For more information about compactions and the compaction file selection process, see compaction
9.2.8. Speculative Execution
Speculative Execution of MapReduce tasks is on by default, and for HBase clusters it is generally
advised to turn off Speculative Execution at a system-level unless you need it for a specific case,
where it can be configured per-job. Set the properties mapreduce.map.speculative and
mapreduce.reduce.speculative to false.
9.3. Other Configurations
9.3.1. Balancer
The balancer is a periodic operation which is run on the master to redistribute regions on the
cluster. It is configured via hbase.balancer.period and defaults to 300000 (5 minutes).
See master.processes.loadbalancer for more information on the LoadBalancer.
9.3.2. Disabling Blockcache
Do not turn off block cache (You’d do it by setting hfile.block.cache.size to zero). Currently we do
not do well if you do this because the RegionServer will spend all its time loading HFile indices over
and over again. If your working set is such that block cache does you no good, at least size the block
cache such that HFile indices will stay up in the cache (you can get a rough idea on the size you
need by surveying RegionServer UIs; you’ll see index block size accounted near the top of the
webpage).
9.3.3. Nagle’s or the small package problem
If a big 40ms or so occasional delay is seen in operations against HBase, try the Nagles' setting. For
example, see the user mailing list thread, Inconsistent scan performance with caching set to 1 and
the issue cited therein where setting notcpdelay improved scan speeds. You might also see the
graphs on the tail of HBASE-7008 Set scanner caching to a better default where our Lars Hofhansl
78
tries various data sizes w/ Nagle’s on and off measuring the effect.
9.3.4. Better Mean Time to Recover (MTTR)
This section is about configurations that will make servers come back faster after a fail. See the
Deveraj Das and Nicolas Liochon blog post Introduction to HBase Mean Time to Recover (MTTR) for
a brief introduction.
The issue HBASE-8354 forces Namenode into loop with lease recovery requests is messy but has a
bunch of good discussion toward the end on low timeouts and how to cause faster recovery
including citation of fixes added to HDFS. Read the Varun Sharma comments. The below suggested
configurations are Varun’s suggestions distilled and tested. Make sure you are running on a late-
version HDFS so you have the fixes he refers to and himself adds to HDFS that help HBase MTTR
(e.g. HDFS-3703, HDFS-3712, and HDFS-4791 — Hadoop 2 for sure has them and late Hadoop 1 has
some). Set the following in the RegionServer.
<property>
Ê <name>hbase.lease.recovery.dfs.timeout</name>
Ê <value>23000</value>
Ê <description>How much time we allow elapse between calls to recover lease.
Ê Should be larger than the dfs timeout.</description>
</property>
<property>
Ê <name>dfs.client.socket-timeout</name>
Ê <value>10000</value>
Ê <description>Down the DFS timeout from 60 to 10 seconds.</description>
</property>
And on the NameNode/DataNode side, set the following to enable 'staleness' introduced in HDFS-
3703, HDFS-3912.
79
<property>
Ê <name>dfs.client.socket-timeout</name>
Ê <value>10000</value>
Ê <description>Down the DFS timeout from 60 to 10 seconds.</description>
</property>
<property>
Ê <name>dfs.datanode.socket.write.timeout</name>
Ê <value>10000</value>
Ê <description>Down the DFS timeout from 8 * 60 to 10 seconds.</description>
</property>
<property>
Ê <name>ipc.client.connect.timeout</name>
Ê <value>3000</value>
Ê <description>Down from 60 seconds to 3.</description>
</property>
<property>
Ê <name>ipc.client.connect.max.retries.on.timeouts</name>
Ê <value>2</value>
Ê <description>Down from 45 seconds to 3 (2 == 3 retries).</description>
</property>
<property>
Ê <name>dfs.namenode.avoid.read.stale.datanode</name>
Ê <value>true</value>
Ê <description>Enable stale state in hdfs</description>
</property>
<property>
Ê <name>dfs.namenode.stale.datanode.interval</name>
Ê <value>20000</value>
Ê <description>Down from default 30 seconds</description>
</property>
<property>
Ê <name>dfs.namenode.avoid.write.stale.datanode</name>
Ê <value>true</value>
Ê <description>Enable stale state in hdfs</description>
</property>
9.3.5. JMX
JMX (Java Management Extensions) provides built-in instrumentation that enables you to monitor
and manage the Java VM. To enable monitoring and management from remote systems, you need
to set system property com.sun.management.jmxremote.port (the port number through which you
want to enable JMX RMI connections) when you start the Java VM. See the official documentation
for more information. Historically, besides above port mentioned, JMX opens two additional
random TCP listening ports, which could lead to port conflict problem. (See HBASE-10289 for
details)
As an alternative, you can use the coprocessor-based JMX implementation provided by HBase. To
enable it, add below property in hbase-site.xml:
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<property>
Ê <name>hbase.coprocessor.regionserver.classes</name>
Ê <value>org.apache.hadoop.hbase.JMXListener</value>
</property>
DO NOT set com.sun.management.jmxremote.port for Java VM at the same time.
Currently it supports Master and RegionServer Java VM. By default, the JMX listens on TCP port
10102, you can further configure the port using below properties:
<property>
Ê <name>regionserver.rmi.registry.port</name>
Ê <value>61130</value>
</property>
<property>
Ê <name>regionserver.rmi.connector.port</name>
Ê <value>61140</value>
</property>
The registry port can be shared with connector port in most cases, so you only need to configure
regionserver.rmi.registry.port. However if you want to use SSL communication, the 2 ports must be
configured to different values.
By default the password authentication and SSL communication is disabled. To enable password
authentication, you need to update hbase-env.sh like below:
export HBASE_JMX_BASE="-Dcom.sun.management.jmxremote.authenticate=true
\
Ê -Dcom.sun.management.jmxremote.password.file=your_password_file
\
Ê -Dcom.sun.management.jmxremote.access.file=your_access_file"
export HBASE_MASTER_OPTS="$HBASE_MASTER_OPTS $HBASE_JMX_BASE "
export HBASE_REGIONSERVER_OPTS="$HBASE_REGIONSERVER_OPTS $HBASE_JMX_BASE "
See example password/access file under $JRE_HOME/lib/management.
To enable SSL communication with password authentication, follow below steps:
81
#1. generate a key pair, stored in myKeyStore
keytool -genkey -alias jconsole -keystore myKeyStore
#2. export it to file jconsole.cert
keytool -export -alias jconsole -keystore myKeyStore -file jconsole.cert
#3. copy jconsole.cert to jconsole client machine, import it to jconsoleKeyStore
keytool -import -alias jconsole -keystore jconsoleKeyStore -file jconsole.cert
And then update hbase-env.sh like below:
export HBASE_JMX_BASE="-Dcom.sun.management.jmxremote.ssl=true
\
Ê -Djavax.net.ssl.keyStore=/home/tianq/myKeyStore
\
Ê -Djavax.net.ssl.keyStorePassword=your_password_in_step_1
\
Ê -Dcom.sun.management.jmxremote.authenticate=true
\
Ê -Dcom.sun.management.jmxremote.password.file=your_password file
\
Ê -Dcom.sun.management.jmxremote.access.file=your_access_file"
export HBASE_MASTER_OPTS="$HBASE_MASTER_OPTS $HBASE_JMX_BASE "
export HBASE_REGIONSERVER_OPTS="$HBASE_REGIONSERVER_OPTS $HBASE_JMX_BASE "
Finally start jconsole on the client using the key store:
jconsole -J-Djavax.net.ssl.trustStore=/home/tianq/jconsoleKeyStore
To enable the HBase JMX implementation on Master, you also need to add below
property in hbase-site.xml:
<property>
Ê <name>hbase.coprocessor.master.classes</name>
Ê <value>org.apache.hadoop.hbase.JMXListener</value>
</property>
The corresponding properties for port configuration are master.rmi.registry.port (by default
10101) and master.rmi.connector.port (by default the same as registry.port)
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Chapter 10. Dynamic Configuration
It is possible to change a subset of the configuration without requiring a server restart. In the HBase
shell, the operations update_config and update_all_config will prompt a server or all servers to
reload configuration.
Only a subset of all configurations can currently be changed in the running server. Here are those
configurations:
Table 3. Configurations support dynamically change
Key
hbase.ipc.server.fallback-to-simple-auth-allowed
hbase.cleaner.scan.dir.concurrent.size
hbase.regionserver.thread.compaction.large
hbase.regionserver.thread.compaction.small
hbase.regionserver.thread.split
hbase.regionserver.throughput.controller
hbase.regionserver.thread.hfilecleaner.throttle
hbase.regionserver.hfilecleaner.large.queue.size
hbase.regionserver.hfilecleaner.small.queue.size
hbase.regionserver.hfilecleaner.large.thread.count
hbase.regionserver.hfilecleaner.small.thread.count
hbase.regionserver.hfilecleaner.thread.timeout.msec
hbase.regionserver.hfilecleaner.thread.check.interval.msec
hbase.regionserver.flush.throughput.controller
hbase.hstore.compaction.max.size
hbase.hstore.compaction.max.size.offpeak
hbase.hstore.compaction.min.size
hbase.hstore.compaction.min
hbase.hstore.compaction.max
hbase.hstore.compaction.ratio
hbase.hstore.compaction.ratio.offpeak
hbase.regionserver.thread.compaction.throttle
hbase.hregion.majorcompaction
hbase.hregion.majorcompaction.jitter
hbase.hstore.min.locality.to.skip.major.compact
hbase.hstore.compaction.date.tiered.max.storefile.age.millis
hbase.hstore.compaction.date.tiered.incoming.window.min
hbase.hstore.compaction.date.tiered.window.policy.class
83
Key
hbase.hstore.compaction.date.tiered.single.output.for.minor.compaction
hbase.hstore.compaction.date.tiered.window.factory.class
hbase.offpeak.start.hour
hbase.offpeak.end.hour
hbase.oldwals.cleaner.thread.size
hbase.oldwals.cleaner.thread.timeout.msec
hbase.oldwals.cleaner.thread.check.interval.msec
hbase.procedure.worker.keep.alive.time.msec
hbase.procedure.worker.add.stuck.percentage
hbase.procedure.worker.monitor.interval.msec
hbase.procedure.worker.stuck.threshold.msec
hbase.regions.slop
hbase.regions.overallSlop
hbase.balancer.tablesOnMaster
hbase.balancer.tablesOnMaster.systemTablesOnly
hbase.util.ip.to.rack.determiner
hbase.ipc.server.max.callqueue.length
hbase.ipc.server.priority.max.callqueue.length
hbase.ipc.server.callqueue.type
hbase.ipc.server.callqueue.codel.target.delay
hbase.ipc.server.callqueue.codel.interval
hbase.ipc.server.callqueue.codel.lifo.threshold
hbase.master.balancer.stochastic.maxSteps
hbase.master.balancer.stochastic.stepsPerRegion
hbase.master.balancer.stochastic.maxRunningTime
hbase.master.balancer.stochastic.runMaxSteps
hbase.master.balancer.stochastic.numRegionLoadsToRemember
hbase.master.loadbalance.bytable
hbase.master.balancer.stochastic.minCostNeedBalance
hbase.master.balancer.stochastic.localityCost
hbase.master.balancer.stochastic.rackLocalityCost
hbase.master.balancer.stochastic.readRequestCost
hbase.master.balancer.stochastic.writeRequestCost
hbase.master.balancer.stochastic.memstoreSizeCost
hbase.master.balancer.stochastic.storefileSizeCost
hbase.master.balancer.stochastic.regionReplicaHostCostKey
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Key
hbase.master.balancer.stochastic.regionReplicaRackCostKey
hbase.master.balancer.stochastic.regionCountCost
hbase.master.balancer.stochastic.primaryRegionCountCost
hbase.master.balancer.stochastic.moveCost
hbase.master.balancer.stochastic.maxMovePercent
hbase.master.balancer.stochastic.tableSkewCost
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Upgrading
You cannot skip major versions when upgrading. If you are upgrading from version 0.98.x to 2.x,
you must first go from 0.98.x to 1.2.x and then go from 1.2.x to 2.x.
Review Apache HBase Configuration, in particular Hadoop. Familiarize yourself with Support and
Testing Expectations.
86
Chapter 11. HBase version number and
compatibility
11.1. Aspirational Semantic Versioning
Starting with the 1.0.0 release, HBase is working towards Semantic Versioning for its release
versioning. In summary:
Given a version number MAJOR.MINOR.PATCH, increment the:
•MAJOR version when you make incompatible API changes,
•MINOR version when you add functionality in a backwards-compatible manner, and
•PATCH version when you make backwards-compatible bug fixes.
•Additional labels for pre-release and build metadata are available as extensions to the
MAJOR.MINOR.PATCH format.
Compatibility Dimensions
In addition to the usual API versioning considerations HBase has other compatibility dimensions
that we need to consider.
Client-Server wire protocol compatibility
•Allows updating client and server out of sync.
•We could only allow upgrading the server first. I.e. the server would be backward compatible to
an old client, that way new APIs are OK.
•Example: A user should be able to use an old client to connect to an upgraded cluster.
Server-Server protocol compatibility
•Servers of different versions can co-exist in the same cluster.
•The wire protocol between servers is compatible.
•Workers for distributed tasks, such as replication and log splitting, can co-exist in the same
cluster.
•Dependent protocols (such as using ZK for coordination) will also not be changed.
•Example: A user can perform a rolling upgrade.
File format compatibility
•Support file formats backward and forward compatible
•Example: File, ZK encoding, directory layout is upgraded automatically as part of an HBase
upgrade. User can downgrade to the older version and everything will continue to work.
Client API compatibility
•Allow changing or removing existing client APIs.
•An API needs to be deprecated for a major version before we will change/remove it.
87
•APIs available in a patch version will be available in all later patch versions. However, new APIs
may be added which will not be available in earlier patch versions.
•New APIs introduced in a patch version will only be added in a source compatible way [1: See
'Source Compatibility' https://blogs.oracle.com/darcy/entry/kinds_of_compatibility]: i.e. code that
implements public APIs will continue to compile.
◦Example: A user using a newly deprecated API does not need to modify application code
with HBase API calls until the next major version. *
Client Binary compatibility
•Client code written to APIs available in a given patch release can run unchanged (no
recompilation needed) against the new jars of later patch versions.
•Client code written to APIs available in a given patch release might not run against the old jars
from an earlier patch version.
◦Example: Old compiled client code will work unchanged with the new jars.
•If a Client implements an HBase Interface, a recompile MAY be required upgrading to a newer
minor version (See release notes for warning about incompatible changes). All effort will be
made to provide a default implementation so this case should not arise.
Server-Side Limited API compatibility (taken from Hadoop)
•Internal APIs are marked as Stable, Evolving, or Unstable
•This implies binary compatibility for coprocessors and plugins (pluggable classes, including
replication) as long as these are only using marked interfaces/classes.
•Example: Old compiled Coprocessor, Filter, or Plugin code will work unchanged with the new
jars.
Dependency Compatibility
•An upgrade of HBase will not require an incompatible upgrade of a dependent project, except
for Apache Hadoop.
•An upgrade of HBase will not require an incompatible upgrade of the Java runtime.
•Example: Upgrading HBase to a version that supports Dependency Compatibility won’t require
that you upgrade your Apache ZooKeeper service.
•Example: If your current version of HBase supported running on JDK 8, then an upgrade to a
version that supports Dependency Compatibility will also run on JDK 8.
Hadoop Versions
Previously, we tried to maintain dependency compatibility for the underly Hadoop
service but over the last few years this has proven untenable. While the HBase
project attempts to maintain support for older versions of Hadoop, we drop the
"supported" designator for minor versions that fail to continue to see releases.
Additionally, the Hadoop project has its own set of compatibility guidelines, which
means in some cases having to update to a newer supported minor release might
break some of our compatibility promises.
Operational Compatibility
88
•Metric changes
•Behavioral changes of services
•JMX APIs exposed via the /jmx/ endpoint
Summary
•A patch upgrade is a drop-in replacement. Any change that is not Java binary and source
compatible would not be allowed. [2: See http://docs.oracle.com/javase/specs/jls/se7/html/jls-
13.html.] Downgrading versions within patch releases may not be compatible.
•A minor upgrade requires no application/client code modification. Ideally it would be a drop-in
replacement but client code, coprocessors, filters, etc might have to be recompiled if new jars
are used.
•A major upgrade allows the HBase community to make breaking changes.
Table 4. Compatibility Matrix [4: Note that this indicates what could break, not that it will break. We
will/should add specifics in our release notes.]
Major Minor Patch
Client-Server wire
Compatibility
N Y Y
Server-Server
Compatibility
N Y Y
File Format
Compatibility
N [3:
comp_matrix_offline_u
pgrade_note,Running
an offline upgrade tool
without downgrade
might be needed. We
will typically only
support migrating data
from major version X
to major version X+1.]
Y Y
Client API
Compatibility
N Y Y
Client Binary
Compatibility
NNY
Server-Side Limited API Compatibility
Stable N Y Y
Evolving N N Y
Unstable N N N
Dependency
Compatibility
N Y Y
Operational
Compatibility
NNY
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11.1.1. HBase API Surface
HBase has a lot of API points, but for the compatibility matrix above, we differentiate between
Client API, Limited Private API, and Private API. HBase uses Apache Yetus Audience Annotations to
guide downstream expectations for stability.
•InterfaceAudience (javadocs): captures the intended audience, possible values include:
◦Public: safe for end users and external projects
◦LimitedPrivate: used for internals we expect to be pluggable, such as coprocessors
◦Private: strictly for use within HBase itself Classes which are defined as IA.Private may be
used as parameters or return values for interfaces which are declared IA.LimitedPrivate.
Treat the IA.Private object as opaque; do not try to access its methods or fields directly.
•InterfaceStability (javadocs): describes what types of interface changes are permitted. Possible
values include:
◦Stable: the interface is fixed and is not expected to change
◦Evolving: the interface may change in future minor verisons
◦Unstable: the interface may change at any time
Please keep in mind the following interactions between the InterfaceAudience and
InterfaceStability annotations within the HBase project:
•IA.Public classes are inherently stable and adhere to our stability guarantees relating to the
type of upgrade (major, minor, or patch).
•IA.LimitedPrivate classes should always be annotated with one of the given InterfaceStability
values. If they are not, you should presume they are IS.Unstable.
•IA.Private classes should be considered implicitly unstable, with no guarantee of stability
between releases.
HBase Client API
HBase Client API consists of all the classes or methods that are marked with
InterfaceAudience.Public interface. All main classes in hbase-client and dependent modules
have either InterfaceAudience.Public, InterfaceAudience.LimitedPrivate, or
InterfaceAudience.Private marker. Not all classes in other modules (hbase-server, etc) have the
marker. If a class is not annotated with one of these, it is assumed to be a
InterfaceAudience.Private class.
HBase LimitedPrivate API
LimitedPrivate annotation comes with a set of target consumers for the interfaces. Those
consumers are coprocessors, phoenix, replication endpoint implementations or similar. At this
point, HBase only guarantees source and binary compatibility for these interfaces between
patch versions.
HBase Private API
All classes annotated with InterfaceAudience.Private or all classes that do not have the
annotation are for HBase internal use only. The interfaces and method signatures can change at
90
any point in time. If you are relying on a particular interface that is marked Private, you should
open a jira to propose changing the interface to be Public or LimitedPrivate, or an interface
exposed for this purpose.
Binary Compatibility
When we say two HBase versions are compatible, we mean that the versions are wire and binary
compatible. Compatible HBase versions means that clients can talk to compatible but differently
versioned servers. It means too that you can just swap out the jars of one version and replace them
with the jars of another, compatible version and all will just work. Unless otherwise specified,
HBase point versions are (mostly) binary compatible. You can safely do rolling upgrades between
binary compatible versions; i.e. across maintenance releases: e.g. from 1.2.4 to 1.2.6. See link:[Does
compatibility between versions also mean binary compatibility?] discussion on the HBase dev
mailing list.
11.2. Rolling Upgrades
A rolling upgrade is the process by which you update the servers in your cluster a server at a time.
You can rolling upgrade across HBase versions if they are binary or wire compatible. See Rolling
Upgrade Between Versions that are Binary/Wire Compatible for more on what this means. Coarsely,
a rolling upgrade is a graceful stop each server, update the software, and then restart. You do this
for each server in the cluster. Usually you upgrade the Master first and then the RegionServers. See
Rolling Restart for tools that can help use the rolling upgrade process.
For example, in the below, HBase was symlinked to the actual HBase install. On upgrade, before
running a rolling restart over the cluster, we changed the symlink to point at the new HBase
software version and then ran
$ HADOOP_HOME=~/hadoop-2.6.0-CRC-SNAPSHOT ~/hbase/bin/rolling-restart.sh --config
~/conf_hbase
The rolling-restart script will first gracefully stop and restart the master, and then each of the
RegionServers in turn. Because the symlink was changed, on restart the server will come up using
the new HBase version. Check logs for errors as the rolling upgrade proceeds.
Rolling Upgrade Between Versions that are Binary/Wire Compatible
Unless otherwise specified, HBase minor versions are binary compatible. You can do a Rolling
Upgrades between HBase point versions. For example, you can go to 1.2.4 from 1.2.6 by doing a
rolling upgrade across the cluster replacing the 1.2.4 binary with a 1.2.6 binary.
In the minor version-particular sections below, we call out where the versions are wire/protocol
compatible and in this case, it is also possible to do a Rolling Upgrades.
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Chapter 12. Rollback
Sometimes things don’t go as planned when attempting an upgrade. This section explains how to
perform a rollback to an earlier HBase release. Note that this should only be needed between Major
and some Minor releases. You should always be able to downgrade between HBase Patch releases
within the same Minor version. These instructions may require you to take steps before you start
the upgrade process, so be sure to read through this section beforehand.
12.1. Caveats
Rollback vs Downgrade
This section describes how to perform a rollback on an upgrade between HBase minor and major
versions. In this document, rollback refers to the process of taking an upgraded cluster and
restoring it to the old version while losing all changes that have occurred since upgrade. By contrast,
a cluster downgrade would restore an upgraded cluster to the old version while maintaining any
data written since the upgrade. We currently only offer instructions to rollback HBase clusters.
Further, rollback only works when these instructions are followed prior to performing the upgrade.
When these instructions talk about rollback vs downgrade of prerequisite cluster services (i.e.
HDFS), you should treat leaving the service version the same as a degenerate case of downgrade.
Replication
Unless you are doing an all-service rollback, the HBase cluster will lose any configured peers for
HBase replication. If your cluster is configured for HBase replication, then prior to following these
instructions you should document all replication peers. After performing the rollback you should
then add each documented peer back to the cluster. For more information on enabling HBase
replication, listing peers, and adding a peer see Managing and Configuring Cluster Replication. Note
also that data written to the cluster since the upgrade may or may not have already been replicated
to any peers. Determining which, if any, peers have seen replication data as well as rolling back the
data in those peers is out of the scope of this guide.
Data Locality
Unless you are doing an all-service rollback, going through a rollback procedure will likely destroy
all locality for Region Servers. You should expect degraded performance until after the cluster has
had time to go through compactions to restore data locality. Optionally, you can force a compaction
to speed this process up at the cost of generating cluster load.
Configurable Locations
The instructions below assume default locations for the HBase data directory and the HBase znode.
Both of these locations are configurable and you should verify the value used in your cluster before
proceeding. In the event that you have a different value, just replace the default with the one found
in your configuration * HBase data directory is configured via the key 'hbase.rootdir' and has a
default value of '/hbase'. * HBase znode is configured via the key 'zookeeper.znode.parent' and has
a default value of '/hbase'.
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12.2. All service rollback
If you will be performing a rollback of both the HDFS and ZooKeeper services, then HBase’s data
will be rolled back in the process.
Requirements
•Ability to rollback HDFS and ZooKeeper
Before upgrade
No additional steps are needed pre-upgrade. As an extra precautionary measure, you may wish to
use distcp to back up the HBase data off of the cluster to be upgraded. To do so, follow the steps in
the 'Before upgrade' section of 'Rollback after HDFS downgrade' but copy to another HDFS instance
instead of within the same instance.
Performing a rollback
1. Stop HBase
2. Perform a rollback for HDFS and ZooKeeper (HBase should remain stopped)
3. Change the installed version of HBase to the previous version
4. Start HBase
5. Verify HBase contents—use the HBase shell to list tables and scan some known values.
12.3. Rollback after HDFS rollback and ZooKeeper
downgrade
If you will be rolling back HDFS but going through a ZooKeeper downgrade, then HBase will be in
an inconsistent state. You must ensure the cluster is not started until you complete this process.
Requirements
•Ability to rollback HDFS
•Ability to downgrade ZooKeeper
Before upgrade
No additional steps are needed pre-upgrade. As an extra precautionary measure, you may wish to
use distcp to back up the HBase data off of the cluster to be upgraded. To do so, follow the steps in
the 'Before upgrade' section of 'Rollback after HDFS downgrade' but copy to another HDFS instance
instead of within the same instance.
Performing a rollback
1. Stop HBase
2. Perform a rollback for HDFS and a downgrade for ZooKeeper (HBase should remain stopped)
3. Change the installed version of HBase to the previous version
4. Clean out ZooKeeper information related to HBase. WARNING: This step will permanently
destroy all replication peers. Please see the section on HBase Replication under Caveats for
more information.
93
Clean HBase information out of ZooKeeper
[hpnewton@gateway_node.example.com ~]$ zookeeper-client -server
zookeeper1.example.com:2181,zookeeper2.example.com:2181,zookeeper3.example.com:2181
Welcome to ZooKeeper!
JLine support is disabled
rmr /hbase
quit
Quitting...
5. Start HBase
6. Verify HBase contents—use the HBase shell to list tables and scan some known values.
12.4. Rollback after HDFS downgrade
If you will be performing an HDFS downgrade, then you’ll need to follow these instructions
regardless of whether ZooKeeper goes through rollback, downgrade, or reinstallation.
Requirements
•Ability to downgrade HDFS
•Pre-upgrade cluster must be able to run MapReduce jobs
•HDFS super user access
•Sufficient space in HDFS for at least two copies of the HBase data directory
Before upgrade
Before beginning the upgrade process, you must take a complete backup of HBase’s backing data.
The following instructions cover backing up the data within the current HDFS instance.
Alternatively, you can use the distcp command to copy the data to another HDFS cluster.
1. Stop the HBase cluster
2. Copy the HBase data directory to a backup location using the distcp command as the HDFS
super user (shown below on a security enabled cluster)
Using distcp to backup the HBase data directory
[hpnewton@gateway_node.example.com ~]$ kinit -k -t hdfs.keytab hdfs@EXAMPLE.COM
[hpnewton@gateway_node.example.com ~]$ hadoop distcp /hbase /hbase-pre-upgrade-
backup
3. Distcp will launch a mapreduce job to handle copying the files in a distributed fashion. Check
the output of the distcp command to ensure this job completed successfully.
Performing a rollback
1. Stop HBase
2. Perform a downgrade for HDFS and a downgrade/rollback for ZooKeeper (HBase should remain
stopped)
94
3. Change the installed version of HBase to the previous version
4. Restore the HBase data directory from prior to the upgrade as the HDFS super user (shown
below on a security enabled cluster). If you backed up your data on another HDFS cluster
instead of locally, you will need to use the distcp command to copy it back to the current HDFS
cluster.
Restore the HBase data directory
[hpnewton@gateway_node.example.com ~]$ kinit -k -t hdfs.keytab hdfs@EXAMPLE.COM
[hpnewton@gateway_node.example.com ~]$ hdfs dfs -mv /hbase /hbase-upgrade-rollback
[hpnewton@gateway_node.example.com ~]$ hdfs dfs -mv /hbase-pre-upgrade-backup
/hbase
5. Clean out ZooKeeper information related to HBase. WARNING: This step will permanently
destroy all replication peers. Please see the section on HBase Replication under Caveats for
more information.
Clean HBase information out of ZooKeeper
[hpnewton@gateway_node.example.com ~]$ zookeeper-client -server
zookeeper1.example.com:2181,zookeeper2.example.com:2181,zookeeper3.example.com:2181
Welcome to ZooKeeper!
JLine support is disabled
rmr /hbase
quit
Quitting...
6. Start HBase
7. Verify HBase contents–use the HBase shell to list tables and scan some known values.
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Chapter 13. Upgrade Paths
13.1. Upgrading from 1.x to 2.x
In this section we will first call out significant changes compared to the prior stable HBase release
and then go over the upgrade process. Be sure to read the former with care so you avoid suprises.
13.1.1. Changes of Note!
First we’ll cover deployment / operational changes that you might hit when upgrading to HBase
2.0+. After that we’ll call out changes for downstream applications. Please note that Coprocessors
are covered in the operational section. Also note that this section is not meant to convey
information about new features that may be of interest to you. For a complete summary of changes,
please see the CHANGES.txt file in the source release artifact for the version you are planning to
upgrade to.
Update to basic prerequisite minimums in HBase 2.0+
As noted in the section Basic Prerequisites, HBase 2.0+ requires a minimum of Java 8 and Hadoop
2.6. The HBase community recommends ensuring you have already completed any needed
upgrades in prerequisites prior to upgrading your HBase version.
HBCK must match HBase server version
You must not use an HBase 1.x version of HBCK against an HBase 2.0+ cluster. HBCK is strongly tied
to the HBase server version. Using the HBCK tool from an earlier release against an HBase 2.0+
cluster will destructively alter said cluster in unrecoverable ways.
As of HBase 2.0, HBCK (A.K.A HBCK1 or hbck1) is a read-only tool that can report the status of some
non-public system internals. You should not rely on the format nor content of these internals to
remain consistent across HBase releases.
To read about HBCK’s replacement, see HBase HBCK2 in Apache HBase Operational Management.
Configuration settings no longer in HBase 2.0+
The following configuration settings are no longer applicable or available. For details, please see
the detailed release notes.
•hbase.config.read.zookeeper.config (see ZooKeeper configs no longer read from zoo.cfg for
migration details)
•hbase.zookeeper.useMulti (HBase now always uses ZK’s multi functionality)
•hbase.rpc.client.threads.max
•hbase.rpc.client.nativetransport
•hbase.fs.tmp.dir
•hbase.bucketcache.combinedcache.enabled
•hbase.bucketcache.ioengine no longer supports the 'heap' value.
•hbase.bulkload.staging.dir
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•hbase.balancer.tablesOnMaster wasn’t removed, strictly speaking, but its meaning has
fundamentally changed and users should not set it. See the section "Master hosting regions"
feature broken and unsupported for details.
•hbase.master.distributed.log.replay See the section "Distributed Log Replay" feature broken and
removed for details
•hbase.regionserver.disallow.writes.when.recovering See the section "Distributed Log Replay"
feature broken and removed for details
•hbase.regionserver.wal.logreplay.batch.size See the section "Distributed Log Replay" feature
broken and removed for details
•hbase.master.catalog.timeout
•hbase.regionserver.catalog.timeout
•hbase.metrics.exposeOperationTimes
•hbase.metrics.showTableName
•hbase.online.schema.update.enable (HBase now always supports this)
•hbase.thrift.htablepool.size.max
Configuration properties that were renamed in HBase 2.0+
The following properties have been renamed. Attempts to set the old property will be ignored at
run time.
Table 5. Renamed properties
Old name New name
hbase.rpc.server.nativetransport hbase.netty.nativetransport
hbase.netty.rpc.server.worker.count hbase.netty.worker.count
hbase.hfile.compactions.discharger.interval hbase.hfile.compaction.discharger.interval
hbase.hregion.percolumnfamilyflush.size.lower.
bound
hbase.hregion.percolumnfamilyflush.size.lower.
bound.min
Configuration settings with different defaults in HBase 2.0+
The following configuration settings changed their default value. Where applicable, the value to set
to restore the behavior of HBase 1.2 is given.
•hbase.security.authorization now defaults to false. set to true to restore same behavior as
previous default.
•hbase.client.retries.number is now set to 10. Previously it was 35. Downstream users are
advised to use client timeouts as described in section Timeout settings instead.
•hbase.client.serverside.retries.multiplier is now set to 3. Previously it was 10. Downstream users
are advised to use client timesout as describe in section Timeout settings instead.
•hbase.master.fileSplitTimeout is now set to 10 minutes. Previously it was 30 seconds.
•hbase.regionserver.logroll.multiplier is now set to 0.5. Previously it was 0.95. This change is tied
with the following doubling of block size. Combined, these two configuration changes should
make for WALs of about the same size as those in hbase-1.x but there should be less incidence of
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small blocks because we fail to roll the WAL before we hit the blocksize threshold. See HBASE-
19148 for discussion.
•hbase.regionserver.hlog.blocksize defaults to 2x the HDFS default block size for the WAL dir.
Previously it was equal to the HDFS default block size for the WAL dir.
•hbase.client.start.log.errors.counter changed to 5. Previously it was 9.
•hbase.ipc.server.callqueue.type changed to 'fifo'. In HBase versions 1.0 - 1.2 it was 'deadline'. In
prior and later 1.x versions it already defaults to 'fifo'.
•hbase.hregion.memstore.chunkpool.maxsize is 1.0 by default. Previously it was 0.0. Effectively,
this means previously we would not use a chunk pool when our memstore is onheap and now
we will. See the section Long GC pauses for more infromation about the MSLAB chunk pool.
•hbase.master.cleaner.interval is now set to 10 minutes. Previously it was 1 minute.
•hbase.master.procedure.threads will now default to 1/4 of the number of available CPUs, but
not less than 16 threads. Previously it would be number of threads equal to number of CPUs.
•hbase.hstore.blockingStoreFiles is now 16. Previously it was 10.
•hbase.http.max.threads is now 16. Previously it was 10.
•hbase.client.max.perserver.tasks is now 2. Previously it was 5.
•hbase.normalizer.period is now 5 minutes. Previously it was 30 minutes.
•hbase.regionserver.region.split.policy is now SteppingSplitPolicy. Previously it was
IncreasingToUpperBoundRegionSplitPolicy.
•replication.source.ratio is now 0.5. Previously it was 0.1.
"Master hosting regions" feature broken and unsupported
The feature "Master acts as region server" and associated follow-on work available in HBase 1.y is
non-functional in HBase 2.y and should not be used in a production setting due to deadlock on
Master initialization. Downstream users are advised to treat related configuration settings as
experimental and the feature as inappropriate for production settings.
A brief summary of related changes:
•Master no longer carries regions by default
•hbase.balancer.tablesOnMaster is a boolean, default false (if it holds an HBase 1.x list of tables,
will default to false)
•hbase.balancer.tablesOnMaster.systemTablesOnly is boolean to keep user tables off master.
default false
•those wishing to replicate old list-of-servers config should deploy a stand-alone RegionServer
process and then rely on Region Server Groups
"Distributed Log Replay" feature broken and removed
The Distributed Log Replay feature was broken and has been removed from HBase 2.y+. As a
consequence all related configs, metrics, RPC fields, and logging have also been removed. Note that
this feature was found to be unreliable in the run up to HBase 1.0, defaulted to being unused, and
was effectively removed in HBase 1.2.0 when we started ignoring the config that turns it on
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(HBASE-14465). If you are currently using the feature, be sure to perform a clean shutdown, ensure
all DLR work is complete, and disable the feature prior to upgrading.
prefix-tree encoding removed
The prefix-tree encoding was removed from HBase 2.0.0 (HBASE-19179). It was (late!) deprecated in
hbase-1.2.7, hbase-1.4.0, and hbase-1.3.2.
This feature was removed because it as not being actively maintained. If interested in reviving this
sweet facility which improved random read latencies at the expensive of slowed writes, write the
HBase developers list at dev at hbase dot apache dot org.
The prefix-tree encoding needs to be removed from all tables before upgrading to HBase 2.0+. To do
that first you need to change the encoding from PREFIX_TREE to something else that is supported in
HBase 2.0. After that you have to major compact the tables that were using PREFIX_TREE encoding
before. To check which column families are using incompatible data block encoding you can use
Pre-Upgrade Validator.
Changed metrics
The following metrics have changed names:
•Metrics previously published under the name "AssignmentManger" [sic] are now published
under the name "AssignmentManager"
The following metrics have changed their meaning:
•The metric 'blockCacheEvictionCount' published on a per-region server basis no longer includes
blocks removed from the cache due to the invalidation of the hfiles they are from (e.g. via
compaction).
•The metric 'totalRequestCount' increments once per request; previously it incremented by the
number of Actions carried in the request; e.g. if a request was a multi made of four Gets and two
Puts, we’d increment 'totalRequestCount' by six; now we increment by one regardless. Expect to
see lower values for this metric in hbase-2.0.0.
•The 'readRequestCount' now counts reads that return a non-empty row where in older hbases,
we’d increment 'readRequestCount' whether a Result or not. This change will flatten the profile
of the read-requests graphs if requests for non-existent rows. A YCSB read-heavy workload can
do this dependent on how the database was loaded.
The following metrics have been removed:
•Metrics related to the Distributed Log Replay feature are no longer present. They were
previsouly found in the region server context under the name 'replay'. See the section
"Distributed Log Replay" feature broken and removed for details.
The following metrics have been added:
•'totalRowActionRequestCount' is a count of region row actions summing reads and writes.
Changed logging
HBase-2.0.0 now uses slf4j as its logging frontend. Prevously, we used log4j (1.2). For most the
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transition should be seamless; slf4j does a good job interpreting log4j.properties logging
configuration files such that you should not notice any difference in your log system emissions.
That said, your log4j.properties may need freshening. See HBASE-20351 for example, where a stale
log configuration file manifest as netty configuration being dumped at DEBUG level as preamble on
every shell command invocation.
ZooKeeper configs no longer read from zoo.cfg
HBase no longer optionally reads the 'zoo.cfg' file for ZooKeeper related configuration settings. If
you previously relied on the 'hbase.config.read.zookeeper.config' config for this functionality, you
should migrate any needed settings to the hbase-site.xml file while adding the prefix
'hbase.zookeeper.property.' to each property name.
Changes in permissions
The following permission related changes either altered semantics or defaults:
•Permissions granted to a user now merge with existing permissions for that user, rather than
over-writing them. (see the release note on HBASE-17472 for details)
•Region Server Group commands (added in 1.4.0) now require admin privileges.
Most Admin APIs don’t work against an HBase 2.0+ cluster from pre-HBase 2.0 clients
A number of admin commands are known to not work when used from a pre-HBase 2.0 client. This
includes an HBase Shell that has the library jars from pre-HBase 2.0. You will need to plan for an
outage of use of admin APIs and commands until you can also update to the needed client version.
The following client operations do not work against HBase 2.0+ cluster when executed from a pre-
HBase 2.0 client:
•list_procedures
•split
•merge_region
•list_quotas
•enable_table_replication
•disable_table_replication
•Snapshot related commands
Deprecated in 1.0 admin commands have been removed.
The following commands that were deprecated in 1.0 have been removed. Where applicable the
replacement command is listed.
•The 'hlog' command has been removed. Downstream users should rely on the 'wal' command
instead.
Region Server memory consumption changes.
Users upgrading from versions prior to HBase 1.4 should read the instructions in section Region
Server memory consumption changes..
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Additionally, HBase 2.0 has changed how memstore memory is tracked for flushing decisions.
Previously, both the data size and overhead for storage were used to calculate utilization against
the flush threashold. Now, only data size is used to make these per-region decisions. Globally the
addition of the storage overhead is used to make decisions about forced flushes.
Web UI for splitting and merging operate on row prefixes
Previously, the Web UI included functionality on table status pages to merge or split based on an
encoded region name. In HBase 2.0, instead this functionality works by taking a row prefix.
Special upgrading for Replication users from pre-HBase 1.4
User running versions of HBase prior to the 1.4.0 release that make use of replication should be
sure to read the instructions in the section Replication peer’s TableCFs config.
HBase shell changes
The HBase shell command relies on a bundled JRuby instance. This bundled JRuby been updated
from version 1.6.8 to version 9.1.10.0. The represents a change from Ruby 1.8 to Ruby 2.3.3, which
introduces non-compatible language changes for user scripts.
The HBase shell command now ignores the '--return-values' flag that was present in early HBase 1.4
releases. Instead the shell always behaves as though that flag were passed. If you wish to avoid
having expression results printed in the console you should alter your IRB configuration as noted in
the section irbrc.
Coprocessor APIs have changed in HBase 2.0+
All Coprocessor APIs have been refactored to improve supportability around binary API
compatibility for future versions of HBase. If you or applications you rely on have custom HBase
coprocessors, you should read the release notes for HBASE-18169 for details of changes you will
need to make prior to upgrading to HBase 2.0+.
For example, if you had a BaseRegionObserver in HBase 1.2 then at a minimum you will need to
update it to implement both RegionObserver and RegionCoprocessor and add the method
...
Ê @Override
Ê public Optional<RegionObserver> getRegionObserver() {
Ê return Optional.of(this);
Ê }
...
HBase 2.0+ can no longer write HFile v2 files.
HBase has simplified our internal HFile handling. As a result, we can no longer write HFile versions
earlier than the default of version 3. Upgrading users should ensure that hfile.format.version is not
set to 2 in hbase-site.xml before upgrading. Failing to do so will cause Region Server failure. HBase
can still read HFiles written in the older version 2 format.
HBase 2.0+ can no longer read Sequence File based WAL file.
HBase can no longer read the deprecated WAL files written in the Apache Hadoop Sequence File
format. The hbase.regionserver.hlog.reader.impl and hbase.regionserver.hlog.reader.impl
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configuration entries should be set to use the Protobuf based WAL reader / writer classes. This
implementation has been the default since HBase 0.96, so legacy WAL files should not be a concern
for most downstream users.
A clean cluster shutdown should ensure there are no WAL files. If you are unsure of a given WAL
file’s format you can use the hbase wal command to parse files while the HBase cluster is offline. In
HBase 2.0+, this command will not be able to read a Sequence File based WAL. For more
information on the tool see the section WALPrettyPrinter.
Change in behavior for filters
The Filter ReturnCode NEXT_ROW has been redefined as skipping to next row in current family, not
to next row in all family. it’s more reasonable, because ReturnCode is a concept in store level, not in
region level.
Downstream HBase 2.0+ users should use the shaded client
Downstream users are strongly urged to rely on the Maven coordinates org.apache.hbase:hbase-
shaded-client for their runtime use. This artifact contains all the needed implementation details for
talking to an HBase cluster while minimizing the number of third party dependencies exposed.
Note that this artifact exposes some classes in the org.apache.hadoop package space (e.g.
o.a.h.configuration.Configuration) so that we can maintain source compatibility with our public
API. Those classes are included so that they can be altered to use the same relocated third party
dependencies as the rest of the HBase client code. In the event that you need to also use Hadoop in
your code, you should ensure all Hadoop related jars precede the HBase client jar in your classpath.
Downstream HBase 2.0+ users of MapReduce must switch to new artifact
Downstream users of HBase’s integration for Apache Hadoop MapReduce must switch to relying on
the org.apache.hbase:hbase-shaded-mapreduce module for their runtime use. Historically,
downstream users relied on either the org.apache.hbase:hbase-server or org.apache.hbase:hbase-
shaded-server artifacts for these classes. Both uses are no longer supported and in the vast majority
of cases will fail at runtime.
Note that this artifact exposes some classes in the org.apache.hadoop package space (e.g.
o.a.h.configuration.Configuration) so that we can maintain source compatibility with our public
API. Those classes are included so that they can be altered to use the same relocated third party
dependencies as the rest of the HBase client code. In the event that you need to also use Hadoop in
your code, you should ensure all Hadoop related jars precede the HBase client jar in your classpath.
Significant changes to runtime classpath
A number of internal dependencies for HBase were updated or removed from the runtime
classpath. Downstream client users who do not follow the guidance in Downstream HBase 2.0+
users should use the shaded client will have to examine the set of dependencies Maven pulls in for
impact. Downstream users of LimitedPrivate Coprocessor APIs will need to examine the runtime
environment for impact. For details on our new handling of third party libraries that have
historically been a problem with respect to harmonizing compatible runtime versions, see the
reference guide section The hbase-thirdparty dependency and shading/relocation.
Multiple breaking changes to source and binary compatibility for client API
The Java client API for HBase has a number of changes that break both source and binary
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compatibility for details see the Compatibility Check Report for the release you’ll be upgrading to.
Tracing implementation changes
The backing implementation of HBase’s tracing features was updated from Apache HTrace 3 to
HTrace 4, which includes several breaking changes. While HTrace 3 and 4 can coexist in the same
runtime, they will not integrate with each other, leading to disjoint trace information.
The internal changes to HBase during this upgrade were sufficient for compilation, but it has not
been confirmed that there are no regressions in tracing functionality. Please consider this feature
expiremental for the immediate future.
If you previously relied on client side tracing integrated with HBase operations, it is recommended
that you upgrade your usage to HTrace 4 as well.
HFile lose forward compatability
HFiles generated by 2.0.0, 2.0.1, 2.1.0 are not forward compatible to 1.4.6-, 1.3.2.1-, 1.2.6.1-, and other
inactive releases. Why HFile lose compatability is hbase in new versions (2.0.0, 2.0.1, 2.1.0) use
protobuf to serialize/deserialize TimeRangeTracker (TRT) while old versions use
DataInput/DataOutput. To solve this, We have to put HBASE-21012 to 2.x and put HBASE-21013 in
1.x. For more information, please check HBASE-21008.
Performance
You will likely see a change in the performance profile on upgrade to hbase-2.0.0 given read and
write paths have undergone significant change. On release, writes may be slower with reads about
the same or much better, dependent on context. Be prepared to spend time re-tuning (See Apache
HBase Performance Tuning). Performance is also an area that is now under active review so look
forward to improvement in coming releases (See HBASE-20188 TESTING Performance).
Integration Tests and Kerberos
Integration Tests (IntegrationTests*) used to rely on the Kerberos credential cache for
authentication against secured clusters. This used to lead to tests failing due to authentication
failures when the tickets in the credential cache expired. As of hbase-2.0.0 (and hbase-1.3.0+), the
integration test clients will make use of the configuration properties hbase.client.keytab.file and
hbase.client.kerberos.principal. They are required. The clients will perform a login from the
configured keytab file and automatically refresh the credentials in the background for the process
lifetime (See HBASE-16231).
13.1.2. Upgrading Coprocessors to 2.0
Coprocessors have changed substantially in 2.0 ranging from top level design changes in class
hierarchies to changed/removed methods, interfaces, etc. (Parent jira: HBASE-18169 Coprocessor fix
and cleanup before 2.0.0 release). Some of the reasons for such widespread changes:
1. Pass Interfaces instead of Implementations; e.g. TableDescriptor instead of HTableDescriptor
and Region instead of HRegion (HBASE-18241 Change client.Table and client.Admin to not use
HTableDescriptor).
2. Design refactor so implementers need to fill out less boilerplate and so we can do more compile-
time checking (HBASE-17732)
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3. Purge Protocol Buffers from Coprocessor API (HBASE-18859, HBASE-16769, etc)
4. Cut back on what we expose to Coprocessors removing hooks on internals that were too private
to expose (for eg. HBASE-18453 CompactionRequest should not be exposed to user directly;
HBASE-18298 RegionServerServices Interface cleanup for CP expose; etc)
To use coprocessors in 2.0, they should be rebuilt against new API otherwise they will fail to load
and HBase processes will die.
Suggested order of changes to upgrade the coprocessors:
1. Directly implement observer interfaces instead of extending Base*Observer classes. Change Foo
extends BaseXXXObserver to Foo implements XXXObserver. (HBASE-17312).
2. Adapt to design change from Inheritence to Composition (HBASE-17732) by following this
example.
3. getTable() has been removed from the CoprocessorEnvrionment, coprocessors should self-
manage Table instances.
Some examples of writing coprocessors with new API can be found in hbase-example module here .
Lastly, if an api has been changed/removed that breaks you in an irreparable way, and if there’s a
good justification to add it back, bring it our notice (dev@hbase.apache.org).
13.1.3. Rolling Upgrade from 1.x to 2.x
Rolling upgrades are currently an experimental feature. They have had limited testing. There are
likely corner cases as yet uncovered in our limited experience so you should be careful if you go
this route. The stop/upgrade/start described in the next section, Upgrade process from 1.x to 2.x, is
the safest route.
That said, the below is a prescription for a rolling upgrade of a 1.4 cluster.
Pre-Requirements
•Upgrade to the latest 1.4.x release. Pre 1.4 releases may also work but are not tested, so please
upgrade to 1.4.3+ before upgrading to 2.x, unless you are an expert and familiar with the region
assignment and crash processing. See the section Upgrading from pre-1.4 to 1.4+ on how to
upgrade to 1.4.x.
•Make sure that the zk-less assignment is enabled, i.e, set hbase.assignment.usezk to false. This is
the most important thing. It allows the 1.x master to assign/unassign regions to/from 2.x region
servers. See the release note section of HBASE-11059 on how to migrate from zk based
assignment to zk less assignment.
•We have tested rolling upgrading from 1.4.3 to 2.1.0, but it should also work if you want to
upgrade to 2.0.x.
Instructions
1. Unload a region server and upgrade it to 2.1.0. With HBASE-17931 in place, the meta region and
regions for other system tables will be moved to this region server immediately. If not, please
move them manually to the new region server. This is very important because
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◦The schema of meta region is hard coded, if meta is on an old region server, then the new
region servers can not access it as it does not have some families, for example, table state.
◦Client with lower version can communicate with server with higher version, but not vice
versa. If the meta region is on an old region server, the new region server will use a client
with higher version to communicate with a server with lower version, this may introduce
strange problems.
2. Rolling upgrade all other region servers.
3. Upgrading masters.
It is OK that during the rolling upgrading there are region server crashes. The 1.x master can assign
regions to both 1.x and 2.x region servers, and HBASE-19166 fixed a problem so that 1.x region
server can also read the WALs written by 2.x region server and split them.
please read the Changes of Note! section carefully before rolling upgrading. Make
sure that you do not use the removed features in 2.0, for example, the prefix-tree
encoding, the old hfile format, etc. They could both fail the upgrading and leave
the cluster in an intermediate state and hard to recover.
If you have success running this prescription, please notify the dev list with a note
on your experience and/or update the above with any deviations you may have
taken so others going this route can benefit from your efforts.
13.1.4. Upgrade process from 1.x to 2.x
To upgrade an existing HBase 1.x cluster, you should:
•Clean shutdown of existing 1.x cluster
•Update coprocessors
•Upgrade Master roles first
•Upgrade RegionServers
•(Eventually) Upgrade Clients
13.2. Upgrading from pre-1.4 to 1.4+
13.2.1. Region Server memory consumption changes.
Users upgrading from versions prior to HBase 1.4 should be aware that the estimates of heap usage
by the memstore objects (KeyValue, object and array header sizes, etc) have been made more
accurate for heap sizes up to 32G (using CompressedOops), resulting in them dropping by 10-50% in
practice. This also results in less number of flushes and compactions due to "fatter" flushes. YMMV.
As a result, the actual heap usage of the memstore before being flushed may increase by up to
100%. If configured memory limits for the region server had been tuned based on observed usage,
this change could result in worse GC behavior or even OutOfMemory errors. Set the environment
property (not hbase-site.xml) "hbase.memorylayout.use.unsafe" to false to disable.
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13.2.2. Replication peer’s TableCFs config
Before 1.4, the table name can’t include namespace for replication peer’s TableCFs config. It was
fixed by add TableCFs to ReplicationPeerConfig which was stored on Zookeeper. So when upgrade
to 1.4, you have to update the original ReplicationPeerConfig data on Zookeeper firstly. There are
four steps to upgrade when your cluster have a replication peer with TableCFs config.
•Disable the replication peer.
•If master has permission to write replication peer znode, then rolling update master directly. If
not, use TableCFsUpdater tool to update the replication peer’s config.
$ bin/hbase org.apache.hadoop.hbase.replication.master.TableCFsUpdater update
•Rolling update regionservers.
•Enable the replication peer.
Notes:
•Can’t use the old client(before 1.4) to change the replication peer’s config. Because the client will
write config to Zookeeper directly, the old client will miss TableCFs config. And the old client
write TableCFs config to the old tablecfs znode, it will not work for new version regionserver.
13.2.3. Raw scan now ignores TTL
Doing a raw scan will now return results that have expired according to TTL settings.
13.3. Upgrading from pre-1.3 to 1.3+
If running Integration Tests under Kerberos, see Integration Tests and Kerberos.
13.4. Upgrading to 1.x
Please consult the documentation published specifically for the version of HBase that you are
upgrading to for details on the upgrade process.
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The Apache HBase Shell
The Apache HBase Shell is (J)Ruby's IRB with some HBase particular commands added. Anything
you can do in IRB, you should be able to do in the HBase Shell.
To run the HBase shell, do as follows:
$ ./bin/hbase shell
Type help and then <RETURN> to see a listing of shell commands and options. Browse at least the
paragraphs at the end of the help output for the gist of how variables and command arguments are
entered into the HBase shell; in particular note how table names, rows, and columns, etc., must be
quoted.
See shell exercises for example basic shell operation.
Here is a nicely formatted listing of all shell commands by Rajeshbabu Chintaguntla.
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Chapter 14. Scripting with Ruby
For examples scripting Apache HBase, look in the HBase bin directory. Look at the files that end in
*.rb. To run one of these files, do as follows:
$ ./bin/hbase org.jruby.Main PATH_TO_SCRIPT
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Chapter 15. Running the Shell in Non-
Interactive Mode
A new non-interactive mode has been added to the HBase Shell (HBASE-11658). Non-interactive
mode captures the exit status (success or failure) of HBase Shell commands and passes that status
back to the command interpreter. If you use the normal interactive mode, the HBase Shell will only
ever return its own exit status, which will nearly always be 0 for success.
To invoke non-interactive mode, pass the -n or --non-interactive option to HBase Shell.
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Chapter 16. HBase Shell in OS Scripts
You can use the HBase shell from within operating system script interpreters like the Bash shell
which is the default command interpreter for most Linux and UNIX distributions. The following
guidelines use Bash syntax, but could be adjusted to work with C-style shells such as csh or tcsh,
and could probably be modified to work with the Microsoft Windows script interpreter as well.
Submissions are welcome.
Spawning HBase Shell commands in this way is slow, so keep that in mind when
you are deciding when combining HBase operations with the operating system
command line is appropriate.
Example 4. Passing Commands to the HBase Shell
You can pass commands to the HBase Shell in non-interactive mode (see
hbase.shell.noninteractive) using the echo command and the | (pipe) operator. Be sure to
escape characters in the HBase commands which would otherwise be interpreted by the shell.
Some debug-level output has been truncated from the example below.
$ echo "describe 'test1'" | ./hbase shell -n
Version 0.98.3-hadoop2, rd5e65a9144e315bb0a964e7730871af32f5018d5, Sat May 31
19:56:09 PDT 2014
describe 'test1'
DESCRIPTION ENABLED
Ê'test1', {NAME => 'cf', DATA_BLOCK_ENCODING => 'NON true
ÊE', BLOOMFILTER => 'ROW', REPLICATION_SCOPE => '0',
Ê VERSIONS => '1', COMPRESSION => 'NONE', MIN_VERSIO
ÊNS => '0', TTL => 'FOREVER', KEEP_DELETED_CELLS =>
Ê'false', BLOCKSIZE => '65536', IN_MEMORY => 'false'
Ê, BLOCKCACHE => 'true'}
1 row(s) in 3.2410 seconds
To suppress all output, echo it to /dev/null:
$ echo "describe 'test'" | ./hbase shell -n > /dev/null 2>&1
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Example 5. Checking the Result of a Scripted Command
Since scripts are not designed to be run interactively, you need a way to check whether your
command failed or succeeded. The HBase shell uses the standard convention of returning a
value of 0 for successful commands, and some non-zero value for failed commands. Bash
stores a command’s return value in a special environment variable called $?. Because that
variable is overwritten each time the shell runs any command, you should store the result in a
different, script-defined variable.
This is a naive script that shows one way to store the return value and make a decision based
upon it.
#!/bin/bash
echo "describe 'test'" | ./hbase shell -n > /dev/null 2>&1
status=$?
echo "The status was " $status
if ($status == 0); then
Ê echo "The command succeeded"
else
Ê echo "The command may have failed."
fi
return $status
16.1. Checking for Success or Failure In Scripts
Getting an exit code of 0 means that the command you scripted definitely succeeded. However,
getting a non-zero exit code does not necessarily mean the command failed. The command could
have succeeded, but the client lost connectivity, or some other event obscured its success. This is
because RPC commands are stateless. The only way to be sure of the status of an operation is to
check. For instance, if your script creates a table, but returns a non-zero exit value, you should
check whether the table was actually created before trying again to create it.
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Chapter 17. Read HBase Shell Commands
from a Command File
You can enter HBase Shell commands into a text file, one command per line, and pass that file to
the HBase Shell.
Example Command File
create 'test', 'cf'
list 'test'
put 'test', 'row1', 'cf:a', 'value1'
put 'test', 'row2', 'cf:b', 'value2'
put 'test', 'row3', 'cf:c', 'value3'
put 'test', 'row4', 'cf:d', 'value4'
scan 'test'
get 'test', 'row1'
disable 'test'
enable 'test'
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Example 6. Directing HBase Shell to Execute the Commands
Pass the path to the command file as the only argument to the hbase shell command. Each
command is executed and its output is shown. If you do not include the exit command in your
script, you are returned to the HBase shell prompt. There is no way to programmatically check
each individual command for success or failure. Also, though you see the output for each
command, the commands themselves are not echoed to the screen so it can be difficult to line
up the command with its output.
$ ./hbase shell ./sample_commands.txt
0 row(s) in 3.4170 seconds
TABLE
test
1 row(s) in 0.0590 seconds
0 row(s) in 0.1540 seconds
0 row(s) in 0.0080 seconds
0 row(s) in 0.0060 seconds
0 row(s) in 0.0060 seconds
ROW COLUMN+CELL
Êrow1 column=cf:a, timestamp=1407130286968, value=value1
Êrow2 column=cf:b, timestamp=1407130286997, value=value2
Êrow3 column=cf:c, timestamp=1407130287007, value=value3
Êrow4 column=cf:d, timestamp=1407130287015, value=value4
4 row(s) in 0.0420 seconds
COLUMN CELL
Êcf:a timestamp=1407130286968, value=value1
1 row(s) in 0.0110 seconds
0 row(s) in 1.5630 seconds
0 row(s) in 0.4360 seconds
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Chapter 18. Passing VM Options to the Shell
You can pass VM options to the HBase Shell using the HBASE_SHELL_OPTS environment variable. You
can set this in your environment, for instance by editing ~/.bashrc, or set it as part of the command
to launch HBase Shell. The following example sets several garbage-collection-related variables, just
for the lifetime of the VM running the HBase Shell. The command should be run all on a single line,
but is broken by the \ character, for readability.
$ HBASE_SHELL_OPTS="-verbose:gc -XX:+PrintGCApplicationStoppedTime
-XX:+PrintGCDateStamps \
Ê -XX:+PrintGCDetails -Xloggc:$HBASE_HOME/logs/gc-hbase.log" ./bin/hbase shell
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Chapter 19. Overriding configuration
starting the HBase Shell
As of hbase-2.0.5/hbase-2.1.3/hbase-2.2.0/hbase-1.4.10/hbase-1.5.0, you can pass or override hbase
configuration as specified in hbase-*.xml by passing your key/values prefixed with -D on the
command-line as follows:
$ ./bin/hbase shell
-Dhbase.zookeeper.quorum=ZK0.remote.cluster.example.org,ZK1.remote.cluster.example.org
,ZK2.remote.cluster.example.org -Draining=false
...
hbase(main):001:0> @shell.hbase.configuration.get("hbase.zookeeper.quorum")
=>
"ZK0.remote.cluster.example.org,ZK1.remote.cluster.example.org,ZK2.remote.cluster.exam
ple.org"
hbase(main):002:0> @shell.hbase.configuration.get("raining")
=> "false"
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Chapter 20. Shell Tricks
20.1. Table variables
HBase 0.95 adds shell commands that provides jruby-style object-oriented references for tables.
Previously all of the shell commands that act upon a table have a procedural style that always took
the name of the table as an argument. HBase 0.95 introduces the ability to assign a table to a jruby
variable. The table reference can be used to perform data read write operations such as puts, scans,
and gets well as admin functionality such as disabling, dropping, describing tables.
For example, previously you would always specify a table name:
hbase(main):000:0> create 't', 'f'
0 row(s) in 1.0970 seconds
hbase(main):001:0> put 't', 'rold', 'f', 'v'
0 row(s) in 0.0080 seconds
hbase(main):002:0> scan 't'
ROW COLUMN+CELL
Êrold column=f:, timestamp=1378473207660, value=v
1 row(s) in 0.0130 seconds
hbase(main):003:0> describe 't'
DESCRIPTION
ENABLED
Ê't', {NAME => 'f', DATA_BLOCK_ENCODING => 'NONE', BLOOMFILTER => 'ROW', REPLICATION_
true
ÊSCOPE => '0', VERSIONS => '1', COMPRESSION => 'NONE', MIN_VERSIONS => '0', TTL => '2
Ê147483647', KEEP_DELETED_CELLS => 'false', BLOCKSIZE => '65536', IN_MEMORY => 'false
Ê', BLOCKCACHE => 'true'}
1 row(s) in 1.4430 seconds
hbase(main):004:0> disable 't'
0 row(s) in 14.8700 seconds
hbase(main):005:0> drop 't'
0 row(s) in 23.1670 seconds
hbase(main):006:0>
Now you can assign the table to a variable and use the results in jruby shell code.
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hbase(main):007 > t = create 't', 'f'
0 row(s) in 1.0970 seconds
=> Hbase::Table - t
hbase(main):008 > t.put 'r', 'f', 'v'
0 row(s) in 0.0640 seconds
hbase(main):009 > t.scan
ROW COLUMN+CELL
Êr column=f:, timestamp=1331865816290, value=v
1 row(s) in 0.0110 seconds
hbase(main):010:0> t.describe
DESCRIPTION
ENABLED
Ê't', {NAME => 'f', DATA_BLOCK_ENCODING => 'NONE', BLOOMFILTER => 'ROW', REPLICATION_
true
ÊSCOPE => '0', VERSIONS => '1', COMPRESSION => 'NONE', MIN_VERSIONS => '0', TTL => '2
Ê147483647', KEEP_DELETED_CELLS => 'false', BLOCKSIZE => '65536', IN_MEMORY => 'false
Ê', BLOCKCACHE => 'true'}
1 row(s) in 0.0210 seconds
hbase(main):038:0> t.disable
0 row(s) in 6.2350 seconds
hbase(main):039:0> t.drop
0 row(s) in 0.2340 seconds
If the table has already been created, you can assign a Table to a variable by using the get_table
method:
hbase(main):011 > create 't','f'
0 row(s) in 1.2500 seconds
=> Hbase::Table - t
hbase(main):012:0> tab = get_table 't'
0 row(s) in 0.0010 seconds
=> Hbase::Table - t
hbase(main):013:0> tab.put 'r1' ,'f', 'v'
0 row(s) in 0.0100 seconds
hbase(main):014:0> tab.scan
ROW COLUMN+CELL
Êr1 column=f:, timestamp=1378473876949, value=v
1 row(s) in 0.0240 seconds
hbase(main):015:0>
The list functionality has also been extended so that it returns a list of table names as strings. You
can then use jruby to script table operations based on these names. The list_snapshots command
also acts similarly.
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hbase(main):016 > tables = list('t.*')
TABLE
t
1 row(s) in 0.1040 seconds
=> #<#<Class:0x7677ce29>:0x21d377a4>
hbase(main):017:0> tables.map { |t| disable t ; drop t}
0 row(s) in 2.2510 seconds
=> [nil]
hbase(main):018:0>
20.2. irbrc
Create an .irbrc file for yourself in your home directory. Add customizations. A useful one is
command history so commands are save across Shell invocations:
$ more .irbrc
require 'irb/ext/save-history'
IRB.conf[:SAVE_HISTORY] = 100
IRB.conf[:HISTORY_FILE] = "#{ENV['HOME']}/.irb-save-history"
If you’d like to avoid printing the result of evaluting each expression to stderr, for example the
array of tables returned from the "list" command:
$ echo "IRB.conf[:ECHO] = false" >>~/.irbrc
See the ruby documentation of .irbrc to learn about other possible configurations.
20.3. LOG data to timestamp
To convert the date '08/08/16 20:56:29' from an hbase log into a timestamp, do:
hbase(main):021:0> import java.text.SimpleDateFormat
hbase(main):022:0> import java.text.ParsePosition
hbase(main):023:0> SimpleDateFormat.new("yy/MM/dd HH:mm:ss").parse("08/08/16
20:56:29", ParsePosition.new(0)).getTime() => 1218920189000
To go the other direction:
hbase(main):021:0> import java.util.Date
hbase(main):022:0> Date.new(1218920189000).toString() => "Sat Aug 16 20:56:29 UTC
2008"
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To output in a format that is exactly like that of the HBase log format will take a little messing with
SimpleDateFormat.
20.4. Query Shell Configuration
hbase(main):001:0> @shell.hbase.configuration.get("hbase.rpc.timeout")
=> "60000"
To set a config in the shell:
hbase(main):005:0> @shell.hbase.configuration.setInt("hbase.rpc.timeout", 61010)
hbase(main):006:0> @shell.hbase.configuration.get("hbase.rpc.timeout")
=> "61010"
20.5. Pre-splitting tables with the HBase Shell
You can use a variety of options to pre-split tables when creating them via the HBase Shell create
command.
The simplest approach is to specify an array of split points when creating the table. Note that when
specifying string literals as split points, these will create split points based on the underlying byte
representation of the string. So when specifying a split point of '10', we are actually specifying the
byte split point '\x31\30'.
The split points will define n+1 regions where n is the number of split points. The lowest region will
contain all keys from the lowest possible key up to but not including the first split point key. The
next region will contain keys from the first split point up to, but not including the next split point
key. This will continue for all split points up to the last. The last region will be defined from the last
split point up to the maximum possible key.
hbase>create 't1','f',SPLITS => ['10','20','30']
In the above example, the table 't1' will be created with column family 'f', pre-split to four regions.
Note the first region will contain all keys from '\x00' up to '\x30' (as '\x31' is the ASCII code for '1').
You can pass the split points in a file using following variation. In this example, the splits are read
from a file corresponding to the local path on the local filesystem. Each line in the file specifies a
split point key.
hbase>create 't14','f',SPLITS_FILE=>'splits.txt'
The other options are to automatically compute splits based on a desired number of regions and a
splitting algorithm. HBase supplies algorithms for splitting the key range based on uniform splits or
based on hexadecimal keys, but you can provide your own splitting algorithm to subdivide the key
119
range.
# create table with four regions based on random bytes keys
hbase>create 't2','f1', { NUMREGIONS => 4 , SPLITALGO => 'UniformSplit' }
# create table with five regions based on hex keys
hbase>create 't3','f1', { NUMREGIONS => 5, SPLITALGO => 'HexStringSplit' }
As the HBase Shell is effectively a Ruby environment, you can use simple Ruby scripts to compute
splits algorithmically.
# generate splits for long (Ruby fixnum) key range from start to end key
hbase(main):070:0> def gen_splits(start_key,end_key,num_regions)
hbase(main):071:1> results=[]
hbase(main):072:1> range=end_key-start_key
hbase(main):073:1> incr=(range/num_regions).floor
hbase(main):074:1> for i in 1 .. num_regions-1
hbase(main):075:2> results.push([i*incr+start_key].pack("N"))
hbase(main):076:2> end
hbase(main):077:1> return results
hbase(main):078:1> end
hbase(main):079:0>
hbase(main):080:0> splits=gen_splits(1,2000000,10)
=> ["\000\003\r@", "\000\006\032\177", "\000\t'\276", "\000\f4\375", "\000\017B<",
"\000\022O{", "\000\025\\\272", "\000\030i\371", "\000\ew8"]
hbase(main):081:0> create 'test_splits','f',SPLITS=>splits
0 row(s) in 0.2670 seconds
=> Hbase::Table - test_splits
Note that the HBase Shell command truncate effectively drops and recreates the table with default
options which will discard any pre-splitting. If you need to truncate a pre-split table, you must drop
and recreate the table explicitly to re-specify custom split options.
20.6. Debug
20.6.1. Shell debug switch
You can set a debug switch in the shell to see more output — e.g. more of the stack trace on
exception — when you run a command:
hbase> debug <RETURN>
20.6.2. DEBUG log level
To enable DEBUG level logging in the shell, launch it with the -d option.
120
$ ./bin/hbase shell -d
20.7. Commands
20.7.1. count
Count command returns the number of rows in a table. It’s quite fast when configured with the
right CACHE
hbase> count '<tablename>', CACHE => 1000
The above count fetches 1000 rows at a time. Set CACHE lower if your rows are big. Default is to
fetch one row at a time.
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Data Model
In HBase, data is stored in tables, which have rows and columns. This is a terminology overlap with
relational databases (RDBMSs), but this is not a helpful analogy. Instead, it can be helpful to think of
an HBase table as a multi-dimensional map.
HBase Data Model Terminology
Table
An HBase table consists of multiple rows.
Row
A row in HBase consists of a row key and one or more columns with values associated with
them. Rows are sorted alphabetically by the row key as they are stored. For this reason, the
design of the row key is very important. The goal is to store data in such a way that related rows
are near each other. A common row key pattern is a website domain. If your row keys are
domains, you should probably store them in reverse (org.apache.www, org.apache.mail,
org.apache.jira). This way, all of the Apache domains are near each other in the table, rather
than being spread out based on the first letter of the subdomain.
Column
A column in HBase consists of a column family and a column qualifier, which are delimited by a
: (colon) character.
Column Family
Column families physically colocate a set of columns and their values, often for performance
reasons. Each column family has a set of storage properties, such as whether its values should be
cached in memory, how its data is compressed or its row keys are encoded, and others. Each row
in a table has the same column families, though a given row might not store anything in a given
column family.
Column Qualifier
A column qualifier is added to a column family to provide the index for a given piece of data.
Given a column family content, a column qualifier might be content:html, and another might be
content:pdf. Though column families are fixed at table creation, column qualifiers are mutable
and may differ greatly between rows.
Cell
A cell is a combination of row, column family, and column qualifier, and contains a value and a
timestamp, which represents the value’s version.
Timestamp
A timestamp is written alongside each value, and is the identifier for a given version of a value.
By default, the timestamp represents the time on the RegionServer when the data was written,
but you can specify a different timestamp value when you put data into the cell.
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Chapter 21. Conceptual View
You can read a very understandable explanation of the HBase data model in the blog post
Understanding HBase and BigTable by Jim R. Wilson. Another good explanation is available in the
PDF Introduction to Basic Schema Design by Amandeep Khurana.
It may help to read different perspectives to get a solid understanding of HBase schema design. The
linked articles cover the same ground as the information in this section.
The following example is a slightly modified form of the one on page 2 of the BigTable paper. There
is a table called webtable that contains two rows (com.cnn.www and com.example.www) and three
column families named contents, anchor, and people. In this example, for the first row (com.cnn.www),
anchor contains two columns (anchor:cssnsi.com, anchor:my.look.ca) and contents contains one
column (contents:html). This example contains 5 versions of the row with the row key com.cnn.www,
and one version of the row with the row key com.example.www. The contents:html column qualifier
contains the entire HTML of a given website. Qualifiers of the anchor column family each contain
the external site which links to the site represented by the row, along with the text it used in the
anchor of its link. The people column family represents people associated with the site.
Column Names
By convention, a column name is made of its column family prefix and a qualifier.
For example, the column contents:html is made up of the column family contents
and the html qualifier. The colon character (:) delimits the column family from the
column family qualifier.
Table 6. Table webtable
Row Key Time Stamp ColumnFamily
contents
ColumnFamily
anchor
ColumnFamily
people
"com.cnn.www" t9 anchor:cnnsi.com
= "CNN"
"com.cnn.www" t8 anchor:my.look.ca
= "CNN.com"
"com.cnn.www" t6 contents:html =
"<html>…"
"com.cnn.www" t5 contents:html =
"<html>…"
"com.cnn.www" t3 contents:html =
"<html>…"
"com.example.ww
w"
t5 contents:html =
"<html>…"
people:author =
"John Doe"
Cells in this table that appear to be empty do not take space, or in fact exist, in HBase. This is what
makes HBase "sparse." A tabular view is not the only possible way to look at data in HBase, or even
the most accurate. The following represents the same information as a multi-dimensional map. This
is only a mock-up for illustrative purposes and may not be strictly accurate.
123
{
Ê "com.cnn.www": {
Ê contents: {
Ê t6: contents:html: "<html>..."
Ê t5: contents:html: "<html>..."
Ê t3: contents:html: "<html>..."
Ê }
Ê anchor: {
Ê t9: anchor:cnnsi.com = "CNN"
Ê t8: anchor:my.look.ca = "CNN.com"
Ê }
Ê people: {}
Ê }
Ê "com.example.www": {
Ê contents: {
Ê t5: contents:html: "<html>..."
Ê }
Ê anchor: {}
Ê people: {
Ê t5: people:author: "John Doe"
Ê }
Ê }
}
124
Chapter 22. Physical View
Although at a conceptual level tables may be viewed as a sparse set of rows, they are physically
stored by column family. A new column qualifier (column_family:column_qualifier) can be added
to an existing column family at any time.
Table 7. ColumnFamily anchor
Row Key Time Stamp Column Family anchor
"com.cnn.www" t9 anchor:cnnsi.com = "CNN"
"com.cnn.www" t8 anchor:my.look.ca = "CNN.com"
Table 8. ColumnFamily contents
Row Key Time Stamp ColumnFamily contents:
"com.cnn.www" t6 contents:html = "<html>…"
"com.cnn.www" t5 contents:html = "<html>…"
"com.cnn.www" t3 contents:html = "<html>…"
The empty cells shown in the conceptual view are not stored at all. Thus a request for the value of
the contents:html column at time stamp t8 would return no value. Similarly, a request for an
anchor:my.look.ca value at time stamp t9 would return no value. However, if no timestamp is
supplied, the most recent value for a particular column would be returned. Given multiple
versions, the most recent is also the first one found, since timestamps are stored in descending
order. Thus a request for the values of all columns in the row com.cnn.www if no timestamp is
specified would be: the value of contents:html from timestamp t6, the value of anchor:cnnsi.com
from timestamp t9, the value of anchor:my.look.ca from timestamp t8.
For more information about the internals of how Apache HBase stores data, see regions.arch.
125
Chapter 23. Namespace
A namespace is a logical grouping of tables analogous to a database in relation database systems.
This abstraction lays the groundwork for upcoming multi-tenancy related features:
•Quota Management (HBASE-8410) - Restrict the amount of resources (i.e. regions, tables) a
namespace can consume.
•Namespace Security Administration (HBASE-9206) - Provide another level of security
administration for tenants.
•Region server groups (HBASE-6721) - A namespace/table can be pinned onto a subset of
RegionServers thus guaranteeing a coarse level of isolation.
23.1. Namespace management
A namespace can be created, removed or altered. Namespace membership is determined during
table creation by specifying a fully-qualified table name of the form:
<table namespace>:<table qualifier>
Example 7. Examples
#Create a namespace
create_namespace 'my_ns'
#create my_table in my_ns namespace
create 'my_ns:my_table', 'fam'
#drop namespace
drop_namespace 'my_ns'
#alter namespace
alter_namespace 'my_ns', {METHOD => 'set', 'PROPERTY_NAME' => 'PROPERTY_VALUE'}
23.2. Predefined namespaces
There are two predefined special namespaces:
•hbase - system namespace, used to contain HBase internal tables
•default - tables with no explicit specified namespace will automatically fall into this namespace
126
Example 8. Examples
#namespace=foo and table qualifier=bar
create 'foo:bar', 'fam'
#namespace=default and table qualifier=bar
create 'bar', 'fam'
127
Chapter 24. Table
Tables are declared up front at schema definition time.
128
Chapter 25. Row
Row keys are uninterpreted bytes. Rows are lexicographically sorted with the lowest order
appearing first in a table. The empty byte array is used to denote both the start and end of a tables'
namespace.
129
Chapter 26. Column Family
Columns in Apache HBase are grouped into column families. All column members of a column
family have the same prefix. For example, the columns courses:history and courses:math are both
members of the courses column family. The colon character (:) delimits the column family from the
column family qualifier. The column family prefix must be composed of printable characters. The
qualifying tail, the column family qualifier, can be made of any arbitrary bytes. Column families
must be declared up front at schema definition time whereas columns do not need to be defined at
schema time but can be conjured on the fly while the table is up and running.
Physically, all column family members are stored together on the filesystem. Because tunings and
storage specifications are done at the column family level, it is advised that all column family
members have the same general access pattern and size characteristics.
130
Chapter 27. Cells
A {row, column, version} tuple exactly specifies a cell in HBase. Cell content is uninterpreted bytes
131
Chapter 28. Data Model Operations
The four primary data model operations are Get, Put, Scan, and Delete. Operations are applied via
Table instances.
28.1. Get
Get returns attributes for a specified row. Gets are executed via Table.get
28.2. Put
Put either adds new rows to a table (if the key is new) or can update existing rows (if the key
already exists). Puts are executed via Table.put (non-writeBuffer) or Table.batch (non-writeBuffer)
28.3. Scans
Scan allow iteration over multiple rows for specified attributes.
The following is an example of a Scan on a Table instance. Assume that a table is populated with
rows with keys "row1", "row2", "row3", and then another set of rows with the keys "abc1", "abc2",
and "abc3". The following example shows how to set a Scan instance to return the rows beginning
with "row".
public static final byte[] CF = "cf".getBytes();
public static final byte[] ATTR = "attr".getBytes();
...
Table table = ... // instantiate a Table instance
Scan scan = new Scan();
scan.addColumn(CF, ATTR);
scan.setRowPrefixFilter(Bytes.toBytes("row"));
ResultScanner rs = table.getScanner(scan);
try {
Ê for (Result r = rs.next(); r != null; r = rs.next()) {
Ê // process result...
Ê }
} finally {
Ê rs.close(); // always close the ResultScanner!
}
Note that generally the easiest way to specify a specific stop point for a scan is by using the
InclusiveStopFilter class.
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28.4. Delete
Delete removes a row from a table. Deletes are executed via Table.delete.
HBase does not modify data in place, and so deletes are handled by creating new markers called
tombstones. These tombstones, along with the dead values, are cleaned up on major compactions.
See version.delete for more information on deleting versions of columns, and see compaction for
more information on compactions.
133
Chapter 29. Versions
A {row, column, version} tuple exactly specifies a cell in HBase. It’s possible to have an unbounded
number of cells where the row and column are the same but the cell address differs only in its
version dimension.
While rows and column keys are expressed as bytes, the version is specified using a long integer.
Typically this long contains time instances such as those returned by java.util.Date.getTime() or
System.currentTimeMillis(), that is: the difference, measured in milliseconds, between the current
time and midnight, January 1, 1970 UTC.
The HBase version dimension is stored in decreasing order, so that when reading from a store file,
the most recent values are found first.
There is a lot of confusion over the semantics of cell versions, in HBase. In particular:
•If multiple writes to a cell have the same version, only the last written is fetchable.
•It is OK to write cells in a non-increasing version order.
Below we describe how the version dimension in HBase currently works. See HBASE-2406 for
discussion of HBase versions. Bending time in HBase makes for a good read on the version, or time,
dimension in HBase. It has more detail on versioning than is provided here.
As of this writing, the limitation Overwriting values at existing timestamps mentioned in the article
no longer holds in HBase. This section is basically a synopsis of this article by Bruno Dumon.
29.1. Specifying the Number of Versions to Store
The maximum number of versions to store for a given column is part of the column schema and is
specified at table creation, or via an alter command, via HColumnDescriptor.DEFAULT_VERSIONS. Prior
to HBase 0.96, the default number of versions kept was 3, but in 0.96 and newer has been changed
to 1.
Example 9. Modify the Maximum Number of Versions for a Column Family
This example uses HBase Shell to keep a maximum of 5 versions of all columns in column
family f1. You could also use HColumnDescriptor.
hbase> alter ‘t1′, NAME => ‘f1′, VERSIONS => 5
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Example 10. Modify the Minimum Number of Versions for a Column Family
You can also specify the minimum number of versions to store per column family. By default,
this is set to 0, which means the feature is disabled. The following example sets the minimum
number of versions on all columns in column family f1 to 2, via HBase Shell. You could also use
HColumnDescriptor.
hbase> alter ‘t1′, NAME => ‘f1′, MIN_VERSIONS => 2
Starting with HBase 0.98.2, you can specify a global default for the maximum number of versions
kept for all newly-created columns, by setting hbase.column.max.version in hbase-site.xml. See
hbase.column.max.version.
29.2. Versions and HBase Operations
In this section we look at the behavior of the version dimension for each of the core HBase
operations.
29.2.1. Get/Scan
Gets are implemented on top of Scans. The below discussion of Get applies equally to Scans.
By default, i.e. if you specify no explicit version, when doing a get, the cell whose version has the
largest value is returned (which may or may not be the latest one written, see later). The default
behavior can be modified in the following ways:
•to return more than one version, see Get.setMaxVersions()
•to return versions other than the latest, see Get.setTimeRange()
To retrieve the latest version that is less than or equal to a given value, thus giving the 'latest'
state of the record at a certain point in time, just use a range from 0 to the desired version and
set the max versions to 1.
29.2.2. Default Get Example
The following Get will only retrieve the current version of the row
public static final byte[] CF = "cf".getBytes();
public static final byte[] ATTR = "attr".getBytes();
...
Get get = new Get(Bytes.toBytes("row1"));
Result r = table.get(get);
byte[] b = r.getValue(CF, ATTR); // returns current version of value
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29.2.3. Versioned Get Example
The following Get will return the last 3 versions of the row.
public static final byte[] CF = "cf".getBytes();
public static final byte[] ATTR = "attr".getBytes();
...
Get get = new Get(Bytes.toBytes("row1"));
get.setMaxVersions(3); // will return last 3 versions of row
Result r = table.get(get);
byte[] b = r.getValue(CF, ATTR); // returns current version of value
List<KeyValue> kv = r.getColumn(CF, ATTR); // returns all versions of this column
29.2.4. Put
Doing a put always creates a new version of a cell, at a certain timestamp. By default the system
uses the server’s currentTimeMillis, but you can specify the version (= the long integer) yourself, on
a per-column level. This means you could assign a time in the past or the future, or use the long
value for non-time purposes.
To overwrite an existing value, do a put at exactly the same row, column, and version as that of the
cell you want to overwrite.
Implicit Version Example
The following Put will be implicitly versioned by HBase with the current time.
public static final byte[] CF = "cf".getBytes();
public static final byte[] ATTR = "attr".getBytes();
...
Put put = new Put(Bytes.toBytes(row));
put.add(CF, ATTR, Bytes.toBytes( data));
table.put(put);
Explicit Version Example
The following Put has the version timestamp explicitly set.
public static final byte[] CF = "cf".getBytes();
public static final byte[] ATTR = "attr".getBytes();
...
Put put = new Put( Bytes.toBytes(row));
long explicitTimeInMs = 555; // just an example
put.add(CF, ATTR, explicitTimeInMs, Bytes.toBytes(data));
table.put(put);
Caution: the version timestamp is used internally by HBase for things like time-to-live calculations.
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It’s usually best to avoid setting this timestamp yourself. Prefer using a separate timestamp
attribute of the row, or have the timestamp as a part of the row key, or both.
29.2.5. Delete
There are three different types of internal delete markers. See Lars Hofhansl’s blog for discussion
of his attempt adding another, Scanning in HBase: Prefix Delete Marker.
•Delete: for a specific version of a column.
•Delete column: for all versions of a column.
•Delete family: for all columns of a particular ColumnFamily
When deleting an entire row, HBase will internally create a tombstone for each ColumnFamily (i.e.,
not each individual column).
Deletes work by creating tombstone markers. For example, let’s suppose we want to delete a row.
For this you can specify a version, or else by default the currentTimeMillis is used. What this means
is delete all cells where the version is less than or equal to this version. HBase never modifies data in
place, so for example a delete will not immediately delete (or mark as deleted) the entries in the
storage file that correspond to the delete condition. Rather, a so-called tombstone is written, which
will mask the deleted values. When HBase does a major compaction, the tombstones are processed
to actually remove the dead values, together with the tombstones themselves. If the version you
specified when deleting a row is larger than the version of any value in the row, then you can
consider the complete row to be deleted.
For an informative discussion on how deletes and versioning interact, see the thread Put
w/timestamp → Deleteall → Put w/ timestamp fails up on the user mailing list.
Also see keyvalue for more information on the internal KeyValue format.
Delete markers are purged during the next major compaction of the store, unless the
KEEP_DELETED_CELLS option is set in the column family (See Keeping Deleted Cells). To keep the
deletes for a configurable amount of time, you can set the delete TTL via the
hbase.hstore.time.to.purge.deletes property in hbase-site.xml. If hbase.hstore.time.to.purge.deletes
is not set, or set to 0, all delete markers, including those with timestamps in the future, are purged
during the next major compaction. Otherwise, a delete marker with a timestamp in the future is
kept until the major compaction which occurs after the time represented by the marker’s
timestamp plus the value of hbase.hstore.time.to.purge.deletes, in milliseconds.
This behavior represents a fix for an unexpected change that was introduced in
HBase 0.94, and was fixed in HBASE-10118. The change has been backported to
HBase 0.94 and newer branches.
29.3. Optional New Version and Delete behavior in
HBase-2.0.0
In hbase-2.0.0, the operator can specify an alternate version and delete treatment by setting the
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column descriptor property NEW_VERSION_BEHAVIOR to true (To set a property on a column family
descriptor, you must first disable the table and then alter the column family descriptor; see Keeping
Deleted Cells for an example of editing an attribute on a column family descriptor).
The 'new version behavior', undoes the limitations listed below whereby a Delete ALWAYS
overshadows a Put if at the same location — i.e. same row, column family, qualifier and
timestamp — regardless of which arrived first. Version accounting is also changed as deleted
versions are considered toward total version count. This is done to ensure results are not changed
should a major compaction intercede. See HBASE-15968 and linked issues for discussion.
Running with this new configuration currently costs; we factor the Cell MVCC on every compare so
we burn more CPU. The slow down will depend. In testing we’ve seen between 0% and 25%
degradation.
If replicating, it is advised that you run with the new serial replication feature (See HBASE-9465; the
serial replication feature did NOT make it into hbase-2.0.0 but should arrive in a subsequent hbase-
2.x release) as now the order in which Mutations arrive is a factor.
29.4. Current Limitations
The below limitations are addressed in hbase-2.0.0. See the section above, Optional New Version
and Delete behavior in HBase-2.0.0.
29.4.1. Deletes mask Puts
Deletes mask puts, even puts that happened after the delete was entered. See HBASE-2256.
Remember that a delete writes a tombstone, which only disappears after then next major
compaction has run. Suppose you do a delete of everything ⇐ T. After this you do a new put with a
timestamp ⇐ T. This put, even if it happened after the delete, will be masked by the delete
tombstone. Performing the put will not fail, but when you do a get you will notice the put did have
no effect. It will start working again after the major compaction has run. These issues should not be
a problem if you use always-increasing versions for new puts to a row. But they can occur even if
you do not care about time: just do delete and put immediately after each other, and there is some
chance they happen within the same millisecond.
29.4.2. Major compactions change query results
…create three cell versions at t1, t2 and t3, with a maximum-versions setting of 2. So when getting all
versions, only the values at t2 and t3 will be returned. But if you delete the version at t2 or t3, the one
at t1 will appear again. Obviously, once a major compaction has run, such behavior will not be the
case anymore… (See Garbage Collection in Bending time in HBase.)
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Chapter 30. Sort Order
All data model operations HBase return data in sorted order. First by row, then by ColumnFamily,
followed by column qualifier, and finally timestamp (sorted in reverse, so newest records are
returned first).
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Chapter 31. Column Metadata
There is no store of column metadata outside of the internal KeyValue instances for a
ColumnFamily. Thus, while HBase can support not only a wide number of columns per row, but a
heterogeneous set of columns between rows as well, it is your responsibility to keep track of the
column names.
The only way to get a complete set of columns that exist for a ColumnFamily is to process all the
rows. For more information about how HBase stores data internally, see keyvalue.
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Chapter 32. Joins
Whether HBase supports joins is a common question on the dist-list, and there is a simple answer: it
doesn’t, at not least in the way that RDBMS' support them (e.g., with equi-joins or outer-joins in
SQL). As has been illustrated in this chapter, the read data model operations in HBase are Get and
Scan.
However, that doesn’t mean that equivalent join functionality can’t be supported in your
application, but you have to do it yourself. The two primary strategies are either denormalizing the
data upon writing to HBase, or to have lookup tables and do the join between HBase tables in your
application or MapReduce code (and as RDBMS' demonstrate, there are several strategies for this
depending on the size of the tables, e.g., nested loops vs. hash-joins). So which is the best approach?
It depends on what you are trying to do, and as such there isn’t a single answer that works for
every use case.
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Chapter 33. ACID
See ACID Semantics. Lars Hofhansl has also written a note on ACID in HBase.
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HBase and Schema Design
A good introduction on the strength and weaknesses modelling on the various non-rdbms
datastores is to be found in Ian Varley’s Master thesis, No Relation: The Mixed Blessings of Non-
Relational Databases. It is a little dated now but a good background read if you have a moment on
how HBase schema modeling differs from how it is done in an RDBMS. Also, read keyvalue for how
HBase stores data internally, and the section on schema.casestudies.
The documentation on the Cloud Bigtable website, Designing Your Schema, is pertinent and nicely
done and lessons learned there equally apply here in HBase land; just divide any quoted values by
~10 to get what works for HBase: e.g. where it says individual values can be ~10MBs in size, HBase
can do similar — perhaps best to go smaller if you can — and where it says a maximum of 100
column families in Cloud Bigtable, think ~10 when modeling on HBase.
See also Robert Yokota’s HBase Application Archetypes (an update on work done by other HBasers),
for a helpful categorization of use cases that do well on top of the HBase model.
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Chapter 34. Schema Creation
HBase schemas can be created or updated using the The Apache HBase Shell or by using Admin in
the Java API.
Tables must be disabled when making ColumnFamily modifications, for example:
Configuration config = HBaseConfiguration.create();
Admin admin = new Admin(conf);
TableName table = TableName.valueOf("myTable");
admin.disableTable(table);
HColumnDescriptor cf1 = ...;
admin.addColumn(table, cf1); // adding new ColumnFamily
HColumnDescriptor cf2 = ...;
admin.modifyColumn(table, cf2); // modifying existing ColumnFamily
admin.enableTable(table);
See client dependencies for more information about configuring client connections.
online schema changes are supported in the 0.92.x codebase, but the 0.90.x
codebase requires the table to be disabled.
34.1. Schema Updates
When changes are made to either Tables or ColumnFamilies (e.g. region size, block size), these
changes take effect the next time there is a major compaction and the StoreFiles get re-written.
See store for more information on StoreFiles.
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Chapter 35. Table Schema Rules Of Thumb
There are many different data sets, with different access patterns and service-level expectations.
Therefore, these rules of thumb are only an overview. Read the rest of this chapter to get more
details after you have gone through this list.
•Aim to have regions sized between 10 and 50 GB.
•Aim to have cells no larger than 10 MB, or 50 MB if you use mob. Otherwise, consider storing
your cell data in HDFS and store a pointer to the data in HBase.
•A typical schema has between 1 and 3 column families per table. HBase tables should not be
designed to mimic RDBMS tables.
•Around 50-100 regions is a good number for a table with 1 or 2 column families. Remember that
a region is a contiguous segment of a column family.
•Keep your column family names as short as possible. The column family names are stored for
every value (ignoring prefix encoding). They should not be self-documenting and descriptive
like in a typical RDBMS.
•If you are storing time-based machine data or logging information, and the row key is based on
device ID or service ID plus time, you can end up with a pattern where older data regions never
have additional writes beyond a certain age. In this type of situation, you end up with a small
number of active regions and a large number of older regions which have no new writes. For
these situations, you can tolerate a larger number of regions because your resource
consumption is driven by the active regions only.
•If only one column family is busy with writes, only that column family accomulates memory. Be
aware of write patterns when allocating resources.
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RegionServer Sizing Rules of Thumb
Lars Hofhansl wrote a great blog post about RegionServer memory sizing. The upshot is that you
probably need more memory than you think you need. He goes into the impact of region size,
memstore size, HDFS replication factor, and other things to check.
Personally I would place the maximum disk space per machine that can be
served exclusively with HBase around 6T, unless you have a very read-
heavy workload. In that case the Java heap should be 32GB (20G regions,
128M memstores, the rest defaults).
— Lars Hofhansl, http://hadoop-hbase.blogspot.com/2013/01/hbase-region-server-memory-sizing.html
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Chapter 36. On the number of column
families
HBase currently does not do well with anything above two or three column families so keep the
number of column families in your schema low. Currently, flushing and compactions are done on a
per Region basis so if one column family is carrying the bulk of the data bringing on flushes, the
adjacent families will also be flushed even though the amount of data they carry is small. When
many column families exist the flushing and compaction interaction can make for a bunch of
needless i/o (To be addressed by changing flushing and compaction to work on a per column family
basis). For more information on compactions, see Compaction.
Try to make do with one column family if you can in your schemas. Only introduce a second and
third column family in the case where data access is usually column scoped; i.e. you query one
column family or the other but usually not both at the one time.
36.1. Cardinality of ColumnFamilies
Where multiple ColumnFamilies exist in a single table, be aware of the cardinality (i.e., number of
rows). If ColumnFamilyA has 1 million rows and ColumnFamilyB has 1 billion rows,
ColumnFamilyA’s data will likely be spread across many, many regions (and RegionServers). This
makes mass scans for ColumnFamilyA less efficient.
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Chapter 37. Rowkey Design
37.1. Hotspotting
Rows in HBase are sorted lexicographically by row key. This design optimizes for scans, allowing
you to store related rows, or rows that will be read together, near each other. However, poorly
designed row keys are a common source of hotspotting. Hotspotting occurs when a large amount of
client traffic is directed at one node, or only a few nodes, of a cluster. This traffic may represent
reads, writes, or other operations. The traffic overwhelms the single machine responsible for
hosting that region, causing performance degradation and potentially leading to region
unavailability. This can also have adverse effects on other regions hosted by the same region server
as that host is unable to service the requested load. It is important to design data access patterns
such that the cluster is fully and evenly utilized.
To prevent hotspotting on writes, design your row keys such that rows that truly do need to be in
the same region are, but in the bigger picture, data is being written to multiple regions across the
cluster, rather than one at a time. Some common techniques for avoiding hotspotting are described
below, along with some of their advantages and drawbacks.
Salting
Salting in this sense has nothing to do with cryptography, but refers to adding random data to the
start of a row key. In this case, salting refers to adding a randomly-assigned prefix to the row key to
cause it to sort differently than it otherwise would. The number of possible prefixes correspond to
the number of regions you want to spread the data across. Salting can be helpful if you have a few
"hot" row key patterns which come up over and over amongst other more evenly-distributed rows.
Consider the following example, which shows that salting can spread write load across multiple
RegionServers, and illustrates some of the negative implications for reads.
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Example 11. Salting Example
Suppose you have the following list of row keys, and your table is split such that there is one
region for each letter of the alphabet. Prefix 'a' is one region, prefix 'b' is another. In this table,
all rows starting with 'f' are in the same region. This example focuses on rows with keys like
the following:
foo0001
foo0002
foo0003
foo0004
Now, imagine that you would like to spread these across four different regions. You decide to
use four different salts: a, b, c, and d. In this scenario, each of these letter prefixes will be on a
different region. After applying the salts, you have the following rowkeys instead. Since you
can now write to four separate regions, you theoretically have four times the throughput when
writing that you would have if all the writes were going to the same region.
a-foo0003
b-foo0001
c-foo0004
d-foo0002
Then, if you add another row, it will randomly be assigned one of the four possible salt values
and end up near one of the existing rows.
a-foo0003
b-foo0001
c-foo0003
c-foo0004
d-foo0002
Since this assignment will be random, you will need to do more work if you want to retrieve
the rows in lexicographic order. In this way, salting attempts to increase throughput on writes,
but has a cost during reads.
Hashing
Instead of a random assignment, you could use a one-way hash that would cause a given row to
always be "salted" with the same prefix, in a way that would spread the load across the
RegionServers, but allow for predictability during reads. Using a deterministic hash allows the
client to reconstruct the complete rowkey and use a Get operation to retrieve that row as normal.
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Example 12. Hashing Example
Given the same situation in the salting example above, you could instead apply a one-way hash
that would cause the row with key foo0003 to always, and predictably, receive the a prefix.
Then, to retrieve that row, you would already know the key. You could also optimize things so
that certain pairs of keys were always in the same region, for instance.
Reversing the Key
A third common trick for preventing hotspotting is to reverse a fixed-width or numeric row key so
that the part that changes the most often (the least significant digit) is first. This effectively
randomizes row keys, but sacrifices row ordering properties.
See https://communities.intel.com/community/itpeernetwork/datastack/blog/2013/11/10/discussion-
on-designing-hbase-tables, and article on Salted Tables from the Phoenix project, and the discussion
in the comments of HBASE-11682 for more information about avoiding hotspotting.
37.2. Monotonically Increasing Row Keys/Timeseries
Data
In the HBase chapter of Tom White’s book Hadoop: The Definitive Guide (O’Reilly) there is a an
optimization note on watching out for a phenomenon where an import process walks in lock-step
with all clients in concert pounding one of the table’s regions (and thus, a single node), then moving
onto the next region, etc. With monotonically increasing row-keys (i.e., using a timestamp), this will
happen. See this comic by IKai Lan on why monotonically increasing row keys are problematic in
BigTable-like datastores: monotonically increasing values are bad. The pile-up on a single region
brought on by monotonically increasing keys can be mitigated by randomizing the input records to
not be in sorted order, but in general it’s best to avoid using a timestamp or a sequence (e.g. 1, 2, 3)
as the row-key.
If you do need to upload time series data into HBase, you should study OpenTSDB as a successful
example. It has a page describing the schema it uses in HBase. The key format in OpenTSDB is
effectively [metric_type][event_timestamp], which would appear at first glance to contradict the
previous advice about not using a timestamp as the key. However, the difference is that the
timestamp is not in the lead position of the key, and the design assumption is that there are dozens
or hundreds (or more) of different metric types. Thus, even with a continual stream of input data
with a mix of metric types, the Puts are distributed across various points of regions in the table.
See schema.casestudies for some rowkey design examples.
37.3. Try to minimize row and column sizes
In HBase, values are always freighted with their coordinates; as a cell value passes through the
system, it’ll be accompanied by its row, column name, and timestamp - always. If your rows and
column names are large, especially compared to the size of the cell value, then you may run up
against some interesting scenarios. One such is the case described by Marc Limotte at the tail of
HBASE-3551 (recommended!). Therein, the indices that are kept on HBase storefiles (StoreFile
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(HFile)) to facilitate random access may end up occupying large chunks of the HBase allotted RAM
because the cell value coordinates are large. Mark in the above cited comment suggests upping the
block size so entries in the store file index happen at a larger interval or modify the table schema so
it makes for smaller rows and column names. Compression will also make for larger indices. See
the thread a question storefileIndexSize up on the user mailing list.
Most of the time small inefficiencies don’t matter all that much. Unfortunately, this is a case where
they do. Whatever patterns are selected for ColumnFamilies, attributes, and rowkeys they could be
repeated several billion times in your data.
See keyvalue for more information on HBase stores data internally to see why this is important.
37.3.1. Column Families
Try to keep the ColumnFamily names as small as possible, preferably one character (e.g. "d" for
data/default).
See KeyValue for more information on HBase stores data internally to see why this is important.
37.3.2. Attributes
Although verbose attribute names (e.g., "myVeryImportantAttribute") are easier to read, prefer
shorter attribute names (e.g., "via") to store in HBase.
See keyvalue for more information on HBase stores data internally to see why this is important.
37.3.3. Rowkey Length
Keep them as short as is reasonable such that they can still be useful for required data access (e.g.
Get vs. Scan). A short key that is useless for data access is not better than a longer key with better
get/scan properties. Expect tradeoffs when designing rowkeys.
37.3.4. Byte Patterns
A long is 8 bytes. You can store an unsigned number up to 18,446,744,073,709,551,615 in those eight
bytes. If you stored this number as a String — presuming a byte per character — you need nearly 3x
the bytes.
Not convinced? Below is some sample code that you can run on your own.
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// long
//
long l = 1234567890L;
byte[] lb = Bytes.toBytes(l);
System.out.println("long bytes length: " + lb.length); // returns 8
String s = String.valueOf(l);
byte[] sb = Bytes.toBytes(s);
System.out.println("long as string length: " + sb.length); // returns 10
// hash
//
MessageDigest md = MessageDigest.getInstance("MD5");
byte[] digest = md.digest(Bytes.toBytes(s));
System.out.println("md5 digest bytes length: " + digest.length); // returns 16
String sDigest = new String(digest);
byte[] sbDigest = Bytes.toBytes(sDigest);
System.out.println("md5 digest as string length: " + sbDigest.length); // returns
26
Unfortunately, using a binary representation of a type will make your data harder to read outside
of your code. For example, this is what you will see in the shell when you increment a value:
hbase(main):001:0> incr 't', 'r', 'f:q', 1
COUNTER VALUE = 1
hbase(main):002:0> get 't', 'r'
COLUMN CELL
Êf:q timestamp=1369163040570, value=\x00\x00
\x00\x00\x00\x00\x00\x01
1 row(s) in 0.0310 seconds
The shell makes a best effort to print a string, and it this case it decided to just print the hex. The
same will happen to your row keys inside the region names. It can be okay if you know what’s
being stored, but it might also be unreadable if arbitrary data can be put in the same cells. This is
the main trade-off.
37.4. Reverse Timestamps
Reverse Scan API
HBASE-4811 implements an API to scan a table or a range within a table in reverse,
reducing the need to optimize your schema for forward or reverse scanning. This
feature is available in HBase 0.98 and later. See Scan.setReversed() for more
information.
A common problem in database processing is quickly finding the most recent version of a value. A
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technique using reverse timestamps as a part of the key can help greatly with a special case of this
problem. Also found in the HBase chapter of Tom White’s book Hadoop: The Definitive Guide
(O’Reilly), the technique involves appending (Long.MAX_VALUE - timestamp) to the end of any key, e.g.
[key][reverse_timestamp].
The most recent value for [key] in a table can be found by performing a Scan for [key] and
obtaining the first record. Since HBase keys are in sorted order, this key sorts before any older row-
keys for [key] and thus is first.
This technique would be used instead of using Number of Versions where the intent is to hold onto
all versions "forever" (or a very long time) and at the same time quickly obtain access to any other
version by using the same Scan technique.
37.5. Rowkeys and ColumnFamilies
Rowkeys are scoped to ColumnFamilies. Thus, the same rowkey could exist in each ColumnFamily
that exists in a table without collision.
37.6. Immutability of Rowkeys
Rowkeys cannot be changed. The only way they can be "changed" in a table is if the row is deleted
and then re-inserted. This is a fairly common question on the HBase dist-list so it pays to get the
rowkeys right the first time (and/or before you’ve inserted a lot of data).
37.7. Relationship Between RowKeys and Region Splits
If you pre-split your table, it is critical to understand how your rowkey will be distributed across
the region boundaries. As an example of why this is important, consider the example of using
displayable hex characters as the lead position of the key (e.g., "0000000000000000" to
"ffffffffffffffff"). Running those key ranges through Bytes.split (which is the split strategy used
when creating regions in Admin.createTable(byte[] startKey, byte[] endKey, numRegions) for 10
regions will generate the following splits…
48 48 48 48 48 48 48 48 48 48 48 48 48 48 48 48 // 0
54 -10 -10 -10 -10 -10 -10 -10 -10 -10 -10 -10 -10 -10 -10 -10 // 6
61 -67 -67 -67 -67 -67 -67 -67 -67 -67 -67 -67 -67 -67 -67 -68 // =
68 -124 -124 -124 -124 -124 -124 -124 -124 -124 -124 -124 -124 -124 -124 -126 // D
75 75 75 75 75 75 75 75 75 75 75 75 75 75 75 72 // K
82 18 18 18 18 18 18 18 18 18 18 18 18 18 18 14 // R
88 -40 -40 -40 -40 -40 -40 -40 -40 -40 -40 -40 -40 -40 -40 -44 // X
95 -97 -97 -97 -97 -97 -97 -97 -97 -97 -97 -97 -97 -97 -97 -102 // _
102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 // f
(note: the lead byte is listed to the right as a comment.) Given that the first split is a '0' and the last
split is an 'f', everything is great, right? Not so fast.
The problem is that all the data is going to pile up in the first 2 regions and the last region thus
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creating a "lumpy" (and possibly "hot") region problem. To understand why, refer to an ASCII Table.
'0' is byte 48, and 'f' is byte 102, but there is a huge gap in byte values (bytes 58 to 96) that will never
appear in this keyspace because the only values are [0-9] and [a-f]. Thus, the middle regions will
never be used. To make pre-splitting work with this example keyspace, a custom definition of splits
(i.e., and not relying on the built-in split method) is required.
Lesson #1: Pre-splitting tables is generally a best practice, but you need to pre-split them in such a
way that all the regions are accessible in the keyspace. While this example demonstrated the
problem with a hex-key keyspace, the same problem can happen with any keyspace. Know your
data.
Lesson #2: While generally not advisable, using hex-keys (and more generally, displayable data) can
still work with pre-split tables as long as all the created regions are accessible in the keyspace.
To conclude this example, the following is an example of how appropriate splits can be pre-created
for hex-keys:.
public static boolean createTable(Admin admin, HTableDescriptor table, byte[][]
splits)
throws IOException {
Ê try {
Ê admin.createTable( table, splits );
Ê return true;
Ê } catch (TableExistsException e) {
Ê logger.info("table " + table.getNameAsString() + " already exists");
Ê // the table already exists...
Ê return false;
Ê }
}
public static byte[][] getHexSplits(String startKey, String endKey, int numRegions) {
Ê byte[][] splits = new byte[numRegions-1][];
Ê BigInteger lowestKey = new BigInteger(startKey, 16);
Ê BigInteger highestKey = new BigInteger(endKey, 16);
Ê BigInteger range = highestKey.subtract(lowestKey);
Ê BigInteger regionIncrement = range.divide(BigInteger.valueOf(numRegions));
Ê lowestKey = lowestKey.add(regionIncrement);
Ê for(int i=0; i < numRegions-1;i++) {
Ê BigInteger key = lowestKey.add(regionIncrement.multiply(BigInteger.valueOf(i)));
Ê byte[] b = String.format("%016x", key).getBytes();
Ê splits[i] = b;
Ê }
Ê return splits;
}
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Chapter 38. Number of Versions
38.1. Maximum Number of Versions
The maximum number of row versions to store is configured per column family via
HColumnDescriptor. The default for max versions is 1. This is an important parameter because as
described in Data Model section HBase does not overwrite row values, but rather stores different
values per row by time (and qualifier). Excess versions are removed during major compactions.
The number of max versions may need to be increased or decreased depending on application
needs.
It is not recommended setting the number of max versions to an exceedingly high level (e.g.,
hundreds or more) unless those old values are very dear to you because this will greatly increase
StoreFile size.
38.2. Minimum Number of Versions
Like maximum number of row versions, the minimum number of row versions to keep is
configured per column family via HColumnDescriptor. The default for min versions is 0, which
means the feature is disabled. The minimum number of row versions parameter is used together
with the time-to-live parameter and can be combined with the number of row versions parameter
to allow configurations such as "keep the last T minutes worth of data, at most N versions, but keep
at least M versions around" (where M is the value for minimum number of row versions, M<N). This
parameter should only be set when time-to-live is enabled for a column family and must be less
than the number of row versions.
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Chapter 39. Supported Datatypes
HBase supports a "bytes-in/bytes-out" interface via Put and Result, so anything that can be
converted to an array of bytes can be stored as a value. Input could be strings, numbers, complex
objects, or even images as long as they can rendered as bytes.
There are practical limits to the size of values (e.g., storing 10-50MB objects in HBase would
probably be too much to ask); search the mailing list for conversations on this topic. All rows in
HBase conform to the Data Model, and that includes versioning. Take that into consideration when
making your design, as well as block size for the ColumnFamily.
39.1. Counters
One supported datatype that deserves special mention are "counters" (i.e., the ability to do atomic
increments of numbers). See Increment in Table.
Synchronization on counters are done on the RegionServer, not in the client.
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Chapter 40. Joins
If you have multiple tables, don’t forget to factor in the potential for Joins into the schema design.
157
Chapter 41. Time To Live (TTL)
ColumnFamilies can set a TTL length in seconds, and HBase will automatically delete rows once the
expiration time is reached. This applies to all versions of a row - even the current one. The TTL time
encoded in the HBase for the row is specified in UTC.
Store files which contains only expired rows are deleted on minor compaction. Setting
hbase.store.delete.expired.storefile to false disables this feature. Setting minimum number of
versions to other than 0 also disables this.
See HColumnDescriptor for more information.
Recent versions of HBase also support setting time to live on a per cell basis. See HBASE-10560 for
more information. Cell TTLs are submitted as an attribute on mutation requests (Appends,
Increments, Puts, etc.) using Mutation#setTTL. If the TTL attribute is set, it will be applied to all cells
updated on the server by the operation. There are two notable differences between cell TTL
handling and ColumnFamily TTLs:
•Cell TTLs are expressed in units of milliseconds instead of seconds.
•A cell TTLs cannot extend the effective lifetime of a cell beyond a ColumnFamily level TTL
setting.
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Chapter 42. Keeping Deleted Cells
By default, delete markers extend back to the beginning of time. Therefore, Get or Scan operations
will not see a deleted cell (row or column), even when the Get or Scan operation indicates a time
range before the delete marker was placed.
ColumnFamilies can optionally keep deleted cells. In this case, deleted cells can still be retrieved, as
long as these operations specify a time range that ends before the timestamp of any delete that
would affect the cells. This allows for point-in-time queries even in the presence of deletes.
Deleted cells are still subject to TTL and there will never be more than "maximum number of
versions" deleted cells. A new "raw" scan options returns all deleted rows and the delete markers.
Change the Value of KEEP_DELETED_CELLS Using HBase Shell
hbase> hbase> alter ‘t1′, NAME => ‘f1′, KEEP_DELETED_CELLS => true
Example 13. Change the Value of KEEP_DELETED_CELLS Using the API
...
HColumnDescriptor.setKeepDeletedCells(true);
...
Let us illustrate the basic effect of setting the KEEP_DELETED_CELLS attribute on a table.
First, without:
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create 'test', {NAME=>'e', VERSIONS=>2147483647}
put 'test', 'r1', 'e:c1', 'value', 10
put 'test', 'r1', 'e:c1', 'value', 12
put 'test', 'r1', 'e:c1', 'value', 14
delete 'test', 'r1', 'e:c1', 11
hbase(main):017:0> scan 'test', {RAW=>true, VERSIONS=>1000}
ROW COLUMN+CELL
Êr1 column=e:c1, timestamp=14, value
=value
Êr1 column=e:c1, timestamp=12, value
=value
Êr1 column=e:c1, timestamp=11, type
=DeleteColumn
Êr1 column=e:c1, timestamp=10, value
=value
1 row(s) in 0.0120 seconds
hbase(main):018:0> flush 'test'
0 row(s) in 0.0350 seconds
hbase(main):019:0> scan 'test', {RAW=>true, VERSIONS=>1000}
ROW COLUMN+CELL
Êr1 column=e:c1, timestamp=14, value
=value
Êr1 column=e:c1, timestamp=12, value
=value
Êr1 column=e:c1, timestamp=11, type
=DeleteColumn
1 row(s) in 0.0120 seconds
hbase(main):020:0> major_compact 'test'
0 row(s) in 0.0260 seconds
hbase(main):021:0> scan 'test', {RAW=>true, VERSIONS=>1000}
ROW COLUMN+CELL
Êr1 column=e:c1, timestamp=14, value
=value
Êr1 column=e:c1, timestamp=12, value
=value
1 row(s) in 0.0120 seconds
Notice how delete cells are let go.
Now let’s run the same test only with KEEP_DELETED_CELLS set on the table (you can do table or per-
column-family):
hbase(main):005:0> create 'test', {NAME=>'e', VERSIONS=>2147483647, KEEP_DELETED_CELLS
=> true}
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0 row(s) in 0.2160 seconds
=> Hbase::Table - test
hbase(main):006:0> put 'test', 'r1', 'e:c1', 'value', 10
0 row(s) in 0.1070 seconds
hbase(main):007:0> put 'test', 'r1', 'e:c1', 'value', 12
0 row(s) in 0.0140 seconds
hbase(main):008:0> put 'test', 'r1', 'e:c1', 'value', 14
0 row(s) in 0.0160 seconds
hbase(main):009:0> delete 'test', 'r1', 'e:c1', 11
0 row(s) in 0.0290 seconds
hbase(main):010:0> scan 'test', {RAW=>true, VERSIONS=>1000}
ROW
COLUMN+CELL
Êr1
column=e:c1, timestamp=14, value=value
Êr1
column=e:c1, timestamp=12, value=value
Êr1
column=e:c1, timestamp=11, type=DeleteColumn
Êr1
column=e:c1, timestamp=10, value=value
1 row(s) in 0.0550 seconds
hbase(main):011:0> flush 'test'
0 row(s) in 0.2780 seconds
hbase(main):012:0> scan 'test', {RAW=>true, VERSIONS=>1000}
ROW
COLUMN+CELL
Êr1
column=e:c1, timestamp=14, value=value
Êr1
column=e:c1, timestamp=12, value=value
Êr1
column=e:c1, timestamp=11, type=DeleteColumn
Êr1
column=e:c1, timestamp=10, value=value
1 row(s) in 0.0620 seconds
hbase(main):013:0> major_compact 'test'
0 row(s) in 0.0530 seconds
hbase(main):014:0> scan 'test', {RAW=>true, VERSIONS=>1000}
ROW
COLUMN+CELL
Êr1
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column=e:c1, timestamp=14, value=value
Êr1
column=e:c1, timestamp=12, value=value
Êr1
column=e:c1, timestamp=11, type=DeleteColumn
Êr1
column=e:c1, timestamp=10, value=value
1 row(s) in 0.0650 seconds
KEEP_DELETED_CELLS is to avoid removing Cells from HBase when the only reason to remove
them is the delete marker. So with KEEP_DELETED_CELLS enabled deleted cells would get removed
if either you write more versions than the configured max, or you have a TTL and Cells are in
excess of the configured timeout, etc.
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Chapter 43. Secondary Indexes and
Alternate Query Paths
This section could also be titled "what if my table rowkey looks like this but I also want to query my
table like that." A common example on the dist-list is where a row-key is of the format "user-
timestamp" but there are reporting requirements on activity across users for certain time ranges.
Thus, selecting by user is easy because it is in the lead position of the key, but time is not.
There is no single answer on the best way to handle this because it depends on…
•Number of users
•Data size and data arrival rate
•Flexibility of reporting requirements (e.g., completely ad-hoc date selection vs. pre-configured
ranges)
•Desired execution speed of query (e.g., 90 seconds may be reasonable to some for an ad-hoc
report, whereas it may be too long for others)
and solutions are also influenced by the size of the cluster and how much processing power you
have to throw at the solution. Common techniques are in sub-sections below. This is a
comprehensive, but not exhaustive, list of approaches.
It should not be a surprise that secondary indexes require additional cluster space and processing.
This is precisely what happens in an RDBMS because the act of creating an alternate index requires
both space and processing cycles to update. RDBMS products are more advanced in this regard to
handle alternative index management out of the box. However, HBase scales better at larger data
volumes, so this is a feature trade-off.
Pay attention to Apache HBase Performance Tuning when implementing any of these approaches.
Additionally, see the David Butler response in this dist-list thread HBase, mail # user -
Stargate+hbase
43.1. Filter Query
Depending on the case, it may be appropriate to use Client Request Filters. In this case, no
secondary index is created. However, don’t try a full-scan on a large table like this from an
application (i.e., single-threaded client).
43.2. Periodic-Update Secondary Index
A secondary index could be created in another table which is periodically updated via a
MapReduce job. The job could be executed intra-day, but depending on load-strategy it could still
potentially be out of sync with the main data table.
See mapreduce.example.readwrite for more information.
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43.3. Dual-Write Secondary Index
Another strategy is to build the secondary index while publishing data to the cluster (e.g., write to
data table, write to index table). If this is approach is taken after a data table already exists, then
bootstrapping will be needed for the secondary index with a MapReduce job (see
secondary.indexes.periodic).
43.4. Summary Tables
Where time-ranges are very wide (e.g., year-long report) and where the data is voluminous,
summary tables are a common approach. These would be generated with MapReduce jobs into
another table.
See mapreduce.example.summary for more information.
43.5. Coprocessor Secondary Index
Coprocessors act like RDBMS triggers. These were added in 0.92. For more information, see
coprocessors
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Chapter 44. Constraints
HBase currently supports 'constraints' in traditional (SQL) database parlance. The advised usage for
Constraints is in enforcing business rules for attributes in the table (e.g. make sure values are in the
range 1-10). Constraints could also be used to enforce referential integrity, but this is strongly
discouraged as it will dramatically decrease the write throughput of the tables where integrity
checking is enabled. Extensive documentation on using Constraints can be found at Constraint
since version 0.94.
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Chapter 45. Schema Design Case Studies
The following will describe some typical data ingestion use-cases with HBase, and how the rowkey
design and construction can be approached. Note: this is just an illustration of potential
approaches, not an exhaustive list. Know your data, and know your processing requirements.
It is highly recommended that you read the rest of the HBase and Schema Design first, before
reading these case studies.
The following case studies are described:
•Log Data / Timeseries Data
•Log Data / Timeseries on Steroids
•Customer/Order
•Tall/Wide/Middle Schema Design
•List Data
45.1. Case Study - Log Data and Timeseries Data
Assume that the following data elements are being collected.
•Hostname
•Timestamp
•Log event
•Value/message
We can store them in an HBase table called LOG_DATA, but what will the rowkey be? From these
attributes the rowkey will be some combination of hostname, timestamp, and log-event - but what
specifically?
45.1.1. Timestamp In The Rowkey Lead Position
The rowkey [timestamp][hostname][log-event] suffers from the monotonically increasing rowkey
problem described in Monotonically Increasing Row Keys/Timeseries Data.
There is another pattern frequently mentioned in the dist-lists about "bucketing" timestamps, by
performing a mod operation on the timestamp. If time-oriented scans are important, this could be a
useful approach. Attention must be paid to the number of buckets, because this will require the
same number of scans to return results.
long bucket = timestamp % numBuckets;
to construct:
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[bucket][timestamp][hostname][log-event]
As stated above, to select data for a particular timerange, a Scan will need to be performed for each
bucket. 100 buckets, for example, will provide a wide distribution in the keyspace but it will require
100 Scans to obtain data for a single timestamp, so there are trade-offs.
45.1.2. Host In The Rowkey Lead Position
The rowkey [hostname][log-event][timestamp] is a candidate if there is a large-ish number of hosts
to spread the writes and reads across the keyspace. This approach would be useful if scanning by
hostname was a priority.
45.1.3. Timestamp, or Reverse Timestamp?
If the most important access path is to pull most recent events, then storing the timestamps as
reverse-timestamps (e.g., timestamp = Long.MAX_VALUE – timestamp) will create the property of being
able to do a Scan on [hostname][log-event] to obtain the most recently captured events.
Neither approach is wrong, it just depends on what is most appropriate for the situation.
Reverse Scan API
HBASE-4811 implements an API to scan a table or a range within a table in reverse,
reducing the need to optimize your schema for forward or reverse scanning. This
feature is available in HBase 0.98 and later. See Scan.setReversed() for more
information.
45.1.4. Variable Length or Fixed Length Rowkeys?
It is critical to remember that rowkeys are stamped on every column in HBase. If the hostname is a
and the event type is e1 then the resulting rowkey would be quite small. However, what if the
ingested hostname is myserver1.mycompany.com and the event type is
com.package1.subpackage2.subsubpackage3.ImportantService?
It might make sense to use some substitution in the rowkey. There are at least two approaches:
hashed and numeric. In the Hostname In The Rowkey Lead Position example, it might look like this:
Composite Rowkey With Hashes:
•[MD5 hash of hostname] = 16 bytes
•[MD5 hash of event-type] = 16 bytes
•[timestamp] = 8 bytes
Composite Rowkey With Numeric Substitution:
For this approach another lookup table would be needed in addition to LOG_DATA, called
LOG_TYPES. The rowkey of LOG_TYPES would be:
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•[type] (e.g., byte indicating hostname vs. event-type)
•[bytes] variable length bytes for raw hostname or event-type.
A column for this rowkey could be a long with an assigned number, which could be obtained by
using an HBase counter
So the resulting composite rowkey would be:
•[substituted long for hostname] = 8 bytes
•[substituted long for event type] = 8 bytes
•[timestamp] = 8 bytes
In either the Hash or Numeric substitution approach, the raw values for hostname and event-type
can be stored as columns.
45.2. Case Study - Log Data and Timeseries Data on
Steroids
This effectively is the OpenTSDB approach. What OpenTSDB does is re-write data and pack rows
into columns for certain time-periods. For a detailed explanation, see: http://opentsdb.net/
schema.html, and Lessons Learned from OpenTSDB from HBaseCon2012.
But this is how the general concept works: data is ingested, for example, in this manner…
[hostname][log-event][timestamp1]
[hostname][log-event][timestamp2]
[hostname][log-event][timestamp3]
with separate rowkeys for each detailed event, but is re-written like this…
[hostname][log-event][timerange]
and each of the above events are converted into columns stored with a time-offset relative to the
beginning timerange (e.g., every 5 minutes). This is obviously a very advanced processing
technique, but HBase makes this possible.
45.3. Case Study - Customer/Order
Assume that HBase is used to store customer and order information. There are two core record-
types being ingested: a Customer record type, and Order record type.
The Customer record type would include all the things that you’d typically expect:
•Customer number
•Customer name
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•Address (e.g., city, state, zip)
•Phone numbers, etc.
The Order record type would include things like:
•Customer number
•Order number
•Sales date
•A series of nested objects for shipping locations and line-items (see Order Object Design for
details)
Assuming that the combination of customer number and sales order uniquely identify an order,
these two attributes will compose the rowkey, and specifically a composite key such as:
[customer number][order number]
for an ORDER table. However, there are more design decisions to make: are the raw values the best
choices for rowkeys?
The same design questions in the Log Data use-case confront us here. What is the keyspace of the
customer number, and what is the format (e.g., numeric? alphanumeric?) As it is advantageous to
use fixed-length keys in HBase, as well as keys that can support a reasonable spread in the
keyspace, similar options appear:
Composite Rowkey With Hashes:
•[MD5 of customer number] = 16 bytes
•[MD5 of order number] = 16 bytes
Composite Numeric/Hash Combo Rowkey:
•[substituted long for customer number] = 8 bytes
•[MD5 of order number] = 16 bytes
45.3.1. Single Table? Multiple Tables?
A traditional design approach would have separate tables for CUSTOMER and SALES. Another
option is to pack multiple record types into a single table (e.g., CUSTOMER++).
Customer Record Type Rowkey:
•[customer-id]
•[type] = type indicating `1' for customer record type
Order Record Type Rowkey:
•[customer-id]
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•[type] = type indicating `2' for order record type
•[order]
The advantage of this particular CUSTOMER++ approach is that organizes many different record-
types by customer-id (e.g., a single scan could get you everything about that customer). The
disadvantage is that it’s not as easy to scan for a particular record-type.
45.3.2. Order Object Design
Now we need to address how to model the Order object. Assume that the class structure is as
follows:
Order
(an Order can have multiple ShippingLocations
LineItem
(a ShippingLocation can have multiple LineItems
there are multiple options on storing this data.
Completely Normalized
With this approach, there would be separate tables for ORDER, SHIPPING_LOCATION, and
LINE_ITEM.
The ORDER table’s rowkey was described above: schema.casestudies.custorder
The SHIPPING_LOCATION’s composite rowkey would be something like this:
•[order-rowkey]
•[shipping location number] (e.g., 1st location, 2nd, etc.)
The LINE_ITEM table’s composite rowkey would be something like this:
•[order-rowkey]
•[shipping location number] (e.g., 1st location, 2nd, etc.)
•[line item number] (e.g., 1st lineitem, 2nd, etc.)
Such a normalized model is likely to be the approach with an RDBMS, but that’s not your only
option with HBase. The cons of such an approach is that to retrieve information about any Order,
you will need:
•Get on the ORDER table for the Order
•Scan on the SHIPPING_LOCATION table for that order to get the ShippingLocation instances
•Scan on the LINE_ITEM for each ShippingLocation
granted, this is what an RDBMS would do under the covers anyway, but since there are no joins in
HBase you’re just more aware of this fact.
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Single Table With Record Types
With this approach, there would exist a single table ORDER that would contain
The Order rowkey was described above: schema.casestudies.custorder
•[order-rowkey]
•[ORDER record type]
The ShippingLocation composite rowkey would be something like this:
•[order-rowkey]
•[SHIPPING record type]
•[shipping location number] (e.g., 1st location, 2nd, etc.)
The LineItem composite rowkey would be something like this:
•[order-rowkey]
•[LINE record type]
•[shipping location number] (e.g., 1st location, 2nd, etc.)
•[line item number] (e.g., 1st lineitem, 2nd, etc.)
Denormalized
A variant of the Single Table With Record Types approach is to denormalize and flatten some of the
object hierarchy, such as collapsing the ShippingLocation attributes onto each LineItem instance.
The LineItem composite rowkey would be something like this:
•[order-rowkey]
•[LINE record type]
•[line item number] (e.g., 1st lineitem, 2nd, etc., care must be taken that there are unique across
the entire order)
and the LineItem columns would be something like this:
•itemNumber
•quantity
•price
•shipToLine1 (denormalized from ShippingLocation)
•shipToLine2 (denormalized from ShippingLocation)
•shipToCity (denormalized from ShippingLocation)
•shipToState (denormalized from ShippingLocation)
•shipToZip (denormalized from ShippingLocation)
The pros of this approach include a less complex object hierarchy, but one of the cons is that
updating gets more complicated in case any of this information changes.
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Object BLOB
With this approach, the entire Order object graph is treated, in one way or another, as a BLOB. For
example, the ORDER table’s rowkey was described above: schema.casestudies.custorder, and a
single column called "order" would contain an object that could be deserialized that contained a
container Order, ShippingLocations, and LineItems.
There are many options here: JSON, XML, Java Serialization, Avro, Hadoop Writables, etc. All of
them are variants of the same approach: encode the object graph to a byte-array. Care should be
taken with this approach to ensure backward compatibility in case the object model changes such
that older persisted structures can still be read back out of HBase.
Pros are being able to manage complex object graphs with minimal I/O (e.g., a single HBase Get per
Order in this example), but the cons include the aforementioned warning about backward
compatibility of serialization, language dependencies of serialization (e.g., Java Serialization only
works with Java clients), the fact that you have to deserialize the entire object to get any piece of
information inside the BLOB, and the difficulty in getting frameworks like Hive to work with
custom objects like this.
45.4. Case Study - "Tall/Wide/Middle" Schema Design
Smackdown
This section will describe additional schema design questions that appear on the dist-list,
specifically about tall and wide tables. These are general guidelines and not laws - each application
must consider its own needs.
45.4.1. Rows vs. Versions
A common question is whether one should prefer rows or HBase’s built-in-versioning. The context
is typically where there are "a lot" of versions of a row to be retained (e.g., where it is significantly
above the HBase default of 1 max versions). The rows-approach would require storing a timestamp
in some portion of the rowkey so that they would not overwrite with each successive update.
Preference: Rows (generally speaking).
45.4.2. Rows vs. Columns
Another common question is whether one should prefer rows or columns. The context is typically
in extreme cases of wide tables, such as having 1 row with 1 million attributes, or 1 million rows
with 1 columns apiece.
Preference: Rows (generally speaking). To be clear, this guideline is in the context is in extremely
wide cases, not in the standard use-case where one needs to store a few dozen or hundred columns.
But there is also a middle path between these two options, and that is "Rows as Columns."
45.4.3. Rows as Columns
The middle path between Rows vs. Columns is packing data that would be a separate row into
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columns, for certain rows. OpenTSDB is the best example of this case where a single row represents
a defined time-range, and then discrete events are treated as columns. This approach is often more
complex, and may require the additional complexity of re-writing your data, but has the advantage
of being I/O efficient. For an overview of this approach, see schema.casestudies.log-steroids.
45.5. Case Study - List Data
The following is an exchange from the user dist-list regarding a fairly common question: how to
handle per-user list data in Apache HBase.
•QUESTION *
We’re looking at how to store a large amount of (per-user) list data in HBase, and we were trying to
figure out what kind of access pattern made the most sense. One option is store the majority of the
data in a key, so we could have something like:
<FixedWidthUserName><FixedWidthValueId1>:"" (no value)
<FixedWidthUserName><FixedWidthValueId2>:"" (no value)
<FixedWidthUserName><FixedWidthValueId3>:"" (no value)
The other option we had was to do this entirely using:
<FixedWidthUserName><FixedWidthPageNum0>:<FixedWidthLength><FixedIdNextPageNum><ValueI
d1><ValueId2><ValueId3>...
<FixedWidthUserName><FixedWidthPageNum1>:<FixedWidthLength><FixedIdNextPageNum><ValueI
d1><ValueId2><ValueId3>...
where each row would contain multiple values. So in one case reading the first thirty values would
be:
scan { STARTROW => 'FixedWidthUsername' LIMIT => 30}
And in the second case it would be
get 'FixedWidthUserName\x00\x00\x00\x00'
The general usage pattern would be to read only the first 30 values of these lists, with infrequent
access reading deeper into the lists. Some users would have ⇐ 30 total values in these lists, and
some users would have millions (i.e. power-law distribution)
The single-value format seems like it would take up more space on HBase, but would offer some
improved retrieval / pagination flexibility. Would there be any significant performance advantages
to be able to paginate via gets vs paginating with scans?
My initial understanding was that doing a scan should be faster if our paging size is unknown (and
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caching is set appropriately), but that gets should be faster if we’ll always need the same page size.
I’ve ended up hearing different people tell me opposite things about performance. I assume the
page sizes would be relatively consistent, so for most use cases we could guarantee that we only
wanted one page of data in the fixed-page-length case. I would also assume that we would have
infrequent updates, but may have inserts into the middle of these lists (meaning we’d need to
update all subsequent rows).
Thanks for help / suggestions / follow-up questions.
•ANSWER *
If I understand you correctly, you’re ultimately trying to store triples in the form "user, valueid,
value", right? E.g., something like:
"user123, firstname, Paul",
"user234, lastname, Smith"
(But the usernames are fixed width, and the valueids are fixed width).
And, your access pattern is along the lines of: "for user X, list the next 30 values, starting with
valueid Y". Is that right? And these values should be returned sorted by valueid?
The tl;dr version is that you should probably go with one row per user+value, and not build a
complicated intra-row pagination scheme on your own unless you’re really sure it is needed.
Your two options mirror a common question people have when designing HBase schemas: should I
go "tall" or "wide"? Your first schema is "tall": each row represents one value for one user, and so
there are many rows in the table for each user; the row key is user + valueid, and there would be
(presumably) a single column qualifier that means "the value". This is great if you want to scan over
rows in sorted order by row key (thus my question above, about whether these ids are sorted
correctly). You can start a scan at any user+valueid, read the next 30, and be done. What you’re
giving up is the ability to have transactional guarantees around all the rows for one user, but it
doesn’t sound like you need that. Doing it this way is generally recommended (see here
https://hbase.apache.org/book.html#schema.smackdown).
Your second option is "wide": you store a bunch of values in one row, using different qualifiers
(where the qualifier is the valueid). The simple way to do that would be to just store ALL values for
one user in a single row. I’m guessing you jumped to the "paginated" version because you’re
assuming that storing millions of columns in a single row would be bad for performance, which
may or may not be true; as long as you’re not trying to do too much in a single request, or do things
like scanning over and returning all of the cells in the row, it shouldn’t be fundamentally worse.
The client has methods that allow you to get specific slices of columns.
Note that neither case fundamentally uses more disk space than the other; you’re just "shifting"
part of the identifying information for a value either to the left (into the row key, in option one) or
to the right (into the column qualifiers in option 2). Under the covers, every key/value still stores
the whole row key, and column family name. (If this is a bit confusing, take an hour and watch Lars
George’s excellent video about understanding HBase schema design: http://www.youtube.com/
watch?v=_HLoH_PgrLk).
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A manually paginated version has lots more complexities, as you note, like having to keep track of
how many things are in each page, re-shuffling if new values are inserted, etc. That seems
significantly more complex. It might have some slight speed advantages (or disadvantages!) at
extremely high throughput, and the only way to really know that would be to try it out. If you don’t
have time to build it both ways and compare, my advice would be to start with the simplest option
(one row per user+value). Start simple and iterate! :)
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Chapter 46. Operational and Performance
Configuration Options
46.1. Tune HBase Server RPC Handling
•Set hbase.regionserver.handler.count (in hbase-site.xml) to cores x spindles for concurrency.
•Optionally, split the call queues into separate read and write queues for differentiated service.
The parameter hbase.ipc.server.callqueue.handler.factor specifies the number of call queues:
◦0 means a single shared queue
◦1 means one queue for each handler.
◦A value between 0 and 1 allocates the number of queues proportionally to the number of
handlers. For instance, a value of .5 shares one queue between each two handlers.
•Use hbase.ipc.server.callqueue.read.ratio (hbase.ipc.server.callqueue.read.share in 0.98) to
split the call queues into read and write queues:
◦0.5 means there will be the same number of read and write queues
◦< 0.5 for more read than write
◦> 0.5 for more write than read
•Set hbase.ipc.server.callqueue.scan.ratio (HBase 1.0+) to split read call queues into small-read
and long-read queues:
◦0.5 means that there will be the same number of short-read and long-read queues
◦< 0.5 for more short-read
◦> 0.5 for more long-read
46.2. Disable Nagle for RPC
Disable Nagle’s algorithm. Delayed ACKs can add up to ~200ms to RPC round trip time. Set the
following parameters:
•In Hadoop’s core-site.xml:
◦ipc.server.tcpnodelay = true
◦ipc.client.tcpnodelay = true
•In HBase’s hbase-site.xml:
◦hbase.ipc.client.tcpnodelay = true
◦hbase.ipc.server.tcpnodelay = true
46.3. Limit Server Failure Impact
Detect regionserver failure as fast as reasonable. Set the following parameters:
•In hbase-site.xml, set zookeeper.session.timeout to 30 seconds or less to bound failure detection
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(20-30 seconds is a good start).
•Detect and avoid unhealthy or failed HDFS DataNodes: in hdfs-site.xml and hbase-site.xml, set
the following parameters:
◦dfs.namenode.avoid.read.stale.datanode = true
◦dfs.namenode.avoid.write.stale.datanode = true
46.4. Optimize on the Server Side for Low Latency
Skip the network for local blocks when the RegionServer goes to read from HDFS by exploiting
HDFS’s Short-Circuit Local Reads facility. Note how setup must be done both at the datanode and on
the dfsclient ends of the conneciton — i.e. at the RegionServer and how both ends need to have
loaded the hadoop native .so library. After configuring your hadoop setting
dfs.client.read.shortcircuit to true and configuring the dfs.domain.socket.path path for the datanode
and dfsclient to share and restarting, next configure the regionserver/dfsclient side.
•In hbase-site.xml, set the following parameters:
◦dfs.client.read.shortcircuit = true
◦dfs.client.read.shortcircuit.skip.checksum = true so we don’t double checksum (HBase
does its own checksumming to save on i/os. See hbase.regionserver.checksum.verify for
more on this.
◦dfs.domain.socket.path to match what was set for the datanodes.
◦dfs.client.read.shortcircuit.buffer.size = 131072 Important to avoid OOME — hbase has a
default it uses if unset, see hbase.dfs.client.read.shortcircuit.buffer.size; its default is
131072.
•Ensure data locality. In hbase-site.xml, set hbase.hstore.min.locality.to.skip.major.compact =
0.7 (Meaning that 0.7 <= n <= 1)
•Make sure DataNodes have enough handlers for block transfers. In hdfs-site.xml, set the
following parameters:
◦dfs.datanode.max.xcievers >= 8192
◦dfs.datanode.handler.count = number of spindles
Check the RegionServer logs after restart. You should only see complaint if misconfiguration.
Otherwise, shortcircuit read operates quietly in background. It does not provide metrics so no
optics on how effective it is but read latencies should show a marked improvement, especially if
good data locality, lots of random reads, and dataset is larger than available cache.
Other advanced configurations that you might play with, especially if shortcircuit functionality is
complaining in the logs, include dfs.client.read.shortcircuit.streams.cache.size and
dfs.client.socketcache.capacity. Documentation is sparse on these options. You’ll have to read
source code.
For more on short-circuit reads, see Colin’s old blog on rollout, How Improved Short-Circuit Local
Reads Bring Better Performance and Security to Hadoop. The HDFS-347 issue also makes for an
interesting read showing the HDFS community at its best (caveat a few comments).
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46.5. JVM Tuning
46.5.1. Tune JVM GC for low collection latencies
•Use the CMS collector: -XX:+UseConcMarkSweepGC
•Keep eden space as small as possible to minimize average collection time. Example:
-XX:CMSInitiatingOccupancyFraction=70
•Optimize for low collection latency rather than throughput: -Xmn512m
•Collect eden in parallel: -XX:+UseParNewGC
•Avoid collection under pressure: -XX:+UseCMSInitiatingOccupancyOnly
•Limit per request scanner result sizing so everything fits into survivor space but doesn’t tenure.
In hbase-site.xml, set hbase.client.scanner.max.result.size to 1/8th of eden space (with -Xmn512m
this is ~51MB )
•Set max.result.size x handler.count less than survivor space
46.5.2. OS-Level Tuning
•Turn transparent huge pages (THP) off:
echo never > /sys/kernel/mm/transparent_hugepage/enabled
echo never > /sys/kernel/mm/transparent_hugepage/defrag
•Set vm.swappiness = 0
•Set vm.min_free_kbytes to at least 1GB (8GB on larger memory systems)
•Disable NUMA zone reclaim with vm.zone_reclaim_mode = 0
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Chapter 47. Special Cases
47.1. For applications where failing quickly is better
than waiting
•In hbase-site.xml on the client side, set the following parameters:
◦Set hbase.client.pause = 1000
◦Set hbase.client.retries.number = 3
◦If you want to ride over splits and region moves, increase hbase.client.retries.number
substantially (>= 20)
◦Set the RecoverableZookeeper retry count: zookeeper.recovery.retry = 1 (no retry)
•In hbase-site.xml on the server side, set the Zookeeper session timeout for detecting server
failures: zookeeper.session.timeout ⇐ 30 seconds (20-30 is good).
47.2. For applications that can tolerate slightly out of
date information
HBase timeline consistency (HBASE-10070) With read replicas enabled, read-only copies of
regions (replicas) are distributed over the cluster. One RegionServer services the default or primary
replica, which is the only replica that can service writes. Other RegionServers serve the secondary
replicas, follow the primary RegionServer, and only see committed updates. The secondary replicas
are read-only, but can serve reads immediately while the primary is failing over, cutting read
availability blips from seconds to milliseconds. Phoenix supports timeline consistency as of 4.4.0
Tips:
•Deploy HBase 1.0.0 or later.
•Enable timeline consistent replicas on the server side.
•Use one of the following methods to set timeline consistency:
◦Use ALTER SESSION SET CONSISTENCY = 'TIMELINE’
◦Set the connection property Consistency to timeline in the JDBC connect string
47.3. More Information
See the Performance section perf.schema for more information about operational and performance
schema design options, such as Bloom Filters, Table-configured regionsizes, compression, and
blocksizes.
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HBase and MapReduce
Apache MapReduce is a software framework used to analyze large amounts of data. It is provided
by Apache Hadoop. MapReduce itself is out of the scope of this document. A good place to get
started with MapReduce is https://hadoop.apache.org/docs/r2.6.0/hadoop-mapreduce-client/hadoop-
mapreduce-client-core/MapReduceTutorial.html. MapReduce version 2 (MR2)is now part of YARN.
This chapter discusses specific configuration steps you need to take to use MapReduce on data
within HBase. In addition, it discusses other interactions and issues between HBase and MapReduce
jobs. Finally, it discusses Cascading, an alternative API for MapReduce.
mapred and mapreduce
There are two mapreduce packages in HBase as in MapReduce itself:
org.apache.hadoop.hbase.mapred and org.apache.hadoop.hbase.mapreduce. The
former does old-style API and the latter the new mode. The latter has more facility
though you can usually find an equivalent in the older package. Pick the package
that goes with your MapReduce deploy. When in doubt or starting over, pick
org.apache.hadoop.hbase.mapreduce. In the notes below, we refer to
o.a.h.h.mapreduce but replace with o.a.h.h.mapred if that is what you are using.
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Chapter 48. HBase, MapReduce, and the
CLASSPATH
By default, MapReduce jobs deployed to a MapReduce cluster do not have access to either the HBase
configuration under $HBASE_CONF_DIR or the HBase classes.
To give the MapReduce jobs the access they need, you could add hbase-site.xml_to
_$HADOOP_HOME/conf and add HBase jars to the $HADOOP_HOME/lib directory. You would then
need to copy these changes across your cluster. Or you could edit $HADOOP_HOME/conf/hadoop-
env.sh and add hbase dependencies to the HADOOP_CLASSPATH variable. Neither of these approaches is
recommended because it will pollute your Hadoop install with HBase references. It also requires
you restart the Hadoop cluster before Hadoop can use the HBase data.
The recommended approach is to let HBase add its dependency jars and use HADOOP_CLASSPATH or
-libjars.
Since HBase 0.90.x, HBase adds its dependency JARs to the job configuration itself. The
dependencies only need to be available on the local CLASSPATH and from here they’ll be picked up
and bundled into the fat job jar deployed to the MapReduce cluster. A basic trick just passes the full
hbase classpath — all hbase and dependent jars as well as configurations — to the mapreduce job
runner letting hbase utility pick out from the full-on classpath what it needs adding them to the
MapReduce job configuration (See the source at
TableMapReduceUtil#addDependencyJars(org.apache.hadoop.mapreduce.Job) for how this is done).
The following example runs the bundled HBase RowCounter MapReduce job against a table named
usertable. It sets into HADOOP_CLASSPATH the jars hbase needs to run in an MapReduce context
(including configuration files such as hbase-site.xml). Be sure to use the correct version of the
HBase JAR for your system; replace the VERSION string in the below command line w/ the version
of your local hbase install. The backticks (` symbols) cause the shell to execute the sub-commands,
setting the output of hbase classpath into HADOOP_CLASSPATH. This example assumes you use a BASH-
compatible shell.
$ HADOOP_CLASSPATH=`${HBASE_HOME}/bin/hbase classpath` \
Ê ${HADOOP_HOME}/bin/hadoop jar ${HBASE_HOME}/lib/hbase-mapreduce-VERSION.jar \
Ê org.apache.hadoop.hbase.mapreduce.RowCounter usertable
The above command will launch a row counting mapreduce job against the hbase cluster that is
pointed to by your local configuration on a cluster that the hadoop configs are pointing to.
The main for the hbase-mapreduce.jar is a Driver that lists a few basic mapreduce tasks that ship
with hbase. For example, presuming your install is hbase 2.0.0-SNAPSHOT:
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$ HADOOP_CLASSPATH=`${HBASE_HOME}/bin/hbase classpath` \
Ê ${HADOOP_HOME}/bin/hadoop jar ${HBASE_HOME}/lib/hbase-mapreduce-2.0.0-SNAPSHOT.jar
An example program must be given as the first argument.
Valid program names are:
Ê CellCounter: Count cells in HBase table.
Ê WALPlayer: Replay WAL files.
Ê completebulkload: Complete a bulk data load.
Ê copytable: Export a table from local cluster to peer cluster.
Ê export: Write table data to HDFS.
Ê exportsnapshot: Export the specific snapshot to a given FileSystem.
Ê import: Import data written by Export.
Ê importtsv: Import data in TSV format.
Ê rowcounter: Count rows in HBase table.
Ê verifyrep: Compare the data from tables in two different clusters. WARNING: It
doesn't work for incrementColumnValues'd cells since the timestamp is changed after
being appended to the log.
You can use the above listed shortnames for mapreduce jobs as in the below re-run of the row
counter job (again, presuming your install is hbase 2.0.0-SNAPSHOT):
$ HADOOP_CLASSPATH=`${HBASE_HOME}/bin/hbase classpath` \
Ê ${HADOOP_HOME}/bin/hadoop jar ${HBASE_HOME}/lib/hbase-mapreduce-2.0.0-SNAPSHOT.jar \
Ê rowcounter usertable
You might find the more selective hbase mapredcp tool output of interest; it lists the minimum set of
jars needed to run a basic mapreduce job against an hbase install. It does not include configuration.
You’ll probably need to add these if you want your MapReduce job to find the target cluster. You’ll
probably have to also add pointers to extra jars once you start to do anything of substance. Just
specify the extras by passing the system propery -Dtmpjars when you run hbase mapredcp.
For jobs that do not package their dependencies or call TableMapReduceUtil#addDependencyJars, the
following command structure is necessary:
$ HADOOP_CLASSPATH=`${HBASE_HOME}/bin/hbase mapredcp`:${HBASE_HOME}/conf hadoop jar
MyApp.jar MyJobMainClass -libjars $(${HBASE_HOME}/bin/hbase mapredcp | tr ':' ',') ...
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The example may not work if you are running HBase from its build directory
rather than an installed location. You may see an error like the following:
java.lang.RuntimeException: java.lang.ClassNotFoundException:
org.apache.hadoop.hbase.mapreduce.RowCounter$RowCounterMapper
If this occurs, try modifying the command as follows, so that it uses the HBase JARs
from the target/ directory within the build environment.
$ HADOOP_CLASSPATH=${HBASE_BUILD_HOME}/hbase-mapreduce/target/hbase-
mapreduce-VERSION-SNAPSHOT.jar:`${HBASE_BUILD_HOME}/bin/hbase
classpath` ${HADOOP_HOME}/bin/hadoop jar ${HBASE_BUILD_HOME}/hbase-
mapreduce/target/hbase-mapreduce-VERSION-SNAPSHOT.jar rowcounter
usertable
Notice to MapReduce users of HBase between 0.96.1 and 0.98.4
Some MapReduce jobs that use HBase fail to launch. The symptom is an exception
similar to the following:
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Exception in thread "main" java.lang.IllegalAccessError: class
Ê com.google.protobuf.ZeroCopyLiteralByteString cannot access its
superclass
Ê com.google.protobuf.LiteralByteString
Ê at java.lang.ClassLoader.defineClass1(Native Method)
Ê at java.lang.ClassLoader.defineClass(ClassLoader.java:792)
Ê at
java.security.SecureClassLoader.defineClass(SecureClassLoader.java:142)
Ê at java.net.URLClassLoader.defineClass(URLClassLoader.java:449)
Ê at java.net.URLClassLoader.access$100(URLClassLoader.java:71)
Ê at java.net.URLClassLoader$1.run(URLClassLoader.java:361)
Ê at java.net.URLClassLoader$1.run(URLClassLoader.java:355)
Ê at java.security.AccessController.doPrivileged(Native Method)
Ê at java.net.URLClassLoader.findClass(URLClassLoader.java:354)
Ê at java.lang.ClassLoader.loadClass(ClassLoader.java:424)
Ê at java.lang.ClassLoader.loadClass(ClassLoader.java:357)
Ê at
Ê
org.apache.hadoop.hbase.protobuf.ProtobufUtil.toScan(ProtobufUtil.java:
818)
Ê at
Ê
org.apache.hadoop.hbase.mapreduce.TableMapReduceUtil.convertScanToStrin
g(TableMapReduceUtil.java:433)
Ê at
Ê
org.apache.hadoop.hbase.mapreduce.TableMapReduceUtil.initTableMapperJob
(TableMapReduceUtil.java:186)
Ê at
Ê
org.apache.hadoop.hbase.mapreduce.TableMapReduceUtil.initTableMapperJob
(TableMapReduceUtil.java:147)
Ê at
Ê
org.apache.hadoop.hbase.mapreduce.TableMapReduceUtil.initTableMapperJob
(TableMapReduceUtil.java:270)
Ê at
Ê
org.apache.hadoop.hbase.mapreduce.TableMapReduceUtil.initTableMapperJob
(TableMapReduceUtil.java:100)
...
This is caused by an optimization introduced in HBASE-9867 that inadvertently
introduced a classloader dependency.
This affects both jobs using the -libjars option and "fat jar," those which package
their runtime dependencies in a nested lib folder.
In order to satisfy the new classloader requirements, hbase-protocol.jar must be
included in Hadoop’s classpath. See HBase, MapReduce, and the CLASSPATH for
184
current recommendations for resolving classpath errors. The following is included
for historical purposes.
This can be resolved system-wide by including a reference to the hbase-
protocol.jar in Hadoop’s lib directory, via a symlink or by copying the jar into the
new location.
This can also be achieved on a per-job launch basis by including it in the
HADOOP_CLASSPATH environment variable at job submission time. When launching
jobs that package their dependencies, all three of the following job launching
commands satisfy this requirement:
$ HADOOP_CLASSPATH=/path/to/hbase-protocol.jar:/path/to/hbase/conf
hadoop jar MyJob.jar MyJobMainClass
$ HADOOP_CLASSPATH=$(hbase mapredcp):/path/to/hbase/conf hadoop jar
MyJob.jar MyJobMainClass
$ HADOOP_CLASSPATH=$(hbase classpath) hadoop jar MyJob.jar
MyJobMainClass
For jars that do not package their dependencies, the following command structure
is necessary:
$ HADOOP_CLASSPATH=$(hbase mapredcp):/etc/hbase/conf hadoop jar
MyApp.jar MyJobMainClass -libjars $(hbase mapredcp | tr ':' ',') ...
See also HBASE-10304 for further discussion of this issue.
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Chapter 49. MapReduce Scan Caching
TableMapReduceUtil now restores the option to set scanner caching (the number of rows which are
cached before returning the result to the client) on the Scan object that is passed in. This
functionality was lost due to a bug in HBase 0.95 (HBASE-11558), which is fixed for HBase 0.98.5 and
0.96.3. The priority order for choosing the scanner caching is as follows:
1. Caching settings which are set on the scan object.
2. Caching settings which are specified via the configuration option hbase.client.scanner.caching,
which can either be set manually in hbase-site.xml or via the helper method
TableMapReduceUtil.setScannerCaching().
3. The default value HConstants.DEFAULT_HBASE_CLIENT_SCANNER_CACHING, which is set to 100.
Optimizing the caching settings is a balance between the time the client waits for a result and the
number of sets of results the client needs to receive. If the caching setting is too large, the client
could end up waiting for a long time or the request could even time out. If the setting is too small,
the scan needs to return results in several pieces. If you think of the scan as a shovel, a bigger cache
setting is analogous to a bigger shovel, and a smaller cache setting is equivalent to more shoveling
in order to fill the bucket.
The list of priorities mentioned above allows you to set a reasonable default, and override it for
specific operations.
See the API documentation for Scan for more details.
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Chapter 50. Bundled HBase MapReduce Jobs
The HBase JAR also serves as a Driver for some bundled MapReduce jobs. To learn about the
bundled MapReduce jobs, run the following command.
$ ${HADOOP_HOME}/bin/hadoop jar ${HBASE_HOME}/hbase-mapreduce-VERSION.jar
An example program must be given as the first argument.
Valid program names are:
Ê copytable: Export a table from local cluster to peer cluster
Ê completebulkload: Complete a bulk data load.
Ê export: Write table data to HDFS.
Ê import: Import data written by Export.
Ê importtsv: Import data in TSV format.
Ê rowcounter: Count rows in HBase table
Each of the valid program names are bundled MapReduce jobs. To run one of the jobs, model your
command after the following example.
$ ${HADOOP_HOME}/bin/hadoop jar ${HBASE_HOME}/hbase-mapreduce-VERSION.jar rowcounter
myTable
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Chapter 51. HBase as a MapReduce Job Data
Source and Data Sink
HBase can be used as a data source, TableInputFormat, and data sink, TableOutputFormat or
MultiTableOutputFormat, for MapReduce jobs. Writing MapReduce jobs that read or write HBase, it
is advisable to subclass TableMapper and/or TableReducer. See the do-nothing pass-through classes
IdentityTableMapper and IdentityTableReducer for basic usage. For a more involved example, see
RowCounter or review the org.apache.hadoop.hbase.mapreduce.TestTableMapReduce unit test.
If you run MapReduce jobs that use HBase as source or sink, need to specify source and sink table
and column names in your configuration.
When you read from HBase, the TableInputFormat requests the list of regions from HBase and
makes a map, which is either a map-per-region or mapreduce.job.maps map, whichever is smaller. If
your job only has two maps, raise mapreduce.job.maps to a number greater than the number of
regions. Maps will run on the adjacent TaskTracker/NodeManager if you are running a
TaskTracer/NodeManager and RegionServer per node. When writing to HBase, it may make sense
to avoid the Reduce step and write back into HBase from within your map. This approach works
when your job does not need the sort and collation that MapReduce does on the map-emitted data.
On insert, HBase 'sorts' so there is no point double-sorting (and shuffling data around your
MapReduce cluster) unless you need to. If you do not need the Reduce, your map might emit counts
of records processed for reporting at the end of the job, or set the number of Reduces to zero and
use TableOutputFormat. If running the Reduce step makes sense in your case, you should typically
use multiple reducers so that load is spread across the HBase cluster.
A new HBase partitioner, the HRegionPartitioner, can run as many reducers the number of existing
regions. The HRegionPartitioner is suitable when your table is large and your upload will not
greatly alter the number of existing regions upon completion. Otherwise use the default partitioner.
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Chapter 52. Writing HFiles Directly During
Bulk Import
If you are importing into a new table, you can bypass the HBase API and write your content directly
to the filesystem, formatted into HBase data files (HFiles). Your import will run faster, perhaps an
order of magnitude faster. For more on how this mechanism works, see Bulk Loading.
189
Chapter 53. RowCounter Example
The included RowCounter MapReduce job uses TableInputFormat and does a count of all rows in the
specified table. To run it, use the following command:
$ ./bin/hadoop jar hbase-X.X.X.jar
This will invoke the HBase MapReduce Driver class. Select rowcounter from the choice of jobs
offered. This will print rowcounter usage advice to standard output. Specify the tablename, column
to count, and output directory. If you have classpath errors, see HBase, MapReduce, and the
CLASSPATH.
190
Chapter 54. Map-Task Splitting
54.1. The Default HBase MapReduce Splitter
When TableInputFormat is used to source an HBase table in a MapReduce job, its splitter will make
a map task for each region of the table. Thus, if there are 100 regions in the table, there will be 100
map-tasks for the job - regardless of how many column families are selected in the Scan.
54.2. Custom Splitters
For those interested in implementing custom splitters, see the method getSplits in
TableInputFormatBase. That is where the logic for map-task assignment resides.
191
Chapter 55. HBase MapReduce Examples
55.1. HBase MapReduce Read Example
The following is an example of using HBase as a MapReduce source in read-only manner.
Specifically, there is a Mapper instance but no Reducer, and nothing is being emitted from the
Mapper. The job would be defined as follows…
Configuration config = HBaseConfiguration.create();
Job job = new Job(config, "ExampleRead");
job.setJarByClass(MyReadJob.class); // class that contains mapper
Scan scan = new Scan();
scan.setCaching(500); // 1 is the default in Scan, which will be bad for
MapReduce jobs
scan.setCacheBlocks(false); // don't set to true for MR jobs
// set other scan attrs
...
TableMapReduceUtil.initTableMapperJob(
Ê tableName, // input HBase table name
Ê scan, // Scan instance to control CF and attribute selection
Ê MyMapper.class, // mapper
Ê null, // mapper output key
Ê null, // mapper output value
Ê job);
job.setOutputFormatClass(NullOutputFormat.class); // because we aren't emitting
anything from mapper
boolean b = job.waitForCompletion(true);
if (!b) {
Ê throw new IOException("error with job!");
}
…and the mapper instance would extend TableMapper…
public static class MyMapper extends TableMapper<Text, Text> {
Ê public void map(ImmutableBytesWritable row, Result value, Context context) throws
InterruptedException, IOException {
Ê // process data for the row from the Result instance.
Ê }
}
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55.2. HBase MapReduce Read/Write Example
The following is an example of using HBase both as a source and as a sink with MapReduce. This
example will simply copy data from one table to another.
Configuration config = HBaseConfiguration.create();
Job job = new Job(config,"ExampleReadWrite");
job.setJarByClass(MyReadWriteJob.class); // class that contains mapper
Scan scan = new Scan();
scan.setCaching(500); // 1 is the default in Scan, which will be bad for
MapReduce jobs
scan.setCacheBlocks(false); // don't set to true for MR jobs
// set other scan attrs
TableMapReduceUtil.initTableMapperJob(
Ê sourceTable, // input table
Ê scan, // Scan instance to control CF and attribute selection
Ê MyMapper.class, // mapper class
Ê null, // mapper output key
Ê null, // mapper output value
Ê job);
TableMapReduceUtil.initTableReducerJob(
Ê targetTable, // output table
Ê null, // reducer class
Ê job);
job.setNumReduceTasks(0);
boolean b = job.waitForCompletion(true);
if (!b) {
Ê throw new IOException("error with job!");
}
An explanation is required of what TableMapReduceUtil is doing, especially with the reducer.
TableOutputFormat is being used as the outputFormat class, and several parameters are being set
on the config (e.g., TableOutputFormat.OUTPUT_TABLE), as well as setting the reducer output key to
ImmutableBytesWritable and reducer value to Writable. These could be set by the programmer on the
job and conf, but TableMapReduceUtil tries to make things easier.
The following is the example mapper, which will create a Put and matching the input Result and
emit it. Note: this is what the CopyTable utility does.
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public static class MyMapper extends TableMapper<ImmutableBytesWritable, Put> {
Ê public void map(ImmutableBytesWritable row, Result value, Context context) throws
IOException, InterruptedException {
Ê // this example is just copying the data from the source table...
Ê context.write(row, resultToPut(row,value));
Ê }
Ê private static Put resultToPut(ImmutableBytesWritable key, Result result) throws
IOException {
Ê Put put = new Put(key.get());
Ê for (KeyValue kv : result.raw()) {
Ê put.add(kv);
Ê }
Ê return put;
Ê }
}
There isn’t actually a reducer step, so TableOutputFormat takes care of sending the Put to the target
table.
This is just an example, developers could choose not to use TableOutputFormat and connect to the
target table themselves.
55.3. HBase MapReduce Read/Write Example With
Multi-Table Output
TODO: example for MultiTableOutputFormat.
55.4. HBase MapReduce Summary to HBase Example
The following example uses HBase as a MapReduce source and sink with a summarization step.
This example will count the number of distinct instances of a value in a table and write those
summarized counts in another table.
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Configuration config = HBaseConfiguration.create();
Job job = new Job(config,"ExampleSummary");
job.setJarByClass(MySummaryJob.class); // class that contains mapper and reducer
Scan scan = new Scan();
scan.setCaching(500); // 1 is the default in Scan, which will be bad for
MapReduce jobs
scan.setCacheBlocks(false); // don't set to true for MR jobs
// set other scan attrs
TableMapReduceUtil.initTableMapperJob(
Ê sourceTable, // input table
Ê scan, // Scan instance to control CF and attribute selection
Ê MyMapper.class, // mapper class
Ê Text.class, // mapper output key
Ê IntWritable.class, // mapper output value
Ê job);
TableMapReduceUtil.initTableReducerJob(
Ê targetTable, // output table
Ê MyTableReducer.class, // reducer class
Ê job);
job.setNumReduceTasks(1); // at least one, adjust as required
boolean b = job.waitForCompletion(true);
if (!b) {
Ê throw new IOException("error with job!");
}
In this example mapper a column with a String-value is chosen as the value to summarize upon.
This value is used as the key to emit from the mapper, and an IntWritable represents an instance
counter.
public static class MyMapper extends TableMapper<Text, IntWritable> {
Ê public static final byte[] CF = "cf".getBytes();
Ê public static final byte[] ATTR1 = "attr1".getBytes();
Ê private final IntWritable ONE = new IntWritable(1);
Ê private Text text = new Text();
Ê public void map(ImmutableBytesWritable row, Result value, Context context) throws
IOException, InterruptedException {
Ê String val = new String(value.getValue(CF, ATTR1));
Ê text.set(val); // we can only emit Writables...
Ê context.write(text, ONE);
Ê }
}
In the reducer, the "ones" are counted (just like any other MR example that does this), and then
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emits a Put.
public static class MyTableReducer extends TableReducer<Text, IntWritable,
ImmutableBytesWritable> {
Ê public static final byte[] CF = "cf".getBytes();
Ê public static final byte[] COUNT = "count".getBytes();
Ê public void reduce(Text key, Iterable<IntWritable> values, Context context) throws
IOException, InterruptedException {
Ê int i = 0;
Ê for (IntWritable val : values) {
Ê i += val.get();
Ê }
Ê Put put = new Put(Bytes.toBytes(key.toString()));
Ê put.add(CF, COUNT, Bytes.toBytes(i));
Ê context.write(null, put);
Ê }
}
55.5. HBase MapReduce Summary to File Example
This very similar to the summary example above, with exception that this is using HBase as a
MapReduce source but HDFS as the sink. The differences are in the job setup and in the reducer.
The mapper remains the same.
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Configuration config = HBaseConfiguration.create();
Job job = new Job(config,"ExampleSummaryToFile");
job.setJarByClass(MySummaryFileJob.class); // class that contains mapper and
reducer
Scan scan = new Scan();
scan.setCaching(500); // 1 is the default in Scan, which will be bad for
MapReduce jobs
scan.setCacheBlocks(false); // don't set to true for MR jobs
// set other scan attrs
TableMapReduceUtil.initTableMapperJob(
Ê sourceTable, // input table
Ê scan, // Scan instance to control CF and attribute selection
Ê MyMapper.class, // mapper class
Ê Text.class, // mapper output key
Ê IntWritable.class, // mapper output value
Ê job);
job.setReducerClass(MyReducer.class); // reducer class
job.setNumReduceTasks(1); // at least one, adjust as required
FileOutputFormat.setOutputPath(job, new Path("/tmp/mr/mySummaryFile")); // adjust
directories as required
boolean b = job.waitForCompletion(true);
if (!b) {
Ê throw new IOException("error with job!");
}
As stated above, the previous Mapper can run unchanged with this example. As for the Reducer, it
is a "generic" Reducer instead of extending TableMapper and emitting Puts.
public static class MyReducer extends Reducer<Text, IntWritable, Text, IntWritable> {
Ê public void reduce(Text key, Iterable<IntWritable> values, Context context) throws
IOException, InterruptedException {
Ê int i = 0;
Ê for (IntWritable val : values) {
Ê i += val.get();
Ê }
Ê context.write(key, new IntWritable(i));
Ê }
}
55.6. HBase MapReduce Summary to HBase Without
Reducer
It is also possible to perform summaries without a reducer - if you use HBase as the reducer.
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An HBase target table would need to exist for the job summary. The Table method
incrementColumnValue would be used to atomically increment values. From a performance
perspective, it might make sense to keep a Map of values with their values to be incremented for
each map-task, and make one update per key at during the cleanup method of the mapper.
However, your mileage may vary depending on the number of rows to be processed and unique
keys.
In the end, the summary results are in HBase.
55.7. HBase MapReduce Summary to RDBMS
Sometimes it is more appropriate to generate summaries to an RDBMS. For these cases, it is possible
to generate summaries directly to an RDBMS via a custom reducer. The setup method can connect
to an RDBMS (the connection information can be passed via custom parameters in the context) and
the cleanup method can close the connection.
It is critical to understand that number of reducers for the job affects the summarization
implementation, and you’ll have to design this into your reducer. Specifically, whether it is
designed to run as a singleton (one reducer) or multiple reducers. Neither is right or wrong, it
depends on your use-case. Recognize that the more reducers that are assigned to the job, the more
simultaneous connections to the RDBMS will be created - this will scale, but only to a point.
public static class MyRdbmsReducer extends Reducer<Text, IntWritable, Text,
IntWritable> {
Ê private Connection c = null;
Ê public void setup(Context context) {
Ê // create DB connection...
Ê }
Ê public void reduce(Text key, Iterable<IntWritable> values, Context context) throws
IOException, InterruptedException {
Ê // do summarization
Ê // in this example the keys are Text, but this is just an example
Ê }
Ê public void cleanup(Context context) {
Ê // close db connection
Ê }
}
In the end, the summary results are written to your RDBMS table/s.
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Chapter 56. Accessing Other HBase Tables in
a MapReduce Job
Although the framework currently allows one HBase table as input to a MapReduce job, other
HBase tables can be accessed as lookup tables, etc., in a MapReduce job via creating an Table
instance in the setup method of the Mapper.
public class MyMapper extends TableMapper<Text, LongWritable> {
Ê private Table myOtherTable;
Ê public void setup(Context context) {
Ê // In here create a Connection to the cluster and save it or use the Connection
Ê // from the existing table
Ê myOtherTable = connection.getTable("myOtherTable");
Ê }
Ê public void map(ImmutableBytesWritable row, Result value, Context context) throws
IOException, InterruptedException {
Ê // process Result...
Ê // use 'myOtherTable' for lookups
Ê }
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Chapter 57. Speculative Execution
It is generally advisable to turn off speculative execution for MapReduce jobs that use HBase as a
source. This can either be done on a per-Job basis through properties, or on the entire cluster.
Especially for longer running jobs, speculative execution will create duplicate map-tasks which will
double-write your data to HBase; this is probably not what you want.
See spec.ex for more information.
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Chapter 58. Cascading
Cascading is an alternative API for MapReduce, which actually uses MapReduce, but allows you to
write your MapReduce code in a simplified way.
The following example shows a Cascading Flow which "sinks" data into an HBase cluster. The same
hBaseTap API could be used to "source" data as well.
// read data from the default filesystem
// emits two fields: "offset" and "line"
Tap source = new Hfs( new TextLine(), inputFileLhs );
// store data in an HBase cluster
// accepts fields "num", "lower", and "upper"
// will automatically scope incoming fields to their proper familyname, "left" or
"right"
Fields keyFields = new Fields( "num" );
String[] familyNames = {"left", "right"};
Fields[] valueFields = new Fields[] {new Fields( "lower" ), new Fields( "upper" ) };
Tap hBaseTap = new HBaseTap( "multitable", new HBaseScheme( keyFields, familyNames,
valueFields ), SinkMode.REPLACE );
// a simple pipe assembly to parse the input into fields
// a real app would likely chain multiple Pipes together for more complex processing
Pipe parsePipe = new Each( "insert", new Fields( "line" ), new RegexSplitter( new
Fields( "num", "lower", "upper" ), " " ) );
// "plan" a cluster executable Flow
// this connects the source Tap and hBaseTap (the sink Tap) to the parsePipe
Flow parseFlow = new FlowConnector( properties ).connect( source, hBaseTap, parsePipe
);
// start the flow, and block until complete
parseFlow.complete();
// open an iterator on the HBase table we stuffed data into
TupleEntryIterator iterator = parseFlow.openSink();
while(iterator.hasNext())
Ê {
Ê // print out each tuple from HBase
Ê System.out.println( "iterator.next() = " + iterator.next() );
Ê }
iterator.close();
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Securing Apache HBase
Reporting Security Bugs
To protect existing HBase installations from exploitation, please
do not use JIRA to report security-related bugs. Instead, send your
report to the mailing list private@hbase.apache.org, which allows
anyone to send messages, but restricts who can read them.
Someone on that list will contact you to follow up on your report.
HBase adheres to the Apache Software Foundation’s policy on reported
vulnerabilities, available at http://apache.org/security/.
If you wish to send an encrypted report, you can use the GPG details provided for
the general ASF security list. This will likely increase the response time to your
report.
HBase provides mechanisms to secure various components and aspects of HBase and how it relates
to the rest of the Hadoop infrastructure, as well as clients and resources outside Hadoop.
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Chapter 59. Using Secure HTTP (HTTPS) for
the Web UI
A default HBase install uses insecure HTTP connections for Web UIs for the master and region
servers. To enable secure HTTP (HTTPS) connections instead, set hbase.ssl.enabled to true in hbase-
site.xml. This does not change the port used by the Web UI. To change the port for the web UI for a
given HBase component, configure that port’s setting in hbase-site.xml. These settings are:
•hbase.master.info.port
•hbase.regionserver.info.port
If you enable HTTPS, clients should avoid using the non-secure HTTP connection.
If you enable secure HTTP, clients should connect to HBase using the https:// URL.
Clients using the http:// URL will receive an HTTP response of 200, but will not
receive any data. The following exception is logged:
javax.net.ssl.SSLException: Unrecognized SSL message, plaintext
connection?
This is because the same port is used for HTTP and HTTPS.
HBase uses Jetty for the Web UI. Without modifying Jetty itself, it does not seem
possible to configure Jetty to redirect one port to another on the same host. See
Nick Dimiduk’s contribution on this Stack Overflow thread for more information.
If you know how to fix this without opening a second port for HTTPS, patches are
appreciated.
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Chapter 60. Using SPNEGO for Kerberos
authentication with Web UIs
Kerberos-authentication to HBase Web UIs can be enabled via configuring SPNEGO with the
hbase.security.authentication.ui property in hbase-site.xml. Enabling this authentication requires
that HBase is also configured to use Kerberos authentication for RPCs (e.g
hbase.security.authentication = kerberos).
<property>
Ê <name>hbase.security.authentication.ui</name>
Ê <value>kerberos</value>
Ê <description>Controls what kind of authentication should be used for the HBase web
UIs.</description>
</property>
<property>
Ê <name>hbase.security.authentication</name>
Ê <value>kerberos</value>
Ê <description>The Kerberos keytab file to use for SPNEGO authentication by the web
server.</description>
</property>
A number of properties exist to configure SPNEGO authentication for the web server:
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<property>
Ê <name>hbase.security.authentication.spnego.kerberos.principal</name>
Ê <value>HTTP/_HOST@EXAMPLE.COM</value>
Ê <description>Required for SPNEGO, the Kerberos principal to use for SPNEGO
authentication by the
Ê web server. The _HOST keyword will be automatically substituted with the node's
Ê hostname.</description>
</property>
<property>
Ê <name>hbase.security.authentication.spnego.kerberos.keytab</name>
Ê <value>/etc/security/keytabs/spnego.service.keytab</value>
Ê <description>Required for SPNEGO, the Kerberos keytab file to use for SPNEGO
authentication by the
Ê web server.</description>
</property>
<property>
Ê <name>hbase.security.authentication.spnego.kerberos.name.rules</name>
Ê <value></value>
Ê <description>Optional, Hadoop-style `auth_to_local` rules which will be parsed and
used in the
Ê handling of Kerberos principals</description>
</property>
<property>
Ê <name>hbase.security.authentication.signature.secret.file</name>
Ê <value></value>
Ê <description>Optional, a file whose contents will be used as a secret to sign the
HTTP cookies
Ê as a part of the SPNEGO authentication handshake. If this is not provided, Java's
`Random` library
Ê will be used for the secret.</description>
</property>
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Chapter 61. Secure Client Access to Apache
HBase
Newer releases of Apache HBase (>= 0.92) support optional SASL authentication of clients. See also
Matteo Bertozzi’s article on Understanding User Authentication and Authorization in Apache
HBase.
This describes how to set up Apache HBase and clients for connection to secure HBase resources.
61.1. Prerequisites
Hadoop Authentication Configuration
To run HBase RPC with strong authentication, you must set hbase.security.authentication to
kerberos. In this case, you must also set hadoop.security.authentication to kerberos in core-
site.xml. Otherwise, you would be using strong authentication for HBase but not for the
underlying HDFS, which would cancel out any benefit.
Kerberos KDC
You need to have a working Kerberos KDC.
61.2. Server-side Configuration for Secure Operation
First, refer to security.prerequisites and ensure that your underlying HDFS configuration is secure.
Add the following to the hbase-site.xml file on every server machine in the cluster:
<property>
Ê <name>hbase.security.authentication</name>
Ê <value>kerberos</value>
</property>
<property>
Ê <name>hbase.security.authorization</name>
Ê <value>true</value>
</property>
<property>
<name>hbase.coprocessor.region.classes</name>
Ê <value>org.apache.hadoop.hbase.security.token.TokenProvider</value>
</property>
A full shutdown and restart of HBase service is required when deploying these configuration
changes.
61.3. Client-side Configuration for Secure Operation
First, refer to Prerequisites and ensure that your underlying HDFS configuration is secure.
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Add the following to the hbase-site.xml file on every client:
<property>
Ê <name>hbase.security.authentication</name>
Ê <value>kerberos</value>
</property>
Before 2.2.0 version, the client environment must be logged in to Kerberos from KDC or keytab via
the kinit command before communication with the HBase cluster will be possible.
Since 2.2.0, client can specify the following configurations in hbase-site.xml:
<property>
Ê <name>hbase.client.keytab.file</name>
Ê <value>/local/path/to/client/keytab</value>
</property>
<property>
Ê <name>hbase.client.keytab.principal</name>
Ê <value>foo@EXAMPLE.COM</value>
</property>
Then application can automatically do the login and credential renewal jobs without client
interference.
It’s optional feature, client, who upgrades to 2.2.0, can still keep their login and credential renewal
logic already did in older version, as long as keeping hbase.client.keytab.file and
hbase.client.keytab.principal are unset.
Be advised that if the hbase.security.authentication in the client- and server-side site files do not
match, the client will not be able to communicate with the cluster.
Once HBase is configured for secure RPC it is possible to optionally configure encrypted
communication. To do so, add the following to the hbase-site.xml file on every client:
<property>
Ê <name>hbase.rpc.protection</name>
Ê <value>privacy</value>
</property>
This configuration property can also be set on a per-connection basis. Set it in the Configuration
supplied to Table:
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Configuration conf = HBaseConfiguration.create();
Connection connection = ConnectionFactory.createConnection(conf);
conf.set("hbase.rpc.protection", "privacy");
try (Connection connection = ConnectionFactory.createConnection(conf);
Ê Table table = connection.getTable(TableName.valueOf(tablename))) {
Ê .... do your stuff
}
Expect a ~10% performance penalty for encrypted communication.
61.4. Client-side Configuration for Secure Operation -
Thrift Gateway
Add the following to the hbase-site.xml file for every Thrift gateway:
<property>
Ê <name>hbase.thrift.keytab.file</name>
Ê <value>/etc/hbase/conf/hbase.keytab</value>
</property>
<property>
Ê <name>hbase.thrift.kerberos.principal</name>
Ê <value>$USER/_HOST@HADOOP.LOCALDOMAIN</value>
Ê <!-- TODO: This may need to be HTTP/_HOST@<REALM> and _HOST may not work.
Ê You may have to put the concrete full hostname.
Ê -->
</property>
<!-- Add these if you need to configure a different DNS interface from the default -->
<property>
Ê <name>hbase.thrift.dns.interface</name>
Ê <value>default</value>
</property>
<property>
Ê <name>hbase.thrift.dns.nameserver</name>
Ê <value>default</value>
</property>
Substitute the appropriate credential and keytab for $USER and $KEYTAB respectively.
In order to use the Thrift API principal to interact with HBase, it is also necessary to add the
hbase.thrift.kerberos.principal to the acl table. For example, to give the Thrift API principal,
thrift_server, administrative access, a command such as this one will suffice:
grant 'thrift_server', 'RWCA'
For more information about ACLs, please see the Access Control Labels (ACLs) section
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The Thrift gateway will authenticate with HBase using the supplied credential. No authentication
will be performed by the Thrift gateway itself. All client access via the Thrift gateway will use the
Thrift gateway’s credential and have its privilege.
61.5. Configure the Thrift Gateway to Authenticate on
Behalf of the Client
Client-side Configuration for Secure Operation - Thrift Gateway describes how to authenticate a
Thrift client to HBase using a fixed user. As an alternative, you can configure the Thrift gateway to
authenticate to HBase on the client’s behalf, and to access HBase using a proxy user. This was
implemented in HBASE-11349 for Thrift 1, and HBASE-11474 for Thrift 2.
Limitations with Thrift Framed Transport
If you use framed transport, you cannot yet take advantage of this feature, because
SASL does not work with Thrift framed transport at this time.
To enable it, do the following.
1. Be sure Thrift is running in secure mode, by following the procedure described in Client-side
Configuration for Secure Operation - Thrift Gateway.
2. Be sure that HBase is configured to allow proxy users, as described in REST Gateway
Impersonation Configuration.
3. In hbase-site.xml for each cluster node running a Thrift gateway, set the property
hbase.thrift.security.qop to one of the following three values:
◦privacy - authentication, integrity, and confidentiality checking.
◦integrity - authentication and integrity checking
◦authentication - authentication checking only
4. Restart the Thrift gateway processes for the changes to take effect. If a node is running Thrift,
the output of the jps command will list a ThriftServer process. To stop Thrift on a node, run the
command bin/hbase-daemon.sh stop thrift. To start Thrift on a node, run the command
bin/hbase-daemon.sh start thrift.
61.6. Configure the Thrift Gateway to Use the doAs
Feature
Configure the Thrift Gateway to Authenticate on Behalf of the Client describes how to configure the
Thrift gateway to authenticate to HBase on the client’s behalf, and to access HBase using a proxy
user. The limitation of this approach is that after the client is initialized with a particular set of
credentials, it cannot change these credentials during the session. The doAs feature provides a
flexible way to impersonate multiple principals using the same client. This feature was
implemented in HBASE-12640 for Thrift 1, but is currently not available for Thrift 2.
To enable the doAs feature, add the following to the hbase-site.xml file for every Thrift gateway:
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<property>
Ê <name>hbase.regionserver.thrift.http</name>
Ê <value>true</value>
</property>
<property>
Ê <name>hbase.thrift.support.proxyuser</name>
Ê <value>true/value>
</property>
To allow proxy users when using doAs impersonation, add the following to the hbase-site.xml file
for every HBase node:
<property>
Ê <name>hadoop.security.authorization</name>
Ê <value>true</value>
</property>
<property>
Ê <name>hadoop.proxyuser.$USER.groups</name>
Ê <value>$GROUPS</value>
</property>
<property>
Ê <name>hadoop.proxyuser.$USER.hosts</name>
Ê <value>$GROUPS</value>
</property>
Take a look at the demo client to get an overall idea of how to use this feature in your client.
61.7. Client-side Configuration for Secure Operation -
REST Gateway
Add the following to the hbase-site.xml file for every REST gateway:
<property>
Ê <name>hbase.rest.keytab.file</name>
Ê <value>$KEYTAB</value>
</property>
<property>
Ê <name>hbase.rest.kerberos.principal</name>
Ê <value>$USER/_HOST@HADOOP.LOCALDOMAIN</value>
</property>
Substitute the appropriate credential and keytab for $USER and $KEYTAB respectively.
The REST gateway will authenticate with HBase using the supplied credential.
In order to use the REST API principal to interact with HBase, it is also necessary to add the
210
hbase.rest.kerberos.principal to the acl table. For example, to give the REST API principal,
rest_server, administrative access, a command such as this one will suffice:
grant 'rest_server', 'RWCA'
For more information about ACLs, please see the Access Control Labels (ACLs) section
HBase REST gateway supports SPNEGO HTTP authentication for client access to the gateway. To
enable REST gateway Kerberos authentication for client access, add the following to the hbase-
site.xml file for every REST gateway.
<property>
Ê <name>hbase.rest.support.proxyuser</name>
Ê <value>true</value>
</property>
<property>
Ê <name>hbase.rest.authentication.type</name>
Ê <value>kerberos</value>
</property>
<property>
Ê <name>hbase.rest.authentication.kerberos.principal</name>
Ê <value>HTTP/_HOST@HADOOP.LOCALDOMAIN</value>
</property>
<property>
Ê <name>hbase.rest.authentication.kerberos.keytab</name>
Ê <value>$KEYTAB</value>
</property>
<!-- Add these if you need to configure a different DNS interface from the default -->
<property>
Ê <name>hbase.rest.dns.interface</name>
Ê <value>default</value>
</property>
<property>
Ê <name>hbase.rest.dns.nameserver</name>
Ê <value>default</value>
</property>
Substitute the keytab for HTTP for $KEYTAB.
HBase REST gateway supports different 'hbase.rest.authentication.type': simple, kerberos. You can
also implement a custom authentication by implementing Hadoop AuthenticationHandler, then
specify the full class name as 'hbase.rest.authentication.type' value. For more information, refer to
SPNEGO HTTP authentication.
61.8. REST Gateway Impersonation Configuration
By default, the REST gateway doesn’t support impersonation. It accesses the HBase on behalf of
clients as the user configured as in the previous section. To the HBase server, all requests are from
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the REST gateway user. The actual users are unknown. You can turn on the impersonation support.
With impersonation, the REST gateway user is a proxy user. The HBase server knows the actual/real
user of each request. So it can apply proper authorizations.
To turn on REST gateway impersonation, we need to configure HBase servers (masters and region
servers) to allow proxy users; configure REST gateway to enable impersonation.
To allow proxy users, add the following to the hbase-site.xml file for every HBase server:
<property>
Ê <name>hadoop.security.authorization</name>
Ê <value>true</value>
</property>
<property>
Ê <name>hadoop.proxyuser.$USER.groups</name>
Ê <value>$GROUPS</value>
</property>
<property>
Ê <name>hadoop.proxyuser.$USER.hosts</name>
Ê <value>$GROUPS</value>
</property>
Substitute the REST gateway proxy user for $USER, and the allowed group list for $GROUPS.
To enable REST gateway impersonation, add the following to the hbase-site.xml file for every REST
gateway.
<property>
Ê <name>hbase.rest.authentication.type</name>
Ê <value>kerberos</value>
</property>
<property>
Ê <name>hbase.rest.authentication.kerberos.principal</name>
Ê <value>HTTP/_HOST@HADOOP.LOCALDOMAIN</value>
</property>
<property>
Ê <name>hbase.rest.authentication.kerberos.keytab</name>
Ê <value>$KEYTAB</value>
</property>
Substitute the keytab for HTTP for $KEYTAB.
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Chapter 62. Simple User Access to Apache
HBase
Newer releases of Apache HBase (>= 0.92) support optional SASL authentication of clients. See also
Matteo Bertozzi’s article on Understanding User Authentication and Authorization in Apache
HBase.
This describes how to set up Apache HBase and clients for simple user access to HBase resources.
62.1. Simple versus Secure Access
The following section shows how to set up simple user access. Simple user access is not a secure
method of operating HBase. This method is used to prevent users from making mistakes. It can be
used to mimic the Access Control using on a development system without having to set up
Kerberos.
This method is not used to prevent malicious or hacking attempts. To make HBase secure against
these types of attacks, you must configure HBase for secure operation. Refer to the section Secure
Client Access to Apache HBase and complete all of the steps described there.
62.2. Prerequisites
None
62.3. Server-side Configuration for Simple User Access
Operation
Add the following to the hbase-site.xml file on every server machine in the cluster:
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<property>
Ê <name>hbase.security.authentication</name>
Ê <value>simple</value>
</property>
<property>
Ê <name>hbase.security.authorization</name>
Ê <value>true</value>
</property>
<property>
Ê <name>hbase.coprocessor.master.classes</name>
Ê <value>org.apache.hadoop.hbase.security.access.AccessController</value>
</property>
<property>
Ê <name>hbase.coprocessor.region.classes</name>
Ê <value>org.apache.hadoop.hbase.security.access.AccessController</value>
</property>
<property>
Ê <name>hbase.coprocessor.regionserver.classes</name>
Ê <value>org.apache.hadoop.hbase.security.access.AccessController</value>
</property>
For 0.94, add the following to the hbase-site.xml file on every server machine in the cluster:
<property>
Ê <name>hbase.rpc.engine</name>
Ê <value>org.apache.hadoop.hbase.ipc.SecureRpcEngine</value>
</property>
<property>
Ê <name>hbase.coprocessor.master.classes</name>
Ê <value>org.apache.hadoop.hbase.security.access.AccessController</value>
</property>
<property>
Ê <name>hbase.coprocessor.region.classes</name>
Ê <value>org.apache.hadoop.hbase.security.access.AccessController</value>
</property>
A full shutdown and restart of HBase service is required when deploying these configuration
changes.
62.4. Client-side Configuration for Simple User Access
Operation
Add the following to the hbase-site.xml file on every client:
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<property>
Ê <name>hbase.security.authentication</name>
Ê <value>simple</value>
</property>
For 0.94, add the following to the hbase-site.xml file on every server machine in the cluster:
<property>
Ê <name>hbase.rpc.engine</name>
Ê <value>org.apache.hadoop.hbase.ipc.SecureRpcEngine</value>
</property>
Be advised that if the hbase.security.authentication in the client- and server-side site files do not
match, the client will not be able to communicate with the cluster.
62.4.1. Client-side Configuration for Simple User Access Operation - Thrift
Gateway
The Thrift gateway user will need access. For example, to give the Thrift API user, thrift_server,
administrative access, a command such as this one will suffice:
grant 'thrift_server', 'RWCA'
For more information about ACLs, please see the Access Control Labels (ACLs) section
The Thrift gateway will authenticate with HBase using the supplied credential. No authentication
will be performed by the Thrift gateway itself. All client access via the Thrift gateway will use the
Thrift gateway’s credential and have its privilege.
62.4.2. Client-side Configuration for Simple User Access Operation - REST
Gateway
The REST gateway will authenticate with HBase using the supplied credential. No authentication
will be performed by the REST gateway itself. All client access via the REST gateway will use the
REST gateway’s credential and have its privilege.
The REST gateway user will need access. For example, to give the REST API user, rest_server,
administrative access, a command such as this one will suffice:
grant 'rest_server', 'RWCA'
For more information about ACLs, please see the Access Control Labels (ACLs) section
It should be possible for clients to authenticate with the HBase cluster through the REST gateway in
a pass-through manner via SPNEGO HTTP authentication. This is future work.
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Chapter 63. Securing Access to HDFS and
ZooKeeper
Secure HBase requires secure ZooKeeper and HDFS so that users cannot access and/or modify the
metadata and data from under HBase. HBase uses HDFS (or configured file system) to keep its data
files as well as write ahead logs (WALs) and other data. HBase uses ZooKeeper to store some
metadata for operations (master address, table locks, recovery state, etc).
63.1. Securing ZooKeeper Data
ZooKeeper has a pluggable authentication mechanism to enable access from clients using different
methods. ZooKeeper even allows authenticated and un-authenticated clients at the same time. The
access to znodes can be restricted by providing Access Control Lists (ACLs) per znode. An ACL
contains two components, the authentication method and the principal. ACLs are NOT enforced
hierarchically. See ZooKeeper Programmers Guide for details.
HBase daemons authenticate to ZooKeeper via SASL and kerberos (See SASL Authentication with
ZooKeeper). HBase sets up the znode ACLs so that only the HBase user and the configured hbase
superuser (hbase.superuser) can access and modify the data. In cases where ZooKeeper is used for
service discovery or sharing state with the client, the znodes created by HBase will also allow
anyone (regardless of authentication) to read these znodes (clusterId, master address, meta
location, etc), but only the HBase user can modify them.
63.2. Securing File System (HDFS) Data
All of the data under management is kept under the root directory in the file system (
hbase.rootdir). Access to the data and WAL files in the filesystem should be restricted so that users
cannot bypass the HBase layer, and peek at the underlying data files from the file system. HBase
assumes the filesystem used (HDFS or other) enforces permissions hierarchically. If sufficient
protection from the file system (both authorization and authentication) is not provided, HBase level
authorization control (ACLs, visibility labels, etc) is meaningless since the user can always access
the data from the file system.
HBase enforces the posix-like permissions 700 (rwx------) to its root directory. It means that only
the HBase user can read or write the files in FS. The default setting can be changed by configuring
hbase.rootdir.perms in hbase-site.xml. A restart of the active master is needed so that it changes the
used permissions. For versions before 1.2.0, you can check whether HBASE-13780 is committed, and
if not, you can manually set the permissions for the root directory if needed. Using HDFS, the
command would be:
sudo -u hdfs hadoop fs -chmod 700 /hbase
You should change /hbase if you are using a different hbase.rootdir.
In secure mode, SecureBulkLoadEndpoint should be configured and used for properly handing of
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users files created from MR jobs to the HBase daemons and HBase user. The staging directory in the
distributed file system used for bulk load (hbase.bulkload.staging.dir, defaults to /tmp/hbase-
staging) should have (mode 711, or rwx—x—x) so that users can access the staging directory created
under that parent directory, but cannot do any other operation. See Secure Bulk Load for how to
configure SecureBulkLoadEndPoint.
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Chapter 64. Securing Access To Your Data
After you have configured secure authentication between HBase client and server processes and
gateways, you need to consider the security of your data itself. HBase provides several strategies for
securing your data:
•Role-based Access Control (RBAC) controls which users or groups can read and write to a given
HBase resource or execute a coprocessor endpoint, using the familiar paradigm of roles.
•Visibility Labels which allow you to label cells and control access to labelled cells, to further
restrict who can read or write to certain subsets of your data. Visibility labels are stored as tags.
See hbase.tags for more information.
•Transparent encryption of data at rest on the underlying filesystem, both in HFiles and in the
WAL. This protects your data at rest from an attacker who has access to the underlying
filesystem, without the need to change the implementation of the client. It can also protect
against data leakage from improperly disposed disks, which can be important for legal and
regulatory compliance.
Server-side configuration, administration, and implementation details of each of these features are
discussed below, along with any performance trade-offs. An example security configuration is given
at the end, to show these features all used together, as they might be in a real-world scenario.
All aspects of security in HBase are in active development and evolving rapidly.
Any strategy you employ for security of your data should be thoroughly tested. In
addition, some of these features are still in the experimental stage of development.
To take advantage of many of these features, you must be running HBase 0.98+ and
using the HFile v3 file format.
Protecting Sensitive Files
Several procedures in this section require you to copy files between cluster nodes.
When copying keys, configuration files, or other files containing sensitive strings,
use a secure method, such as ssh, to avoid leaking sensitive data.
Procedure: Basic Server-Side Configuration
1. Enable HFile v3, by setting hfile.format.version to 3 in hbase-site.xml. This is the default for
HBase 1.0 and newer.
<property>
Ê <name>hfile.format.version</name>
Ê <value>3</value>
</property>
2. Enable SASL and Kerberos authentication for RPC and ZooKeeper, as described in
security.prerequisites and SASL Authentication with ZooKeeper.
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64.1. Tags
Tags are a feature of HFile v3. A tag is a piece of metadata which is part of a cell, separate from the
key, value, and version. Tags are an implementation detail which provides a foundation for other
security-related features such as cell-level ACLs and visibility labels. Tags are stored in the HFiles
themselves. It is possible that in the future, tags will be used to implement other HBase features.
You don’t need to know a lot about tags in order to use the security features they enable.
64.1.1. Implementation Details
Every cell can have zero or more tags. Every tag has a type and the actual tag byte array.
Just as row keys, column families, qualifiers and values can be encoded (see
data.block.encoding.types), tags can also be encoded as well. You can enable or disable tag encoding
at the level of the column family, and it is enabled by default. Use the
HColumnDescriptor#setCompressionTags(boolean compressTags) method to manage encoding settings
on a column family. You also need to enable the DataBlockEncoder for the column family, for
encoding of tags to take effect.
You can enable compression of each tag in the WAL, if WAL compression is also enabled, by setting
the value of hbase.regionserver.wal.tags.enablecompression to true in hbase-site.xml. Tag
compression uses dictionary encoding.
Coprocessors that run server-side on RegionServers can perform get and set operations on cell Tags.
Tags are stripped out at the RPC layer before the read response is sent back, so clients do not see
these tags. Tag compression is not supported when using WAL encryption.
64.2. Access Control Labels (ACLs)
64.2.1. How It Works
ACLs in HBase are based upon a user’s membership in or exclusion from groups, and a given
group’s permissions to access a given resource. ACLs are implemented as a coprocessor called
AccessController.
HBase does not maintain a private group mapping, but relies on a Hadoop group mapper, which
maps between entities in a directory such as LDAP or Active Directory, and HBase users. Any
supported Hadoop group mapper will work. Users are then granted specific permissions (Read,
Write, Execute, Create, Admin) against resources (global, namespaces, tables, cells, or endpoints).
With Kerberos and Access Control enabled, client access to HBase is authenticated
and user data is private unless access has been explicitly granted.
HBase has a simpler security model than relational databases, especially in terms of client
operations. No distinction is made between an insert (new record) and update (of existing record),
for example, as both collapse down into a Put.
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Understanding Access Levels
HBase access levels are granted independently of each other and allow for different types of
operations at a given scope.
•Read (R) - can read data at the given scope
•Write (W) - can write data at the given scope
•Execute (X) - can execute coprocessor endpoints at the given scope
•Create (C) - can create tables or drop tables (even those they did not create) at the given scope
•Admin (A) - can perform cluster operations such as balancing the cluster or assigning regions at
the given scope
The possible scopes are:
•Superuser - superusers can perform any operation available in HBase, to any resource. The user
who runs HBase on your cluster is a superuser, as are any principals assigned to the
configuration property hbase.superuser in hbase-site.xml on the HMaster.
•Global - permissions granted at global scope allow the admin to operate on all tables of the
cluster.
•Namespace - permissions granted at namespace scope apply to all tables within a given
namespace.
•Table - permissions granted at table scope apply to data or metadata within a given table.
•ColumnFamily - permissions granted at ColumnFamily scope apply to cells within that
ColumnFamily.
•Cell - permissions granted at cell scope apply to that exact cell coordinate (key, value,
timestamp). This allows for policy evolution along with data.
To change an ACL on a specific cell, write an updated cell with new ACL to the precise
coordinates of the original.
If you have a multi-versioned schema and want to update ACLs on all visible versions, you need
to write new cells for all visible versions. The application has complete control over policy
evolution.
The exception to the above rule is append and increment processing. Appends and increments
can carry an ACL in the operation. If one is included in the operation, then it will be applied to
the result of the append or increment. Otherwise, the ACL of the existing cell you are appending to
or incrementing is preserved.
The combination of access levels and scopes creates a matrix of possible access levels that can be
granted to a user. In a production environment, it is useful to think of access levels in terms of what
is needed to do a specific job. The following list describes appropriate access levels for some
common types of HBase users. It is important not to grant more access than is required for a given
user to perform their required tasks.
•Superusers - In a production system, only the HBase user should have superuser access. In a
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development environment, an administrator may need superuser access in order to quickly
control and manage the cluster. However, this type of administrator should usually be a Global
Admin rather than a superuser.
•Global Admins - A global admin can perform tasks and access every table in HBase. In a typical
production environment, an admin should not have Read or Write permissions to data within
tables.
•A global admin with Admin permissions can perform cluster-wide operations on the cluster,
such as balancing, assigning or unassigning regions, or calling an explicit major compaction.
This is an operations role.
•A global admin with Create permissions can create or drop any table within HBase. This is more
of a DBA-type role.
In a production environment, it is likely that different users will have only one of Admin and
Create permissions.
In the current implementation, a Global Admin with Admin permission can
grant himself Read and Write permissions on a table and gain access to that
table’s data. For this reason, only grant Global Admin permissions to trusted
user who actually need them.
Also be aware that a Global Admin with Create permission can perform a Put
operation on the ACL table, simulating a grant or revoke and circumventing the
authorization check for Global Admin permissions.
Due to these issues, be cautious with granting Global Admin privileges.
•Namespace Admins - a namespace admin with Create permissions can create or drop tables
within that namespace, and take and restore snapshots. A namespace admin with Admin
permissions can perform operations such as splits or major compactions on tables within that
namespace.
•Table Admins - A table admin can perform administrative operations only on that table. A table
admin with Create permissions can create snapshots from that table or restore that table from a
snapshot. A table admin with Admin permissions can perform operations such as splits or major
compactions on that table.
•Users - Users can read or write data, or both. Users can also execute coprocessor endpoints, if
given Executable permissions.
Table 9. Real-World Example of Access Levels
Job Title Scope Permissions Description
Senior Administrator Global Access, Create Manages the cluster
and gives access to
Junior Administrators.
Junior Administrator Global Create Creates tables and gives
access to Table
Administrators.
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Job Title Scope Permissions Description
Table Administrator Table Access Maintains a table from
an operations point of
view.
Data Analyst Table Read Creates reports from
HBase data.
Web Application Table Read, Write Puts data into HBase
and uses HBase data to
perform operations.
ACL Matrix
For more details on how ACLs map to specific HBase operations and tasks, see appendix acl matrix.
Implementation Details
Cell-level ACLs are implemented using tags (see Tags). In order to use cell-level ACLs, you must be
using HFile v3 and HBase 0.98 or newer.
1. Files created by HBase are owned by the operating system user running the HBase process. To
interact with HBase files, you should use the API or bulk load facility.
2. HBase does not model "roles" internally in HBase. Instead, group names can be granted
permissions. This allows external modeling of roles via group membership. Groups are created
and manipulated externally to HBase, via the Hadoop group mapping service.
Server-Side Configuration
1. As a prerequisite, perform the steps in Procedure: Basic Server-Side Configuration.
2. Install and configure the AccessController coprocessor, by setting the following properties in
hbase-site.xml. These properties take a list of classes.
If you use the AccessController along with the VisibilityController, the
AccessController must come first in the list, because with both components
active, the VisibilityController will delegate access control on its system tables
to the AccessController. For an example of using both together, see Security
Configuration Example.
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<property>
Ê <name>hbase.security.authorization</name>
Ê <value>true</value>
</property>
<property>
Ê <name>hbase.coprocessor.region.classes</name>
Ê <value>org.apache.hadoop.hbase.security.access.AccessController,
org.apache.hadoop.hbase.security.token.TokenProvider</value>
</property>
<property>
Ê <name>hbase.coprocessor.master.classes</name>
Ê <value>org.apache.hadoop.hbase.security.access.AccessController</value>
</property>
<property>
Ê <name>hbase.coprocessor.regionserver.classes</name>
Ê <value>org.apache.hadoop.hbase.security.access.AccessController</value>
</property>
<property>
Ê <name>hbase.security.exec.permission.checks</name>
Ê <value>true</value>
</property>
Optionally, you can enable transport security, by setting hbase.rpc.protection to privacy. This
requires HBase 0.98.4 or newer.
3. Set up the Hadoop group mapper in the Hadoop namenode’s core-site.xml. This is a Hadoop file,
not an HBase file. Customize it to your site’s needs. Following is an example.
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<property>
Ê <name>hadoop.security.group.mapping</name>
Ê <value>org.apache.hadoop.security.LdapGroupsMapping</value>
</property>
<property>
Ê <name>hadoop.security.group.mapping.ldap.url</name>
Ê <value>ldap://server</value>
</property>
<property>
Ê <name>hadoop.security.group.mapping.ldap.bind.user</name>
Ê <value>Administrator@example-ad.local</value>
</property>
<property>
Ê <name>hadoop.security.group.mapping.ldap.bind.password</name>
Ê <value>****</value>
</property>
<property>
Ê <name>hadoop.security.group.mapping.ldap.base</name>
Ê <value>dc=example-ad,dc=local</value>
</property>
<property>
Ê <name>hadoop.security.group.mapping.ldap.search.filter.user</name>
Ê <value>(&amp;(objectClass=user)(sAMAccountName={0}))</value>
</property>
<property>
Ê <name>hadoop.security.group.mapping.ldap.search.filter.group</name>
Ê <value>(objectClass=group)</value>
</property>
<property>
Ê <name>hadoop.security.group.mapping.ldap.search.attr.member</name>
Ê <value>member</value>
</property>
<property>
Ê <name>hadoop.security.group.mapping.ldap.search.attr.group.name</name>
Ê <value>cn</value>
</property>
4. Optionally, enable the early-out evaluation strategy. Prior to HBase 0.98.0, if a user was not
granted access to a column family, or at least a column qualifier, an AccessDeniedException
would be thrown. HBase 0.98.0 removed this exception in order to allow cell-level exceptional
grants. To restore the old behavior in HBase 0.98.0-0.98.6, set hbase.security.access.early_out to
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true in hbase-site.xml. In HBase 0.98.6, the default has been returned to true.
5. Distribute your configuration and restart your cluster for changes to take effect.
6. To test your configuration, log into HBase Shell as a given user and use the whoami command to
report the groups your user is part of. In this example, the user is reported as being a member
of the services group.
hbase> whoami
service (auth:KERBEROS)
Ê groups: services
Administration
Administration tasks can be performed from HBase Shell or via an API.
API Examples
Many of the API examples below are taken from source files hbase-
server/src/test/java/org/apache/hadoop/hbase/security/access/TestAccessController.j
ava and hbase-
server/src/test/java/org/apache/hadoop/hbase/security/access/SecureTestUtil.java.
Neither the examples, nor the source files they are taken from, are part of the
public HBase API, and are provided for illustration only. Refer to the official API
for usage instructions.
1. User and Group Administration
Users and groups are maintained external to HBase, in your directory.
2. Granting Access To A Namespace, Table, Column Family, or Cell
There are a few different types of syntax for grant statements. The first, and most familiar, is as
follows, with the table and column family being optional:
grant 'user', 'RWXCA', 'TABLE', 'CF', 'CQ'
Groups and users are granted access in the same way, but groups are prefixed with an @ symbol.
In the same way, tables and namespaces are specified in the same way, but namespaces are
prefixed with an @ symbol.
It is also possible to grant multiple permissions against the same resource in a single statement,
as in this example. The first sub-clause maps users to ACLs and the second sub-clause specifies
the resource.
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HBase Shell support for granting and revoking access at the cell level is for
testing and verification support, and should not be employed for production
use because it won’t apply the permissions to cells that don’t exist yet. The
correct way to apply cell level permissions is to do so in the application code
when storing the values.
ACL Granularity and Evaluation Order
ACLs are evaluated from least granular to most granular, and when an ACL is reached that
grants permission, evaluation stops. This means that cell ACLs do not override ACLs at less
granularity.
Example 14. HBase Shell
◦Global:
hbase> grant '@admins', 'RWXCA'
◦Namespace:
hbase> grant 'service', 'RWXCA', '@test-NS'
◦Table:
hbase> grant 'service', 'RWXCA', 'user'
◦Column Family:
hbase> grant '@developers', 'RW', 'user', 'i'
◦Column Qualifier:
hbase> grant 'service, 'RW', 'user', 'i', 'foo'
◦Cell:
The syntax for granting cell ACLs uses the following syntax:
grant <table>, \
Ê { '<user-or-group>' => \
Ê '<permissions>', ... }, \
Ê { <scanner-specification> }
◦<user-or-group> is the user or group name, prefixed with @ in the case of a group.
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◦<permissions> is a string containing any or all of "RWXCA", though only R and W are
meaningful at cell scope.
◦<scanner-specification> is the scanner specification syntax and conventions used by the
'scan' shell command. For some examples of scanner specifications, issue the following
HBase Shell command.
hbase> help "scan"
If you need to enable cell acl,the hfile.format.version option in hbase-site.xml should be
greater than or equal to 3,and the hbase.security.access.early_out option should be set
to false.This example grants read access to the 'testuser' user and read/write access to
the 'developers' group, on cells in the 'pii' column which match the filter.
hbase> grant 'user', \
Ê { '@developers' => 'RW', 'testuser' => 'R' }, \
Ê { COLUMNS => 'pii', FILTER => "(PrefixFilter ('test'))" }
The shell will run a scanner with the given criteria, rewrite the found cells with new
ACLs, and store them back to their exact coordinates.
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Example 15. API
The following example shows how to grant access at the table level.
public static void grantOnTable(final HBaseTestingUtility util, final String
user,
Ê final TableName table, final byte[] family, final byte[] qualifier,
Ê final Permission.Action... actions) throws Exception {
Ê SecureTestUtil.updateACLs(util, new Callable<Void>() {
Ê @Override
Ê public Void call() throws Exception {
Ê try (Connection connection = ConnectionFactory.createConnection(util
.getConfiguration());
Ê Table acl = connection.getTable(AccessControlLists.ACL_TABLE_NAME))
{
Ê BlockingRpcChannel service = acl.coprocessorService(HConstants
.EMPTY_START_ROW);
Ê AccessControlService.BlockingInterface protocol =
Ê AccessControlService.newBlockingStub(service);
Ê AccessControlUtil.grant(null, protocol, user, table, family, qualifier,
false, actions);
Ê }
Ê return null;
Ê }
Ê });
}
To grant permissions at the cell level, you can use the Mutation.setACL method:
Mutation.setACL(String user, Permission perms)
Mutation.setACL(Map<String, Permission> perms)
Specifically, this example provides read permission to a user called user1 on any cells
contained in a particular Put operation:
put.setACL(“user1”, new Permission(Permission.Action.READ))
3. Revoking Access Control From a Namespace, Table, Column Family, or Cell
The revoke command and API are twins of the grant command and API, and the syntax is
exactly the same. The only exception is that you cannot revoke permissions at the cell level. You
can only revoke access that has previously been granted, and a revoke statement is not the same
thing as explicit denial to a resource.
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HBase Shell support for granting and revoking access is for testing and
verification support, and should not be employed for production use because it
won’t apply the permissions to cells that don’t exist yet. The correct way to
apply cell-level permissions is to do so in the application code when storing the
values.
Example 16. Revoking Access To a Table
public static void revokeFromTable(final HBaseTestingUtility util, final String
user,
Ê final TableName table, final byte[] family, final byte[] qualifier,
Ê final Permission.Action... actions) throws Exception {
Ê SecureTestUtil.updateACLs(util, new Callable<Void>() {
Ê @Override
Ê public Void call() throws Exception {
Ê Configuration conf = HBaseConfiguration.create();
Ê Connection connection = ConnectionFactory.createConnection(conf);
Ê Table acl = connection.getTable(util.getConfiguration(),
AccessControlLists.ACL_TABLE_NAME);
Ê try {
Ê BlockingRpcChannel service = acl.coprocessorService(HConstants
.EMPTY_START_ROW);
Ê AccessControlService.BlockingInterface protocol =
Ê AccessControlService.newBlockingStub(service);
Ê ProtobufUtil.revoke(protocol, user, table, family, qualifier, actions);
Ê } finally {
Ê acl.close();
Ê }
Ê return null;
Ê }
Ê });
}
4. Showing a User’s Effective Permissions
HBase Shell
hbase> user_permission 'user'
hbase> user_permission '.*'
hbase> user_permission JAVA_REGEX
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Example 17. API
public static void verifyAllowed(User user, AccessTestAction action, int count)
throws Exception {
Ê try {
Ê Object obj = user.runAs(action);
Ê if (obj != null && obj instanceof List&lt;?&gt;) {
Ê List&lt;?&gt; results = (List&lt;?&gt;) obj;
Ê if (results != null && results.isEmpty()) {
Ê fail("Empty non null results from action for user '" ` user.getShortName()
` "'");
Ê }
Ê assertEquals(count, results.size());
Ê }
Ê } catch (AccessDeniedException ade) {
Ê fail("Expected action to pass for user '" ` user.getShortName() ` "' but was
denied");
Ê }
}
64.3. Visibility Labels
Visibility labels control can be used to only permit users or principals associated with a given label
to read or access cells with that label. For instance, you might label a cell top-secret, and only grant
access to that label to the managers group. Visibility labels are implemented using Tags, which are a
feature of HFile v3, and allow you to store metadata on a per-cell basis. A label is a string, and
labels can be combined into expressions by using logical operators (&, |, or !), and using
parentheses for grouping. HBase does not do any kind of validation of expressions beyond basic
well-formedness. Visibility labels have no meaning on their own, and may be used to denote
sensitivity level, privilege level, or any other arbitrary semantic meaning.
If a user’s labels do not match a cell’s label or expression, the user is denied access to the cell.
In HBase 0.98.6 and newer, UTF-8 encoding is supported for visibility labels and expressions. When
creating labels using the addLabels(conf, labels) method provided by the
org.apache.hadoop.hbase.security.visibility.VisibilityClient class and passing labels in
Authorizations via Scan or Get, labels can contain UTF-8 characters, as well as the logical operators
normally used in visibility labels, with normal Java notations, without needing any escaping
method. However, when you pass a CellVisibility expression via a Mutation, you must enclose the
expression with the CellVisibility.quote() method if you use UTF-8 characters or logical operators.
See TestExpressionParser and the source file hbase-
client/src/test/java/org/apache/hadoop/hbase/client/TestScan.java.
A user adds visibility expressions to a cell during a Put operation. In the default configuration, the
user does not need to have access to a label in order to label cells with it. This behavior is controlled
by the configuration option hbase.security.visibility.mutations.checkauths. If you set this option
to true, the labels the user is modifying as part of the mutation must be associated with the user, or
230
the mutation will fail. Whether a user is authorized to read a labelled cell is determined during a
Get or Scan, and results which the user is not allowed to read are filtered out. This incurs the same
I/O penalty as if the results were returned, but reduces load on the network.
Visibility labels can also be specified during Delete operations. For details about visibility labels and
Deletes, see HBASE-10885.
The user’s effective label set is built in the RPC context when a request is first received by the
RegionServer. The way that users are associated with labels is pluggable. The default plugin passes
through labels specified in Authorizations added to the Get or Scan and checks those against the
calling user’s authenticated labels list. When the client passes labels for which the user is not
authenticated, the default plugin drops them. You can pass a subset of user authenticated labels via
the Get#setAuthorizations(Authorizations(String,…)) and
Scan#setAuthorizations(Authorizations(String,…)); methods.
Groups can be granted visibility labels the same way as users. Groups are prefixed with an @
symbol. When checking visibility labels of a user, the server will include the visibility labels of the
groups of which the user is a member, together with the user’s own labels. When the visibility
labels are retrieved using API VisibilityClient#getAuths or Shell command get_auths for a user, we
will return labels added specifically for that user alone, not the group level labels.
Visibility label access checking is performed by the VisibilityController coprocessor. You can use
interface VisibilityLabelService to provide a custom implementation and/or control the way that
visibility labels are stored with cells. See the source file hbase-
server/src/test/java/org/apache/hadoop/hbase/security/visibility/TestVisibilityLabelsWithCustomVisLa
bService.java for one example.
Visibility labels can be used in conjunction with ACLs.
The labels have to be explicitly defined before they can be used in visibility labels.
See below for an example of how this can be done.
There is currently no way to determine which labels have been applied to a cell.
See HBASE-12470 for details.
Visibility labels are not currently applied for superusers.
Table 10. Examples of Visibility Expressions
Expression Interpretation
fulltime Allow access to users associated with the
fulltime label.
!public Allow access to users not associated with the
public label.
( secret | topsecret ) & !probationary Allow access to users associated with either the
secret or topsecret label and not associated with
the probationary label.
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64.3.1. Server-Side Configuration
1. As a prerequisite, perform the steps in Procedure: Basic Server-Side Configuration.
2. Install and configure the VisibilityController coprocessor by setting the following properties in
hbase-site.xml. These properties take a list of class names.
<property>
Ê <name>hbase.security.authorization</name>
Ê <value>true</value>
</property>
<property>
Ê <name>hbase.coprocessor.region.classes</name>
Ê <value>org.apache.hadoop.hbase.security.visibility.VisibilityController</value>
</property>
<property>
Ê <name>hbase.coprocessor.master.classes</name>
Ê <value>org.apache.hadoop.hbase.security.visibility.VisibilityController</value>
</property>
If you use the AccessController and VisibilityController coprocessors together,
the AccessController must come first in the list, because with both components
active, the VisibilityController will delegate access control on its system tables
to the AccessController.
3. Adjust Configuration
By default, users can label cells with any label, including labels they are not associated with,
which means that a user can Put data that he cannot read. For example, a user could label a cell
with the (hypothetical) 'topsecret' label even if the user is not associated with that label. If you
only want users to be able to label cells with labels they are associated with, set
hbase.security.visibility.mutations.checkauths to true. In that case, the mutation will fail if it
makes use of labels the user is not associated with.
4. Distribute your configuration and restart your cluster for changes to take effect.
64.3.2. Administration
Administration tasks can be performed using the HBase Shell or the Java API. For defining the list of
visibility labels and associating labels with users, the HBase Shell is probably simpler.
232
API Examples
Many of the Java API examples in this section are taken from the source file hbase-
server/src/test/java/org/apache/hadoop/hbase/security/visibility/TestVisibilityLabels.j
ava. Refer to that file or the API documentation for more context.
Neither these examples, nor the source file they were taken from, are part of the
public HBase API, and are provided for illustration only. Refer to the official API
for usage instructions.
1. Define the List of Visibility Labels
HBase Shell
hbase> add_labels [ 'admin', 'service', 'developer', 'test' ]
Example 18. Java API
public static void addLabels() throws Exception {
Ê PrivilegedExceptionAction<VisibilityLabelsResponse> action = new
PrivilegedExceptionAction<VisibilityLabelsResponse>() {
Ê public VisibilityLabelsResponse run() throws Exception {
Ê String[] labels = { SECRET, TOPSECRET, CONFIDENTIAL, PUBLIC, PRIVATE,
COPYRIGHT, ACCENT,
Ê UNICODE_VIS_TAG, UC1, UC2 };
Ê try {
Ê VisibilityClient.addLabels(conf, labels);
Ê } catch (Throwable t) {
Ê throw new IOException(t);
Ê }
Ê return null;
Ê }
Ê };
Ê SUPERUSER.runAs(action);
}
2. Associate Labels with Users
HBase Shell
hbase> set_auths 'service', [ 'service' ]
hbase> set_auths 'testuser', [ 'test' ]
hbase> set_auths 'qa', [ 'test', 'developer' ]
233
hbase> set_auths '@qagroup', [ 'test' ]
+ .Java API
public void testSetAndGetUserAuths() throws Throwable {
Ê final String user = "user1";
Ê PrivilegedExceptionAction<Void> action = new PrivilegedExceptionAction<Void>() {
Ê public Void run() throws Exception {
Ê String[] auths = { SECRET, CONFIDENTIAL };
Ê try {
Ê VisibilityClient.setAuths(conf, auths, user);
Ê } catch (Throwable e) {
Ê }
Ê return null;
Ê }
Ê ...
1. Clear Labels From Users
HBase Shell
hbase> clear_auths 'service', [ 'service' ]
hbase> clear_auths 'testuser', [ 'test' ]
hbase> clear_auths 'qa', [ 'test', 'developer' ]
hbase> clear_auths '@qagroup', [ 'test', 'developer' ]
+ .Java API
234
...
auths = new String[] { SECRET, PUBLIC, CONFIDENTIAL };
VisibilityLabelsResponse response = null;
try {
Ê response = VisibilityClient.clearAuths(conf, auths, user);
} catch (Throwable e) {
Ê fail("Should not have failed");
Ê ...
}
1. Apply a Label or Expression to a Cell
The label is only applied when data is written. The label is associated with a given version of the
cell.
HBase Shell
hbase> set_visibility 'user', 'admin|service|developer', { COLUMNS => 'i' }
hbase> set_visibility 'user', 'admin|service', { COLUMNS => 'pii' }
hbase> set_visibility 'user', 'test', { COLUMNS => [ 'i', 'pii' ], FILTER =>
"(PrefixFilter ('test'))" }
+ NOTE: HBase Shell support for applying labels or permissions to cells is for testing and
verification support, and should not be employed for production use because it won’t apply the
labels to cells that don’t exist yet. The correct way to apply cell level labels is to do so in the
application code when storing the values.
+ .Java API
235
static Table createTableAndWriteDataWithLabels(TableName tableName, String...
labelExps)
Ê throws Exception {
Ê Configuration conf = HBaseConfiguration.create();
Ê Connection connection = ConnectionFactory.createConnection(conf);
Ê Table table = NULL;
Ê try {
Ê table = TEST_UTIL.createTable(tableName, fam);
Ê int i = 1;
Ê List<Put> puts = new ArrayList<Put>();
Ê for (String labelExp : labelExps) {
Ê Put put = new Put(Bytes.toBytes("row" + i));
Ê put.add(fam, qual, HConstants.LATEST_TIMESTAMP, value);
Ê put.setCellVisibility(new CellVisibility(labelExp));
Ê puts.add(put);
Ê i++;
Ê }
Ê table.put(puts);
Ê } finally {
Ê if (table != null) {
Ê table.flushCommits();
Ê }
Ê }
64.3.3. Reading Cells with Labels
When you issue a Scan or Get, HBase uses your default set of authorizations to filter out cells that
you do not have access to. A superuser can set the default set of authorizations for a given user by
using the set_auths HBase Shell command or the VisibilityClient.setAuths() method.
You can specify a different authorization during the Scan or Get, by passing the AUTHORIZATIONS
option in HBase Shell, or the Scan.setAuthorizations() method if you use the API. This authorization
will be combined with your default set as an additional filter. It will further filter your results,
rather than giving you additional authorization.
HBase Shell
hbase> get_auths 'myUser'
hbase> scan 'table1', AUTHORIZATIONS => ['private']
236
Example 19. Java API
...
public Void run() throws Exception {
Ê String[] auths1 = { SECRET, CONFIDENTIAL };
Ê GetAuthsResponse authsResponse = null;
Ê try {
Ê VisibilityClient.setAuths(conf, auths1, user);
Ê try {
Ê authsResponse = VisibilityClient.getAuths(conf, user);
Ê } catch (Throwable e) {
Ê fail("Should not have failed");
Ê }
Ê } catch (Throwable e) {
Ê }
Ê List<String> authsList = new ArrayList<String>();
Ê for (ByteString authBS : authsResponse.getAuthList()) {
Ê authsList.add(Bytes.toString(authBS.toByteArray()));
Ê }
Ê assertEquals(2, authsList.size());
Ê assertTrue(authsList.contains(SECRET));
Ê assertTrue(authsList.contains(CONFIDENTIAL));
Ê return null;
}
...
64.3.4. Implementing Your Own Visibility Label Algorithm
Interpreting the labels authenticated for a given get/scan request is a pluggable algorithm.
You can specify a custom plugin or plugins by using the property
hbase.regionserver.scan.visibility.label.generator.class. The output for the first
ScanLabelGenerator will be the input for the next one, until the end of the list.
The default implementation, which was implemented in HBASE-12466, loads two plugins,
FeedUserAuthScanLabelGenerator and DefinedSetFilterScanLabelGenerator. See Reading Cells with
Labels.
64.3.5. Replicating Visibility Tags as Strings
As mentioned in the above sections, the interface VisibilityLabelService could be used to
implement a different way of storing the visibility expressions in the cells. Clusters with replication
enabled also must replicate the visibility expressions to the peer cluster. If
DefaultVisibilityLabelServiceImpl is used as the implementation for VisibilityLabelService, all the
visibility expression are converted to the corresponding expression based on the ordinals for each
visibility label stored in the labels table. During replication, visible cells are also replicated with the
ordinal-based expression intact. The peer cluster may not have the same labels table with the same
ordinal mapping for the visibility labels. In that case, replicating the ordinals makes no sense. It
237
would be better if the replication occurred with the visibility expressions transmitted as strings. To
replicate the visibility expression as strings to the peer cluster, create a RegionServerObserver
configuration which works based on the implementation of the VisibilityLabelService interface.
The configuration below enables replication of visibility expressions to peer clusters as strings. See
HBASE-11639 for more details.
<property>
Ê <name>hbase.security.authorization</name>
Ê <value>true</value>
</property>
<property>
Ê <name>hbase.coprocessor.regionserver.classes</name>
Ê
<value>org.apache.hadoop.hbase.security.visibility.VisibilityController$VisibilityRepl
ication</value>
</property>
64.4. Transparent Encryption of Data At Rest
HBase provides a mechanism for protecting your data at rest, in HFiles and the WAL, which reside
within HDFS or another distributed filesystem. A two-tier architecture is used for flexible and non-
intrusive key rotation. "Transparent" means that no implementation changes are needed on the
client side. When data is written, it is encrypted. When it is read, it is decrypted on demand.
64.4.1. How It Works
The administrator provisions a master key for the cluster, which is stored in a key provider
accessible to every trusted HBase process, including the HMaster, RegionServers, and clients (such
as HBase Shell) on administrative workstations. The default key provider is integrated with the Java
KeyStore API and any key management systems with support for it. Other custom key provider
implementations are possible. The key retrieval mechanism is configured in the hbase-site.xml
configuration file. The master key may be stored on the cluster servers, protected by a secure
KeyStore file, or on an external keyserver, or in a hardware security module. This master key is
resolved as needed by HBase processes through the configured key provider.
Next, encryption use can be specified in the schema, per column family, by creating or modifying a
column descriptor to include two additional attributes: the name of the encryption algorithm to use
(currently only "AES" is supported), and optionally, a data key wrapped (encrypted) with the cluster
master key. If a data key is not explicitly configured for a ColumnFamily, HBase will create a
random data key per HFile. This provides an incremental improvement in security over the
alternative. Unless you need to supply an explicit data key, such as in a case where you are
generating encrypted HFiles for bulk import with a given data key, only specify the encryption
algorithm in the ColumnFamily schema metadata and let HBase create data keys on demand. Per
Column Family keys facilitate low impact incremental key rotation and reduce the scope of any
external leak of key material. The wrapped data key is stored in the ColumnFamily schema
metadata, and in each HFile for the Column Family, encrypted with the cluster master key. After the
Column Family is configured for encryption, any new HFiles will be written encrypted. To ensure
238
encryption of all HFiles, trigger a major compaction after enabling this feature.
When the HFile is opened, the data key is extracted from the HFile, decrypted with the cluster
master key, and used for decryption of the remainder of the HFile. The HFile will be unreadable if
the master key is not available. If a remote user somehow acquires access to the HFile data because
of some lapse in HDFS permissions, or from inappropriately discarded media, it will not be possible
to decrypt either the data key or the file data.
It is also possible to encrypt the WAL. Even though WALs are transient, it is necessary to encrypt
the WALEdits to avoid circumventing HFile protections for encrypted column families, in the event
that the underlying filesystem is compromised. When WAL encryption is enabled, all WALs are
encrypted, regardless of whether the relevant HFiles are encrypted.
64.4.2. Server-Side Configuration
This procedure assumes you are using the default Java keystore implementation. If you are using a
custom implementation, check its documentation and adjust accordingly.
1. Create a secret key of appropriate length for AES encryption, using the keytool utility.
$ keytool -keystore /path/to/hbase/conf/hbase.jks \
Ê -storetype jceks -storepass **** \
Ê -genseckey -keyalg AES -keysize 128 \
Ê -alias <alias>
Replace **** with the password for the keystore file and <alias> with the username of the HBase
service account, or an arbitrary string. If you use an arbitrary string, you will need to configure
HBase to use it, and that is covered below. Specify a keysize that is appropriate. Do not specify a
separate password for the key, but press Return when prompted.
2. Set appropriate permissions on the keyfile and distribute it to all the HBase servers.
The previous command created a file called hbase.jks in the HBase conf/ directory. Set the
permissions and ownership on this file such that only the HBase service account user can read
the file, and securely distribute the key to all HBase servers.
3. Configure the HBase daemons.
Set the following properties in hbase-site.xml on the region servers, to configure HBase daemons
to use a key provider backed by the KeyStore file or retrieving the cluster master key. In the
example below, replace **** with the password.
239
<property>
Ê <name>hbase.crypto.keyprovider</name>
Ê <value>org.apache.hadoop.hbase.io.crypto.KeyStoreKeyProvider</value>
</property>
<property>
Ê <name>hbase.crypto.keyprovider.parameters</name>
Ê <value>jceks:///path/to/hbase/conf/hbase.jks?password=****</value>
</property>
By default, the HBase service account name will be used to resolve the cluster master key.
However, you can store it with an arbitrary alias (in the keytool command). In that case, set the
following property to the alias you used.
<property>
Ê <name>hbase.crypto.master.key.name</name>
Ê <value>my-alias</value>
</property>
You also need to be sure your HFiles use HFile v3, in order to use transparent encryption. This is
the default configuration for HBase 1.0 onward. For previous versions, set the following
property in your hbase-site.xml file.
<property>
Ê <name>hfile.format.version</name>
Ê <value>3</value>
</property>
Optionally, you can use a different cipher provider, either a Java Cryptography Encryption (JCE)
algorithm provider or a custom HBase cipher implementation.
◦JCE:
▪Install a signed JCE provider (supporting AES/CTR/NoPadding mode with 128 bit keys)
▪Add it with highest preference to the JCE site configuration file
$JAVA_HOME/lib/security/java.security.
▪Update hbase.crypto.algorithm.aes.provider and hbase.crypto.algorithm.rng.provider
options in hbase-site.xml.
◦Custom HBase Cipher:
▪Implement org.apache.hadoop.hbase.io.crypto.CipherProvider.
▪Add the implementation to the server classpath.
▪Update hbase.crypto.cipherprovider in hbase-site.xml.
4. Configure WAL encryption.
Configure WAL encryption in every RegionServer’s hbase-site.xml, by setting the following
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properties. You can include these in the HMaster’s hbase-site.xml as well, but the HMaster does
not have a WAL and will not use them.
<property>
Ê <name>hbase.regionserver.hlog.reader.impl</name>
Ê <value>org.apache.hadoop.hbase.regionserver.wal.SecureProtobufLogReader</value>
</property>
<property>
Ê <name>hbase.regionserver.hlog.writer.impl</name>
Ê <value>org.apache.hadoop.hbase.regionserver.wal.SecureProtobufLogWriter</value>
</property>
<property>
Ê <name>hbase.regionserver.wal.encryption</name>
Ê <value>true</value>
</property>
5. Configure permissions on the hbase-site.xml file.
Because the keystore password is stored in the hbase-site.xml, you need to ensure that only the
HBase user can read the hbase-site.xml file, using file ownership and permissions.
6. Restart your cluster.
Distribute the new configuration file to all nodes and restart your cluster.
64.4.3. Administration
Administrative tasks can be performed in HBase Shell or the Java API.
Java API
Java API examples in this section are taken from the source file hbase-
server/src/test/java/org/apache/hadoop/hbase/util/TestHBaseFsckEncryption.java. .
Neither these examples, nor the source files they are taken from, are part of the
public HBase API, and are provided for illustration only. Refer to the official API
for usage instructions.
Enable Encryption on a Column Family
To enable encryption on a column family, you can either use HBase Shell or the Java API. After
enabling encryption, trigger a major compaction. When the major compaction completes, the
HFiles will be encrypted.
Rotate the Data Key
To rotate the data key, first change the ColumnFamily key in the column descriptor, then trigger
a major compaction. When compaction is complete, all HFiles will be re-encrypted using the
new data key. Until the compaction completes, the old HFiles will still be readable using the old
key.
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Switching Between Using a Random Data Key and Specifying A Key
If you configured a column family to use a specific key and you want to return to the default
behavior of using a randomly-generated key for that column family, use the Java API to alter the
HColumnDescriptor so that no value is sent with the key ENCRYPTION_KEY.
Rotate the Master Key
To rotate the master key, first generate and distribute the new key. Then update the KeyStore to
contain a new master key, and keep the old master key in the KeyStore using a different alias.
Next, configure fallback to the old master key in the hbase-site.xml file.
64.5. Secure Bulk Load
Bulk loading in secure mode is a bit more involved than normal setup, since the client has to
transfer the ownership of the files generated from the MapReduce job to HBase. Secure bulk
loading is implemented by a coprocessor, named SecureBulkLoadEndpoint, which uses a staging
directory configured by the configuration property hbase.bulkload.staging.dir, which defaults to
/tmp/hbase-staging/.
Secure Bulk Load Algorithm
•One time only, create a staging directory which is world-traversable and owned by the user
which runs HBase (mode 711, or rwx—x—x). A listing of this directory will look similar to the
following:
$ ls -ld /tmp/hbase-staging
drwx--x--x 2 hbase hbase 68 3 Sep 14:54 /tmp/hbase-staging
•A user writes out data to a secure output directory owned by that user. For example,
/user/foo/data.
•Internally, HBase creates a secret staging directory which is globally readable/writable (
-rwxrwxrwx, 777). For example, /tmp/hbase-staging/averylongandrandomdirectoryname. The
name and location of this directory is not exposed to the user. HBase manages creation and
deletion of this directory.
•The user makes the data world-readable and world-writable, moves it into the random staging
directory, then calls the SecureBulkLoadClient#bulkLoadHFiles method.
The strength of the security lies in the length and randomness of the secret directory.
To enable secure bulk load, add the following properties to hbase-site.xml.
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<property>
Ê <name>hbase.security.authorization</name>
Ê <value>true</value>
</property>
<property>
Ê <name>hbase.bulkload.staging.dir</name>
Ê <value>/tmp/hbase-staging</value>
</property>
<property>
Ê <name>hbase.coprocessor.region.classes</name>
Ê <value>org.apache.hadoop.hbase.security.token.TokenProvider,
Ê
org.apache.hadoop.hbase.security.access.AccessController,org.apache.hadoop.hbase.secur
ity.access.SecureBulkLoadEndpoint</value>
</property>
64.6. Secure Enable
After hbase-2.x, the default 'hbase.security.authorization' changed. Before hbase-2.x, it defaulted to
true, in later HBase versions, the default became false. So to enable hbase authorization, the
following propertie must be configured in hbase-site.xml. See HBASE-19483;
<property>
Ê <name>hbase.security.authorization</name>
Ê <value>true</value>
</property>
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Chapter 65. Security Configuration Example
This configuration example includes support for HFile v3, ACLs, Visibility Labels, and transparent
encryption of data at rest and the WAL. All options have been discussed separately in the sections
above.
Example 20. Example Security Settings in hbase-site.xml
<!-- HFile v3 Support -->
<property>
Ê <name>hfile.format.version</name>
Ê <value>3</value>
</property>
<!-- HBase Superuser -->
<property>
Ê <name>hbase.superuser</name>
Ê <value>hbase,admin</value>
</property>
<!-- Coprocessors for ACLs and Visibility Tags -->
<property>
Ê <name>hbase.security.authorization</name>
Ê <value>true</value>
</property>
<property>
Ê <name>hbase.coprocessor.region.classes</name>
Ê <value>org.apache.hadoop.hbase.security.access.AccessController,
Ê org.apache.hadoop.hbase.security.visibility.VisibilityController,
Ê org.apache.hadoop.hbase.security.token.TokenProvider</value>
</property>
<property>
Ê <name>hbase.coprocessor.master.classes</name>
Ê <value>org.apache.hadoop.hbase.security.access.AccessController,
Ê org.apache.hadoop.hbase.security.visibility.VisibilityController</value>
</property>
<property>
Ê <name>hbase.coprocessor.regionserver.classes</name>
Ê <value>org.apache.hadoop.hbase.security.access.AccessController</value>
</property>
<!-- Executable ACL for Coprocessor Endpoints -->
<property>
Ê <name>hbase.security.exec.permission.checks</name>
Ê <value>true</value>
</property>
<!-- Whether a user needs authorization for a visibility tag to set it on a cell
-->
<property>
Ê <name>hbase.security.visibility.mutations.checkauth</name>
Ê <value>false</value>
</property>
244
<!-- Secure RPC Transport -->
<property>
Ê <name>hbase.rpc.protection</name>
Ê <value>privacy</value>
Ê</property>
Ê<!-- Transparent Encryption -->
<property>
Ê <name>hbase.crypto.keyprovider</name>
Ê <value>org.apache.hadoop.hbase.io.crypto.KeyStoreKeyProvider</value>
</property>
<property>
Ê <name>hbase.crypto.keyprovider.parameters</name>
Ê <value>jceks:///path/to/hbase/conf/hbase.jks?password=***</value>
</property>
<property>
Ê <name>hbase.crypto.master.key.name</name>
Ê <value>hbase</value>
</property>
<!-- WAL Encryption -->
<property>
Ê <name>hbase.regionserver.hlog.reader.impl</name>
Ê <value>org.apache.hadoop.hbase.regionserver.wal.SecureProtobufLogReader</value>
</property>
<property>
Ê <name>hbase.regionserver.hlog.writer.impl</name>
Ê <value>org.apache.hadoop.hbase.regionserver.wal.SecureProtobufLogWriter</value>
</property>
<property>
Ê <name>hbase.regionserver.wal.encryption</name>
Ê <value>true</value>
</property>
<!-- For key rotation -->
<property>
Ê <name>hbase.crypto.master.alternate.key.name</name>
Ê <value>hbase.old</value>
</property>
<!-- Secure Bulk Load -->
<property>
Ê <name>hbase.bulkload.staging.dir</name>
Ê <value>/tmp/hbase-staging</value>
</property>
<property>
Ê <name>hbase.coprocessor.region.classes</name>
Ê <value>org.apache.hadoop.hbase.security.token.TokenProvider,
Ê
org.apache.hadoop.hbase.security.access.AccessController,org.apache.hadoop.hbase.s
ecurity.access.SecureBulkLoadEndpoint</value>
</property>
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Example 21. Example Group Mapper in Hadoop core-site.xml
Adjust these settings to suit your environment.
<property>
Ê <name>hadoop.security.group.mapping</name>
Ê <value>org.apache.hadoop.security.LdapGroupsMapping</value>
</property>
<property>
Ê <name>hadoop.security.group.mapping.ldap.url</name>
Ê <value>ldap://server</value>
</property>
<property>
Ê <name>hadoop.security.group.mapping.ldap.bind.user</name>
Ê <value>Administrator@example-ad.local</value>
</property>
<property>
Ê <name>hadoop.security.group.mapping.ldap.bind.password</name>
Ê <value>****</value> <!-- Replace with the actual password -->
</property>
<property>
Ê <name>hadoop.security.group.mapping.ldap.base</name>
Ê <value>dc=example-ad,dc=local</value>
</property>
<property>
Ê <name>hadoop.security.group.mapping.ldap.search.filter.user</name>
Ê <value>(&amp;(objectClass=user)(sAMAccountName={0}))</value>
</property>
<property>
Ê <name>hadoop.security.group.mapping.ldap.search.filter.group</name>
Ê <value>(objectClass=group)</value>
</property>
<property>
Ê <name>hadoop.security.group.mapping.ldap.search.attr.member</name>
Ê <value>member</value>
</property>
<property>
Ê <name>hadoop.security.group.mapping.ldap.search.attr.group.name</name>
Ê <value>cn</value>
</property>
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Architecture
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Chapter 66. Overview
66.1. NoSQL?
HBase is a type of "NoSQL" database. "NoSQL" is a general term meaning that the database isn’t an
RDBMS which supports SQL as its primary access language, but there are many types of NoSQL
databases: BerkeleyDB is an example of a local NoSQL database, whereas HBase is very much a
distributed database. Technically speaking, HBase is really more a "Data Store" than "Data Base"
because it lacks many of the features you find in an RDBMS, such as typed columns, secondary
indexes, triggers, and advanced query languages, etc.
However, HBase has many features which supports both linear and modular scaling. HBase
clusters expand by adding RegionServers that are hosted on commodity class servers. If a cluster
expands from 10 to 20 RegionServers, for example, it doubles both in terms of storage and as well
as processing capacity. An RDBMS can scale well, but only up to a point - specifically, the size of a
single database server - and for the best performance requires specialized hardware and storage
devices. HBase features of note are:
•Strongly consistent reads/writes: HBase is not an "eventually consistent" DataStore. This makes
it very suitable for tasks such as high-speed counter aggregation.
•Automatic sharding: HBase tables are distributed on the cluster via regions, and regions are
automatically split and re-distributed as your data grows.
•Automatic RegionServer failover
•Hadoop/HDFS Integration: HBase supports HDFS out of the box as its distributed file system.
•MapReduce: HBase supports massively parallelized processing via MapReduce for using HBase
as both source and sink.
•Java Client API: HBase supports an easy to use Java API for programmatic access.
•Thrift/REST API: HBase also supports Thrift and REST for non-Java front-ends.
•Block Cache and Bloom Filters: HBase supports a Block Cache and Bloom Filters for high volume
query optimization.
•Operational Management: HBase provides build-in web-pages for operational insight as well as
JMX metrics.
66.2. When Should I Use HBase?
HBase isn’t suitable for every problem.
First, make sure you have enough data. If you have hundreds of millions or billions of rows, then
HBase is a good candidate. If you only have a few thousand/million rows, then using a traditional
RDBMS might be a better choice due to the fact that all of your data might wind up on a single node
(or two) and the rest of the cluster may be sitting idle.
Second, make sure you can live without all the extra features that an RDBMS provides (e.g., typed
columns, secondary indexes, transactions, advanced query languages, etc.) An application built
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against an RDBMS cannot be "ported" to HBase by simply changing a JDBC driver, for example.
Consider moving from an RDBMS to HBase as a complete redesign as opposed to a port.
Third, make sure you have enough hardware. Even HDFS doesn’t do well with anything less than 5
DataNodes (due to things such as HDFS block replication which has a default of 3), plus a
NameNode.
HBase can run quite well stand-alone on a laptop - but this should be considered a development
configuration only.
66.3. What Is The Difference Between HBase and
Hadoop/HDFS?
HDFS is a distributed file system that is well suited for the storage of large files. Its documentation
states that it is not, however, a general purpose file system, and does not provide fast individual
record lookups in files. HBase, on the other hand, is built on top of HDFS and provides fast record
lookups (and updates) for large tables. This can sometimes be a point of conceptual confusion.
HBase internally puts your data in indexed "StoreFiles" that exist on HDFS for high-speed lookups.
See the Data Model and the rest of this chapter for more information on how HBase achieves its
goals.
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Chapter 67. Catalog Tables
The catalog table hbase:meta exists as an HBase table and is filtered out of the HBase shell’s list
command, but is in fact a table just like any other.
67.1. hbase:meta
The hbase:meta table (previously called .META.) keeps a list of all regions in the system, and the
location of hbase:meta is stored in ZooKeeper.
The hbase:meta table structure is as follows:
Key
•Region key of the format ([table],[region start key],[region id])
Values
•info:regioninfo (serialized HRegionInfo instance for this region)
•info:server (server:port of the RegionServer containing this region)
•info:serverstartcode (start-time of the RegionServer process containing this region)
When a table is in the process of splitting, two other columns will be created, called info:splitA and
info:splitB. These columns represent the two daughter regions. The values for these columns are
also serialized HRegionInfo instances. After the region has been split, eventually this row will be
deleted.
Note on HRegionInfo
The empty key is used to denote table start and table end. A region with an empty
start key is the first region in a table. If a region has both an empty start and an
empty end key, it is the only region in the table
In the (hopefully unlikely) event that programmatic processing of catalog metadata is required, see
the RegionInfo.parseFrom utility.
67.2. Startup Sequencing
First, the location of hbase:meta is looked up in ZooKeeper. Next, hbase:meta is updated with server
and startcode values.
For information on region-RegionServer assignment, see Region-RegionServer Assignment.
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Chapter 68. Client
The HBase client finds the RegionServers that are serving the particular row range of interest. It
does this by querying the hbase:meta table. See hbase:meta for details. After locating the required
region(s), the client contacts the RegionServer serving that region, rather than going through the
master, and issues the read or write request. This information is cached in the client so that
subsequent requests need not go through the lookup process. Should a region be reassigned either
by the master load balancer or because a RegionServer has died, the client will requery the catalog
tables to determine the new location of the user region.
See Runtime Impact for more information about the impact of the Master on HBase Client
communication.
Administrative functions are done via an instance of Admin
68.1. Cluster Connections
The API changed in HBase 1.0. For connection configuration information, see Client configuration
and dependencies connecting to an HBase cluster.
68.1.1. API as of HBase 1.0.0
It’s been cleaned up and users are returned Interfaces to work against rather than particular types.
In HBase 1.0, obtain a Connection object from ConnectionFactory and thereafter, get from it instances
of Table, Admin, and RegionLocator on an as-need basis. When done, close the obtained instances.
Finally, be sure to cleanup your Connection instance before exiting. Connections are heavyweight
objects but thread-safe so you can create one for your application and keep the instance around.
Table, Admin and RegionLocator instances are lightweight. Create as you go and then let go as soon as
you are done by closing them. See the Client Package Javadoc Description for example usage of the
new HBase 1.0 API.
68.1.2. API before HBase 1.0.0
Instances of HTable are the way to interact with an HBase cluster earlier than 1.0.0. Table instances
are not thread-safe. Only one thread can use an instance of Table at any given time. When creating
Table instances, it is advisable to use the same HBaseConfiguration instance. This will ensure
sharing of ZooKeeper and socket instances to the RegionServers which is usually what you want.
For example, this is preferred:
HBaseConfiguration conf = HBaseConfiguration.create();
HTable table1 = new HTable(conf, "myTable");
HTable table2 = new HTable(conf, "myTable");
as opposed to this:
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HBaseConfiguration conf1 = HBaseConfiguration.create();
HTable table1 = new HTable(conf1, "myTable");
HBaseConfiguration conf2 = HBaseConfiguration.create();
HTable table2 = new HTable(conf2, "myTable");
For more information about how connections are handled in the HBase client, see
ConnectionFactory.
Connection Pooling
For applications which require high-end multithreaded access (e.g., web-servers or application
servers that may serve many application threads in a single JVM), you can pre-create a Connection,
as shown in the following example:
Example 22. Pre-Creating a Connection
// Create a connection to the cluster.
Configuration conf = HBaseConfiguration.create();
try (Connection connection = ConnectionFactory.createConnection(conf);
Ê Table table = connection.getTable(TableName.valueOf(tablename))) {
Ê // use table as needed, the table returned is lightweight
}
HTablePool is Deprecated
Previous versions of this guide discussed HTablePool, which was deprecated in
HBase 0.94, 0.95, and 0.96, and removed in 0.98.1, by HBASE-6580, or HConnection,
which is deprecated in HBase 1.0 by Connection. Please use Connection instead.
68.2. WriteBuffer and Batch Methods
In HBase 1.0 and later, HTable is deprecated in favor of Table. Table does not use autoflush. To do
buffered writes, use the BufferedMutator class.
In HBase 2.0 and later, HTable does not use BufferedMutator to execute the Put operation. Refer to
HBASE-18500 for more information.
For additional information on write durability, review the ACID semantics page.
For fine-grained control of batching of Puts or Deletes, see the batch methods on Table.
68.3. Asynchronous Client
It is a new API introduced in HBase 2.0 which aims to provide the ability to access HBase
asynchronously.
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You can obtain an AsyncConnection from ConnectionFactory, and then get a asynchronous table
instance from it to access HBase. When done, close the AsyncConnection instance(usually when your
program exits).
For the asynchronous table, most methods have the same meaning with the old Table interface,
expect that the return value is wrapped with a CompletableFuture usually. We do not have any
buffer here so there is no close method for asynchronous table, you do not need to close it. And it is
thread safe.
There are several differences for scan:
•There is still a getScanner method which returns a ResultScanner. You can use it in the old way
and it works like the old ClientAsyncPrefetchScanner.
•There is a scanAll method which will return all the results at once. It aims to provide a simpler
way for small scans which you want to get the whole results at once usually.
•The Observer Pattern. There is a scan method which accepts a ScanResultConsumer as a
parameter. It will pass the results to the consumer.
Notice that AsyncTable interface is templatized. The template parameter specifies the type of
ScanResultConsumerBase used by scans, which means the observer style scan APIs are different. The
two types of scan consumers are - ScanResultConsumer and AdvancedScanResultConsumer.
ScanResultConsumer needs a separate thread pool which is used to execute the callbacks registered to
the returned CompletableFuture. Because the use of separate thread pool frees up RPC threads,
callbacks are free to do anything. Use this if the callbacks are not quick, or when in doubt.
AdvancedScanResultConsumer executes callbacks inside the framework thread. It is not allowed to do
time consuming work in the callbacks else it will likely block the framework threads and cause
very bad performance impact. As its name, it is designed for advanced users who want to write
high performance code. See org.apache.hadoop.hbase.client.example.HttpProxyExample for how to
write fully asynchronous code with it.
68.4. Asynchronous Admin
You can obtain an AsyncConnection from ConnectionFactory, and then get a AsyncAdmin instance from
it to access HBase. Notice that there are two getAdmin methods to get a AsyncAdmin instance. One
method has one extra thread pool parameter which is used to execute callbacks. It is designed for
normal users. Another method doesn’t need a thread pool and all the callbacks are executed inside
the framework thread so it is not allowed to do time consuming works in the callbacks. It is
designed for advanced users.
The default getAdmin methods will return a AsyncAdmin instance which use default configs. If you
want to customize some configs, you can use getAdminBuilder methods to get a AsyncAdminBuilder for
creating AsyncAdmin instance. Users are free to only set the configs they care about to create a new
AsyncAdmin instance.
For the AsyncAdmin interface, most methods have the same meaning with the old Admin interface,
expect that the return value is wrapped with a CompletableFuture usually.
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For most admin operations, when the returned CompletableFuture is done, it means the admin
operation has also been done. But for compact operation, it only means the compact request was
sent to HBase and may need some time to finish the compact operation. For rollWALWriter method,
it only means the rollWALWriter request was sent to the region server and may need some time to
finish the rollWALWriter operation.
For region name, we only accept byte[] as the parameter type and it may be a full region name or a
encoded region name. For server name, we only accept ServerName as the parameter type. For table
name, we only accept TableName as the parameter type. For list* operations, we only accept Pattern
as the parameter type if you want to do regex matching.
68.5. External Clients
Information on non-Java clients and custom protocols is covered in Apache HBase External APIs
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Chapter 69. Client Request Filters
Get and Scan instances can be optionally configured with filters which are applied on the
RegionServer.
Filters can be confusing because there are many different types, and it is best to approach them by
understanding the groups of Filter functionality.
69.1. Structural
Structural Filters contain other Filters.
69.1.1. FilterList
FilterList represents a list of Filters with a relationship of FilterList.Operator.MUST_PASS_ALL or
FilterList.Operator.MUST_PASS_ONE between the Filters. The following example shows an 'or'
between two Filters (checking for either 'my value' or 'my other value' on the same attribute).
FilterList list = new FilterList(FilterList.Operator.MUST_PASS_ONE);
SingleColumnValueFilter filter1 = new SingleColumnValueFilter(
Ê cf,
Ê column,
Ê CompareOperator.EQUAL,
Ê Bytes.toBytes("my value")
Ê );
list.add(filter1);
SingleColumnValueFilter filter2 = new SingleColumnValueFilter(
Ê cf,
Ê column,
Ê CompareOperator.EQUAL,
Ê Bytes.toBytes("my other value")
Ê );
list.add(filter2);
scan.setFilter(list);
69.2. Column Value
69.2.1. SingleColumnValueFilter
A SingleColumnValueFilter (see: https://hbase.apache.org/apidocs/org/apache/hadoop/hbase/filter/
SingleColumnValueFilter.html) can be used to test column values for equivalence
(CompareOperaor.EQUAL), inequality (CompareOperaor.NOT_EQUAL), or ranges (e.g.,
CompareOperaor.GREATER). The following is an example of testing equivalence of a column to a String
value "my value"…
255
SingleColumnValueFilter filter = new SingleColumnValueFilter(
Ê cf,
Ê column,
Ê CompareOperaor.EQUAL,
Ê Bytes.toBytes("my value")
Ê );
scan.setFilter(filter);
69.2.2. ColumnValueFilter
Introduced in HBase-2.0.0 version as a complementation of SingleColumnValueFilter,
ColumnValueFilter gets matched cell only, while SingleColumnValueFilter gets the entire row (has
other columns and values) to which the matched cell belongs. Parameters of constructor of
ColumnValueFilter are the same as SingleColumnValueFilter.
ColumnValueFilter filter = new ColumnValueFilter(
Ê cf,
Ê column,
Ê CompareOperaor.EQUAL,
Ê Bytes.toBytes("my value")
Ê );
scan.setFilter(filter);
Note. For simple query like "equals to a family:qualifier:value", we highly recommend to use the
following way instead of using SingleColumnValueFilter or ColumnValueFilter:
Scan scan = new Scan();
scan.addColumn(Bytes.toBytes("family"), Bytes.toBytes("qualifier"));
ValueFilter vf = new ValueFilter(CompareOperator.EQUAL,
Ê new BinaryComparator(Bytes.toBytes("value")));
scan.setFilter(vf);
...
This scan will restrict to the specified column 'family:qualifier', avoiding scan unrelated families
and columns, which has better performance, and ValueFilter is the condition used to do the value
filtering.
But if query is much more complicated beyond this book, then please make your good choice case
by case.
69.3. Column Value Comparators
There are several Comparator classes in the Filter package that deserve special mention. These
Comparators are used in concert with other Filters, such as SingleColumnValueFilter.
256
69.3.1. RegexStringComparator
RegexStringComparator supports regular expressions for value comparisons.
RegexStringComparator comp = new RegexStringComparator("my."); // any value that
starts with 'my'
SingleColumnValueFilter filter = new SingleColumnValueFilter(
Ê cf,
Ê column,
Ê CompareOperaor.EQUAL,
Ê comp
Ê );
scan.setFilter(filter);
See the Oracle JavaDoc for supported RegEx patterns in Java.
69.3.2. SubstringComparator
SubstringComparator can be used to determine if a given substring exists in a value. The
comparison is case-insensitive.
SubstringComparator comp = new SubstringComparator("y val"); // looking for 'my
value'
SingleColumnValueFilter filter = new SingleColumnValueFilter(
Ê cf,
Ê column,
Ê CompareOperaor.EQUAL,
Ê comp
Ê );
scan.setFilter(filter);
69.3.3. BinaryPrefixComparator
See BinaryPrefixComparator.
69.3.4. BinaryComparator
See BinaryComparator.
69.4. KeyValue Metadata
As HBase stores data internally as KeyValue pairs, KeyValue Metadata Filters evaluate the existence
of keys (i.e., ColumnFamily:Column qualifiers) for a row, as opposed to values the previous section.
69.4.1. FamilyFilter
FamilyFilter can be used to filter on the ColumnFamily. It is generally a better idea to select
257
ColumnFamilies in the Scan than to do it with a Filter.
69.4.2. QualifierFilter
QualifierFilter can be used to filter based on Column (aka Qualifier) name.
69.4.3. ColumnPrefixFilter
ColumnPrefixFilter can be used to filter based on the lead portion of Column (aka Qualifier) names.
A ColumnPrefixFilter seeks ahead to the first column matching the prefix in each row and for each
involved column family. It can be used to efficiently get a subset of the columns in very wide rows.
Note: The same column qualifier can be used in different column families. This filter returns all
matching columns.
Example: Find all columns in a row and family that start with "abc"
Table t = ...;
byte[] row = ...;
byte[] family = ...;
byte[] prefix = Bytes.toBytes("abc");
Scan scan = new Scan(row, row); // (optional) limit to one row
scan.addFamily(family); // (optional) limit to one family
Filter f = new ColumnPrefixFilter(prefix);
scan.setFilter(f);
scan.setBatch(10); // set this if there could be many columns returned
ResultScanner rs = t.getScanner(scan);
for (Result r = rs.next(); r != null; r = rs.next()) {
Ê for (KeyValue kv : r.raw()) {
Ê // each kv represents a column
Ê }
}
rs.close();
69.4.4. MultipleColumnPrefixFilter
MultipleColumnPrefixFilter behaves like ColumnPrefixFilter but allows specifying multiple
prefixes.
Like ColumnPrefixFilter, MultipleColumnPrefixFilter efficiently seeks ahead to the first column
matching the lowest prefix and also seeks past ranges of columns between prefixes. It can be used
to efficiently get discontinuous sets of columns from very wide rows.
Example: Find all columns in a row and family that start with "abc" or "xyz"
258
Table t = ...;
byte[] row = ...;
byte[] family = ...;
byte[][] prefixes = new byte[][] {Bytes.toBytes("abc"), Bytes.toBytes("xyz")};
Scan scan = new Scan(row, row); // (optional) limit to one row
scan.addFamily(family); // (optional) limit to one family
Filter f = new MultipleColumnPrefixFilter(prefixes);
scan.setFilter(f);
scan.setBatch(10); // set this if there could be many columns returned
ResultScanner rs = t.getScanner(scan);
for (Result r = rs.next(); r != null; r = rs.next()) {
Ê for (KeyValue kv : r.raw()) {
Ê // each kv represents a column
Ê }
}
rs.close();
69.4.5. ColumnRangeFilter
A ColumnRangeFilter allows efficient intra row scanning.
A ColumnRangeFilter can seek ahead to the first matching column for each involved column family.
It can be used to efficiently get a 'slice' of the columns of a very wide row. i.e. you have a million
columns in a row but you only want to look at columns bbbb-bbdd.
Note: The same column qualifier can be used in different column families. This filter returns all
matching columns.
Example: Find all columns in a row and family between "bbbb" (inclusive) and "bbdd" (inclusive)
Table t = ...;
byte[] row = ...;
byte[] family = ...;
byte[] startColumn = Bytes.toBytes("bbbb");
byte[] endColumn = Bytes.toBytes("bbdd");
Scan scan = new Scan(row, row); // (optional) limit to one row
scan.addFamily(family); // (optional) limit to one family
Filter f = new ColumnRangeFilter(startColumn, true, endColumn, true);
scan.setFilter(f);
scan.setBatch(10); // set this if there could be many columns returned
ResultScanner rs = t.getScanner(scan);
for (Result r = rs.next(); r != null; r = rs.next()) {
Ê for (KeyValue kv : r.raw()) {
Ê // each kv represents a column
Ê }
}
rs.close();
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Note: Introduced in HBase 0.92
69.5. RowKey
69.5.1. RowFilter
It is generally a better idea to use the startRow/stopRow methods on Scan for row selection,
however RowFilter can also be used.
69.6. Utility
69.6.1. FirstKeyOnlyFilter
This is primarily used for rowcount jobs. See FirstKeyOnlyFilter.
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Chapter 70. Master
HMaster is the implementation of the Master Server. The Master server is responsible for monitoring
all RegionServer instances in the cluster, and is the interface for all metadata changes. In a
distributed cluster, the Master typically runs on the NameNode. J Mohamed Zahoor goes into some
more detail on the Master Architecture in this blog posting, HBase HMaster Architecture .
70.1. Startup Behavior
If run in a multi-Master environment, all Masters compete to run the cluster. If the active Master
loses its lease in ZooKeeper (or the Master shuts down), then the remaining Masters jostle to take
over the Master role.
70.2. Runtime Impact
A common dist-list question involves what happens to an HBase cluster when the Master goes
down. Because the HBase client talks directly to the RegionServers, the cluster can still function in a
"steady state". Additionally, per Catalog Tables, hbase:meta exists as an HBase table and is not
resident in the Master. However, the Master controls critical functions such as RegionServer
failover and completing region splits. So while the cluster can still run for a short time without the
Master, the Master should be restarted as soon as possible.
70.3. Interface
The methods exposed by HMasterInterface are primarily metadata-oriented methods:
•Table (createTable, modifyTable, removeTable, enable, disable)
•ColumnFamily (addColumn, modifyColumn, removeColumn)
•Region (move, assign, unassign) For example, when the Admin method disableTable is invoked, it
is serviced by the Master server.
70.4. Processes
The Master runs several background threads:
70.4.1. LoadBalancer
Periodically, and when there are no regions in transition, a load balancer will run and move
regions around to balance the cluster’s load. See Balancer for configuring this property.
See Region-RegionServer Assignment for more information on region assignment.
70.4.2. CatalogJanitor
Periodically checks and cleans up the hbase:meta table. See hbase:meta for more information on the
meta table.
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70.5. MasterProcWAL
HMaster records administrative operations and their running states, such as the handling of a
crashed server, table creation, and other DDLs, into its own WAL file. The WALs are stored under
the MasterProcWALs directory. The Master WALs are not like RegionServer WALs. Keeping up the
Master WAL allows us run a state machine that is resilient across Master failures. For example, if a
HMaster was in the middle of creating a table encounters an issue and fails, the next active
HMaster can take up where the previous left off and carry the operation to completion. Since
hbase-2.0.0, a new AssignmentManager (A.K.A AMv2) was introduced and the HMaster handles
region assignment operations, server crash processing, balancing, etc., all via AMv2 persisting all
state and transitions into MasterProcWALs rather than up into ZooKeeper, as we do in hbase-1.x.
See AMv2 Description for Devs (and Procedure Framework (Pv2): HBASE-12439 for its basis) if you
would like to learn more about the new AssignmentManager.
70.5.1. Configurations for MasterProcWAL
Here are the list of configurations that effect MasterProcWAL operation. You should not have to
change your defaults.
hbase.procedure.store.wal.periodic.roll.msec
Description
Frequency of generating a new WAL
Default
1h (3600000 in msec)
hbase.procedure.store.wal.roll.threshold
Description
Threshold in size before the WAL rolls. Every time the WAL reaches this size or the above
period, 1 hour, passes since last log roll, the HMaster will generate a new WAL.
Default
32MB (33554432 in byte)
hbase.procedure.store.wal.warn.threshold
Description
If the number of WALs goes beyond this threshold, the following message should appear in the
HMaster log with WARN level when rolling.
procedure WALs count=xx above the warning threshold 64. check running procedures to
see if something is stuck.
Default
64
hbase.procedure.store.wal.max.retries.before.roll
Description
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Max number of retry when syncing slots (records) to its underlying storage, such as HDFS. Every
attempt, the following message should appear in the HMaster log.
unable to sync slots, retry=xx
Default
3
hbase.procedure.store.wal.sync.failure.roll.max
Description
After the above 3 retrials, the log is rolled and the retry count is reset to 0, thereon a new set of
retrial starts. This configuration controls the max number of attempts of log rolling upon sync
failure. That is, HMaster is allowed to fail to sync 9 times in total. Once it exceeds, the following
log should appear in the HMaster log.
Sync slots after log roll failed, abort.
Default
3
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Chapter 71. RegionServer
HRegionServer is the RegionServer implementation. It is responsible for serving and managing
regions. In a distributed cluster, a RegionServer runs on a DataNode.
71.1. Interface
The methods exposed by HRegionRegionInterface contain both data-oriented and region-
maintenance methods:
•Data (get, put, delete, next, etc.)
•Region (splitRegion, compactRegion, etc.) For example, when the Admin method majorCompact is
invoked on a table, the client is actually iterating through all regions for the specified table and
requesting a major compaction directly to each region.
71.2. Processes
The RegionServer runs a variety of background threads:
71.2.1. CompactSplitThread
Checks for splits and handle minor compactions.
71.2.2. MajorCompactionChecker
Checks for major compactions.
71.2.3. MemStoreFlusher
Periodically flushes in-memory writes in the MemStore to StoreFiles.
71.2.4. LogRoller
Periodically checks the RegionServer’s WAL.
71.3. Coprocessors
Coprocessors were added in 0.92. There is a thorough Blog Overview of CoProcessors posted.
Documentation will eventually move to this reference guide, but the blog is the most current
information available at this time.
71.4. Block Cache
HBase provides two different BlockCache implementations to cache data read from HDFS: the
default on-heap LruBlockCache and the BucketCache, which is (usually) off-heap. This section
discusses benefits and drawbacks of each implementation, how to choose the appropriate option,
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and configuration options for each.
Block Cache Reporting: UI
See the RegionServer UI for detail on caching deploy. See configurations, sizings,
current usage, time-in-the-cache, and even detail on block counts and types.
71.4.1. Cache Choices
LruBlockCache is the original implementation, and is entirely within the Java heap. BucketCache is
optional and mainly intended for keeping block cache data off-heap, although BucketCache can also
be a file-backed cache.
When you enable BucketCache, you are enabling a two tier caching system. We used to describe the
tiers as "L1" and "L2" but have deprecated this terminology as of hbase-2.0.0. The "L1" cache
referred to an instance of LruBlockCache and "L2" to an off-heap BucketCache. Instead, when
BucketCache is enabled, all DATA blocks are kept in the BucketCache tier and meta blocks — INDEX
and BLOOM blocks — are on-heap in the LruBlockCache. Management of these two tiers and the
policy that dictates how blocks move between them is done by CombinedBlockCache.
71.4.2. General Cache Configurations
Apart from the cache implementation itself, you can set some general configuration options to
control how the cache performs. See CacheConfig. After setting any of these options, restart or
rolling restart your cluster for the configuration to take effect. Check logs for errors or unexpected
behavior.
See also Prefetch Option for Blockcache, which discusses a new option introduced in HBASE-9857.
71.4.3. LruBlockCache Design
The LruBlockCache is an LRU cache that contains three levels of block priority to allow for scan-
resistance and in-memory ColumnFamilies:
•Single access priority: The first time a block is loaded from HDFS it normally has this priority
and it will be part of the first group to be considered during evictions. The advantage is that
scanned blocks are more likely to get evicted than blocks that are getting more usage.
•Multi access priority: If a block in the previous priority group is accessed again, it upgrades to
this priority. It is thus part of the second group considered during evictions.
•In-memory access priority: If the block’s family was configured to be "in-memory", it will be
part of this priority disregarding the number of times it was accessed. Catalog tables are
configured like this. This group is the last one considered during evictions.
To mark a column family as in-memory, call
HColumnDescriptor.setInMemory(true);
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if creating a table from java, or set IN_MEMORY ⇒ true when creating or altering a table in the shell:
e.g.
hbase(main):003:0> create 't', {NAME => 'f', IN_MEMORY => 'true'}
For more information, see the LruBlockCache source
71.4.4. LruBlockCache Usage
Block caching is enabled by default for all the user tables which means that any read operation will
load the LRU cache. This might be good for a large number of use cases, but further tunings are
usually required in order to achieve better performance. An important concept is the working set
size, or WSS, which is: "the amount of memory needed to compute the answer to a problem". For a
website, this would be the data that’s needed to answer the queries over a short amount of time.
The way to calculate how much memory is available in HBase for caching is:
number of region servers * heap size * hfile.block.cache.size * 0.99
The default value for the block cache is 0.4 which represents 40% of the available heap. The last
value (99%) is the default acceptable loading factor in the LRU cache after which eviction is started.
The reason it is included in this equation is that it would be unrealistic to say that it is possible to
use 100% of the available memory since this would make the process blocking from the point
where it loads new blocks. Here are some examples:
•One region server with the heap size set to 1 GB and the default block cache size will have 405
MB of block cache available.
•20 region servers with the heap size set to 8 GB and a default block cache size will have 63.3 of
block cache.
•100 region servers with the heap size set to 24 GB and a block cache size of 0.5 will have about
1.16 TB of block cache.
Your data is not the only resident of the block cache. Here are others that you may have to take into
account:
Catalog Tables
The hbase:meta table is forced into the block cache and have the in-memory priority which
means that they are harder to evict.
The hbase:meta tables can occupy a few MBs depending on the number of regions.
HFiles Indexes
An HFile is the file format that HBase uses to store data in HDFS. It contains a multi-layered
index which allows HBase to seek to the data without having to read the whole file. The size of
those indexes is a factor of the block size (64KB by default), the size of your keys and the amount
of data you are storing. For big data sets it’s not unusual to see numbers around 1GB per region
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server, although not all of it will be in cache because the LRU will evict indexes that aren’t used.
Keys
The values that are stored are only half the picture, since each value is stored along with its keys
(row key, family qualifier, and timestamp). See Try to minimize row and column sizes.
Bloom Filters
Just like the HFile indexes, those data structures (when enabled) are stored in the LRU.
Currently the recommended way to measure HFile indexes and bloom filters sizes is to look at the
region server web UI and checkout the relevant metrics. For keys, sampling can be done by using
the HFile command line tool and look for the average key size metric. Since HBase 0.98.3, you can
view details on BlockCache stats and metrics in a special Block Cache section in the UI.
It’s generally bad to use block caching when the WSS doesn’t fit in memory. This is the case when
you have for example 40GB available across all your region servers' block caches but you need to
process 1TB of data. One of the reasons is that the churn generated by the evictions will trigger
more garbage collections unnecessarily. Here are two use cases:
•Fully random reading pattern: This is a case where you almost never access the same row twice
within a short amount of time such that the chance of hitting a cached block is close to 0. Setting
block caching on such a table is a waste of memory and CPU cycles, more so that it will generate
more garbage to pick up by the JVM. For more information on monitoring GC, see JVM Garbage
Collection Logs.
•Mapping a table: In a typical MapReduce job that takes a table in input, every row will be read
only once so there’s no need to put them into the block cache. The Scan object has the option of
turning this off via the setCaching method (set it to false). You can still keep block caching
turned on on this table if you need fast random read access. An example would be counting the
number of rows in a table that serves live traffic, caching every block of that table would create
massive churn and would surely evict data that’s currently in use.
Caching META blocks only (DATA blocks in fscache)
An interesting setup is one where we cache META blocks only and we read DATA blocks in on each
access. If the DATA blocks fit inside fscache, this alternative may make sense when access is
completely random across a very large dataset. To enable this setup, alter your table and for each
column family set BLOCKCACHE ⇒ 'false'. You are 'disabling' the BlockCache for this column family
only. You can never disable the caching of META blocks. Since HBASE-4683 Always cache index and
bloom blocks, we will cache META blocks even if the BlockCache is disabled.
71.4.5. Off-heap Block Cache
How to Enable BucketCache
The usual deploy of BucketCache is via a managing class that sets up two caching tiers: an on-heap
cache implemented by LruBlockCache and a second cache implemented with BucketCache. The
managing class is CombinedBlockCache by default. The previous link describes the caching 'policy'
implemented by CombinedBlockCache. In short, it works by keeping meta blocks — INDEX and
BLOOM in the on-heap LruBlockCache tier — and DATA blocks are kept in the BucketCache tier.
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Pre-hbase-2.0.0 versions
Fetching will always be slower when fetching from BucketCache in pre-hbase-2.0.0, as
compared to the native on-heap LruBlockCache. However, latencies tend to be less erratic
across time, because there is less garbage collection when you use BucketCache since it is
managing BlockCache allocations, not the GC. If the BucketCache is deployed in off-heap
mode, this memory is not managed by the GC at all. This is why you’d use BucketCache in
pre-2.0.0, so your latencies are less erratic, to mitigate GCs and heap fragmentation, and so
you can safely use more memory. See Nick Dimiduk’s BlockCache 101 for comparisons
running on-heap vs off-heap tests. Also see Comparing BlockCache Deploys which finds that
if your dataset fits inside your LruBlockCache deploy, use it otherwise if you are
experiencing cache churn (or you want your cache to exist beyond the vagaries of java GC),
use BucketCache.
In pre-2.0.0, one can configure the BucketCache so it receives the victim of an
LruBlockCache eviction. All Data and index blocks are cached in L1 first. When eviction
happens from L1, the blocks (or victims) will get moved to L2. Set cacheDataInL1 via
(HColumnDescriptor.setCacheDataInL1(true) or in the shell, creating or amending column
families setting CACHE_DATA_IN_L1 to true: e.g.
hbase(main):003:0> create 't', {NAME => 't', CONFIGURATION => {CACHE_DATA_IN_L1 =>
'true'}}
hbase-2.0.0+ versions
HBASE-11425 changed the HBase read path so it could hold the read-data off-heap avoiding
copying of cached data on to the java heap. See Offheap read-path. In hbase-2.0.0, off-heap
latencies approach those of on-heap cache latencies with the added benefit of NOT
provoking GC.
From HBase 2.0.0 onwards, the notions of L1 and L2 have been deprecated. When
BucketCache is turned on, the DATA blocks will always go to BucketCache and
INDEX/BLOOM blocks go to on heap LRUBlockCache. cacheDataInL1 support hase been
removed.
The BucketCache Block Cache can be deployed off-heap, file or mmaped file mode.
You set which via the hbase.bucketcache.ioengine setting. Setting it to offheap will have BucketCache
make its allocations off-heap, and an ioengine setting of file:PATH_TO_FILE will direct BucketCache
to use file caching (Useful in particular if you have some fast I/O attached to the box such as SSDs).
From 2.0.0, it is possible to have more than one file backing the BucketCache. This is very useful
specially when the Cache size requirement is high. For multiple backing files, configure ioengine as
files:PATH_TO_FILE1,PATH_TO_FILE2,PATH_TO_FILE3. BucketCache can be configured to use an
mmapped file also. Configure ioengine as mmap:PATH_TO_FILE for this.
It is possible to deploy a tiered setup where we bypass the CombinedBlockCache policy and have
BucketCache working as a strict L2 cache to the L1 LruBlockCache. For such a setup, set
hbase.bucketcache.combinedcache.enabled to false. In this mode, on eviction from L1, blocks go to
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L2. When a block is cached, it is cached first in L1. When we go to look for a cached block, we look
first in L1 and if none found, then search L2. Let us call this deploy format, Raw L1+L2. NOTE: This
L1+L2 mode is removed from 2.0.0. When BucketCache is used, it will be strictly the DATA cache
and the LruBlockCache will cache INDEX/META blocks.
Other BucketCache configs include: specifying a location to persist cache to across restarts, how
many threads to use writing the cache, etc. See the CacheConfig.html class for configuration options
and descriptions.
To check it enabled, look for the log line describing cache setup; it will detail how BucketCache has
been deployed. Also see the UI. It will detail the cache tiering and their configuration.
BucketCache Example Configuration
This sample provides a configuration for a 4 GB off-heap BucketCache with a 1 GB on-heap cache.
Configuration is performed on the RegionServer.
Setting hbase.bucketcache.ioengine and hbase.bucketcache.size > 0 enables CombinedBlockCache. Let
us presume that the RegionServer has been set to run with a 5G heap: i.e. HBASE_HEAPSIZE=5g.
1. First, edit the RegionServer’s hbase-env.sh and set HBASE_OFFHEAPSIZE to a value greater than the
off-heap size wanted, in this case, 4 GB (expressed as 4G). Let’s set it to 5G. That’ll be 4G for our
off-heap cache and 1G for any other uses of off-heap memory (there are other users of off-heap
memory other than BlockCache; e.g. DFSClient in RegionServer can make use of off-heap
memory). See Direct Memory Usage In HBase.
HBASE_OFFHEAPSIZE=5G
2. Next, add the following configuration to the RegionServer’s hbase-site.xml.
<property>
Ê <name>hbase.bucketcache.ioengine</name>
Ê <value>offheap</value>
</property>
<property>
Ê <name>hfile.block.cache.size</name>
Ê <value>0.2</value>
</property>
<property>
Ê <name>hbase.bucketcache.size</name>
Ê <value>4196</value>
</property>
3. Restart or rolling restart your cluster, and check the logs for any issues.
In the above, we set the BucketCache to be 4G. We configured the on-heap LruBlockCache have 20%
(0.2) of the RegionServer’s heap size (0.2 * 5G = 1G). In other words, you configure the L1
LruBlockCache as you would normally (as if there were no L2 cache present).
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HBASE-10641 introduced the ability to configure multiple sizes for the buckets of the BucketCache,
in HBase 0.98 and newer. To configurable multiple bucket sizes, configure the new property
hbase.bucketcache.bucket.sizes to a comma-separated list of block sizes, ordered from smallest to
largest, with no spaces. The goal is to optimize the bucket sizes based on your data access patterns.
The following example configures buckets of size 4096 and 8192.
<property>
Ê <name>hbase.bucketcache.bucket.sizes</name>
Ê <value>4096,8192</value>
</property>
Direct Memory Usage In HBase
The default maximum direct memory varies by JVM. Traditionally it is 64M or
some relation to allocated heap size (-Xmx) or no limit at all (JDK7 apparently).
HBase servers use direct memory, in particular short-circuit reading (See
Leveraging local data), the hosted DFSClient will allocate direct memory buffers.
How much the DFSClient uses is not easy to quantify; it is the number of open
HFiles * hbase.dfs.client.read.shortcircuit.buffer.size where
hbase.dfs.client.read.shortcircuit.buffer.size is set to 128k in HBase — see
hbase-default.xml default configurations. If you do off-heap block caching, you’ll be
making use of direct memory. The RPCServer uses a ByteBuffer pool. From 2.0.0,
these buffers are off-heap ByteBuffers. Starting your JVM, make sure the
-XX:MaxDirectMemorySize setting in conf/hbase-env.sh considers off-heap BlockCache
(hbase.bucketcache.size), DFSClient usage, RPC side ByteBufferPool max size. This
has to be bit higher than sum of off heap BlockCache size and max ByteBufferPool
size. Allocating an extra of 1-2 GB for the max direct memory size has worked in
tests. Direct memory, which is part of the Java process heap, is separate from the
object heap allocated by -Xmx. The value allocated by MaxDirectMemorySize must
not exceed physical RAM, and is likely to be less than the total available RAM due
to other memory requirements and system constraints.
You can see how much memory — on-heap and off-heap/direct — a RegionServer is
configured to use and how much it is using at any one time by looking at the Server
Metrics: Memory tab in the UI. It can also be gotten via JMX. In particular the direct
memory currently used by the server can be found on the
java.nio.type=BufferPool,name=direct bean. Terracotta has a good write up on
using off-heap memory in Java. It is for their product BigMemory but a lot of the
issues noted apply in general to any attempt at going off-heap. Check it out.
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hbase.bucketcache.percentage.in.combinedcache
This is a pre-HBase 1.0 configuration removed because it was confusing. It was a
float that you would set to some value between 0.0 and 1.0. Its default was 0.9. If
the deploy was using CombinedBlockCache, then the LruBlockCache L1 size was
calculated to be (1 - hbase.bucketcache.percentage.in.combinedcache) * size-of-
bucketcache and the BucketCache size was
hbase.bucketcache.percentage.in.combinedcache * size-of-bucket-cache. where
size-of-bucket-cache itself is EITHER the value of the configuration
hbase.bucketcache.size IF it was specified as Megabytes OR hbase.bucketcache.size
* -XX:MaxDirectMemorySize if hbase.bucketcache.size is between 0 and 1.0.
In 1.0, it should be more straight-forward. Onheap LruBlockCache size is set as a
fraction of java heap using hfile.block.cache.size setting (not the best name) and
BucketCache is set as above in absolute Megabytes.
71.4.6. Compressed BlockCache
HBASE-11331 introduced lazy BlockCache decompression, more simply referred to as compressed
BlockCache. When compressed BlockCache is enabled data and encoded data blocks are cached in
the BlockCache in their on-disk format, rather than being decompressed and decrypted before
caching.
For a RegionServer hosting more data than can fit into cache, enabling this feature with SNAPPY
compression has been shown to result in 50% increase in throughput and 30% improvement in
mean latency while, increasing garbage collection by 80% and increasing overall CPU load by 2%.
See HBASE-11331 for more details about how performance was measured and achieved. For a
RegionServer hosting data that can comfortably fit into cache, or if your workload is sensitive to
extra CPU or garbage-collection load, you may receive less benefit.
The compressed BlockCache is disabled by default. To enable it, set
hbase.block.data.cachecompressed to true in hbase-site.xml on all RegionServers.
71.5. RegionServer Offheap Read/Write Path
71.5.1. Offheap read-path
In hbase-2.0.0, HBASE-11425 changed the HBase read path so it could hold the read-data off-heap
avoiding copying of cached data on to the java heap. This reduces GC pauses given there is less
garbage made and so less to clear. The off-heap read path has a performance that is similar/better
to that of the on-heap LRU cache. This feature is available since HBase 2.0.0. If the BucketCache is in
file mode, fetching will always be slower compared to the native on-heap LruBlockCache. Refer to
below blogs for more details and test results on off heaped read path Offheaping the Read Path in
Apache HBase: Part 1 of 2 and Offheap Read-Path in Production - The Alibaba story
For an end-to-end off-heaped read-path, first of all there should be an off-heap backed Off-heap
Block Cache(BC). Configure 'hbase.bucketcache.ioengine' to off-heap in hbase-site.xml. Also specify
the total capacity of the BC using hbase.bucketcache.size config. Please remember to adjust value of
'HBASE_OFFHEAPSIZE' in hbase-env.sh. This is how we specify the max possible off-heap memory
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allocation for the RegionServer java process. This should be bigger than the off-heap BC size. Please
keep in mind that there is no default for hbase.bucketcache.ioengine which means the BC is turned
OFF by default (See Direct Memory Usage In HBase).
Next thing to tune is the ByteBuffer pool on the RPC server side. The buffers from this pool will be
used to accumulate the cell bytes and create a result cell block to send back to the client side.
hbase.ipc.server.reservoir.enabled can be used to turn this pool ON or OFF. By default this pool is
ON and available. HBase will create off heap ByteBuffers and pool them. Please make sure not to
turn this OFF if you want end-to-end off-heaping in read path. If this pool is turned off, the server
will create temp buffers on heap to accumulate the cell bytes and make a result cell block. This can
impact the GC on a highly read loaded server. The user can tune this pool with respect to how many
buffers are in the pool and what should be the size of each ByteBuffer. Use the config
hbase.ipc.server.reservoir.initial.buffer.size to tune each of the buffer sizes. Default is 64 KB.
When the read pattern is a random row read load and each of the rows are smaller in size
compared to this 64 KB, try reducing this. When the result size is larger than one ByteBuffer size,
the server will try to grab more than one buffer and make a result cell block out of these. When the
pool is running out of buffers, the server will end up creating temporary on-heap buffers.
The maximum number of ByteBuffers in the pool can be tuned using the config
'hbase.ipc.server.reservoir.initial.max'. Its value defaults to 64 * region server handlers configured
(See the config 'hbase.regionserver.handler.count'). The math is such that by default we consider 2
MB as the result cell block size per read result and each handler will be handling a read. For 2 MB
size, we need 32 buffers each of size 64 KB (See default buffer size in pool). So per handler 32
ByteBuffers(BB). We allocate twice this size as the max BBs count such that one handler can be
creating the response and handing it to the RPC Responder thread and then handling a new request
creating a new response cell block (using pooled buffers). Even if the responder could not send
back the first TCP reply immediately, our count should allow that we should still have enough
buffers in our pool without having to make temporary buffers on the heap. Again for smaller sized
random row reads, tune this max count. There are lazily created buffers and the count is the max
count to be pooled.
If you still see GC issues even after making end-to-end read path off-heap, look for issues in the
appropriate buffer pool. Check the below RegionServer log with INFO level:
Pool already reached its max capacity : XXX and no free buffers now. Consider
increasing the value for 'hbase.ipc.server.reservoir.initial.max' ?
The setting for HBASE_OFFHEAPSIZE in hbase-env.sh should consider this off heap buffer pool at
the RPC side also. We need to config this max off heap size for the RegionServer as a bit higher than
the sum of this max pool size and the off heap cache size. The TCP layer will also need to create
direct bytebuffers for TCP communication. Also the DFS client will need some off-heap to do its
workings especially if short-circuit reads are configured. Allocating an extra of 1 - 2 GB for the max
direct memory size has worked in tests.
If you are using co processors and refer the Cells in the read results, DO NOT store reference to
these Cells out of the scope of the CP hook methods. Some times the CPs need store info about the
cell (Like its row key) for considering in the next CP hook call etc. For such cases, pls clone the
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required fields of the entire Cell as per the use cases. [ See CellUtil#cloneXXX(Cell) APIs ]
71.5.2. Offheap write-path
TODO
71.6. RegionServer Splitting Implementation
As write requests are handled by the region server, they accumulate in an in-memory storage
system called the memstore. Once the memstore fills, its content are written to disk as additional
store files. This event is called a memstore flush. As store files accumulate, the RegionServer will
compact them into fewer, larger files. After each flush or compaction finishes, the amount of data
stored in the region has changed. The RegionServer consults the region split policy to determine if
the region has grown too large or should be split for another policy-specific reason. A region split
request is enqueued if the policy recommends it.
Logically, the process of splitting a region is simple. We find a suitable point in the keyspace of the
region where we should divide the region in half, then split the region’s data into two new regions
at that point. The details of the process however are not simple. When a split happens, the newly
created daughter regions do not rewrite all the data into new files immediately. Instead, they create
small files similar to symbolic link files, named Reference files, which point to either the top or
bottom part of the parent store file according to the split point. The reference file is used just like a
regular data file, but only half of the records are considered. The region can only be split if there
are no more references to the immutable data files of the parent region. Those reference files are
cleaned gradually by compactions, so that the region will stop referring to its parents files, and can
be split further.
Although splitting the region is a local decision made by the RegionServer, the split process itself
must coordinate with many actors. The RegionServer notifies the Master before and after the split,
updates the .META. table so that clients can discover the new daughter regions, and rearranges the
directory structure and data files in HDFS. Splitting is a multi-task process. To enable rollback in
case of an error, the RegionServer keeps an in-memory journal about the execution state. The steps
taken by the RegionServer to execute the split are illustrated in RegionServer Split Process. Each
step is labeled with its step number. Actions from RegionServers or Master are shown in red, while
actions from the clients are show in green.
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Figure 1. RegionServer Split Process
1. The RegionServer decides locally to split the region, and prepares the split. THE SPLIT
TRANSACTION IS STARTED. As a first step, the RegionServer acquires a shared read lock on
the table to prevent schema modifications during the splitting process. Then it creates a znode
in zookeeper under /hbase/region-in-transition/region-name, and sets the znode’s state to
SPLITTING.
2. The Master learns about this znode, since it has a watcher for the parent region-in-transition
znode.
3. The RegionServer creates a sub-directory named .splits under the parent’s region directory in
HDFS.
4. The RegionServer closes the parent region and marks the region as offline in its local data
structures. THE SPLITTING REGION IS NOW OFFLINE. At this point, client requests coming to
the parent region will throw NotServingRegionException. The client will retry with some backoff.
The closing region is flushed.
5. The RegionServer creates region directories under the .splits directory, for daughter regions A
and B, and creates necessary data structures. Then it splits the store files, in the sense that it
creates two Reference files per store file in the parent region. Those reference files will point to
the parent region’s files.
6. The RegionServer creates the actual region directory in HDFS, and moves the reference files for
each daughter.
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7. The RegionServer sends a Put request to the .META. table, to set the parent as offline in the
.META. table and add information about daughter regions. At this point, there won’t be
individual entries in .META. for the daughters. Clients will see that the parent region is split if
they scan .META., but won’t know about the daughters until they appear in .META.. Also, if this
Put to .META. succeeds, the parent will be effectively split. If the RegionServer fails before this
RPC succeeds, Master and the next Region Server opening the region will clean dirty state about
the region split. After the .META. update, though, the region split will be rolled-forward by
Master.
8. The RegionServer opens daughters A and B in parallel.
9. The RegionServer adds the daughters A and B to .META., together with information that it hosts
the regions. THE SPLIT REGIONS (DAUGHTERS WITH REFERENCES TO PARENT) ARE NOW
ONLINE. After this point, clients can discover the new regions and issue requests to them.
Clients cache the .META. entries locally, but when they make requests to the RegionServer or
.META., their caches will be invalidated, and they will learn about the new regions from .META..
10. The RegionServer updates znode /hbase/region-in-transition/region-name in ZooKeeper to state
SPLIT, so that the master can learn about it. The balancer can freely re-assign the daughter
regions to other region servers if necessary. THE SPLIT TRANSACTION IS NOW FINISHED.
11. After the split, .META. and HDFS will still contain references to the parent region. Those
references will be removed when compactions in daughter regions rewrite the data files.
Garbage collection tasks in the master periodically check whether the daughter regions still
refer to the parent region’s files. If not, the parent region will be removed.
71.7. Write Ahead Log (WAL)
71.7.1. Purpose
The Write Ahead Log (WAL) records all changes to data in HBase, to file-based storage. Under
normal operations, the WAL is not needed because data changes move from the MemStore to
StoreFiles. However, if a RegionServer crashes or becomes unavailable before the MemStore is
flushed, the WAL ensures that the changes to the data can be replayed. If writing to the WAL fails,
the entire operation to modify the data fails.
HBase uses an implementation of the WAL interface. Usually, there is only one instance of a WAL
per RegionServer. An exception is the RegionServer that is carrying hbase:meta; the meta table gets
its own dedicated WAL. The RegionServer records Puts and Deletes to its WAL, before recording
them these Mutations MemStore for the affected Store.
The HLog
Prior to 2.0, the interface for WALs in HBase was named HLog. In 0.94, HLog was
the name of the implementation of the WAL. You will likely find references to the
HLog in documentation tailored to these older versions.
The WAL resides in HDFS in the /hbase/WALs/ directory, with subdirectories per region.
For more general information about the concept of write ahead logs, see the Wikipedia Write-
Ahead Log article.
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71.7.2. WAL Providers
In HBase, there are a number of WAL imlementations (or 'Providers'). Each is known by a short
name label (that unfortunately is not always descriptive). You set the provider in hbase-site.xml
passing the WAL provder short-name as the value on the hbase.wal.provider property (Set the
provider for hbase:meta using the hbase.wal.meta_provider property, otherwise it uses the same
provider configured by hbase.wal.provider).
•asyncfs: The default. New since hbase-2.0.0 (HBASE-15536, HBASE-14790). This AsyncFSWAL
provider, as it identifies itself in RegionServer logs, is built on a new non-blocking dfsclient
implementation. It is currently resident in the hbase codebase but intent is to move it back up
into HDFS itself. WALs edits are written concurrently ("fan-out") style to each of the WAL-block
replicas on each DataNode rather than in a chained pipeline as the default client does. Latencies
should be better. See Apache HBase Improements and Practices at Xiaomi at slide 14 onward for
more detail on implementation.
•filesystem: This was the default in hbase-1.x releases. It is built on the blocking DFSClient and
writes to replicas in classic DFSCLient pipeline mode. In logs it identifies as FSHLog or
FSHLogProvider.
•multiwal: This provider is made of multiple instances of asyncfs or filesystem. See the next
section for more on multiwal.
Look for the lines like the below in the RegionServer log to see which provider is in place (The
below shows the default AsyncFSWALProvider):
2018-04-02 13:22:37,983 INFO [regionserver/ve0528:16020] wal.WALFactory:
Instantiating WALProvider of type class org.apache.hadoop.hbase.wal.AsyncFSWALProvider
As the AsyncFSWAL hacks into the internal of DFSClient implementation, it will be
easily broken by upgrading the hadoop dependencies, even for a simple patch
release. So if you do not specify the wal provider explicitly, we will first try to use
the asyncfs, if failed, we will fall back to use filesystem. And notice that this may
not always work, so if you still have problem starting HBase due to the problem of
starting AsyncFSWAL, please specify filesystem explicitly in the config file.
EC support has been added to hadoop-3.x, and it is incompatible with WAL as the
EC output stream does not support hflush/hsync. In order to create a non-EC file in
an EC directory, we need to use the new builder-based create API for FileSystem,
but it is only introduced in hadoop-2.9+ and for HBase we still need to support
hadoop-2.7.x. So please do not enable EC for the WAL directory until we find a way
to deal with it.
71.7.3. MultiWAL
With a single WAL per RegionServer, the RegionServer must write to the WAL serially, because
HDFS files must be sequential. This causes the WAL to be a performance bottleneck.
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HBase 1.0 introduces support MultiWal in HBASE-5699. MultiWAL allows a RegionServer to write
multiple WAL streams in parallel, by using multiple pipelines in the underlying HDFS instance,
which increases total throughput during writes. This parallelization is done by partitioning
incoming edits by their Region. Thus, the current implementation will not help with increasing the
throughput to a single Region.
RegionServers using the original WAL implementation and those using the MultiWAL
implementation can each handle recovery of either set of WALs, so a zero-downtime configuration
update is possible through a rolling restart.
Configure MultiWAL
To configure MultiWAL for a RegionServer, set the value of the property hbase.wal.provider to
multiwal by pasting in the following XML:
<property>
Ê <name>hbase.wal.provider</name>
Ê <value>multiwal</value>
</property>
Restart the RegionServer for the changes to take effect.
To disable MultiWAL for a RegionServer, unset the property and restart the RegionServer.
71.7.4. WAL Flushing
TODO (describe).
71.7.5. WAL Splitting
A RegionServer serves many regions. All of the regions in a region server share the same active
WAL file. Each edit in the WAL file includes information about which region it belongs to. When a
region is opened, the edits in the WAL file which belong to that region need to be replayed.
Therefore, edits in the WAL file must be grouped by region so that particular sets can be replayed to
regenerate the data in a particular region. The process of grouping the WAL edits by region is called
log splitting. It is a critical process for recovering data if a region server fails.
Log splitting is done by the HMaster during cluster start-up or by the ServerShutdownHandler as a
region server shuts down. So that consistency is guaranteed, affected regions are unavailable until
data is restored. All WAL edits need to be recovered and replayed before a given region can become
available again. As a result, regions affected by log splitting are unavailable until the process
completes.
Procedure: Log Splitting, Step by Step
1. The /hbase/WALs/<host>,<port>,<startcode> directory is renamed.
Renaming the directory is important because a RegionServer may still be up and accepting
requests even if the HMaster thinks it is down. If the RegionServer does not respond
immediately and does not heartbeat its ZooKeeper session, the HMaster may interpret this as a
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RegionServer failure. Renaming the logs directory ensures that existing, valid WAL files which
are still in use by an active but busy RegionServer are not written to by accident.
The new directory is named according to the following pattern:
/hbase/WALs/<host>,<port>,<startcode>-splitting
An example of such a renamed directory might look like the following:
/hbase/WALs/srv.example.com,60020,1254173957298-splitting
2. Each log file is split, one at a time.
The log splitter reads the log file one edit entry at a time and puts each edit entry into the buffer
corresponding to the edit’s region. At the same time, the splitter starts several writer threads.
Writer threads pick up a corresponding buffer and write the edit entries in the buffer to a
temporary recovered edit file. The temporary edit file is stored to disk with the following
naming pattern:
/hbase/<table_name>/<region_id>/recovered.edits/.temp
This file is used to store all the edits in the WAL log for this region. After log splitting completes,
the .temp file is renamed to the sequence ID of the first log written to the file.
To determine whether all edits have been written, the sequence ID is compared to the sequence
of the last edit that was written to the HFile. If the sequence of the last edit is greater than or
equal to the sequence ID included in the file name, it is clear that all writes from the edit file
have been completed.
3. After log splitting is complete, each affected region is assigned to a RegionServer.
When the region is opened, the recovered.edits folder is checked for recovered edits files. If any
such files are present, they are replayed by reading the edits and saving them to the MemStore.
After all edit files are replayed, the contents of the MemStore are written to disk (HFile) and the
edit files are deleted.
Handling of Errors During Log Splitting
If you set the hbase.hlog.split.skip.errors option to true, errors are treated as follows:
•Any error encountered during splitting will be logged.
•The problematic WAL log will be moved into the .corrupt directory under the hbase rootdir,
•Processing of the WAL will continue
If the hbase.hlog.split.skip.errors option is set to false, the default, the exception will be
propagated and the split will be logged as failed. See HBASE-2958 When hbase.hlog.split.skip.errors
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is set to false, we fail the split but that’s it. We need to do more than just fail split if this flag is set.
How EOFExceptions are treated when splitting a crashed RegionServer’s WALs
If an EOFException occurs while splitting logs, the split proceeds even when
hbase.hlog.split.skip.errors is set to false. An EOFException while reading the last log in the set of
files to split is likely, because the RegionServer was likely in the process of writing a record at the
time of a crash. For background, see HBASE-2643 Figure how to deal with eof splitting logs
Performance Improvements during Log Splitting
WAL log splitting and recovery can be resource intensive and take a long time, depending on the
number of RegionServers involved in the crash and the size of the regions. Enabling or Disabling
Distributed Log Splitting was developed to improve performance during log splitting.
Enabling or Disabling Distributed Log Splitting
Distributed log processing is enabled by default since HBase 0.92. The setting is controlled by the
hbase.master.distributed.log.splitting property, which can be set to true or false, but defaults to
true.
Distributed Log Splitting, Step by Step
After configuring distributed log splitting, the HMaster controls the process. The HMaster enrolls
each RegionServer in the log splitting process, and the actual work of splitting the logs is done by
the RegionServers. The general process for log splitting, as described in Distributed Log Splitting,
Step by Step still applies here.
1. If distributed log processing is enabled, the HMaster creates a split log manager instance when
the cluster is started.
a. The split log manager manages all log files which need to be scanned and split.
b. The split log manager places all the logs into the ZooKeeper splitWAL node (/hbase/splitWAL)
as tasks.
c. You can view the contents of the splitWAL by issuing the following zkCli command. Example
output is shown.
ls /hbase/splitWAL
[hdfs%3A%2F%2Fhost2.sample.com%3A56020%2Fhbase%2FWALs%2Fhost8.sample.com%2C57020
%2C1340474893275-splitting%2Fhost8.sample.com%253A57020.1340474893900,
hdfs%3A%2F%2Fhost2.sample.com%3A56020%2Fhbase%2FWALs%2Fhost3.sample.com%2C57020%
2C1340474893299-splitting%2Fhost3.sample.com%253A57020.1340474893931,
hdfs%3A%2F%2Fhost2.sample.com%3A56020%2Fhbase%2FWALs%2Fhost4.sample.com%2C57020%
2C1340474893287-splitting%2Fhost4.sample.com%253A57020.1340474893946]
The output contains some non-ASCII characters. When decoded, it looks much more simple:
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[hdfs://host2.sample.com:56020/hbase/WALs
/host8.sample.com,57020,1340474893275-splitting
/host8.sample.com%3A57020.1340474893900,
hdfs://host2.sample.com:56020/hbase/WALs
/host3.sample.com,57020,1340474893299-splitting
/host3.sample.com%3A57020.1340474893931,
hdfs://host2.sample.com:56020/hbase/WALs
/host4.sample.com,57020,1340474893287-splitting
/host4.sample.com%3A57020.1340474893946]
The listing represents WAL file names to be scanned and split, which is a list of log splitting
tasks.
2. The split log manager monitors the log-splitting tasks and workers.
The split log manager is responsible for the following ongoing tasks:
◦Once the split log manager publishes all the tasks to the splitWAL znode, it monitors these
task nodes and waits for them to be processed.
◦Checks to see if there are any dead split log workers queued up. If it finds tasks claimed by
unresponsive workers, it will resubmit those tasks. If the resubmit fails due to some
ZooKeeper exception, the dead worker is queued up again for retry.
◦Checks to see if there are any unassigned tasks. If it finds any, it create an ephemeral rescan
node so that each split log worker is notified to re-scan unassigned tasks via the
nodeChildrenChanged ZooKeeper event.
◦Checks for tasks which are assigned but expired. If any are found, they are moved back to
TASK_UNASSIGNED state again so that they can be retried. It is possible that these tasks are
assigned to slow workers, or they may already be finished. This is not a problem, because
log splitting tasks have the property of idempotence. In other words, the same log splitting
task can be processed many times without causing any problem.
◦The split log manager watches the HBase split log znodes constantly. If any split log task
node data is changed, the split log manager retrieves the node data. The node data contains
the current state of the task. You can use the zkCli get command to retrieve the current state
of a task. In the example output below, the first line of the output shows that the task is
currently unassigned.
get
/hbase/splitWAL/hdfs%3A%2F%2Fhost2.sample.com%3A56020%2Fhbase%2FWALs%2Fhost6.sam
ple.com%2C57020%2C1340474893287-
splitting%2Fhost6.sample.com%253A57020.1340474893945
unassigned host2.sample.com:57000
cZxid = 0×7115
ctime = Sat Jun 23 11:13:40 PDT 2012
...
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Based on the state of the task whose data is changed, the split log manager does one of the
following:
◦Resubmit the task if it is unassigned
◦Heartbeat the task if it is assigned
◦Resubmit or fail the task if it is resigned (see Reasons a Task Will Fail)
◦Resubmit or fail the task if it is completed with errors (see Reasons a Task Will Fail)
◦Resubmit or fail the task if it could not complete due to errors (see Reasons a Task Will Fail)
◦Delete the task if it is successfully completed or failed
Reasons a Task Will Fail
▪The task has been deleted.
▪The node no longer exists.
▪The log status manager failed to move the state of the task to
TASK_UNASSIGNED.
▪The number of resubmits is over the resubmit threshold.
3. Each RegionServer’s split log worker performs the log-splitting tasks.
Each RegionServer runs a daemon thread called the split log worker, which does the work to
split the logs. The daemon thread starts when the RegionServer starts, and registers itself to
watch HBase znodes. If any splitWAL znode children change, it notifies a sleeping worker
thread to wake up and grab more tasks. If a worker’s current task’s node data is changed, the
worker checks to see if the task has been taken by another worker. If so, the worker thread
stops work on the current task.
The worker monitors the splitWAL znode constantly. When a new task appears, the split log
worker retrieves the task paths and checks each one until it finds an unclaimed task, which it
attempts to claim. If the claim was successful, it attempts to perform the task and updates the
task’s state property based on the splitting outcome. At this point, the split log worker scans for
another unclaimed task.
How the Split Log Worker Approaches a Task
◦It queries the task state and only takes action if the task is in `TASK_UNASSIGNED `state.
◦If the task is in TASK_UNASSIGNED state, the worker attempts to set the state to TASK_OWNED by
itself. If it fails to set the state, another worker will try to grab it. The split log manager will
also ask all workers to rescan later if the task remains unassigned.
◦If the worker succeeds in taking ownership of the task, it tries to get the task state again to
make sure it really gets it asynchronously. In the meantime, it starts a split task executor to
do the actual work:
▪Get the HBase root folder, create a temp folder under the root, and split the log file to the
temp folder.
▪If the split was successful, the task executor sets the task to state TASK_DONE.
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▪If the worker catches an unexpected IOException, the task is set to state TASK_ERR.
▪If the worker is shutting down, set the task to state TASK_RESIGNED.
▪If the task is taken by another worker, just log it.
4. The split log manager monitors for uncompleted tasks.
The split log manager returns when all tasks are completed successfully. If all tasks are
completed with some failures, the split log manager throws an exception so that the log splitting
can be retried. Due to an asynchronous implementation, in very rare cases, the split log
manager loses track of some completed tasks. For that reason, it periodically checks for
remaining uncompleted task in its task map or ZooKeeper. If none are found, it throws an
exception so that the log splitting can be retried right away instead of hanging there waiting for
something that won’t happen.
71.7.6. WAL Compression
The content of the WAL can be compressed using LRU Dictionary compression. This can be used to
speed up WAL replication to different datanodes. The dictionary can store up to 215 elements;
eviction starts after this number is exceeded.
To enable WAL compression, set the hbase.regionserver.wal.enablecompression property to true.
The default value for this property is false. By default, WAL tag compression is turned on when
WAL compression is enabled. You can turn off WAL tag compression by setting the
hbase.regionserver.wal.tags.enablecompression property to 'false'.
A possible downside to WAL compression is that we lose more data from the last block in the WAL
if it ill-terminated mid-write. If entries in this last block were added with new dictionary entries but
we failed persist the amended dictionary because of an abrupt termination, a read of this last block
may not be able to resolve last-written entries.
71.7.7. Durability
It is possible to set durability on each Mutation or on a Table basis. Options include:
•SKIP_WAL: Do not write Mutations to the WAL (See the next section, Disabling the WAL).
•ASYNC_WAL: Write the WAL asynchronously; do not hold-up clients waiting on the sync of their
write to the filesystem but return immediately. The edit becomes visible. Meanwhile, in the
background, the Mutation will be flushed to the WAL at some time later. This option currently
may lose data. See HBASE-16689.
•SYNC_WAL: The default. Each edit is sync’d to HDFS before we return success to the client.
•FSYNC_WAL: Each edit is fsync’d to HDFS and the filesystem before we return success to the
client.
Do not confuse the ASYNC_WAL option on a Mutation or Table with the AsyncFSWAL writer; they
are distinct options unfortunately closely named
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71.7.8. Disabling the WAL
It is possible to disable the WAL, to improve performance in certain specific situations. However,
disabling the WAL puts your data at risk. The only situation where this is recommended is during a
bulk load. This is because, in the event of a problem, the bulk load can be re-run with no risk of
data loss.
The WAL is disabled by calling the HBase client field Mutation.writeToWAL(false). Use the
Mutation.setDurability(Durability.SKIP_WAL) and Mutation.getDurability() methods to set and get
the field’s value. There is no way to disable the WAL for only a specific table.
If you disable the WAL for anything other than bulk loads, your data is at risk.
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Chapter 72. Regions
Regions are the basic element of availability and distribution for tables, and are comprised of a
Store per Column Family. The hierarchy of objects is as follows:
Table (HBase table)
Ê Region (Regions for the table)
Ê Store (Store per ColumnFamily for each Region for the table)
Ê MemStore (MemStore for each Store for each Region for the table)
Ê StoreFile (StoreFiles for each Store for each Region for the table)
Ê Block (Blocks within a StoreFile within a Store for each Region for
the table)
For a description of what HBase files look like when written to HDFS, see Browsing HDFS for HBase
Objects.
72.1. Considerations for Number of Regions
In general, HBase is designed to run with a small (20-200) number of relatively large (5-20Gb)
regions per server. The considerations for this are as follows:
72.1.1. Why should I keep my Region count low?
Typically you want to keep your region count low on HBase for numerous reasons. Usually right
around 100 regions per RegionServer has yielded the best results. Here are some of the reasons
below for keeping region count low:
1. MSLAB (MemStore-local allocation buffer) requires 2MB per MemStore (that’s 2MB per family
per region). 1000 regions that have 2 families each is 3.9GB of heap used, and it’s not even
storing data yet. NB: the 2MB value is configurable.
2. If you fill all the regions at somewhat the same rate, the global memory usage makes it that it
forces tiny flushes when you have too many regions which in turn generates compactions.
Rewriting the same data tens of times is the last thing you want. An example is filling 1000
regions (with one family) equally and let’s consider a lower bound for global MemStore usage of
5GB (the region server would have a big heap). Once it reaches 5GB it will force flush the biggest
region, at that point they should almost all have about 5MB of data so it would flush that
amount. 5MB inserted later, it would flush another region that will now have a bit over 5MB of
data, and so on. This is currently the main limiting factor for the number of regions; see
Number of regions per RS - upper bound for detailed formula.
3. The master as is is allergic to tons of regions, and will take a lot of time assigning them and
moving them around in batches. The reason is that it’s heavy on ZK usage, and it’s not very
async at the moment (could really be improved — and has been improved a bunch in 0.96
HBase).
4. In older versions of HBase (pre-HFile v2, 0.90 and previous), tons of regions on a few RS can
cause the store file index to rise, increasing heap usage and potentially creating memory
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pressure or OOME on the RSs
Another issue is the effect of the number of regions on MapReduce jobs; it is typical to have one
mapper per HBase region. Thus, hosting only 5 regions per RS may not be enough to get sufficient
number of tasks for a MapReduce job, while 1000 regions will generate far too many tasks.
See Determining region count and size for configuration guidelines.
72.2. Region-RegionServer Assignment
This section describes how Regions are assigned to RegionServers.
72.2.1. Startup
When HBase starts regions are assigned as follows (short version):
1. The Master invokes the AssignmentManager upon startup.
2. The AssignmentManager looks at the existing region assignments in hbase:meta.
3. If the region assignment is still valid (i.e., if the RegionServer is still online) then the assignment
is kept.
4. If the assignment is invalid, then the LoadBalancerFactory is invoked to assign the region. The
load balancer (StochasticLoadBalancer by default in HBase 1.0) assign the region to a
RegionServer.
5. hbase:meta is updated with the RegionServer assignment (if needed) and the RegionServer start
codes (start time of the RegionServer process) upon region opening by the RegionServer.
72.2.2. Failover
When a RegionServer fails:
1. The regions immediately become unavailable because the RegionServer is down.
2. The Master will detect that the RegionServer has failed.
3. The region assignments will be considered invalid and will be re-assigned just like the startup
sequence.
4. In-flight queries are re-tried, and not lost.
5. Operations are switched to a new RegionServer within the following amount of time:
ZooKeeper session timeout + split time + assignment/replay time
72.2.3. Region Load Balancing
Regions can be periodically moved by the LoadBalancer.
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72.2.4. Region State Transition
HBase maintains a state for each region and persists the state in hbase:meta. The state of the
hbase:meta region itself is persisted in ZooKeeper. You can see the states of regions in transition in
the Master web UI. Following is the list of possible region states.
Possible Region States
•OFFLINE: the region is offline and not opening
•OPENING: the region is in the process of being opened
•OPEN: the region is open and the RegionServer has notified the master
•FAILED_OPEN: the RegionServer failed to open the region
•CLOSING: the region is in the process of being closed
•CLOSED: the RegionServer has closed the region and notified the master
•FAILED_CLOSE: the RegionServer failed to close the region
•SPLITTING: the RegionServer notified the master that the region is splitting
•SPLIT: the RegionServer notified the master that the region has finished splitting
•SPLITTING_NEW: this region is being created by a split which is in progress
•MERGING: the RegionServer notified the master that this region is being merged with another
region
•MERGED: the RegionServer notified the master that this region has been merged
•MERGING_NEW: this region is being created by a merge of two regions
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Figure 2. Region State Transitions
Graph Legend
•Brown: Offline state, a special state that can be transient (after closed before opening), terminal
(regions of disabled tables), or initial (regions of newly created tables)
•Palegreen: Online state that regions can serve requests
•Lightblue: Transient states
•Red: Failure states that need OPS attention
•Gold: Terminal states of regions split/merged
•Grey: Initial states of regions created through split/merge
Transition State Descriptions
1. The master moves a region from OFFLINE to OPENING state and tries to assign the region to a
RegionServer. The RegionServer may or may not have received the open region request. The
master retries sending the open region request to the RegionServer until the RPC goes through
or the master runs out of retries. After the RegionServer receives the open region request, the
RegionServer begins opening the region.
2. If the master is running out of retries, the master prevents the RegionServer from opening the
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region by moving the region to CLOSING state and trying to close it, even if the RegionServer is
starting to open the region.
3. After the RegionServer opens the region, it continues to try to notify the master until the master
moves the region to OPEN state and notifies the RegionServer. The region is now open.
4. If the RegionServer cannot open the region, it notifies the master. The master moves the region
to CLOSED state and tries to open the region on a different RegionServer.
5. If the master cannot open the region on any of a certain number of regions, it moves the region
to FAILED_OPEN state, and takes no further action until an operator intervenes from the HBase
shell, or the server is dead.
6. The master moves a region from OPEN to CLOSING state. The RegionServer holding the region may
or may not have received the close region request. The master retries sending the close request
to the server until the RPC goes through or the master runs out of retries.
7. If the RegionServer is not online, or throws NotServingRegionException, the master moves the
region to OFFLINE state and re-assigns it to a different RegionServer.
8. If the RegionServer is online, but not reachable after the master runs out of retries, the master
moves the region to FAILED_CLOSE state and takes no further action until an operator intervenes
from the HBase shell, or the server is dead.
9. If the RegionServer gets the close region request, it closes the region and notifies the master.
The master moves the region to CLOSED state and re-assigns it to a different RegionServer.
10. Before assigning a region, the master moves the region to OFFLINE state automatically if it is in
CLOSED state.
11. When a RegionServer is about to split a region, it notifies the master. The master moves the
region to be split from OPEN to SPLITTING state and add the two new regions to be created to the
RegionServer. These two regions are in SPLITTING_NEW state initially.
12. After notifying the master, the RegionServer starts to split the region. Once past the point of no
return, the RegionServer notifies the master again so the master can update the hbase:meta
table. However, the master does not update the region states until it is notified by the server
that the split is done. If the split is successful, the splitting region is moved from SPLITTING to
SPLIT state and the two new regions are moved from SPLITTING_NEW to OPEN state.
13. If the split fails, the splitting region is moved from SPLITTING back to OPEN state, and the two new
regions which were created are moved from SPLITTING_NEW to OFFLINE state.
14. When a RegionServer is about to merge two regions, it notifies the master first. The master
moves the two regions to be merged from OPEN to MERGING state, and adds the new region which
will hold the contents of the merged regions region to the RegionServer. The new region is in
MERGING_NEW state initially.
15. After notifying the master, the RegionServer starts to merge the two regions. Once past the point
of no return, the RegionServer notifies the master again so the master can update the META.
However, the master does not update the region states until it is notified by the RegionServer
that the merge has completed. If the merge is successful, the two merging regions are moved
from MERGING to MERGED state and the new region is moved from MERGING_NEW to OPEN state.
16. If the merge fails, the two merging regions are moved from MERGING back to OPEN state, and the
new region which was created to hold the contents of the merged regions is moved from
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MERGING_NEW to OFFLINE state.
17. For regions in FAILED_OPEN or FAILED_CLOSE states, the master tries to close them again when they
are reassigned by an operator via HBase Shell.
72.3. Region-RegionServer Locality
Over time, Region-RegionServer locality is achieved via HDFS block replication. The HDFS client
does the following by default when choosing locations to write replicas:
1. First replica is written to local node
2. Second replica is written to a random node on another rack
3. Third replica is written on the same rack as the second, but on a different node chosen
randomly
4. Subsequent replicas are written on random nodes on the cluster. See Replica Placement: The
First Baby Steps on this page: HDFS Architecture
Thus, HBase eventually achieves locality for a region after a flush or a compaction. In a
RegionServer failover situation a RegionServer may be assigned regions with non-local StoreFiles
(because none of the replicas are local), however as new data is written in the region, or the table is
compacted and StoreFiles are re-written, they will become "local" to the RegionServer.
For more information, see Replica Placement: The First Baby Steps on this page: HDFS Architecture
and also Lars George’s blog on HBase and HDFS locality.
72.4. Region Splits
Regions split when they reach a configured threshold. Below we treat the topic in short. For a
longer exposition, see Apache HBase Region Splitting and Merging by our Enis Soztutar.
Splits run unaided on the RegionServer; i.e. the Master does not participate. The RegionServer splits
a region, offlines the split region and then adds the daughter regions to hbase:meta, opens daughters
on the parent’s hosting RegionServer and then reports the split to the Master. See Managed Splitting
for how to manually manage splits (and for why you might do this).
72.4.1. Custom Split Policies
You can override the default split policy using a custom RegionSplitPolicy(HBase 0.94+). Typically a
custom split policy should extend HBase’s default split policy:
IncreasingToUpperBoundRegionSplitPolicy.
The policy can set globally through the HBase configuration or on a per-table basis.
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Configuring the Split Policy Globally in hbase-site.xml
<property>
Ê <name>hbase.regionserver.region.split.policy</name>
Ê <value>
org.apache.hadoop.hbase.regionserver.IncreasingToUpperBoundRegionSplitPolicy</value>
</property>
Configuring a Split Policy On a Table Using the Java API
HTableDescriptor tableDesc = new HTableDescriptor("test");
tableDesc.setValue(HTableDescriptor.SPLIT_POLICY, ConstantSizeRegionSplitPolicy.class
.getName());
tableDesc.addFamily(new HColumnDescriptor(Bytes.toBytes("cf1")));
admin.createTable(tableDesc);
----
Configuring the Split Policy On a Table Using HBase Shell
hbase> create 'test', {METADATA => {'SPLIT_POLICY' =>
'org.apache.hadoop.hbase.regionserver.ConstantSizeRegionSplitPolicy'}},{NAME => 'cf1'}
The policy can be set globally through the HBaseConfiguration used or on a per table basis:
HTableDescriptor myHtd = ...;
myHtd.setValue(HTableDescriptor.SPLIT_POLICY, MyCustomSplitPolicy.class.getName());
The DisabledRegionSplitPolicy policy blocks manual region splitting.
72.5. Manual Region Splitting
It is possible to manually split your table, either at table creation (pre-splitting), or at a later time as
an administrative action. You might choose to split your region for one or more of the following
reasons. There may be other valid reasons, but the need to manually split your table might also
point to problems with your schema design.
Reasons to Manually Split Your Table
•Your data is sorted by timeseries or another similar algorithm that sorts new data at the end of
the table. This means that the Region Server holding the last region is always under load, and
the other Region Servers are idle, or mostly idle. See also Monotonically Increasing Row
Keys/Timeseries Data.
•You have developed an unexpected hotspot in one region of your table. For instance, an
application which tracks web searches might be inundated by a lot of searches for a celebrity in
the event of news about that celebrity. See perf.one.region for more discussion about this
particular scenario.
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•After a big increase in the number of RegionServers in your cluster, to get the load spread out
quickly.
•Before a bulk-load which is likely to cause unusual and uneven load across regions.
See Managed Splitting for a discussion about the dangers and possible benefits of managing
splitting completely manually.
The DisabledRegionSplitPolicy policy blocks manual region splitting.
72.5.1. Determining Split Points
The goal of splitting your table manually is to improve the chances of balancing the load across the
cluster in situations where good rowkey design alone won’t get you there. Keeping that in mind, the
way you split your regions is very dependent upon the characteristics of your data. It may be that
you already know the best way to split your table. If not, the way you split your table depends on
what your keys are like.
Alphanumeric Rowkeys
If your rowkeys start with a letter or number, you can split your table at letter or number
boundaries. For instance, the following command creates a table with regions that split at each
vowel, so the first region has A-D, the second region has E-H, the third region has I-N, the fourth
region has O-V, and the fifth region has U-Z.
Using a Custom Algorithm
The RegionSplitter tool is provided with HBase, and uses a SplitAlgorithm to determine split
points for you. As parameters, you give it the algorithm, desired number of regions, and column
families. It includes three split algorithms. The first is the HexStringSplit algorithm, which
assumes the row keys are hexadecimal strings. The second is the DecimalStringSplit algorithm,
which assumes the row keys are decimal strings in the range 00000000 to 99999999. The third,
UniformSplit, assumes the row keys are random byte arrays. You will probably need to develop
your own SplitAlgorithm, using the provided ones as models.
72.6. Online Region Merges
Both Master and RegionServer participate in the event of online region merges. Client sends merge
RPC to the master, then the master moves the regions together to the RegionServer where the more
heavily loaded region resided. Finally the master sends the merge request to this RegionServer
which then runs the merge. Similar to process of region splitting, region merges run as a local
transaction on the RegionServer. It offlines the regions and then merges two regions on the file
system, atomically delete merging regions from hbase:meta and adds the merged region to
hbase:meta, opens the merged region on the RegionServer and reports the merge to the Master.
An example of region merges in the HBase shell
$ hbase> merge_region 'ENCODED_REGIONNAME', 'ENCODED_REGIONNAME'
$ hbase> merge_region 'ENCODED_REGIONNAME', 'ENCODED_REGIONNAME', true
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It’s an asynchronous operation and call returns immediately without waiting merge completed.
Passing true as the optional third parameter will force a merge. Normally only adjacent regions can
be merged. The force parameter overrides this behaviour and is for expert use only.
72.7. Store
A Store hosts a MemStore and 0 or more StoreFiles (HFiles). A Store corresponds to a column family
for a table for a given region.
72.7.1. MemStore
The MemStore holds in-memory modifications to the Store. Modifications are Cells/KeyValues.
When a flush is requested, the current MemStore is moved to a snapshot and is cleared. HBase
continues to serve edits from the new MemStore and backing snapshot until the flusher reports
that the flush succeeded. At this point, the snapshot is discarded. Note that when the flush happens,
MemStores that belong to the same region will all be flushed.
72.7.2. MemStore Flush
A MemStore flush can be triggered under any of the conditions listed below. The minimum flush
unit is per region, not at individual MemStore level.
1. When a MemStore reaches the size specified by hbase.hregion.memstore.flush.size, all
MemStores that belong to its region will be flushed out to disk.
2. When the overall MemStore usage reaches the value specified by
hbase.regionserver.global.memstore.upperLimit, MemStores from various regions will be
flushed out to disk to reduce overall MemStore usage in a RegionServer.
The flush order is based on the descending order of a region’s MemStore usage.
Regions will have their MemStores flushed until the overall MemStore usage drops to or slightly
below hbase.regionserver.global.memstore.lowerLimit.
3. When the number of WAL log entries in a given region server’s WAL reaches the value specified
in hbase.regionserver.max.logs, MemStores from various regions will be flushed out to disk to
reduce the number of logs in the WAL.
The flush order is based on time.
Regions with the oldest MemStores are flushed first until WAL count drops below
hbase.regionserver.max.logs.
72.7.3. Scans
•When a client issues a scan against a table, HBase generates RegionScanner objects, one per
region, to serve the scan request.
•The RegionScanner object contains a list of StoreScanner objects, one per column family.
•Each StoreScanner object further contains a list of StoreFileScanner objects, corresponding to
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each StoreFile and HFile of the corresponding column family, and a list of KeyValueScanner
objects for the MemStore.
•The two lists are merged into one, which is sorted in ascending order with the scan object for
the MemStore at the end of the list.
•When a StoreFileScanner object is constructed, it is associated with a
MultiVersionConcurrencyControl read point, which is the current memstoreTS, filtering out any
new updates beyond the read point.
72.7.4. StoreFile (HFile)
StoreFiles are where your data lives.
HFile Format
The HFile file format is based on the SSTable file described in the BigTable [2006] paper and on
Hadoop’s TFile (The unit test suite and the compression harness were taken directly from TFile).
Schubert Zhang’s blog post on HFile: A Block-Indexed File Format to Store Sorted Key-Value Pairs
makes for a thorough introduction to HBase’s HFile. Matteo Bertozzi has also put up a helpful
description, HBase I/O: HFile.
For more information, see the HFile source code. Also see HBase file format with inline blocks
(version 2) for information about the HFile v2 format that was included in 0.92.
HFile Tool
To view a textualized version of HFile content, you can use the hbase hfile tool. Type the following
to see usage:
$ ${HBASE_HOME}/bin/hbase hfile
For example, to view the content of the file
hdfs://10.81.47.41:8020/hbase/default/TEST/1418428042/DSMP/4759508618286845475, type the
following:
Ê$ ${HBASE_HOME}/bin/hbase hfile -v -f
hdfs://10.81.47.41:8020/hbase/default/TEST/1418428042/DSMP/4759508618286845475
If you leave off the option -v to see just a summary on the HFile. See usage for other things to do
with the hfile tool.
In the output of this tool, you might see 'seqid=0' for certain keys in places such as
'Mid-key'/'firstKey'/'lastKey'. These are 'KeyOnlyKeyValue' type instances - meaning
their seqid is irrelevant & we just need the keys of these Key-Value instances.
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StoreFile Directory Structure on HDFS
For more information of what StoreFiles look like on HDFS with respect to the directory structure,
see Browsing HDFS for HBase Objects.
72.7.5. Blocks
StoreFiles are composed of blocks. The blocksize is configured on a per-ColumnFamily basis.
Compression happens at the block level within StoreFiles. For more information on compression,
see Compression and Data Block Encoding In HBase.
For more information on blocks, see the HFileBlock source code.
72.7.6. KeyValue
The KeyValue class is the heart of data storage in HBase. KeyValue wraps a byte array and takes
offsets and lengths into the passed array which specify where to start interpreting the content as
KeyValue.
The KeyValue format inside a byte array is:
•keylength
•valuelength
•key
•value
The Key is further decomposed as:
•rowlength
•row (i.e., the rowkey)
•columnfamilylength
•columnfamily
•columnqualifier
•timestamp
•keytype (e.g., Put, Delete, DeleteColumn, DeleteFamily)
KeyValue instances are not split across blocks. For example, if there is an 8 MB KeyValue, even if the
block-size is 64kb this KeyValue will be read in as a coherent block. For more information, see the
KeyValue source code.
Example
To emphasize the points above, examine what happens with two Puts for two different columns for
the same row:
•Put #1: rowkey=row1, cf:attr1=value1
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•Put #2: rowkey=row1, cf:attr2=value2
Even though these are for the same row, a KeyValue is created for each column:
Key portion for Put #1:
•rowlength -----------→ 4
•row -----------------→ row1
•columnfamilylength --→ 2
•columnfamily --------→ cf
•columnqualifier -----→ attr1
•timestamp -----------→ server time of Put
•keytype -------------→ Put
Key portion for Put #2:
•rowlength -----------→ 4
•row -----------------→ row1
•columnfamilylength --→ 2
•columnfamily --------→ cf
•columnqualifier -----→ attr2
•timestamp -----------→ server time of Put
•keytype -------------→ Put
It is critical to understand that the rowkey, ColumnFamily, and column (aka columnqualifier) are
embedded within the KeyValue instance. The longer these identifiers are, the bigger the KeyValue
is.
72.7.7. Compaction
Ambiguous Terminology
•A StoreFile is a facade of HFile. In terms of compaction, use of StoreFile seems to have prevailed
in the past.
•A Store is the same thing as a ColumnFamily. StoreFiles are related to a Store, or ColumnFamily.
•If you want to read more about StoreFiles versus HFiles and Stores versus ColumnFamilies, see
HBASE-11316.
When the MemStore reaches a given size (hbase.hregion.memstore.flush.size), it flushes its contents
to a StoreFile. The number of StoreFiles in a Store increases over time. Compaction is an operation
which reduces the number of StoreFiles in a Store, by merging them together, in order to increase
performance on read operations. Compactions can be resource-intensive to perform, and can either
help or hinder performance depending on many factors.
Compactions fall into two categories: minor and major. Minor and major compactions differ in the
following ways.
Minor compactions usually select a small number of small, adjacent StoreFiles and rewrite them as
a single StoreFile. Minor compactions do not drop (filter out) deletes or expired versions, because of
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potential side effects. See Compaction and Deletions and Compaction and Versions for information
on how deletes and versions are handled in relation to compactions. The end result of a minor
compaction is fewer, larger StoreFiles for a given Store.
The end result of a major compaction is a single StoreFile per Store. Major compactions also process
delete markers and max versions. See Compaction and Deletions and Compaction and Versions for
information on how deletes and versions are handled in relation to compactions.
Compaction and Deletions
When an explicit deletion occurs in HBase, the data is not actually deleted. Instead, a tombstone
marker is written. The tombstone marker prevents the data from being returned with queries.
During a major compaction, the data is actually deleted, and the tombstone marker is removed
from the StoreFile. If the deletion happens because of an expired TTL, no tombstone is created.
Instead, the expired data is filtered out and is not written back to the compacted StoreFile.
Compaction and Versions
When you create a Column Family, you can specify the maximum number of versions to keep, by
specifying HColumnDescriptor.setMaxVersions(int versions). The default value is 3. If more versions
than the specified maximum exist, the excess versions are filtered out and not written back to the
compacted StoreFile.
Major Compactions Can Impact Query Results
In some situations, older versions can be inadvertently resurrected if a newer
version is explicitly deleted. See Major compactions change query results for a
more in-depth explanation. This situation is only possible before the compaction
finishes.
In theory, major compactions improve performance. However, on a highly loaded system, major
compactions can require an inappropriate number of resources and adversely affect performance.
In a default configuration, major compactions are scheduled automatically to run once in a 7-day
period. This is sometimes inappropriate for systems in production. You can manage major
compactions manually. See Managed Compactions.
Compactions do not perform region merges. See Merge for more information on region merging.
Compaction Switch
We can switch on and off the compactions at region servers. Switching off compactions will also
interrupt any currently ongoing compactions. It can be done dynamically using the
"compaction_switch" command from hbase shell. If done from the command line, this setting will
be lost on restart of the server. To persist the changes across region servers modify the
configuration hbase.regionserver .compaction.enabled in hbase-site.xml and restart HBase.
Compaction Policy - HBase 0.96.x and newer
Compacting large StoreFiles, or too many StoreFiles at once, can cause more IO load than your
cluster is able to handle without causing performance problems. The method by which HBase
selects which StoreFiles to include in a compaction (and whether the compaction is a minor or
major compaction) is called the compaction policy.
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Prior to HBase 0.96.x, there was only one compaction policy. That original compaction policy is still
available as RatioBasedCompactionPolicy. The new compaction default policy, called
ExploringCompactionPolicy, was subsequently backported to HBase 0.94 and HBase 0.95, and is the
default in HBase 0.96 and newer. It was implemented in HBASE-7842. In short,
ExploringCompactionPolicy attempts to select the best possible set of StoreFiles to compact with the
least amount of work, while the RatioBasedCompactionPolicy selects the first set that meets the
criteria.
Regardless of the compaction policy used, file selection is controlled by several configurable
parameters and happens in a multi-step approach. These parameters will be explained in context,
and then will be given in a table which shows their descriptions, defaults, and implications of
changing them.
Being Stuck
When the MemStore gets too large, it needs to flush its contents to a StoreFile. However, Stores are
configured with a bound on the number StoreFiles, hbase.hstore.blockingStoreFiles, and if in
excess, the MemStore flush must wait until the StoreFile count is reduced by one or more
compactions. If the MemStore is too large and the number of StoreFiles is also too high, the
algorithm is said to be "stuck". By default we’ll wait on compactions up to
hbase.hstore.blockingWaitTime milliseconds. If this period expires, we’ll flush anyways even though
we are in excess of the hbase.hstore.blockingStoreFiles count.
Upping the hbase.hstore.blockingStoreFiles count will allow flushes to happen but a Store with
many StoreFiles in will likely have higher read latencies. Try to figure why Compactions are not
keeping up. Is it a write spurt that is bringing about this situation or is a regular occurance and the
cluster is under-provisioned for the volume of writes?
The ExploringCompactionPolicy Algorithm
The ExploringCompactionPolicy algorithm considers each possible set of adjacent StoreFiles before
choosing the set where compaction will have the most benefit.
One situation where the ExploringCompactionPolicy works especially well is when you are bulk-
loading data and the bulk loads create larger StoreFiles than the StoreFiles which are holding data
older than the bulk-loaded data. This can "trick" HBase into choosing to perform a major
compaction each time a compaction is needed, and cause a lot of extra overhead. With the
ExploringCompactionPolicy, major compactions happen much less frequently because minor
compactions are more efficient.
In general, ExploringCompactionPolicy is the right choice for most situations, and thus is the
default compaction policy. You can also use ExploringCompactionPolicy along with Experimental:
Stripe Compactions.
The logic of this policy can be examined in hbase-
server/src/main/java/org/apache/hadoop/hbase/regionserver/compactions/ExploringCompactionPoli
cy.java. The following is a walk-through of the logic of the ExploringCompactionPolicy.
1. Make a list of all existing StoreFiles in the Store. The rest of the algorithm filters this list to come
up with the subset of HFiles which will be chosen for compaction.
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2. If this was a user-requested compaction, attempt to perform the requested compaction type,
regardless of what would normally be chosen. Note that even if the user requests a major
compaction, it may not be possible to perform a major compaction. This may be because not all
StoreFiles in the Column Family are available to compact or because there are too many Stores
in the Column Family.
3. Some StoreFiles are automatically excluded from consideration. These include:
◦StoreFiles that are larger than hbase.hstore.compaction.max.size
◦StoreFiles that were created by a bulk-load operation which explicitly excluded compaction.
You may decide to exclude StoreFiles resulting from bulk loads, from compaction. To do this,
specify the hbase.mapreduce.hfileoutputformat.compaction.exclude parameter during the
bulk load operation.
4. Iterate through the list from step 1, and make a list of all potential sets of StoreFiles to compact
together. A potential set is a grouping of hbase.hstore.compaction.min contiguous StoreFiles in
the list. For each set, perform some sanity-checking and figure out whether this is the best
compaction that could be done:
◦If the number of StoreFiles in this set (not the size of the StoreFiles) is fewer than
hbase.hstore.compaction.min or more than hbase.hstore.compaction.max, take it out of
consideration.
◦Compare the size of this set of StoreFiles with the size of the smallest possible compaction
that has been found in the list so far. If the size of this set of StoreFiles represents the
smallest compaction that could be done, store it to be used as a fall-back if the algorithm is
"stuck" and no StoreFiles would otherwise be chosen. See Being Stuck.
◦Do size-based sanity checks against each StoreFile in this set of StoreFiles.
▪If the size of this StoreFile is larger than hbase.hstore.compaction.max.size, take it out of
consideration.
▪If the size is greater than or equal to hbase.hstore.compaction.min.size, sanity-check it
against the file-based ratio to see whether it is too large to be considered.
The sanity-checking is successful if:
▪There is only one StoreFile in this set, or
▪For each StoreFile, its size multiplied by hbase.hstore.compaction.ratio (or
hbase.hstore.compaction.ratio.offpeak if off-peak hours are configured and it is during
off-peak hours) is less than the sum of the sizes of the other HFiles in the set.
5. If this set of StoreFiles is still in consideration, compare it to the previously-selected best
compaction. If it is better, replace the previously-selected best compaction with this one.
6. When the entire list of potential compactions has been processed, perform the best compaction
that was found. If no StoreFiles were selected for compaction, but there are multiple StoreFiles,
assume the algorithm is stuck (see Being Stuck) and if so, perform the smallest compaction that
was found in step 3.
RatioBasedCompactionPolicy Algorithm
The RatioBasedCompactionPolicy was the only compaction policy prior to HBase 0.96, though
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ExploringCompactionPolicy has now been backported to HBase 0.94 and 0.95. To use the
RatioBasedCompactionPolicy rather than the ExploringCompactionPolicy, set
hbase.hstore.defaultengine.compactionpolicy.class to RatioBasedCompactionPolicy in the hbase-
site.xml file. To switch back to the ExploringCompactionPolicy, remove the setting from the hbase-
site.xml.
The following section walks you through the algorithm used to select StoreFiles for compaction in
the RatioBasedCompactionPolicy.
1. The first phase is to create a list of all candidates for compaction. A list is created of all
StoreFiles not already in the compaction queue, and all StoreFiles newer than the newest file
that is currently being compacted. This list of StoreFiles is ordered by the sequence ID. The
sequence ID is generated when a Put is appended to the write-ahead log (WAL), and is stored in
the metadata of the HFile.
2. Check to see if the algorithm is stuck (see Being Stuck, and if so, a major compaction is forced.
This is a key area where The ExploringCompactionPolicy Algorithm is often a better choice than
the RatioBasedCompactionPolicy.
3. If the compaction was user-requested, try to perform the type of compaction that was
requested. Note that a major compaction may not be possible if all HFiles are not available for
compaction or if too many StoreFiles exist (more than hbase.hstore.compaction.max).
4. Some StoreFiles are automatically excluded from consideration. These include:
◦StoreFiles that are larger than hbase.hstore.compaction.max.size
◦StoreFiles that were created by a bulk-load operation which explicitly excluded compaction.
You may decide to exclude StoreFiles resulting from bulk loads, from compaction. To do this,
specify the hbase.mapreduce.hfileoutputformat.compaction.exclude parameter during the
bulk load operation.
5. The maximum number of StoreFiles allowed in a major compaction is controlled by the
hbase.hstore.compaction.max parameter. If the list contains more than this number of StoreFiles,
a minor compaction is performed even if a major compaction would otherwise have been done.
However, a user-requested major compaction still occurs even if there are more than
hbase.hstore.compaction.max StoreFiles to compact.
6. If the list contains fewer than hbase.hstore.compaction.min StoreFiles to compact, a minor
compaction is aborted. Note that a major compaction can be performed on a single HFile. Its
function is to remove deletes and expired versions, and reset locality on the StoreFile.
7. The value of the hbase.hstore.compaction.ratio parameter is multiplied by the sum of StoreFiles
smaller than a given file, to determine whether that StoreFile is selected for compaction during
a minor compaction. For instance, if hbase.hstore.compaction.ratio is 1.2, FileX is 5MB, FileY is
2MB, and FileZ is 3MB:
5 <= 1.2 x (2 + 3) or 5 <= 6
In this scenario, FileX is eligible for minor compaction. If FileX were 7MB, it would not be
eligible for minor compaction. This ratio favors smaller StoreFile. You can configure a different
ratio for use in off-peak hours, using the parameter hbase.hstore.compaction.ratio.offpeak, if
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you also configure hbase.offpeak.start.hour and hbase.offpeak.end.hour.
8. If the last major compaction was too long ago and there is more than one StoreFile to be
compacted, a major compaction is run, even if it would otherwise have been minor. By default,
the maximum time between major compactions is 7 days, plus or minus a 4.8 hour period, and
determined randomly within those parameters. Prior to HBase 0.96, the major compaction
period was 24 hours. See hbase.hregion.majorcompaction in the table below to tune or disable
time-based major compactions.
Parameters Used by Compaction Algorithm
This table contains the main configuration parameters for compaction. This list is not exhaustive.
To tune these parameters from the defaults, edit the hbase-default.xml file. For a full list of all
configuration parameters available, see config.files
hbase.hstore.compaction.min
The minimum number of StoreFiles which must be eligible for compaction before compaction
can run. The goal of tuning hbase.hstore.compaction.min is to avoid ending up with too many tiny
StoreFiles to compact. Setting this value to 2 would cause a minor compaction each time you
have two StoreFiles in a Store, and this is probably not appropriate. If you set this value too high,
all the other values will need to be adjusted accordingly. For most cases, the default value is
appropriate. In previous versions of HBase, the parameter hbase.hstore.compaction.min was
called hbase.hstore.compactionThreshold.
Default: 3
hbase.hstore.compaction.max
The maximum number of StoreFiles which will be selected for a single minor compaction,
regardless of the number of eligible StoreFiles. Effectively, the value of
hbase.hstore.compaction.max controls the length of time it takes a single compaction to complete.
Setting it larger means that more StoreFiles are included in a compaction. For most cases, the
default value is appropriate.
Default: 10
hbase.hstore.compaction.min.size
A StoreFile smaller than this size will always be eligible for minor compaction. StoreFiles this
size or larger are evaluated by hbase.hstore.compaction.ratio to determine if they are eligible.
Because this limit represents the "automatic include" limit for all StoreFiles smaller than this
value, this value may need to be reduced in write-heavy environments where many files in the
1-2 MB range are being flushed, because every StoreFile will be targeted for compaction and the
resulting StoreFiles may still be under the minimum size and require further compaction. If this
parameter is lowered, the ratio check is triggered more quickly. This addressed some issues seen
in earlier versions of HBase but changing this parameter is no longer necessary in most
situations.
Default:128 MB
hbase.hstore.compaction.max.size
A StoreFile larger than this size will be excluded from compaction. The effect of raising
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hbase.hstore.compaction.max.size is fewer, larger StoreFiles that do not get compacted often. If
you feel that compaction is happening too often without much benefit, you can try raising this
value.
Default: Long.MAX_VALUE
hbase.hstore.compaction.ratio
For minor compaction, this ratio is used to determine whether a given StoreFile which is larger
than hbase.hstore.compaction.min.size is eligible for compaction. Its effect is to limit compaction
of large StoreFile. The value of hbase.hstore.compaction.ratio is expressed as a floating-point
decimal.
•A large ratio, such as 10, will produce a single giant StoreFile. Conversely, a value of .25, will
produce behavior similar to the BigTable compaction algorithm, producing four StoreFiles.
•A moderate value of between 1.0 and 1.4 is recommended. When tuning this value, you are
balancing write costs with read costs. Raising the value (to something like 1.4) will have more
write costs, because you will compact larger StoreFiles. However, during reads, HBase will
need to seek through fewer StoreFiles to accomplish the read. Consider this approach if you
cannot take advantage of Bloom Filters.
•Alternatively, you can lower this value to something like 1.0 to reduce the background cost of
writes, and use to limit the number of StoreFiles touched during reads. For most cases, the
default value is appropriate.
Default: 1.2F
hbase.hstore.compaction.ratio.offpeak
The compaction ratio used during off-peak compactions, if off-peak hours are also configured
(see below). Expressed as a floating-point decimal. This allows for more aggressive (or less
aggressive, if you set it lower than hbase.hstore.compaction.ratio) compaction during a set time
period. Ignored if off-peak is disabled (default). This works the same as
hbase.hstore.compaction.ratio.
Default: 5.0F
hbase.offpeak.start.hour
The start of off-peak hours, expressed as an integer between 0 and 23, inclusive. Set to -1 to
disable off-peak.
Default: -1 (disabled)
hbase.offpeak.end.hour
The end of off-peak hours, expressed as an integer between 0 and 23, inclusive. Set to -1 to
disable off-peak.
Default: -1 (disabled)
hbase.regionserver.thread.compaction.throttle
There are two different thread pools for compactions, one for large compactions and the other
for small compactions. This helps to keep compaction of lean tables (such as hbase:meta) fast. If a
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compaction is larger than this threshold, it goes into the large compaction pool. In most cases,
the default value is appropriate.
Default: 2 x hbase.hstore.compaction.max x hbase.hregion.memstore.flush.size (which defaults
to 128)
hbase.hregion.majorcompaction
Time between major compactions, expressed in milliseconds. Set to 0 to disable time-based
automatic major compactions. User-requested and size-based major compactions will still run.
This value is multiplied by hbase.hregion.majorcompaction.jitter to cause compaction to start at
a somewhat-random time during a given window of time.
Default: 7 days (604800000 milliseconds)
hbase.hregion.majorcompaction.jitter
A multiplier applied to hbase.hregion.majorcompaction to cause compaction to occur a given
amount of time either side of hbase.hregion.majorcompaction. The smaller the number, the closer
the compactions will happen to the hbase.hregion.majorcompaction interval. Expressed as a
floating-point decimal.
Default: .50F
Compaction File Selection
Legacy Information
This section has been preserved for historical reasons and refers to the way
compaction worked prior to HBase 0.96.x. You can still use this behavior if you
enable RatioBasedCompactionPolicy Algorithm. For information on the way that
compactions work in HBase 0.96.x and later, see Compaction.
To understand the core algorithm for StoreFile selection, there is some ASCII-art in the Store source
code that will serve as useful reference.
It has been copied below:
/* normal skew:
Ê*
Ê* older ----> newer
Ê* _
Ê* | | _
Ê* | | | | _
Ê* --|-|- |-|- |-|---_-------_------- minCompactSize
Ê* | | | | | | | | _ | |
Ê* | | | | | | | | | | | |
Ê* | | | | | | | | | | | |
Ê*/
Important knobs:
•hbase.hstore.compaction.ratio Ratio used in compaction file selection algorithm (default 1.2f).
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•hbase.hstore.compaction.min (in HBase v 0.90 this is called hbase.hstore.compactionThreshold)
(files) Minimum number of StoreFiles per Store to be selected for a compaction to occur (default
2).
•hbase.hstore.compaction.max (files) Maximum number of StoreFiles to compact per minor
compaction (default 10).
•hbase.hstore.compaction.min.size (bytes) Any StoreFile smaller than this setting with
automatically be a candidate for compaction. Defaults to hbase.hregion.memstore.flush.size
(128 mb).
•hbase.hstore.compaction.max.size (.92) (bytes) Any StoreFile larger than this setting with
automatically be excluded from compaction (default Long.MAX_VALUE).
The minor compaction StoreFile selection logic is size based, and selects a file for compaction when
the file ⇐ sum(smaller_files) * hbase.hstore.compaction.ratio.
Minor Compaction File Selection - Example #1 (Basic Example)
This example mirrors an example from the unit test TestCompactSelection.
•hbase.hstore.compaction.ratio = 1.0f
•hbase.hstore.compaction.min = 3 (files)
•hbase.hstore.compaction.max = 5 (files)
•hbase.hstore.compaction.min.size = 10 (bytes)
•hbase.hstore.compaction.max.size = 1000 (bytes)
The following StoreFiles exist: 100, 50, 23, 12, and 12 bytes apiece (oldest to newest). With the above
parameters, the files that would be selected for minor compaction are 23, 12, and 12.
Why?
•100 → No, because sum(50, 23, 12, 12) * 1.0 = 97.
•50 → No, because sum(23, 12, 12) * 1.0 = 47.
•23 → Yes, because sum(12, 12) * 1.0 = 24.
•12 → Yes, because the previous file has been included, and because this does not exceed the
max-file limit of 5
•12 → Yes, because the previous file had been included, and because this does not exceed the
max-file limit of 5.
Minor Compaction File Selection - Example #2 (Not Enough Files ToCompact)
This example mirrors an example from the unit test TestCompactSelection.
•hbase.hstore.compaction.ratio = 1.0f
•hbase.hstore.compaction.min = 3 (files)
•hbase.hstore.compaction.max = 5 (files)
•hbase.hstore.compaction.min.size = 10 (bytes)
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•hbase.hstore.compaction.max.size = 1000 (bytes)
The following StoreFiles exist: 100, 25, 12, and 12 bytes apiece (oldest to newest). With the above
parameters, no compaction will be started.
Why?
•100 → No, because sum(25, 12, 12) * 1.0 = 47
•25 → No, because sum(12, 12) * 1.0 = 24
•12 → No. Candidate because sum(12) * 1.0 = 12, there are only 2 files to compact and that is less
than the threshold of 3
•12 → No. Candidate because the previous StoreFile was, but there are not enough files to
compact
Minor Compaction File Selection - Example #3 (Limiting Files To Compact)
This example mirrors an example from the unit test TestCompactSelection.
•hbase.hstore.compaction.ratio = 1.0f
•hbase.hstore.compaction.min = 3 (files)
•hbase.hstore.compaction.max = 5 (files)
•hbase.hstore.compaction.min.size = 10 (bytes)
•hbase.hstore.compaction.max.size = 1000 (bytes)
The following StoreFiles exist: 7, 6, 5, 4, 3, 2, and 1 bytes apiece (oldest to newest). With the above
parameters, the files that would be selected for minor compaction are 7, 6, 5, 4, 3.
Why?
•7 → Yes, because sum(6, 5, 4, 3, 2, 1) * 1.0 = 21. Also, 7 is less than the min-size
•6 → Yes, because sum(5, 4, 3, 2, 1) * 1.0 = 15. Also, 6 is less than the min-size.
•5 → Yes, because sum(4, 3, 2, 1) * 1.0 = 10. Also, 5 is less than the min-size.
•4 → Yes, because sum(3, 2, 1) * 1.0 = 6. Also, 4 is less than the min-size.
•3 → Yes, because sum(2, 1) * 1.0 = 3. Also, 3 is less than the min-size.
•2 → No. Candidate because previous file was selected and 2 is less than the min-size, but the
max-number of files to compact has been reached.
•1 → No. Candidate because previous file was selected and 1 is less than the min-size, but max-
number of files to compact has been reached.
Impact of Key Configuration Options
This information is now included in the configuration parameter table in
Parameters Used by Compaction Algorithm.
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Date Tiered Compaction
Date tiered compaction is a date-aware store file compaction strategy that is beneficial for time-
range scans for time-series data.
When To Use Date Tiered Compactions
Consider using Date Tiered Compaction for reads for limited time ranges, especially scans of recent
data
Don’t use it for
•random gets without a limited time range
•frequent deletes and updates
•Frequent out of order data writes creating long tails, especially writes with future timestamps
•frequent bulk loads with heavily overlapping time ranges
Performance Improvements
Performance testing has shown that the performance of time-range scans improve greatly for
limited time ranges, especially scans of recent data.
Enabling Date Tiered Compaction
You can enable Date Tiered compaction for a table or a column family, by setting its
hbase.hstore.engine.class to org.apache.hadoop.hbase.regionserver.DateTieredStoreEngine.
You also need to set hbase.hstore.blockingStoreFiles to a high number, such as 60, if using all
default settings, rather than the default value of 12). Use 1.5~2 x projected file count if changing the
parameters, Projected file count = windows per tier x tier count + incoming window min + files
older than max age
You also need to set hbase.hstore.compaction.max to the same value as
hbase.hstore.blockingStoreFiles to unblock major compaction.
Procedure: Enable Date Tiered Compaction
1. Run one of following commands in the HBase shell. Replace the table name orders_table with
the name of your table.
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alter 'orders_table', CONFIGURATION => {'hbase.hstore.engine.class' =>
'org.apache.hadoop.hbase.regionserver.DateTieredStoreEngine',
'hbase.hstore.blockingStoreFiles' => '60', 'hbase.hstore.compaction.min'=>'2',
'hbase.hstore.compaction.max'=>'60'}
alter 'orders_table', {NAME => 'blobs_cf', CONFIGURATION => {
'hbase.hstore.engine.class' =>
'org.apache.hadoop.hbase.regionserver.DateTieredStoreEngine',
'hbase.hstore.blockingStoreFiles' => '60', 'hbase.hstore.compaction.min'=>'2',
'hbase.hstore.compaction.max'=>'60'}}
create 'orders_table', 'blobs_cf', CONFIGURATION => {'hbase.hstore.engine.class' =>
'org.apache.hadoop.hbase.regionserver.DateTieredStoreEngine',
'hbase.hstore.blockingStoreFiles' => '60', 'hbase.hstore.compaction.min'=>'2',
'hbase.hstore.compaction.max'=>'60'}
2. Configure other options if needed. See Configuring Date Tiered Compaction for more
information.
Procedure: Disable Date Tiered Compaction
1. Set the hbase.hstore.engine.class option to either nil or
org.apache.hadoop.hbase.regionserver.DefaultStoreEngine. Either option has the same effect.
Make sure you set the other options you changed to the original settings too.
alter 'orders_table', CONFIGURATION => {'hbase.hstore.engine.class' =>
'org.apache.hadoop.hbase.regionserver.DefaultStoreEngine'，
'hbase.hstore.blockingStoreFiles' => '12', 'hbase.hstore.compaction.min'=>'6',
'hbase.hstore.compaction.max'=>'12'}}
When you change the store engine either way, a major compaction will likely be performed on most
regions. This is not necessary on new tables.
Configuring Date Tiered Compaction
Each of the settings for date tiered compaction should be configured at the table or column family
level. If you use HBase shell, the general command pattern is as follows:
alter 'orders_table', CONFIGURATION => {'key' => 'value', ..., 'key' => 'value'}}
Tier Parameters
You can configure your date tiers by changing the settings for the following parameters:
Table 11. Date Tier Parameters
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Setting Notes
hbase.hstore.compaction.date.tiered.max.storef
ile.age.millis Files with max-timestamp smaller than this will
no longer be compacted.Default at
Long.MAX_VALUE.
hbase.hstore.compaction.date.tiered.base.windo
w.millis Base window size in milliseconds. Default at 6
hours.
hbase.hstore.compaction.date.tiered.windows.pe
r.tier Number of windows per tier. Default at 4.
hbase.hstore.compaction.date.tiered.incoming.w
indow.min Minimal number of files to compact in the
incoming window. Set it to expected number of
files in the window to avoid wasteful
compaction. Default at 6.
hbase.hstore.compaction.date.tiered.window.pol
icy.class The policy to select store files within the same
time window. It doesn’t apply to the incoming
window. Default at exploring compaction. This
is to avoid wasteful compaction.
Compaction Throttler
With tiered compaction all servers in the cluster will promote windows to higher tier at the same
time, so using a compaction throttle is recommended: Set hbase.regionserver.throughput.controller
to org.apache.hadoop.hbase.regionserver.compactions.PressureAwareCompactionThroughputController.
For more information about date tiered compaction, please refer to the design
specification at https://docs.google.com/document/d/
1_AmlNb2N8Us1xICsTeGDLKIqL6T-oHoRLZ323MG_uy8
Experimental: Stripe Compactions
Stripe compactions is an experimental feature added in HBase 0.98 which aims to improve
compactions for large regions or non-uniformly distributed row keys. In order to achieve smaller
and/or more granular compactions, the StoreFiles within a region are maintained separately for
several row-key sub-ranges, or "stripes", of the region. The stripes are transparent to the rest of
HBase, so other operations on the HFiles or data work without modification.
Stripe compactions change the HFile layout, creating sub-regions within regions. These sub-regions
are easier to compact, and should result in fewer major compactions. This approach alleviates some
of the challenges of larger regions.
Stripe compaction is fully compatible with Compaction and works in conjunction with either the
ExploringCompactionPolicy or RatioBasedCompactionPolicy. It can be enabled for existing tables,
and the table will continue to operate normally if it is disabled later.
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When To Use Stripe Compactions
Consider using stripe compaction if you have either of the following:
•Large regions. You can get the positive effects of smaller regions without additional overhead
for MemStore and region management overhead.
•Non-uniform keys, such as time dimension in a key. Only the stripes receiving the new keys will
need to compact. Old data will not compact as often, if at all
Performance Improvements
Performance testing has shown that the performance of reads improves somewhat, and variability
of performance of reads and writes is greatly reduced. An overall long-term performance
improvement is seen on large non-uniform-row key regions, such as a hash-prefixed timestamp
key. These performance gains are the most dramatic on a table which is already large. It is possible
that the performance improvement might extend to region splits.
Enabling Stripe Compaction
You can enable stripe compaction for a table or a column family, by setting its
hbase.hstore.engine.class to org.apache.hadoop.hbase.regionserver.StripeStoreEngine. You also
need to set the hbase.hstore.blockingStoreFiles to a high number, such as 100 (rather than the
default value of 10).
Procedure: Enable Stripe Compaction
1. Run one of following commands in the HBase shell. Replace the table name orders_table with
the name of your table.
alter 'orders_table', CONFIGURATION => {'hbase.hstore.engine.class' =>
'org.apache.hadoop.hbase.regionserver.StripeStoreEngine',
'hbase.hstore.blockingStoreFiles' => '100'}
alter 'orders_table', {NAME => 'blobs_cf', CONFIGURATION => {
'hbase.hstore.engine.class' =>
'org.apache.hadoop.hbase.regionserver.StripeStoreEngine',
'hbase.hstore.blockingStoreFiles' => '100'}}
create 'orders_table', 'blobs_cf', CONFIGURATION => {'hbase.hstore.engine.class' =>
'org.apache.hadoop.hbase.regionserver.StripeStoreEngine',
'hbase.hstore.blockingStoreFiles' => '100'}
2. Configure other options if needed. See Configuring Stripe Compaction for more information.
3. Enable the table.
Procedure: Disable Stripe Compaction
1. Set the hbase.hstore.engine.class option to either nil or
org.apache.hadoop.hbase.regionserver.DefaultStoreEngine. Either option has the same effect.
alter 'orders_table', CONFIGURATION => {'hbase.hstore.engine.class' =>
'rg.apache.hadoop.hbase.regionserver.DefaultStoreEngine'}
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2. Enable the table.
When you enable a large table after changing the store engine either way, a major compaction will
likely be performed on most regions. This is not necessary on new tables.
Configuring Stripe Compaction
Each of the settings for stripe compaction should be configured at the table or column family level.
If you use HBase shell, the general command pattern is as follows:
alter 'orders_table', CONFIGURATION => {'key' => 'value', ..., 'key' => 'value'}}
Region and stripe sizing
You can configure your stripe sizing based upon your region sizing. By default, your new regions
will start with one stripe. On the next compaction after the stripe has grown too large (16 x
MemStore flushes size), it is split into two stripes. Stripe splitting continues as the region grows,
until the region is large enough to split.
You can improve this pattern for your own data. A good rule is to aim for a stripe size of at least 1
GB, and about 8-12 stripes for uniform row keys. For example, if your regions are 30 GB, 12 x 2.5 GB
stripes might be a good starting point.
Table 12. Stripe Sizing Settings
Setting Notes
hbase.store.stripe.initialStripeCount The number of stripes to create when stripe
compaction is enabled. You can use it as follows:
•For relatively uniform row keys, if you know
the approximate target number of stripes
from the above, you can avoid some splitting
overhead by starting with several stripes (2,
5, 10…). If the early data is not
representative of overall row key
distribution, this will not be as efficient.
•For existing tables with a large amount of
data, this setting will effectively pre-split
your stripes.
•For keys such as hash-prefixed sequential
keys, with more than one hash prefix per
region, pre-splitting may make sense.
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Setting Notes
hbase.store.stripe.sizeToSplit The maximum size a stripe grows before
splitting. Use this in conjunction with
hbase.store.stripe.splitPartCount to control the
target stripe size (sizeToSplit = splitPartsCount
* target stripe size), according to the above
sizing considerations.
hbase.store.stripe.splitPartCount The number of new stripes to create when
splitting a stripe. The default is 2, which is
appropriate for most cases. For non-uniform
row keys, you can experiment with increasing
the number to 3 or 4, to isolate the arriving
updates into narrower slice of the region
without additional splits being required.
MemStore Size Settings
By default, the flush creates several files from one MemStore, according to existing stripe
boundaries and row keys to flush. This approach minimizes write amplification, but can be
undesirable if the MemStore is small and there are many stripes, because the files will be too small.
In this type of situation, you can set hbase.store.stripe.compaction.flushToL0 to true. This will
cause a MemStore flush to create a single file instead. When at least
hbase.store.stripe.compaction.minFilesL0 such files (by default, 4) accumulate, they will be
compacted into striped files.
Normal Compaction Configuration and Stripe Compaction
All the settings that apply to normal compactions (see Parameters Used by Compaction Algorithm)
apply to stripe compactions. The exceptions are the minimum and maximum number of files,
which are set to higher values by default because the files in stripes are smaller. To control these
for stripe compactions, use hbase.store.stripe.compaction.minFiles and
hbase.store.stripe.compaction.maxFiles, rather than hbase.hstore.compaction.min and
hbase.hstore.compaction.max.
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Chapter 73. Bulk Loading
73.1. Overview
HBase includes several methods of loading data into tables. The most straightforward method is to
either use the TableOutputFormat class from a MapReduce job, or use the normal client APIs;
however, these are not always the most efficient methods.
The bulk load feature uses a MapReduce job to output table data in HBase’s internal data format,
and then directly loads the generated StoreFiles into a running cluster. Using bulk load will use less
CPU and network resources than simply using the HBase API.
73.2. Bulk Load Architecture
The HBase bulk load process consists of two main steps.
73.2.1. Preparing data via a MapReduce job
The first step of a bulk load is to generate HBase data files (StoreFiles) from a MapReduce job using
HFileOutputFormat2. This output format writes out data in HBase’s internal storage format so that
they can be later loaded very efficiently into the cluster.
In order to function efficiently, HFileOutputFormat2 must be configured such that each output HFile
fits within a single region. In order to do this, jobs whose output will be bulk loaded into HBase use
Hadoop’s TotalOrderPartitioner class to partition the map output into disjoint ranges of the key
space, corresponding to the key ranges of the regions in the table.
HFileOutputFormat2 includes a convenience function, configureIncrementalLoad(), which
automatically sets up a TotalOrderPartitioner based on the current region boundaries of a table.
73.2.2. Completing the data load
After a data import has been prepared, either by using the importtsv tool with the
“importtsv.bulk.output” option or by some other MapReduce job using the HFileOutputFormat, the
completebulkload tool is used to import the data into the running cluster. This command line tool
iterates through the prepared data files, and for each one determines the region the file belongs to.
It then contacts the appropriate RegionServer which adopts the HFile, moving it into its storage
directory and making the data available to clients.
If the region boundaries have changed during the course of bulk load preparation, or between the
preparation and completion steps, the completebulkload utility will automatically split the data files
into pieces corresponding to the new boundaries. This process is not optimally efficient, so users
should take care to minimize the delay between preparing a bulk load and importing it into the
cluster, especially if other clients are simultaneously loading data through other means.
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$ hadoop jar hbase-server-VERSION.jar completebulkload [-c
/path/to/hbase/config/hbase-site.xml] /user/todd/myoutput mytable
The -c config-file option can be used to specify a file containing the appropriate hbase
parameters (e.g., hbase-site.xml) if not supplied already on the CLASSPATH (In addition, the
CLASSPATH must contain the directory that has the zookeeper configuration file if zookeeper is
NOT managed by HBase).
If the target table does not already exist in HBase, this tool will create the table
automatically.
73.3. See Also
For more information about the referenced utilities, see ImportTsv and CompleteBulkLoad.
See How-to: Use HBase Bulk Loading, and Why for a recent blog on current state of bulk loading.
73.4. Advanced Usage
Although the importtsv tool is useful in many cases, advanced users may want to generate data
programmatically, or import data from other formats. To get started doing so, dig into
ImportTsv.java and check the JavaDoc for HFileOutputFormat.
The import step of the bulk load can also be done programmatically. See the LoadIncrementalHFiles
class for more information.
73.5. Bulk Loading Replication
HBASE-13153 adds replication support for bulk loaded HFiles, available since HBase 1.3/2.0. This
feature is enabled by setting hbase.replication.bulkload.enabled to true (default is false). You also
need to copy the source cluster configuration files to the destination cluster.
Additional configurations are required too:
1. hbase.replication.source.fs.conf.provider
This defines the class which loads the source cluster file system client configuration in the
destination cluster. This should be configured for all the RS in the destination cluster. Default is
org.apache.hadoop.hbase.replication.regionserver.DefaultSourceFSConfigurationProvider.
2. hbase.replication.conf.dir
This represents the base directory where the file system client configurations of the source
cluster are copied to the destination cluster. This should be configured for all the RS in the
destination cluster. Default is $HBASE_CONF_DIR.
3. hbase.replication.cluster.id
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This configuration is required in the cluster where replication for bulk loaded data is enabled. A
source cluster is uniquely identified by the destination cluster using this id. This should be
configured for all the RS in the source cluster configuration file for all the RS.
For example: If source cluster FS client configurations are copied to the destination cluster under
directory /home/user/dc1/, then hbase.replication.cluster.id should be configured as dc1 and
hbase.replication.conf.dir as /home/user.
DefaultSourceFSConfigurationProvider supports only xml type files. It loads source
cluster FS client configuration only once, so if source cluster FS client
configuration files are updated, every peer(s) cluster RS must be restarted to
reload the configuration.
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Chapter 74. HDFS
As HBase runs on HDFS (and each StoreFile is written as a file on HDFS), it is important to have an
understanding of the HDFS Architecture especially in terms of how it stores files, handles failovers,
and replicates blocks.
See the Hadoop documentation on HDFS Architecture for more information.
74.1. NameNode
The NameNode is responsible for maintaining the filesystem metadata. See the above HDFS
Architecture link for more information.
74.2. DataNode
The DataNodes are responsible for storing HDFS blocks. See the above HDFS Architecture link for
more information.
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Chapter 75. Timeline-consistent High
Available Reads
The current Assignment Manager V2 does not work well with region replica, so
this feature maybe broken. Use it with caution.
75.1. Introduction
HBase, architecturally, always had the strong consistency guarantee from the start. All reads and
writes are routed through a single region server, which guarantees that all writes happen in an
order, and all reads are seeing the most recent committed data.
However, because of this single homing of the reads to a single location, if the server becomes
unavailable, the regions of the table that were hosted in the region server become unavailable for
some time. There are three phases in the region recovery process - detection, assignment, and
recovery. Of these, the detection is usually the longest and is presently in the order of 20-30 seconds
depending on the ZooKeeper session timeout. During this time and before the recovery is complete,
the clients will not be able to read the region data.
However, for some use cases, either the data may be read-only, or doing reads against some stale
data is acceptable. With timeline-consistent high available reads, HBase can be used for these kind
of latency-sensitive use cases where the application can expect to have a time bound on the read
completion.
For achieving high availability for reads, HBase provides a feature called region replication. In this
model, for each region of a table, there will be multiple replicas that are opened in different
RegionServers. By default, the region replication is set to 1, so only a single region replica is
deployed and there will not be any changes from the original model. If region replication is set to 2
or more, then the master will assign replicas of the regions of the table. The Load Balancer ensures
that the region replicas are not co-hosted in the same region servers and also in the same rack (if
possible).
All of the replicas for a single region will have a unique replica_id, starting from 0. The region
replica having replica_id==0 is called the primary region, and the others secondary regions or
secondaries. Only the primary can accept writes from the client, and the primary will always
contain the latest changes. Since all writes still have to go through the primary region, the writes
are not highly-available (meaning they might block for some time if the region becomes
unavailable).
75.2. Timeline Consistency
With this feature, HBase introduces a Consistency definition, which can be provided per read
operation (get or scan).
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public enum Consistency {
Ê STRONG,
Ê TIMELINE
}
Consistency.STRONG is the default consistency model provided by HBase. In case the table has region
replication = 1, or in a table with region replicas but the reads are done with this consistency, the
read is always performed by the primary regions, so that there will not be any change from the
previous behaviour, and the client always observes the latest data.
In case a read is performed with Consistency.TIMELINE, then the read RPC will be sent to the
primary region server first. After a short interval (hbase.client.primaryCallTimeout.get, 10ms by
default), parallel RPC for secondary region replicas will also be sent if the primary does not respond
back. After this, the result is returned from whichever RPC is finished first. If the response came
back from the primary region replica, we can always know that the data is latest. For this
Result.isStale() API has been added to inspect the staleness. If the result is from a secondary region,
then Result.isStale() will be set to true. The user can then inspect this field to possibly reason about
the data.
In terms of semantics, TIMELINE consistency as implemented by HBase differs from pure eventual
consistency in these respects:
•Single homed and ordered updates: Region replication or not, on the write side, there is still
only 1 defined replica (primary) which can accept writes. This replica is responsible for
ordering the edits and preventing conflicts. This guarantees that two different writes are not
committed at the same time by different replicas and the data diverges. With this, there is no
need to do read-repair or last-timestamp-wins kind of conflict resolution.
•The secondaries also apply the edits in the order that the primary committed them. This way
the secondaries will contain a snapshot of the primaries data at any point in time. This is similar
to RDBMS replications and even HBase’s own multi-datacenter replication, however in a single
cluster.
•On the read side, the client can detect whether the read is coming from up-to-date data or is
stale data. Also, the client can issue reads with different consistency requirements on a per-
operation basis to ensure its own semantic guarantees.
•The client can still observe edits out-of-order, and can go back in time, if it observes reads from
one secondary replica first, then another secondary replica. There is no stickiness to region
replicas or a transaction-id based guarantee. If required, this can be implemented later though.
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Figure 3. Timeline Consistency
To better understand the TIMELINE semantics, let’s look at the above diagram. Let’s say that there
are two clients, and the first one writes x=1 at first, then x=2 and x=3 later. As above, all writes are
handled by the primary region replica. The writes are saved in the write ahead log (WAL), and
replicated to the other replicas asynchronously. In the above diagram, notice that replica_id=1
received 2 updates, and its data shows that x=2, while the replica_id=2 only received a single
update, and its data shows that x=1.
If client1 reads with STRONG consistency, it will only talk with the replica_id=0, and thus is
guaranteed to observe the latest value of x=3. In case of a client issuing TIMELINE consistency
reads, the RPC will go to all replicas (after primary timeout) and the result from the first response
will be returned back. Thus the client can see either 1, 2 or 3 as the value of x. Let’s say that the
primary region has failed and log replication cannot continue for some time. If the client does
multiple reads with TIMELINE consistency, she can observe x=2 first, then x=1, and so on.
75.3. Tradeoffs
Having secondary regions hosted for read availability comes with some tradeoffs which should be
carefully evaluated per use case. Following are advantages and disadvantages.
Advantages
•High availability for read-only tables
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•High availability for stale reads
•Ability to do very low latency reads with very high percentile (99.9%+) latencies for stale reads
Disadvantages
•Double / Triple MemStore usage (depending on region replication count) for tables with region
replication > 1
•Increased block cache usage
•Extra network traffic for log replication
•Extra backup RPCs for replicas
To serve the region data from multiple replicas, HBase opens the regions in secondary mode in the
region servers. The regions opened in secondary mode will share the same data files with the
primary region replica, however each secondary region replica will have its own MemStore to keep
the unflushed data (only primary region can do flushes). Also to serve reads from secondary
regions, the blocks of data files may be also cached in the block caches for the secondary regions.
75.4. Where is the code
This feature is delivered in two phases, Phase 1 and 2. The first phase is done in time for HBase-
1.0.0 release. Meaning that using HBase-1.0.x, you can use all the features that are marked for Phase
1. Phase 2 is committed in HBase-1.1.0, meaning all HBase versions after 1.1.0 should contain Phase
2 items.
75.5. Propagating writes to region replicas
As discussed above writes only go to the primary region replica. For propagating the writes from
the primary region replica to the secondaries, there are two different mechanisms. For read-only
tables, you do not need to use any of the following methods. Disabling and enabling the table
should make the data available in all region replicas. For mutable tables, you have to use only one
of the following mechanisms: storefile refresher, or async wal replication. The latter is
recommended.
75.5.1. StoreFile Refresher
The first mechanism is store file refresher which is introduced in HBase-1.0+. Store file refresher is
a thread per region server, which runs periodically, and does a refresh operation for the store files
of the primary region for the secondary region replicas. If enabled, the refresher will ensure that
the secondary region replicas see the new flushed, compacted or bulk loaded files from the primary
region in a timely manner. However, this means that only flushed data can be read back from the
secondary region replicas, and after the refresher is run, making the secondaries lag behind the
primary for an a longer time.
For turning this feature on, you should configure hbase.regionserver.storefile.refresh.period to a
non-zero value. See Configuration section below.
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75.5.2. Asnyc WAL replication
The second mechanism for propagation of writes to secondaries is done via “Async WAL
Replication” feature and is only available in HBase-1.1+. This works similarly to HBase’s multi-
datacenter replication, but instead the data from a region is replicated to the secondary regions.
Each secondary replica always receives and observes the writes in the same order that the primary
region committed them. In some sense, this design can be thought of as “in-cluster replication”,
where instead of replicating to a different datacenter, the data goes to secondary regions to keep
secondary region’s in-memory state up to date. The data files are shared between the primary
region and the other replicas, so that there is no extra storage overhead. However, the secondary
regions will have recent non-flushed data in their memstores, which increases the memory
overhead. The primary region writes flush, compaction, and bulk load events to its WAL as well,
which are also replicated through wal replication to secondaries. When they observe the
flush/compaction or bulk load event, the secondary regions replay the event to pick up the new files
and drop the old ones.
Committing writes in the same order as in primary ensures that the secondaries won’t diverge from
the primary regions data, but since the log replication is asynchronous, the data might still be stale
in secondary regions. Since this feature works as a replication endpoint, the performance and
latency characteristics is expected to be similar to inter-cluster replication.
Async WAL Replication is disabled by default. You can enable this feature by setting
hbase.region.replica.replication.enabled to true. Asyn WAL Replication feature will add a new
replication peer named region_replica_replication as a replication peer when you create a table
with region replication > 1 for the first time. Once enabled, if you want to disable this feature, you
need to do two actions: * Set configuration property hbase.region.replica.replication.enabled to
false in hbase-site.xml (see Configuration section below) * Disable the replication peer named
region_replica_replication in the cluster using hbase shell or Admin class:
Ê hbase> disable_peer 'region_replica_replication'
75.6. Store File TTL
In both of the write propagation approaches mentioned above, store files of the primary will be
opened in secondaries independent of the primary region. So for files that the primary compacted
away, the secondaries might still be referring to these files for reading. Both features are using
HFileLinks to refer to files, but there is no protection (yet) for guaranteeing that the file will not be
deleted prematurely. Thus, as a guard, you should set the configuration property
hbase.master.hfilecleaner.ttl to a larger value, such as 1 hour to guarantee that you will not
receive IOExceptions for requests going to replicas.
75.7. Region replication for META table’s region
Currently, Async WAL Replication is not done for the META table’s WAL. The meta table’s secondary
replicas still refreshes themselves from the persistent store files. Hence the
hbase.regionserver.meta.storefile.refresh.period needs to be set to a certain non-zero value for
refreshing the meta store files. Note that this configuration is configured differently than
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hbase.regionserver.storefile.refresh.period.
75.8. Memory accounting
The secondary region replicas refer to the data files of the primary region replica, but they have
their own memstores (in HBase-1.1+) and uses block cache as well. However, one distinction is that
the secondary region replicas cannot flush the data when there is memory pressure for their
memstores. They can only free up memstore memory when the primary region does a flush and
this flush is replicated to the secondary. Since in a region server hosting primary replicas for some
regions and secondaries for some others, the secondaries might cause extra flushes to the primary
regions in the same host. In extreme situations, there can be no memory left for adding new writes
coming from the primary via wal replication. For unblocking this situation (and since secondary
cannot flush by itself), the secondary is allowed to do a “store file refresh” by doing a file system list
operation to pick up new files from primary, and possibly dropping its memstore. This refresh will
only be performed if the memstore size of the biggest secondary region replica is at least
hbase.region.replica.storefile.refresh.memstore.multiplier (default 4) times bigger than the
biggest memstore of a primary replica. One caveat is that if this is performed, the secondary can
observe partial row updates across column families (since column families are flushed
independently). The default should be good to not do this operation frequently. You can set this
value to a large number to disable this feature if desired, but be warned that it might cause the
replication to block forever.
75.9. Secondary replica failover
When a secondary region replica first comes online, or fails over, it may have served some edits
from its memstore. Since the recovery is handled differently for secondary replicas, the secondary
has to ensure that it does not go back in time before it starts serving requests after assignment. For
doing that, the secondary waits until it observes a full flush cycle (start flush, commit flush) or a
“region open event” replicated from the primary. Until this happens, the secondary region replica
will reject all read requests by throwing an IOException with message “The region’s reads are
disabled”. However, the other replicas will probably still be available to read, thus not causing any
impact for the rpc with TIMELINE consistency. To facilitate faster recovery, the secondary region
will trigger a flush request from the primary when it is opened. The configuration property
hbase.region.replica.wait.for.primary.flush (enabled by default) can be used to disable this
feature if needed.
75.10. Configuration properties
To use highly available reads, you should set the following properties in hbase-site.xml file. There is
no specific configuration to enable or disable region replicas. Instead you can change the number
of region replicas per table to increase or decrease at the table creation or with alter table. The
following configuration is for using async wal replication and using meta replicas of 3.
75.10.1. Server side properties
<property>
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Ê <name>hbase.regionserver.storefile.refresh.period</name>
Ê <value>0</value>
Ê <description>
Ê The period (in milliseconds) for refreshing the store files for the secondary
regions. 0 means this feature is disabled. Secondary regions sees new files (from
flushes and compactions) from primary once the secondary region refreshes the list of
files in the region (there is no notification mechanism). But too frequent refreshes
might cause extra Namenode pressure. If the files cannot be refreshed for longer than
HFile TTL (hbase.master.hfilecleaner.ttl) the requests are rejected. Configuring HFile
TTL to a larger value is also recommended with this setting.
Ê </description>
</property>
<property>
Ê <name>hbase.regionserver.meta.storefile.refresh.period</name>
Ê <value>300000</value>
Ê <description>
Ê The period (in milliseconds) for refreshing the store files for the hbase:meta
tables secondary regions. 0 means this feature is disabled. Secondary regions sees new
files (from flushes and compactions) from primary once the secondary region refreshes
the list of files in the region (there is no notification mechanism). But too frequent
refreshes might cause extra Namenode pressure. If the files cannot be refreshed for
longer than HFile TTL (hbase.master.hfilecleaner.ttl) the requests are rejected.
Configuring HFile TTL to a larger value is also recommended with this setting. This
should be a non-zero number if meta replicas are enabled (via hbase.meta.replica.count
set to greater than 1).
Ê </description>
</property>
<property>
Ê <name>hbase.region.replica.replication.enabled</name>
Ê <value>true</value>
Ê <description>
Ê Whether asynchronous WAL replication to the secondary region replicas is enabled
or not. If this is enabled, a replication peer named "region_replica_replication" will
be created which will tail the logs and replicate the mutations to region replicas for
tables that have region replication > 1. If this is enabled once, disabling this
replication also requires disabling the replication peer using shell or Admin java
class. Replication to secondary region replicas works over standard inter-cluster
replication.
Ê </description>
</property>
<property>
Ê <name>hbase.region.replica.replication.memstore.enabled</name>
Ê <value>true</value>
Ê <description>
Ê If you set this to `false`, replicas do not receive memstore updates from
Ê the primary RegionServer. If you set this to `true`, you can still disable
Ê memstore replication on a per-table basis, by setting the table's
Ê `REGION_MEMSTORE_REPLICATION` configuration property to `false`. If
Ê memstore replication is disabled, the secondaries will only receive
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Ê updates for events like flushes and bulkloads, and will not have access to
Ê data which the primary has not yet flushed. This preserves the guarantee
Ê of row-level consistency, even when the read requests `Consistency.TIMELINE`.
Ê </description>
</property>
<property>
Ê <name>hbase.master.hfilecleaner.ttl</name>
Ê <value>3600000</value>
Ê <description>
Ê The period (in milliseconds) to keep store files in the archive folder before
deleting them from the file system.</description>
</property>
<property>
Ê <name>hbase.meta.replica.count</name>
Ê <value>3</value>
Ê <description>
Ê Region replication count for the meta regions. Defaults to 1.
Ê </description>
</property>
<property>
Ê <name>hbase.region.replica.storefile.refresh.memstore.multiplier</name>
Ê <value>4</value>
Ê <description>
Ê The multiplier for a “store file refresh” operation for the secondary region
replica. If a region server has memory pressure, the secondary region will refresh it’s
store files if the memstore size of the biggest secondary replica is bigger this many
times than the memstore size of the biggest primary replica. Set this to a very big
value to disable this feature (not recommended).
Ê </description>
</property>
<property>
Ê<name>hbase.region.replica.wait.for.primary.flush</name>
Ê <value>true</value>
Ê <description>
Ê Whether to wait for observing a full flush cycle from the primary before start
serving data in a secondary. Disabling this might cause the secondary region replicas
to go back in time for reads between region movements.
Ê </description>
</property>
One thing to keep in mind also is that, region replica placement policy is only enforced by the
StochasticLoadBalancer which is the default balancer. If you are using a custom load balancer
property in hbase-site.xml (hbase.master.loadbalancer.class) replicas of regions might end up being
hosted in the same server.
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75.10.2. Client side properties
Ensure to set the following for all clients (and servers) that will use region replicas.
<property>
Ê <name>hbase.ipc.client.specificThreadForWriting</name>
Ê <value>true</value>
Ê <description>
Ê Whether to enable interruption of RPC threads at the client side. This is
required for region replicas with fallback RPC’s to secondary regions.
Ê </description>
</property>
<property>
Ê <name>hbase.client.primaryCallTimeout.get</name>
Ê <value>10000</value>
Ê <description>
Ê The timeout (in microseconds), before secondary fallback RPC’s are submitted for
get requests with Consistency.TIMELINE to the secondary replicas of the regions.
Defaults to 10ms. Setting this lower will increase the number of RPC’s, but will lower
the p99 latencies.
Ê </description>
</property>
<property>
Ê <name>hbase.client.primaryCallTimeout.multiget</name>
Ê <value>10000</value>
Ê <description>
Ê The timeout (in microseconds), before secondary fallback RPC’s are submitted for
multi-get requests (Table.get(List<Get>)) with Consistency.TIMELINE to the secondary
replicas of the regions. Defaults to 10ms. Setting this lower will increase the number
of RPC’s, but will lower the p99 latencies.
Ê </description>
</property>
<property>
Ê <name>hbase.client.replicaCallTimeout.scan</name>
Ê <value>1000000</value>
Ê <description>
Ê The timeout (in microseconds), before secondary fallback RPC’s are submitted for
scan requests with Consistency.TIMELINE to the secondary replicas of the regions.
Defaults to 1 sec. Setting this lower will increase the number of RPC’s, but will lower
the p99 latencies.
Ê </description>
</property>
<property>
Ê <name>hbase.meta.replicas.use</name>
Ê <value>true</value>
Ê <description>
Ê Whether to use meta table replicas or not. Default is false.
Ê </description>
</property>
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Note HBase-1.0.x users should use hbase.ipc.client.allowsInterrupt rather than
hbase.ipc.client.specificThreadForWriting.
75.11. User Interface
In the masters user interface, the region replicas of a table are also shown together with the
primary regions. You can notice that the replicas of a region will share the same start and end keys
and the same region name prefix. The only difference would be the appended replica_id (which is
encoded as hex), and the region encoded name will be different. You can also see the replica ids
shown explicitly in the UI.
75.12. Creating a table with region replication
Region replication is a per-table property. All tables have REGION_REPLICATION = 1 by default, which
means that there is only one replica per region. You can set and change the number of replicas per
region of a table by supplying the REGION_REPLICATION property in the table descriptor.
75.12.1. Shell
create 't1', 'f1', {REGION_REPLICATION => 2}
describe 't1'
for i in 1..100
put 't1', "r#{i}", 'f1:c1', i
end
flush 't1'
75.12.2. Java
HTableDescriptor htd = new HTableDescriptor(TableName.valueOf(“test_table”));
htd.setRegionReplication(2);
...
admin.createTable(htd);
You can also use setRegionReplication() and alter table to increase, decrease the region replication
for a table.
75.13. Read API and Usage
75.13.1. Shell
You can do reads in shell using a the Consistency.TIMELINE semantics as follows
hbase(main):001:0> get 't1','r6', {CONSISTENCY => "TIMELINE"}
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You can simulate a region server pausing or becoming unavailable and do a read from the
secondary replica:
$ kill -STOP <pid or primary region server>
hbase(main):001:0> get 't1','r6', {CONSISTENCY => "TIMELINE"}
Using scans is also similar
hbase> scan 't1', {CONSISTENCY => 'TIMELINE'}
75.13.2. Java
You can set the consistency for Gets and Scans and do requests as follows.
Get get = new Get(row);
get.setConsistency(Consistency.TIMELINE);
...
Result result = table.get(get);
You can also pass multiple gets:
Get get1 = new Get(row);
get1.setConsistency(Consistency.TIMELINE);
...
ArrayList<Get> gets = new ArrayList<Get>();
gets.add(get1);
...
Result[] results = table.get(gets);
And Scans:
Scan scan = new Scan();
scan.setConsistency(Consistency.TIMELINE);
...
ResultScanner scanner = table.getScanner(scan);
You can inspect whether the results are coming from primary region or not by calling the
Result.isStale() method:
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Result result = table.get(get);
if (result.isStale()) {
Ê ...
}
75.14. Resources
1. More information about the design and implementation can be found at the jira issue: HBASE-
10070
2. HBaseCon 2014 talk: HBase Read High Availability Using Timeline-Consistent Region Replicas
also contains some details and slides.
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Chapter 76. Storing Medium-sized Objects
(MOB)
Data comes in many sizes, and saving all of your data in HBase, including binary data such as
images and documents, is ideal. While HBase can technically handle binary objects with cells that
are larger than 100 KB in size, HBase’s normal read and write paths are optimized for values
smaller than 100KB in size. When HBase deals with large numbers of objects over this threshold,
referred to here as medium objects, or MOBs, performance is degraded due to write amplification
caused by splits and compactions. When using MOBs, ideally your objects will be between 100KB
and 10MB (see the FAQ). HBase FIX_VERSION_NUMBER adds support for better managing large
numbers of MOBs while maintaining performance, consistency, and low operational overhead.
MOB support is provided by the work done in HBASE-11339. To take advantage of MOB, you need to
use HFile version 3. Optionally, configure the MOB file reader’s cache settings for each
RegionServer (see Configuring the MOB Cache), then configure specific columns to hold MOB data.
Client code does not need to change to take advantage of HBase MOB support. The feature is
transparent to the client.
MOB compaction
MOB data is flushed into MOB files after MemStore flush. There will be lots of MOB files after some
time. To reduce MOB file count, there is a periodic task which compacts small MOB files into a large
one (MOB compaction).
76.1. Configuring Columns for MOB
You can configure columns to support MOB during table creation or alteration, either in HBase
Shell or via the Java API. The two relevant properties are the boolean IS_MOB and the MOB_THRESHOLD,
which is the number of bytes at which an object is considered to be a MOB. Only IS_MOB is required.
If you do not specify the MOB_THRESHOLD, the default threshold value of 100 KB is used.
Configure a Column for MOB Using HBase Shell
hbase> create 't1', {NAME => 'f1', IS_MOB => true, MOB_THRESHOLD => 102400}
hbase> alter 't1', {NAME => 'f1', IS_MOB => true, MOB_THRESHOLD => 102400}
Example 23. Configure a Column for MOB Using the Java API
...
HColumnDescriptor hcd = new HColumnDescriptor(“f”);
hcd.setMobEnabled(true);
...
hcd.setMobThreshold(102400L);
...
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76.2. Configure MOB Compaction Policy
By default, MOB files for one specific day are compacted into one large MOB file. To reduce MOB
file count more, there are other MOB Compaction policies supported.
daily policy - compact MOB Files for one day into one large MOB file (default policy) weekly policy -
compact MOB Files for one week into one large MOB file montly policy - compact MOB Files for one
month into one large MOB File
Configure MOB compaction policy Using HBase Shell
hbase> create 't1', {NAME => 'f1', IS_MOB => true, MOB_THRESHOLD => 102400,
MOB_COMPACT_PARTITION_POLICY => 'daily'}
hbase> create 't1', {NAME => 'f1', IS_MOB => true, MOB_THRESHOLD => 102400,
MOB_COMPACT_PARTITION_POLICY => 'weekly'}
hbase> create 't1', {NAME => 'f1', IS_MOB => true, MOB_THRESHOLD => 102400,
MOB_COMPACT_PARTITION_POLICY => 'monthly'}
hbase> alter 't1', {NAME => 'f1', IS_MOB => true, MOB_THRESHOLD => 102400,
MOB_COMPACT_PARTITION_POLICY => 'daily'}
hbase> alter 't1', {NAME => 'f1', IS_MOB => true, MOB_THRESHOLD => 102400,
MOB_COMPACT_PARTITION_POLICY => 'weekly'}
hbase> alter 't1', {NAME => 'f1', IS_MOB => true, MOB_THRESHOLD => 102400,
MOB_COMPACT_PARTITION_POLICY => 'monthly'}
76.3. Configure MOB Compaction mergeable threshold
If the size of a mob file is less than this value, it’s regarded as a small file and needs to be merged in
mob compaction. The default value is 1280MB.
<property>
Ê <name>hbase.mob.compaction.mergeable.threshold</name>
Ê <value>10000000000</value>
</property>
76.4. Testing MOB
The utility org.apache.hadoop.hbase.IntegrationTestIngestWithMOB is provided to assist with testing
the MOB feature. The utility is run as follows:
$ sudo -u hbase hbase org.apache.hadoop.hbase.IntegrationTestIngestWithMOB \
Ê -threshold 1024 \
Ê -minMobDataSize 512 \
Ê -maxMobDataSize 5120
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•threshold is the threshold at which cells are considered to be MOBs. The default is 1 kB,
expressed in bytes.
•minMobDataSize is the minimum value for the size of MOB data. The default is 512 B, expressed in
bytes.
•maxMobDataSize is the maximum value for the size of MOB data. The default is 5 kB, expressed in
bytes.
76.5. Configuring the MOB Cache
Because there can be a large number of MOB files at any time, as compared to the number of
HFiles, MOB files are not always kept open. The MOB file reader cache is a LRU cache which keeps
the most recently used MOB files open. To configure the MOB file reader’s cache on each
RegionServer, add the following properties to the RegionServer’s hbase-site.xml, customize the
configuration to suit your environment, and restart or rolling restart the RegionServer.
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Example 24. Example MOB Cache Configuration
<property>
Ê <name>hbase.mob.file.cache.size</name>
Ê <value>1000</value>
Ê <description>
Ê Number of opened file handlers to cache.
Ê A larger value will benefit reads by providing more file handlers per mob
Ê file cache and would reduce frequent file opening and closing.
Ê However, if this is set too high, this could lead to a "too many opened file
handers"
Ê The default value is 1000.
Ê </description>
</property>
<property>
Ê <name>hbase.mob.cache.evict.period</name>
Ê <value>3600</value>
Ê <description>
Ê The amount of time in seconds after which an unused file is evicted from the
Ê MOB cache. The default value is 3600 seconds.
Ê </description>
</property>
<property>
Ê <name>hbase.mob.cache.evict.remain.ratio</name>
Ê <value>0.5f</value>
Ê <description>
Ê A multiplier (between 0.0 and 1.0), which determines how many files remain
cached
Ê after the threshold of files that remains cached after a cache eviction
occurs
Ê which is triggered by reaching the `hbase.mob.file.cache.size` threshold.
Ê The default value is 0.5f, which means that half the files (the least-
recently-used
Ê ones) are evicted.
Ê </description>
</property>
76.6. MOB Optimization Tasks
76.6.1. Manually Compacting MOB Files
To manually compact MOB files, rather than waiting for the configuration to trigger compaction,
use the compact or major_compact HBase shell commands. These commands require the first
argument to be the table name, and take a column family as the second argument. and take a
compaction type as the third argument.
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hbase> compact 't1', 'c1’, ‘MOB’
hbase> major_compact 't1', 'c1’, ‘MOB’
These commands are also available via Admin.compact and Admin.majorCompact methods.
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In-memory Compaction
332
Chapter 77. Overview
In-memory Compaction (A.K.A Accordion) is a new feature in hbase-2.0.0. It was first introduced on
the Apache HBase Blog at Accordion: HBase Breathes with In-Memory Compaction. Quoting the
blog:
Accordion reapplies the LSM principal [Log-Structured-Merge Tree, the
design pattern upon which HBase is based] to MemStore, in order to
eliminate redundancies and other overhead while the data is still in RAM.
Doing so decreases the frequency of flushes to HDFS, thereby reducing the
write amplification and the overall disk footprint. With less flushes, the
write operations are stalled less frequently as the MemStore overflows,
therefore the write performance is improved. Less data on disk also implies
less pressure on the block cache, higher hit rates, and eventually better read
response times. Finally, having less disk writes also means having less
compaction happening in the background, i.e., less cycles are stolen from
productive (read and write) work. All in all, the effect of in-memory
compaction can be envisioned as a catalyst that enables the system move
faster as a whole.
A developer view is available at Accordion: Developer View of In-Memory Compaction.
In-memory compaction works best when high data churn; overwrites or over-versions can be
eliminated while the data is still in memory. If the writes are all uniques, it may drag write
throughput (In-memory compaction costs CPU). We suggest you test and compare before deploying
to production.
In this section we describe how to enable Accordion and the available configurations.
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Chapter 78. Enabling
To enable in-memory compactions, set the IN_MEMORY_COMPACTION attribute on per column
family where you want the behavior. The IN_MEMORY_COMPACTION attribute can have one of
four values.
•NONE: No in-memory compaction.
•BASIC: Basic policy enables flushing and keeps a pipeline of flushes until we trip the pipeline
maximum threshold and then we flush to disk. No in-memory compaction but can help
throughput as data is moved from the profligate, native ConcurrentSkipListMap data-type to
more compact (and efficient) data types.
•EAGER: This is BASIC policy plus in-memory compaction of flushes (much like the on-disk
compactions done to hfiles); on compaction we apply on-disk rules eliminating versions,
duplicates, ttl’d cells, etc.
•ADAPTIVE: Adaptive compaction adapts to the workload. It applies either index compaction or
data compaction based on the ratio of duplicate cells in the data. Experimental.
To enable BASIC on the info column family in the table radish, disable the table and add the
attribute to the info column family, and then reenable:
hbase(main):002:0> disable 'radish'
Took 0.5570 seconds
hbase(main):003:0> alter 'radish', {NAME => 'info', IN_MEMORY_COMPACTION => 'BASIC'}
Updating all regions with the new schema...
All regions updated.
Done.
Took 1.2413 seconds
hbase(main):004:0> describe 'radish'
Table radish is DISABLED
radish
COLUMN FAMILIES DESCRIPTION
{NAME => 'info', VERSIONS => '1', EVICT_BLOCKS_ON_CLOSE => 'false',
NEW_VERSION_BEHAVIOR => 'false', KEEP_DELETED_CELLS => 'FALSE', CACHE_DATA_ON_WRITE =>
'false', DATA_BLOCK_ENCODING => 'NONE', TTL => 'FOREVER', MIN_VERSIONS => '0',
REPLICATION_SCOPE => '0', BLOOMFILTER => 'ROW', CACHE_INDEX_ON_WRITE => 'false',
IN_MEMORY => 'false', CACHE_BLOOMS_ON_WRITE => 'false', PREFETCH_BLOCKS_ON_OPEN =>
'false', COMPRESSION => 'NONE', BLOCKCACHE => 'true', BLOCKSIZE => '65536', METADATA
=> {
'IN_MEMORY_COMPACTION' => 'BASIC'}}
1 row(s)
Took 0.0239 seconds
hbase(main):005:0> enable 'radish'
Took 0.7537 seconds
Note how the IN_MEMORY_COMPACTION attribute shows as part of the METADATA map.
There is also a global configuration, hbase.hregion.compacting.memstore.type which you can set in
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your hbase-site.xml file. Use it to set the default on creation of a new table (On creation of a column
family Store, we look first to the column family configuration looking for the
IN_MEMORY_COMPACTION setting, and if none, we then consult the
hbase.hregion.compacting.memstore.type value using its content; default is BASIC).
By default, new hbase system tables will have BASIC in-memory compaction set. To specify
otherwise, on new table-creation, set hbase.hregion.compacting.memstore.type to NONE (Note,
setting this value post-creation of system tables will not have a retroactive effect; you will have to
alter your tables to set the in-memory attribute to NONE).
When an in-memory flush happens is calculated by dividing the configured region flush size (Set in
the table descriptor or read from hbase.hregion.memstore.flush.size) by the number of column
families and then multiplying by hbase.memstore.inmemoryflush.threshold.factor. Default is 0.014.
The number of flushes carried by the pipeline is monitored so as to fit within the bounds of
memstore sizing but you can also set a maximum on the number of flushes total by setting
hbase.hregion.compacting.pipeline.segments.limit. Default is 2.
When a column family Store is created, it says what memstore type is in effect. As of this writing
there is the old-school DefaultMemStore which fills a ConcurrentSkipListMap and then flushes to
disk or the new CompactingMemStore that is the implementation that provides this new in-memory
compactions facility. Here is a log-line from a RegionServer that shows a column family Store
named family configured to use a CompactingMemStore:
Note how the IN_MEMORY_COMPACTION attribute shows as part of the _METADATA_ map.
2018-03-30 11:02:24,466 INFO [Time-limited test] regionserver.HStore(325):
Store=family, memstore type=CompactingMemStore, storagePolicy=HOT,
verifyBulkLoads=false, parallelPutCountPrintThreshold=10
Enable TRACE-level logging on the CompactingMemStore class
(org.apache.hadoop.hbase.regionserver.CompactingMemStore) to see detail on its operation.
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Backup and Restore
336
Chapter 79. Overview
Backup and restore is a standard operation provided by many databases. An effective backup and
restore strategy helps ensure that users can recover data in case of unexpected failures. The HBase
backup and restore feature helps ensure that enterprises using HBase as a canonical data
repository can recover from catastrophic failures. Another important feature is the ability to
restore the database to a particular point-in-time, commonly referred to as a snapshot.
The HBase backup and restore feature provides the ability to create full backups and incremental
backups on tables in an HBase cluster. The full backup is the foundation on which incremental
backups are applied to build iterative snapshots. Incremental backups can be run on a schedule to
capture changes over time, for example by using a Cron task. Incremental backups are more cost-
effective than full backups because they only capture the changes since the last backup and they
also enable administrators to restore the database to any prior incremental backup. Furthermore,
the utilities also enable table-level data backup-and-recovery if you do not want to restore the
entire dataset of the backup.
The backup and restore feature supplements the HBase Replication feature. While HBase
replication is ideal for creating "hot" copies of the data (where the replicated data is immediately
available for query), the backup and restore feature is ideal for creating "cold" copies of data
(where a manual step must be taken to restore the system). Previously, users only had the ability to
create full backups via the ExportSnapshot functionality. The incremental backup implementation
is the novel improvement over the previous "art" provided by ExportSnapshot.
The backup and restore feature uses DistCp to transfer files between clusters . HADOOP-15850 fixes
a bug where CopyCommitter#concatFileChunks unconditionally tried to concatenate the files being
DistCp’ed to target cluster (though the files are independent) . Without the fix from HADOOP-15850
, the transfer would fail. So the backup and restore feature need hadoop version as below
•2.7.x
•2.8.x
•2.9.2+
•2.10.0+
•3.0.4+
•3.1.2+
•3.2.0+
•3.3.0+
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Chapter 80. Terminology
The backup and restore feature introduces new terminology which can be used to understand how
control flows through the system.
•A backup: A logical unit of data and metadata which can restore a table to its state at a specific
point in time.
•Full backup: a type of backup which wholly encapsulates the contents of the table at a point in
time.
•Incremental backup: a type of backup which contains the changes in a table since a full backup.
•Backup set: A user-defined name which references one or more tables over which a backup can
be executed.
•Backup ID: A unique names which identifies one backup from the rest, e.g.
backupId_1467823988425
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Chapter 81. Planning
There are some common strategies which can be used to implement backup and restore in your
environment. The following section shows how these strategies are implemented and identifies
potential tradeoffs with each.
This backup and restore tools has not been tested on Transparent Data Encryption
(TDE) enabled HDFS clusters. This is related to the open issue HBASE-16178.
81.1. Backup within a cluster
This strategy stores the backups on the same cluster as where the backup was taken. This approach
is only appropriate for testing as it does not provide any additional safety on top of what the
software itself already provides.
Figure 4. Intra-Cluster Backup
81.2. Backup using a dedicated cluster
This strategy provides greater fault tolerance and provides a path towards disaster recovery. In this
setting, you will store the backup on a separate HDFS cluster by supplying the backup destination
cluster’s HDFS URL to the backup utility. You should consider backing up to a different physical
location, such as a different data center.
Typically, a backup-dedicated HDFS cluster uses a more economical hardware profile to save
money.
Figure 5. Dedicated HDFS Cluster Backup
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81.3. Backup to the Cloud or a storage vendor
appliance
Another approach to safeguarding HBase incremental backups is to store the data on provisioned,
secure servers that belong to third-party vendors and that are located off-site. The vendor can be a
public cloud provider or a storage vendor who uses a Hadoop-compatible file system, such as S3
and other HDFS-compatible destinations.
Figure 6. Backup to Cloud or Vendor Storage Solutions
The HBase backup utility does not support backup to multiple destinations. A
workaround is to manually create copies of the backup files from HDFS or S3.
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Chapter 82. First-time configuration steps
This section contains the necessary configuration changes that must be made in order to use the
backup and restore feature. As this feature makes significant use of YARN’s MapReduce framework
to parallelize these I/O heavy operations, configuration changes extend outside of just hbase-
site.xml.
82.1. Allow the "hbase" system user in YARN
The YARN container-executor.cfg configuration file must have the following property setting:
allowed.system.users=hbase. No spaces are allowed in entries of this configuration file.
Skipping this step will result in runtime errors when executing the first backup
tasks.
Example of a valid container-executor.cfg file for backup and restore:
yarn.nodemanager.log-dirs=/var/log/hadoop/mapred
yarn.nodemanager.linux-container-executor.group=yarn
banned.users=hdfs,yarn,mapred,bin
allowed.system.users=hbase
min.user.id=500
82.2. HBase specific changes
Add the following properties to hbase-site.xml and restart HBase if it is already running.
The ",…" is an ellipsis meant to imply that this is a comma-separated list of values,
not literal text which should be added to hbase-site.xml.
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<property>
Ê <name>hbase.backup.enable</name>
Ê <value>true</value>
</property>
<property>
Ê <name>hbase.master.logcleaner.plugins</name>
Ê <value>org.apache.hadoop.hbase.backup.master.BackupLogCleaner,...</value>
</property>
<property>
Ê <name>hbase.procedure.master.classes</name>
Ê <value>org.apache.hadoop.hbase.backup.master.LogRollMasterProcedureManager,...<
/value>
</property>
<property>
Ê <name>hbase.procedure.regionserver.classes</name>
Ê <value>org.apache.hadoop.hbase.backup.regionserver
.LogRollRegionServerProcedureManager,...</value>
</property>
<property>
Ê <name>hbase.coprocessor.region.classes</name>
Ê <value>org.apache.hadoop.hbase.backup.BackupObserver,...</value>
</property>
<property>
Ê <name>hbase.master.hfilecleaner.plugins</name>
Ê <value>org.apache.hadoop.hbase.backup.BackupHFileCleaner,...</value>
</property>
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Chapter 83. Backup and Restore commands
This covers the command-line utilities that administrators would run to create, restore, and merge
backups. Tools to inspect details on specific backup sessions is covered in the next section,
Administration of Backup Images.
Run the command hbase backup help <command> to access the online help that provides basic
information about a command and its options. The below information is captured in this help
message for each command.
83.1. Creating a Backup Image
For HBase clusters also using Apache Phoenix: include the SQL system catalog
tables in the backup. In the event that you need to restore the HBase backup,
access to the system catalog tables enable you to resume Phoenix interoperability
with the restored data.
The first step in running the backup and restore utilities is to perform a full backup and to store the
data in a separate image from the source. At a minimum, you must do this to get a baseline before
you can rely on incremental backups.
Run the following command as HBase superuser:
hbase backup create <type> <backup_path>
After the command finishes running, the console prints a SUCCESS or FAILURE status message. The
SUCCESS message includes a backup ID. The backup ID is the Unix time (also known as Epoch time)
that the HBase master received the backup request from the client.
Record the backup ID that appears at the end of a successful backup. In case the
source cluster fails and you need to recover the dataset with a restore operation,
having the backup ID readily available can save time.
83.1.1. Positional Command-Line Arguments
type
The type of backup to execute: full or incremental. As a reminder, an incremental backup
requires a full backup to already exist.
backup_path
The backup_path argument specifies the full filesystem URI of where to store the backup image.
Valid prefixes are hdfs:, webhdfs:, s3a: or other compatible Hadoop File System implementations.
83.1.2. Named Command-Line Arguments
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-t <table_name[,table_name]>
A comma-separated list of tables to back up. If no tables are specified, all tables are backed up.
No regular-expression or wildcard support is present; all table names must be explicitly listed.
See Backup Sets for more information about peforming operations on collections of tables.
Mutually exclusive with the -s option; one of these named options are required.
-s <backup_set_name>
Identify tables to backup based on a backup set. See Using Backup Sets for the purpose and
usage of backup sets. Mutually exclusive with the -t option.
-w <number_workers>
(Optional) Specifies the number of parallel workers to copy data to backup destination. Backups
are currently executed by MapReduce jobs so this value corresponds to the number of Mappers
that will be spawned by the job.
-b <bandwidth_per_worker>
(Optional) Specifies the bandwidth of each worker in MB per second.
-d
(Optional) Enables "DEBUG" mode which prints additional logging about the backup creation.
-q <name>
(Optional) Allows specification of the name of a YARN queue which the MapReduce job to create
the backup should be executed in. This option is useful to prevent backup tasks from stealing
resources away from other MapReduce jobs of high importance.
83.1.3. Example usage
$ hbase backup create full hdfs://host5:8020/data/backup -t SALES2,SALES3 -w 3
This command creates a full backup image of two tables, SALES2 and SALES3, in the HDFS instance
who NameNode is host5:8020 in the path /data/backup. The -w option specifies that no more than
three parallel works complete the operation.
83.2. Restoring a Backup Image
Run the following command as an HBase superuser. You can only restore a backup on a running
HBase cluster because the data must be redistributed the RegionServers for the operation to
complete successfully.
hbase restore <backup_path> <backup_id>
83.2.1. Positional Command-Line Arguments
backup_path
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The backup_path argument specifies the full filesystem URI of where to store the backup image.
Valid prefixes are hdfs:, webhdfs:, s3a: or other compatible Hadoop File System implementations.
backup_id
The backup ID that uniquely identifies the backup image to be restored.
83.2.2. Named Command-Line Arguments
-t <table_name[,table_name]>
A comma-separated list of tables to restore. See Backup Sets for more information about
peforming operations on collections of tables. Mutually exclusive with the -s option; one of these
named options are required.
-s <backup_set_name>
Identify tables to backup based on a backup set. See Using Backup Sets for the purpose and
usage of backup sets. Mutually exclusive with the -t option.
-q <name>
(Optional) Allows specification of the name of a YARN queue which the MapReduce job to create
the backup should be executed in. This option is useful to prevent backup tasks from stealing
resources away from other MapReduce jobs of high importance.
-c
(Optional) Perform a dry-run of the restore. The actions are checked, but not executed.
-m <target_tables>
(Optional) A comma-separated list of tables to restore into. If this option is not provided, the
original table name is used. When this option is provided, there must be an equal number of
entries provided in the -t option.
-o
(Optional) Overwrites the target table for the restore if the table already exists.
83.2.3. Example of Usage
hbase restore /tmp/backup_incremental backupId_1467823988425 -t mytable1,mytable2
This command restores two tables of an incremental backup image. In this example: •
/tmp/backup_incremental is the path to the directory containing the backup image. •
backupId_1467823988425 is the backup ID. • mytable1 and mytable2 are the names of tables in the
backup image to be restored.
83.3. Merging Incremental Backup Images
This command can be used to merge two or more incremental backup images into a single
incremental backup image. This can be used to consolidate multiple, small incremental backup
images into a single larger incremental backup image. This command could be used to merge
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hourly incremental backups into a daily incremental backup image, or daily incremental backups
into a weekly incremental backup.
$ hbase backup merge <backup_ids>
83.3.1. Positional Command-Line Arguments
backup_ids
A comma-separated list of incremental backup image IDs that are to be combined into a single
image.
83.3.2. Named Command-Line Arguments
None.
83.3.3. Example usage
$ hbase backup merge backupId_1467823988425,backupId_1467827588425
83.4. Using Backup Sets
Backup sets can ease the administration of HBase data backups and restores by reducing the
amount of repetitive input of table names. You can group tables into a named backup set with the
hbase backup set add command. You can then use the -set option to invoke the name of a backup
set in the hbase backup create or hbase restore rather than list individually every table in the
group. You can have multiple backup sets.
Note the differentiation between the hbase backup set add command and the -set
option. The hbase backup set add command must be run before using the -set
option in a different command because backup sets must be named and defined
before using backup sets as a shortcut.
If you run the hbase backup set add command and specify a backup set name that does not yet exist
on your system, a new set is created. If you run the command with the name of an existing backup
set name, then the tables that you specify are added to the set.
In this command, the backup set name is case-sensitive.
The metadata of backup sets are stored within HBase. If you do not have access to
the original HBase cluster with the backup set metadata, then you must specify
individual table names to restore the data.
To create a backup set, run the following command as the HBase superuser:
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$ hbase backup set <subcommand> <backup_set_name> <tables>
83.4.1. Backup Set Subcommands
The following list details subcommands of the hbase backup set command.
You must enter one (and no more than one) of the following subcommands after
hbase backup set to complete an operation. Also, the backup set name is case-
sensitive in the command-line utility.
add
Adds table[s] to a backup set. Specify a backup_set_name value after this argument to create a
backup set.
remove
Removes tables from the set. Specify the tables to remove in the tables argument.
list
Lists all backup sets.
describe
Displays a description of a backup set. The information includes whether the set has full or
incremental backups, start and end times of the backups, and a list of the tables in the set. This
subcommand must precede a valid value for the backup_set_name value.
delete
Deletes a backup set. Enter the value for the backup_set_name option directly after the hbase
backup set delete command.
83.4.2. Positional Command-Line Arguments
backup_set_name
Use to assign or invoke a backup set name. The backup set name must contain only printable
characters and cannot have any spaces.
tables
List of tables (or a single table) to include in the backup set. Enter the table names as a comma-
separated list. If no tables are specified, all tables are included in the set.
Maintain a log or other record of the case-sensitive backup set names and the
corresponding tables in each set on a separate or remote cluster, backup strategy.
This information can help you in case of failure on the primary cluster.
83.4.3. Example of Usage
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$ hbase backup set add Q1Data TEAM3,TEAM_4
Depending on the environment, this command results in one of the following actions:
•If the Q1Data backup set does not exist, a backup set containing tables TEAM_3 and TEAM_4 is
created.
•If the Q1Data backup set exists already, the tables TEAM_3 and TEAM_4 are added to the Q1Data
backup set.
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Chapter 84. Administration of Backup
Images
The hbase backup command has several subcommands that help with administering backup images
as they accumulate. Most production environments require recurring backups, so it is necessary to
have utilities to help manage the data of the backup repository. Some subcommands enable you to
find information that can help identify backups that are relevant in a search for particular data.
You can also delete backup images.
The following list details each hbase backup subcommand that can help administer backups. Run the
full command-subcommand line as the HBase superuser.
84.1. Managing Backup Progress
You can monitor a running backup in another terminal session by running the hbase backup
progress command and specifying the backup ID as an argument.
For example, run the following command as hbase superuser to view the progress of a backup
$ hbase backup progress <backup_id>
84.1.1. Positional Command-Line Arguments
backup_id
Specifies the backup that you want to monitor by seeing the progress information. The backupId
is case-sensitive.
84.1.2. Named Command-Line Arguments
None.
84.1.3. Example usage
hbase backup progress backupId_1467823988425
84.2. Managing Backup History
This command displays a log of backup sessions. The information for each session includes backup
ID, type (full or incremental), the tables in the backup, status, and start and end time. Specify the
number of backup sessions to display with the optional -n argument.
$ hbase backup history <backup_id>
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84.2.1. Positional Command-Line Arguments
backup_id
Specifies the backup that you want to monitor by seeing the progress information. The backupId
is case-sensitive.
84.2.2. Named Command-Line Arguments
-n <num_records>
(Optional) The maximum number of backup records (Default: 10).
-p <backup_root_path>
The full filesystem URI of where backup images are stored.
-s <backup_set_name>
The name of the backup set to obtain history for. Mutually exclusive with the -t option.
-t <table_name>
The name of table to obtain history for. Mutually exclusive with the -s option.
84.2.3. Example usage
$ hbase backup history
$ hbase backup history -n 20
$ hbase backup history -t WebIndexRecords
84.3. Describing a Backup Image
This command can be used to obtain information about a specific backup image.
$ hbase backup describe <backup_id>
84.3.1. Positional Command-Line Arguments
backup_id
The ID of the backup image to describe.
84.3.2. Named Command-Line Arguments
None.
84.3.3. Example usage
$ hbase backup describe backupId_1467823988425
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84.4. Deleting a Backup Image
This command can be used to delete a backup image which is no longer needed.
$ hbase backup delete <backup_id>
84.4.1. Positional Command-Line Arguments
backup_id
The ID to the backup image which should be deleted.
84.4.2. Named Command-Line Arguments
None.
84.4.3. Example usage
$ hbase backup delete backupId_1467823988425
84.5. Backup Repair Command
This command attempts to correct any inconsistencies in persisted backup metadata which exists
as the result of software errors or unhandled failure scenarios. While the backup implementation
tries to correct all errors on its own, this tool may be necessary in the cases where the system
cannot automatically recover on its own.
$ hbase backup repair
84.5.1. Positional Command-Line Arguments
None.
84.6. Named Command-Line Arguments
None.
84.6.1. Example usage
$ hbase backup repair
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Chapter 85. Configuration keys
The backup and restore feature includes both required and optional configuration keys.
85.1. Required properties
hbase.backup.enable: Controls whether or not the feature is enabled (Default: false). Set this value
to true.
hbase.master.logcleaner.plugins: A comma-separated list of classes invoked when cleaning logs in
the HBase Master. Set this value to org.apache.hadoop.hbase.backup.master.BackupLogCleaner or
append it to the current value.
hbase.procedure.master.classes: A comma-separated list of classes invoked with the Procedure
framework in the Master. Set this value to
org.apache.hadoop.hbase.backup.master.LogRollMasterProcedureManager or append it to the current
value.
hbase.procedure.regionserver.classes: A comma-separated list of classes invoked with the Procedure
framework in the RegionServer. Set this value to
org.apache.hadoop.hbase.backup.regionserver.LogRollRegionServerProcedureManager or append it to
the current value.
hbase.coprocessor.region.classes: A comma-separated list of RegionObservers deployed on tables.
Set this value to org.apache.hadoop.hbase.backup.BackupObserver or append it to the current value.
hbase.master.hfilecleaner.plugins: A comma-separated list of HFileCleaners deployed on the Master.
Set this value to org.apache.hadoop.hbase.backup.BackupHFileCleaner or append it to the current
value.
85.2. Optional properties
hbase.backup.system.ttl: The time-to-live in seconds of data in the hbase:backup tables (default:
forever). This property is only relevant prior to the creation of the hbase:backup table. Use the alter
command in the HBase shell to modify the TTL when this table already exists. See the below section
for more details on the impact of this configuration property.
hbase.backup.attempts.max: The number of attempts to perform when taking hbase table snapshots
(default: 10).
hbase.backup.attempts.pause.ms: The amount of time to wait between failed snapshot attempts in
milliseconds (default: 10000).
hbase.backup.logroll.timeout.millis: The amount of time (in milliseconds) to wait for RegionServers
to execute a WAL rolling in the Master’s procedure framework (default: 30000).
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Chapter 86. Best Practices
86.1. Formulate a restore strategy and test it.
Before you rely on a backup and restore strategy for your production environment, identify how
backups must be performed, and more importantly, how restores must be performed. Test the plan
to ensure that it is workable. At a minimum, store backup data from a production cluster on a
different cluster or server. To further safeguard the data, use a backup location that is at a different
physical location.
If you have a unrecoverable loss of data on your primary production cluster as a result of computer
system issues, you may be able to restore the data from a different cluster or server at the same
site. However, a disaster that destroys the whole site renders locally stored backups useless.
Consider storing the backup data and necessary resources (both computing capacity and operator
expertise) to restore the data at a site sufficiently remote from the production site. In the case of a
catastrophe at the whole primary site (fire, earthquake, etc.), the remote backup site can be very
valuable.
86.2. Secure a full backup image first.
As a baseline, you must complete a full backup of HBase data at least once before you can rely on
incremental backups. The full backup should be stored outside of the source cluster. To ensure
complete dataset recovery, you must run the restore utility with the option to restore baseline full
backup. The full backup is the foundation of your dataset. Incremental backup data is applied on
top of the full backup during the restore operation to return you to the point in time when backup
was last taken.
86.3. Define and use backup sets for groups of tables
that are logical subsets of the entire dataset.
You can group tables into an object called a backup set. A backup set can save time when you have
a particular group of tables that you expect to repeatedly back up or restore.
When you create a backup set, you type table names to include in the group. The backup set
includes not only groups of related tables, but also retains the HBase backup metadata. Afterwards,
you can invoke the backup set name to indicate what tables apply to the command execution
instead of entering all the table names individually.
86.4. Document the backup and restore strategy, and
ideally log information about each backup.
Document the whole process so that the knowledge base can transfer to new administrators after
employee turnover. As an extra safety precaution, also log the calendar date, time, and other
relevant details about the data of each backup. This metadata can potentially help locate a
particular dataset in case of source cluster failure or primary site disaster. Maintain duplicate
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copies of all documentation: one copy at the production cluster site and another at the backup
location or wherever it can be accessed by an administrator remotely from the production cluster.
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Chapter 87. Scenario: Safeguarding
Application Datasets on Amazon S3
This scenario describes how a hypothetical retail business uses backups to safeguard application
data and then restore the dataset after failure.
The HBase administration team uses backup sets to store data from a group of tables that have
interrelated information for an application called green. In this example, one table contains
transaction records and the other contains customer details. The two tables need to be backed up
and be recoverable as a group.
The admin team also wants to ensure daily backups occur automatically.
Figure 7. Tables Composing The Backup Set
The following is an outline of the steps and examples of commands that are used to backup the data
for the green application and to recover the data later. All commands are run when logged in as
HBase superuser.
•A backup set called green_set is created as an alias for both the transactions table and the
customer table. The backup set can be used for all operations to avoid typing each table name.
The backup set name is case-sensitive and should be formed with only printable characters and
without spaces.
$ hbase backup set add green_set transactions
$ hbase backup set add green_set customer
•The first backup of green_set data must be a full backup. The following command example
shows how credentials are passed to Amazon S3 and specifies the file system with the s3a:
prefix.
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$ ACCESS_KEY=ABCDEFGHIJKLMNOPQRST
$ SECRET_KEY=123456789abcdefghijklmnopqrstuvwxyzABCD
$ sudo -u hbase hbase backup create full\
Ê s3a://$ACCESS_KEY:SECRET_KEY@prodhbasebackups/backups -s green_set
•Incremental backups should be run according to a schedule that ensures essential data recovery
in the event of a catastrophe. At this retail company, the HBase admin team decides that
automated daily backups secures the data sufficiently. The team decides that they can
implement this by modifying an existing Cron job that is defined in /etc/crontab. Consequently,
IT modifies the Cron job by adding the following line:
@daily hbase hbase backup create incremental
s3a://$ACCESS_KEY:$SECRET_KEY@prodhbasebackups/backups -s green_set
•A catastrophic IT incident disables the production cluster that the green application uses. An
HBase system administrator of the backup cluster must restore the green_set dataset to the
point in time closest to the recovery objective.
If the administrator of the backup HBase cluster has the backup ID with
relevant details in accessible records, the following search with the hdfs dfs
-ls command and manually scanning the backup ID list can be bypassed.
Consider continuously maintaining and protecting a detailed log of backup IDs
outside the production cluster in your environment.
The HBase administrator runs the following command on the directory where backups are
stored to print the list of successful backup IDs on the console:
`hdfs dfs -ls -t /prodhbasebackups/backups`
•The admin scans the list to see which backup was created at a date and time closest to the
recovery objective. To do this, the admin converts the calendar timestamp of the recovery point
in time to Unix time because backup IDs are uniquely identified with Unix time. The backup IDs
are listed in reverse chronological order, meaning the most recent successful backup appears
first.
The admin notices that the following line in the command output corresponds with the
green_set backup that needs to be restored:
/prodhbasebackups/backups/backup_1467823988425`
•The admin restores green_set invoking the backup ID and the -overwrite option. The -overwrite
option truncates all existing data in the destination and populates the tables with data from the
backup dataset. Without this flag, the backup data is appended to the existing data in the
destination. In this case, the admin decides to overwrite the data because it is corrupted.
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$ sudo -u hbase hbase restore -s green_set \
Ê s3a://$ACCESS_KEY:$SECRET_KEY@prodhbasebackups/backups backup_1467823988425 \
-overwrite
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Chapter 88. Security of Backup Data
With this feature which makes copying data to remote locations, it’s important to take a moment to
clearly state the procedural concerns that exist around data security. Like the HBase replication
feature, backup and restore provides the constructs to automatically copy data from within a
corporate boundary to some system outside of that boundary. It is imperative when storing
sensitive data that with backup and restore, much less any feature which extracts data from HBase,
the locations to which data is being sent has undergone a security audit to ensure that only
authenticated users are allowed to access that data.
For example, with the above example of backing up data to S3, it is of the utmost importance that
the proper permissions are assigned to the S3 bucket to ensure that only a minimum set of
authorized users are allowed to access this data. Because the data is no longer being accessed via
HBase, and its authentication and authorization controls, we must ensure that the filesystem
storing that data is providing a comparable level of security. This is a manual step which users
must implement on their own.
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Chapter 89. Technical Details of Incremental
Backup and Restore
HBase incremental backups enable more efficient capture of HBase table images than previous
attempts at serial backup and restore solutions, such as those that only used HBase Export and
Import APIs. Incremental backups use Write Ahead Logs (WALs) to capture the data changes since
the previous backup was created. A WAL roll (create new WALs) is executed across all
RegionServers to track the WALs that need to be in the backup.
After the incremental backup image is created, the source backup files usually are on same node as
the data source. A process similar to the DistCp (distributed copy) tool is used to move the source
backup files to the target file systems. When a table restore operation starts, a two-step process is
initiated. First, the full backup is restored from the full backup image. Second, all WAL files from
incremental backups between the last full backup and the incremental backup being restored are
converted to HFiles, which the HBase Bulk Load utility automatically imports as restored data in
the table.
You can only restore on a live HBase cluster because the data must be redistributed to complete the
restore operation successfully.
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Chapter 90. A Warning on File System
Growth
As a reminder, incremental backups are implemented via retaining the write-ahead logs which
HBase primarily uses for data durability. Thus, to ensure that all data needing to be included in a
backup is still available in the system, the HBase backup and restore feature retains all write-ahead
logs since the last backup until the next incremental backup is executed.
Like HBase Snapshots, this can have an expectedly large impact on the HDFS usage of HBase for
high volume tables. Take care in enabling and using the backup and restore feature, specifically
with a mind to removing backup sessions when they are not actively being used.
The only automated, upper-bound on retained write-ahead logs for backup and restore is based on
the TTL of the hbase:backup system table which, as of the time this document is written, is infinite
(backup table entries are never automatically deleted). This requires that administrators perform
backups on a schedule whose frequency is relative to the amount of available space on HDFS (e.g.
less available HDFS space requires more aggressive backup merges and deletions). As a reminder,
the TTL can be altered on the hbase:backup table using the alter command in the HBase shell.
Modifying the configuration property hbase.backup.system.ttl in hbase-site.xml after the system
table exists has no effect.
360
Chapter 91. Capacity Planning
When designing a distributed system deployment, it is critical that some basic mathmatical rigor is
executed to ensure sufficient computational capacity is available given the data and software
requirements of the system. For this feature, the availability of network capacity is the largest
bottleneck when estimating the performance of some implementation of backup and restore. The
second most costly function is the speed at which data can be read/written.
91.1. Full Backups
To estimate the duration of a full backup, we have to understand the general actions which are
invoked:
•Write-ahead log roll on each RegionServer: ones to tens of seconds per RegionServer in parallel.
Relative to the load on each RegionServer.
•Take an HBase snapshot of the table(s): tens of seconds. Relative to the number of regions and
files that comprise the table.
•Export the snapshot to the destination: see below. Relative to the size of the data and the
network bandwidth to the destination.
To approximate how long the final step will take, we have to make some assumptions on hardware.
Be aware that these will not be accurate for your system — these are numbers that your or your
administrator know for your system. Let’s say the speed of reading data from HDFS on a single
node is capped at 80MB/s (across all Mappers that run on that host), a modern network interface
controller (NIC) supports 10Gb/s, the top-of-rack switch can handle 40Gb/s, and the WAN between
your clusters is 10Gb/s. This means that you can only ship data to your remote at a speed of
1.25GB/s — meaning that 16 nodes (1.25 * 1024 / 80 = 16) participating in the ExportSnapshot
should be able to fully saturate the link between clusters. With more nodes in the cluster, we can
still saturate the network but at a lesser impact on any one node which helps ensure local SLAs are
made. If the size of the snapshot is 10TB, this would full backup would take in the ballpark of 2.5
hours (10 * 1024 / 1.25 / (60 * 60) = 2.23hrs)
As a general statement, it is very likely that the WAN bandwidth between your local cluster and the
remote storage is the largest bottleneck to the speed of a full backup.
When the concern is restricting the computational impact of backups to a "production system", the
above formulas can be reused with the optional command-line arguments to hbase backup create:
-b, -w, -q. The -b option defines the bandwidth at which each worker (Mapper) would write data.
The -w argument limits the number of workers that would be spawned in the DistCp job. The -q
allows the user to specify a YARN queue which can limit the specific nodes where the workers will
be spawned — this can quarantine the backup workers performing the copy to a set of non-critical
nodes. Relating the -b and -w options to our earlier equations: -b would be used to restrict each
node from reading data at the full 80MB/s and -w is used to limit the job from spawning 16 worker
tasks.
361
91.2. Incremental Backup
Like we did for full backups, we have to understand the incremental backup process to
approximate its runtime and cost.
•Identify new write-ahead logs since last full or incremental backup: negligible. Apriori
knowledge from the backup system table(s).
•Read, filter, and write "minimized" HFiles equivalent to the WALs: dominated by the speed of
writing data. Relative to write speed of HDFS.
•DistCp the HFiles to the destination: see above.
For the second step, the dominating cost of this operation would be the re-writing the data (under
the assumption that a majority of the data in the WAL is preserved). In this case, we can assume an
aggregate write speed of 30MB/s per node. Continuing our 16-node cluster example, this would
require approximately 15 minutes to perform this step for 50GB of data (50 * 1024 / 60 / 60 = 14.2).
The amount of time to start the DistCp MapReduce job would likely dominate the actual time taken
to copy the data (50 / 1.25 = 40 seconds) and can be ignored.
362
Chapter 92. Limitations of the Backup and
Restore Utility
Serial backup operations
Backup operations cannot be run concurrently. An operation includes actions like create, delete,
restore, and merge. Only one active backup session is supported. HBASE-16391 will introduce
multiple-backup sessions support.
No means to cancel backups
Both backup and restore operations cannot be canceled. (HBASE-15997, HBASE-15998). The
workaround to cancel a backup would be to kill the client-side backup command (control-C),
ensure all relevant MapReduce jobs have exited, and then run the hbase backup repair command to
ensure the system backup metadata is consistent.
Backups can only be saved to a single location
Copying backup information to multiple locations is an exercise left to the user. HBASE-15476 will
introduce the ability to specify multiple-backup destinations intrinsically.
HBase superuser access is required
Only an HBase superuser (e.g. hbase) is allowed to perform backup/restore, can pose a problem for
shared HBase installations. Current mitigations would require coordination with system
administrators to build and deploy a backup and restore strategy (HBASE-14138).
Backup restoration is an online operation
To perform a restore from a backup, it requires that the HBase cluster is online as a caveat of the
current implementation (HBASE-16573).
Some operations may fail and require re-run
The HBase backup feature is primarily client driven. While there is the standard HBase retry logic
built into the HBase Connection, persistent errors in executing operations may propagate back to
the client (e.g. snapshot failure due to region splits). The backup implementation should be moved
from client-side into the ProcedureV2 framework in the future which would provide additional
robustness around transient/retryable failures. The hbase backup repair command is meant to
correct states which the system cannot automatically detect and recover from.
Avoidance of declaration of public API
While the Java API to interact with this feature exists and its implementation is separated from an
interface, insufficient rigor has been applied to determine if it is exactly what we intend to ship to
users. As such, it is marked as for a Private audience with the expectation that, as users begin to try
the feature, there will be modifications that would necessitate breaking compatibility (HBASE-
17517).
363
Lack of global metrics for backup and restore
Individual backup and restore operations contain metrics about the amount of work the operation
included, but there is no centralized location (e.g. the Master UI) which present information for
consumption (HBASE-16565).
364
Synchronous Replication
365
Chapter 93. Background
The current replication in HBase in asynchronous. So if the master cluster crashes, the slave cluster
may not have the newest data. If users want strong consistency then they can not switch to the
slave cluster.
366
Chapter 94. Design
Please see the design doc on HBASE-19064
367
Chapter 95. Operation and maintenance
Case.1 Setup two synchronous replication clusters
•Add a synchronous peer in both source cluster and peer cluster.
For source cluster:
hbase> add_peer '1', CLUSTER_KEY => 'lg-hadoop-tst-st01.bj:10010,lg-hadoop-tst-
st02.bj:10010,lg-hadoop-tst-st03.bj:10010:/hbase/test-hbase-slave', REMOTE_WAL_DIR=>
'hdfs://lg-hadoop-tst-st01.bj:20100/hbase/test-hbase-slave/remoteWALs', TABLE_CFS =>
{"ycsb-test"=>[]}
For peer cluster:
hbase> add_peer '1', CLUSTER_KEY => 'lg-hadoop-tst-st01.bj:10010,lg-hadoop-tst-
st02.bj:10010,lg-hadoop-tst-st03.bj:10010:/hbase/test-hbase', REMOTE_WAL_DIR=>
'hdfs://lg-hadoop-tst-st01.bj:20100/hbase/test-hbase/remoteWALs', TABLE_CFS => {"ycsb-
test"=>[]}
For synchronous replication, the current implementation require that we have the
same peer id for both source and peer cluster. Another thing that need attention is:
the peer does not support cluster-level, namespace-level, or cf-level replication,
only support table-level replication now.
•Transit the peer cluster to be STANDBY state
hbase> transit_peer_sync_replication_state '1', 'STANDBY'
•Transit the source cluster to be ACTIVE state
hbase> transit_peer_sync_replication_state '1', 'ACTIVE'
Now, the synchronous replication has been set up successfully. the HBase client can only request to
source cluster, if request to peer cluster, the peer cluster which is STANDBY state now will reject the
read/write requests.
Case.2 How to operate when standby cluster crashed
If the standby cluster has been crashed, it will fail to write remote WAL for the active cluster. So
we need to transit the source cluster to DOWNGRANDE_ACTIVE state, which means source
cluster won’t write any remote WAL any more, but the normal replication (asynchronous
Replication) can still work fine, it queue the newly written WALs, but the replication block until
the peer cluster come back.
368
hbase> transit_peer_sync_replication_state '1', 'DOWNGRADE_ACTIVE'
Once the peer cluster come back, we can just transit the source cluster to ACTIVE, to ensure that the
replication will be synchronous.
hbase> transit_peer_sync_replication_state '1', 'ACTIVE'
Case.3 How to operate when active cluster crashed
If the active cluster has been crashed (it may be not reachable now), so let’s just transit the
standby cluster to DOWNGRANDE_ACTIVE state, and after that, we should redirect all the
requests from client to the DOWNGRADE_ACTIVE cluster.
hbase> transit_peer_sync_replication_state '1', 'DOWNGRADE_ACTIVE'
If the crashed cluster come back again, we just need to transit it to STANDBY directly. Otherwise if
you transit the cluster to DOWNGRADE_ACTIVE, the original ACTIVE cluster may have redundant
data compared to the current ACTIVE cluster. Because we designed to write source cluster WALs
and remote cluster WALs concurrently, so it’s possible that the source cluster WALs has more data
than the remote cluster, which result in data inconsistency. The procedure of transiting ACTIVE to
STANDBY has no problem, because we’ll skip to replay the original WALs.
hbase> transit_peer_sync_replication_state '1', 'STANDBY'
After that, we can promote the DOWNGRADE_ACTIVE cluster to ACTIVE now, to ensure that the
replication will be synchronous.
hbase> transit_peer_sync_replication_state '1', 'ACTIVE'
369
Apache HBase APIs
This chapter provides information about performing operations using HBase native APIs. This
information is not exhaustive, and provides a quick reference in addition to the User API Reference.
The examples here are not comprehensive or complete, and should be used for purposes of
illustration only.
Apache HBase also works with multiple external APIs. See Apache HBase External APIs for more
information.
370
Chapter 96. Examples
Example 25. Create, modify and delete a Table Using Java
package com.example.hbase.admin;
import java.io.IOException;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.hbase.HBaseConfiguration;
import org.apache.hadoop.hbase.HColumnDescriptor;
import org.apache.hadoop.hbase.HConstants;
import org.apache.hadoop.hbase.HTableDescriptor;
import org.apache.hadoop.hbase.TableName;
import org.apache.hadoop.hbase.client.Admin;
import org.apache.hadoop.hbase.client.Connection;
import org.apache.hadoop.hbase.client.ConnectionFactory;
import org.apache.hadoop.hbase.io.compress.Compression.Algorithm;
public class Example {
Ê private static final String TABLE_NAME = "MY_TABLE_NAME_TOO";
Ê private static final String CF_DEFAULT = "DEFAULT_COLUMN_FAMILY";
Ê public static void createOrOverwrite(Admin admin, HTableDescriptor table) throws
IOException {
Ê if (admin.tableExists(table.getTableName())) {
Ê admin.disableTable(table.getTableName());
Ê admin.deleteTable(table.getTableName());
Ê }
Ê admin.createTable(table);
Ê }
Ê public static void createSchemaTables(Configuration config) throws IOException {
Ê try (Connection connection = ConnectionFactory.createConnection(config);
Ê Admin admin = connection.getAdmin()) {
Ê HTableDescriptor table = new HTableDescriptor(TableName.valueOf(TABLE_NAME)
);
Ê table.addFamily(new HColumnDescriptor(CF_DEFAULT).setCompressionType
(Algorithm.NONE));
Ê System.out.print("Creating table. ");
Ê createOrOverwrite(admin, table);
Ê System.out.println(" Done.");
Ê }
Ê }
371
Ê public static void modifySchema (Configuration config) throws IOException {
Ê try (Connection connection = ConnectionFactory.createConnection(config);
Ê Admin admin = connection.getAdmin()) {
Ê TableName tableName = TableName.valueOf(TABLE_NAME);
Ê if (!admin.tableExists(tableName)) {
Ê System.out.println("Table does not exist.");
Ê System.exit(-1);
Ê }
Ê HTableDescriptor table = admin.getTableDescriptor(tableName);
Ê // Update existing table
Ê HColumnDescriptor newColumn = new HColumnDescriptor("NEWCF");
Ê newColumn.setCompactionCompressionType(Algorithm.GZ);
Ê newColumn.setMaxVersions(HConstants.ALL_VERSIONS);
Ê admin.addColumn(tableName, newColumn);
Ê // Update existing column family
Ê HColumnDescriptor existingColumn = new HColumnDescriptor(CF_DEFAULT);
Ê existingColumn.setCompactionCompressionType(Algorithm.GZ);
Ê existingColumn.setMaxVersions(HConstants.ALL_VERSIONS);
Ê table.modifyFamily(existingColumn);
Ê admin.modifyTable(tableName, table);
Ê // Disable an existing table
Ê admin.disableTable(tableName);
Ê // Delete an existing column family
Ê admin.deleteColumn(tableName, CF_DEFAULT.getBytes("UTF-8"));
Ê // Delete a table (Need to be disabled first)
Ê admin.deleteTable(tableName);
Ê }
Ê }
Ê public static void main(String... args) throws IOException {
Ê Configuration config = HBaseConfiguration.create();
Ê //Add any necessary configuration files (hbase-site.xml, core-site.xml)
Ê config.addResource(new Path(System.getenv("HBASE_CONF_DIR"), "hbase-site.xml"
));
Ê config.addResource(new Path(System.getenv("HADOOP_CONF_DIR"), "core-site.xml"
));
Ê createSchemaTables(config);
Ê modifySchema(config);
Ê }
}
372
Apache HBase External APIs
This chapter will cover access to Apache HBase either through non-Java languages and through
custom protocols. For information on using the native HBase APIs, refer to User API Reference and
the HBase APIs chapter.
373
Chapter 97. REST
Representational State Transfer (REST) was introduced in 2000 in the doctoral dissertation of Roy
Fielding, one of the principal authors of the HTTP specification.
REST itself is out of the scope of this documentation, but in general, REST allows client-server
interactions via an API that is tied to the URL itself. This section discusses how to configure and run
the REST server included with HBase, which exposes HBase tables, rows, cells, and metadata as URL
specified resources. There is also a nice series of blogs on How-to: Use the Apache HBase REST
Interface by Jesse Anderson.
97.1. Starting and Stopping the REST Server
The included REST server can run as a daemon which starts an embedded Jetty servlet container
and deploys the servlet into it. Use one of the following commands to start the REST server in the
foreground or background. The port is optional, and defaults to 8080.
# Foreground
$ bin/hbase rest start -p <port>
# Background, logging to a file in $HBASE_LOGS_DIR
$ bin/hbase-daemon.sh start rest -p <port>
To stop the REST server, use Ctrl-C if you were running it in the foreground, or the following
command if you were running it in the background.
$ bin/hbase-daemon.sh stop rest
97.2. Configuring the REST Server and Client
For information about configuring the REST server and client for SSL, as well as doAs impersonation
for the REST server, see Configure the Thrift Gateway to Authenticate on Behalf of the Client and
other portions of the Securing Apache HBase chapter.
97.3. Using REST Endpoints
The following examples use the placeholder server http://example.com:8000, and the following
commands can all be run using curl or wget commands. You can request plain text (the default),
XML , or JSON output by adding no header for plain text, or the header "Accept: text/xml" for XML,
"Accept: application/json" for JSON, or "Accept: application/x-protobuf" to for protocol buffers.
Unless specified, use GET requests for queries, PUT or POST requests for creation or
mutation, and DELETE for deletion.
Table 13. Cluster-Wide Endpoints
374
Endpoint HTTP
Verb
Description Example
/version/clust
er
GET Version of HBase
running on this cluster
curl -vi -X GET \
Ê -H "Accept: text/xml" \
Ê "http://example.com:8000/version/cluster"
/status/cluste
r
GET Cluster status curl -vi -X GET \
Ê -H "Accept: text/xml" \
Ê "http://example.com:8000/status/cluster"
/ GET List of all non-system
tables
curl -vi -X GET \
Ê -H "Accept: text/xml" \
Ê "http://example.com:8000/"
Table 14. Namespace Endpoints
Endpoint HTTP
Verb
Description Example
/namespaces GET List all namespaces curl -vi -X GET \
Ê -H "Accept: text/xml" \
Ê "http://example.com:8000/namespaces/"
/namespaces/na
mespace
GET Describe a specific
namespace
curl -vi -X GET \
Ê -H "Accept: text/xml" \
Ê
"http://example.com:8000/namespaces/special_
ns"
/namespaces/na
mespace
POST Create a new
namespace
curl -vi -X POST \
Ê -H "Accept: text/xml" \
Ê "example.com:8000/namespaces/special_ns"
/namespaces/na
mespace/tables
GET List all tables in a
specific namespace
curl -vi -X GET \
Ê -H "Accept: text/xml" \
Ê
"http://example.com:8000/namespaces/special_
ns/tables"
/namespaces/na
mespace
PUT Alter an existing
namespace. Currently
not used.
curl -vi -X PUT \
Ê -H "Accept: text/xml" \
Ê
"http://example.com:8000/namespaces/special_
ns
/namespaces/na
mespace
DELETE Delete a namespace.
The namespace must
be empty.
curl -vi -X DELETE \
Ê -H "Accept: text/xml" \
Ê "example.com:8000/namespaces/special_ns"
Table 15. Table Endpoints
Endpoint HTTP
Verb
Description Example
/table/schema GET Describe the schema of
the specified table.
curl -vi -X GET \
Ê -H "Accept: text/xml" \
Ê "http://example.com:8000/users/schema"
375
Endpoint HTTP
Verb
Description Example
/table/schema POST Update an existing
table with the provided
schema fragment
curl -vi -X POST \
Ê -H "Accept: text/xml" \
Ê -H "Content-Type: text/xml" \
Ê -d '&lt;?xml version="1.0" encoding="UTF-
8"?>&lt;TableSchema
name="users">&lt;ColumnSchema name="cf"
KEEP_DELETED_CELLS="true"
/>&lt;/TableSchema>' \
Ê "http://example.com:8000/users/schema"
/table/schema PUT Create a new table, or
replace an existing
table’s schema
curl -vi -X PUT \
Ê -H "Accept: text/xml" \
Ê -H "Content-Type: text/xml" \
Ê -d '&lt;?xml version="1.0" encoding="UTF-
8"?>&lt;TableSchema
name="users">&lt;ColumnSchema name="cf"
/>&lt;/TableSchema>' \
Ê "http://example.com:8000/users/schema"
/table/schema DELETE Delete the table. You
must use the /
table/schema endpoint,
not just /table/.
curl -vi -X DELETE \
Ê -H "Accept: text/xml" \
Ê "http://example.com:8000/users/schema"
/table/regions GET List the table regions curl -vi -X GET \
Ê -H "Accept: text/xml" \
Ê "http://example.com:8000/users/regions
Table 16. Endpoints for Get Operations
Endpoint HTTP
Verb
Description Example
/table/row GET Get all columns of a
single row. Values are
Base-64 encoded. This
requires the "Accept"
request header with a
type that can hold
multiple columns (like
xml, json or protobuf).
curl -vi -X GET \
Ê -H "Accept: text/xml" \
Ê "http://example.com:8000/users/row1"
/table/row/col
umn:qualifier/
timestamp
GET Get the value of a single
column. Values are
Base-64 encoded.
curl -vi -X GET \
Ê -H "Accept: text/xml" \
Ê
"http://example.com:8000/users/row1/cf:a/145
8586888395"
/table/row/col
umn:qualifier
GET Get the value of a single
column. Values are
Base-64 encoded.
curl -vi -X GET \
Ê -H "Accept: text/xml" \
Ê "http://example.com:8000/users/row1/cf:a"
curl -vi -X GET \
Ê -H "Accept: text/xml" \
Ê
"http://example.com:8000/users/row1/cf:a/"
376
Endpoint HTTP
Verb
Description Example
/table/row/col
umn:qualifier/
?v=number_of_v
ersions
GET Multi-Get a specified
number of versions of a
given cell. Values are
Base-64 encoded.
curl -vi -X GET \
Ê -H "Accept: text/xml" \
Ê
"http://example.com:8000/users/row1/cf:a?v=2
"
Table 17. Endpoints for Scan Operations
Endpoint HTTP
Verb
Description Example
/table/scanner
/
PUT Get a Scanner object.
Required by all other
Scan operations. Adjust
the batch parameter to
the number of rows the
scan should return in a
batch. See the next
example for adding
filters to your scanner.
The scanner endpoint
URL is returned as the
Location in the HTTP
response. The other
examples in this table
assume that the
scanner endpoint is
http://example.com:800
0/users/scanner/145869
072824375522207.
curl -vi -X PUT \
Ê -H "Accept: text/xml" \
Ê -H "Content-Type: text/xml" \
Ê -d '<Scanner batch="1"/>' \
Ê "http://example.com:8000/users/scanner/"
377
Endpoint HTTP
Verb
Description Example
/table/scanner
/
PUT To supply filters to the
Scanner object or
configure the Scanner
in any other way, you
can create a text file
and add your filter to
the file. For example, to
return only rows for
which keys start with
<codeph>u123</codeph
> and use a batch size
of 100, the filter file
would look like this:
[source,xml] ----
<Scanner batch="100">
<filter> { "type":
"PrefixFilter", "value":
"u123" } </filter>
</Scanner> ----
Pass the file to the -d
argument of the curl
request.
curl -vi -X PUT \
Ê -H "Accept: text/xml" \
Ê -H "Content-Type:text/xml" \
Ê -d @filter.txt \
Ê "http://example.com:8000/users/scanner/"
/table/scanner
/scanner-id
GET Get the next batch from
the scanner. Cell values
are byte-encoded. If the
scanner has been
exhausted, HTTP status
204 is returned.
curl -vi -X GET \
Ê -H "Accept: text/xml" \
Ê
"http://example.com:8000/users/scanner/14586
9072824375522207"
table/scanner/
scanner-id
DELETE Deletes the scanner and
frees the resources it
used.
curl -vi -X DELETE \
Ê -H "Accept: text/xml" \
Ê
"http://example.com:8000/users/scanner/14586
9072824375522207"
Table 18. Endpoints for Put Operations
378
Endpoint HTTP
Verb
Description Example
/table/row_key PUT Write a row to a table.
The row, column
qualifier, and value
must each be Base-64
encoded. To encode a
string, use the base64
command-line utility.
To decode the string,
use base64 -d. The
payload is in the --data
argument, and the
/users/fakerow value is
a placeholder. Insert
multiple rows by
adding them to the
<CellSet> element. You
can also save the data
to be inserted to a file
and pass it to the -d
parameter with syntax
like -d @filename.txt.
curl -vi -X PUT \
Ê -H "Accept: text/xml" \
Ê -H "Content-Type: text/xml" \
Ê -d '<?xml version="1.0" encoding="UTF-8"
standalone="yes"?><CellSet><Row
key="cm93NQo="><Cell
column="Y2Y6ZQo=">dmFsdWU1Cg==</Cell></Row><
/CellSet>' \
Ê "http://example.com:8000/users/fakerow"
curl -vi -X PUT \
Ê -H "Accept: text/json" \
Ê -H "Content-Type: text/json" \
Ê -d '{"Row":[{"key":"cm93NQo=", "Cell":
[{"column":"Y2Y6ZQo=",
"$":"dmFsdWU1Cg=="}]}]}'' \
Ê "example.com:8000/users/fakerow"
97.4. REST XML Schema
<schema xmlns="http://www.w3.org/2001/XMLSchema" xmlns:tns="RESTSchema">
Ê <element name="Version" type="tns:Version"></element>
Ê <complexType name="Version">
Ê <attribute name="REST" type="string"></attribute>
Ê <attribute name="JVM" type="string"></attribute>
Ê <attribute name="OS" type="string"></attribute>
Ê <attribute name="Server" type="string"></attribute>
Ê <attribute name="Jersey" type="string"></attribute>
Ê </complexType>
Ê <element name="TableList" type="tns:TableList"></element>
Ê <complexType name="TableList">
Ê <sequence>
Ê <element name="table" type="tns:Table" maxOccurs="unbounded" minOccurs="1"
></element>
Ê </sequence>
Ê </complexType>
Ê <complexType name="Table">
Ê <sequence>
Ê <element name="name" type="string"></element>
379
Ê </sequence>
Ê </complexType>
Ê <element name="TableInfo" type="tns:TableInfo"></element>
Ê <complexType name="TableInfo">
Ê <sequence>
Ê <element name="region" type="tns:TableRegion" maxOccurs="unbounded" minOccurs="
1"></element>
Ê </sequence>
Ê <attribute name="name" type="string"></attribute>
Ê </complexType>
Ê <complexType name="TableRegion">
Ê <attribute name="name" type="string"></attribute>
Ê <attribute name="id" type="int"></attribute>
Ê <attribute name="startKey" type="base64Binary"></attribute>
Ê <attribute name="endKey" type="base64Binary"></attribute>
Ê <attribute name="location" type="string"></attribute>
Ê </complexType>
Ê <element name="TableSchema" type="tns:TableSchema"></element>
Ê <complexType name="TableSchema">
Ê <sequence>
Ê <element name="column" type="tns:ColumnSchema" maxOccurs="unbounded" minOccurs=
"1"></element>
Ê </sequence>
Ê <attribute name="name" type="string"></attribute>
Ê <anyAttribute></anyAttribute>
Ê </complexType>
Ê <complexType name="ColumnSchema">
Ê <attribute name="name" type="string"></attribute>
Ê <anyAttribute></anyAttribute>
Ê </complexType>
Ê <element name="CellSet" type="tns:CellSet"></element>
Ê <complexType name="CellSet">
Ê <sequence>
Ê <element name="row" type="tns:Row" maxOccurs="unbounded" minOccurs="1"
></element>
Ê </sequence>
Ê </complexType>
Ê <element name="Row" type="tns:Row"></element>
Ê <complexType name="Row">
Ê <sequence>
Ê <element name="key" type="base64Binary"></element>
380
Ê <element name="cell" type="tns:Cell" maxOccurs="unbounded" minOccurs="1"
></element>
Ê </sequence>
Ê </complexType>
Ê <element name="Cell" type="tns:Cell"></element>
Ê <complexType name="Cell">
Ê <sequence>
Ê <element name="value" maxOccurs="1" minOccurs="1">
Ê <simpleType><restriction base="base64Binary">
Ê </simpleType>
Ê </element>
Ê </sequence>
Ê <attribute name="column" type="base64Binary" />
Ê <attribute name="timestamp" type="int" />
Ê </complexType>
Ê <element name="Scanner" type="tns:Scanner"></element>
Ê <complexType name="Scanner">
Ê <sequence>
Ê <element name="column" type="base64Binary" minOccurs="0" maxOccurs="unbounded"
></element>
Ê </sequence>
Ê <sequence>
Ê <element name="filter" type="string" minOccurs="0" maxOccurs="1"></element>
Ê </sequence>
Ê <attribute name="startRow" type="base64Binary"></attribute>
Ê <attribute name="endRow" type="base64Binary"></attribute>
Ê <attribute name="batch" type="int"></attribute>
Ê <attribute name="startTime" type="int"></attribute>
Ê <attribute name="endTime" type="int"></attribute>
Ê </complexType>
Ê <element name="StorageClusterVersion" type="tns:StorageClusterVersion" />
Ê <complexType name="StorageClusterVersion">
Ê <attribute name="version" type="string"></attribute>
Ê </complexType>
Ê <element name="StorageClusterStatus"
Ê type="tns:StorageClusterStatus">
Ê </element>
Ê <complexType name="StorageClusterStatus">
Ê <sequence>
Ê <element name="liveNode" type="tns:Node"
Ê maxOccurs="unbounded" minOccurs="0">
Ê </element>
Ê <element name="deadNode" type="string" maxOccurs="unbounded"
381
Ê minOccurs="0">
Ê </element>
Ê </sequence>
Ê <attribute name="regions" type="int"></attribute>
Ê <attribute name="requests" type="int"></attribute>
Ê <attribute name="averageLoad" type="float"></attribute>
Ê </complexType>
Ê <complexType name="Node">
Ê <sequence>
Ê <element name="region" type="tns:Region"
Ê maxOccurs="unbounded" minOccurs="0">
Ê </element>
Ê </sequence>
Ê <attribute name="name" type="string"></attribute>
Ê <attribute name="startCode" type="int"></attribute>
Ê <attribute name="requests" type="int"></attribute>
Ê <attribute name="heapSizeMB" type="int"></attribute>
Ê <attribute name="maxHeapSizeMB" type="int"></attribute>
Ê </complexType>
Ê <complexType name="Region">
Ê <attribute name="name" type="base64Binary"></attribute>
Ê <attribute name="stores" type="int"></attribute>
Ê <attribute name="storefiles" type="int"></attribute>
Ê <attribute name="storefileSizeMB" type="int"></attribute>
Ê <attribute name="memstoreSizeMB" type="int"></attribute>
Ê <attribute name="storefileIndexSizeMB" type="int"></attribute>
Ê </complexType>
</schema>
97.5. REST Protobufs Schema
message Version {
Ê optional string restVersion = 1;
Ê optional string jvmVersion = 2;
Ê optional string osVersion = 3;
Ê optional string serverVersion = 4;
Ê optional string jerseyVersion = 5;
}
message StorageClusterStatus {
Ê message Region {
Ê required bytes name = 1;
Ê optional int32 stores = 2;
Ê optional int32 storefiles = 3;
Ê optional int32 storefileSizeMB = 4;
Ê optional int32 memstoreSizeMB = 5;
382
Ê optional int32 storefileIndexSizeMB = 6;
Ê }
Ê message Node {
Ê required string name = 1; // name:port
Ê optional int64 startCode = 2;
Ê optional int32 requests = 3;
Ê optional int32 heapSizeMB = 4;
Ê optional int32 maxHeapSizeMB = 5;
Ê repeated Region regions = 6;
Ê }
Ê // node status
Ê repeated Node liveNodes = 1;
Ê repeated string deadNodes = 2;
Ê // summary statistics
Ê optional int32 regions = 3;
Ê optional int32 requests = 4;
Ê optional double averageLoad = 5;
}
message TableList {
Ê repeated string name = 1;
}
message TableInfo {
Ê required string name = 1;
Ê message Region {
Ê required string name = 1;
Ê optional bytes startKey = 2;
Ê optional bytes endKey = 3;
Ê optional int64 id = 4;
Ê optional string location = 5;
Ê }
Ê repeated Region regions = 2;
}
message TableSchema {
Ê optional string name = 1;
Ê message Attribute {
Ê required string name = 1;
Ê required string value = 2;
Ê }
Ê repeated Attribute attrs = 2;
Ê repeated ColumnSchema columns = 3;
Ê // optional helpful encodings of commonly used attributes
Ê optional bool inMemory = 4;
Ê optional bool readOnly = 5;
}
message ColumnSchema {
Ê optional string name = 1;
Ê message Attribute {
383
Ê required string name = 1;
Ê required string value = 2;
Ê }
Ê repeated Attribute attrs = 2;
Ê // optional helpful encodings of commonly used attributes
Ê optional int32 ttl = 3;
Ê optional int32 maxVersions = 4;
Ê optional string compression = 5;
}
message Cell {
Ê optional bytes row = 1; // unused if Cell is in a CellSet
Ê optional bytes column = 2;
Ê optional int64 timestamp = 3;
Ê optional bytes data = 4;
}
message CellSet {
Ê message Row {
Ê required bytes key = 1;
Ê repeated Cell values = 2;
Ê }
Ê repeated Row rows = 1;
}
message Scanner {
Ê optional bytes startRow = 1;
Ê optional bytes endRow = 2;
Ê repeated bytes columns = 3;
Ê optional int32 batch = 4;
Ê optional int64 startTime = 5;
Ê optional int64 endTime = 6;
}
384
Chapter 98. Thrift
Documentation about Thrift has moved to Thrift.
385
Chapter 99. C/C++ Apache HBase Client
FB’s Chip Turner wrote a pure C/C++ client. Check it out.
C++ client implementation. To see HBASE-14850.
386
Chapter 100. Using Java Data Objects (JDO)
with HBase
Java Data Objects (JDO) is a standard way to access persistent data in databases, using plain old Java
objects (POJO) to represent persistent data.
Dependencies
This code example has the following dependencies:
1. HBase 0.90.x or newer
2. commons-beanutils.jar (https://commons.apache.org/)
3. commons-pool-1.5.5.jar (https://commons.apache.org/)
4. transactional-tableindexed for HBase 0.90 (https://github.com/hbase-trx/hbase-transactional-
tableindexed)
Download hbase-jdo
Download the code from http://code.google.com/p/hbase-jdo/.
Example 26. JDO Example
This example uses JDO to create a table and an index, insert a row into a table, get a row, get a
column value, perform a query, and do some additional HBase operations.
package com.apache.hadoop.hbase.client.jdo.examples;
import java.io.File;
import java.io.FileInputStream;
import java.io.InputStream;
import java.util.Hashtable;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.hbase.client.tableindexed.IndexedTable;
import com.apache.hadoop.hbase.client.jdo.AbstractHBaseDBO;
import com.apache.hadoop.hbase.client.jdo.HBaseBigFile;
import com.apache.hadoop.hbase.client.jdo.HBaseDBOImpl;
import com.apache.hadoop.hbase.client.jdo.query.DeleteQuery;
import com.apache.hadoop.hbase.client.jdo.query.HBaseOrder;
import com.apache.hadoop.hbase.client.jdo.query.HBaseParam;
import com.apache.hadoop.hbase.client.jdo.query.InsertQuery;
import com.apache.hadoop.hbase.client.jdo.query.QSearch;
import com.apache.hadoop.hbase.client.jdo.query.SelectQuery;
import com.apache.hadoop.hbase.client.jdo.query.UpdateQuery;
/**
Ê* Hbase JDO Example.
Ê*
387
Ê* dependency library.
Ê* - commons-beanutils.jar
Ê* - commons-pool-1.5.5.jar
Ê* - hbase0.90.0-transactionl.jar
Ê*
Ê* you can expand Delete,Select,Update,Insert Query classes.
Ê*
Ê*/
public class HBaseExample {
Ê public static void main(String[] args) throws Exception {
Ê AbstractHBaseDBO dbo = new HBaseDBOImpl();
Ê //*drop if table is already exist.*
Ê if(dbo.isTableExist("user")){
Ê dbo.deleteTable("user");
Ê }
Ê //*create table*
Ê dbo.createTableIfNotExist("user",HBaseOrder.DESC,"account");
Ê //dbo.createTableIfNotExist("user",HBaseOrder.ASC,"account");
Ê //create index.
Ê String[] cols={"id","name"};
Ê dbo.addIndexExistingTable("user","account",cols);
Ê //insert
Ê InsertQuery insert = dbo.createInsertQuery("user");
Ê UserBean bean = new UserBean();
Ê bean.setFamily("account");
Ê bean.setAge(20);
Ê bean.setEmail("ncanis@gmail.com");
Ê bean.setId("ncanis");
Ê bean.setName("ncanis");
Ê bean.setPassword("1111");
Ê insert.insert(bean);
Ê //select 1 row
Ê SelectQuery select = dbo.createSelectQuery("user");
Ê UserBean resultBean = (UserBean)select.select(bean.getRow(),UserBean.class);
Ê // select column value.
Ê String value = (String)select.selectColumn(bean.getRow(),"account","id",
String.class);
Ê // search with option (QSearch has EQUAL, NOT_EQUAL, LIKE)
Ê // select id,password,name,email from account where id='ncanis' limit
startRow,20
Ê HBaseParam param = new HBaseParam();
Ê param.setPage(bean.getRow(),20);
Ê param.addColumn("id","password","name","email");
Ê param.addSearchOption("id","ncanis",QSearch.EQUAL);
388
Ê select.search("account", param, UserBean.class);
Ê // search column value is existing.
Ê boolean isExist = select.existColumnValue("account","id","ncanis".getBytes());
Ê // update password.
Ê UpdateQuery update = dbo.createUpdateQuery("user");
Ê Hashtable<String, byte[]> colsTable = new Hashtable<String, byte[]>();
Ê colsTable.put("password","2222".getBytes());
Ê update.update(bean.getRow(),"account",colsTable);
Ê //delete
Ê DeleteQuery delete = dbo.createDeleteQuery("user");
Ê delete.deleteRow(resultBean.getRow());
Ê ////////////////////////////////////
Ê // etc
Ê // HTable pool with apache commons pool
Ê // borrow and release. HBasePoolManager(maxActive, minIdle etc..)
Ê IndexedTable table = dbo.getPool().borrow("user");
Ê dbo.getPool().release(table);
Ê // upload bigFile by hadoop directly.
Ê HBaseBigFile bigFile = new HBaseBigFile();
Ê File file = new File("doc/movie.avi");
Ê FileInputStream fis = new FileInputStream(file);
Ê Path rootPath = new Path("/files/");
Ê String filename = "movie.avi";
Ê bigFile.uploadFile(rootPath,filename,fis,true);
Ê // receive file stream from hadoop.
Ê Path p = new Path(rootPath,filename);
Ê InputStream is = bigFile.path2Stream(p,4096);
Ê }
}
389
Chapter 101. Scala
101.1. Setting the Classpath
To use Scala with HBase, your CLASSPATH must include HBase’s classpath as well as the Scala JARs
required by your code. First, use the following command on a server running the HBase
RegionServer process, to get HBase’s classpath.
$ ps aux |grep regionserver| awk -F 'java.library.path=' {'print $2'} | awk {'print
$1'}
/usr/lib/hadoop/lib/native:/usr/lib/hbase/lib/native/Linux-amd64-64
Set the $CLASSPATH environment variable to include the path you found in the previous step, plus the
path of scala-library.jar and each additional Scala-related JAR needed for your project.
$ export
CLASSPATH=$CLASSPATH:/usr/lib/hadoop/lib/native:/usr/lib/hbase/lib/native/Linux-amd64-
64:/path/to/scala-library.jar
101.2. Scala SBT File
Your build.sbt file needs the following resolvers and libraryDependencies to work with HBase.
resolvers += "Apache HBase" at
"https://repository.apache.org/content/repositories/releases"
resolvers += "Thrift" at "https://people.apache.org/~rawson/repo/"
libraryDependencies ++= Seq(
Ê "org.apache.hadoop" % "hadoop-core" % "0.20.2",
Ê "org.apache.hbase" % "hbase" % "0.90.4"
)
101.3. Example Scala Code
This example lists HBase tables, creates a new table, and adds a row to it.
390
import org.apache.hadoop.hbase.HBaseConfiguration
import
org.apache.hadoop.hbase.client.{Connection,ConnectionFactory,HBaseAdmin,HTable,Put,Get
}
import org.apache.hadoop.hbase.util.Bytes
val conf = new HBaseConfiguration()
val connection = ConnectionFactory.createConnection(conf);
val admin = connection.getAdmin();
// list the tables
val listtables=admin.listTables()
listtables.foreach(println)
// let's insert some data in 'mytable' and get the row
val table = new HTable(conf, "mytable")
val theput= new Put(Bytes.toBytes("rowkey1"))
theput.add(Bytes.toBytes("ids"),Bytes.toBytes("id1"),Bytes.toBytes("one"))
table.put(theput)
val theget= new Get(Bytes.toBytes("rowkey1"))
val result=table.get(theget)
val value=result.value()
println(Bytes.toString(value))
391
Chapter 102. Jython
102.1. Setting the Classpath
To use Jython with HBase, your CLASSPATH must include HBase’s classpath as well as the Jython
JARs required by your code.
Set the path to directory containing the jython.jar and each additional Jython-related JAR needed
for your project. Then export HBASE_CLASSPATH pointing to the $JYTHON_HOME env. variable.
$ export HBASE_CLASSPATH=/directory/jython.jar
Start a Jython shell with HBase and Hadoop JARs in the classpath: $ bin/hbase
org.python.util.jython
102.2. Jython Code Examples
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Example 27. Table Creation, Population, Get, and Delete with Jython
The following Jython code example checks for table, if it exists, deletes it and then creates it.
Then it populates the table with data and fetches the data.
import java.lang
from org.apache.hadoop.hbase import HBaseConfiguration, HTableDescriptor,
HColumnDescriptor, TableName
from org.apache.hadoop.hbase.client import Admin, Connection, ConnectionFactory,
Get, Put, Result, Table
from org.apache.hadoop.conf import Configuration
# First get a conf object. This will read in the configuration
# that is out in your hbase-*.xml files such as location of the
# hbase master node.
conf = HBaseConfiguration.create()
connection = ConnectionFactory.createConnection(conf)
admin = connection.getAdmin()
# Create a table named 'test' that has a column family
# named 'content'.
tableName = TableName.valueOf("test")
table = connection.getTable(tableName)
desc = HTableDescriptor(tableName)
desc.addFamily(HColumnDescriptor("content"))
# Drop and recreate if it exists
if admin.tableExists(tableName):
Ê admin.disableTable(tableName)
Ê admin.deleteTable(tableName)
admin.createTable(desc)
# Add content to 'column:' on a row named 'row_x'
row = 'row_x'
put = Put(row)
put.addColumn("content", "qual", "some content")
table.put(put)
# Now fetch the content just added, returns a byte[]
get = Get(row)
result = table.get(get)
data = java.lang.String(result.getValue("content", "qual"), "UTF8")
print "The fetched row contains the value '%s'" % data
393
Example 28. Table Scan Using Jython
This example scans a table and returns the results that match a given family qualifier.
import java.lang
from org.apache.hadoop.hbase import TableName, HBaseConfiguration
from org.apache.hadoop.hbase.client import Connection, ConnectionFactory, Result,
ResultScanner, Table, Admin
from org.apache.hadoop.conf import Configuration
conf = HBaseConfiguration.create()
connection = ConnectionFactory.createConnection(conf)
admin = connection.getAdmin()
tableName = TableName.valueOf('wiki')
table = connection.getTable(tableName)
cf = "title"
attr = "attr"
scanner = table.getScanner(cf)
while 1:
Ê result = scanner.next()
Ê if not result:
Ê break
Ê print java.lang.String(result.row), java.lang.String(result.getValue(cf,
attr))
394
Thrift API and Filter Language
Apache Thrift is a cross-platform, cross-language development framework. HBase includes a Thrift
API and filter language. The Thrift API relies on client and server processes.
You can configure Thrift for secure authentication at the server and client side, by following the
procedures in Client-side Configuration for Secure Operation - Thrift Gateway and Configure the
Thrift Gateway to Authenticate on Behalf of the Client.
The rest of this chapter discusses the filter language provided by the Thrift API.
395
Chapter 103. Filter Language
Thrift Filter Language was introduced in HBase 0.92. It allows you to perform server-side filtering
when accessing HBase over Thrift or in the HBase shell. You can find out more about shell
integration by using the scan help command in the shell.
You specify a filter as a string, which is parsed on the server to construct the filter.
103.1. General Filter String Syntax
A simple filter expression is expressed as a string:
“FilterName (argument, argument,... , argument)”
Keep the following syntax guidelines in mind.
•Specify the name of the filter followed by the comma-separated argument list in parentheses.
•If the argument represents a string, it should be enclosed in single quotes (').
•Arguments which represent a boolean, an integer, or a comparison operator (such as <, >, or !=),
should not be enclosed in quotes
•The filter name must be a single word. All ASCII characters are allowed except for whitespace,
single quotes and parentheses.
•The filter’s arguments can contain any ASCII character. If single quotes are present in the
argument, they must be escaped by an additional preceding single quote.
103.2. Compound Filters and Operators
Binary Operators
AND
If the AND operator is used, the key-value must satisfy both filters.
OR
If the OR operator is used, the key-value must satisfy at least one of the filters.
Unary Operators
SKIP
For a particular row, if any of the key-values fail the filter condition, the entire row is skipped.
WHILE
For a particular row, key-values will be emitted until a key-value is reached that fails the filter
condition.
396
Example 29. Compound Operators
You can combine multiple operators to create a hierarchy of filters, such as the following
example:
(Filter1 AND Filter2) OR (Filter3 AND Filter4)
103.3. Order of Evaluation
1. Parentheses have the highest precedence.
2. The unary operators SKIP and WHILE are next, and have the same precedence.
3. The binary operators follow. AND has highest precedence, followed by OR.
Example 30. Precedence Example
Filter1 AND Filter2 OR Filter
is evaluated as
(Filter1 AND Filter2) OR Filter3
Filter1 AND SKIP Filter2 OR Filter3
is evaluated as
(Filter1 AND (SKIP Filter2)) OR Filter3
You can use parentheses to explicitly control the order of evaluation.
103.4. Compare Operator
The following compare operators are provided:
1. LESS (<)
2. LESS_OR_EQUAL (⇐)
3. EQUAL (=)
4. NOT_EQUAL (!=)
5. GREATER_OR_EQUAL (>=)
6. GREATER (>)
7. NO_OP (no operation)
The client should use the symbols (<, ⇐, =, !=, >, >=) to express compare operators.
397
103.5. Comparator
A comparator can be any of the following:
1. BinaryComparator - This lexicographically compares against the specified byte array using
Bytes.compareTo(byte[], byte[])
2. BinaryPrefixComparator - This lexicographically compares against a specified byte array. It only
compares up to the length of this byte array.
3. RegexStringComparator - This compares against the specified byte array using the given regular
expression. Only EQUAL and NOT_EQUAL comparisons are valid with this comparator
4. SubStringComparator - This tests if the given substring appears in a specified byte array. The
comparison is case insensitive. Only EQUAL and NOT_EQUAL comparisons are valid with this
comparator
The general syntax of a comparator is: ComparatorType:ComparatorValue
The ComparatorType for the various comparators is as follows:
1. BinaryComparator - binary
2. BinaryPrefixComparator - binaryprefix
3. RegexStringComparator - regexstring
4. SubStringComparator - substring
The ComparatorValue can be any value.
Example ComparatorValues
1. binary:abc will match everything that is lexicographically greater than "abc"
2. binaryprefix:abc will match everything whose first 3 characters are lexicographically equal to
"abc"
3. regexstring:ab*yz will match everything that doesn’t begin with "ab" and ends with "yz"
4. substring:abc123 will match everything that begins with the substring "abc123"
103.6. Example PHP Client Program that uses the Filter
Language
398
<?
Ê $_SERVER['PHP_ROOT'] = realpath(dirname(__FILE__).'/..');
Ê require_once $_SERVER['PHP_ROOT'].'/flib/__flib.php';
Ê flib_init(FLIB_CONTEXT_SCRIPT);
Ê require_module('storage/hbase');
Ê $hbase = new HBase('<server_name_running_thrift_server>', <port on which thrift
server is running>);
Ê $hbase->open();
Ê $client = $hbase->getClient();
Ê $result = $client->scannerOpenWithFilterString('table_name', "(PrefixFilter ('row2')
AND (QualifierFilter (>=, 'binary:xyz'))) AND (TimestampsFilter ( 123, 456))");
Ê $to_print = $client->scannerGetList($result,1);
Ê while ($to_print) {
Ê print_r($to_print);
Ê $to_print = $client->scannerGetList($result,1);
Ê }
Ê $client->scannerClose($result);
?>
103.7. Example Filter Strings
•"PrefixFilter ('Row') AND PageFilter (1) AND FirstKeyOnlyFilter ()" will return all key-value
pairs that match the following conditions:
1. The row containing the key-value should have prefix Row
2. The key-value must be located in the first row of the table
3. The key-value pair must be the first key-value in the row
•"(RowFilter (=, 'binary:Row 1') AND TimeStampsFilter (74689, 89734)) OR ColumnRangeFilter
('abc', true, 'xyz', false))" will return all key-value pairs that match both the following
conditions:
◦The key-value is in a row having row key Row 1
◦The key-value must have a timestamp of either 74689 or 89734.
◦Or it must match the following condition:
▪The key-value pair must be in a column that is lexicographically >= abc and < xyzÊ
•"SKIP ValueFilter (0)" will skip the entire row if any of the values in the row is not 0
103.8. Individual Filter Syntax
KeyOnlyFilter
This filter doesn’t take any arguments. It returns only the key component of each key-value.
FirstKeyOnlyFilter
This filter doesn’t take any arguments. It returns only the first key-value from each row.
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PrefixFilter
This filter takes one argument – a prefix of a row key. It returns only those key-values present in
a row that starts with the specified row prefix
ColumnPrefixFilter
This filter takes one argument – a column prefix. It returns only those key-values present in a
column that starts with the specified column prefix. The column prefix must be of the form:
“qualifier”.
MultipleColumnPrefixFilter
This filter takes a list of column prefixes. It returns key-values that are present in a column that
starts with any of the specified column prefixes. Each of the column prefixes must be of the
form: “qualifier”.
ColumnCountGetFilter
This filter takes one argument – a limit. It returns the first limit number of columns in the table.
PageFilter
This filter takes one argument – a page size. It returns page size number of rows from the table.
ColumnPaginationFilter
This filter takes two arguments – a limit and offset. It returns limit number of columns after
offset number of columns. It does this for all the rows.
InclusiveStopFilter
This filter takes one argument – a row key on which to stop scanning. It returns all key-values
present in rows up to and including the specified row.
TimeStampsFilter
This filter takes a list of timestamps. It returns those key-values whose timestamps matches any
of the specified timestamps.
RowFilter
This filter takes a compare operator and a comparator. It compares each row key with the
comparator using the compare operator and if the comparison returns true, it returns all the
key-values in that row.
Family Filter
This filter takes a compare operator and a comparator. It compares each column family name
with the comparator using the compare operator and if the comparison returns true, it returns
all the Cells in that column family.
QualifierFilter
This filter takes a compare operator and a comparator. It compares each qualifier name with the
comparator using the compare operator and if the comparison returns true, it returns all the
key-values in that column.
ValueFilter
400
This filter takes a compare operator and a comparator. It compares each value with the
comparator using the compare operator and if the comparison returns true, it returns that key-
value.
DependentColumnFilter
This filter takes two arguments – a family and a qualifier. It tries to locate this column in each
row and returns all key-values in that row that have the same timestamp. If the row doesn’t
contain the specified column – none of the key-values in that row will be returned.
SingleColumnValueFilter
This filter takes a column family, a qualifier, a compare operator and a comparator. If the
specified column is not found – all the columns of that row will be emitted. If the column is
found and the comparison with the comparator returns true, all the columns of the row will be
emitted. If the condition fails, the row will not be emitted.
SingleColumnValueExcludeFilter
This filter takes the same arguments and behaves same as SingleColumnValueFilter – however, if
the column is found and the condition passes, all the columns of the row will be emitted except
for the tested column value.
ColumnRangeFilter
This filter is used for selecting only those keys with columns that are between minColumn and
maxColumn. It also takes two boolean variables to indicate whether to include the minColumn
and maxColumn or not.
401
HBase and Spark
Apache Spark is a software framework that is used to process data in memory in a distributed
manner, and is replacing MapReduce in many use cases.
Spark itself is out of scope of this document, please refer to the Spark site for more information on
the Spark project and subprojects. This document will focus on 4 main interaction points between
Spark and HBase. Those interaction points are:
Basic Spark
The ability to have an HBase Connection at any point in your Spark DAG.
Spark Streaming
The ability to have an HBase Connection at any point in your Spark Streaming application.
Spark Bulk Load
The ability to write directly to HBase HFiles for bulk insertion into HBase
SparkSQL/DataFrames
The ability to write SparkSQL that draws on tables that are represented in HBase.
The following sections will walk through examples of all these interaction points.
402
Chapter 104. Basic Spark
This section discusses Spark HBase integration at the lowest and simplest levels. All the other
interaction points are built upon the concepts that will be described here.
At the root of all Spark and HBase integration is the HBaseContext. The HBaseContext takes in
HBase configurations and pushes them to the Spark executors. This allows us to have an HBase
Connection per Spark Executor in a static location.
For reference, Spark Executors can be on the same nodes as the Region Servers or on different
nodes there is no dependence of co-location. Think of every Spark Executor as a multi-threaded
client application. This allows any Spark Tasks running on the executors to access the shared
Connection object.
Example 31. HBaseContext Usage Example
This example shows how HBaseContext can be used to do a foreachPartition on a RDD in
Scala:
val sc = new SparkContext("local", "test")
val config = new HBaseConfiguration()
...
val hbaseContext = new HBaseContext(sc, config)
rdd.hbaseForeachPartition(hbaseContext, (it, conn) => {
Êval bufferedMutator = conn.getBufferedMutator(TableName.valueOf("t1"))
Êit.foreach((putRecord) => {
. val put = new Put(putRecord._1)
. putRecord._2.foreach((putValue) => put.addColumn(putValue._1, putValue._2,
putValue._3))
. bufferedMutator.mutate(put)
Ê})
ÊbufferedMutator.flush()
ÊbufferedMutator.close()
})
Here is the same example implemented in Java:
403
JavaSparkContext jsc = new JavaSparkContext(sparkConf);
try {
Ê List<byte[]> list = new ArrayList<>();
Ê list.add(Bytes.toBytes("1"));
Ê ...
Ê list.add(Bytes.toBytes("5"));
Ê JavaRDD<byte[]> rdd = jsc.parallelize(list);
Ê Configuration conf = HBaseConfiguration.create();
Ê JavaHBaseContext hbaseContext = new JavaHBaseContext(jsc, conf);
Ê hbaseContext.foreachPartition(rdd,
Ê new VoidFunction<Tuple2<Iterator<byte[]>, Connection>>() {
Ê public void call(Tuple2<Iterator<byte[]>, Connection> t)
Ê throws Exception {
Ê Table table = t._2().getTable(TableName.valueOf(tableName));
Ê BufferedMutator mutator = t._2().getBufferedMutator(TableName.valueOf
(tableName));
Ê while (t._1().hasNext()) {
Ê byte[] b = t._1().next();
Ê Result r = table.get(new Get(b));
Ê if (r.getExists()) {
Ê mutator.mutate(new Put(b));
Ê }
Ê }
Ê mutator.flush();
Ê mutator.close();
Ê table.close();
Ê }
Ê });
} finally {
Ê jsc.stop();
}
All functionality between Spark and HBase will be supported both in Scala and in Java, with the
exception of SparkSQL which will support any language that is supported by Spark. For the
remaining of this documentation we will focus on Scala examples for now.
The examples above illustrate how to do a foreachPartition with a connection. A number of other
Spark base functions are supported out of the box:
bulkPut
For massively parallel sending of puts to HBase
bulkDelete
For massively parallel sending of deletes to HBase
404
bulkGet
For massively parallel sending of gets to HBase to create a new RDD
mapPartition
To do a Spark Map function with a Connection object to allow full access to HBase
hBaseRDD
To simplify a distributed scan to create a RDD
For examples of all these functionalities, see the HBase-Spark Module.
405
Chapter 105. Spark Streaming
Spark Streaming is a micro batching stream processing framework built on top of Spark. HBase and
Spark Streaming make great companions in that HBase can help serve the following benefits
alongside Spark Streaming.
•A place to grab reference data or profile data on the fly
•A place to store counts or aggregates in a way that supports Spark Streaming promise of only
once processing.
The HBase-Spark module’s integration points with Spark Streaming are similar to its normal Spark
integration points, in that the following commands are possible straight off a Spark Streaming
DStream.
bulkPut
For massively parallel sending of puts to HBase
bulkDelete
For massively parallel sending of deletes to HBase
bulkGet
For massively parallel sending of gets to HBase to create a new RDD
mapPartition
To do a Spark Map function with a Connection object to allow full access to HBase
hBaseRDD
To simplify a distributed scan to create a RDD
406
Example 32. bulkPut Example with DStreams
Below is an example of bulkPut with DStreams. It is very close in feel to the RDD bulk put.
val sc = new SparkContext("local", "test")
val config = new HBaseConfiguration()
val hbaseContext = new HBaseContext(sc, config)
val ssc = new StreamingContext(sc, Milliseconds(200))
val rdd1 = ...
val rdd2 = ...
val queue = mutable.Queue[RDD[(Array[Byte], Array[(Array[Byte],
Ê Array[Byte], Array[Byte])])]]()
queue += rdd1
queue += rdd2
val dStream = ssc.queueStream(queue)
dStream.hbaseBulkPut(
Ê hbaseContext,
Ê TableName.valueOf(tableName),
Ê (putRecord) => {
Ê val put = new Put(putRecord._1)
Ê putRecord._2.foreach((putValue) => put.addColumn(putValue._1, putValue._2,
putValue._3))
Ê put
Ê })
There are three inputs to the hbaseBulkPut function. . The hbaseContext that carries the
configuration boardcast information link us to the HBase Connections in the executors . The
table name of the table we are putting data into . A function that will convert a record in the
DStream into an HBase Put object.
407
Chapter 106. Bulk Load
There are two options for bulk loading data into HBase with Spark. There is the basic bulk load
functionality that will work for cases where your rows have millions of columns and cases where
your columns are not consolidated and partitions before the on the map side of the Spark bulk load
process.
There is also a thin record bulk load option with Spark, this second option is designed for tables
that have less then 10k columns per row. The advantage of this second option is higher throughput
and less over all load on the Spark shuffle operation.
Both implementations work more or less like the MapReduce bulk load process in that a partitioner
partitions the rowkeys based on region splits and the row keys are sent to the reducers in order, so
that HFiles can be written out directly from the reduce phase.
In Spark terms, the bulk load will be implemented around a the Spark
repartitionAndSortWithinPartitions followed by a Spark foreachPartition.
First lets look at an example of using the basic bulk load functionality
408
Example 33. Bulk Loading Example
The following example shows bulk loading with Spark.
val sc = new SparkContext("local", "test")
val config = new HBaseConfiguration()
val hbaseContext = new HBaseContext(sc, config)
val stagingFolder = ...
val rdd = sc.parallelize(Array(
Ê (Bytes.toBytes("1"),
Ê (Bytes.toBytes(columnFamily1), Bytes.toBytes("a"),
Bytes.toBytes("foo1"))),
Ê (Bytes.toBytes("3"),
Ê (Bytes.toBytes(columnFamily1), Bytes.toBytes("b"),
Bytes.toBytes("foo2.b"))), ...
rdd.hbaseBulkLoad(TableName.valueOf(tableName),
Ê t => {
Ê val rowKey = t._1
Ê val family:Array[Byte] = t._2(0)._1
Ê val qualifier = t._2(0)._2
Ê val value = t._2(0)._3
Ê val keyFamilyQualifier= new KeyFamilyQualifier(rowKey, family, qualifier)
Ê Seq((keyFamilyQualifier, value)).iterator
Ê },
Ê stagingFolder.getPath)
val load = new LoadIncrementalHFiles(config)
load.doBulkLoad(new Path(stagingFolder.getPath),
Ê conn.getAdmin, table, conn.getRegionLocator(TableName.valueOf(tableName)))
The hbaseBulkLoad function takes three required parameters:
1. The table name of the table we intend to bulk load too
2. A function that will convert a record in the RDD to a tuple key value par. With the tuple key
being a KeyFamilyQualifer object and the value being the cell value. The KeyFamilyQualifer
object will hold the RowKey, Column Family, and Column Qualifier. The shuffle will partition on
the RowKey but will sort by all three values.
3. The temporary path for the HFile to be written out too
Following the Spark bulk load command, use the HBase’s LoadIncrementalHFiles object to load the
newly created HFiles into HBase.
Additional Parameters for Bulk Loading with Spark
409
You can set the following attributes with additional parameter options on hbaseBulkLoad.
•Max file size of the HFiles
•A flag to exclude HFiles from compactions
•Column Family settings for compression, bloomType, blockSize, and dataBlockEncoding
Example 34. Using Additional Parameters
val sc = new SparkContext("local", "test")
val config = new HBaseConfiguration()
val hbaseContext = new HBaseContext(sc, config)
val stagingFolder = ...
val rdd = sc.parallelize(Array(
Ê (Bytes.toBytes("1"),
Ê (Bytes.toBytes(columnFamily1), Bytes.toBytes("a"),
Bytes.toBytes("foo1"))),
Ê (Bytes.toBytes("3"),
Ê (Bytes.toBytes(columnFamily1), Bytes.toBytes("b"),
Bytes.toBytes("foo2.b"))), ...
val familyHBaseWriterOptions = new java.util.HashMap[Array[Byte],
FamilyHFileWriteOptions]
val f1Options = new FamilyHFileWriteOptions("GZ", "ROW", 128, "PREFIX")
familyHBaseWriterOptions.put(Bytes.toBytes("columnFamily1"), f1Options)
rdd.hbaseBulkLoad(TableName.valueOf(tableName),
Ê t => {
Ê val rowKey = t._1
Ê val family:Array[Byte] = t._2(0)._1
Ê val qualifier = t._2(0)._2
Ê val value = t._2(0)._3
Ê val keyFamilyQualifier= new KeyFamilyQualifier(rowKey, family, qualifier)
Ê Seq((keyFamilyQualifier, value)).iterator
Ê },
Ê stagingFolder.getPath,
Ê familyHBaseWriterOptions,
Ê compactionExclude = false,
Ê HConstants.DEFAULT_MAX_FILE_SIZE)
val load = new LoadIncrementalHFiles(config)
load.doBulkLoad(new Path(stagingFolder.getPath),
Ê conn.getAdmin, table, conn.getRegionLocator(TableName.valueOf(tableName)))
410
Now lets look at how you would call the thin record bulk load implementation
Example 35. Using thin record bulk load
val sc = new SparkContext("local", "test")
val config = new HBaseConfiguration()
val hbaseContext = new HBaseContext(sc, config)
val stagingFolder = ...
val rdd = sc.parallelize(Array(
Ê ("1",
Ê (Bytes.toBytes(columnFamily1), Bytes.toBytes("a"),
Bytes.toBytes("foo1"))),
Ê ("3",
Ê (Bytes.toBytes(columnFamily1), Bytes.toBytes("b"),
Bytes.toBytes("foo2.b"))), ...
rdd.hbaseBulkLoadThinRows(hbaseContext,
Ê TableName.valueOf(tableName),
Ê t => {
Ê val rowKey = t._1
Ê val familyQualifiersValues = new FamiliesQualifiersValues
Ê t._2.foreach(f => {
Ê val family:Array[Byte] = f._1
Ê val qualifier = f._2
Ê val value:Array[Byte] = f._3
Ê familyQualifiersValues +=(family, qualifier, value)
Ê })
Ê (new ByteArrayWrapper(Bytes.toBytes(rowKey)), familyQualifiersValues)
Ê },
Ê stagingFolder.getPath,
Ê new java.util.HashMap[Array[Byte], FamilyHFileWriteOptions],
Ê compactionExclude = false,
Ê 20)
val load = new LoadIncrementalHFiles(config)
load.doBulkLoad(new Path(stagingFolder.getPath),
Ê conn.getAdmin, table, conn.getRegionLocator(TableName.valueOf(tableName)))
Note that the big difference in using bulk load for thin rows is the function returns a tuple with the
first value being the row key and the second value being an object of FamiliesQualifiersValues,
which will contain all the values for this row for all column families.
411
Chapter 107. SparkSQL/DataFrames
HBase-Spark Connector (in HBase-Spark Module) leverages DataSource API (SPARK-3247)
introduced in Spark-1.2.0, bridges the gap between simple HBase KV store and complex relational
SQL queries and enables users to perform complex data analytical work on top of HBase using
Spark. HBase Dataframe is a standard Spark Dataframe, and is able to interact with any other data
sources such as Hive, Orc, Parquet, JSON, etc. HBase-Spark Connector applies critical techniques
such as partition pruning, column pruning, predicate pushdown and data locality.
To use HBase-Spark connector, users need to define the Catalog for the schema mapping between
HBase and Spark tables, prepare the data and populate the HBase table, then load HBase
DataFrame. After that, users can do integrated query and access records in HBase table with SQL
query. Following illustrates the basic procedure.
107.1. Define catalog
def catalog = s"""{
ÊÊÊÊÊÊÊ|"table":{"namespace":"default", "name":"table1"},
ÊÊÊÊÊÊÊ|"rowkey":"key",
ÊÊÊÊÊÊÊ|"columns":{
ÊÊÊÊÊÊÊÊÊ|"col0":{"cf":"rowkey", "col":"key", "type":"string"},
ÊÊÊÊÊÊÊÊÊ|"col1":{"cf":"cf1", "col":"col1", "type":"boolean"},
ÊÊÊÊÊÊÊÊÊ|"col2":{"cf":"cf2", "col":"col2", "type":"double"},
ÊÊÊÊÊÊÊÊÊ|"col3":{"cf":"cf3", "col":"col3", "type":"float"},
ÊÊÊÊÊÊÊÊÊ|"col4":{"cf":"cf4", "col":"col4", "type":"int"},
ÊÊÊÊÊÊÊÊÊ|"col5":{"cf":"cf5", "col":"col5", "type":"bigint"},
ÊÊÊÊÊÊÊÊÊ|"col6":{"cf":"cf6", "col":"col6", "type":"smallint"},
ÊÊÊÊÊÊÊÊÊ|"col7":{"cf":"cf7", "col":"col7", "type":"string"},
ÊÊÊÊÊÊÊÊÊ|"col8":{"cf":"cf8", "col":"col8", "type":"tinyint"}
ÊÊÊÊÊÊÊ|}
ÊÊÊÊÊ|}""".stripMargin
Catalog defines a mapping between HBase and Spark tables. There are two critical parts of this
catalog. One is the rowkey definition and the other is the mapping between table column in Spark
and the column family and column qualifier in HBase. The above defines a schema for a HBase
table with name as table1, row key as key and a number of columns (col1 - col8). Note that the
rowkey also has to be defined in details as a column (col0), which has a specific cf (rowkey).
107.2. Save the DataFrame
412
case class HBaseRecord(
Ê col0: String,
Ê col1: Boolean,
Ê col2: Double,
Ê col3: Float,
Ê col4: Int, ÊÊÊÊÊÊ
Ê col5: Long,
Ê col6: Short,
Ê col7: String,
Ê col8: Byte)
object HBaseRecord
{
ÊÊÊÊÊÊÊÊÊÊÊÊÊÊÊÊÊÊÊÊÊÊÊÊÊÊÊÊÊÊÊÊÊÊÊÊÊÊÊÊÊÊÊÊÊÊÊÊÊÊÊÊÊÊÊÊÊÊÊÊÊÊÊÊÊÊÊÊÊÊÊÊÊÊÊÊÊÊÊÊÊÊÊÊÊÊ
ÊÊÊÊÊÊÊÊÊÊÊÊÊÊÊÊÊÊÊÊÊÊ
Ê def apply(i: Int, t: String): HBaseRecord = {
Ê val s = s"""row${"%03d".format(i)}""" ÊÊÊÊÊÊ
Ê HBaseRecord(s,
Ê i % 2 == 0,
Ê i.toDouble,
Ê i.toFloat, Ê
Ê i,
Ê i.toLong,
Ê i.toShort, Ê
Ê s"String$i: $t", ÊÊÊÊÊ
Ê i.toByte)
Ê }
}
val data = (0 to 255).map { i => ÊHBaseRecord(i, "extra")}
sc.parallelize(data).toDF.write.options(
ÊMap(HBaseTableCatalog.tableCatalog -> catalog, HBaseTableCatalog.newTable -> "5"))
Ê.format("org.apache.hadoop.hbase.spark ")
Ê.save()
data prepared by the user is a local Scala collection which has 256 HBaseRecord objects.
sc.parallelize(data) function distributes data to form an RDD. toDF returns a DataFrame. write
function returns a DataFrameWriter used to write the DataFrame to external storage systems (e.g.
HBase here). Given a DataFrame with specified schema catalog, save function will create an HBase
table with 5 regions and save the DataFrame inside.
107.3. Load the DataFrame
413
def withCatalog(cat: String): DataFrame = {
Ê sqlContext
Ê .read
Ê .options(Map(HBaseTableCatalog.tableCatalog->cat))
Ê .format("org.apache.hadoop.hbase.spark")
Ê .load()
}
val df = withCatalog(catalog)
In ‘withCatalog’ function, sqlContext is a variable of SQLContext, which is the entry point for
working with structured data (rows and columns) in Spark. read returns a DataFrameReader that
can be used to read data in as a DataFrame. option function adds input options for the underlying
data source to the DataFrameReader, and format function specifies the input data source format for
the DataFrameReader. The load() function loads input in as a DataFrame. The date frame df
returned by withCatalog function could be used to access HBase table, such as 4.4 and 4.5.
107.4. Language Integrated Query
val s = df.filter(($"col0" <= "row050" && $"col0" > "row040") ||
Ê $"col0" === "row005" ||
Ê $"col0" <= "row005")
Ê .select("col0", "col1", "col4")
s.show
DataFrame can do various operations, such as join, sort, select, filter, orderBy and so on. df.filter
above filters rows using the given SQL expression. select selects a set of columns: col0, col1 and
col4.
107.5. SQL Query
df.registerTempTable("table1")
sqlContext.sql("select count(col1) from table1").show
registerTempTable registers df DataFrame as a temporary table using the table name table1. The
lifetime of this temporary table is tied to the SQLContext that was used to create df. sqlContext.sql
function allows the user to execute SQL queries.
107.6. Others
414
Example 36. Query with different timestamps
In HBaseSparkConf, four parameters related to timestamp can be set. They are TIMESTAMP,
MIN_TIMESTAMP, MAX_TIMESTAMP and MAX_VERSIONS respectively. Users can query
records with different timestamps or time ranges with MIN_TIMESTAMP and
MAX_TIMESTAMP. In the meantime, use concrete value instead of tsSpecified and oldMs in the
examples below.
The example below shows how to load df DataFrame with different timestamps. tsSpecified is
specified by the user. HBaseTableCatalog defines the HBase and Relation relation schema.
writeCatalog defines catalog for the schema mapping.
val df = sqlContext.read
Ê .options(Map(HBaseTableCatalog.tableCatalog -> writeCatalog,
HBaseSparkConf.TIMESTAMP -> tsSpecified.toString))
Ê .format("org.apache.hadoop.hbase.spark")
Ê .load()
The example below shows how to load df DataFrame with different time ranges. oldMs is
specified by the user.
val df = sqlContext.read
Ê .options(Map(HBaseTableCatalog.tableCatalog -> writeCatalog,
HBaseSparkConf.MIN_TIMESTAMP -> "0",
Ê HBaseSparkConf.MAX_TIMESTAMP -> oldMs.toString))
Ê .format("org.apache.hadoop.hbase.spark")
Ê .load()
After loading df DataFrame, users can query data.
df.registerTempTable("table")
sqlContext.sql("select count(col1) from table").show
Example 37. Native Avro support
HBase-Spark Connector support different data formats like Avro, Jason, etc. The use case below
shows how spark supports Avro. User can persist the Avro record into HBase directly.
Internally, the Avro schema is converted to a native Spark Catalyst data type automatically.
Note that both key-value parts in an HBase table can be defined in Avro format.
1) Define catalog for the schema mapping:
415
def catalog = s"""{
Ê |"table":{"namespace":"default", "name":"Avrotable"},
Ê |"rowkey":"key",
Ê |"columns":{
Ê |"col0":{"cf":"rowkey", "col":"key", "type":"string"},
Ê |"col1":{"cf":"cf1", "col":"col1", "type":"binary"}
Ê |}
Ê |}""".stripMargin
catalog is a schema for a HBase table named Avrotable. row key as key and one column col1.
The rowkey also has to be defined in details as a column (col0), which has a specific cf
(rowkey).
2) Prepare the Data:
416
Êobject AvroHBaseRecord {
Ê val schemaString =
Ê s"""{"namespace": "example.avro",
Ê | "type": "record", "name": "User",
Ê | "fields": [
Ê | {"name": "name", "type": "string"},
Ê | {"name": "favorite_number", "type": ["int", "null"]},
Ê | {"name": "favorite_color", "type": ["string", "null"]},
Ê | {"name": "favorite_array", "type": {"type": "array", "items":
"string"}},
Ê | {"name": "favorite_map", "type": {"type": "map", "values":
"int"}}
Ê | ] }""".stripMargin
Ê val avroSchema: Schema = {
Ê val p = new Schema.Parser
Ê p.parse(schemaString)
Ê }
Ê def apply(i: Int): AvroHBaseRecord = {
Ê val user = new GenericData.Record(avroSchema);
Ê user.put("name", s"name${"%03d".format(i)}")
Ê user.put("favorite_number", i)
Ê user.put("favorite_color", s"color${"%03d".format(i)}")
Ê val favoriteArray = new GenericData.Array[String](2,
avroSchema.getField("favorite_array").schema())
Ê favoriteArray.add(s"number${i}")
Ê favoriteArray.add(s"number${i+1}")
Ê user.put("favorite_array", favoriteArray)
Ê import collection.JavaConverters._
Ê val favoriteMap = Map[String, Int](("key1" -> i), ("key2" -> (i+1))).asJava
Ê user.put("favorite_map", favoriteMap)
Ê val avroByte = AvroSedes.serialize(user, avroSchema)
Ê AvroHBaseRecord(s"name${"%03d".format(i)}", avroByte)
Ê }
Ê}
Êval data = (0 to 255).map { i =>
Ê AvroHBaseRecord(i)
Ê}
schemaString is defined first, then it is parsed to get avroSchema. avroSchema is used to generate
AvroHBaseRecord. data prepared by users is a local Scala collection which has 256
AvroHBaseRecord objects.
3) Save DataFrame:
417
Êsc.parallelize(data).toDF.write.options(
Ê Map(HBaseTableCatalog.tableCatalog -> catalog, HBaseTableCatalog.newTable ->
"5"))
Ê .format("org.apache.spark.sql.execution.datasources.hbase")
Ê .save()
Given a data frame with specified schema catalog, above will create an HBase table with 5
regions and save the data frame inside.
4) Load the DataFrame
def avroCatalog = s"""{
Ê |"table":{"namespace":"default", "name":"avrotable"},
Ê |"rowkey":"key",
Ê |"columns":{
Ê |"col0":{"cf":"rowkey", "col":"key", "type":"string"},
Ê |"col1":{"cf":"cf1", "col":"col1", "avro":"avroSchema"}
Ê |}
Ê |}""".stripMargin
Êdef withCatalog(cat: String): DataFrame = {
Ê sqlContext
Ê .read
Ê .options(Map("avroSchema" -> AvroHBaseRecord.schemaString,
HBaseTableCatalog.tableCatalog -> avroCatalog))
Ê .format("org.apache.spark.sql.execution.datasources.hbase")
Ê .load()
Ê}
Êval df = withCatalog(catalog)
In withCatalog function, read returns a DataFrameReader that can be used to read data in as a
DataFrame. The option function adds input options for the underlying data source to the
DataFrameReader. There are two options: one is to set avroSchema as
AvroHBaseRecord.schemaString, and one is to set HBaseTableCatalog.tableCatalog as avroCatalog.
The load() function loads input in as a DataFrame. The date frame df returned by withCatalog
function could be used to access the HBase table.
5) SQL Query
Êdf.registerTempTable("avrotable")
Êval c = sqlContext.sql("select count(1) from avrotable").
After loading df DataFrame, users can query data. registerTempTable registers df DataFrame
as a temporary table using the table name avrotable. sqlContext.sql function allows the user
to execute SQL queries.
418
Apache HBase Coprocessors
HBase Coprocessors are modeled after Google BigTable’s coprocessor implementation
(http://research.google.com/people/jeff/SOCC2010-keynote-slides.pdf pages 41-42.).
The coprocessor framework provides mechanisms for running your custom code directly on the
RegionServers managing your data. Efforts are ongoing to bridge gaps between HBase’s
implementation and BigTable’s architecture. For more information see HBASE-4047.
The information in this chapter is primarily sourced and heavily reused from the following
resources:
1. Mingjie Lai’s blog post Coprocessor Introduction.
2. Gaurav Bhardwaj’s blog post The How To Of HBase Coprocessors.
Use Coprocessors At Your Own Risk
Coprocessors are an advanced feature of HBase and are intended to be used by
system developers only. Because coprocessor code runs directly on the
RegionServer and has direct access to your data, they introduce the risk of data
corruption, man-in-the-middle attacks, or other malicious data access. Currently,
there is no mechanism to prevent data corruption by coprocessors, though work is
underway on HBASE-4047.
+ In addition, there is no resource isolation, so a well-intentioned but misbehaving
coprocessor can severely degrade cluster performance and stability.
419
Chapter 108. Coprocessor Overview
In HBase, you fetch data using a Get or Scan, whereas in an RDBMS you use a SQL query. In order to
fetch only the relevant data, you filter it using a HBase Filter , whereas in an RDBMS you use a WHERE
predicate.
After fetching the data, you perform computations on it. This paradigm works well for "small data"
with a few thousand rows and several columns. However, when you scale to billions of rows and
millions of columns, moving large amounts of data across your network will create bottlenecks at
the network layer, and the client needs to be powerful enough and have enough memory to handle
the large amounts of data and the computations. In addition, the client code can grow large and
complex.
In this scenario, coprocessors might make sense. You can put the business computation code into a
coprocessor which runs on the RegionServer, in the same location as the data, and returns the
result to the client.
This is only one scenario where using coprocessors can provide benefit. Following are some
analogies which may help to explain some of the benefits of coprocessors.
108.1. Coprocessor Analogies
Triggers and Stored Procedure
An Observer coprocessor is similar to a trigger in a RDBMS in that it executes your code either
before or after a specific event (such as a Get or Put) occurs. An endpoint coprocessor is similar
to a stored procedure in a RDBMS because it allows you to perform custom computations on the
data on the RegionServer itself, rather than on the client.
MapReduce
MapReduce operates on the principle of moving the computation to the location of the data.
Coprocessors operate on the same principal.
AOP
If you are familiar with Aspect Oriented Programming (AOP), you can think of a coprocessor as
applying advice by intercepting a request and then running some custom code, before passing
the request on to its final destination (or even changing the destination).
108.2. Coprocessor Implementation Overview
1. Your class should implement one of the Coprocessor interfaces - Coprocessor, RegionObserver,
CoprocessorService - to name a few.
2. Load the coprocessor, either statically (from the configuration) or dynamically, using HBase
Shell. For more details see Loading Coprocessors.
3. Call the coprocessor from your client-side code. HBase handles the coprocessor transparently.
The framework API is provided in the coprocessor package.
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Chapter 109. Types of Coprocessors
109.1. Observer Coprocessors
Observer coprocessors are triggered either before or after a specific event occurs. Observers that
happen before an event use methods that start with a pre prefix, such as prePut. Observers that
happen just after an event override methods that start with a post prefix, such as postPut.
109.1.1. Use Cases for Observer Coprocessors
Security
Before performing a Get or Put operation, you can check for permission using preGet or prePut
methods.
Referential Integrity
HBase does not directly support the RDBMS concept of refential integrity, also known as foreign
keys. You can use a coprocessor to enforce such integrity. For instance, if you have a business
rule that every insert to the users table must be followed by a corresponding entry in the
user_daily_attendance table, you could implement a coprocessor to use the prePut method on
user to insert a record into user_daily_attendance.
Secondary Indexes
You can use a coprocessor to maintain secondary indexes. For more information, see
SecondaryIndexing.
109.1.2. Types of Observer Coprocessor
RegionObserver
A RegionObserver coprocessor allows you to observe events on a region, such as Get and Put
operations. See RegionObserver.
RegionServerObserver
A RegionServerObserver allows you to observe events related to the RegionServer’s operation,
such as starting, stopping, or performing merges, commits, or rollbacks. See
RegionServerObserver.
MasterObserver
A MasterObserver allows you to observe events related to the HBase Master, such as table
creation, deletion, or schema modification. See MasterObserver.
WalObserver
A WalObserver allows you to observe events related to writes to the Write-Ahead Log (WAL). See
WALObserver.
Examples provides working examples of observer coprocessors.
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109.2. Endpoint Coprocessor
Endpoint processors allow you to perform computation at the location of the data. See Coprocessor
Analogy. An example is the need to calculate a running average or summation for an entire table
which spans hundreds of regions.
In contrast to observer coprocessors, where your code is run transparently, endpoint coprocessors
must be explicitly invoked using the CoprocessorService() method available in Table or HTable.
Starting with HBase 0.96, endpoint coprocessors are implemented using Google Protocol Buffers
(protobuf). For more details on protobuf, see Google’s Protocol Buffer Guide. Endpoints Coprocessor
written in version 0.94 are not compatible with version 0.96 or later. See HBASE-5448). To upgrade
your HBase cluster from 0.94 or earlier to 0.96 or later, you need to reimplement your coprocessor.
Coprocessor Endpoints should make no use of HBase internals and only avail of public APIs; ideally
a CPEP should depend on Interfaces and data structures only. This is not always possible but
beware that doing so makes the Endpoint brittle, liable to breakage as HBase internals evolve.
HBase internal APIs annotated as private or evolving do not have to respect semantic versioning
rules or general java rules on deprecation before removal. While generated protobuf files are
absent the hbase audience annotations — they are created by the protobuf protoc tool which knows
nothing of how HBase works — they should be consided @InterfaceAudience.Private so are liable to
change.
Examples provides working examples of endpoint coprocessors.
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Chapter 110. Loading Coprocessors
To make your coprocessor available to HBase, it must be loaded, either statically (through the HBase
configuration) or dynamically (using HBase Shell or the Java API).
110.1. Static Loading
Follow these steps to statically load your coprocessor. Keep in mind that you must restart HBase to
unload a coprocessor that has been loaded statically.
1. Define the Coprocessor in hbase-site.xml, with a <property> element with a <name> and a
<value> sub-element. The <name> should be one of the following:
◦hbase.coprocessor.region.classes for RegionObservers and Endpoints.
◦hbase.coprocessor.wal.classes for WALObservers.
◦hbase.coprocessor.master.classes for MasterObservers.
<value> must contain the fully-qualified class name of your coprocessor’s implementation
class.
For example to load a Coprocessor (implemented in class SumEndPoint.java) you have to
create following entry in RegionServer’s 'hbase-site.xml' file (generally located under 'conf'
directory):
<property>
Ê <name>hbase.coprocessor.region.classes</name>
Ê <value>org.myname.hbase.coprocessor.endpoint.SumEndPoint</value>
</property>
If multiple classes are specified for loading, the class names must be comma-separated. The
framework attempts to load all the configured classes using the default class loader.
Therefore, the jar file must reside on the server-side HBase classpath.
Coprocessors which are loaded in this way will be active on all regions of all tables. These
are also called system Coprocessor. The first listed Coprocessors will be assigned the priority
Coprocessor.Priority.SYSTEM. Each subsequent coprocessor in the list will have its priority
value incremented by one (which reduces its priority, because priorities have the natural
sort order of Integers).
When calling out to registered observers, the framework executes their callbacks methods
in the sorted order of their priority.
Ties are broken arbitrarily.
2. Put your code on HBase’s classpath. One easy way to do this is to drop the jar (containing you
code and all the dependencies) into the lib/ directory in the HBase installation.
3. Restart HBase.
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110.2. Static Unloading
1. Delete the coprocessor’s <property> element, including sub-elements, from hbase-site.xml.
2. Restart HBase.
3. Optionally, remove the coprocessor’s JAR file from the classpath or HBase’s lib/ directory.
110.3. Dynamic Loading
You can also load a coprocessor dynamically, without restarting HBase. This may seem preferable to
static loading, but dynamically loaded coprocessors are loaded on a per-table basis, and are only
available to the table for which they were loaded. For this reason, dynamically loaded tables are
sometimes called Table Coprocessor.
In addition, dynamically loading a coprocessor acts as a schema change on the table, and the table
must be taken offline to load the coprocessor.
There are three ways to dynamically load Coprocessor.
Assumptions
The below mentioned instructions makes the following assumptions:
•A JAR called coprocessor.jar contains the Coprocessor implementation along
with all of its dependencies.
•The JAR is available in HDFS in some location like
hdfs://<namenode>:<port>/user/<hadoop-user>/coprocessor.jar.
110.3.1. Using HBase Shell
1. Disable the table using HBase Shell:
hbase> disable 'users'
2. Load the Coprocessor, using a command like the following:
hbase alter 'users', METHOD => 'table_att', 'Coprocessor'=>
'hdfs://<namenode>:<port>/
user/<hadoop-user>/coprocessor.jar|
org.myname.hbase.Coprocessor.RegionObserverExample|1073741823|
arg1=1,arg2=2'
The Coprocessor framework will try to read the class information from the coprocessor table
attribute value. The value contains four pieces of information which are separated by the pipe
(|) character.
◦File path: The jar file containing the Coprocessor implementation must be in a location
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where all region servers can read it.
You could copy the file onto the local disk on each region server, but it is recommended to
store it in HDFS.
HBASE-14548 allows a directory containing the jars or some wildcards to be specified, such
as: hdfs://<namenode>:<port>/user/<hadoop-user>/ or
hdfs://<namenode>:<port>/user/<hadoop-user>/*.jar. Please note that if a directory is
specified, all jar files(.jar) in the directory are added. It does not search for files in sub-
directories. Do not use a wildcard if you would like to specify a directory. This enhancement
applies to the usage via the JAVA API as well.
◦Class name: The full class name of the Coprocessor.
◦Priority: An integer. The framework will determine the execution sequence of all configured
observers registered at the same hook using priorities. This field can be left blank. In that
case the framework will assign a default priority value.
◦Arguments (Optional): This field is passed to the Coprocessor implementation. This is
optional.
3. Enable the table.
hbase(main):003:0> enable 'users'
4. Verify that the coprocessor loaded:
hbase(main):04:0> describe 'users'
The coprocessor should be listed in the TABLE_ATTRIBUTES.
110.3.2. Using the Java API (all HBase versions)
The following Java code shows how to use the setValue() method of HTableDescriptor to load a
coprocessor on the users table.
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TableName tableName = TableName.valueOf("users");
String path = "hdfs://<namenode>:<port>/user/<hadoop-user>/coprocessor.jar";
Configuration conf = HBaseConfiguration.create();
Connection connection = ConnectionFactory.createConnection(conf);
Admin admin = connection.getAdmin();
admin.disableTable(tableName);
HTableDescriptor hTableDescriptor = new HTableDescriptor(tableName);
HColumnDescriptor columnFamily1 = new HColumnDescriptor("personalDet");
columnFamily1.setMaxVersions(3);
hTableDescriptor.addFamily(columnFamily1);
HColumnDescriptor columnFamily2 = new HColumnDescriptor("salaryDet");
columnFamily2.setMaxVersions(3);
hTableDescriptor.addFamily(columnFamily2);
hTableDescriptor.setValue("COPROCESSOR$1", path + "|"
+ RegionObserverExample.class.getCanonicalName() + "|"
+ Coprocessor.PRIORITY_USER);
admin.modifyTable(tableName, hTableDescriptor);
admin.enableTable(tableName);
110.3.3. Using the Java API (HBase 0.96+ only)
In HBase 0.96 and newer, the addCoprocessor() method of HTableDescriptor provides an easier way
to load a coprocessor dynamically.
TableName tableName = TableName.valueOf("users");
Path path = new Path("hdfs://<namenode>:<port>/user/<hadoop-user>/coprocessor.jar");
Configuration conf = HBaseConfiguration.create();
Connection connection = ConnectionFactory.createConnection(conf);
Admin admin = connection.getAdmin();
admin.disableTable(tableName);
HTableDescriptor hTableDescriptor = new HTableDescriptor(tableName);
HColumnDescriptor columnFamily1 = new HColumnDescriptor("personalDet");
columnFamily1.setMaxVersions(3);
hTableDescriptor.addFamily(columnFamily1);
HColumnDescriptor columnFamily2 = new HColumnDescriptor("salaryDet");
columnFamily2.setMaxVersions(3);
hTableDescriptor.addFamily(columnFamily2);
hTableDescriptor.addCoprocessor(RegionObserverExample.class.getCanonicalName(), path,
Coprocessor.PRIORITY_USER, null);
admin.modifyTable(tableName, hTableDescriptor);
admin.enableTable(tableName);
There is no guarantee that the framework will load a given Coprocessor
successfully. For example, the shell command neither guarantees a jar file exists at
a particular location nor verifies whether the given class is actually contained in
the jar file.
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110.4. Dynamic Unloading
110.4.1. Using HBase Shell
1. Disable the table.
hbase> disable 'users'
2. Alter the table to remove the coprocessor.
hbase> alter 'users', METHOD => 'table_att_unset', NAME => 'coprocessor$1'
3. Enable the table.
hbase> enable 'users'
110.4.2. Using the Java API
Reload the table definition without setting the value of the coprocessor either by using setValue()
or addCoprocessor() methods. This will remove any coprocessor attached to the table.
TableName tableName = TableName.valueOf("users");
String path = "hdfs://<namenode>:<port>/user/<hadoop-user>/coprocessor.jar";
Configuration conf = HBaseConfiguration.create();
Connection connection = ConnectionFactory.createConnection(conf);
Admin admin = connection.getAdmin();
admin.disableTable(tableName);
HTableDescriptor hTableDescriptor = new HTableDescriptor(tableName);
HColumnDescriptor columnFamily1 = new HColumnDescriptor("personalDet");
columnFamily1.setMaxVersions(3);
hTableDescriptor.addFamily(columnFamily1);
HColumnDescriptor columnFamily2 = new HColumnDescriptor("salaryDet");
columnFamily2.setMaxVersions(3);
hTableDescriptor.addFamily(columnFamily2);
admin.modifyTable(tableName, hTableDescriptor);
admin.enableTable(tableName);
In HBase 0.96 and newer, you can instead use the removeCoprocessor() method of the
HTableDescriptor class.
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Chapter 111. Examples
HBase ships examples for Observer Coprocessor.
A more detailed example is given below.
These examples assume a table called users, which has two column families personalDet and
salaryDet, containing personal and salary details. Below is the graphical representation of the users
table.
Table 19. Users Table
personalDet salaryDet
rowkey name lastname dob gross net allowances
admin Admin Admin
cdickens Charles Dickens 02/07/1812 10000 8000 2000
jverne Jules Verne 02/08/1828 12000 9000 3000
111.1. Observer Example
The following Observer coprocessor prevents the details of the user admin from being returned in a
Get or Scan of the users table.
1. Write a class that implements the RegionCoprocessor, RegionObserver class.
2. Override the preGetOp() method (the preGet() method is deprecated) to check whether the client
has queried for the rowkey with value admin. If so, return an empty result. Otherwise, process
the request as normal.
3. Put your code and dependencies in a JAR file.
4. Place the JAR in HDFS where HBase can locate it.
5. Load the Coprocessor.
6. Write a simple program to test it.
Following are the implementation of the above steps:
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public class RegionObserverExample implements RegionCoprocessor, RegionObserver {
Ê private static final byte[] ADMIN = Bytes.toBytes("admin");
Ê private static final byte[] COLUMN_FAMILY = Bytes.toBytes("details");
Ê private static final byte[] COLUMN = Bytes.toBytes("Admin_det");
Ê private static final byte[] VALUE = Bytes.toBytes("You can't see Admin details");
Ê @Override
Ê public Optional<RegionObserver> getRegionObserver() {
Ê return Optional.of(this);
Ê }
Ê @Override
Ê public void preGetOp(final ObserverContext<RegionCoprocessorEnvironment> e, final
Get get, final List<Cell> results)
Ê throws IOException {
Ê if (Bytes.equals(get.getRow(),ADMIN)) {
Ê Cell c = CellUtil.createCell(get.getRow(),COLUMN_FAMILY, COLUMN,
Ê System.currentTimeMillis(), (byte)4, VALUE);
Ê results.add(c);
Ê e.bypass();
Ê }
Ê }
}
Overriding the preGetOp() will only work for Get operations. You also need to override the
preScannerOpen() method to filter the admin row from scan results.
@Override
public RegionScanner preScannerOpen(final ObserverContext<
RegionCoprocessorEnvironment> e, final Scan scan,
final RegionScanner s) throws IOException {
Ê Filter filter = new RowFilter(CompareOp.NOT_EQUAL, new BinaryComparator(ADMIN));
Ê scan.setFilter(filter);
Ê return s;
}
This method works but there is a side effect. If the client has used a filter in its scan, that filter will
be replaced by this filter. Instead, you can explicitly remove any admin results from the scan:
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@Override
public boolean postScannerNext(final ObserverContext<RegionCoprocessorEnvironment> e,
final InternalScanner s,
final List<Result> results, final int limit, final boolean hasMore) throws IOException
{
Ê Result result = null;
Ê Iterator<Result> iterator = results.iterator();
Ê while (iterator.hasNext()) {
Ê result = iterator.next();
Ê if (Bytes.equals(result.getRow(), ROWKEY)) {
Ê iterator.remove();
Ê break;
Ê }
Ê }
Ê return hasMore;
}
111.2. Endpoint Example
Still using the users table, this example implements a coprocessor to calculate the sum of all
employee salaries, using an endpoint coprocessor.
1. Create a '.proto' file defining your service.
option java_package = "org.myname.hbase.coprocessor.autogenerated";
option java_outer_classname = "Sum";
option java_generic_services = true;
option java_generate_equals_and_hash = true;
option optimize_for = SPEED;
message SumRequest {
Ê required string family = 1;
Ê required string column = 2;
}
message SumResponse {
Ê required int64 sum = 1 [default = 0];
}
service SumService {
Ê rpc getSum(SumRequest)
Ê returns (SumResponse);
}
2. Execute the protoc command to generate the Java code from the above .proto' file.
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$ mkdir src
$ protoc --java_out=src ./sum.proto
This will generate a class call Sum.java.
3. Write a class that extends the generated service class, implement the Coprocessor and
CoprocessorService classes, and override the service method.
If you load a coprocessor from hbase-site.xml and then load the same
coprocessor again using HBase Shell, it will be loaded a second time. The same
class will exist twice, and the second instance will have a higher ID (and thus a
lower priority). The effect is that the duplicate coprocessor is effectively
ignored.
public class SumEndPoint extends Sum.SumService implements Coprocessor,
CoprocessorService {
Ê private RegionCoprocessorEnvironment env;
Ê @Override
Ê public Service getService() {
Ê return this;
Ê }
Ê @Override
Ê public void start(CoprocessorEnvironment env) throws IOException {
Ê if (env instanceof RegionCoprocessorEnvironment) {
Ê this.env = (RegionCoprocessorEnvironment)env;
Ê } else {
Ê throw new CoprocessorException("Must be loaded on a table region!");
Ê }
Ê }
Ê @Override
Ê public void stop(CoprocessorEnvironment env) throws IOException {
Ê // do nothing
Ê }
Ê @Override
Ê public void getSum(RpcController controller, Sum.SumRequest request,
RpcCallback<Sum.SumResponse> done) {
Ê Scan scan = new Scan();
Ê scan.addFamily(Bytes.toBytes(request.getFamily()));
Ê scan.addColumn(Bytes.toBytes(request.getFamily()), Bytes.toBytes(request
.getColumn()));
Ê Sum.SumResponse response = null;
Ê InternalScanner scanner = null;
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Ê try {
Ê scanner = env.getRegion().getScanner(scan);
Ê List<Cell> results = new ArrayList<>();
Ê boolean hasMore = false;
Ê long sum = 0L;
Ê do {
Ê hasMore = scanner.next(results);
Ê for (Cell cell : results) {
Ê sum = sum + Bytes.toLong(CellUtil.cloneValue(cell));
Ê }
Ê results.clear();
Ê } while (hasMore);
Ê response = Sum.SumResponse.newBuilder().setSum(sum).build();
Ê } catch (IOException ioe) {
Ê ResponseConverter.setControllerException(controller, ioe);
Ê } finally {
Ê if (scanner != null) {
Ê try {
Ê scanner.close();
Ê } catch (IOException ignored) {}
Ê }
Ê }
Ê done.run(response);
Ê }
}
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Configuration conf = HBaseConfiguration.create();
Connection connection = ConnectionFactory.createConnection(conf);
TableName tableName = TableName.valueOf("users");
Table table = connection.getTable(tableName);
final Sum.SumRequest request = Sum.SumRequest.newBuilder().setFamily("salaryDet")
.setColumn("gross").build();
try {
Ê Map<byte[], Long> results = table.coprocessorService(
Ê Sum.SumService.class,
Ê null, /* start key */
Ê null, /* end key */
Ê new Batch.Call<Sum.SumService, Long>() {
Ê @Override
Ê public Long call(Sum.SumService aggregate) throws IOException {
Ê BlockingRpcCallback<Sum.SumResponse> rpcCallback = new
BlockingRpcCallback<>();
Ê aggregate.getSum(null, request, rpcCallback);
Ê Sum.SumResponse response = rpcCallback.get();
Ê return response.hasSum() ? response.getSum() : 0L;
Ê }
Ê }
Ê );
Ê for (Long sum : results.values()) {
Ê System.out.println("Sum = " + sum);
Ê }
} catch (ServiceException e) {
Ê e.printStackTrace();
} catch (Throwable e) {
Ê e.printStackTrace();
}
4. Load the Coprocessor.
5. Write a client code to call the Coprocessor.
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Chapter 112. Guidelines For Deploying A
Coprocessor
Bundling Coprocessors
You can bundle all classes for a coprocessor into a single JAR on the RegionServer’s classpath, for
easy deployment. Otherwise, place all dependencies on the RegionServer’s classpath so that they
can be loaded during RegionServer start-up. The classpath for a RegionServer is set in the
RegionServer’s hbase-env.sh file.
Automating Deployment
You can use a tool such as Puppet, Chef, or Ansible to ship the JAR for the coprocessor to the
required location on your RegionServers' filesystems and restart each RegionServer, to automate
coprocessor deployment. Details for such set-ups are out of scope of this document.
Updating a Coprocessor
Deploying a new version of a given coprocessor is not as simple as disabling it, replacing the JAR,
and re-enabling the coprocessor. This is because you cannot reload a class in a JVM unless you
delete all the current references to it. Since the current JVM has reference to the existing
coprocessor, you must restart the JVM, by restarting the RegionServer, in order to replace it. This
behavior is not expected to change.
Coprocessor Logging
The Coprocessor framework does not provide an API for logging beyond standard Java logging.
Coprocessor Configuration
If you do not want to load coprocessors from the HBase Shell, you can add their configuration
properties to hbase-site.xml. In Using HBase Shell, two arguments are set: arg1=1,arg2=2. These
could have been added to hbase-site.xml as follows:
<property>
Ê <name>arg1</name>
Ê <value>1</value>
</property>
<property>
Ê <name>arg2</name>
Ê <value>2</value>
</property>
Then you can read the configuration using code like the following:
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Configuration conf = HBaseConfiguration.create();
Connection connection = ConnectionFactory.createConnection(conf);
TableName tableName = TableName.valueOf("users");
Table table = connection.getTable(tableName);
Get get = new Get(Bytes.toBytes("admin"));
Result result = table.get(get);
for (Cell c : result.rawCells()) {
Ê System.out.println(Bytes.toString(CellUtil.cloneRow(c))
Ê + "==> " + Bytes.toString(CellUtil.cloneFamily(c))
Ê + "{" + Bytes.toString(CellUtil.cloneQualifier(c))
Ê + ":" + Bytes.toLong(CellUtil.cloneValue(c)) + "}");
}
Scan scan = new Scan();
ResultScanner scanner = table.getScanner(scan);
for (Result res : scanner) {
Ê for (Cell c : res.rawCells()) {
Ê System.out.println(Bytes.toString(CellUtil.cloneRow(c))
Ê + " ==> " + Bytes.toString(CellUtil.cloneFamily(c))
Ê + " {" + Bytes.toString(CellUtil.cloneQualifier(c))
Ê + ":" + Bytes.toLong(CellUtil.cloneValue(c))
Ê + "}");
Ê }
}
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Chapter 113. Restricting Coprocessor Usage
Restricting arbitrary user coprocessors can be a big concern in multitenant environments. HBase
provides a continuum of options for ensuring only expected coprocessors are running:
•hbase.coprocessor.enabled: Enables or disables all coprocessors. This will limit the functionality
of HBase, as disabling all coprocessors will disable some security providers. An example
coproccessor so affected is org.apache.hadoop.hbase.security.access.AccessController.
◦hbase.coprocessor.user.enabled: Enables or disables loading coprocessors on tables (i.e. user
coprocessors).
◦One can statically load coprocessors via the following tunables in hbase-site.xml:
▪hbase.coprocessor.regionserver.classes: A comma-separated list of coprocessors that are
loaded by region servers
▪hbase.coprocessor.region.classes: A comma-separated list of RegionObserver and
Endpoint coprocessors
▪hbase.coprocessor.user.region.classes: A comma-separated list of coprocessors that are
loaded by all regions
▪hbase.coprocessor.master.classes: A comma-separated list of coprocessors that are
loaded by the master (MasterObserver coprocessors)
▪hbase.coprocessor.wal.classes: A comma-separated list of WALObserver coprocessors to
load
◦hbase.coprocessor.abortonerror: Whether to abort the daemon which has loaded the
coprocessor if the coprocessor should error other than IOError. If this is set to false and an
access controller coprocessor should have a fatal error the coprocessor will be
circumvented, as such in secure installations this is advised to be true; however, one may
override this on a per-table basis for user coprocessors, to ensure they do not abort their
running region server and are instead unloaded on error.
◦hbase.coprocessor.region.whitelist.paths: A comma separated list available for those
loading org.apache.hadoop.hbase.security.access.CoprocessorWhitelistMasterObserver
whereby one can use the following options to white-list paths from which coprocessors may
be loaded.
▪Coprocessors on the classpath are implicitly white-listed
▪* to wildcard all coprocessor paths
▪An entire filesystem (e.g. hdfs://my-cluster/)
▪A wildcard path to be evaluated by FilenameUtils.wildcardMatch
▪Note: Path can specify scheme or not (e.g. file:///usr/hbase/lib/coprocessors or for all
filesystems /usr/hbase/lib/coprocessors)
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Apache HBase Performance Tuning
437
Chapter 114. Operating System
114.1. Memory
RAM, RAM, RAM. Don’t starve HBase.
114.2. 64-bit
Use a 64-bit platform (and 64-bit JVM).
114.3. Swapping
Watch out for swapping. Set swappiness to 0.
114.4. CPU
Make sure you have set up your Hadoop to use native, hardware checksumming. See
link:[hadoop.native.lib].
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Chapter 115. Network
Perhaps the most important factor in avoiding network issues degrading Hadoop and HBase
performance is the switching hardware that is used, decisions made early in the scope of the
project can cause major problems when you double or triple the size of your cluster (or more).
Important items to consider:
•Switching capacity of the device
•Number of systems connected
•Uplink capacity
115.1. Single Switch
The single most important factor in this configuration is that the switching capacity of the
hardware is capable of handling the traffic which can be generated by all systems connected to the
switch. Some lower priced commodity hardware can have a slower switching capacity than could
be utilized by a full switch.
115.2. Multiple Switches
Multiple switches are a potential pitfall in the architecture. The most common configuration of
lower priced hardware is a simple 1Gbps uplink from one switch to another. This often overlooked
pinch point can easily become a bottleneck for cluster communication. Especially with MapReduce
jobs that are both reading and writing a lot of data the communication across this uplink could be
saturated.
Mitigation of this issue is fairly simple and can be accomplished in multiple ways:
•Use appropriate hardware for the scale of the cluster which you’re attempting to build.
•Use larger single switch configurations i.e. single 48 port as opposed to 2x 24 port
•Configure port trunking for uplinks to utilize multiple interfaces to increase cross switch
bandwidth.
115.3. Multiple Racks
Multiple rack configurations carry the same potential issues as multiple switches, and can suffer
performance degradation from two main areas:
•Poor switch capacity performance
•Insufficient uplink to another rack
If the switches in your rack have appropriate switching capacity to handle all the hosts at full
speed, the next most likely issue will be caused by homing more of your cluster across racks. The
easiest way to avoid issues when spanning multiple racks is to use port trunking to create a bonded
uplink to other racks. The downside of this method however, is in the overhead of ports that could
439
potentially be used. An example of this is, creating an 8Gbps port channel from rack A to rack B,
using 8 of your 24 ports to communicate between racks gives you a poor ROI, using too few
however can mean you’re not getting the most out of your cluster.
Using 10Gbe links between racks will greatly increase performance, and assuming your switches
support a 10Gbe uplink or allow for an expansion card will allow you to save your ports for
machines as opposed to uplinks.
115.4. Network Interfaces
Are all the network interfaces functioning correctly? Are you sure? See the Troubleshooting Case
Study in Case Study #1 (Performance Issue On A Single Node).
115.5. Network Consistency and Partition Tolerance
The CAP Theorem states that a distributed system can maintain two out of the following three
characteristics: - *C*onsistency — all nodes see the same data. - *A*vailability — every request
receives a response about whether it succeeded or failed. - *P*artition tolerance — the system
continues to operate even if some of its components become unavailable to the others.
HBase favors consistency and partition tolerance, where a decision has to be made. Coda Hale
explains why partition tolerance is so important, in http://codahale.com/you-cant-sacrifice-
partition-tolerance/.
Robert Yokota used an automated testing framework called Jepson to test HBase’s partition
tolerance in the face of network partitions, using techniques modeled after Aphyr’s Call Me Maybe
series. The results, available as a blog post and an addendum, show that HBase performs correctly.
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Chapter 116. Java
116.1. The Garbage Collector and Apache HBase
116.1.1. Long GC pauses
In his presentation, Avoiding Full GCs with MemStore-Local Allocation Buffers, Todd Lipcon
describes two cases of stop-the-world garbage collections common in HBase, especially during
loading; CMS failure modes and old generation heap fragmentation brought.
To address the first, start the CMS earlier than default by adding
-XX:CMSInitiatingOccupancyFraction and setting it down from defaults. Start at 60 or 70 percent (The
lower you bring down the threshold, the more GCing is done, the more CPU used). To address the
second fragmentation issue, Todd added an experimental facility, (MSLAB), that must be explicitly
enabled in Apache HBase 0.90.x (It’s defaulted to be on in Apache 0.92.x HBase). Set
hbase.hregion.memstore.mslab.enabled to true in your Configuration. See the cited slides for
background and detail. The latest JVMs do better regards fragmentation so make sure you are
running a recent release. Read down in the message, Identifying concurrent mode failures caused
by fragmentation. Be aware that when enabled, each MemStore instance will occupy at least an
MSLAB instance of memory. If you have thousands of regions or lots of regions each with many
column families, this allocation of MSLAB may be responsible for a good portion of your heap
allocation and in an extreme case cause you to OOME. Disable MSLAB in this case, or lower the
amount of memory it uses or float less regions per server.
If you have a write-heavy workload, check out HBASE-8163 MemStoreChunkPool: An improvement
for JAVA GC when using MSLAB. It describes configurations to lower the amount of young GC
during write-heavy loadings. If you do not have HBASE-8163 installed, and you are trying to
improve your young GC times, one trick to consider — courtesy of our Liang Xie — is to set the GC
config -XX:PretenureSizeThreshold in hbase-env.sh to be just smaller than the size of
hbase.hregion.memstore.mslab.chunksize so MSLAB allocations happen in the tenured space directly
rather than first in the young gen. You’d do this because these MSLAB allocations are going to likely
make it to the old gen anyways and rather than pay the price of a copies between s0 and s1 in eden
space followed by the copy up from young to old gen after the MSLABs have achieved sufficient
tenure, save a bit of YGC churn and allocate in the old gen directly.
Other sources of long GCs can be the JVM itself logging. See Eliminating Large JVM GC Pauses
Caused by Background IO Traffic
For more information about GC logs, see JVM Garbage Collection Logs.
Consider also enabling the off-heap Block Cache. This has been shown to mitigate GC pause times.
See Block Cache
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Chapter 117. HBase Configurations
See Recommended Configurations.
117.1. Improving the 99th Percentile
Try link:[hedged_reads].
117.2. Managing Compactions
For larger systems, managing link:[compactions and splits] may be something you want to consider.
117.3. hbase.regionserver.handler.count
See [hbase.regionserver.handler.count].
117.4. hfile.block.cache.size
See [hfile.block.cache.size]. A memory setting for the RegionServer process.
117.5. Prefetch Option for Blockcache
HBASE-9857 adds a new option to prefetch HFile contents when opening the BlockCache, if a
Column family or RegionServer property is set. This option is available for HBase 0.98.3 and later.
The purpose is to warm the BlockCache as rapidly as possible after the cache is opened, using in-
memory table data, and not counting the prefetching as cache misses. This is great for fast reads,
but is not a good idea if the data to be preloaded will not fit into the BlockCache. It is useful for
tuning the IO impact of prefetching versus the time before all data blocks are in cache.
To enable prefetching on a given column family, you can use HBase Shell or use the API.
Enable Prefetch Using HBase Shell
hbase> create 'MyTable', { NAME => 'myCF', PREFETCH_BLOCKS_ON_OPEN => 'true' }
Example 38. Enable Prefetch Using the API
// ...
HTableDescriptor tableDesc = new HTableDescriptor("myTable");
HColumnDescriptor cfDesc = new HColumnDescriptor("myCF");
cfDesc.setPrefetchBlocksOnOpen(true);
tableDesc.addFamily(cfDesc);
// ...
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See the API documentation for CacheConfig.
To see prefetch in operation, enable TRACE level logging on
org.apache.hadoop.hbase.io.hfile.HFileReaderImpl in hbase-2.0+ or on
org.apache.hadoop.hbase.io.hfile.HFileReaderV2 in earlier versions, hbase-1.x, of HBase.
117.6. hbase.regionserver.global.memstore.size
See [hbase.regionserver.global.memstore.size]. This memory setting is often adjusted for the
RegionServer process depending on needs.
117.7.
hbase.regionserver.global.memstore.size.lower.limit
See [hbase.regionserver.global.memstore.size.lower.limit]. This memory setting is often adjusted
for the RegionServer process depending on needs.
117.8. hbase.hstore.blockingStoreFiles
See [hbase.hstore.blockingStoreFiles]. If there is blocking in the RegionServer logs, increasing this
can help.
117.9. hbase.hregion.memstore.block.multiplier
See [hbase.hregion.memstore.block.multiplier]. If there is enough RAM, increasing this can help.
117.10. hbase.regionserver.checksum.verify
Have HBase write the checksum into the datablock and save having to do the checksum seek
whenever you read.
See [hbase.regionserver.checksum.verify], [hbase.hstore.bytes.per.checksum] and
[hbase.hstore.checksum.algorithm]. For more information see the release note on HBASE-5074
support checksums in HBase block cache.
117.11. Tuning callQueue Options
HBASE-11355 introduces several callQueue tuning mechanisms which can increase performance.
See the JIRA for some benchmarking information.
To increase the number of callqueues, set hbase.ipc.server.num.callqueue to a value greater than 1.
To split the callqueue into separate read and write queues, set
hbase.ipc.server.callqueue.read.ratio to a value between 0 and 1. This factor weights the queues
toward writes (if below .5) or reads (if above .5). Another way to say this is that the factor
determines what percentage of the split queues are used for reads. The following examples
illustrate some of the possibilities. Note that you always have at least one write queue, no matter
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what setting you use.
•The default value of 0 does not split the queue.
•A value of .3 uses 30% of the queues for reading and 60% for writing. Given a value of 10 for
hbase.ipc.server.num.callqueue, 3 queues would be used for reads and 7 for writes.
•A value of .5 uses the same number of read queues and write queues. Given a value of 10 for
hbase.ipc.server.num.callqueue, 5 queues would be used for reads and 5 for writes.
•A value of .6 uses 60% of the queues for reading and 30% for reading. Given a value of 10 for
hbase.ipc.server.num.callqueue, 7 queues would be used for reads and 3 for writes.
•A value of 1.0 uses one queue to process write requests, and all other queues process read
requests. A value higher than 1.0 has the same effect as a value of 1.0. Given a value of 10 for
hbase.ipc.server.num.callqueue, 9 queues would be used for reads and 1 for writes.
You can also split the read queues so that separate queues are used for short reads (from Get
operations) and long reads (from Scan operations), by setting the
hbase.ipc.server.callqueue.scan.ratio option. This option is a factor between 0 and 1, which
determine the ratio of read queues used for Gets and Scans. More queues are used for Gets if the
value is below .5 and more are used for scans if the value is above .5. No matter what setting you
use, at least one read queue is used for Get operations.
•A value of 0 does not split the read queue.
•A value of .3 uses 60% of the read queues for Gets and 30% for Scans. Given a value of 20 for
hbase.ipc.server.num.callqueue and a value of .5 for hbase.ipc.server.callqueue.read.ratio, 10
queues would be used for reads, out of those 10, 7 would be used for Gets and 3 for Scans.
•A value of .5 uses half the read queues for Gets and half for Scans. Given a value of 20 for
hbase.ipc.server.num.callqueue and a value of .5 for hbase.ipc.server.callqueue.read.ratio, 10
queues would be used for reads, out of those 10, 5 would be used for Gets and 5 for Scans.
•A value of .6 uses 30% of the read queues for Gets and 60% for Scans. Given a value of 20 for
hbase.ipc.server.num.callqueue and a value of .5 for hbase.ipc.server.callqueue.read.ratio, 10
queues would be used for reads, out of those 10, 3 would be used for Gets and 7 for Scans.
•A value of 1.0 uses all but one of the read queues for Scans. Given a value of 20 for
hbase.ipc.server.num.callqueue and a value of`.5` for hbase.ipc.server.callqueue.read.ratio,
10 queues would be used for reads, out of those 10, 1 would be used for Gets and 9 for Scans.
You can use the new option hbase.ipc.server.callqueue.handler.factor to programmatically tune
the number of queues:
•A value of 0 uses a single shared queue between all the handlers.
•A value of 1 uses a separate queue for each handler.
•A value between 0 and 1 tunes the number of queues against the number of handlers. For
instance, a value of .5 shares one queue between each two handlers.
Having more queues, such as in a situation where you have one queue per handler, reduces
contention when adding a task to a queue or selecting it from a queue. The trade-off is that if
you have some queues with long-running tasks, a handler may end up waiting to execute from
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that queue rather than processing another queue which has waiting tasks.
For these values to take effect on a given RegionServer, the RegionServer must be restarted. These
parameters are intended for testing purposes and should be used carefully.
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Chapter 118. ZooKeeper
See ZooKeeper for information on configuring ZooKeeper, and see the part about having a
dedicated disk.
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Chapter 119. Schema Design
119.1. Number of Column Families
See On the number of column families.
119.2. Key and Attribute Lengths
See Try to minimize row and column sizes. See also However… for compression caveats.
119.3. Table RegionSize
The regionsize can be set on a per-table basis via setFileSize on HTableDescriptor in the event
where certain tables require different regionsizes than the configured default regionsize.
See Determining region count and size for more information.
119.4. Bloom Filters
A Bloom filter, named for its creator, Burton Howard Bloom, is a data structure which is designed
to predict whether a given element is a member of a set of data. A positive result from a Bloom
filter is not always accurate, but a negative result is guaranteed to be accurate. Bloom filters are
designed to be "accurate enough" for sets of data which are so large that conventional hashing
mechanisms would be impractical. For more information about Bloom filters in general, refer to
http://en.wikipedia.org/wiki/Bloom_filter.
In terms of HBase, Bloom filters provide a lightweight in-memory structure to reduce the number
of disk reads for a given Get operation (Bloom filters do not work with Scans) to only the StoreFiles
likely to contain the desired Row. The potential performance gain increases with the number of
parallel reads.
The Bloom filters themselves are stored in the metadata of each HFile and never need to be
updated. When an HFile is opened because a region is deployed to a RegionServer, the Bloom filter
is loaded into memory.
HBase includes some tuning mechanisms for folding the Bloom filter to reduce the size and keep
the false positive rate within a desired range.
Bloom filters were introduced in HBASE-1200. Since HBase 0.96, row-based Bloom filters are
enabled by default. (HBASE-8450)
For more information on Bloom filters in relation to HBase, see Bloom Filters for more information,
or the following Quora discussion: How are bloom filters used in HBase?.
119.4.1. When To Use Bloom Filters
Since HBase 0.96, row-based Bloom filters are enabled by default. You may choose to disable them
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or to change some tables to use row+column Bloom filters, depending on the characteristics of your
data and how it is loaded into HBase.
To determine whether Bloom filters could have a positive impact, check the value of
blockCacheHitRatio in the RegionServer metrics. If Bloom filters are enabled, the value of
blockCacheHitRatio should increase, because the Bloom filter is filtering out blocks that are
definitely not needed.
You can choose to enable Bloom filters for a row or for a row+column combination. If you generally
scan entire rows, the row+column combination will not provide any benefit. A row-based Bloom
filter can operate on a row+column Get, but not the other way around. However, if you have a large
number of column-level Puts, such that a row may be present in every StoreFile, a row-based filter
will always return a positive result and provide no benefit. Unless you have one column per row,
row+column Bloom filters require more space, in order to store more keys. Bloom filters work best
when the size of each data entry is at least a few kilobytes in size.
Overhead will be reduced when your data is stored in a few larger StoreFiles, to avoid extra disk IO
during low-level scans to find a specific row.
Bloom filters need to be rebuilt upon deletion, so may not be appropriate in environments with a
large number of deletions.
119.4.2. Enabling Bloom Filters
Bloom filters are enabled on a Column Family. You can do this by using the setBloomFilterType
method of HColumnDescriptor or using the HBase API. Valid values are NONE, ROW (default), or
ROWCOL. See When To Use Bloom Filters for more information on ROW versus ROWCOL. See also the API
documentation for HColumnDescriptor.
The following example creates a table and enables a ROWCOL Bloom filter on the colfam1 column
family.
hbase> create 'mytable',{NAME => 'colfam1', BLOOMFILTER => 'ROWCOL'}
119.4.3. Configuring Server-Wide Behavior of Bloom Filters
You can configure the following settings in the hbase-site.xml.
Parameter Default Description
io.storefile.bloom.enabled yes Set to no to kill bloom filters
server-wide if something goes
wrong
io.storefile.bloom.error.rate .01 The average false positive rate
for bloom filters. Folding is used
to maintain the false positive
rate. Expressed as a decimal
representation of a percentage.
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Parameter Default Description
io.storefile.bloom.max.fold 7 The guaranteed maximum fold
rate. Changing this setting
should not be necessary and is
not recommended.
io.storefile.bloom.max.keys 128000000 For default (single-block) Bloom
filters, this specifies the
maximum number of keys.
io.storefile.delete.family.bloom.
enabled
true Master switch to enable Delete
Family Bloom filters and store
them in the StoreFile.
io.storefile.bloom.block.size 131072 Target Bloom block size. Bloom
filter blocks of approximately
this size are interleaved with
data blocks.
hfile.block.bloom.cacheonwrite false Enables cache-on-write for
inline blocks of a compound
Bloom filter.
119.5. ColumnFamily BlockSize
The blocksize can be configured for each ColumnFamily in a table, and defaults to 64k. Larger cell
values require larger blocksizes. There is an inverse relationship between blocksize and the
resulting StoreFile indexes (i.e., if the blocksize is doubled then the resulting indexes should be
roughly halved).
See HColumnDescriptor and Storefor more information.
119.6. In-Memory ColumnFamilies
ColumnFamilies can optionally be defined as in-memory. Data is still persisted to disk, just like any
other ColumnFamily. In-memory blocks have the highest priority in the Block Cache, but it is not a
guarantee that the entire table will be in memory.
See HColumnDescriptor for more information.
119.7. Compression
Production systems should use compression with their ColumnFamily definitions. See Compression
and Data Block Encoding In HBase for more information.
119.7.1. However…
Compression deflates data on disk. When it’s in-memory (e.g., in the MemStore) or on the wire (e.g.,
transferring between RegionServer and Client) it’s inflated. So while using ColumnFamily
compression is a best practice, but it’s not going to completely eliminate the impact of over-sized
Keys, over-sized ColumnFamily names, or over-sized Column names.
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See Try to minimize row and column sizes on for schema design tips, and KeyValue for more
information on HBase stores data internally.
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Chapter 120. HBase General Patterns
120.1. Constants
When people get started with HBase they have a tendency to write code that looks like this:
Get get = new Get(rowkey);
Result r = table.get(get);
byte[] b = r.getValue(Bytes.toBytes("cf"), Bytes.toBytes("attr")); // returns current
version of value
But especially when inside loops (and MapReduce jobs), converting the columnFamily and column-
names to byte-arrays repeatedly is surprisingly expensive. It’s better to use constants for the byte-
arrays, like this:
public static final byte[] CF = "cf".getBytes();
public static final byte[] ATTR = "attr".getBytes();
...
Get get = new Get(rowkey);
Result r = table.get(get);
byte[] b = r.getValue(CF, ATTR); // returns current version of value
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Chapter 121. Writing to HBase
121.1. Batch Loading
Use the bulk load tool if you can. See Bulk Loading. Otherwise, pay attention to the below.
121.2. Table Creation: Pre-Creating Regions
Tables in HBase are initially created with one region by default. For bulk imports, this means that
all clients will write to the same region until it is large enough to split and become distributed
across the cluster. A useful pattern to speed up the bulk import process is to pre-create empty
regions. Be somewhat conservative in this, because too-many regions can actually degrade
performance.
There are two different approaches to pre-creating splits using the HBase API. The first approach is
to rely on the default Admin strategy (which is implemented in Bytes.split)…
byte[] startKey = ...; // your lowest key
byte[] endKey = ...; // your highest key
int numberOfRegions = ...; // # of regions to create
admin.createTable(table, startKey, endKey, numberOfRegions);
And the other approach, using the HBase API, is to define the splits yourself…
byte[][] splits = ...; // create your own splits
admin.createTable(table, splits);
You can achieve a similar effect using the HBase Shell to create tables by specifying split options.
# create table with specific split points
hbase>create 't1','f1',SPLITS => ['\x10\x00', '\x20\x00', '\x30\x00', '\x40\x00']
# create table with four regions based on random bytes keys
hbase>create 't2','f1', { NUMREGIONS => 4 , SPLITALGO => 'UniformSplit' }
# create table with five regions based on hex keys
create 't3','f1', { NUMREGIONS => 5, SPLITALGO => 'HexStringSplit' }
See Relationship Between RowKeys and Region Splits for issues related to understanding your
keyspace and pre-creating regions. See manual region splitting decisions for discussion on
manually pre-splitting regions. See Pre-splitting tables with the HBase Shell for more details of
using the HBase Shell to pre-split tables.
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121.3. Table Creation: Deferred Log Flush
The default behavior for Puts using the Write Ahead Log (WAL) is that WAL edits will be written
immediately. If deferred log flush is used, WAL edits are kept in memory until the flush period. The
benefit is aggregated and asynchronous WAL- writes, but the potential downside is that if the
RegionServer goes down the yet-to-be-flushed edits are lost. This is safer, however, than not using
WAL at all with Puts.
Deferred log flush can be configured on tables via HTableDescriptor. The default value of
hbase.regionserver.optionallogflushinterval is 1000ms.
121.4. HBase Client: Turn off WAL on Puts
A frequent request is to disable the WAL to increase performance of Puts. This is only appropriate
for bulk loads, as it puts your data at risk by removing the protection of the WAL in the event of a
region server crash. Bulk loads can be re-run in the event of a crash, with little risk of data loss.
If you disable the WAL for anything other than bulk loads, your data is at risk.
In general, it is best to use WAL for Puts, and where loading throughput is a concern to use bulk
loading techniques instead. For normal Puts, you are not likely to see a performance improvement
which would outweigh the risk. To disable the WAL, see Disabling the WAL.
121.5. HBase Client: Group Puts by RegionServer
In addition to using the writeBuffer, grouping Put`s by RegionServer can reduce the number of
client RPC calls per writeBuffer flush. There is a utility `HTableUtil currently on MASTER
that does this, but you can either copy that or implement your own version for those still on 0.90.x
or earlier.
121.6. MapReduce: Skip The Reducer
When writing a lot of data to an HBase table from a MR job (e.g., with TableOutputFormat), and
specifically where Puts are being emitted from the Mapper, skip the Reducer step. When a Reducer
step is used, all of the output (Puts) from the Mapper will get spooled to disk, then sorted/shuffled to
other Reducers that will most likely be off-node. It’s far more efficient to just write directly to
HBase.
For summary jobs where HBase is used as a source and a sink, then writes will be coming from the
Reducer step (e.g., summarize values then write out result). This is a different processing problem
than from the above case.
121.7. Anti-Pattern: One Hot Region
If all your data is being written to one region at a time, then re-read the section on processing
timeseries data.
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Also, if you are pre-splitting regions and all your data is still winding up in a single region even
though your keys aren’t monotonically increasing, confirm that your keyspace actually works with
the split strategy. There are a variety of reasons that regions may appear "well split" but won’t work
with your data. As the HBase client communicates directly with the RegionServers, this can be
obtained via RegionLocator.getRegionLocation.
See Table Creation: Pre-Creating Regions, as well as HBase Configurations
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Chapter 122. Reading from HBase
The mailing list can help if you are having performance issues. For example, here is a good general
thread on what to look at addressing read-time issues: HBase Random Read latency > 100ms
122.1. Scan Caching
If HBase is used as an input source for a MapReduce job, for example, make sure that the input
Scan instance to the MapReduce job has setCaching set to something greater than the default (which
is 1). Using the default value means that the map-task will make call back to the region-server for
every record processed. Setting this value to 500, for example, will transfer 500 rows at a time to the
client to be processed. There is a cost/benefit to have the cache value be large because it costs more
in memory for both client and RegionServer, so bigger isn’t always better.
122.1.1. Scan Caching in MapReduce Jobs
Scan settings in MapReduce jobs deserve special attention. Timeouts can result (e.g.,
UnknownScannerException) in Map tasks if it takes longer to process a batch of records before the
client goes back to the RegionServer for the next set of data. This problem can occur because there
is non-trivial processing occurring per row. If you process rows quickly, set caching higher. If you
process rows more slowly (e.g., lots of transformations per row, writes), then set caching lower.
Timeouts can also happen in a non-MapReduce use case (i.e., single threaded HBase client doing a
Scan), but the processing that is often performed in MapReduce jobs tends to exacerbate this issue.
122.2. Scan Attribute Selection
Whenever a Scan is used to process large numbers of rows (and especially when used as a
MapReduce source), be aware of which attributes are selected. If scan.addFamily is called then all of
the attributes in the specified ColumnFamily will be returned to the client. If only a small number
of the available attributes are to be processed, then only those attributes should be specified in the
input scan because attribute over-selection is a non-trivial performance penalty over large datasets.
122.3. Avoid scan seeks
When columns are selected explicitly with scan.addColumn, HBase will schedule seek operations to
seek between the selected columns. When rows have few columns and each column has only a few
versions this can be inefficient. A seek operation is generally slower if does not seek at least past 5-
10 columns/versions or 512-1024 bytes.
In order to opportunistically look ahead a few columns/versions to see if the next column/version
can be found that way before a seek operation is scheduled, a new attribute Scan.HINT_LOOKAHEAD
can be set on the Scan object. The following code instructs the RegionServer to attempt two
iterations of next before a seek is scheduled:
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Scan scan = new Scan();
scan.addColumn(...);
scan.setAttribute(Scan.HINT_LOOKAHEAD, Bytes.toBytes(2));
table.getScanner(scan);
122.4. MapReduce - Input Splits
For MapReduce jobs that use HBase tables as a source, if there a pattern where the "slow" map tasks
seem to have the same Input Split (i.e., the RegionServer serving the data), see the Troubleshooting
Case Study in Case Study #1 (Performance Issue On A Single Node).
122.5. Close ResultScanners
This isn’t so much about improving performance but rather avoiding performance problems. If you
forget to close ResultScanners you can cause problems on the RegionServers. Always have
ResultScanner processing enclosed in try/catch blocks.
Scan scan = new Scan();
// set attrs...
ResultScanner rs = table.getScanner(scan);
try {
Ê for (Result r = rs.next(); r != null; r = rs.next()) {
Ê // process result...
} finally {
Ê rs.close(); // always close the ResultScanner!
}
table.close();
122.6. Block Cache
Scan instances can be set to use the block cache in the RegionServer via the setCacheBlocks method.
For input Scans to MapReduce jobs, this should be false. For frequently accessed rows, it is
advisable to use the block cache.
Cache more data by moving your Block Cache off-heap. See Off-heap Block Cache
122.7. Optimal Loading of Row Keys
When performing a table scan where only the row keys are needed (no families, qualifiers, values
or timestamps), add a FilterList with a MUST_PASS_ALL operator to the scanner using setFilter. The
filter list should include both a FirstKeyOnlyFilter and a KeyOnlyFilter. Using this filter
combination will result in a worst case scenario of a RegionServer reading a single value from disk
and minimal network traffic to the client for a single row.
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122.8. Concurrency: Monitor Data Spread
When performing a high number of concurrent reads, monitor the data spread of the target tables.
If the target table(s) have too few regions then the reads could likely be served from too few nodes.
See Table Creation: Pre-Creating Regions, as well as HBase Configurations
122.9. Bloom Filters
Enabling Bloom Filters can save your having to go to disk and can help improve read latencies.
Bloom filters were developed over in HBase-1200 Add bloomfilters. For description of the
development process — why static blooms rather than dynamic — and for an overview of the
unique properties that pertain to blooms in HBase, as well as possible future directions, see the
Development Process section of the document BloomFilters in HBase attached to HBASE-1200. The
bloom filters described here are actually version two of blooms in HBase. In versions up to 0.19.x,
HBase had a dynamic bloom option based on work done by the European Commission One-Lab
Project 034819. The core of the HBase bloom work was later pulled up into Hadoop to implement
org.apache.hadoop.io.BloomMapFile. Version 1 of HBase blooms never worked that well. Version 2
is a rewrite from scratch though again it starts with the one-lab work.
See also Bloom Filters.
122.9.1. Bloom StoreFile footprint
Bloom filters add an entry to the StoreFile general FileInfo data structure and then two extra
entries to the StoreFile metadata section.
BloomFilter in the StoreFile``FileInfo data structure
FileInfo has a BLOOM_FILTER_TYPE entry which is set to NONE, ROW or ROWCOL.
BloomFilter entries in StoreFile metadata
BLOOM_FILTER_META holds Bloom Size, Hash Function used, etc. It’s small in size and is cached on
StoreFile.Reader load
BLOOM_FILTER_DATA is the actual bloomfilter data. Obtained on-demand. Stored in the LRU cache, if it
is enabled (It’s enabled by default).
122.9.2. Bloom Filter Configuration
io.storefile.bloom.enabled global kill switch
io.storefile.bloom.enabled in Configuration serves as the kill switch in case something goes wrong.
Default = true.
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io.storefile.bloom.error.rate
io.storefile.bloom.error.rate = average false positive rate. Default = 1%. Decrease rate by ½ (e.g. to
.5%) == +1 bit per bloom entry.
io.storefile.bloom.max.fold
io.storefile.bloom.max.fold = guaranteed minimum fold rate. Most people should leave this alone.
Default = 7, or can collapse to at least 1/128th of original size. See the Development Process section
of the document BloomFilters in HBase for more on what this option means.
122.10. Hedged Reads
Hedged reads are a feature of HDFS, introduced in Hadoop 2.4.0 with HDFS-5776. Normally, a single
thread is spawned for each read request. However, if hedged reads are enabled, the client waits
some configurable amount of time, and if the read does not return, the client spawns a second read
request, against a different block replica of the same data. Whichever read returns first is used, and
the other read request is discarded.
Hedged reads are "…very good at eliminating outlier datanodes, which in turn makes them very
good choice for latency sensitive setups. But, if you are looking for maximizing throughput, hedged
reads tend to create load amplification as things get slower in general. In short, the thing to watch
out for is the non-graceful performance degradation when you are running close a certain
throughput threshold." (Quote from Ashu Pachauri in HBASE-17083).
Other concerns to keep in mind while running with hedged reads enabled include:
•They may lead to network congestion. See HBASE-17083
•Make sure you set the thread pool large enough so as blocking on the pool does not become a
bottleneck (Again see HBASE-17083)
(From Yu Li up in HBASE-17083)
Because an HBase RegionServer is a HDFS client, you can enable hedged reads in HBase, by adding
the following properties to the RegionServer’s hbase-site.xml and tuning the values to suit your
environment.
Configuration for Hedged Reads
•dfs.client.hedged.read.threadpool.size - the number of threads dedicated to servicing hedged
reads. If this is set to 0 (the default), hedged reads are disabled.
•dfs.client.hedged.read.threshold.millis - the number of milliseconds to wait before spawning
a second read thread.
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Example 39. Hedged Reads Configuration Example
<property>
Ê <name>dfs.client.hedged.read.threadpool.size</name>
Ê <value>20</value> <!-- 20 threads -->
</property>
<property>
Ê <name>dfs.client.hedged.read.threshold.millis</name>
Ê <value>10</value> <!-- 10 milliseconds -->
</property>
Use the following metrics to tune the settings for hedged reads on your cluster. See HBase Metrics
for more information.
Metrics for Hedged Reads
•hedgedReadOps - the number of times hedged read threads have been triggered. This could
indicate that read requests are often slow, or that hedged reads are triggered too quickly.
•hedgeReadOpsWin - the number of times the hedged read thread was faster than the original
thread. This could indicate that a given RegionServer is having trouble servicing requests.
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Chapter 123. Deleting from HBase
123.1. Using HBase Tables as Queues
HBase tables are sometimes used as queues. In this case, special care must be taken to regularly
perform major compactions on tables used in this manner. As is documented in Data Model,
marking rows as deleted creates additional StoreFiles which then need to be processed on reads.
Tombstones only get cleaned up with major compactions.
See also Compaction and Admin.majorCompact.
123.2. Delete RPC Behavior
Be aware that Table.delete(Delete) doesn’t use the writeBuffer. It will execute an RegionServer RPC
with each invocation. For a large number of deletes, consider Table.delete(List).
See hbase.client.Delete
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Chapter 124. HDFS
Because HBase runs on HDFS it is important to understand how it works and how it affects HBase.
124.1. Current Issues With Low-Latency Reads
The original use-case for HDFS was batch processing. As such, there low-latency reads were
historically not a priority. With the increased adoption of Apache HBase this is changing, and
several improvements are already in development. See the Umbrella Jira Ticket for HDFS
Improvements for HBase.
124.2. Leveraging local data
Since Hadoop 1.0.0 (also 0.22.1, 0.23.1, CDH3u3 and HDP 1.0) via HDFS-2246, it is possible for the
DFSClient to take a "short circuit" and read directly from the disk instead of going through the
DataNode when the data is local. What this means for HBase is that the RegionServers can read
directly off their machine’s disks instead of having to open a socket to talk to the DataNode, the
former being generally much faster. See JD’s Performance Talk. Also see HBase, mail # dev - read
short circuit thread for more discussion around short circuit reads.
To enable "short circuit" reads, it will depend on your version of Hadoop. The original shortcircuit
read patch was much improved upon in Hadoop 2 in HDFS-347. See http://blog.cloudera.com/blog/
2013/08/how-improved-short-circuit-local-reads-bring-better-performance-and-security-to-hadoop/
for details on the difference between the old and new implementations. See Hadoop shortcircuit
reads configuration page for how to enable the latter, better version of shortcircuit. For example,
here is a minimal config. enabling short-circuit reads added to hbase-site.xml:
<property>
Ê <name>dfs.client.read.shortcircuit</name>
Ê <value>true</value>
Ê <description>
Ê This configuration parameter turns on short-circuit local reads.
Ê </description>
</property>
<property>
Ê <name>dfs.domain.socket.path</name>
Ê <value>/home/stack/sockets/short_circuit_read_socket_PORT</value>
Ê <description>
Ê Optional. This is a path to a UNIX domain socket that will be used for
Ê communication between the DataNode and local HDFS clients.
Ê If the string "_PORT" is present in this path, it will be replaced by the
Ê TCP port of the DataNode.
Ê </description>
</property>
Be careful about permissions for the directory that hosts the shared domain socket; dfsclient will
complain if open to other than the hbase user.
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If you are running on an old Hadoop, one that is without HDFS-347 but that has HDFS-2246, you
must set two configurations. First, the hdfs-site.xml needs to be amended. Set the property
dfs.block.local-path-access.user to be the only user that can use the shortcut. This has to be the
user that started HBase. Then in hbase-site.xml, set dfs.client.read.shortcircuit to be true
Services — at least the HBase RegionServers — will need to be restarted in order to pick up the new
configurations.
dfs.client.read.shortcircuit.buffer.size
The default for this value is too high when running on a highly trafficked HBase. In
HBase, if this value has not been set, we set it down from the default of 1M to 128k
(Since HBase 0.98.0 and 0.96.1). See HBASE-8143 HBase on Hadoop 2 with local
short circuit reads (ssr) causes OOM). The Hadoop DFSClient in HBase will allocate
a direct byte buffer of this size for each block it has open; given HBase keeps its
HDFS files open all the time, this can add up quickly.
124.3. Performance Comparisons of HBase vs. HDFS
A fairly common question on the dist-list is why HBase isn’t as performant as HDFS files in a batch
context (e.g., as a MapReduce source or sink). The short answer is that HBase is doing a lot more
than HDFS (e.g., reading the KeyValues, returning the most current row or specified timestamps,
etc.), and as such HBase is 4-5 times slower than HDFS in this processing context. There is room for
improvement and this gap will, over time, be reduced, but HDFS will always be faster in this use-
case.
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Chapter 125. Amazon EC2
Performance questions are common on Amazon EC2 environments because it is a shared
environment. You will not see the same throughput as a dedicated server. In terms of running tests
on EC2, run them several times for the same reason (i.e., it’s a shared environment and you don’t
know what else is happening on the server).
If you are running on EC2 and post performance questions on the dist-list, please state this fact up-
front that because EC2 issues are practically a separate class of performance issues.
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Chapter 126. Collocating HBase and
MapReduce
It is often recommended to have different clusters for HBase and MapReduce. A better qualification
of this is: don’t collocate an HBase that serves live requests with a heavy MR workload. OLTP and
OLAP-optimized systems have conflicting requirements and one will lose to the other, usually the
former. For example, short latency-sensitive disk reads will have to wait in line behind longer reads
that are trying to squeeze out as much throughput as possible. MR jobs that write to HBase will also
generate flushes and compactions, which will in turn invalidate blocks in the Block Cache.
If you need to process the data from your live HBase cluster in MR, you can ship the deltas with
CopyTable or use replication to get the new data in real time on the OLAP cluster. In the worst case,
if you really need to collocate both, set MR to use less Map and Reduce slots than you’d normally
configure, possibly just one.
When HBase is used for OLAP operations, it’s preferable to set it up in a hardened way like
configuring the ZooKeeper session timeout higher and giving more memory to the MemStores (the
argument being that the Block Cache won’t be used much since the workloads are usually long
scans).
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Chapter 127. Case Studies
For Performance and Troubleshooting Case Studies, see Apache HBase Case Studies.
465
Troubleshooting and Debugging
Apache HBase
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Chapter 128. General Guidelines
Always start with the master log (TODO: Which lines?). Normally it’s just printing the same lines
over and over again. If not, then there’s an issue. Google or search-hadoop.com should return some
hits for those exceptions you’re seeing.
An error rarely comes alone in Apache HBase, usually when something gets screwed up what will
follow may be hundreds of exceptions and stack traces coming from all over the place. The best
way to approach this type of problem is to walk the log up to where it all began, for example one
trick with RegionServers is that they will print some metrics when aborting so grepping for Dump
should get you around the start of the problem.
RegionServer suicides are 'normal', as this is what they do when something goes wrong. For
example, if ulimit and max transfer threads (the two most important initial settings, see [ulimit]
and dfs.datanode.max.transfer.threads ) aren’t changed, it will make it impossible at some point for
DataNodes to create new threads that from the HBase point of view is seen as if HDFS was gone.
Think about what would happen if your MySQL database was suddenly unable to access files on
your local file system, well it’s the same with HBase and HDFS. Another very common reason to see
RegionServers committing seppuku is when they enter prolonged garbage collection pauses that
last longer than the default ZooKeeper session timeout. For more information on GC pauses, see the
3 part blog post by Todd Lipcon and Long GC pauses above.
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Chapter 129. Logs
The key process logs are as follows… (replace <user> with the user that started the service, and
<hostname> for the machine name)
NameNode: $HADOOP_HOME/logs/hadoop-<user>-namenode-<hostname>.log
DataNode: $HADOOP_HOME/logs/hadoop-<user>-datanode-<hostname>.log
JobTracker: $HADOOP_HOME/logs/hadoop-<user>-jobtracker-<hostname>.log
TaskTracker: $HADOOP_HOME/logs/hadoop-<user>-tasktracker-<hostname>.log
HMaster: $HBASE_HOME/logs/hbase-<user>-master-<hostname>.log
RegionServer: $HBASE_HOME/logs/hbase-<user>-regionserver-<hostname>.log
ZooKeeper: TODO
129.1. Log Locations
For stand-alone deployments the logs are obviously going to be on a single machine, however this is
a development configuration only. Production deployments need to run on a cluster.
129.1.1. NameNode
The NameNode log is on the NameNode server. The HBase Master is typically run on the NameNode
server, and well as ZooKeeper.
For smaller clusters the JobTracker/ResourceManager is typically run on the NameNode server as
well.
129.1.2. DataNode
Each DataNode server will have a DataNode log for HDFS, as well as a RegionServer log for HBase.
Additionally, each DataNode server will also have a TaskTracker/NodeManager log for MapReduce
task execution.
129.2. Log Levels
129.2.1. Enabling RPC-level logging
Enabling the RPC-level logging on a RegionServer can often give insight on timings at the server.
Once enabled, the amount of log spewed is voluminous. It is not recommended that you leave this
logging on for more than short bursts of time. To enable RPC-level logging, browse to the
RegionServer UI and click on Log Level. Set the log level to DEBUG for the package
org.apache.hadoop.ipc (That’s right, for hadoop.ipc, NOT, hbase.ipc). Then tail the RegionServers log.
Analyze.
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To disable, set the logging level back to INFO level.
129.3. JVM Garbage Collection Logs
All example Garbage Collection logs in this section are based on Java 8 output. The
introduction of Unified Logging in Java 9 and newer will result in very different
looking logs.
HBase is memory intensive, and using the default GC you can see long pauses in all threads
including the Juliet Pause aka "GC of Death". To help debug this or confirm this is happening GC
logging can be turned on in the Java virtual machine.
To enable, in hbase-env.sh, uncomment one of the below lines :
# This enables basic gc logging to the .out file.
# export SERVER_GC_OPTS="-verbose:gc -XX:+PrintGCDetails -XX:+PrintGCDateStamps"
# This enables basic gc logging to its own file.
# export SERVER_GC_OPTS="-verbose:gc -XX:+PrintGCDetails -XX:+PrintGCDateStamps
-Xloggc:<FILE-PATH>"
# This enables basic GC logging to its own file with automatic log rolling. Only
applies to jdk 1.6.0_34+ and 1.7.0_2+.
# export SERVER_GC_OPTS="-verbose:gc -XX:+PrintGCDetails -XX:+PrintGCDateStamps
-Xloggc:<FILE-PATH> -XX:+UseGCLogFileRotation -XX:NumberOfGCLogFiles=1
-XX:GCLogFileSize=512M"
# If <FILE-PATH> is not replaced, the log file(.gc) would be generated in the
HBASE_LOG_DIR.
At this point you should see logs like so:
64898.952: [GC [1 CMS-initial-mark: 2811538K(3055704K)] 2812179K(3061272K), 0.0007360
secs] [Times: user=0.00 sys=0.00, real=0.00 secs]
64898.953: [CMS-concurrent-mark-start]
64898.971: [GC 64898.971: [ParNew: 5567K->576K(5568K), 0.0101110 secs] 2817105K-
>2812715K(3061272K), 0.0102200 secs] [Times: user=0.07 sys=0.00, real=0.01 secs]
In this section, the first line indicates a 0.0007360 second pause for the CMS to initially mark. This
pauses the entire VM, all threads for that period of time.
The third line indicates a "minor GC", which pauses the VM for 0.0101110 seconds - aka 10
milliseconds. It has reduced the "ParNew" from about 5.5m to 576k. Later on in this cycle we see:
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64901.445: [CMS-concurrent-mark: 1.542/2.492 secs] [Times: user=10.49 sys=0.33, real
=2.49 secs]
64901.445: [CMS-concurrent-preclean-start]
64901.453: [GC 64901.453: [ParNew: 5505K->573K(5568K), 0.0062440 secs] 2868746K-
>2864292K(3061272K), 0.0063360 secs] [Times: user=0.05 sys=0.00, real=0.01 secs]
64901.476: [GC 64901.476: [ParNew: 5563K->575K(5568K), 0.0072510 secs] 2869283K-
>2864837K(3061272K), 0.0073320 secs] [Times: user=0.05 sys=0.01, real=0.01 secs]
64901.500: [GC 64901.500: [ParNew: 5517K->573K(5568K), 0.0120390 secs] 2869780K-
>2865267K(3061272K), 0.0121150 secs] [Times: user=0.09 sys=0.00, real=0.01 secs]
64901.529: [GC 64901.529: [ParNew: 5507K->569K(5568K), 0.0086240 secs] 2870200K-
>2865742K(3061272K), 0.0087180 secs] [Times: user=0.05 sys=0.00, real=0.01 secs]
64901.554: [GC 64901.555: [ParNew: 5516K->575K(5568K), 0.0107130 secs] 2870689K-
>2866291K(3061272K), 0.0107820 secs] [Times: user=0.06 sys=0.00, real=0.01 secs]
64901.578: [CMS-concurrent-preclean: 0.070/0.133 secs] [Times: user=0.48 sys=0.01,
real=0.14 secs]
64901.578: [CMS-concurrent-abortable-preclean-start]
64901.584: [GC 64901.584: [ParNew: 5504K->571K(5568K), 0.0087270 secs] 2871220K-
>2866830K(3061272K), 0.0088220 secs] [Times: user=0.05 sys=0.00, real=0.01 secs]
64901.609: [GC 64901.609: [ParNew: 5512K->569K(5568K), 0.0063370 secs] 2871771K-
>2867322K(3061272K), 0.0064230 secs] [Times: user=0.06 sys=0.00, real=0.01 secs]
64901.615: [CMS-concurrent-abortable-preclean: 0.007/0.037 secs] [Times: user=0.13
sys=0.00, real=0.03 secs]
64901.616: [GC[YG occupancy: 645 K (5568 K)]64901.616: [Rescan (parallel) , 0.0020210
secs]64901.618: [weak refs processing, 0.0027950 secs] [1 CMS-remark: 2866753K(
3055704K)] 2867399K(3061272K), 0.0049380 secs] [Times: user=0.00 sys=0.01, real=0.01
secs]
64901.621: [CMS-concurrent-sweep-start]
The first line indicates that the CMS concurrent mark (finding garbage) has taken 2.4 seconds. But
this is a concurrent 2.4 seconds, Java has not been paused at any point in time.
There are a few more minor GCs, then there is a pause at the 2nd last line:
64901.616: [GC[YG occupancy: 645 K (5568 K)]64901.616: [Rescan (parallel) , 0.0020210
secs]64901.618: [weak refs processing, 0.0027950 secs] [1 CMS-remark: 2866753K(
3055704K)] 2867399K(3061272K), 0.0049380 secs] [Times: user=0.00 sys=0.01, real=0.01
secs]
The pause here is 0.0049380 seconds (aka 4.9 milliseconds) to 'remark' the heap.
At this point the sweep starts, and you can watch the heap size go down:
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64901.637: [GC 64901.637: [ParNew: 5501K->569K(5568K), 0.0097350 secs] 2871958K-
>2867441K(3061272K), 0.0098370 secs] [Times: user=0.05 sys=0.00, real=0.01 secs]
... lines removed ...
64904.936: [GC 64904.936: [ParNew: 5532K->568K(5568K), 0.0070720 secs] 1365024K-
>1360689K(3061272K), 0.0071930 secs] [Times: user=0.05 sys=0.00, real=0.01 secs]
64904.953: [CMS-concurrent-sweep: 2.030/3.332 secs] [Times: user=9.57 sys=0.26, real
=3.33 secs]
At this point, the CMS sweep took 3.332 seconds, and heap went from about ~ 2.8 GB to 1.3 GB
(approximate).
The key points here is to keep all these pauses low. CMS pauses are always low, but if your ParNew
starts growing, you can see minor GC pauses approach 100ms, exceed 100ms and hit as high at
400ms.
This can be due to the size of the ParNew, which should be relatively small. If your ParNew is very
large after running HBase for a while, in one example a ParNew was about 150MB, then you might
have to constrain the size of ParNew (The larger it is, the longer the collections take but if it’s too
small, objects are promoted to old gen too quickly). In the below we constrain new gen size to 64m.
Add the below line in hbase-env.sh:
export SERVER_GC_OPTS="$SERVER_GC_OPTS -XX:NewSize=64m -XX:MaxNewSize=64m"
Similarly, to enable GC logging for client processes, uncomment one of the below lines in hbase-
env.sh:
# This enables basic gc logging to the .out file.
# export CLIENT_GC_OPTS="-verbose:gc -XX:+PrintGCDetails -XX:+PrintGCDateStamps"
# This enables basic gc logging to its own file.
# export CLIENT_GC_OPTS="-verbose:gc -XX:+PrintGCDetails -XX:+PrintGCDateStamps
-Xloggc:<FILE-PATH>"
# This enables basic GC logging to its own file with automatic log rolling. Only
applies to jdk 1.6.0_34+ and 1.7.0_2+.
# export CLIENT_GC_OPTS="-verbose:gc -XX:+PrintGCDetails -XX:+PrintGCDateStamps
-Xloggc:<FILE-PATH> -XX:+UseGCLogFileRotation -XX:NumberOfGCLogFiles=1
-XX:GCLogFileSize=512M"
# If <FILE-PATH> is not replaced, the log file(.gc) would be generated in the
HBASE_LOG_DIR .
For more information on GC pauses, see the 3 part blog post by Todd Lipcon and Long GC pauses
above.
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Chapter 130. Resources
130.1. search-hadoop.com
search-hadoop.com indexes all the mailing lists and is great for historical searches. Search here
first when you have an issue as its more than likely someone has already had your problem.
130.2. Mailing Lists
Ask a question on the Apache HBase mailing lists. The 'dev' mailing list is aimed at the community
of developers actually building Apache HBase and for features currently under development, and
'user' is generally used for questions on released versions of Apache HBase. Before going to the
mailing list, make sure your question has not already been answered by searching the mailing list
archives first. Use search-hadoop.com. Take some time crafting your question. See Getting Answers
for ideas on crafting good questions. A quality question that includes all context and exhibits
evidence the author has tried to find answers in the manual and out on lists is more likely to get a
prompt response.
130.3. Slack
See http://apache-hbase.slack.com Channel on Slack
130.4. IRC
(You will probably get a more prompt response on the Slack channel)
#hbase on irc.freenode.net
130.5. JIRA
JIRA is also really helpful when looking for Hadoop/HBase-specific issues.
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Chapter 131. Tools
131.1. Builtin Tools
131.1.1. Master Web Interface
The Master starts a web-interface on port 16010 by default.
The Master web UI lists created tables and their definition (e.g., ColumnFamilies, blocksize, etc.).
Additionally, the available RegionServers in the cluster are listed along with selected high-level
metrics (requests, number of regions, usedHeap, maxHeap). The Master web UI allows navigation
to each RegionServer’s web UI.
131.1.2. RegionServer Web Interface
RegionServers starts a web-interface on port 16030 by default.
The RegionServer web UI lists online regions and their start/end keys, as well as point-in-time
RegionServer metrics (requests, regions, storeFileIndexSize, compactionQueueSize, etc.).
See HBase Metrics for more information in metric definitions.
131.1.3. zkcli
zkcli is a very useful tool for investigating ZooKeeper-related issues. To invoke:
./hbase zkcli -server host:port <cmd> <args>
The commands (and arguments) are:
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Ê connect host:port
Ê get path [watch]
Ê ls path [watch]
Ê set path data [version]
Ê delquota [-n|-b] path
Ê quit
Ê printwatches on|off
Ê create [-s] [-e] path data acl
Ê stat path [watch]
Ê close
Ê ls2 path [watch]
Ê history
Ê listquota path
Ê setAcl path acl
Ê getAcl path
Ê sync path
Ê redo cmdno
Ê addauth scheme auth
Ê delete path [version]
Ê setquota -n|-b val path
131.1.4. Maintenance Mode
If the cluster has gotten stuck in some state and the standard techniques aren’t making progress, it
is possible to restart the cluster in "maintenance mode." This mode features drastically reduced
capabilities and surface area, making it easier to enact very low-level changes such as
repairing/recovering the hbase:meta table.
To enter maintenance mode, set hbase.master.maintenance_mode to true either in your hbase-
site.xml or via system propery when starting the master process (-D…=true). Entering and exiting
this mode requires a service restart, however the typical use will be when HBase Master is already
facing startup difficulties.
When maintenance mode is enabled, the master will host all system tables - ensure that it has
enough memory to do so. RegionServers will not be assigned any regions from user-space tables; in
fact, they will go completely unused while in maintenance mode. Additionally, the master will not
load any coprocessors, will not run any normalization or merge/split operations, and will not
enforce quotas.
131.2. External Tools
131.2.1. tail
tail is the command line tool that lets you look at the end of a file. Add the -f option and it will
refresh when new data is available. It’s useful when you are wondering what’s happening, for
example, when a cluster is taking a long time to shutdown or startup as you can just fire a new
terminal and tail the master log (and maybe a few RegionServers).
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131.2.2. top
top is probably one of the most important tools when first trying to see what’s running on a
machine and how the resources are consumed. Here’s an example from production system:
top - 14:46:59 up 39 days, 11:55, 1 user, load average: 3.75, 3.57, 3.84
Tasks: 309 total, 1 running, 308 sleeping, 0 stopped, 0 zombie
Cpu(s): 4.5%us, 1.6%sy, 0.0%ni, 91.7%id, 1.4%wa, 0.1%hi, 0.6%si, 0.0%st
Mem: 24414432k total, 24296956k used, 117476k free, 7196k buffers
Swap: 16008732k total, 14348k used, 15994384k free, 11106908k cached
Ê PID USER PR NI VIRT RES SHR S %CPU %MEM TIME+ COMMAND
15558 hadoop 18 -2 3292m 2.4g 3556 S 79 10.4 6523:52 java
13268 hadoop 18 -2 8967m 8.2g 4104 S 21 35.1 5170:30 java
Ê8895 hadoop 18 -2 1581m 497m 3420 S 11 2.1 4002:32 java
…
Here we can see that the system load average during the last five minutes is 3.75, which very
roughly means that on average 3.75 threads were waiting for CPU time during these 5 minutes. In
general, the perfect utilization equals to the number of cores, under that number the machine is
under utilized and over that the machine is over utilized. This is an important concept, see this
article to understand it more: http://www.linuxjournal.com/article/9001.
Apart from load, we can see that the system is using almost all its available RAM but most of it is
used for the OS cache (which is good). The swap only has a few KBs in it and this is wanted, high
numbers would indicate swapping activity which is the nemesis of performance of Java systems.
Another way to detect swapping is when the load average goes through the roof (although this
could also be caused by things like a dying disk, among others).
The list of processes isn’t super useful by default, all we know is that 3 java processes are using
about 111% of the CPUs. To know which is which, simply type c and each line will be expanded.
Typing 1 will give you the detail of how each CPU is used instead of the average for all of them like
shown here.
131.2.3. jps
jps is shipped with every JDK and gives the java process ids for the current user (if root, then it
gives the ids for all users). Example:
hadoop@sv4borg12:~$ jps
1322 TaskTracker
17789 HRegionServer
27862 Child
1158 DataNode
25115 HQuorumPeer
2950 Jps
19750 ThriftServer
18776 jmx
475
In order, we see a:
•Hadoop TaskTracker, manages the local Childs
•HBase RegionServer, serves regions
•Child, its MapReduce task, cannot tell which type exactly
•Hadoop TaskTracker, manages the local Childs
•Hadoop DataNode, serves blocks
•HQuorumPeer, a ZooKeeper ensemble member
•Jps, well… it’s the current process
•ThriftServer, it’s a special one will be running only if thrift was started
•jmx, this is a local process that’s part of our monitoring platform ( poorly named maybe). You
probably don’t have that.
You can then do stuff like checking out the full command line that started the process:
hadoop@sv4borg12:~$ ps aux | grep HRegionServer
hadoop 17789 155 35.2 9067824 8604364 ? S&lt;l Mar04 9855:48
/usr/java/jdk1.6.0_14/bin/java -Xmx8000m -XX:+DoEscapeAnalysis -XX:+AggressiveOpts
-XX:+UseConcMarkSweepGC -XX:NewSize=64m -XX:MaxNewSize=64m
-XX:CMSInitiatingOccupancyFraction=88 -verbose:gc -XX:+PrintGCDetails
-XX:+PrintGCTimeStamps -Xloggc:/export1/hadoop/logs/gc-hbase.log
-Dcom.sun.management.jmxremote.port=10102
-Dcom.sun.management.jmxremote.authenticate=true
-Dcom.sun.management.jmxremote.ssl=false
-Dcom.sun.management.jmxremote.password.file=/home/hadoop/hbase/conf/jmxremote.passwor
d -Dcom.sun.management.jmxremote -Dhbase.log.dir=/export1/hadoop/logs
-Dhbase.log.file=hbase-hadoop-regionserver-sv4borg12.log
-Dhbase.home.dir=/home/hadoop/hbase -Dhbase.id.str=hadoop
-Dhbase.root.logger=INFO,DRFA -Djava.library.path=/home/hadoop/hbase/lib/native/Linux
-amd64-64 -classpath /home/hadoop/hbase/bin/../conf:[many
jars]:/home/hadoop/hadoop/conf org.apache.hadoop.hbase.regionserver.HRegionServer
start
131.2.4. jstack
jstack is one of the most important tools when trying to figure out what a java process is doing
apart from looking at the logs. It has to be used in conjunction with jps in order to give it a process
id. It shows a list of threads, each one has a name, and they appear in the order that they were
created (so the top ones are the most recent threads). Here are a few example:
The main thread of a RegionServer waiting for something to do from the master:
476
"regionserver60020" prio=10 tid=0x0000000040ab4000 nid=0x45cf waiting on condition
[0x00007f16b6a96000..0x00007f16b6a96a70]
java.lang.Thread.State: TIMED_WAITING (parking)
Ê at sun.misc.Unsafe.park(Native Method)
Ê - parking to wait for <0x00007f16cd5c2f30> (a java.util.concurrent.locks
.AbstractQueuedSynchronizer$ConditionObject)
Ê at java.util.concurrent.locks.LockSupport.parkNanos(LockSupport.java:198)
Ê at java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject
.awaitNanos(AbstractQueuedSynchronizer.java:1963)
Ê at java.util.concurrent.LinkedBlockingQueue.poll(LinkedBlockingQueue.java:395)
Ê at org.apache.hadoop.hbase.regionserver.HRegionServer.run(HRegionServer.java:647)
Ê at java.lang.Thread.run(Thread.java:619)
The MemStore flusher thread that is currently flushing to a file:
477
"regionserver60020.cacheFlusher" daemon prio=10 tid=0x0000000040f4e000 nid=0x45eb in
Object.wait() [0x00007f16b5b86000..0x00007f16b5b87af0]
java.lang.Thread.State: WAITING (on object monitor)
Ê at java.lang.Object.wait(Native Method)
Ê at java.lang.Object.wait(Object.java:485)
Ê at org.apache.hadoop.ipc.Client.call(Client.java:803)
Ê - locked <0x00007f16cb14b3a8> (a org.apache.hadoop.ipc.Client$Call)
Ê at org.apache.hadoop.ipc.RPC$Invoker.invoke(RPC.java:221)
Ê at $Proxy1.complete(Unknown Source)
Ê at sun.reflect.GeneratedMethodAccessor38.invoke(Unknown Source)
Ê at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl
.java:25)
Ê at java.lang.reflect.Method.invoke(Method.java:597)
Ê at org.apache.hadoop.io.retry.RetryInvocationHandler.invokeMethod
(RetryInvocationHandler.java:82)
Ê at org.apache.hadoop.io.retry.RetryInvocationHandler.invoke(
RetryInvocationHandler.java:59)
Ê at $Proxy1.complete(Unknown Source)
Ê at org.apache.hadoop.hdfs.DFSClient$DFSOutputStream.closeInternal(DFSClient.java
:3390)
Ê - locked <0x00007f16cb14b470> (a org.apache.hadoop.hdfs.DFSClient$DFSOutputStream)
Ê at org.apache.hadoop.hdfs.DFSClient$DFSOutputStream.close(DFSClient.java:3304)
Ê at org.apache.hadoop.fs.FSDataOutputStream$PositionCache.close(FSDataOutputStream
.java:61)
Ê at org.apache.hadoop.fs.FSDataOutputStream.close(FSDataOutputStream.java:86)
Ê at org.apache.hadoop.hbase.io.hfile.HFile$Writer.close(HFile.java:650)
Ê at org.apache.hadoop.hbase.regionserver.StoreFile$Writer.close(StoreFile.java:853)
Ê at org.apache.hadoop.hbase.regionserver.Store.internalFlushCache(Store.java:467)
Ê - locked <0x00007f16d00e6f08> (a java.lang.Object)
Ê at org.apache.hadoop.hbase.regionserver.Store.flushCache(Store.java:427)
Ê at org.apache.hadoop.hbase.regionserver.Store.access$100(Store.java:80)
Ê at org.apache.hadoop.hbase.regionserver.Store$StoreFlusherImpl.flushCache(Store
.java:1359)
Ê at org.apache.hadoop.hbase.regionserver.HRegion.internalFlushcache(HRegion.java
:907)
Ê at org.apache.hadoop.hbase.regionserver.HRegion.internalFlushcache(HRegion.java
:834)
Ê at org.apache.hadoop.hbase.regionserver.HRegion.flushcache(HRegion.java:786)
Ê at org.apache.hadoop.hbase.regionserver.MemStoreFlusher.flushRegion
(MemStoreFlusher.java:250)
Ê at org.apache.hadoop.hbase.regionserver.MemStoreFlusher.flushRegion
(MemStoreFlusher.java:224)
Ê at org.apache.hadoop.hbase.regionserver.MemStoreFlusher.run(MemStoreFlusher.java
:146)
A handler thread that’s waiting for stuff to do (like put, delete, scan, etc.):
478
"IPC Server handler 16 on 60020" daemon prio=10 tid=0x00007f16b011d800 nid=0x4a5e
waiting on condition [0x00007f16afefd000..0x00007f16afefd9f0]
Ê java.lang.Thread.State: WAITING (parking)
Ê at sun.misc.Unsafe.park(Native Method)
Ê - parking to wait for <0x00007f16cd3f8dd8> (a java.util.concurrent.locks
.AbstractQueuedSynchronizer$ConditionObject)
Ê at java.util.concurrent.locks.LockSupport.park(LockSupport.java:158)
Ê at java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject
.await(AbstractQueuedSynchronizer.java:1925)
Ê at java.util.concurrent.LinkedBlockingQueue.take(LinkedBlockingQueue.java
:358)
Ê at org.apache.hadoop.hbase.ipc.HBaseServer$Handler.run(HBaseServer.java:
1013)
And one that’s busy doing an increment of a counter (it’s in the phase where it’s trying to create a
scanner in order to read the last value):
"IPC Server handler 66 on 60020" daemon prio=10 tid=0x00007f16b006e800 nid=0x4a90
runnable [0x00007f16acb77000..0x00007f16acb77cf0]
Ê java.lang.Thread.State: RUNNABLE
Ê at org.apache.hadoop.hbase.regionserver.KeyValueHeap.<init>(KeyValueHeap
.java:56)
Ê at org.apache.hadoop.hbase.regionserver.StoreScanner.<init>(StoreScanner
.java:79)
Ê at org.apache.hadoop.hbase.regionserver.Store.getScanner(Store.java:1202)
Ê at org.apache.hadoop.hbase.regionserver.HRegion$RegionScanner.<init>(
HRegion.java:2209)
Ê at org.apache.hadoop.hbase.regionserver.HRegion.instantiateInternalScanner
(HRegion.java:1063)
Ê at org.apache.hadoop.hbase.regionserver.HRegion.getScanner(HRegion.java:
1055)
Ê at org.apache.hadoop.hbase.regionserver.HRegion.getScanner(HRegion.java:
1039)
Ê at org.apache.hadoop.hbase.regionserver.HRegion.getLastIncrement(HRegion
.java:2875)
Ê at org.apache.hadoop.hbase.regionserver.HRegion.incrementColumnValue(
HRegion.java:2978)
Ê at org.apache.hadoop.hbase.regionserver.HRegionServer.incrementColumnValue
(HRegionServer.java:2433)
Ê at sun.reflect.GeneratedMethodAccessor20.invoke(Unknown Source)
Ê at sun.reflect.DelegatingMethodAccessorImpl.invoke
(DelegatingMethodAccessorImpl.java:25)
Ê at java.lang.reflect.Method.invoke(Method.java:597)
Ê at org.apache.hadoop.hbase.ipc.HBaseRPC$Server.call(HBaseRPC.java:560)
Ê at org.apache.hadoop.hbase.ipc.HBaseServer$Handler.run(HBaseServer.java:
1027)
A thread that receives data from HDFS:
479
"IPC Client (47) connection to sv4borg9/10.4.24.40:9000 from hadoop" daemon prio=10
tid=0x00007f16a02d0000 nid=0x4fa3 runnable [0x00007f16b517d000..0x00007f16b517dbf0]
Ê java.lang.Thread.State: RUNNABLE
Ê at sun.nio.ch.EPollArrayWrapper.epollWait(Native Method)
Ê at sun.nio.ch.EPollArrayWrapper.poll(EPollArrayWrapper.java:215)
Ê at sun.nio.ch.EPollSelectorImpl.doSelect(EPollSelectorImpl.java:65)
Ê at sun.nio.ch.SelectorImpl.lockAndDoSelect(SelectorImpl.java:69)
Ê - locked <0x00007f17d5b68c00> (a sun.nio.ch.Util$1)
Ê - locked <0x00007f17d5b68be8> (a java.util.Collections$UnmodifiableSet)
Ê - locked <0x00007f1877959b50> (a sun.nio.ch.EPollSelectorImpl)
Ê at sun.nio.ch.SelectorImpl.select(SelectorImpl.java:80)
Ê at org.apache.hadoop.net.SocketIOWithTimeout$SelectorPool.select
(SocketIOWithTimeout.java:332)
Ê at org.apache.hadoop.net.SocketIOWithTimeout.doIO(SocketIOWithTimeout.java
:157)
Ê at org.apache.hadoop.net.SocketInputStream.read(SocketInputStream.java:155)
Ê at org.apache.hadoop.net.SocketInputStream.read(SocketInputStream.java:128)
Ê at java.io.FilterInputStream.read(FilterInputStream.java:116)
Ê at org.apache.hadoop.ipc.Client$Connection$PingInputStream.read(Client.java
:304)
Ê at java.io.BufferedInputStream.fill(BufferedInputStream.java:218)
Ê at java.io.BufferedInputStream.read(BufferedInputStream.java:237)
Ê - locked <0x00007f1808539178> (a java.io.BufferedInputStream)
Ê at java.io.DataInputStream.readInt(DataInputStream.java:370)
Ê at org.apache.hadoop.ipc.Client$Connection.receiveResponse(Client.java:569)
Ê at org.apache.hadoop.ipc.Client$Connection.run(Client.java:477)
And here is a master trying to recover a lease after a RegionServer died:
480
"LeaseChecker" daemon prio=10 tid=0x00000000407ef800 nid=0x76cd waiting on condition
[0x00007f6d0eae2000..0x00007f6d0eae2a70]
--
Ê java.lang.Thread.State: WAITING (on object monitor)
Ê at java.lang.Object.wait(Native Method)
Ê at java.lang.Object.wait(Object.java:485)
Ê at org.apache.hadoop.ipc.Client.call(Client.java:726)
Ê - locked <0x00007f6d1cd28f80> (a org.apache.hadoop.ipc.Client$Call)
Ê at org.apache.hadoop.ipc.RPC$Invoker.invoke(RPC.java:220)
Ê at $Proxy1.recoverBlock(Unknown Source)
Ê at org.apache.hadoop.hdfs.DFSClient$DFSOutputStream.processDatanodeError
(DFSClient.java:2636)
Ê at org.apache.hadoop.hdfs.DFSClient$DFSOutputStream.<init>(DFSClient.java
:2832)
Ê at org.apache.hadoop.hdfs.DFSClient.append(DFSClient.java:529)
Ê at org.apache.hadoop.hdfs.DistributedFileSystem.append(
DistributedFileSystem.java:186)
Ê at org.apache.hadoop.fs.FileSystem.append(FileSystem.java:530)
Ê at org.apache.hadoop.hbase.util.FSUtils.recoverFileLease(FSUtils.java:619)
Ê at org.apache.hadoop.hbase.regionserver.wal.HLog.splitLog(HLog.java:1322)
Ê at org.apache.hadoop.hbase.regionserver.wal.HLog.splitLog(HLog.java:1210)
Ê at org.apache.hadoop.hbase.master.HMaster.splitLogAfterStartup(HMaster.java
:648)
Ê at org.apache.hadoop.hbase.master.HMaster.joinCluster(HMaster.java:572)
Ê at org.apache.hadoop.hbase.master.HMaster.run(HMaster.java:503)
131.2.5. OpenTSDB
OpenTSDB is an excellent alternative to Ganglia as it uses Apache HBase to store all the time series
and doesn’t have to downsample. Monitoring your own HBase cluster that hosts OpenTSDB is a
good exercise.
Here’s an example of a cluster that’s suffering from hundreds of compactions launched almost all
around the same time, which severely affects the IO performance: (TODO: insert graph plotting
compactionQueueSize)
It’s a good practice to build dashboards with all the important graphs per machine and per cluster
so that debugging issues can be done with a single quick look. For example, at StumbleUpon there’s
one dashboard per cluster with the most important metrics from both the OS and Apache HBase.
You can then go down at the machine level and get even more detailed metrics.
131.2.6. clusterssh+top
clusterssh+top, it’s like a poor man’s monitoring system and it can be quite useful when you have
only a few machines as it’s very easy to setup. Starting clusterssh will give you one terminal per
machine and another terminal in which whatever you type will be retyped in every window. This
means that you can type top once and it will start it for all of your machines at the same time giving
you full view of the current state of your cluster. You can also tail all the logs at the same time, edit
files, etc.
481
Chapter 132. Client
For more information on the HBase client, see client.
132.1. ScannerTimeoutException or
UnknownScannerException
This is thrown if the time between RPC calls from the client to RegionServer exceeds the scan
timeout. For example, if Scan.setCaching is set to 500, then there will be an RPC call to fetch the next
batch of rows every 500 .next() calls on the ResultScanner because data is being transferred in
blocks of 500 rows to the client. Reducing the setCaching value may be an option, but setting this
value too low makes for inefficient processing on numbers of rows.
See Scan Caching.
132.2. Performance Differences in Thrift and Java APIs
Poor performance, or even ScannerTimeoutExceptions, can occur if Scan.setCaching is too high, as
discussed in ScannerTimeoutException or UnknownScannerException. If the Thrift client uses the
wrong caching settings for a given workload, performance can suffer compared to the Java API. To
set caching for a given scan in the Thrift client, use the scannerGetList(scannerId, numRows) method,
where numRows is an integer representing the number of rows to cache. In one case, it was found
that reducing the cache for Thrift scans from 1000 to 100 increased performance to near parity
with the Java API given the same queries.
See also Jesse Andersen’s blog post about using Scans with Thrift.
132.3. LeaseException when calling Scanner.next
In some situations clients that fetch data from a RegionServer get a LeaseException instead of the
usual ScannerTimeoutException or UnknownScannerException. Usually the source of the exception
is org.apache.hadoop.hbase.regionserver.Leases.removeLease(Leases.java:230) (line number may
vary). It tends to happen in the context of a slow/freezing RegionServer#next call. It can be
prevented by having hbase.rpc.timeout > hbase.client.scanner.timeout.period. Harsh J investigated
the issue as part of the mailing list thread HBase, mail # user - Lease does not exist exceptions
132.4. Shell or client application throws lots of scary
exceptions during normal operation
Since 0.20.0 the default log level for `org.apache.hadoop.hbase.*`is DEBUG.
On your clients, edit $HBASE_HOME/conf/log4j.properties and change this:
log4j.logger.org.apache.hadoop.hbase=DEBUG to this: log4j.logger.org.apache.hadoop.hbase=INFO, or
even log4j.logger.org.apache.hadoop.hbase=WARN.
482
132.5. Long Client Pauses With Compression
This is a fairly frequent question on the Apache HBase dist-list. The scenario is that a client is
typically inserting a lot of data into a relatively un-optimized HBase cluster. Compression can
exacerbate the pauses, although it is not the source of the problem.
See Table Creation: Pre-Creating Regions on the pattern for pre-creating regions and confirm that
the table isn’t starting with a single region.
See HBase Configurations for cluster configuration, particularly hbase.hstore.blockingStoreFiles,
hbase.hregion.memstore.block.multiplier, MAX_FILESIZE (region size), and MEMSTORE_FLUSHSIZE.
A slightly longer explanation of why pauses can happen is as follows: Puts are sometimes blocked
on the MemStores which are blocked by the flusher thread which is blocked because there are too
many files to compact because the compactor is given too many small files to compact and has to
compact the same data repeatedly. This situation can occur even with minor compactions.
Compounding this situation, Apache HBase doesn’t compress data in memory. Thus, the 64MB that
lives in the MemStore could become a 6MB file after compression - which results in a smaller
StoreFile. The upside is that more data is packed into the same region, but performance is achieved
by being able to write larger files - which is why HBase waits until the flushsize before writing a
new StoreFile. And smaller StoreFiles become targets for compaction. Without compression the
files are much bigger and don’t need as much compaction, however this is at the expense of I/O.
For additional information, see this thread on Long client pauses with compression.
132.6. Secure Client Connect ([Caused by
GSSException: No valid credentials provided…])
You may encounter the following error:
Secure Client Connect ([Caused by GSSException: No valid credentials provided
Ê (Mechanism level: Request is a replay (34) V PROCESS_TGS)])
This issue is caused by bugs in the MIT Kerberos replay_cache component, #1201 and #5924. These
bugs caused the old version of krb5-server to erroneously block subsequent requests sent from a
Principal. This caused krb5-server to block the connections sent from one Client (one HTable
instance with multi-threading connection instances for each RegionServer); Messages, such as
Request is a replay (34), are logged in the client log You can ignore the messages, because HTable
will retry 5 * 10 (50) times for each failed connection by default. HTable will throw IOException if
any connection to the RegionServer fails after the retries, so that the user client code for HTable
instance can handle it further. NOTE: HTable is deprecated in HBase 1.0, in favor of Table.
Alternatively, update krb5-server to a version which solves these issues, such as krb5-server-1.10.3.
See JIRA HBASE-10379 for more details.
483
132.7. ZooKeeper Client Connection Errors
Errors like this…
11/07/05 11:26:41 WARN zookeeper.ClientCnxn: Session 0x0 for server null,
Êunexpected error, closing socket connection and attempting reconnect
Êjava.net.ConnectException: Connection refused: no further information
Ê at sun.nio.ch.SocketChannelImpl.checkConnect(Native Method)
Ê at sun.nio.ch.SocketChannelImpl.finishConnect(Unknown Source)
Ê at org.apache.zookeeper.ClientCnxn$SendThread.run(ClientCnxn.java:1078)
Ê11/07/05 11:26:43 INFO zookeeper.ClientCnxn: Opening socket connection to
Êserver localhost/127.0.0.1:2181
Ê11/07/05 11:26:44 WARN zookeeper.ClientCnxn: Session 0x0 for server null,
Êunexpected error, closing socket connection and attempting reconnect
Êjava.net.ConnectException: Connection refused: no further information
Ê at sun.nio.ch.SocketChannelImpl.checkConnect(Native Method)
Ê at sun.nio.ch.SocketChannelImpl.finishConnect(Unknown Source)
Ê at org.apache.zookeeper.ClientCnxn$SendThread.run(ClientCnxn.java:1078)
Ê11/07/05 11:26:45 INFO zookeeper.ClientCnxn: Opening socket connection to
Êserver localhost/127.0.0.1:2181
…are either due to ZooKeeper being down, or unreachable due to network issues.
The utility zkcli may help investigate ZooKeeper issues.
132.8. Client running out of memory though heap size
seems to be stable (but the off-heap/direct heap keeps
growing)
You are likely running into the issue that is described and worked through in the mail thread
HBase, mail # user - Suspected memory leak and continued over in HBase, mail # dev - FeedbackRe:
Suspected memory leak. A workaround is passing your client-side JVM a reasonable value for
-XX:MaxDirectMemorySize. By default, the MaxDirectMemorySize is equal to your -Xmx max heapsize
setting (if -Xmx is set). Try setting it to something smaller (for example, one user had success setting
it to 1g when they had a client-side heap of 12g). If you set it too small, it will bring on FullGCs so
keep it a bit hefty. You want to make this setting client-side only especially if you are running the
new experimental server-side off-heap cache since this feature depends on being able to use big
direct buffers (You may have to keep separate client-side and server-side config dirs).
484
132.9. Secure Client Cannot Connect ([Caused by
GSSException: No valid credentials
provided(Mechanism level: Failed to find any
Kerberos tgt)])
There can be several causes that produce this symptom.
First, check that you have a valid Kerberos ticket. One is required in order to set up communication
with a secure Apache HBase cluster. Examine the ticket currently in the credential cache, if any, by
running the klist command line utility. If no ticket is listed, you must obtain a ticket by running the
kinit command with either a keytab specified, or by interactively entering a password for the
desired principal.
Then, consult the Java Security Guide troubleshooting section. The most common problem
addressed there is resolved by setting javax.security.auth.useSubjectCredsOnly system property
value to false.
Because of a change in the format in which MIT Kerberos writes its credentials cache, there is a bug
in the Oracle JDK 6 Update 26 and earlier that causes Java to be unable to read the Kerberos
credentials cache created by versions of MIT Kerberos 1.8.1 or higher. If you have this problematic
combination of components in your environment, to work around this problem, first log in with
kinit and then immediately refresh the credential cache with kinit -R. The refresh will rewrite the
credential cache without the problematic formatting.
Prior to JDK 1.4, the JCE was an unbundled product, and as such, the JCA and JCE were regularly
referred to as separate, distinct components. As JCE is now bundled in the JDK 7.0, the distinction is
becoming less apparent. Since the JCE uses the same architecture as the JCA, the JCE should be more
properly thought of as a part of the JCA.
You may need to install the Java Cryptography Extension, or JCE because of JDK 1.5 or earlier
version. Insure the JCE jars are on the classpath on both server and client systems.
You may also need to download the unlimited strength JCE policy files. Uncompress and extract the
downloaded file, and install the policy jars into <java-home>/lib/security.
485
Chapter 133. MapReduce
133.1. You Think You’re On The Cluster, But You’re
Actually Local
This following stacktrace happened using ImportTsv, but things like this can happen on any job with
a mis-configuration.
Ê WARN mapred.LocalJobRunner: job_local_0001
java.lang.IllegalArgumentException: Can't read partitions file
Ê at
org.apache.hadoop.hbase.mapreduce.hadoopbackport.TotalOrderPartitioner.setConf(TotalOr
derPartitioner.java:111)
Ê at org.apache.hadoop.util.ReflectionUtils.setConf(ReflectionUtils.java:62)
Ê at org.apache.hadoop.util.ReflectionUtils.newInstance(ReflectionUtils.java:117)
Ê at org.apache.hadoop.mapred.MapTask$NewOutputCollector.<init>(MapTask.java:560)
Ê at org.apache.hadoop.mapred.MapTask.runNewMapper(MapTask.java:639)
Ê at org.apache.hadoop.mapred.MapTask.run(MapTask.java:323)
Ê at org.apache.hadoop.mapred.LocalJobRunner$Job.run(LocalJobRunner.java:210)
Caused by: java.io.FileNotFoundException: File _partition.lst does not exist.
Ê at
org.apache.hadoop.fs.RawLocalFileSystem.getFileStatus(RawLocalFileSystem.java:383)
Ê at
org.apache.hadoop.fs.FilterFileSystem.getFileStatus(FilterFileSystem.java:251)
Ê at org.apache.hadoop.fs.FileSystem.getLength(FileSystem.java:776)
Ê at org.apache.hadoop.io.SequenceFile$Reader.<init>(SequenceFile.java:1424)
Ê at org.apache.hadoop.io.SequenceFile$Reader.<init>(SequenceFile.java:1419)
Ê at
org.apache.hadoop.hbase.mapreduce.hadoopbackport.TotalOrderPartitioner.readPartitions(
TotalOrderPartitioner.java:296)
…see the critical portion of the stack? It’s…
at org.apache.hadoop.mapred.LocalJobRunner$Job.run(LocalJobRunner.java:210)
LocalJobRunner means the job is running locally, not on the cluster.
To solve this problem, you should run your MR job with your HADOOP_CLASSPATH set to include the
HBase dependencies. The "hbase classpath" utility can be used to do this easily. For example
(substitute VERSION with your HBase version):
HADOOP_CLASSPATH=`hbase classpath` hadoop jar $HBASE_HOME/hbase-mapreduce-VERSION.jar
rowcounter usertable
See HBase, MapReduce, and the CLASSPATH for more information on HBase MapReduce jobs and
486
classpaths.
133.2. Launching a job, you get
java.lang.IllegalAccessError:
com/google/protobuf/HBaseZeroCopyByteString or
class com.google.protobuf.ZeroCopyLiteralByteString
cannot access its superclass
com.google.protobuf.LiteralByteString
See HBASE-10304 Running an hbase job jar: IllegalAccessError: class
com.google.protobuf.ZeroCopyLiteralByteString cannot access its superclass
com.google.protobuf.LiteralByteString and HBASE-11118 non environment variable solution for
"IllegalAccessError: class com.google.protobuf.ZeroCopyLiteralByteString cannot access its
superclass com.google.protobuf.LiteralByteString". The issue can also show up when trying to run
spark jobs. See HBASE-10877 HBase non-retriable exception list should be expanded.
487
Chapter 134. NameNode
For more information on the NameNode, see HDFS.
134.1. HDFS Utilization of Tables and Regions
To determine how much space HBase is using on HDFS use the hadoop shell commands from the
NameNode. For example…
hadoop fs -dus /hbase/
…returns the summarized disk utilization for all HBase objects.
hadoop fs -dus /hbase/myTable
…returns the summarized disk utilization for the HBase table 'myTable'.
hadoop fs -du /hbase/myTable
…returns a list of the regions under the HBase table 'myTable' and their disk utilization.
For more information on HDFS shell commands, see the HDFS FileSystem Shell documentation.
134.2. Browsing HDFS for HBase Objects
Sometimes it will be necessary to explore the HBase objects that exist on HDFS. These objects could
include the WALs (Write Ahead Logs), tables, regions, StoreFiles, etc. The easiest way to do this is
with the NameNode web application that runs on port 50070. The NameNode web application will
provide links to the all the DataNodes in the cluster so that they can be browsed seamlessly.
The HDFS directory structure of HBase tables in the cluster is…
/hbase
Ê /data
Ê /<Namespace> (Namespaces in the cluster)
Ê /<Table> (Tables in the cluster)
Ê /<Region> (Regions for the table)
Ê /<ColumnFamily> (ColumnFamilies for the Region for the table)
Ê /<StoreFile> (StoreFiles for the ColumnFamily for the
Regions for the table)
The HDFS directory structure of HBase WAL is..
488
/hbase
Ê /WALs
Ê /<RegionServer> (RegionServers)
Ê /<WAL> (WAL files for the RegionServer)
See the HDFS User Guide for other non-shell diagnostic utilities like fsck.
134.2.1. Zero size WALs with data in them
Problem: when getting a listing of all the files in a RegionServer’s WALs directory, one file has a size
of 0 but it contains data.
Answer: It’s an HDFS quirk. A file that’s currently being written to will appear to have a size of 0
but once it’s closed it will show its true size
134.2.2. Use Cases
Two common use-cases for querying HDFS for HBase objects is research the degree of
uncompaction of a table. If there are a large number of StoreFiles for each ColumnFamily it could
indicate the need for a major compaction. Additionally, after a major compaction if the resulting
StoreFile is "small" it could indicate the need for a reduction of ColumnFamilies for the table.
134.3. Unexpected Filesystem Growth
If you see an unexpected spike in filesystem usage by HBase, two possible culprits are snapshots
and WALs.
Snapshots
When you create a snapshot, HBase retains everything it needs to recreate the table’s state at
that time of the snapshot. This includes deleted cells or expired versions. For this reason, your
snapshot usage pattern should be well-planned, and you should prune snapshots that you no
longer need. Snapshots are stored in /hbase/.hbase-snapshot, and archives needed to restore
snapshots are stored in /hbase/archive/<_tablename_>/<_region_>/<_column_family_>/.
*Do not* manage snapshots or archives manually via HDFS. HBase provides APIs and
HBase Shell commands for managing them. For more information, see
<<ops.snapshots>>.
WAL
Write-ahead logs (WALs) are stored in subdirectories of the HBase root directory, typically
/hbase/, depending on their status. Already-processed WALs are stored in /hbase/oldWALs/ and
corrupt WALs are stored in /hbase/.corrupt/ for examination. If the size of one of these
subdirectories is growing, examine the HBase server logs to find the root cause for why WALs
are not being processed correctly.
If you use replication and /hbase/oldWALs/ is using more space than you expect, remember that
489
WALs are saved when replication is disabled, as long as there are peers.
Do not manage WALs manually via HDFS.
490
Chapter 135. Network
135.1. Network Spikes
If you are seeing periodic network spikes you might want to check the compactionQueues to see if
major compactions are happening.
See Managed Compactions for more information on managing compactions.
135.2. Loopback IP
HBase expects the loopback IP Address to be 127.0.0.1.
135.3. Network Interfaces
Are all the network interfaces functioning correctly? Are you sure? See the Troubleshooting Case
Study in Case Studies.
491
Chapter 136. RegionServer
For more information on the RegionServers, see RegionServer.
136.1. Startup Errors
136.1.1. Master Starts, But RegionServers Do Not
The Master believes the RegionServers have the IP of 127.0.0.1 - which is localhost and resolves to
the master’s own localhost.
The RegionServers are erroneously informing the Master that their IP addresses are 127.0.0.1.
Modify /etc/hosts on the region servers, from…
# Do not remove the following line, or various programs
# that require network functionality will fail.
127.0.0.1 fully.qualified.regionservername regionservername localhost
.localdomain localhost
::1 localhost6.localdomain6 localhost6
... to (removing the master node’s name from localhost)…
# Do not remove the following line, or various programs
# that require network functionality will fail.
127.0.0.1 localhost.localdomain localhost
::1 localhost6.localdomain6 localhost6
136.1.2. Compression Link Errors
Since compression algorithms such as LZO need to be installed and configured on each cluster this
is a frequent source of startup error. If you see messages like this…
11/02/20 01:32:15 ERROR lzo.GPLNativeCodeLoader: Could not load native gpl library
java.lang.UnsatisfiedLinkError: no gplcompression in java.library.path
Ê at java.lang.ClassLoader.loadLibrary(ClassLoader.java:1734)
Ê at java.lang.Runtime.loadLibrary0(Runtime.java:823)
Ê at java.lang.System.loadLibrary(System.java:1028)
... then there is a path issue with the compression libraries. See the Configuration section on
link:[LZO compression configuration].
136.1.3. RegionServer aborts due to lack of hsync for filesystem
In order to provide data durability for writes to the cluster HBase relies on the ability to durably
492
save state in a write ahead log. When using a version of Apache Hadoop Common’s filesystem API
that supports checking on the availability of needed calls, HBase will proactively abort the cluster if
it finds it can’t operate safely.
For RegionServer roles, the failure will show up in logs like this:
2018-04-05 11:36:22,785 ERROR [regionserver/192.168.1.123:16020]
wal.AsyncFSWALProvider: The RegionServer async write ahead log provider relies on the
ability to call hflush and hsync for proper operation during component failures, but
the current FileSystem does not support doing so. Please check the config value of
'hbase.wal.dir' and ensure it points to a FileSystem mount that has suitable
capabilities for output streams.
2018-04-05 11:36:22,799 ERROR [regionserver/192.168.1.123:16020]
regionserver.HRegionServer: ***** ABORTING region server
192.168.1.123,16020,1522946074234: Unhandled: cannot get log writer *****
java.io.IOException: cannot get log writer
Ê at
org.apache.hadoop.hbase.wal.AsyncFSWALProvider.createAsyncWriter(AsyncFSWALProvider.ja
va:112)
Ê at
org.apache.hadoop.hbase.regionserver.wal.AsyncFSWAL.createWriterInstance(AsyncFSWAL.ja
va:612)
Ê at
org.apache.hadoop.hbase.regionserver.wal.AsyncFSWAL.createWriterInstance(AsyncFSWAL.ja
va:124)
Ê at
org.apache.hadoop.hbase.regionserver.wal.AbstractFSWAL.rollWriter(AbstractFSWAL.java:7
59)
Ê at
org.apache.hadoop.hbase.regionserver.wal.AbstractFSWAL.rollWriter(AbstractFSWAL.java:4
89)
Ê at
org.apache.hadoop.hbase.regionserver.wal.AsyncFSWAL.<init>(AsyncFSWAL.java:251)
Ê at
org.apache.hadoop.hbase.wal.AsyncFSWALProvider.createWAL(AsyncFSWALProvider.java:69)
Ê at
org.apache.hadoop.hbase.wal.AsyncFSWALProvider.createWAL(AsyncFSWALProvider.java:44)
Ê at
org.apache.hadoop.hbase.wal.AbstractFSWALProvider.getWAL(AbstractFSWALProvider.java:13
8)
Ê at
org.apache.hadoop.hbase.wal.AbstractFSWALProvider.getWAL(AbstractFSWALProvider.java:57
)
Ê at org.apache.hadoop.hbase.wal.WALFactory.getWAL(WALFactory.java:252)
Ê at
org.apache.hadoop.hbase.regionserver.HRegionServer.getWAL(HRegionServer.java:2105)
Ê at
org.apache.hadoop.hbase.regionserver.HRegionServer.buildServerLoad(HRegionServer.java:
1326)
Ê at
493
org.apache.hadoop.hbase.regionserver.HRegionServer.tryRegionServerReport(HRegionServer
.java:1191)
Ê at
org.apache.hadoop.hbase.regionserver.HRegionServer.run(HRegionServer.java:1007)
Ê at java.lang.Thread.run(Thread.java:745)
Caused by: org.apache.hadoop.hbase.util.CommonFSUtils$StreamLacksCapabilityException:
hflush and hsync
Ê at
org.apache.hadoop.hbase.io.asyncfs.AsyncFSOutputHelper.createOutput(AsyncFSOutputHelpe
r.java:69)
Ê at
org.apache.hadoop.hbase.regionserver.wal.AsyncProtobufLogWriter.initOutput(AsyncProtob
ufLogWriter.java:168)
Ê at
org.apache.hadoop.hbase.regionserver.wal.AbstractProtobufLogWriter.init(AbstractProtob
ufLogWriter.java:167)
Ê at
org.apache.hadoop.hbase.wal.AsyncFSWALProvider.createAsyncWriter(AsyncFSWALProvider.ja
va:99)
Ê ... 15 more
If you are attempting to run in standalone mode and see this error, please walk back through the
section Quick Start - Standalone HBase and ensure you have included all the given configuration
settings.
136.1.4. RegionServer aborts due to can not initialize access to HDFS
We will try to use AsyncFSWAL for HBase-2.x as it has better performance while consuming less
resources. But the problem for AsyncFSWAL is that it hacks into the internal of the DFSClient
implementation, so it will easily be broken when upgrading hadoop, even for a simple patch
release.
If you do not specify the wal provider, we will try to fall back to the old FSHLog if we fail to
initialize AsyncFSWAL, but it may not always work. The failure will show up in logs like this:
18/07/02 18:51:06 WARN concurrent.DefaultPromise: An exception was
thrown by
org.apache.hadoop.hbase.io.asyncfs.FanOutOneBlockAsyncDFSOutputHelper$13.operationComp
lete()
java.lang.Error: Couldn't properly initialize access to HDFS
internals. Please update your WAL Provider to not make use of the
'asyncfs' provider. See HBASE-16110 for more information.
Ê at
org.apache.hadoop.hbase.io.asyncfs.FanOutOneBlockAsyncDFSOutputSaslHelper.<clinit>(Fan
OutOneBlockAsyncDFSOutputSaslHelper.java:268)
Ê at
org.apache.hadoop.hbase.io.asyncfs.FanOutOneBlockAsyncDFSOutputHelper.initialize(FanOu
tOneBlockAsyncDFSOutputHelper.java:661)
Ê at
org.apache.hadoop.hbase.io.asyncfs.FanOutOneBlockAsyncDFSOutputHelper.access$300(FanOu
494
tOneBlockAsyncDFSOutputHelper.java:118)
Ê at
org.apache.hadoop.hbase.io.asyncfs.FanOutOneBlockAsyncDFSOutputHelper$13.operationComp
lete(FanOutOneBlockAsyncDFSOutputHelper.java:720)
Ê at
org.apache.hadoop.hbase.io.asyncfs.FanOutOneBlockAsyncDFSOutputHelper$13.operationComp
lete(FanOutOneBlockAsyncDFSOutputHelper.java:715)
Ê at
org.apache.hbase.thirdparty.io.netty.util.concurrent.DefaultPromise.notifyListener0(De
faultPromise.java:507)
Ê at
org.apache.hbase.thirdparty.io.netty.util.concurrent.DefaultPromise.notifyListeners0(D
efaultPromise.java:500)
Ê at
org.apache.hbase.thirdparty.io.netty.util.concurrent.DefaultPromise.notifyListenersNow
(DefaultPromise.java:479)
Ê at
org.apache.hbase.thirdparty.io.netty.util.concurrent.DefaultPromise.notifyListeners(De
faultPromise.java:420)
Ê at
org.apache.hbase.thirdparty.io.netty.util.concurrent.DefaultPromise.trySuccess(Default
Promise.java:104)
Ê at
org.apache.hbase.thirdparty.io.netty.channel.DefaultChannelPromise.trySuccess(DefaultC
hannelPromise.java:82)
Ê at
org.apache.hbase.thirdparty.io.netty.channel.epoll.AbstractEpollChannel$AbstractEpollU
nsafe.fulfillConnectPromise(AbstractEpollChannel.java:638)
Ê at
org.apache.hbase.thirdparty.io.netty.channel.epoll.AbstractEpollChannel$AbstractEpollU
nsafe.finishConnect(AbstractEpollChannel.java:676)
Ê at
org.apache.hbase.thirdparty.io.netty.channel.epoll.AbstractEpollChannel$AbstractEpollU
nsafe.epollOutReady(AbstractEpollChannel.java:552)
Ê at
org.apache.hbase.thirdparty.io.netty.channel.epoll.EpollEventLoop.processReady(EpollEv
entLoop.java:394)
Ê at
org.apache.hbase.thirdparty.io.netty.channel.epoll.EpollEventLoop.run(EpollEventLoop.j
ava:304)
Ê at
org.apache.hbase.thirdparty.io.netty.util.concurrent.SingleThreadEventExecutor$5.run(S
ingleThreadEventExecutor.java:858)
Ê at
org.apache.hbase.thirdparty.io.netty.util.concurrent.DefaultThreadFactory$DefaultRunna
bleDecorator.run(DefaultThreadFactory.java:138)
Ê at java.lang.Thread.run(Thread.java:748)
ÊCaused by: java.lang.NoSuchMethodException:
org.apache.hadoop.hdfs.DFSClient.decryptEncryptedDataEncryptionKey(org.apache.hadoop.f
s.FileEncryptionInfo)
Ê at java.lang.Class.getDeclaredMethod(Class.java:2130)
495
Ê at
org.apache.hadoop.hbase.io.asyncfs.FanOutOneBlockAsyncDFSOutputSaslHelper.createTransp
arentCryptoHelper(FanOutOneBlockAsyncDFSOutputSaslHelper.java:232)
Ê at
org.apache.hadoop.hbase.io.asyncfs.FanOutOneBlockAsyncDFSOutputSaslHelper.<clinit>(Fan
OutOneBlockAsyncDFSOutputSaslHelper.java:262)
Ê ... 18 more
If you hit this error, please specify FSHLog, i.e, filesystem, explicitly in your config file.
<property>
Ê <name>hbase.wal.provider</name>
Ê <value>filesystem</value>
</property>
And do not forget to send an email to the user@hbase.apache.org or dev@hbase.apache.org to
report the failure and also your hadoop version, we will try to fix the problem ASAP in the next
release.
136.2. Runtime Errors
136.2.1. RegionServer Hanging
Are you running an old JVM (< 1.6.0_u21?)? When you look at a thread dump, does it look like
threads are BLOCKED but no one holds the lock all are blocked on? See HBASE 3622 Deadlock in
HBaseServer (JVM bug?). Adding -XX:+UseMembar to the HBase HBASE_OPTS in conf/hbase-env.sh may
fix it.
136.2.2. java.io.IOException…(Too many open files)
If you see log messages like this…
2010-09-13 01:24:17,336 WARN org.apache.hadoop.hdfs.server.datanode.DataNode:
Disk-related IOException in BlockReceiver constructor. Cause is java.io.IOException:
Too many open files
Ê at java.io.UnixFileSystem.createFileExclusively(Native Method)
Ê at java.io.File.createNewFile(File.java:883)
... see the Getting Started section on link:[ulimit and nproc configuration].
136.2.3. xceiverCount 258 exceeds the limit of concurrent xcievers 256
This typically shows up in the DataNode logs.
See the Getting Started section on link:[xceivers configuration].
496
136.2.4. System instability, and the presence of
"java.lang.OutOfMemoryError: unable to createnew native thread in
exceptions" HDFS DataNode logs or that of any system daemon
See the Getting Started section on ulimit and nproc configuration. The default on recent Linux
distributions is 1024 - which is far too low for HBase.
136.2.5. DFS instability and/or RegionServer lease timeouts
If you see warning messages like this…
2009-02-24 10:01:33,516 WARN org.apache.hadoop.hbase.util.Sleeper: We slept xxx ms,
ten times longer than scheduled: 10000
2009-02-24 10:01:33,516 WARN org.apache.hadoop.hbase.util.Sleeper: We slept xxx ms,
ten times longer than scheduled: 15000
2009-02-24 10:01:36,472 WARN org.apache.hadoop.hbase.regionserver.HRegionServer:
unable to report to master for xxx milliseconds - retrying
... or see full GC compactions then you may be experiencing full GC’s.
136.2.6. "No live nodes contain current block" and/or YouAreDeadException
These errors can happen either when running out of OS file handles or in periods of severe
network problems where the nodes are unreachable.
See the Getting Started section on ulimit and nproc configuration and check your network.
136.2.7. ZooKeeper SessionExpired events
Master or RegionServers shutting down with messages like those in the logs:
497
WARN org.apache.zookeeper.ClientCnxn: Exception
closing session 0x278bd16a96000f to sun.nio.ch.SelectionKeyImpl@355811ec
java.io.IOException: TIMED OUT
Ê at org.apache.zookeeper.ClientCnxn$SendThread.run(ClientCnxn.java:906)
WARN org.apache.hadoop.hbase.util.Sleeper: We slept 79410ms, ten times longer than
scheduled: 5000
INFO org.apache.zookeeper.ClientCnxn: Attempting connection to server hostname/IP:PORT
INFO org.apache.zookeeper.ClientCnxn: Priming connection to java.nio.channels
.SocketChannel[connected local=/IP:PORT remote=hostname/IP:PORT]
INFO org.apache.zookeeper.ClientCnxn: Server connection successful
WARN org.apache.zookeeper.ClientCnxn: Exception closing session 0x278bd16a96000d to
sun.nio.ch.SelectionKeyImpl@3544d65e
java.io.IOException: Session Expired
Ê at org.apache.zookeeper.ClientCnxn$SendThread.readConnectResult(ClientCnxn.
java:589)
Ê at org.apache.zookeeper.ClientCnxn$SendThread.doIO(ClientCnxn.java:709)
Ê at org.apache.zookeeper.ClientCnxn$SendThread.run(ClientCnxn.java:945)
ERROR org.apache.hadoop.hbase.regionserver.HRegionServer: ZooKeeper session expired
The JVM is doing a long running garbage collecting which is pausing every threads (aka "stop the
world"). Since the RegionServer’s local ZooKeeper client cannot send heartbeats, the session times
out. By design, we shut down any node that isn’t able to contact the ZooKeeper ensemble after
getting a timeout so that it stops serving data that may already be assigned elsewhere.
•Make sure you give plenty of RAM (in hbase-env.sh), the default of 1GB won’t be able to sustain
long running imports.
•Make sure you don’t swap, the JVM never behaves well under swapping.
•Make sure you are not CPU starving the RegionServer thread. For example, if you are running a
MapReduce job using 6 CPU-intensive tasks on a machine with 4 cores, you are probably
starving the RegionServer enough to create longer garbage collection pauses.
•Increase the ZooKeeper session timeout
If you wish to increase the session timeout, add the following to your hbase-site.xml to increase the
timeout from the default of 60 seconds to 120 seconds.
<property>
Ê <name>zookeeper.session.timeout</name>
Ê <value>120000</value>
</property>
<property>
Ê <name>hbase.zookeeper.property.tickTime</name>
Ê <value>6000</value>
</property>
Be aware that setting a higher timeout means that the regions served by a failed RegionServer will
take at least that amount of time to be transferred to another RegionServer. For a production
system serving live requests, we would instead recommend setting it lower than 1 minute and over-
498
provision your cluster in order the lower the memory load on each machines (hence having less
garbage to collect per machine).
If this is happening during an upload which only happens once (like initially loading all your data
into HBase), consider bulk loading.
See ZooKeeper, The Cluster Canary for other general information about ZooKeeper troubleshooting.
136.2.8. NotServingRegionException
This exception is "normal" when found in the RegionServer logs at DEBUG level. This exception is
returned back to the client and then the client goes back to hbase:meta to find the new location of
the moved region.
However, if the NotServingRegionException is logged ERROR, then the client ran out of retries and
something probably wrong.
136.2.9. Logs flooded with '2011-01-10 12:40:48,407 INFO
org.apache.hadoop.io.compress.CodecPool: Gotbrand-new compressor'
messages
We are not using the native versions of compression libraries. See HBASE-1900 Put back native
support when hadoop 0.21 is released. Copy the native libs from hadoop under HBase lib dir or
symlink them into place and the message should go away.
136.2.10. Server handler X on 60020 caught:
java.nio.channels.ClosedChannelException
If you see this type of message it means that the region server was trying to read/send data from/to
a client but it already went away. Typical causes for this are if the client was killed (you see a storm
of messages like this when a MapReduce job is killed or fails) or if the client receives a
SocketTimeoutException. It’s harmless, but you should consider digging in a bit more if you aren’t
doing something to trigger them.
136.3. Snapshot Errors Due to Reverse DNS
Several operations within HBase, including snapshots, rely on properly configured reverse DNS.
Some environments, such as Amazon EC2, have trouble with reverse DNS. If you see errors like the
following on your RegionServers, check your reverse DNS configuration:
2013-05-01 00:04:56,356 DEBUG org.apache.hadoop.hbase.procedure.Subprocedure:
Subprocedure 'backup1'
coordinator notified of 'acquire', waiting on 'reached' or 'abort' from coordinator.
In general, the hostname reported by the RegionServer needs to be the same as the hostname the
Master is trying to reach. You can see a hostname mismatch by looking for the following type of
message in the RegionServer’s logs at start-up.
499
2013-05-01 00:03:00,614 INFO org.apache.hadoop.hbase.regionserver.HRegionServer:
Master passed us hostname
to use. Was=myhost-1234, Now=ip-10-55-88-99.ec2.internal
136.4. Shutdown Errors
500
Chapter 137. Master
For more information on the Master, see master.
137.1. Startup Errors
137.1.1. Master says that you need to run the HBase migrations script
Upon running that, the HBase migrations script says no files in root directory.
HBase expects the root directory to either not exist, or to have already been initialized by HBase
running a previous time. If you create a new directory for HBase using Hadoop DFS, this error will
occur. Make sure the HBase root directory does not currently exist or has been initialized by a
previous run of HBase. Sure fire solution is to just use Hadoop dfs to delete the HBase root and let
HBase create and initialize the directory itself.
137.1.2. Packet len6080218 is out of range!
If you have many regions on your cluster and you see an error like that reported above in this
sections title in your logs, see HBASE-4246 Cluster with too many regions cannot withstand some
master failover scenarios.
137.1.3. Master fails to become active due to lack of hsync for filesystem
HBase’s internal framework for cluster operations requires the ability to durably save state in a
write ahead log. When using a version of Apache Hadoop Common’s filesystem API that supports
checking on the availability of needed calls, HBase will proactively abort the cluster if it finds it
can’t operate safely.
For Master roles, the failure will show up in logs like this:
501
2018-04-05 11:18:44,653 ERROR [Thread-21] master.HMaster: Failed to become active
master
java.lang.IllegalStateException: The procedure WAL relies on the ability to hsync for
proper operation during component failures, but the underlying filesystem does not
support doing so. Please check the config value of
'hbase.procedure.store.wal.use.hsync' to set the desired level of robustness and
ensure the config value of 'hbase.wal.dir' points to a FileSystem mount that can
provide it.
Ê at
org.apache.hadoop.hbase.procedure2.store.wal.WALProcedureStore.rollWriter(WALProcedure
Store.java:1034)
Ê at
org.apache.hadoop.hbase.procedure2.store.wal.WALProcedureStore.recoverLease(WALProcedu
reStore.java:374)
Ê at
org.apache.hadoop.hbase.procedure2.ProcedureExecutor.start(ProcedureExecutor.java:530)
Ê at
org.apache.hadoop.hbase.master.HMaster.startProcedureExecutor(HMaster.java:1267)
Ê at
org.apache.hadoop.hbase.master.HMaster.startServiceThreads(HMaster.java:1173)
Ê at
org.apache.hadoop.hbase.master.HMaster.finishActiveMasterInitialization(HMaster.java:8
81)
Ê at
org.apache.hadoop.hbase.master.HMaster.startActiveMasterManager(HMaster.java:2048)
Ê at org.apache.hadoop.hbase.master.HMaster.lambda$run$0(HMaster.java:568)
Ê at java.lang.Thread.run(Thread.java:745)
If you are attempting to run in standalone mode and see this error, please walk back through the
section Quick Start - Standalone HBase and ensure you have included all the given configuration
settings.
137.2. Shutdown Errors
502
Chapter 138. ZooKeeper
138.1. Startup Errors
138.1.1. Could not find my address: xyz in list of ZooKeeper quorum servers
A ZooKeeper server wasn’t able to start, throws that error. xyz is the name of your server.
This is a name lookup problem. HBase tries to start a ZooKeeper server on some machine but that
machine isn’t able to find itself in the hbase.zookeeper.quorum configuration.
Use the hostname presented in the error message instead of the value you used. If you have a DNS
server, you can set hbase.zookeeper.dns.interface and hbase.zookeeper.dns.nameserver in hbase-
site.xml to make sure it resolves to the correct FQDN.
138.2. ZooKeeper, The Cluster Canary
ZooKeeper is the cluster’s "canary in the mineshaft". It’ll be the first to notice issues if any so
making sure its happy is the short-cut to a humming cluster.
See the ZooKeeper Operating Environment Troubleshooting page. It has suggestions and tools for
checking disk and networking performance; i.e. the operating environment your ZooKeeper and
HBase are running in.
Additionally, the utility zkcli may help investigate ZooKeeper issues.
503
Chapter 139. Amazon EC2
139.1. ZooKeeper does not seem to work on Amazon
EC2
HBase does not start when deployed as Amazon EC2 instances. Exceptions like the below appear in
the Master and/or RegionServer logs:
Ê 2009-10-19 11:52:27,030 INFO org.apache.zookeeper.ClientCnxn: Attempting
Ê connection to server ec2-174-129-15-236.compute-1.amazonaws.com/10.244.9.171:2181
Ê 2009-10-19 11:52:27,032 WARN org.apache.zookeeper.ClientCnxn: Exception
Ê closing session 0x0 to sun.nio.ch.SelectionKeyImpl@656dc861
Ê java.net.ConnectException: Connection refused
Security group policy is blocking the ZooKeeper port on a public address. Use the internal EC2 host
names when configuring the ZooKeeper quorum peer list.
139.2. Instability on Amazon EC2
Questions on HBase and Amazon EC2 come up frequently on the HBase dist-list. Search for old
threads using Search Hadoop
139.3. Remote Java Connection into EC2 Cluster Not
Working
See Andrew’s answer here, up on the user list: Remote Java client connection into EC2 instance.
504
Chapter 140. HBase and Hadoop version
issues
140.1. …cannot communicate with client version…
If you see something like the following in your logs ... 2012-09-24 10:20:52,168 FATAL
org.apache.hadoop.hbase.master.HMaster: Unhandled exception. Starting shutdown.
org.apache.hadoop.ipc.RemoteException: Server IPC version 7 cannot communicate with client
version 4 ... …are you trying to talk to an Hadoop 2.0.x from an HBase that has an Hadoop 1.0.x
client? Use the HBase built against Hadoop 2.0 or rebuild your HBase passing the
-Dhadoop.profile=2.0 attribute to Maven (See Building against various hadoop versions. for more).
505
Chapter 141. HBase and HDFS
General configuration guidance for Apache HDFS is out of the scope of this guide. Refer to the
documentation available at https://hadoop.apache.org/ for extensive information about configuring
HDFS. This section deals with HDFS in terms of HBase.
In most cases, HBase stores its data in Apache HDFS. This includes the HFiles containing the data, as
well as the write-ahead logs (WALs) which store data before it is written to the HFiles and protect
against RegionServer crashes. HDFS provides reliability and protection to data in HBase because it
is distributed. To operate with the most efficiency, HBase needs data to be available locally.
Therefore, it is a good practice to run an HDFS DataNode on each RegionServer.
Important Information and Guidelines for HBase and HDFS
HBase is a client of HDFS.
HBase is an HDFS client, using the HDFS DFSClient class, and references to this class appear in
HBase logs with other HDFS client log messages.
Configuration is necessary in multiple places.
Some HDFS configurations relating to HBase need to be done at the HDFS (server) side. Others
must be done within HBase (at the client side). Other settings need to be set at both the server
and client side.
Write errors which affect HBase may be logged in the HDFS logs rather than HBase logs.
When writing, HDFS pipelines communications from one DataNode to another. HBase
communicates to both the HDFS NameNode and DataNode, using the HDFS client classes.
Communication problems between DataNodes are logged in the HDFS logs, not the HBase logs.
HBase communicates with HDFS using two different ports.
HBase communicates with DataNodes using the ipc.Client interface and the DataNode class.
References to these will appear in HBase logs. Each of these communication channels use a
different port (50010 and 50020 by default). The ports are configured in the HDFS configuration,
via the dfs.datanode.address and dfs.datanode.ipc.address parameters.
Errors may be logged in HBase, HDFS, or both.
When troubleshooting HDFS issues in HBase, check logs in both places for errors.
HDFS takes a while to mark a node as dead. You can configure HDFS to avoid using stale
DataNodes.
By default, HDFS does not mark a node as dead until it is unreachable for 630 seconds. In
Hadoop 1.1 and Hadoop 2.x, this can be alleviated by enabling checks for stale DataNodes,
though this check is disabled by default. You can enable the check for reads and writes
separately, via dfs.namenode.avoid.read.stale.datanode and
dfs.namenode.avoid.write.stale.datanode settings. A stale DataNode is one that has not been
reachable for dfs.namenode.stale.datanode.interval (default is 30 seconds). Stale datanodes are
avoided, and marked as the last possible target for a read or write operation. For configuration
details, see the HDFS documentation.
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Settings for HDFS retries and timeouts are important to HBase.
You can configure settings for various retries and timeouts. Always refer to the HDFS
documentation for current recommendations and defaults. Some of the settings important to
HBase are listed here. Defaults are current as of Hadoop 2.3. Check the Hadoop documentation
for the most current values and recommendations.
The HBase Balancer and HDFS Balancer are incompatible
The HDFS balancer attempts to spread HDFS blocks evenly among DataNodes. HBase relies on
compactions to restore locality after a region split or failure. These two types of balancing do not
work well together.
In the past, the generally accepted advice was to turn off the HDFS load balancer and rely on the
HBase balancer, since the HDFS balancer would degrade locality. This advice is still valid if your
HDFS version is lower than 2.7.1.
HDFS-6133 provides the ability to exclude favored-nodes (pinned) blocks from the HDFS load
balancer, by setting the dfs.datanode.block-pinning.enabled property to true in the HDFS service
configuration.
HBase can be enabled to use the HDFS favored-nodes feature by switching the HBase balancer
class (conf: hbase.master.loadbalancer.class) to
org.apache.hadoop.hbase.favored.FavoredNodeLoadBalancer which is documented here.
HDFS-6133 is available in HDFS 2.7.0 and higher, but HBase does not support
running on HDFS 2.7.0, so you must be using HDFS 2.7.1 or higher to use this
feature with HBase.
Connection Timeouts
Connection timeouts occur between the client (HBASE) and the HDFS DataNode. They may occur
when establishing a connection, attempting to read, or attempting to write. The two settings below
are used in combination, and affect connections between the DFSClient and the DataNode, the
ipc.cClient and the DataNode, and communication between two DataNodes.
dfs.client.socket-timeout (default: 60000)
The amount of time before a client connection times out when establishing a connection or
reading. The value is expressed in milliseconds, so the default is 60 seconds.
dfs.datanode.socket.write.timeout (default: 480000)
The amount of time before a write operation times out. The default is 8 minutes, expressed as
milliseconds.
Typical Error Logs
The following types of errors are often seen in the logs.
INFO HDFS.DFSClient: Failed to connect to /xxx50010, add to deadNodes and continue
java.net.SocketTimeoutException: 60000 millis timeout while waiting for channel to be ready for
connect. ch : java.nio.channels.SocketChannel[connection-pending remote=/region-server-
1:50010]:: All DataNodes for a block are dead, and recovery is not possible. Here is the sequence of
events that leads to this error:
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INFO org.apache.hadoop.HDFS.DFSClient: Exception in createBlockOutputStream
java.net.SocketTimeoutException: 69000 millis timeout while waiting for channel to be ready for
connect. ch : java.nio.channels.SocketChannel[connection-pending remote=/ xxx:50010]:: This type
of error indicates a write issue. In this case, the master wants to split the log. It does not have a local
DataNodes so it tries to connect to a remote DataNode, but the DataNode is dead.
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Chapter 142. Running unit or integration
tests
142.1. Runtime exceptions from MiniDFSCluster when
running tests
If you see something like the following
...
java.lang.NullPointerException: null
at org.apache.hadoop.hdfs.MiniDFSCluster.startDataNodes
at org.apache.hadoop.hdfs.MiniDFSCluster.<init>
at org.apache.hadoop.hbase.MiniHBaseCluster.<init>
at org.apache.hadoop.hbase.HBaseTestingUtility.startMiniDFSCluster
at org.apache.hadoop.hbase.HBaseTestingUtility.startMiniCluster
...
or
...
java.io.IOException: Shutting down
at org.apache.hadoop.hbase.MiniHBaseCluster.init
at org.apache.hadoop.hbase.MiniHBaseCluster.<init>
at org.apache.hadoop.hbase.MiniHBaseCluster.<init>
at org.apache.hadoop.hbase.HBaseTestingUtility.startMiniHBaseCluster
at org.apache.hadoop.hbase.HBaseTestingUtility.startMiniCluster
...
... then try issuing the command umask 022 before launching tests. This is a workaround for HDFS-
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509
Chapter 143. Case Studies
For Performance and Troubleshooting Case Studies, see Apache HBase Case Studies.
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Chapter 144. Cryptographic Features
144.1. sun.security.pkcs11.wrapper.PKCS11Exception:
CKR_ARGUMENTS_BAD
This problem manifests as exceptions ultimately caused by:
Caused by: sun.security.pkcs11.wrapper.PKCS11Exception: CKR_ARGUMENTS_BAD
Ê at sun.security.pkcs11.wrapper.PKCS11.C_DecryptUpdate(Native Method)
Ê at sun.security.pkcs11.P11Cipher.implDoFinal(P11Cipher.java:795)
This problem appears to affect some versions of OpenJDK 7 shipped by some Linux vendors. NSS is
configured as the default provider. If the host has an x86_64 architecture, depending on if the
vendor packages contain the defect, the NSS provider will not function correctly.
To work around this problem, find the JRE home directory and edit the file lib/security/java.security.
Edit the file to comment out the line:
security.provider.1=sun.security.pkcs11.SunPKCS11 ${java.home}/lib/security/nss.cfg
Then renumber the remaining providers accordingly.
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Chapter 145. Operating System Specific
Issues
145.1. Page Allocation Failure
This issue is known to affect CentOS 6.2 and possibly CentOS 6.5. It may also affect
some versions of Red Hat Enterprise Linux, according to
https://bugzilla.redhat.com/show_bug.cgi?id=770545.
Some users have reported seeing the following error:
kernel: java: page allocation failure. order:4, mode:0x20
Raising the value of min_free_kbytes was reported to fix this problem. This parameter is set to a
percentage of the amount of RAM on your system, and is described in more detail at
http://www.centos.org/docs/5/html/5.1/Deployment_Guide/s3-proc-sys-vm.html.
To find the current value on your system, run the following command:
[user@host]# cat /proc/sys/vm/min_free_kbytes
Next, raise the value. Try doubling, then quadrupling the value. Note that setting the value too low
or too high could have detrimental effects on your system. Consult your operating system vendor
for specific recommendations.
Use the following command to modify the value of min_free_kbytes, substituting <value> with your
intended value:
[user@host]# echo <value> > /proc/sys/vm/min_free_kbytes
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Chapter 146. JDK Issues
146.1. NoSuchMethodError:
java.util.concurrent.ConcurrentHashMap.keySet
If you see this in your logs:
Caused by: java.lang.NoSuchMethodError: java.util.concurrent.ConcurrentHashMap.keySet
()Ljava/util/concurrent/ConcurrentHashMap$KeySetView;
Ê at org.apache.hadoop.hbase.master.ServerManager
.findServerWithSameHostnamePortWithLock(ServerManager.java:393)
Ê at org.apache.hadoop.hbase.master.ServerManager.checkAndRecordNewServer
(ServerManager.java:307)
Ê at org.apache.hadoop.hbase.master.ServerManager.regionServerStartup(ServerManager
.java:244)
Ê at org.apache.hadoop.hbase.master.MasterRpcServices.regionServerStartup
(MasterRpcServices.java:304)
Ê at org.apache.hadoop.hbase.protobuf.generated.RegionServerStatusProtos
$RegionServerStatusService$2.callBlockingMethod(RegionServerStatusProtos.java:7910)
Ê at org.apache.hadoop.hbase.ipc.RpcServer.call(RpcServer.java:2020)
Ê ... 4 more
then check if you compiled with jdk8 and tried to run it on jdk7. If so, this won’t work. Run on jdk8
or recompile with jdk7. See HBASE-10607 JDK8 NoSuchMethodError involving
ConcurrentHashMap.keySet if running on JRE 7.
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Apache HBase Case Studies
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Chapter 147. Overview
This chapter will describe a variety of performance and troubleshooting case studies that can
provide a useful blueprint on diagnosing Apache HBase cluster issues.
For more information on Performance and Troubleshooting, see Apache HBase Performance
Tuning and Troubleshooting and Debugging Apache HBase.
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Chapter 148. Schema Design
See the schema design case studies here: Schema Design Case Studies
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Chapter 149. Performance/Troubleshooting
149.1. Case Study #1 (Performance Issue On A Single
Node)
149.1.1. Scenario
Following a scheduled reboot, one data node began exhibiting unusual behavior. Routine
MapReduce jobs run against HBase tables which regularly completed in five or six minutes began
taking 30 or 40 minutes to finish. These jobs were consistently found to be waiting on map and
reduce tasks assigned to the troubled data node (e.g., the slow map tasks all had the same Input
Split). The situation came to a head during a distributed copy, when the copy was severely
prolonged by the lagging node.
149.1.2. Hardware
Datanodes:
•Two 12-core processors
•Six Enterprise SATA disks
•24GB of RAM
•Two bonded gigabit NICs
Network:
•10 Gigabit top-of-rack switches
•20 Gigabit bonded interconnects between racks.
149.1.3. Hypotheses
HBase "Hot Spot" Region
We hypothesized that we were experiencing a familiar point of pain: a "hot spot" region in an
HBase table, where uneven key-space distribution can funnel a huge number of requests to a single
HBase region, bombarding the RegionServer process and cause slow response time. Examination of
the HBase Master status page showed that the number of HBase requests to the troubled node was
almost zero. Further, examination of the HBase logs showed that there were no region splits,
compactions, or other region transitions in progress. This effectively ruled out a "hot spot" as the
root cause of the observed slowness.
HBase Region With Non-Local Data
Our next hypothesis was that one of the MapReduce tasks was requesting data from HBase that was
not local to the DataNode, thus forcing HDFS to request data blocks from other servers over the
network. Examination of the DataNode logs showed that there were very few blocks being
requested over the network, indicating that the HBase region was correctly assigned, and that the
majority of the necessary data was located on the node. This ruled out the possibility of non-local
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data causing a slowdown.
Excessive I/O Wait Due To Swapping Or An Over-Worked Or Failing Hard Disk
After concluding that the Hadoop and HBase were not likely to be the culprits, we moved on to
troubleshooting the DataNode’s hardware. Java, by design, will periodically scan its entire memory
space to do garbage collection. If system memory is heavily overcommitted, the Linux kernel may
enter a vicious cycle, using up all of its resources swapping Java heap back and forth from disk to
RAM as Java tries to run garbage collection. Further, a failing hard disk will often retry reads
and/or writes many times before giving up and returning an error. This can manifest as high
iowait, as running processes wait for reads and writes to complete. Finally, a disk nearing the upper
edge of its performance envelope will begin to cause iowait as it informs the kernel that it cannot
accept any more data, and the kernel queues incoming data into the dirty write pool in memory.
However, using vmstat(1) and free(1), we could see that no swap was being used, and the amount
of disk IO was only a few kilobytes per second.
Slowness Due To High Processor Usage
Next, we checked to see whether the system was performing slowly simply due to very high
computational load. top(1) showed that the system load was higher than normal, but vmstat(1) and
mpstat(1) showed that the amount of processor being used for actual computation was low.
Network Saturation (The Winner)
Since neither the disks nor the processors were being utilized heavily, we moved on to the
performance of the network interfaces. The DataNode had two gigabit ethernet adapters, bonded to
form an active-standby interface. ifconfig(8) showed some unusual anomalies, namely interface
errors, overruns, framing errors. While not unheard of, these kinds of errors are exceedingly rare
on modern hardware which is operating as it should:
$ /sbin/ifconfig bond0
bond0 Link encap:Ethernet HWaddr 00:00:00:00:00:00
inet addr:10.x.x.x Bcast:10.x.x.255 Mask:255.255.255.0
UP BROADCAST RUNNING MASTER MULTICAST MTU:1500 Metric:1
RX packets:2990700159 errors:12 dropped:0 overruns:1 frame:6 <--- Look Here!
Errors!
TX packets:3443518196 errors:0 dropped:0 overruns:0 carrier:0
collisions:0 txqueuelen:0
RX bytes:2416328868676 (2.4 TB) TX bytes:3464991094001 (3.4 TB)
These errors immediately lead us to suspect that one or more of the ethernet interfaces might have
negotiated the wrong line speed. This was confirmed both by running an ICMP ping from an
external host and observing round-trip-time in excess of 700ms, and by running ethtool(8) on the
members of the bond interface and discovering that the active interface was operating at 100Mbs/,
full duplex.
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$ sudo ethtool eth0
Settings for eth0:
Supported ports: [ TP ]
Supported link modes: 10baseT/Half 10baseT/Full
Ê 100baseT/Half 100baseT/Full
Ê 1000baseT/Full
Supports auto-negotiation: Yes
Advertised link modes: 10baseT/Half 10baseT/Full
Ê 100baseT/Half 100baseT/Full
Ê 1000baseT/Full
Advertised pause frame use: No
Advertised auto-negotiation: Yes
Link partner advertised link modes: Not reported
Link partner advertised pause frame use: No
Link partner advertised auto-negotiation: No
Speed: 100Mb/s <--- Look Here! Should say
1000Mb/s!
Duplex: Full
Port: Twisted Pair
PHYAD: 1
Transceiver: internal
Auto-negotiation: on
MDI-X: Unknown
Supports Wake-on: umbg
Wake-on: g
Current message level: 0x00000003 (3)
Link detected: yes
In normal operation, the ICMP ping round trip time should be around 20ms, and the interface
speed and duplex should read, "1000MB/s", and, "Full", respectively.
149.1.4. Resolution
After determining that the active ethernet adapter was at the incorrect speed, we used the
ifenslave(8) command to make the standby interface the active interface, which yielded an
immediate improvement in MapReduce performance, and a 10 times improvement in network
throughput:
On the next trip to the datacenter, we determined that the line speed issue was ultimately caused
by a bad network cable, which was replaced.
149.2. Case Study #2 (Performance Research 2012)
Investigation results of a self-described "we’re not sure what’s wrong, but it seems slow" problem.
http://gbif.blogspot.com/2012/03/hbase-performance-evaluation-continued.html
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149.3. Case Study #3 (Performance Research 2010))
Investigation results of general cluster performance from 2010. Although this research is on an
older version of the codebase, this writeup is still very useful in terms of approach.
http://hstack.org/hbase-performance-testing/
149.4. Case Study #4 (max.transfer.threads Config)
Case study of configuring max.transfer.threads (previously known as xcievers) and diagnosing
errors from misconfigurations. http://www.larsgeorge.com/2012/03/hadoop-hbase-and-
xceivers.html
See also dfs.datanode.max.transfer.threads .
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Apache HBase Operational
Management
This chapter will cover operational tools and practices required of a running Apache HBase cluster.
The subject of operations is related to the topics of Troubleshooting and Debugging Apache HBase,
Apache HBase Performance Tuning, and Apache HBase Configuration but is a distinct topic in itself.
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Chapter 150. HBase Tools and Utilities
HBase provides several tools for administration, analysis, and debugging of your cluster. The entry-
point to most of these tools is the bin/hbase command, though some tools are available in the dev-
support/ directory.
To see usage instructions for bin/hbase command, run it with no arguments, or with the -h
argument. These are the usage instructions for HBase 0.98.x. Some commands, such as version, pe,
ltt, clean, are not available in previous versions.
$ bin/hbase
Usage: hbase [<options>] <command> [<args>]
Options:
Ê --config DIR Configuration direction to use. Default: ./conf
Ê --hosts HOSTS Override the list in 'regionservers' file
Ê --auth-as-server Authenticate to ZooKeeper using servers configuration
Commands:
Some commands take arguments. Pass no args or -h for usage.
Ê shell Run the HBase shell
Ê hbck Run the HBase 'fsck' tool. Defaults read-only hbck1.
Ê Pass '-j /path/to/HBCK2.jar' to run hbase-2.x HBCK2.
Ê snapshot Tool for managing snapshots
Ê wal Write-ahead-log analyzer
Ê hfile Store file analyzer
Ê zkcli Run the ZooKeeper shell
Ê master Run an HBase HMaster node
Ê regionserver Run an HBase HRegionServer node
Ê zookeeper Run a ZooKeeper server
Ê rest Run an HBase REST server
Ê thrift Run the HBase Thrift server
Ê thrift2 Run the HBase Thrift2 server
Ê clean Run the HBase clean up script
Ê classpath Dump hbase CLASSPATH
Ê mapredcp Dump CLASSPATH entries required by mapreduce
Ê pe Run PerformanceEvaluation
Ê ltt Run LoadTestTool
Ê canary Run the Canary tool
Ê version Print the version
Ê backup Backup tables for recovery
Ê restore Restore tables from existing backup image
Ê regionsplitter Run RegionSplitter tool
Ê rowcounter Run RowCounter tool
Ê cellcounter Run CellCounter tool
Ê CLASSNAME Run the class named CLASSNAME
Some of the tools and utilities below are Java classes which are passed directly to the bin/hbase
command, as referred to in the last line of the usage instructions. Others, such as hbase shell (The
Apache HBase Shell), hbase upgrade (Upgrading), and hbase thrift (Thrift), are documented
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elsewhere in this guide.
150.1. Canary
The Canary tool can help users "canary-test" the HBase cluster status. The default "region mode"
fetches a row from every column-family of every regions. In "regionserver mode", the Canary tool
will fetch a row from a random region on each of the cluster’s RegionServers. In "zookeeper mode",
the Canary will read the root znode on each member of the zookeeper ensemble.
To see usage, pass the -help parameter (if you pass no parameters, the Canary tool starts executing
in the default region "mode" fetching a row from every region in the cluster).
2018-10-16 13:11:27,037 INFO [main] tool.Canary: Execution thread count=16
Usage: canary [OPTIONS] [<TABLE1> [<TABLE2]...] | [<REGIONSERVER1> [<REGIONSERVER2]..]
Where [OPTIONS] are:
Ê-h,-help show this help and exit.
Ê-regionserver set 'regionserver mode'; gets row from random region on server
Ê-allRegions get from ALL regions when 'regionserver mode', not just random one.
Ê-zookeeper set 'zookeeper mode'; grab zookeeper.znode.parent on each ensemble
member
Ê-daemon continuous check at defined intervals.
Ê-interval <N> interval between checks in seconds
Ê-e consider table/regionserver argument as regular expression
Ê-f <B> exit on first error; default=true
Ê-failureAsError treat read/write failure as error
Ê-t <N> timeout for canary-test run; default=600000ms
Ê-writeSniffing enable write sniffing
Ê-writeTable the table used for write sniffing; default=hbase:canary
Ê-writeTableTimeout <N> timeout for writeTable; default=600000ms
Ê-readTableTimeouts <tableName>=<read timeout>,<tableName>=<read timeout>,...
Ê comma-separated list of table read timeouts (no spaces);
Ê logs 'ERROR' if takes longer. default=600000ms
Ê-permittedZookeeperFailures <N> Ignore first N failures attempting to
Ê connect to individual zookeeper nodes in ensemble
Ê-D<configProperty>=<value> to assign or override configuration params
Ê-Dhbase.canary.read.raw.enabled=<true/false> Set to enable/disable raw scan;
default=false
Canary runs in one of three modes: region (default), regionserver, or zookeeper.
To sniff/probe all regions, pass no arguments.
To sniff/probe all regions of a table, pass tablename.
To sniff/probe regionservers, pass -regionserver, etc.
See http://hbase.apache.org/book.html#_canary for Canary documentation.
The Sink class is instantiated using the hbase.canary.sink.class configuration
property.
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This tool will return non zero error codes to user for collaborating with other monitoring tools,
such as Nagios. The error code definitions are:
private static final int USAGE_EXIT_CODE = 1;
private static final int INIT_ERROR_EXIT_CODE = 2;
private static final int TIMEOUT_ERROR_EXIT_CODE = 3;
private static final int ERROR_EXIT_CODE = 4;
private static final int FAILURE_EXIT_CODE = 5;
Here are some examples based on the following given case: given two Table objects called test-01
and test-02 each with two column family cf1 and cf2 respectively, deployed on 3 RegionServers. See
the following table.
RegionServer test-01 test-02
rs1 r1 r2
rs2 r2
rs3 r2 r1
Following are some example outputs based on the previous given case.
150.1.1. Canary test for every column family (store) of every region of every
table
$ ${HBASE_HOME}/bin/hbase canary
3/12/09 03:26:32 INFO tool.Canary: read from region test-
01,,1386230156732.0e3c7d77ffb6361ea1b996ac1042ca9a. column family cf1 in 2ms
13/12/09 03:26:32 INFO tool.Canary: read from region test-
01,,1386230156732.0e3c7d77ffb6361ea1b996ac1042ca9a. column family cf2 in 2ms
13/12/09 03:26:32 INFO tool.Canary: read from region test-
01,0004883,1386230156732.87b55e03dfeade00f441125159f8ca87. column family cf1 in 4ms
13/12/09 03:26:32 INFO tool.Canary: read from region test-
01,0004883,1386230156732.87b55e03dfeade00f441125159f8ca87. column family cf2 in 1ms
...
13/12/09 03:26:32 INFO tool.Canary: read from region test-
02,,1386559511167.aa2951a86289281beee480f107bb36ee. column family cf1 in 5ms
13/12/09 03:26:32 INFO tool.Canary: read from region test-
02,,1386559511167.aa2951a86289281beee480f107bb36ee. column family cf2 in 3ms
13/12/09 03:26:32 INFO tool.Canary: read from region test-
02,0004883,1386559511167.cbda32d5e2e276520712d84eaaa29d84. column family cf1 in 31ms
13/12/09 03:26:32 INFO tool.Canary: read from region test-
02,0004883,1386559511167.cbda32d5e2e276520712d84eaaa29d84. column family cf2 in 8ms
So you can see, table test-01 has two regions and two column families, so the Canary tool in the
default "region mode" will pick 4 small piece of data from 4 (2 region * 2 store) different stores. This
is a default behavior.
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150.1.2. Canary test for every column family (store) of every region of a
specific table(s)
You can also test one or more specific tables by passing table names.
$ ${HBASE_HOME}/bin/hbase canary test-01 test-02
150.1.3. Canary test with RegionServer granularity
In "regionserver mode", the Canary tool will pick one small piece of data from each RegionServer
(You can also pass one or more RegionServer names as arguments to the canary-test when in
"regionserver mode").
$ ${HBASE_HOME}/bin/hbase canary -regionserver
13/12/09 06:05:17 INFO tool.Canary: Read from table:test-01 on region server:rs2 in
72ms
13/12/09 06:05:17 INFO tool.Canary: Read from table:test-02 on region server:rs3 in
34ms
13/12/09 06:05:17 INFO tool.Canary: Read from table:test-01 on region server:rs1 in
56ms
150.1.4. Canary test with regular expression pattern
You can pass regexes for table names when in "region mode" or for servernames when in
"regionserver mode". The below will test both table test-01 and test-02.
$ ${HBASE_HOME}/bin/hbase canary -e test-0[1-2]
150.1.5. Run canary test as a "daemon"
Run repeatedly with an interval defined via the option -interval (default value is 60 seconds). This
daemon will stop itself and return non-zero error code if any error occur. To have the daemon keep
running across errors, pass the -f flag with its value set to false (see usage above).
$ ${HBASE_HOME}/bin/hbase canary -daemon
To run repeatedly with 5 second intervals and not stop on errors, do the following.
$ ${HBASE_HOME}/bin/hbase canary -daemon -interval 5 -f false
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150.1.6. Force timeout if canary test stuck
In some cases the request is stuck and no response is sent back to the client. This can happen with
dead RegionServers which the master has not yet noticed. Because of this we provide a timeout
option to kill the canary test and return a non-zero error code. The below sets the timeout value to
60 seconds (the default value is 600 seconds).
$ ${HBASE_HOME}/bin/hbase canary -t 60000
150.1.7. Enable write sniffing in canary
By default, the canary tool only checks read operations. To enable the write sniffing, you can run
the canary with the -writeSniffing option set. When write sniffing is enabled, the canary tool will
create an hbase table and make sure the regions of the table are distributed to all region servers. In
each sniffing period, the canary will try to put data to these regions to check the write availability
of each region server.
$ ${HBASE_HOME}/bin/hbase canary -writeSniffing
The default write table is hbase:canary and can be specified with the option -writeTable.
$ ${HBASE_HOME}/bin/hbase canary -writeSniffing -writeTable ns:canary
The default value size of each put is 10 bytes. You can set it via the config key:
hbase.canary.write.value.size.
150.1.8. Treat read / write failure as error
By default, the canary tool only logs read failures — due to e.g. RetriesExhaustedException,
etc. — and will return the 'normal' exit code. To treat read/write failure as errors, you can run
canary with the -treatFailureAsError option. When enabled, read/write failures will result in an
error exit code.
$ ${HBASE_HOME}/bin/hbase canary -treatFailureAsError
150.1.9. Running Canary in a Kerberos-enabled Cluster
To run the Canary in a Kerberos-enabled cluster, configure the following two properties in hbase-
site.xml:
•hbase.client.keytab.file
•hbase.client.kerberos.principal
Kerberos credentials are refreshed every 30 seconds when Canary runs in daemon mode.
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To configure the DNS interface for the client, configure the following optional properties in hbase-
site.xml.
•hbase.client.dns.interface
•hbase.client.dns.nameserver
Example 40. Canary in a Kerberos-Enabled Cluster
This example shows each of the properties with valid values.
<property>
Ê <name>hbase.client.kerberos.principal</name>
Ê <value>hbase/_HOST@YOUR-REALM.COM</value>
</property>
<property>
Ê <name>hbase.client.keytab.file</name>
Ê <value>/etc/hbase/conf/keytab.krb5</value>
</property>
<!-- optional params -->
<property>
Ê <name>hbase.client.dns.interface</name>
Ê <value>default</value>
</property>
<property>
Ê <name>hbase.client.dns.nameserver</name>
Ê <value>default</value>
</property>
150.2. RegionSplitter
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usage: bin/hbase regionsplitter <TABLE> <SPLITALGORITHM>
SPLITALGORITHM is the java class name of a class implementing
Ê SplitAlgorithm, or one of the special strings
Ê HexStringSplit or DecimalStringSplit or
Ê UniformSplit, which are built-in split algorithms.
Ê HexStringSplit treats keys as hexadecimal ASCII, and
Ê DecimalStringSplit treats keys as decimal ASCII, and
Ê UniformSplit treats keys as arbitrary bytes.
Ê-c <region count> Create a new table with a pre-split number of
Ê regions
Ê-D <property=value> Override HBase Configuration Settings
Ê-f <family:family:...> Column Families to create with new table.
Ê Required with -c
Ê --firstrow <arg> First Row in Table for Split Algorithm
Ê-h Print this usage help
Ê --lastrow <arg> Last Row in Table for Split Algorithm
Ê-o <count> Max outstanding splits that have unfinished
Ê major compactions
Ê-r Perform a rolling split of an existing region
Ê --risky Skip verification steps to complete
Ê quickly. STRONGLY DISCOURAGED for production
Ê systems.
For additional detail, see Manual Region Splitting.
150.3. Health Checker
You can configure HBase to run a script periodically and if it fails N times (configurable), have the
server exit. See HBASE-7351 Periodic health check script for configurations and detail.
150.4. Driver
Several frequently-accessed utilities are provided as Driver classes, and executed by the bin/hbase
command. These utilities represent MapReduce jobs which run on your cluster. They are run in the
following way, replacing UtilityName with the utility you want to run. This command assumes you
have set the environment variable HBASE_HOME to the directory where HBase is unpacked on your
server.
${HBASE_HOME}/bin/hbase org.apache.hadoop.hbase.mapreduce.UtilityName
The following utilities are available:
LoadIncrementalHFiles
Complete a bulk data load.
CopyTable
Export a table from the local cluster to a peer cluster.
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Export
Write table data to HDFS.
Import
Import data written by a previous Export operation.
ImportTsv
Import data in TSV format.
RowCounter
Count rows in an HBase table.
CellCounter
Count cells in an HBase table.
replication.VerifyReplication
Compare the data from tables in two different clusters. WARNING: It doesn’t work for
incrementColumnValues’d cells since the timestamp is changed. Note that this command is in a
different package than the others.
Each command except RowCounter and CellCounter accept a single --help argument to print usage
instructions.
150.5. HBase hbck
The hbck tool that shipped with hbase-1.x has been made read-only in hbase-2.x. It is not able to
repair hbase-2.x clusters as hbase internals have changed. Nor should its assessments in read-only
mode be trusted as it does not understand hbase-2.x operation.
A new tool, HBase HBCK2, described in the next section, replaces hbck.
150.6. HBase HBCK2
HBCK2 is the successor to HBase hbck, the hbase-1.x fix tool (A.K.A hbck1). Use it in place of hbck1
making repairs against hbase-2.x installs.
HBCK2 does not ship as part of hbase. It can be found as a subproject of the companion hbase-
operator-tools repository at Apache HBase HBCK2 Tool. HBCK2 was moved out of hbase so it could
evolve at a cadence apart from that of hbase core.
See the [https://github.com/apache/hbase-operator-tools/tree/master/hbase-hbck2](HBCK2) Home
Page for how HBCK2 differs from hbck1, and for how to build and use it.
Once built, you can run HBCK2 as follows:
$ hbase hbck -j /path/to/HBCK2.jar
This will generate HBCK2 usage describing commands and options.
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150.7. HFile Tool
See HFile Tool.
150.8. WAL Tools
150.8.1. FSHLog tool
The main method on FSHLog offers manual split and dump facilities. Pass it WALs or the product of a
split, the content of the recovered.edits. directory.
You can get a textual dump of a WAL file content by doing the following:
Ê$ ./bin/hbase org.apache.hadoop.hbase.regionserver.wal.FSHLog --dump
hdfs://example.org:8020/hbase/WALs/example.org,60020,1283516293161/10.10.21.10%3A60020
.1283973724012
The return code will be non-zero if there are any issues with the file so you can test wholesomeness
of file by redirecting STDOUT to /dev/null and testing the program return.
Similarly you can force a split of a log file directory by doing:
Ê$ ./bin/hbase org.apache.hadoop.hbase.regionserver.wal.FSHLog --split
hdfs://example.org:8020/hbase/WALs/example.org,60020,1283516293161/
WALPrettyPrinter
The WALPrettyPrinter is a tool with configurable options to print the contents of a WAL. You can
invoke it via the HBase cli with the 'wal' command.
Ê$ ./bin/hbase wal
hdfs://example.org:8020/hbase/WALs/example.org,60020,1283516293161/10.10.21.10%3A60020
.1283973724012
WAL Printing in older versions of HBase
Prior to version 2.0, the WALPrettyPrinter was called the HLogPrettyPrinter, after an
internal name for HBase’s write ahead log. In those versions, you can print the
contents of a WAL using the same configuration as above, but with the 'hlog'
command.
Ê$ ./bin/hbase hlog
hdfs://example.org:8020/hbase/.logs/example.org,60020,1283516293161/10.
10.21.10%3A60020.1283973724012
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150.9. Compression Tool
See compression.test.
150.10. CopyTable
CopyTable is a utility that can copy part or of all of a table, either to the same cluster or another
cluster. The target table must first exist. The usage is as follows:
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$ ./bin/hbase org.apache.hadoop.hbase.mapreduce.CopyTable --help
/bin/hbase org.apache.hadoop.hbase.mapreduce.CopyTable --help
Usage: CopyTable [general options] [--starttime=X] [--endtime=Y] [--new.name=NEW] [--
peer.adr=ADR] <tablename>
Options:
Êrs.class hbase.regionserver.class of the peer cluster,
Ê specify if different from current cluster
Êrs.impl hbase.regionserver.impl of the peer cluster,
Êstartrow the start row
Êstoprow the stop row
Êstarttime beginning of the time range (unixtime in millis)
Ê without endtime means from starttime to forever
Êendtime end of the time range. Ignored if no starttime specified.
Êversions number of cell versions to copy
Ênew.name new table's name
Êpeer.adr Address of the peer cluster given in the format
Ê hbase.zookeeer.quorum:hbase.zookeeper.client.port:zookeeper.znode.parent
Êfamilies comma-separated list of families to copy
Ê To copy from cf1 to cf2, give sourceCfName:destCfName.
Ê To keep the same name, just give "cfName"
Êall.cells also copy delete markers and deleted cells
Args:
Êtablename Name of the table to copy
Examples:
ÊTo copy 'TestTable' to a cluster that uses replication for a 1 hour window:
Ê$ bin/hbase org.apache.hadoop.hbase.mapreduce.CopyTable --starttime=1265875194289
--endtime=1265878794289 --peer.adr=server1,server2,server3:2181:/hbase
--families=myOldCf:myNewCf,cf2,cf3 TestTable
For performance consider the following general options:
Ê It is recommended that you set the following to >=100. A higher value uses more
memory but
Ê decreases the round trip time to the server and may increase performance.
Ê -Dhbase.client.scanner.caching=100
Ê The following should always be set to false, to prevent writing data twice, which
may produce
Ê inaccurate results.
Ê -Dmapred.map.tasks.speculative.execution=false
Scanner Caching
Caching for the input Scan is configured via hbase.client.scanner.caching in the
job configuration.
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Versions
By default, CopyTable utility only copies the latest version of row cells unless
--versions=n is explicitly specified in the command.
See Jonathan Hsieh’s Online HBase Backups with CopyTable blog post for more on CopyTable.
150.11. HashTable/SyncTable
HashTable/SyncTable is a two steps tool for synchronizing table data, where each of the steps are
implemented as MapReduce jobs. Similarly to CopyTable, it can be used for partial or entire table
data syncing, under same or remote cluster. However, it performs the sync in a more efficient way
than CopyTable. Instead of copying all cells in specified row key/time period range, HashTable (the
first step) creates hashed indexes for batch of cells on source table and output those as results. On
the next stage, SyncTable scans the source table and now calculates hash indexes for table cells,
compares these hashes with the outputs of HashTable, then it just scans (and compares) cells for
diverging hashes, only updating mismatching cells. This results in less network traffic/data
transfers, which can be impacting when syncing large tables on remote clusters.
150.11.1. Step 1, HashTable
First, run HashTable on the source table cluster (this is the table whose state will be copied to its
counterpart).
Usage:
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$ ./bin/hbase org.apache.hadoop.hbase.mapreduce.HashTable --help
Usage: HashTable [options] <tablename> <outputpath>
Options:
Êbatchsize the target amount of bytes to hash in each batch
Ê rows are added to the batch until this size is reached
Ê (defaults to 8000 bytes)
Ênumhashfiles the number of hash files to create
Ê if set to fewer than number of regions then
Ê the job will create this number of reducers
Ê (defaults to 1/100 of regions -- at least 1)
Êstartrow the start row
Êstoprow the stop row
Êstarttime beginning of the time range (unixtime in millis)
Ê without endtime means from starttime to forever
Êendtime end of the time range. Ignored if no starttime specified.
Êscanbatch scanner batch size to support intra row scans
Êversions number of cell versions to include
Êfamilies comma-separated list of families to include
Args:
Êtablename Name of the table to hash
Êoutputpath Filesystem path to put the output data
Examples:
ÊTo hash 'TestTable' in 32kB batches for a 1 hour window into 50 files:
Ê$ bin/hbase org.apache.hadoop.hbase.mapreduce.HashTable --batchsize=32000
--numhashfiles=50 --starttime=1265875194289 --endtime=1265878794289 --families=cf2,cf3
TestTable /hashes/testTable
The batchsize property defines how much cell data for a given region will be hashed together in a
single hash value. Sizing this properly has a direct impact on the sync efficiency, as it may lead to
less scans executed by mapper tasks of SyncTable (the next step in the process). The rule of thumb
is that, the smaller the number of cells out of sync (lower probability of finding a diff), larger batch
size values can be determined.
150.11.2. Step 2, SyncTable
Once HashTable has completed on source cluster, SyncTable can be ran on target cluster. Just like
replication and other synchronization jobs, it requires that all RegionServers/DataNodes on source
cluster be accessible by NodeManagers on the target cluster (where SyncTable job tasks will be
running).
Usage:
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$ ./bin/hbase org.apache.hadoop.hbase.mapreduce.SyncTable --help
Usage: SyncTable [options] <sourcehashdir> <sourcetable> <targettable>
Options:
Êsourcezkcluster ZK cluster key of the source table
Ê (defaults to cluster in classpath's config)
Êtargetzkcluster ZK cluster key of the target table
Ê (defaults to cluster in classpath's config)
Êdryrun if true, output counters but no writes
Ê (defaults to false)
ÊdoDeletes if false, does not perform deletes
Ê (defaults to true)
ÊdoPuts if false, does not perform puts
Ê (defaults to true)
Args:
Êsourcehashdir path to HashTable output dir for source table
Ê (see org.apache.hadoop.hbase.mapreduce.HashTable)
Êsourcetable Name of the source table to sync from
Êtargettable Name of the target table to sync to
Examples:
ÊFor a dry run SyncTable of tableA from a remote source cluster
Êto a local target cluster:
Ê$ bin/hbase org.apache.hadoop.hbase.mapreduce.SyncTable --dryrun=true
--sourcezkcluster=zk1.example.com,zk2.example.com,zk3.example.com:2181:/hbase
hdfs://nn:9000/hashes/tableA tableA tableA
The dryrun option is useful when a read only, diff report is wanted, as it will produce only
COUNTERS indicating the differences, but will not perform any actual changes. It can be used as an
alternative to VerifyReplication tool.
By default, SyncTable will cause target table to become an exact copy of source table (at least, for
the specified startrow/stoprow or/and starttime/endtime).
Setting doDeletes to false modifies default behaviour to not delete target cells that are missing on
source. Similarly, setting doPuts to false modifies default behaviour to not add missing cells on
target. Setting both doDeletes and doPuts to false would give same effect as setting dryrun to true.
Set doDeletes to false on Two-Way Replication scenarios
On Two-Way Replication or other scenarios where both source and target clusters
can have data ingested, it’s advisable to always set doDeletes option to false, as any
additional cell inserted on SyncTable target cluster and not yet replicated to source
would be deleted, and potentially lost permanently.
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Set sourcezkcluster to the actual source cluster ZK quorum
Although not required, if sourcezkcluster is not set, SyncTable will connect to local
HBase cluster for both source and target, which does not give any meaningful
result.
Remote Clusters on different Kerberos Realms
Currently, SyncTable can’t be ran for remote clusters on different Kerberos realms.
There’s some work in progress to resolve this on HBASE-20586
150.12. Export
Export is a utility that will dump the contents of table to HDFS in a sequence file. The Export can be
run via a Coprocessor Endpoint or MapReduce. Invoke via:
mapreduce-based Export
$ bin/hbase org.apache.hadoop.hbase.mapreduce.Export <tablename> <outputdir>
[<versions> [<starttime> [<endtime>]]]
endpoint-based Export
Make sure the Export coprocessor is enabled by adding
org.apache.hadoop.hbase.coprocessor.Export to hbase.coprocessor.region.classes.
$ bin/hbase org.apache.hadoop.hbase.coprocessor.Export <tablename> <outputdir>
[<versions> [<starttime> [<endtime>]]]
The outputdir is a HDFS directory that does not exist prior to the export. When done, the exported
files will be owned by the user invoking the export command.
The Comparison of Endpoint-based Export And Mapreduce-based Export
Endpoint-based Export Mapreduce-based Export
HBase version requirement 2.0+ 0.2.1+
Maven dependency hbase-endpoint hbase-mapreduce (2.0+), hbase-
server(prior to 2.0)
Requirement before dump mount the endpoint.Export on
the target table
deploy the MapReduce
framework
Read latency low, directly read the data from
region
normal, traditional RPC scan
Read Scalability depend on number of regions depend on number of mappers
(see
TableInputFormatBase#getSplit
s)
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Endpoint-based Export Mapreduce-based Export
Timeout operation timeout. configured
by
hbase.client.operation.timeout
scan timeout. configured by
hbase.client.scanner.timeout.pe
riod
Permission requirement READ, EXECUTE READ
Fault tolerance no depend on MapReduce
To see usage instructions, run the command with no options. Available options
include specifying column families and applying filters during the export.
By default, the Export tool only exports the newest version of a given cell, regardless of the number
of versions stored. To export more than one version, replace <versions> with the desired number
of versions.
Note: caching for the input Scan is configured via hbase.client.scanner.caching in the job
configuration.
150.13. Import
Import is a utility that will load data that has been exported back into HBase. Invoke via:
$ bin/hbase org.apache.hadoop.hbase.mapreduce.Import <tablename> <inputdir>
To see usage instructions, run the command with no options.
To import 0.94 exported files in a 0.96 cluster or onwards, you need to set system property
"hbase.import.version" when running the import command as below:
$ bin/hbase -Dhbase.import.version=0.94 org.apache.hadoop.hbase.mapreduce.Import
<tablename> <inputdir>
150.14. ImportTsv
ImportTsv is a utility that will load data in TSV format into HBase. It has two distinct usages:
loading data from TSV format in HDFS into HBase via Puts, and preparing StoreFiles to be loaded
via the completebulkload.
To load data via Puts (i.e., non-bulk loading):
$ bin/hbase org.apache.hadoop.hbase.mapreduce.ImportTsv -Dimporttsv.columns=a,b,c
<tablename> <hdfs-inputdir>
To generate StoreFiles for bulk-loading:
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$ bin/hbase org.apache.hadoop.hbase.mapreduce.ImportTsv -Dimporttsv.columns=a,b,c
-Dimporttsv.bulk.output=hdfs://storefile-outputdir <tablename> <hdfs-data-inputdir>
These generated StoreFiles can be loaded into HBase via completebulkload.
150.14.1. ImportTsv Options
Running ImportTsv with no arguments prints brief usage information:
Usage: importtsv -Dimporttsv.columns=a,b,c <tablename> <inputdir>
Imports the given input directory of TSV data into the specified table.
The column names of the TSV data must be specified using the -Dimporttsv.columns
option. This option takes the form of comma-separated column names, where each
column name is either a simple column family, or a columnfamily:qualifier. The special
column name HBASE_ROW_KEY is used to designate that this column should be used
as the row key for each imported record. You must specify exactly one column
to be the row key, and you must specify a column name for every column that exists in
the
input data.
By default importtsv will load data directly into HBase. To instead generate
HFiles of data to prepare for a bulk data load, pass the option:
Ê -Dimporttsv.bulk.output=/path/for/output
Ê Note: the target table will be created with default column family descriptors if it
does not already exist.
Other options that may be specified with -D include:
Ê -Dimporttsv.skip.bad.lines=false - fail if encountering an invalid line
Ê '-Dimporttsv.separator=|' - eg separate on pipes instead of tabs
Ê -Dimporttsv.timestamp=currentTimeAsLong - use the specified timestamp for the import
Ê -Dimporttsv.mapper.class=my.Mapper - A user-defined Mapper to use instead of
org.apache.hadoop.hbase.mapreduce.TsvImporterMapper
150.14.2. ImportTsv Example
For example, assume that we are loading data into a table called 'datatsv' with a ColumnFamily
called 'd' with two columns "c1" and "c2".
Assume that an input file exists as follows:
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row1 c1 c2
row2 c1 c2
row3 c1 c2
row4 c1 c2
row5 c1 c2
row6 c1 c2
row7 c1 c2
row8 c1 c2
row9 c1 c2
row10 c1 c2
For ImportTsv to use this input file, the command line needs to look like this:
ÊHADOOP_CLASSPATH=`${HBASE_HOME}/bin/hbase classpath` ${HADOOP_HOME}/bin/hadoop jar
${HBASE_HOME}/hbase-mapreduce-VERSION.jar importtsv
-Dimporttsv.columns=HBASE_ROW_KEY,d:c1,d:c2
-Dimporttsv.bulk.output=hdfs://storefileoutput datatsv hdfs://inputfile
... and in this example the first column is the rowkey, which is why the HBASE_ROW_KEY is used.
The second and third columns in the file will be imported as "d:c1" and "d:c2", respectively.
150.14.3. ImportTsv Warning
If you have preparing a lot of data for bulk loading, make sure the target HBase table is pre-split
appropriately.
150.14.4. See Also
For more information about bulk-loading HFiles into HBase, see arch.bulk.load
150.15. CompleteBulkLoad
The completebulkload utility will move generated StoreFiles into an HBase table. This utility is often
used in conjunction with output from importtsv.
There are two ways to invoke this utility, with explicit classname and via the driver:
Explicit Classname
$ bin/hbase org.apache.hadoop.hbase.tool.LoadIncrementalHFiles
<hdfs://storefileoutput> <tablename>
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Driver
HADOOP_CLASSPATH=`${HBASE_HOME}/bin/hbase classpath` ${HADOOP_HOME}/bin/hadoop jar
${HBASE_HOME}/hbase-server-VERSION.jar completebulkload <hdfs://storefileoutput>
<tablename>
150.15.1. CompleteBulkLoad Warning
Data generated via MapReduce is often created with file permissions that are not compatible with
the running HBase process. Assuming you’re running HDFS with permissions enabled, those
permissions will need to be updated before you run CompleteBulkLoad.
For more information about bulk-loading HFiles into HBase, see arch.bulk.load.
150.16. WALPlayer
WALPlayer is a utility to replay WAL files into HBase.
The WAL can be replayed for a set of tables or all tables, and a timerange can be provided (in
milliseconds). The WAL is filtered to this set of tables. The output can optionally be mapped to
another set of tables.
WALPlayer can also generate HFiles for later bulk importing, in that case only a single table and no
mapping can be specified.
Invoke via:
$ bin/hbase org.apache.hadoop.hbase.mapreduce.WALPlayer [options] <wal inputdir>
<tables> [<tableMappings>]>
For example:
$ bin/hbase org.apache.hadoop.hbase.mapreduce.WALPlayer /backuplogdir
oldTable1,oldTable2 newTable1,newTable2
WALPlayer, by default, runs as a mapreduce job. To NOT run WALPlayer as a mapreduce job on
your cluster, force it to run all in the local process by adding the flags
-Dmapreduce.jobtracker.address=local on the command line.
150.16.1. WALPlayer Options
Running WALPlayer with no arguments prints brief usage information:
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Usage: WALPlayer [options] <wal inputdir> <tables> [<tableMappings>]
Replay all WAL files into HBase.
<tables> is a comma separated list of tables.
If no tables ("") are specified, all tables are imported.
(Be careful, hbase:meta entries will be imported in this case.)
WAL entries can be mapped to new set of tables via <tableMappings>.
<tableMappings> is a comma separated list of target tables.
If specified, each table in <tables> must have a mapping.
By default WALPlayer will load data directly into HBase.
To generate HFiles for a bulk data load instead, pass the following option:
Ê -Dwal.bulk.output=/path/for/output
Ê (Only one table can be specified, and no mapping is allowed!)
Time range options:
Ê -Dwal.start.time=[date|ms]
Ê -Dwal.end.time=[date|ms]
Ê (The start and the end date of timerange. The dates can be expressed
Ê in milliseconds since epoch or in yyyy-MM-dd'T'HH:mm:ss.SS format.
Ê E.g. 1234567890120 or 2009-02-13T23:32:30.12)
Other options:
Ê -Dmapreduce.job.name=jobName
Ê Use the specified mapreduce job name for the wal player
For performance also consider the following options:
Ê -Dmapreduce.map.speculative=false
Ê -Dmapreduce.reduce.speculative=false
150.17. RowCounter
RowCounter is a mapreduce job to count all the rows of a table. This is a good utility to use as a
sanity check to ensure that HBase can read all the blocks of a table if there are any concerns of
metadata inconsistency. It will run the mapreduce all in a single process but it will run faster if you
have a MapReduce cluster in place for it to exploit. It is possible to limit the time range of data to be
scanned by using the --starttime=[starttime] and --endtime=[endtime] flags. The scanned data can
be limited based on keys using the --range=[startKey],[endKey][;[startKey],[endKey]…] option.
$ bin/hbase rowcounter [options] <tablename> [--starttime=<start> --endtime=<end>] [--
range=[startKey],[endKey][;[startKey],[endKey]...]] [<column1> <column2>...]
RowCounter only counts one version per cell.
For performance consider to use -Dhbase.client.scanner.caching=100 and
-Dmapreduce.map.speculative=false options.
150.18. CellCounter
HBase ships another diagnostic mapreduce job called CellCounter. Like RowCounter, it gathers
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more fine-grained statistics about your table. The statistics gathered by CellCounter are more fine-
grained and include:
•Total number of rows in the table.
•Total number of CFs across all rows.
•Total qualifiers across all rows.
•Total occurrence of each CF.
•Total occurrence of each qualifier.
•Total number of versions of each qualifier.
The program allows you to limit the scope of the run. Provide a row regex or prefix to limit the
rows to analyze. Specify a time range to scan the table by using the --starttime=<starttime> and
--endtime=<endtime> flags.
Use hbase.mapreduce.scan.column.family to specify scanning a single column family.
$ bin/hbase cellcounter <tablename> <outputDir> [reportSeparator] [regex or prefix]
[--starttime=<starttime> --endtime=<endtime>]
Note: just like RowCounter, caching for the input Scan is configured via
hbase.client.scanner.caching in the job configuration.
150.19. mlockall
It is possible to optionally pin your servers in physical memory making them less likely to be
swapped out in oversubscribed environments by having the servers call mlockall on startup. See
HBASE-4391 Add ability to start RS as root and call mlockall for how to build the optional library
and have it run on startup.
150.20. Offline Compaction Tool
CompactionTool provides a way of running compactions (either minor or major) as an
independent process from the RegionServer. It reuses same internal implementation classes
executed by RegionServer compaction feature. However, since this runs on a complete separate
independent java process, it releases RegionServers from the overhead involved in rewrite a set of
hfiles, which can be critical for latency sensitive use cases.
Usage:
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$ ./bin/hbase org.apache.hadoop.hbase.regionserver.CompactionTool
Usage: java org.apache.hadoop.hbase.regionserver.CompactionTool \
Ê [-compactOnce] [-major] [-mapred] [-D<property=value>]* files...
Options:
Êmapred Use MapReduce to run compaction.
ÊcompactOnce Execute just one compaction step. (default: while needed)
Êmajor Trigger major compaction.
Note: -D properties will be applied to the conf used.
For example:
ÊTo stop delete of compacted file, pass -Dhbase.compactiontool.delete=false
ÊTo set tmp dir, pass -Dhbase.tmp.dir=ALTERNATE_DIR
Examples:
ÊTo compact the full 'TestTable' using MapReduce:
Ê$ hbase org.apache.hadoop.hbase.regionserver.CompactionTool -mapred
hdfs://hbase/data/default/TestTable
ÊTo compact column family 'x' of the table 'TestTable' region 'abc':
Ê$ hbase org.apache.hadoop.hbase.regionserver.CompactionTool
hdfs://hbase/data/default/TestTable/abc/x
As shown by usage options above, CompactionTool can run as a standalone client or a mapreduce
job. When running as mapreduce job, each family dir is handled as an input split, and is processed
by a separate map task.
The compactionOnce parameter controls how many compaction cycles will be performed until
CompactionTool program decides to finish its work. If omitted, it will assume it should keep
running compactions on each specified family as determined by the given compaction policy
configured. For more info on compaction policy, see compaction.
If a major compaction is desired, major flag can be specified. If omitted, CompactionTool will
assume minor compaction is wanted by default.
It also allows for configuration overrides with -D flag. In the usage section above, for example,
-Dhbase.compactiontool.delete=false option will instruct compaction engine to not delete original
files from temp folder.
Files targeted for compaction must be specified as parent hdfs dirs. It allows for multiple dirs
definition, as long as each for these dirs are either a family, a region, or a table dir. If a table or
region dir is passed, the program will recursively iterate through related sub-folders, effectively
running compaction for each family found below the table/region level.
Since these dirs are nested under hbase hdfs directory tree, CompactionTool requires hbase super
user permissions in order to have access to required hfiles.
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Running in MapReduce mode
MapReduce mode offers the ability to process each family dir in parallel, as a
separate map task. Generally, it would make sense to run in this mode when
specifying one or more table dirs as targets for compactions. The caveat, though, is
that if number of families to be compacted become too large, the related
mapreduce job may have indirect impacts on RegionServers performance . Since
NodeManagers are normally co-located with RegionServers, such large jobs could
compete for IO/Bandwidth resources with the RegionServers.
MajorCompaction completely disabled on RegionServers due performance impacts
Major compactions can be a costly operation (see compaction), and can indeed
impact performance on RegionServers, leading operators to completely disable it
for critical low latency application. CompactionTool could be used as an
alternative in such scenarios, although, additional custom application logic would
need to be implemented, such as deciding scheduling and selection of
tables/regions/families target for a given compaction run.
For additional details about CompactionTool, see also CompactionTool.
150.21. hbase clean
The hbase clean command cleans HBase data from ZooKeeper, HDFS, or both. It is appropriate to
use for testing. Run it with no options for usage instructions. The hbase clean command was
introduced in HBase 0.98.
$ bin/hbase clean
Usage: hbase clean (--cleanZk|--cleanHdfs|--cleanAll)
Options:
Ê --cleanZk cleans hbase related data from zookeeper.
Ê --cleanHdfs cleans hbase related data from hdfs.
Ê --cleanAll cleans hbase related data from both zookeeper and hdfs.
150.22. hbase pe
The hbase pe command runs the PerformanceEvaluation tool, which is used for testing.
The PerformanceEvaluation tool accepts many different options and commands. For usage
instructions, run the command with no options.
The PerformanceEvaluation tool has received many updates in recent HBase releases, including
support for namespaces, support for tags, cell-level ACLs and visibility labels, multiget support for
RPC calls, increased sampling sizes, an option to randomly sleep during testing, and ability to
"warm up" the cluster before testing starts.
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150.23. hbase ltt
The hbase ltt command runs the LoadTestTool utility, which is used for testing.
You must specify either -init_only or at least one of -write, -update, or -read. For general usage
instructions, pass the -h option.
The LoadTestTool has received many updates in recent HBase releases, including support for
namespaces, support for tags, cell-level ACLS and visibility labels, testing security-related features,
ability to specify the number of regions per server, tests for multi-get RPC calls, and tests relating to
replication.
150.24. Pre-Upgrade validator
Pre-Upgrade validator tool can be used to check the cluster for known incompatibilities before
upgrading from HBase 1 to HBase 2.
$ bin/hbase pre-upgrade command ...
150.24.1. Coprocessor validation
HBase supports co-processors for a long time, but the co-processor API can be changed between
major releases. Co-processor validator tries to determine whether the old co-processors are still
compatible with the actual HBase version.
$ bin/hbase pre-upgrade validate-cp [-jar ...] [-class ... | -table ... | -config]
Options:
Ê-e Treat warnings as errors.
Ê-jar <arg> Jar file/directory of the coprocessor.
Ê-table <arg> Table coprocessor(s) to check.
Ê-class <arg> Coprocessor class(es) to check.
Ê-config Scan jar for observers.
The co-processor classes can be explicitly declared by -class option, or they can be obtained from
HBase configuration by -config option. Table level co-processors can be also checked by -table
option. The tool searches for co-processors on its classpath, but it can be extended by the -jar
option. It is possible to test multiple classes with multiple -class, multiple tables with multiple
-table options as well as adding multiple jars to the classpath with multiple -jar options.
The tool can report errors and warnings. Errors mean that HBase won’t be able to load the
coprocessor, because it is incompatible with the current version of HBase. Warnings mean that the
co-processors can be loaded, but they won’t work as expected. If -e option is given, then the tool
will also fail for warnings.
Please note that this tool cannot validate every aspect of jar files, it just does some static checks.
For example:
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$ bin/hbase pre-upgrade validate-cp -jar my-coprocessor.jar -class MyMasterObserver
-class MyRegionObserver
It validates MyMasterObserver and MyRegionObserver classes which are located in my-coprocessor.jar.
$ bin/hbase pre-upgrade validate-cp -table .*
It validates every table level co-processors where the table name matches to .* regular expression.
150.24.2. DataBlockEncoding validation
HBase 2.0 removed PREFIX_TREE Data Block Encoding from column families. For further information
please check prefix-tree encoding removed. To verify that none of the column families are using
incompatible Data Block Encodings in the cluster run the following command.
$ bin/hbase pre-upgrade validate-dbe
This check validates all column families and print out any incompatibilities. For example:
2018-07-13 09:58:32,028 WARN [main] tool.DataBlockEncodingValidator: Incompatible
DataBlockEncoding for table: t, cf: f, encoding: PREFIX_TREE
Which means that Data Block Encoding of table t, column family f is incompatible. To fix, use alter
command in HBase shell:
alter 't', { NAME => 'f', DATA_BLOCK_ENCODING => 'FAST_DIFF' }
Please also validate HFiles, which is described in the next section.
150.24.3. HFile Content validation
Even though Data Block Encoding is changed from PREFIX_TREE it is still possible to have HFiles that
contain data encoded that way. To verify that HFiles are readable with HBase 2 please use HFile
content validator.
$ bin/hbase pre-upgrade validate-hfile
The tool will log the corrupt HFiles and details about the root cause. If the problem is about
PREFIX_TREE encoding it is necessary to change encodings before upgrading to HBase 2.
The following log message shows an example of incorrect HFiles.
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2018-06-05 16:20:46,976 WARN [hfilevalidator-pool1-t3] hbck.HFileCorruptionChecker:
Found corrupt HFile
hdfs://example.com:8020/hbase/data/default/t/72ea7f7d625ee30f959897d1a3e2c350/prefix/7
e6b3d73263c4851bf2b8590a9b3791e
org.apache.hadoop.hbase.io.hfile.CorruptHFileException: Problem reading HFile Trailer
from file
hdfs://example.com:8020/hbase/data/default/t/72ea7f7d625ee30f959897d1a3e2c350/prefix/7
e6b3d73263c4851bf2b8590a9b3791e
Ê ...
Caused by: java.io.IOException: Invalid data block encoding type in file info:
PREFIX_TREE
Ê ...
Caused by: java.lang.IllegalArgumentException: No enum constant
org.apache.hadoop.hbase.io.encoding.DataBlockEncoding.PREFIX_TREE
Ê ...
2018-06-05 16:20:47,322 INFO [main] tool.HFileContentValidator: Corrupted file:
hdfs://example.com:8020/hbase/data/default/t/72ea7f7d625ee30f959897d1a3e2c350/prefix/7
e6b3d73263c4851bf2b8590a9b3791e
2018-06-05 16:20:47,383 INFO [main] tool.HFileContentValidator: Corrupted file:
hdfs://example.com:8020/hbase/archive/data/default/t/56be41796340b757eb7fff1eb5e2a905/
f/29c641ae91c34fc3bee881f45436b6d1
Fixing PREFIX_TREE errors
It’s possible to get PREFIX_TREE errors after changing Data Block Encoding to a supported one. It can
happen because there are some HFiles which still encoded with PREFIX_TREE or there are still some
snapshots.
For fixing HFiles, please run a major compaction on the table (it was default:t according to the log
message):
major_compact 't'
HFiles can be referenced from snapshots, too. It’s the case when the HFile is located under
archive/data. The first step is to determine which snapshot references that HFile (the name of the
file was 29c641ae91c34fc3bee881f45436b6d1 according to the logs):
for snapshot in $(hbase snapshotinfo -list-snapshots 2> /dev/null | tail -n -1 | cut
-f 1 -d \|);
do
Ê echo "checking snapshot named '${snapshot}'";
Ê hbase snapshotinfo -snapshot "${snapshot}" -files 2> /dev/null | grep
29c641ae91c34fc3bee881f45436b6d1;
done
The output of this shell script is:
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checking snapshot named 't_snap'
Ê 1.0 K t/56be41796340b757eb7fff1eb5e2a905/f/29c641ae91c34fc3bee881f45436b6d1
(archive)
Which means t_snap snapshot references the incompatible HFile. If the snapshot is still needed,
then it has to be recreated with HBase shell:
# creating a new namespace for the cleanup process
create_namespace 'pre_upgrade_cleanup'
# creating a new snapshot
clone_snapshot 't_snap', 'pre_upgrade_cleanup:t'
alter 'pre_upgrade_cleanup:t', { NAME => 'f', DATA_BLOCK_ENCODING => 'FAST_DIFF' }
major_compact 'pre_upgrade_cleanup:t'
# removing the invalid snapshot
delete_snapshot 't_snap'
# creating a new snapshot
snapshot 'pre_upgrade_cleanup:t', 't_snap'
# removing temporary table
disable 'pre_upgrade_cleanup:t'
drop 'pre_upgrade_cleanup:t'
drop_namespace 'pre_upgrade_cleanup'
For further information, please refer to HBASE-20649.
150.25. Data Block Encoding Tool
Tests various compression algorithms with different data block encoder for key compression on an
existing HFile. Useful for testing, debugging and benchmarking.
You must specify -f which is the full path of the HFile.
The result shows both the performance (MB/s) of compression/decompression and
encoding/decoding, and the data savings on the HFile.
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$ bin/hbase org.apache.hadoop.hbase.regionserver.DataBlockEncodingTool
Usages: hbase org.apache.hadoop.hbase.regionserver.DataBlockEncodingTool
Options:
Ê -f HFile to analyse (REQUIRED)
Ê -n Maximum number of key/value pairs to process in a single benchmark run.
Ê -b Whether to run a benchmark to measure read throughput.
Ê -c If this is specified, no correctness testing will be done.
Ê -a What kind of compression algorithm use for test. Default value: GZ.
Ê -t Number of times to run each benchmark. Default value: 12.
Ê -omit Number of first runs of every benchmark to omit from statistics. Default
value: 2.
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Chapter 151. Region Management
151.1. Major Compaction
Major compactions can be requested via the HBase shell or Admin.majorCompact.
Note: major compactions do NOT do region merges. See compaction for more information about
compactions.
151.2. Merge
Merge is a utility that can merge adjoining regions in the same table (see
org.apache.hadoop.hbase.util.Merge).
$ bin/hbase org.apache.hadoop.hbase.util.Merge <tablename> <region1> <region2>
If you feel you have too many regions and want to consolidate them, Merge is the utility you need.
Merge must run be done when the cluster is down. See the O’Reilly HBase Book for an example of
usage.
You will need to pass 3 parameters to this application. The first one is the table name. The second
one is the fully qualified name of the first region to merge, like
"table_name,\x0A,1342956111995.7cef47f192318ba7ccc75b1bbf27a82b.". The third one is the fully
qualified name for the second region to merge.
Additionally, there is a Ruby script attached to HBASE-1621 for region merging.
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Chapter 152. Node Management
152.1. Node Decommission
You can stop an individual RegionServer by running the following script in the HBase directory on
the particular node:
$ ./bin/hbase-daemon.sh stop regionserver
The RegionServer will first close all regions and then shut itself down. On shutdown, the
RegionServer’s ephemeral node in ZooKeeper will expire. The master will notice the RegionServer
gone and will treat it as a 'crashed' server; it will reassign the nodes the RegionServer was carrying.
Disable the Load Balancer before Decommissioning a node
If the load balancer runs while a node is shutting down, then there could be
contention between the Load Balancer and the Master’s recovery of the just
decommissioned RegionServer. Avoid any problems by disabling the balancer first.
See lb below.
Kill Node Tool
In hbase-2.0, in the bin directory, we added a script named considerAsDead.sh that
can be used to kill a regionserver. Hardware issues could be detected by
specialized monitoring tools before the zookeeper timeout has expired.
considerAsDead.sh is a simple function to mark a RegionServer as dead. It deletes
all the znodes of the server, starting the recovery process. Plug in the script into
your monitoring/fault detection tools to initiate faster failover. Be careful how you
use this disruptive tool. Copy the script if you need to make use of it in a version of
hbase previous to hbase-2.0.
A downside to the above stop of a RegionServer is that regions could be offline for a good period of
time. Regions are closed in order. If many regions on the server, the first region to close may not be
back online until all regions close and after the master notices the RegionServer’s znode gone. In
Apache HBase 0.90.2, we added facility for having a node gradually shed its load and then
shutdown itself down. Apache HBase 0.90.2 added the graceful_stop.sh script. Here is its usage:
$ ./bin/graceful_stop.sh
Usage: graceful_stop.sh [--config &conf-dir>] [--restart] [--reload] [--thrift] [--
rest] &hostname>
Êthrift If we should stop/start thrift before/after the hbase stop/start
Êrest If we should stop/start rest before/after the hbase stop/start
Êrestart If we should restart after graceful stop
Êreload Move offloaded regions back on to the stopped server
Êdebug Move offloaded regions back on to the stopped server
Êhostname Hostname of server we are to stop
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To decommission a loaded RegionServer, run the following: $ ./bin/graceful_stop.sh HOSTNAME
where HOSTNAME is the host carrying the RegionServer you would decommission.
On HOSTNAME
The HOSTNAME passed to graceful_stop.sh must match the hostname that hbase is
using to identify RegionServers. Check the list of RegionServers in the master UI
for how HBase is referring to servers. It’s usually hostname but can also be FQDN.
Whatever HBase is using, this is what you should pass the graceful_stop.sh
decommission script. If you pass IPs, the script is not yet smart enough to make a
hostname (or FQDN) of it and so it will fail when it checks if server is currently
running; the graceful unloading of regions will not run.
The graceful_stop.sh script will move the regions off the decommissioned RegionServer one at a
time to minimize region churn. It will verify the region deployed in the new location before it will
moves the next region and so on until the decommissioned server is carrying zero regions. At this
point, the graceful_stop.sh tells the RegionServer stop. The master will at this point notice the
RegionServer gone but all regions will have already been redeployed and because the RegionServer
went down cleanly, there will be no WAL logs to split.
Load Balancer
It is assumed that the Region Load Balancer is disabled while the graceful_stop
script runs (otherwise the balancer and the decommission script will end up
fighting over region deployments). Use the shell to disable the balancer:
hbase(main):001:0> balance_switch false
true
0 row(s) in 0.3590 seconds
This turns the balancer OFF. To reenable, do:
hbase(main):001:0> balance_switch true
false
0 row(s) in 0.3590 seconds
The graceful_stop will check the balancer and if enabled, will turn it off before it
goes to work. If it exits prematurely because of error, it will not have reset the
balancer. Hence, it is better to manage the balancer apart from graceful_stop
reenabling it after you are done w/ graceful_stop.
152.1.1. Decommissioning several Regions Servers concurrently
If you have a large cluster, you may want to decommission more than one machine at a time by
gracefully stopping multiple RegionServers concurrently. To gracefully drain multiple
regionservers at the same time, RegionServers can be put into a "draining" state. This is done by
marking a RegionServer as a draining node by creating an entry in ZooKeeper under the
hbase_root/draining znode. This znode has format name,port,startcode just like the regionserver
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entries under hbase_root/rs znode.
Without this facility, decommissioning multiple nodes may be non-optimal because regions that are
being drained from one region server may be moved to other regionservers that are also draining.
Marking RegionServers to be in the draining state prevents this from happening. See this blog post
for more details.
152.1.2. Bad or Failing Disk
It is good having dfs.datanode.failed.volumes.tolerated set if you have a decent number of disks per
machine for the case where a disk plain dies. But usually disks do the "John Wayne" — i.e. take a
while to go down spewing errors in dmesg — or for some reason, run much slower than their
companions. In this case you want to decommission the disk. You have two options. You can
decommission the datanode or, less disruptive in that only the bad disks data will be rereplicated,
can stop the datanode, unmount the bad volume (You can’t umount a volume while the datanode is
using it), and then restart the datanode (presuming you have set
dfs.datanode.failed.volumes.tolerated > 0). The regionserver will throw some errors in its logs as it
recalibrates where to get its data from — it will likely roll its WAL log too — but in general but for
some latency spikes, it should keep on chugging.
Short Circuit Reads
If you are doing short-circuit reads, you will have to move the regions off the
regionserver before you stop the datanode; when short-circuiting reading, though
chmod’d so regionserver cannot have access, because it already has the files open,
it will be able to keep reading the file blocks from the bad disk even though the
datanode is down. Move the regions back after you restart the datanode.
152.2. Rolling Restart
Some cluster configuration changes require either the entire cluster, or the RegionServers, to be
restarted in order to pick up the changes. In addition, rolling restarts are supported for upgrading
to a minor or maintenance release, and to a major release if at all possible. See the release notes for
release you want to upgrade to, to find out about limitations to the ability to perform a rolling
upgrade.
There are multiple ways to restart your cluster nodes, depending on your situation. These methods
are detailed below.
152.2.1. Using the rolling-restart.sh Script
HBase ships with a script, bin/rolling-restart.sh, that allows you to perform rolling restarts on the
entire cluster, the master only, or the RegionServers only. The script is provided as a template for
your own script, and is not explicitly tested. It requires password-less SSH login to be configured
and assumes that you have deployed using a tarball. The script requires you to set some
environment variables before running it. Examine the script and modify it to suit your needs.
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rolling-restart.sh General Usage
$ ./bin/rolling-restart.sh --help
Usage: rolling-restart.sh [--config <hbase-confdir>] [--rs-only] [--master-only] [--
graceful] [--maxthreads xx]
Rolling Restart on RegionServers Only
To perform a rolling restart on the RegionServers only, use the --rs-only option. This might be
necessary if you need to reboot the individual RegionServer or if you make a configuration
change that only affects RegionServers and not the other HBase processes.
Rolling Restart on Masters Only
To perform a rolling restart on the active and backup Masters, use the --master-only option. You
might use this if you know that your configuration change only affects the Master and not the
RegionServers, or if you need to restart the server where the active Master is running.
Graceful Restart
If you specify the --graceful option, RegionServers are restarted using the bin/graceful_stop.sh
script, which moves regions off a RegionServer before restarting it. This is safer, but can delay
the restart.
Limiting the Number of Threads
To limit the rolling restart to using only a specific number of threads, use the --maxthreads
option.
152.2.2. Manual Rolling Restart
To retain more control over the process, you may wish to manually do a rolling restart across your
cluster. This uses the graceful-stop.sh command decommission. In this method, you can restart
each RegionServer individually and then move its old regions back into place, retaining locality. If
you also need to restart the Master, you need to do it separately, and restart the Master before
restarting the RegionServers using this method. The following is an example of such a command.
You may need to tailor it to your environment. This script does a rolling restart of RegionServers
only. It disables the load balancer before moving the regions.
$ for i in `cat conf/regionservers|sort`; do ./bin/graceful_stop.sh --restart --reload
--debug $i; done &> /tmp/log.txt &;
Monitor the output of the /tmp/log.txt file to follow the progress of the script.
152.2.3. Logic for Crafting Your Own Rolling Restart Script
Use the following guidelines if you want to create your own rolling restart script.
1. Extract the new release, verify its configuration, and synchronize it to all nodes of your cluster
using rsync, scp, or another secure synchronization mechanism.
2. Restart the master first. You may need to modify these commands if your new HBase directory
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is different from the old one, such as for an upgrade.
$ ./bin/hbase-daemon.sh stop master; ./bin/hbase-daemon.sh start master
3. Gracefully restart each RegionServer, using a script such as the following, from the Master.
$ for i in `cat conf/regionservers|sort`; do ./bin/graceful_stop.sh --restart
--reload --debug $i; done &> /tmp/log.txt &
If you are running Thrift or REST servers, pass the --thrift or --rest options. For other available
options, run the bin/graceful-stop.sh --help command.
It is important to drain HBase regions slowly when restarting multiple RegionServers.
Otherwise, multiple regions go offline simultaneously and must be reassigned to other nodes,
which may also go offline soon. This can negatively affect performance. You can inject delays
into the script above, for instance, by adding a Shell command such as sleep. To wait for 5
minutes between each RegionServer restart, modify the above script to the following:
$ for i in `cat conf/regionservers|sort`; do ./bin/graceful_stop.sh --restart
--reload --debug $i & sleep 5m; done &> /tmp/log.txt &
4. Restart the Master again, to clear out the dead servers list and re-enable the load balancer.
152.3. Adding a New Node
Adding a new regionserver in HBase is essentially free, you simply start it like this: $ ./bin/hbase-
daemon.sh start regionserver and it will register itself with the master. Ideally you also started a
DataNode on the same machine so that the RS can eventually start to have local files. If you rely on
ssh to start your daemons, don’t forget to add the new hostname in conf/regionservers on the
master.
At this point the region server isn’t serving data because no regions have moved to it yet. If the
balancer is enabled, it will start moving regions to the new RS. On a small/medium cluster this can
have a very adverse effect on latency as a lot of regions will be offline at the same time. It is thus
recommended to disable the balancer the same way it’s done when decommissioning a node and
move the regions manually (or even better, using a script that moves them one by one).
The moved regions will all have 0% locality and won’t have any blocks in cache so the region server
will have to use the network to serve requests. Apart from resulting in higher latency, it may also be
able to use all of your network card’s capacity. For practical purposes, consider that a standard
1GigE NIC won’t be able to read much more than 100MB/s. In this case, or if you are in a OLAP
environment and require having locality, then it is recommended to major compact the moved
regions.
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Chapter 153. HBase Metrics
HBase emits metrics which adhere to the Hadoop Metrics API. Starting with HBase 0.95 [5: The
Metrics system was redone in HBase 0.96. See Migration to the New Metrics Hotness – Metrics2 by
Elliot Clark for detail], HBase is configured to emit a default set of metrics with a default sampling
period of every 10 seconds. You can use HBase metrics in conjunction with Ganglia. You can also
filter which metrics are emitted and extend the metrics framework to capture custom metrics
appropriate for your environment.
153.1. Metric Setup
For HBase 0.95 and newer, HBase ships with a default metrics configuration, or sink. This includes a
wide variety of individual metrics, and emits them every 10 seconds by default. To configure
metrics for a given region server, edit the conf/hadoop-metrics2-hbase.properties file. Restart the
region server for the changes to take effect.
To change the sampling rate for the default sink, edit the line beginning with *.period. To filter
which metrics are emitted or to extend the metrics framework, see https://hadoop.apache.org/docs/
current/api/org/apache/hadoop/metrics2/package-summary.html
HBase Metrics and Ganglia
By default, HBase emits a large number of metrics per region server. Ganglia may
have difficulty processing all these metrics. Consider increasing the capacity of the
Ganglia server or reducing the number of metrics emitted by HBase. See Metrics
Filtering.
153.2. Disabling Metrics
To disable metrics for a region server, edit the conf/hadoop-metrics2-hbase.properties file and
comment out any uncommented lines. Restart the region server for the changes to take effect.
153.3. Discovering Available Metrics
Rather than listing each metric which HBase emits by default, you can browse through the
available metrics, either as a JSON output or via JMX. Different metrics are exposed for the Master
process and each region server process.
Procedure: Access a JSON Output of Available Metrics
1. After starting HBase, access the region server’s web UI, at
http://REGIONSERVER_HOSTNAME:60030 by default (or port 16030 in HBase 1.0+).
2. Click the Metrics Dump link near the top. The metrics for the region server are presented as a
dump of the JMX bean in JSON format. This will dump out all metrics names and their values.
To include metrics descriptions in the listing — this can be useful when you are exploring what
is available — add a query string of ?description=true so your URL becomes
http://REGIONSERVER_HOSTNAME:60030/jmx?description=true. Not all beans and attributes
have descriptions.
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3. To view metrics for the Master, connect to the Master’s web UI instead (defaults to
http://localhost:60010 or port 16010 in HBase 1.0+) and click its Metrics Dump link. To include
metrics descriptions in the listing — this can be useful when you are exploring what is
available — add a query string of ?description=true so your URL becomes
http://REGIONSERVER_HOSTNAME:60010/jmx?description=true. Not all beans and attributes
have descriptions.
You can use many different tools to view JMX content by browsing MBeans. This procedure uses
jvisualvm, which is an application usually available in the JDK.
Procedure: Browse the JMX Output of Available Metrics
1. Start HBase, if it is not already running.
2. Run the command jvisualvm command on a host with a GUI display. You can launch it from the
command line or another method appropriate for your operating system.
3. Be sure the VisualVM-MBeans plugin is installed. Browse to Tools → Plugins. Click Installed and
check whether the plugin is listed. If not, click Available Plugins, select it, and click [ Install ].
When finished, click [ Close ].
4. To view details for a given HBase process, double-click the process in the Local sub-tree in the
left-hand panel. A detailed view opens in the right-hand panel. Click the MBeans tab which
appears as a tab in the top of the right-hand panel.
5. To access the HBase metrics, navigate to the appropriate sub-bean: .* Master: .* RegionServer:
6. The name of each metric and its current value is displayed in the Attributes tab. For a view
which includes more details, including the description of each attribute, click the Metadata tab.
153.4. Units of Measure for Metrics
Different metrics are expressed in different units, as appropriate. Often, the unit of measure is in
the name (as in the metric shippedKBs). Otherwise, use the following guidelines. When in doubt, you
may need to examine the source for a given metric.
•Metrics that refer to a point in time are usually expressed as a timestamp.
•Metrics that refer to an age (such as ageOfLastShippedOp) are usually expressed in milliseconds.
•Metrics that refer to memory sizes are in bytes.
•Sizes of queues (such as sizeOfLogQueue) are expressed as the number of items in the queue.
Determine the size by multiplying by the block size (default is 64 MB in HDFS).
•Metrics that refer to things like the number of a given type of operations (such as logEditsRead)
are expressed as an integer.
153.5. Most Important Master Metrics
Note: Counts are usually over the last metrics reporting interval.
hbase.master.numRegionServers
Number of live regionservers
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hbase.master.numDeadRegionServers
Number of dead regionservers
hbase.master.ritCount
The number of regions in transition
hbase.master.ritCountOverThreshold
The number of regions that have been in transition longer than a threshold time (default: 60
seconds)
hbase.master.ritOldestAge
The age of the longest region in transition, in milliseconds
153.6. Most Important RegionServer Metrics
Note: Counts are usually over the last metrics reporting interval.
hbase.regionserver.regionCount
The number of regions hosted by the regionserver
hbase.regionserver.storeFileCount
The number of store files on disk currently managed by the regionserver
hbase.regionserver.storeFileSize
Aggregate size of the store files on disk
hbase.regionserver.hlogFileCount
The number of write ahead logs not yet archived
hbase.regionserver.totalRequestCount
The total number of requests received
hbase.regionserver.readRequestCount
The number of read requests received
hbase.regionserver.writeRequestCount
The number of write requests received
hbase.regionserver.numOpenConnections
The number of open connections at the RPC layer
hbase.regionserver.numActiveHandler
The number of RPC handlers actively servicing requests
hbase.regionserver.numCallsInGeneralQueue
The number of currently enqueued user requests
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hbase.regionserver.numCallsInReplicationQueue
The number of currently enqueued operations received from replication
hbase.regionserver.numCallsInPriorityQueue
The number of currently enqueued priority (internal housekeeping) requests
hbase.regionserver.flushQueueLength
Current depth of the memstore flush queue. If increasing, we are falling behind with clearing
memstores out to HDFS.
hbase.regionserver.updatesBlockedTime
Number of milliseconds updates have been blocked so the memstore can be flushed
hbase.regionserver.compactionQueueLength
Current depth of the compaction request queue. If increasing, we are falling behind with
storefile compaction.
hbase.regionserver.blockCacheHitCount
The number of block cache hits
hbase.regionserver.blockCacheMissCount
The number of block cache misses
hbase.regionserver.blockCacheExpressHitPercent
The percent of the time that requests with the cache turned on hit the cache
hbase.regionserver.percentFilesLocal
Percent of store file data that can be read from the local DataNode, 0-100
hbase.regionserver.<op>_<measure>
Operation latencies, where <op> is one of Append, Delete, Mutate, Get, Replay, Increment; and
where <measure> is one of min, max, mean, median, 75th_percentile, 95th_percentile,
99th_percentile
hbase.regionserver.slow<op>Count
The number of operations we thought were slow, where <op> is one of the list above
hbase.regionserver.GcTimeMillis
Time spent in garbage collection, in milliseconds
hbase.regionserver.GcTimeMillisParNew
Time spent in garbage collection of the young generation, in milliseconds
hbase.regionserver.GcTimeMillisConcurrentMarkSweep
Time spent in garbage collection of the old generation, in milliseconds
hbase.regionserver.authenticationSuccesses
Number of client connections where authentication succeeded
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hbase.regionserver.authenticationFailures
Number of client connection authentication failures
hbase.regionserver.mutationsWithoutWALCount
Count of writes submitted with a flag indicating they should bypass the write ahead log
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Chapter 154. HBase Monitoring
154.1. Overview
The following metrics are arguably the most important to monitor for each RegionServer for
"macro monitoring", preferably with a system like OpenTSDB. If your cluster is having performance
issues it’s likely that you’ll see something unusual with this group.
HBase
•See rs metrics
OS
•IO Wait
•User CPU
Java
•GC
For more information on HBase metrics, see hbase metrics.
154.2. Slow Query Log
The HBase slow query log consists of parseable JSON structures describing the properties of those
client operations (Gets, Puts, Deletes, etc.) that either took too long to run, or produced too much
output. The thresholds for "too long to run" and "too much output" are configurable, as described
below. The output is produced inline in the main region server logs so that it is easy to discover
further details from context with other logged events. It is also prepended with identifying tags
(responseTooSlow), (responseTooLarge), (operationTooSlow), and (operationTooLarge) in order to
enable easy filtering with grep, in case the user desires to see only slow queries.
154.2.1. Configuration
There are two configuration knobs that can be used to adjust the thresholds for when queries are
logged.
•hbase.ipc.warn.response.time Maximum number of milliseconds that a query can be run
without being logged. Defaults to 10000, or 10 seconds. Can be set to -1 to disable logging by
time.
•hbase.ipc.warn.response.size Maximum byte size of response that a query can return without
being logged. Defaults to 100 megabytes. Can be set to -1 to disable logging by size.
154.2.2. Metrics
The slow query log exposes to metrics to JMX.
•hadoop.regionserver_rpc_slowResponse a global metric reflecting the durations of all responses
that triggered logging.
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•hadoop.regionserver_rpc_methodName.aboveOneSec A metric reflecting the durations of all
responses that lasted for more than one second.
154.2.3. Output
The output is tagged with operation e.g. (operationTooSlow) if the call was a client operation, such as
a Put, Get, or Delete, which we expose detailed fingerprint information for. If not, it is tagged
(responseTooSlow) and still produces parseable JSON output, but with less verbose information
solely regarding its duration and size in the RPC itself. TooLarge is substituted for TooSlow if the
response size triggered the logging, with TooLarge appearing even in the case that both size and
duration triggered logging.
154.2.4. Example
2011-09-08 10:01:25,824 WARN org.apache.hadoop.ipc.HBaseServer: (operationTooSlow): {
"tables":{"riley2":{"puts":[{"totalColumns":11,"families":{"actions":[{"timestamp":131
5501284459,"qualifier":"0","vlen":9667580},{"timestamp":1315501284459,"qualifier":"1",
"vlen":10122412},{"timestamp":1315501284459,"qualifier":"2","vlen":11104617},{"timesta
mp":1315501284459,"qualifier":"3","vlen":13430635}]},"row":"cfcd208495d565ef66e7dff9f9
8764da:0"}],"families":["actions"]}},"processingtimems":956,"client":"10.47.34.63:3362
3","starttimems":1315501284456,"queuetimems":0,"totalPuts":1,"class":"HRegionServer","
responsesize":0,"method":"multiPut"}
Note that everything inside the "tables" structure is output produced by MultiPut’s fingerprint,
while the rest of the information is RPC-specific, such as processing time and client IP/port. Other
client operations follow the same pattern and the same general structure, with necessary
differences due to the nature of the individual operations. In the case that the call is not a client
operation, that detailed fingerprint information will be completely absent.
This particular example, for example, would indicate that the likely cause of slowness is simply a
very large (on the order of 100MB) multiput, as we can tell by the "vlen," or value length, fields of
each put in the multiPut.
154.3. Block Cache Monitoring
Starting with HBase 0.98, the HBase Web UI includes the ability to monitor and report on the
performance of the block cache. To view the block cache reports, click . Following are a few
examples of the reporting capabilities.
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Figure 8. Basic Info
Figure 9. Config
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Figure 10. Stats
Figure 11. L1 and L2
This is not an exhaustive list of all the screens and reports available. Have a look in the Web UI.
154.4. Snapshot Space Usage Monitoring
Starting with HBase 0.95, Snapshot usage information on individual snapshots was shown in the
HBase Master Web UI. This was further enhanced starting with HBase 1.3 to show the total Storefile
size of the Snapshot Set. The following metrics are shown in the Master Web UI with HBase 1.3 and
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later.
•Shared Storefile Size is the Storefile size shared between snapshots and active tables.
•Mob Storefile Size is the Mob Storefile size shared between snapshots and active tables.
•Archived Storefile Size is the Storefile size in Archive.
The format of Archived Storefile Size is NNN(MMM). NNN is the total Storefile size in Archive,
MMM is the total Storefile size in Archive that is specific to the snapshot (not shared with other
snapshots and tables).
Figure 12. Master Snapshot Overview
Figure 13. Snapshot Storefile Stats Example 1
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Figure 14. Snapshot Storefile Stats Example 2
Figure 15. Empty Snapshot Storfile Stats Example
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Chapter 155. Cluster Replication
This information was previously available at Cluster Replication.
HBase provides a cluster replication mechanism which allows you to keep one cluster’s state
synchronized with that of another cluster, using the write-ahead log (WAL) of the source cluster to
propagate the changes. Some use cases for cluster replication include:
•Backup and disaster recovery
•Data aggregation
•Geographic data distribution
•Online data ingestion combined with offline data analytics
Replication is enabled at the granularity of the column family. Before enabling
replication for a column family, create the table and all column families to be
replicated, on the destination cluster.
Replication is asynchronous as we send WAL to another cluster in background,
which means that when you want to do recovery through replication, you could
loss some data. To address this problem, we have introduced a new feature called
synchronous replication. As the mechanism is a bit different so we use a separated
section to describe it. Please see Synchronous Replication.
155.1. Replication Overview
Cluster replication uses a source-push methodology. An HBase cluster can be a source (also called
master or active, meaning that it is the originator of new data), a destination (also called slave or
passive, meaning that it receives data via replication), or can fulfill both roles at once. Replication is
asynchronous, and the goal of replication is eventual consistency. When the source receives an edit
to a column family with replication enabled, that edit is propagated to all destination clusters using
the WAL for that for that column family on the RegionServer managing the relevant region.
When data is replicated from one cluster to another, the original source of the data is tracked via a
cluster ID which is part of the metadata. In HBase 0.96 and newer (HBASE-7709), all clusters which
have already consumed the data are also tracked. This prevents replication loops.
The WALs for each region server must be kept in HDFS as long as they are needed to replicate data
to any slave cluster. Each region server reads from the oldest log it needs to replicate and keeps
track of its progress processing WALs inside ZooKeeper to simplify failure recovery. The position
marker which indicates a slave cluster’s progress, as well as the queue of WALs to process, may be
different for every slave cluster.
The clusters participating in replication can be of different sizes. The master cluster relies on
randomization to attempt to balance the stream of replication on the slave clusters. It is expected
that the slave cluster has storage capacity to hold the replicated data, as well as any data it is
responsible for ingesting. If a slave cluster does run out of room, or is inaccessible for other
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reasons, it throws an error and the master retains the WAL and retries the replication at intervals.
Consistency Across Replicated Clusters
How your application builds on top of the HBase API matters when replication is
in play. HBase’s replication system provides at-least-once delivery of client edits
for an enabled column family to each configured destination cluster. In the event
of failure to reach a given destination, the replication system will retry sending
edits in a way that might repeat a given message. HBase provides two ways of
replication, one is the original replication and the other is serial replication. In the
previous way of replication, there is not a guaranteed order of delivery for client
edits. In the event of a RegionServer failing, recovery of the replication queue
happens independent of recovery of the individual regions that server was
previously handling. This means that it is possible for the not-yet-replicated edits
to be serviced by a RegionServer that is currently slower to replicate than the one
that handles edits from after the failure.
The combination of these two properties (at-least-once delivery and the lack of
message ordering) means that some destination clusters may end up in a different
state if your application makes use of operations that are not idempotent, e.g.
Increments.
To solve the problem, HBase now supports serial replication, which sends edits to
destination cluster as the order of requests from client. See Serial Replication.
Terminology Changes
Previously, terms such as master-master, master-slave, and cyclical were used to
describe replication relationships in HBase. These terms added confusion, and
have been abandoned in favor of discussions about cluster topologies appropriate
for different scenarios.
Cluster Topologies
•A central source cluster might propagate changes out to multiple destination clusters, for
failover or due to geographic distribution.
•A source cluster might push changes to a destination cluster, which might also push its own
changes back to the original cluster.
•Many different low-latency clusters might push changes to one centralized cluster for backup or
resource-intensive data analytics jobs. The processed data might then be replicated back to the
low-latency clusters.
Multiple levels of replication may be chained together to suit your organization’s needs. The
following diagram shows a hypothetical scenario. Use the arrows to follow the data paths.
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Figure 16. Example of a Complex Cluster Replication Configuration
HBase replication borrows many concepts from the statement-based replication design used by
MySQL. Instead of SQL statements, entire WALEdits (consisting of multiple cell inserts coming from
Put and Delete operations on the clients) are replicated in order to maintain atomicity.
155.2. Managing and Configuring Cluster Replication
Cluster Configuration Overview
1. Configure and start the source and destination clusters. Create tables with the same names and
column families on both the source and destination clusters, so that the destination cluster
knows where to store data it will receive.
2. All hosts in the source and destination clusters should be reachable to each other.
3. If both clusters use the same ZooKeeper cluster, you must use a different
zookeeper.znode.parent, because they cannot write in the same folder.
4. On the source cluster, in HBase Shell, add the destination cluster as a peer, using the add_peer
command.
5. On the source cluster, in HBase Shell, enable the table replication, using the
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enable_table_replication command.
6. Check the logs to see if replication is taking place. If so, you will see messages like the following,
coming from the ReplicationSource.
LOG.info("Replicating "+clusterId + " -> " + peerClusterId);
Serial Replication Configuration
See Serial Replication
Cluster Management Commands
add_peer <ID> <CLUSTER_KEY>
Adds a replication relationship between two clusters.
•ID — a unique string, which must not contain a hyphen.
•CLUSTER_KEY: composed using the following template, with appropriate place-holders:
hbase.zookeeper.quorum:hbase.zookeeper.property.clientPort:zookeeper.znode.parent. This
value can be found on the Master UI info page.
•STATE(optional): ENABLED or DISABLED, default value is ENABLED
list_peers
list all replication relationships known by this cluster
enable_peer <ID>
Enable a previously-disabled replication relationship
disable_peer <ID>
Disable a replication relationship. HBase will no longer send edits to that peer cluster, but it still
keeps track of all the new WALs that it will need to replicate if and when it is re-enabled. WALs
are retained when enabling or disabling replication as long as peers exist.
remove_peer <ID>
Disable and remove a replication relationship. HBase will no longer send edits to that peer
cluster or keep track of WALs.
enable_table_replication <TABLE_NAME>
Enable the table replication switch for all its column families. If the table is not found in the
destination cluster then it will create one with the same name and column families.
disable_table_replication <TABLE_NAME>
Disable the table replication switch for all its column families.
155.3. Serial Replication
Note: this feature is introduced in HBase 2.1
Function of serial replication
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Serial replication supports to push logs to the destination cluster in the same order as logs reach to
the source cluster.
Why need serial replication?
In replication of HBase, we push mutations to destination cluster by reading WAL in each region
server. We have a queue for WAL files so we can read them in order of creation time. However,
when region-move or RS failure occurs in source cluster, the hlog entries that are not pushed
before region-move or RS-failure will be pushed by original RS(for region move) or another RS
which takes over the remained hlog of dead RS(for RS failure), and the new entries for the same
region(s) will be pushed by the RS which now serves the region(s), but they push the hlog entries of
a same region concurrently without coordination.
This treatment can possibly lead to data inconsistency between source and destination clusters:
1. there are put and then delete written to source cluster.
2. due to region-move / RS-failure, they are pushed by different replication-source threads to peer
cluster.
3. if delete is pushed to peer cluster before put, and flush and major-compact occurs in peer
cluster before put is pushed to peer cluster, the delete is collected and the put remains in peer
cluster, but in source cluster the put is masked by the delete, hence data inconsistency between
source and destination clusters.
Serial replication configuration
Set the serial flag to true for a repliation peer. And the default serial flag is false.
•Add a new replication peer which serial flag is true
hbase> add_peer '1', CLUSTER_KEY => "server1.cie.com:2181:/hbase", SERIAL => true
•Set a replication peer’s serial flag to false
hbase> set_peer_serial '1', false
•Set a replication peer’s serial flag to true
hbase> set_peer_serial '1', true
The serial replication feature had been done firstly in HBASE-9465 and then reverted and redone in
HBASE-20046. You can find more details in these issues.
155.4. Verifying Replicated Data
The VerifyReplication MapReduce job, which is included in HBase, performs a systematic
comparison of replicated data between two different clusters. Run the VerifyReplication job on the
master cluster, supplying it with the peer ID and table name to use for validation. You can limit the
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verification further by specifying a time range or specific families. The job’s short name is
verifyrep. To run the job, use a command like the following:
+
$ HADOOP_CLASSPATH=`${HBASE_HOME}/bin/hbase classpath` "${HADOOP_HOME}/bin/hadoop" jar
"${HBASE_HOME}/hbase-mapreduce-VERSION.jar" verifyrep --starttime=<timestamp>
--endtime=<timestamp> --families=<myFam> <ID> <tableName>
+ The VerifyReplication command prints out GOODROWS and BADROWS counters to indicate rows that
did and did not replicate correctly.
155.5. Detailed Information About Cluster Replication
Figure 17. Replication Architecture Overview
155.5.1. Life of a WAL Edit
A single WAL edit goes through several steps in order to be replicated to a slave cluster.
1. An HBase client uses a Put or Delete operation to manipulate data in HBase.
2. The region server writes the request to the WAL in a way allows it to be replayed if it is not
written successfully.
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3. If the changed cell corresponds to a column family that is scoped for replication, the edit is
added to the queue for replication.
4. In a separate thread, the edit is read from the log, as part of a batch process. Only the KeyValues
that are eligible for replication are kept. Replicable KeyValues are part of a column family
whose schema is scoped GLOBAL, are not part of a catalog such as hbase:meta, did not originate
from the target slave cluster, and have not already been consumed by the target slave cluster.
5. The edit is tagged with the master’s UUID and added to a buffer. When the buffer is filled, or the
reader reaches the end of the file, the buffer is sent to a random region server on the slave
cluster.
6. The region server reads the edits sequentially and separates them into buffers, one buffer per
table. After all edits are read, each buffer is flushed using Table, HBase’s normal client. The
master’s UUID and the UUIDs of slaves which have already consumed the data are preserved in
the edits they are applied, in order to prevent replication loops.
7. In the master, the offset for the WAL that is currently being replicated is registered in
ZooKeeper.
8. The first three steps, where the edit is inserted, are identical.
9. Again in a separate thread, the region server reads, filters, and edits the log edits in the same
way as above. The slave region server does not answer the RPC call.
10. The master sleeps and tries again a configurable number of times.
11. If the slave region server is still not available, the master selects a new subset of region server to
replicate to, and tries again to send the buffer of edits.
12. Meanwhile, the WALs are rolled and stored in a queue in ZooKeeper. Logs that are archived by
their region server, by moving them from the region server’s log directory to a central log
directory, will update their paths in the in-memory queue of the replicating thread.
13. When the slave cluster is finally available, the buffer is applied in the same way as during
normal processing. The master region server will then replicate the backlog of logs that
accumulated during the outage.
Spreading Queue Failover Load
When replication is active, a subset of region servers in the source cluster is responsible for
shipping edits to the sink. This responsibility must be failed over like all other region server
functions should a process or node crash. The following configuration settings are recommended
for maintaining an even distribution of replication activity over the remaining live servers in the
source cluster:
•Set replication.source.maxretriesmultiplier to 300.
•Set replication.source.sleepforretries to 1 (1 second). This value, combined with the value of
replication.source.maxretriesmultiplier, causes the retry cycle to last about 5 minutes.
•Set replication.sleep.before.failover to 30000 (30 seconds) in the source cluster site
configuration.
Preserving Tags During Replication
By default, the codec used for replication between clusters strips tags, such as cell-level ACLs, from
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cells. To prevent the tags from being stripped, you can use a different codec which does not strip
them. Configure hbase.replication.rpc.codec to use
org.apache.hadoop.hbase.codec.KeyValueCodecWithTags, on both the source and sink RegionServers
involved in the replication. This option was introduced in HBASE-10322.
155.5.2. Replication Internals
Replication State in ZooKeeper
HBase replication maintains its state in ZooKeeper. By default, the state is contained in the base
node /hbase/replication. This node contains two child nodes, the Peers znode and the RS znode.
The Peers Znode
The peers znode is stored in /hbase/replication/peers by default. It consists of a list of all peer
replication clusters, along with the status of each of them. The value of each peer is its cluster
key, which is provided in the HBase Shell. The cluster key contains a list of ZooKeeper nodes in
the cluster’s quorum, the client port for the ZooKeeper quorum, and the base znode for HBase in
HDFS on that cluster.
The RS Znode
The rs znode contains a list of WAL logs which need to be replicated. This list is divided into a set
of queues organized by region server and the peer cluster the region server is shipping the logs
to. The rs znode has one child znode for each region server in the cluster. The child znode name
is the region server’s hostname, client port, and start code. This list includes both live and dead
region servers.
155.5.3. Choosing Region Servers to Replicate To
When a master cluster region server initiates a replication source to a slave cluster, it first connects
to the slave’s ZooKeeper ensemble using the provided cluster key . It then scans the rs/ directory to
discover all the available sinks (region servers that are accepting incoming streams of edits to
replicate) and randomly chooses a subset of them using a configured ratio which has a default
value of 10%. For example, if a slave cluster has 150 machines, 15 will be chosen as potential
recipient for edits that this master cluster region server sends. Because this selection is performed
by each master region server, the probability that all slave region servers are used is very high, and
this method works for clusters of any size. For example, a master cluster of 10 machines replicating
to a slave cluster of 5 machines with a ratio of 10% causes the master cluster region servers to
choose one machine each at random.
A ZooKeeper watcher is placed on the ${zookeeper.znode.parent}/rs node of the slave cluster by
each of the master cluster’s region servers. This watch is used to monitor changes in the
composition of the slave cluster. When nodes are removed from the slave cluster, or if nodes go
down or come back up, the master cluster’s region servers will respond by selecting a new pool of
slave region servers to replicate to.
155.5.4. Keeping Track of Logs
Each master cluster region server has its own znode in the replication znodes hierarchy. It contains
one znode per peer cluster (if 5 slave clusters, 5 znodes are created), and each of these contain a
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queue of WALs to process. Each of these queues will track the WALs created by that region server,
but they can differ in size. For example, if one slave cluster becomes unavailable for some time, the
WALs should not be deleted, so they need to stay in the queue while the others are processed. See
rs.failover.details for an example.
When a source is instantiated, it contains the current WAL that the region server is writing to.
During log rolling, the new file is added to the queue of each slave cluster’s znode just before it is
made available. This ensures that all the sources are aware that a new log exists before the region
server is able to append edits into it, but this operations is now more expensive. The queue items
are discarded when the replication thread cannot read more entries from a file (because it reached
the end of the last block) and there are other files in the queue. This means that if a source is up to
date and replicates from the log that the region server writes to, reading up to the "end" of the
current file will not delete the item in the queue.
A log can be archived if it is no longer used or if the number of logs exceeds
hbase.regionserver.maxlogs because the insertion rate is faster than regions are flushed. When a log
is archived, the source threads are notified that the path for that log changed. If a particular source
has already finished with an archived log, it will just ignore the message. If the log is in the queue,
the path will be updated in memory. If the log is currently being replicated, the change will be done
atomically so that the reader doesn’t attempt to open the file when has already been moved.
Because moving a file is a NameNode operation , if the reader is currently reading the log, it won’t
generate any exception.
155.5.5. Reading, Filtering and Sending Edits
By default, a source attempts to read from a WAL and ship log entries to a sink as quickly as
possible. Speed is limited by the filtering of log entries Only KeyValues that are scoped GLOBAL and
that do not belong to catalog tables will be retained. Speed is also limited by total size of the list of
edits to replicate per slave, which is limited to 64 MB by default. With this configuration, a master
cluster region server with three slaves would use at most 192 MB to store data to replicate. This
does not account for the data which was filtered but not garbage collected.
Once the maximum size of edits has been buffered or the reader reaches the end of the WAL, the
source thread stops reading and chooses at random a sink to replicate to (from the list that was
generated by keeping only a subset of slave region servers). It directly issues a RPC to the chosen
region server and waits for the method to return. If the RPC was successful, the source determines
whether the current file has been emptied or it contains more data which needs to be read. If the
file has been emptied, the source deletes the znode in the queue. Otherwise, it registers the new
offset in the log’s znode. If the RPC threw an exception, the source will retry 10 times before trying
to find a different sink.
155.5.6. Cleaning Logs
If replication is not enabled, the master’s log-cleaning thread deletes old logs using a configured
TTL. This TTL-based method does not work well with replication, because archived logs which have
exceeded their TTL may still be in a queue. The default behavior is augmented so that if a log is past
its TTL, the cleaning thread looks up every queue until it finds the log, while caching queues it has
found. If the log is not found in any queues, the log will be deleted. The next time the cleaning
process needs to look for a log, it starts by using its cached list.
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WALs are saved when replication is enabled or disabled as long as peers exist.
155.5.7. Region Server Failover
When no region servers are failing, keeping track of the logs in ZooKeeper adds no value.
Unfortunately, region servers do fail, and since ZooKeeper is highly available, it is useful for
managing the transfer of the queues in the event of a failure.
Each of the master cluster region servers keeps a watcher on every other region server, in order to
be notified when one dies (just as the master does). When a failure happens, they all race to create
a znode called lock inside the dead region server’s znode that contains its queues. The region server
that creates it successfully then transfers all the queues to its own znode, one at a time since
ZooKeeper does not support renaming queues. After queues are all transferred, they are deleted
from the old location. The znodes that were recovered are renamed with the ID of the slave cluster
appended with the name of the dead server.
Next, the master cluster region server creates one new source thread per copied queue, and each of
the source threads follows the read/filter/ship pattern. The main difference is that those queues will
never receive new data, since they do not belong to their new region server. When the reader hits
the end of the last log, the queue’s znode is deleted and the master cluster region server closes that
replication source.
Given a master cluster with 3 region servers replicating to a single slave with id 2, the following
hierarchy represents what the znodes layout could be at some point in time. The region servers'
znodes all contain a peers znode which contains a single queue. The znode names in the queues
represent the actual file names on HDFS in the form address,port.timestamp.
/hbase/replication/rs/
Ê 1.1.1.1,60020,123456780/
Ê 2/
Ê 1.1.1.1,60020.1234 (Contains a position)
Ê 1.1.1.1,60020.1265
Ê 1.1.1.2,60020,123456790/
Ê 2/
Ê 1.1.1.2,60020.1214 (Contains a position)
Ê 1.1.1.2,60020.1248
Ê 1.1.1.2,60020.1312
Ê 1.1.1.3,60020, 123456630/
Ê 2/
Ê 1.1.1.3,60020.1280 (Contains a position)
Assume that 1.1.1.2 loses its ZooKeeper session. The survivors will race to create a lock, and,
arbitrarily, 1.1.1.3 wins. It will then start transferring all the queues to its local peers znode by
appending the name of the dead server. Right before 1.1.1.3 is able to clean up the old znodes, the
layout will look like the following:
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/hbase/replication/rs/
Ê 1.1.1.1,60020,123456780/
Ê 2/
Ê 1.1.1.1,60020.1234 (Contains a position)
Ê 1.1.1.1,60020.1265
Ê 1.1.1.2,60020,123456790/
Ê lock
Ê 2/
Ê 1.1.1.2,60020.1214 (Contains a position)
Ê 1.1.1.2,60020.1248
Ê 1.1.1.2,60020.1312
Ê 1.1.1.3,60020,123456630/
Ê 2/
Ê 1.1.1.3,60020.1280 (Contains a position)
Ê 2-1.1.1.2,60020,123456790/
Ê 1.1.1.2,60020.1214 (Contains a position)
Ê 1.1.1.2,60020.1248
Ê 1.1.1.2,60020.1312
Some time later, but before 1.1.1.3 is able to finish replicating the last WAL from 1.1.1.2, it dies too.
Some new logs were also created in the normal queues. The last region server will then try to lock
1.1.1.3’s znode and will begin transferring all the queues. The new layout will be:
/hbase/replication/rs/
Ê 1.1.1.1,60020,123456780/
Ê 2/
Ê 1.1.1.1,60020.1378 (Contains a position)
Ê 2-1.1.1.3,60020,123456630/
Ê 1.1.1.3,60020.1325 (Contains a position)
Ê 1.1.1.3,60020.1401
Ê 2-1.1.1.2,60020,123456790-1.1.1.3,60020,123456630/
Ê 1.1.1.2,60020.1312 (Contains a position)
Ê 1.1.1.3,60020,123456630/
Ê lock
Ê 2/
Ê 1.1.1.3,60020.1325 (Contains a position)
Ê 1.1.1.3,60020.1401
Ê 2-1.1.1.2,60020,123456790/
Ê 1.1.1.2,60020.1312 (Contains a position)
155.6. Replication Metrics
The following metrics are exposed at the global region server level and at the peer level:
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source.sizeOfLogQueue
number of WALs to process (excludes the one which is being processed) at the Replication
source
source.shippedOps
number of mutations shipped
source.logEditsRead
number of mutations read from WALs at the replication source
source.ageOfLastShippedOp
age of last batch that was shipped by the replication source
source.completedLogs
The number of write-ahead-log files that have completed their acknowledged sending to the
peer associated with this source. Increments to this metric are a part of normal operation of
HBase replication.
source.completedRecoverQueues
The number of recovery queues this source has completed sending to the associated peer.
Increments to this metric are a part of normal recovery of HBase replication in the face of failed
Region Servers.
source.uncleanlyClosedLogs
The number of write-ahead-log files the replication system considered completed after reaching
the end of readable entries in the face of an uncleanly closed file.
source.ignoredUncleanlyClosedLogContentsInBytes
When a write-ahead-log file is not closed cleanly, there will likely be some entry that has been
partially serialized. This metric contains the number of bytes of such entries the HBase
replication system believes were remaining at the end of files skipped in the face of an uncleanly
closed file. Those bytes should either be in different file or represent a client write that was not
acknowledged.
source.restartedLogReading
The number of times the HBase replication system detected that it failed to correctly parse a
cleanly closed write-ahead-log file. In this circumstance, the system replays the entire log from
the beginning, ensuring that no edits fail to be acknowledged by the associated peer. Increments
to this metric indicate that the HBase replication system is having difficulty correctly handling
failures in the underlying distributed storage system. No dataloss should occur, but you should
check Region Server log files for details of the failures.
source.repeatedLogFileBytes
When the HBase replication system determines that it needs to replay a given write-ahead-log
file, this metric is incremented by the number of bytes the replication system believes had
already been acknowledged by the associated peer prior to starting over.
source.closedLogsWithUnknownFileLength
Incremented when the HBase replication system believes it is at the end of a write-ahead-log file
but it can not determine the length of that file in the underlying distributed storage system.
Could indicate dataloss since the replication system is unable to determine if the end of readable
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entries lines up with the expected end of the file. You should check Region Server log files for
details of the failures.
155.7. Replication Configuration Options
Option Description Default
zookeeper.znode.parent The name of the base
ZooKeeper znode used for
HBase
/hbase
zookeeper.znode.replication The name of the base znode
used for replication
replication
zookeeper.znode.replication.pe
ers
The name of the peer znode peers
zookeeper.znode.replication.pe
ers.state
The name of peer-state znode peer-state
zookeeper.znode.replication.rs The name of the rs znode rs
replication.sleep.before.failover How many milliseconds a
worker should sleep before
attempting to replicate a dead
region server’s WAL queues.
replication.executor.workers The number of region servers a
given region server should
attempt to failover
simultaneously.
1
155.8. Monitoring Replication Status
You can use the HBase Shell command status 'replication' to monitor the replication status on
your cluster. The command has three variations: * status 'replication' — prints the status of each
source and its sinks, sorted by hostname. * status 'replication', 'source' — prints the status for
each replication source, sorted by hostname. * status 'replication', 'sink' — prints the status for
each replication sink, sorted by hostname.
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Chapter 156. Running Multiple Workloads
On a Single Cluster
HBase provides the following mechanisms for managing the performance of a cluster handling
multiple workloads: . Quotas . Request Queues . Multiple-Typed Queues
156.1. Quotas
HBASE-11598 introduces RPC quotas, which allow you to throttle requests based on the following
limits:
1. The number or size of requests(read, write, or read+write) in a given timeframe
2. The number of tables allowed in a namespace
These limits can be enforced for a specified user, table, or namespace.
Enabling Quotas
Quotas are disabled by default. To enable the feature, set the hbase.quota.enabled property to true
in hbase-site.xml file for all cluster nodes.
General Quota Syntax
1. THROTTLE_TYPE can be expressed as READ, WRITE, or the default type(read + write).
2. Timeframes can be expressed in the following units: sec, min, hour, day
3. Request sizes can be expressed in the following units: B (bytes), K (kilobytes), M (megabytes), G
(gigabytes), T (terabytes), P (petabytes)
4. Numbers of requests are expressed as an integer followed by the string req
5. Limits relating to time are expressed as req/time or size/time. For instance 10req/day or
100P/hour.
6. Numbers of tables or regions are expressed as integers.
Setting Request Quotas
You can set quota rules ahead of time, or you can change the throttle at runtime. The change will
propagate after the quota refresh period has expired. This expiration period defaults to 5 minutes.
To change it, modify the hbase.quota.refresh.period property in hbase-site.xml. This property is
expressed in milliseconds and defaults to 300000.
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# Limit user u1 to 10 requests per second
hbase> set_quota TYPE => THROTTLE, USER => 'u1', LIMIT => '10req/sec'
# Limit user u1 to 10 read requests per second
hbase> set_quota TYPE => THROTTLE, THROTTLE_TYPE => READ, USER => 'u1', LIMIT =>
'10req/sec'
# Limit user u1 to 10 M per day everywhere
hbase> set_quota TYPE => THROTTLE, USER => 'u1', LIMIT => '10M/day'
# Limit user u1 to 10 M write size per sec
hbase> set_quota TYPE => THROTTLE, THROTTLE_TYPE => WRITE, USER => 'u1', LIMIT =>
'10M/sec'
# Limit user u1 to 5k per minute on table t2
hbase> set_quota TYPE => THROTTLE, USER => 'u1', TABLE => 't2', LIMIT => '5K/min'
# Limit user u1 to 10 read requests per sec on table t2
hbase> set_quota TYPE => THROTTLE, THROTTLE_TYPE => READ, USER => 'u1', TABLE => 't2',
LIMIT => '10req/sec'
# Remove an existing limit from user u1 on namespace ns2
hbase> set_quota TYPE => THROTTLE, USER => 'u1', NAMESPACE => 'ns2', LIMIT => NONE
# Limit all users to 10 requests per hour on namespace ns1
hbase> set_quota TYPE => THROTTLE, NAMESPACE => 'ns1', LIMIT => '10req/hour'
# Limit all users to 10 T per hour on table t1
hbase> set_quota TYPE => THROTTLE, TABLE => 't1', LIMIT => '10T/hour'
# Remove all existing limits from user u1
hbase> set_quota TYPE => THROTTLE, USER => 'u1', LIMIT => NONE
# List all quotas for user u1 in namespace ns2
hbase> list_quotas USER => 'u1, NAMESPACE => 'ns2'
# List all quotas for namespace ns2
hbase> list_quotas NAMESPACE => 'ns2'
# List all quotas for table t1
hbase> list_quotas TABLE => 't1'
# list all quotas
hbase> list_quotas
You can also place a global limit and exclude a user or a table from the limit by applying the
GLOBAL_BYPASS property.
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hbase> set_quota NAMESPACE => 'ns1', LIMIT => '100req/min' # a per-
namespace request limit
hbase> set_quota USER => 'u1', GLOBAL_BYPASS => true # user u1 is
not affected by the limit
Setting Namespace Quotas
You can specify the maximum number of tables or regions allowed in a given namespace, either
when you create the namespace or by altering an existing namespace, by setting the
hbase.namespace.quota.maxtables property on the namespace.
Limiting Tables Per Namespace
# Create a namespace with a max of 5 tables
hbase> create_namespace 'ns1', {'hbase.namespace.quota.maxtables'=>'5'}
# Alter an existing namespace to have a max of 8 tables
hbase> alter_namespace 'ns2', {METHOD => 'set',
'hbase.namespace.quota.maxtables'=>'8'}
# Show quota information for a namespace
hbase> describe_namespace 'ns2'
# Alter an existing namespace to remove a quota
hbase> alter_namespace 'ns2', {METHOD => 'unset',
NAME=>'hbase.namespace.quota.maxtables'}
Limiting Regions Per Namespace
# Create a namespace with a max of 10 regions
hbase> create_namespace 'ns1', {'hbase.namespace.quota.maxregions'=>'10'
# Show quota information for a namespace
hbase> describe_namespace 'ns1'
# Alter an existing namespace to have a max of 20 tables
hbase> alter_namespace 'ns2', {METHOD => 'set',
'hbase.namespace.quota.maxregions'=>'20'}
# Alter an existing namespace to remove a quota
hbase> alter_namespace 'ns2', {METHOD => 'unset', NAME=>
'hbase.namespace.quota.maxregions'}
156.2. Request Queues
If no throttling policy is configured, when the RegionServer receives multiple requests, they are
now placed into a queue waiting for a free execution slot (HBASE-6721). The simplest queue is a
FIFO queue, where each request waits for all previous requests in the queue to finish before
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running. Fast or interactive queries can get stuck behind large requests.
If you are able to guess how long a request will take, you can reorder requests by pushing the long
requests to the end of the queue and allowing short requests to preempt them. Eventually, you must
still execute the large requests and prioritize the new requests behind them. The short requests will
be newer, so the result is not terrible, but still suboptimal compared to a mechanism which allows
large requests to be split into multiple smaller ones.
HBASE-10993 introduces such a system for deprioritizing long-running scanners. There are two
types of queues, fifo and deadline. To configure the type of queue used, configure the
hbase.ipc.server.callqueue.type property in hbase-site.xml. There is no way to estimate how long
each request may take, so de-prioritization only affects scans, and is based on the number of “next”
calls a scan request has made. An assumption is made that when you are doing a full table scan,
your job is not likely to be interactive, so if there are concurrent requests, you can delay long-
running scans up to a limit tunable by setting the hbase.ipc.server.queue.max.call.delay property.
The slope of the delay is calculated by a simple square root of (numNextCall * weight) where the
weight is configurable by setting the hbase.ipc.server.scan.vtime.weight property.
156.3. Multiple-Typed Queues
You can also prioritize or deprioritize different kinds of requests by configuring a specified number
of dedicated handlers and queues. You can segregate the scan requests in a single queue with a
single handler, and all the other available queues can service short Get requests.
You can adjust the IPC queues and handlers based on the type of workload, using static tuning
options. This approach is an interim first step that will eventually allow you to change the settings
at runtime, and to dynamically adjust values based on the load.
Multiple Queues
To avoid contention and separate different kinds of requests, configure the
hbase.ipc.server.callqueue.handler.factor property, which allows you to increase the number of
queues and control how many handlers can share the same queue., allows admins to increase the
number of queues and decide how many handlers share the same queue.
Using more queues reduces contention when adding a task to a queue or selecting it from a queue.
You can even configure one queue per handler. The trade-off is that if some queues contain long-
running tasks, a handler may need to wait to execute from that queue rather than stealing from
another queue which has waiting tasks.
Read and Write Queues
With multiple queues, you can now divide read and write requests, giving more priority (more
queues) to one or the other type. Use the hbase.ipc.server.callqueue.read.ratio property to choose
to serve more reads or more writes.
Get and Scan Queues
Similar to the read/write split, you can split gets and scans by tuning the
hbase.ipc.server.callqueue.scan.ratio property to give more priority to gets or to scans. A scan
ratio of 0.1 will give more queue/handlers to the incoming gets, which means that more gets can be
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processed at the same time and that fewer scans can be executed at the same time. A value of 0.9
will give more queue/handlers to scans, so the number of scans executed will increase and the
number of gets will decrease.
156.4. Space Quotas
HBASE-16961 introduces a new type of quotas for HBase to leverage: filesystem quotas. These
"space" quotas limit the amount of space on the filesystem that HBase namespaces and tables can
consume. If a user, malicious or ignorant, has the ability to write data into HBase, with enough
time, that user can effectively crash HBase (or worse HDFS) by consuming all available space. When
there is no filesystem space available, HBase crashes because it can no longer create/sync data to
the write-ahead log.
This feature allows a for a limit to be set on the size of a table or namespace. When a space quota is
set on a namespace, the quota’s limit applies to the sum of usage of all tables in that namespace.
When a table with a quota exists in a namespace with a quota, the table quota takes priority over
the namespace quota. This allows for a scenario where a large limit can be placed on a collection of
tables, but a single table in that collection can have a fine-grained limit set.
The existing set_quota and list_quota HBase shell commands can be used to interact with space
quotas. Space quotas are quotas with a TYPE of SPACE and have LIMIT and POLICY attributes. The LIMIT
is a string that refers to the amount of space on the filesystem that the quota subject (e.g. the table
or namespace) may consume. For example, valid values of LIMIT are '10G', '2T', or '256M'. The
POLICY refers to the action that HBase will take when the quota subject’s usage exceeds the LIMIT.
The following are valid POLICY values.
•NO_INSERTS - No new data may be written (e.g. Put, Increment, Append).
•NO_WRITES - Same as NO_INSERTS but Deletes are also disallowed.
•NO_WRITES_COMPACTIONS - Same as NO_WRITES but compactions are also disallowed.
•DISABLE - The table(s) are disabled, preventing all read/write access.
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Setting simple space quotas
# Sets a quota on the table 't1' with a limit of 1GB, disallowing
Puts/Increments/Appends when the table exceeds 1GB
hbase> set_quota TYPE => SPACE, TABLE => 't1', LIMIT => '1G', POLICY => NO_INSERTS
# Sets a quota on the namespace 'ns1' with a limit of 50TB, disallowing
Puts/Increments/Appends/Deletes
hbase> set_quota TYPE => SPACE, NAMESPACE => 'ns1', LIMIT => '50T', POLICY =>
NO_WRITES
# Sets a quota on the table 't3' with a limit of 2TB, disallowing any writes and
compactions when the table exceeds 2TB.
hbase> set_quota TYPE => SPACE, TABLE => 't3', LIMIT => '2T', POLICY =>
NO_WRITES_COMPACTIONS
# Sets a quota on the table 't2' with a limit of 50GB, disabling the table when it
exceeds 50GB
hbase> set_quota TYPE => SPACE, TABLE => 't2', LIMIT => '50G', POLICY => DISABLE
Consider the following scenario to set up quotas on a namespace, overriding the quota on tables in
that namespace
Table and Namespace space quotas
hbase> create_namespace 'ns1'
hbase> create 'ns1:t1'
hbase> create 'ns1:t2'
hbase> create 'ns1:t3'
hbase> set_quota TYPE => SPACE, NAMESPACE => 'ns1', LIMIT => '100T', POLICY =>
NO_INSERTS
hbase> set_quota TYPE => SPACE, TABLE => 'ns1:t2', LIMIT => '200G', POLICY =>
NO_WRITES
hbase> set_quota TYPE => SPACE, TABLE => 'ns1:t3', LIMIT => '20T', POLICY => NO_WRITES
In the above scenario, the tables in the namespace ns1 will not be allowed to consume more than
100TB of space on the filesystem among each other. The table 'ns1:t2' is only allowed to be 200GB in
size, and will disallow all writes when the usage exceeds this limit. The table 'ns1:t3' is allowed to
grow to 20TB in size and also will disallow all writes then the usage exceeds this limit. Because
there is no table quota on 'ns1:t1', this table can grow up to 100TB, but only if 'ns1:t2' and 'ns1:t3'
have a usage of zero bytes. Practically, it’s limit is 100TB less the current usage of 'ns1:t2' and
'ns1:t3'.
156.5. Disabling Automatic Space Quota Deletion
By default, if a table or namespace is deleted that has a space quota, the quota itself is also deleted.
In some cases, it may be desirable for the space quota to not be automatically deleted. In these
cases, the user may configure the system to not delete any space quota automatically via hbase-
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site.xml.
Ê <property>
Ê <name>hbase.quota.remove.on.table.delete</name>
Ê <value>false</value>
Ê </property>
The value is set to true by default.
156.6. HBase Snapshots with Space Quotas
One common area of unintended-filesystem-use with HBase is via HBase snapshots. Because
snapshots exist outside of the management of HBase tables, it is not uncommon for administrators
to suddenly realize that hundreds of gigabytes or terabytes of space is being used by HBase
snapshots which were forgotten and never removed.
HBASE-17748 is the umbrella JIRA issue which expands on the original space quota functionality to
also include HBase snapshots. While this is a confusing subject, the implementation attempts to
present this support in as reasonable and simple of a manner as possible for administrators. This
feature does not make any changes to administrator interaction with space quotas, only in the
internal computation of table/namespace usage. Table and namespace usage will automatically
incorporate the size taken by a snapshot per the rules defined below.
As a review, let’s cover a snapshot’s lifecycle: a snapshot is metadata which points to a list of HFiles
on the filesystem. This is why creating a snapshot is a very cheap operation; no HBase table data is
actually copied to perform a snapshot. Cloning a snapshot into a new table or restoring a table is a
cheap operation for the same reason; the new table references the files which already exist on the
filesystem without a copy. To include snapshots in space quotas, we need to define which table
"owns" a file when a snapshot references the file ("owns" refers to encompassing the filesystem
usage of that file).
Consider a snapshot which was made against a table. When the snapshot refers to a file and the
table no longer refers to that file, the "originating" table "owns" that file. When multiple snapshots
refer to the same file and no table refers to that file, the snapshot with the lowest-sorting name
(lexicographically) is chosen and the table which that snapshot was created from "owns" that file.
HFiles are not "double-counted" hen a table and one or more snapshots refer to that HFile.
When a table is "rematerialized" (via clone_snapshot or restore_snapshot), a similar problem of file
ownership arises. In this case, while the rematerialized table references a file which a snapshot also
references, the table does not "own" the file. The table from which the snapshot was created still
"owns" that file. When the rematerialized table is compacted or the snapshot is deleted, the
rematerialized table will uniquely refer to a new file and "own" the usage of that file. Similarly,
when a table is duplicated via a snapshot and restore_snapshot, the new table will not consume any
quota size until the original table stops referring to the files, either due to a compaction on the
original table, a compaction on the new table, or the original table being deleted.
One new HBase shell command was added to inspect the computed sizes of each snapshot in an
HBase instance.
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hbase> list_snapshot_sizes
SNAPSHOT SIZE
Êt1.s1 1159108
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Chapter 157. HBase Backup
There are two broad strategies for performing HBase backups: backing up with a full cluster
shutdown, and backing up on a live cluster. Each approach has pros and cons.
For additional information, see HBase Backup Options over on the Sematext Blog.
157.1. Full Shutdown Backup
Some environments can tolerate a periodic full shutdown of their HBase cluster, for example if it is
being used a back-end analytic capacity and not serving front-end web-pages. The benefits are that
the NameNode/Master are RegionServers are down, so there is no chance of missing any in-flight
changes to either StoreFiles or metadata. The obvious con is that the cluster is down. The steps
include:
157.1.1. Stop HBase
157.1.2. Distcp
Distcp could be used to either copy the contents of the HBase directory in HDFS to either the same
cluster in another directory, or to a different cluster.
Note: Distcp works in this situation because the cluster is down and there are no in-flight edits to
files. Distcp-ing of files in the HBase directory is not generally recommended on a live cluster.
157.1.3. Restore (if needed)
The backup of the hbase directory from HDFS is copied onto the 'real' hbase directory via distcp.
The act of copying these files creates new HDFS metadata, which is why a restore of the NameNode
edits from the time of the HBase backup isn’t required for this kind of restore, because it’s a restore
(via distcp) of a specific HDFS directory (i.e., the HBase part) not the entire HDFS file-system.
157.2. Live Cluster Backup - Replication
This approach assumes that there is a second cluster. See the HBase page on replication for more
information.
157.3. Live Cluster Backup - CopyTable
The copytable utility could either be used to copy data from one table to another on the same
cluster, or to copy data to another table on another cluster.
Since the cluster is up, there is a risk that edits could be missed in the copy process.
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157.4. Live Cluster Backup - Export
The export approach dumps the content of a table to HDFS on the same cluster. To restore the data,
the import utility would be used.
Since the cluster is up, there is a risk that edits could be missed in the export process.
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Chapter 158. HBase Snapshots
HBase Snapshots allow you to take a copy of a table (both contents and metadata)with a very small
performance impact. A Snapshot is an immutable collection of table metadata and a list of HFiles
that comprised the table at the time the Snapshot was taken. A "clone" of a snapshot creates a new
table from that snapshot, and a "restore" of a snapshot returns the contents of a table to what it was
when the snapshot was created. The "clone" and "restore" operations do not require any data to be
copied, as the underlying HFiles (the files which contain the data for an HBase table) are not
modified with either action. Simiarly, exporting a snapshot to another cluster has little impact on
RegionServers of the local cluster.
Prior to version 0.94.6, the only way to backup or to clone a table is to use CopyTable/ExportTable,
or to copy all the hfiles in HDFS after disabling the table. The disadvantages of these methods are
that you can degrade region server performance (Copy/Export Table) or you need to disable the
table, that means no reads or writes; and this is usually unacceptable.
158.1. Configuration
To turn on the snapshot support just set the hbase.snapshot.enabled property to true. (Snapshots are
enabled by default in 0.95+ and off by default in 0.94.6+)
Ê <property>
Ê <name>hbase.snapshot.enabled</name>
Ê <value>true</value>
Ê </property>
158.2. Take a Snapshot
You can take a snapshot of a table regardless of whether it is enabled or disabled. The snapshot
operation doesn’t involve any data copying.
$ ./bin/hbase shell
hbase> snapshot 'myTable', 'myTableSnapshot-122112'
Take a Snapshot Without Flushing
The default behavior is to perform a flush of data in memory before the snapshot is taken. This
means that data in memory is included in the snapshot. In most cases, this is the desired behavior.
However, if your set-up can tolerate data in memory being excluded from the snapshot, you can
use the SKIP_FLUSH option of the snapshot command to disable and flushing while taking the
snapshot.
hbase> snapshot 'mytable', 'snapshot123', {SKIP_FLUSH => true}
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There is no way to determine or predict whether a very concurrent insert or
update will be included in a given snapshot, whether flushing is enabled or
disabled. A snapshot is only a representation of a table during a window of time.
The amount of time the snapshot operation will take to reach each Region Server
may vary from a few seconds to a minute, depending on the resource load and
speed of the hardware or network, among other factors. There is also no way to
know whether a given insert or update is in memory or has been flushed.
158.3. Listing Snapshots
List all snapshots taken (by printing the names and relative information).
$ ./bin/hbase shell
hbase> list_snapshots
158.4. Deleting Snapshots
You can remove a snapshot, and the files retained for that snapshot will be removed if no longer
needed.
$ ./bin/hbase shell
hbase> delete_snapshot 'myTableSnapshot-122112'
158.5. Clone a table from snapshot
From a snapshot you can create a new table (clone operation) with the same data that you had
when the snapshot was taken. The clone operation, doesn’t involve data copies, and a change to the
cloned table doesn’t impact the snapshot or the original table.
$ ./bin/hbase shell
hbase> clone_snapshot 'myTableSnapshot-122112', 'myNewTestTable'
158.6. Restore a snapshot
The restore operation requires the table to be disabled, and the table will be restored to the state at
the time when the snapshot was taken, changing both data and schema if required.
$ ./bin/hbase shell
hbase> disable 'myTable'
hbase> restore_snapshot 'myTableSnapshot-122112'
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Since Replication works at log level and snapshots at file-system level, after a
restore, the replicas will be in a different state from the master. If you want to use
restore, you need to stop replication and redo the bootstrap.
In case of partial data-loss due to misbehaving client, instead of a full restore that requires the table
to be disabled, you can clone the table from the snapshot and use a Map-Reduce job to copy the data
that you need, from the clone to the main one.
158.7. Snapshots operations and ACLs
If you are using security with the AccessController Coprocessor (See
hbase.accesscontrol.configuration), only a global administrator can take, clone, or restore a
snapshot, and these actions do not capture the ACL rights. This means that restoring a table
preserves the ACL rights of the existing table, while cloning a table creates a new table that has no
ACL rights until the administrator adds them.
158.8. Export to another cluster
The ExportSnapshot tool copies all the data related to a snapshot (hfiles, logs, snapshot metadata) to
another cluster. The tool executes a Map-Reduce job, similar to distcp, to copy files between the two
clusters, and since it works at file-system level the hbase cluster does not have to be online.
To copy a snapshot called MySnapshot to an HBase cluster srv2 (hdfs:///srv2:8082/hbase) using 16
mappers:
$ bin/hbase org.apache.hadoop.hbase.snapshot.ExportSnapshot -snapshot MySnapshot -copy
-to hdfs://srv2:8082/hbase -mappers 16
Limiting Bandwidth Consumption
You can limit the bandwidth consumption when exporting a snapshot, by specifying the -bandwidth
parameter, which expects an integer representing megabytes per second. The following example
limits the above example to 200 MB/sec.
$ bin/hbase org.apache.hadoop.hbase.snapshot.ExportSnapshot -snapshot MySnapshot -copy
-to hdfs://srv2:8082/hbase -mappers 16 -bandwidth 200
158.9. Storing Snapshots in an Amazon S3 Bucket
You can store and retrieve snapshots from Amazon S3, using the following procedure.
You can also store snapshots in Microsoft Azure Blob Storage. See Storing
Snapshots in Microsoft Azure Blob Storage.
Prerequisites
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•You must be using HBase 1.0 or higher and Hadoop 2.6.1 or higher, which is the first
configuration that uses the Amazon AWS SDK.
•You must use the s3a:// protocol to connect to Amazon S3. The older s3n:// and s3:// protocols
have various limitations and do not use the Amazon AWS SDK.
•The s3a:// URI must be configured and available on the server where you run the commands to
export and restore the snapshot.
After you have fulfilled the prerequisites, take the snapshot like you normally would. Afterward,
you can export it using the org.apache.hadoop.hbase.snapshot.ExportSnapshot command like the one
below, substituting your own s3a:// path in the copy-from or copy-to directive and substituting or
modifying other options as required:
$ hbase org.apache.hadoop.hbase.snapshot.ExportSnapshot \
Ê -snapshot MySnapshot \
Ê -copy-from hdfs://srv2:8082/hbase \
Ê -copy-to s3a://<bucket>/<namespace>/hbase \
Ê -chuser MyUser \
Ê -chgroup MyGroup \
Ê -chmod 700 \
Ê -mappers 16
$ hbase org.apache.hadoop.hbase.snapshot.ExportSnapshot \
Ê -snapshot MySnapshot
Ê -copy-from s3a://<bucket>/<namespace>/hbase \
Ê -copy-to hdfs://srv2:8082/hbase \
Ê -chuser MyUser \
Ê -chgroup MyGroup \
Ê -chmod 700 \
Ê -mappers 16
You can also use the org.apache.hadoop.hbase.snapshot.SnapshotInfo utility with the s3a:// path by
including the -remote-dir option.
$ hbase org.apache.hadoop.hbase.snapshot.SnapshotInfo \
Ê -remote-dir s3a://<bucket>/<namespace>/hbase \
Ê -list-snapshots
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Chapter 159. Storing Snapshots in Microsoft
Azure Blob Storage
You can store snapshots in Microsoft Azure Blog Storage using the same techniques as in Storing
Snapshots in an Amazon S3 Bucket.
Prerequisites
•You must be using HBase 1.2 or higher with Hadoop 2.7.1 or higher. No version of HBase
supports Hadoop 2.7.0.
•Your hosts must be configured to be aware of the Azure blob storage filesystem. See
https://hadoop.apache.org/docs/r2.7.1/hadoop-azure/index.html.
After you meet the prerequisites, follow the instructions in Storing Snapshots in an Amazon S3
Bucket, replacingthe protocol specifier with wasb:// or wasbs://.
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Chapter 160. Capacity Planning and Region
Sizing
There are several considerations when planning the capacity for an HBase cluster and performing
the initial configuration. Start with a solid understanding of how HBase handles data internally.
160.1. Node count and hardware/VM configuration
160.1.1. Physical data size
Physical data size on disk is distinct from logical size of your data and is affected by the following:
•Increased by HBase overhead
•See keyvalue and keysize. At least 24 bytes per key-value (cell), can be more. Small keys/values
means more relative overhead.
•KeyValue instances are aggregated into blocks, which are indexed. Indexes also have to be
stored. Blocksize is configurable on a per-ColumnFamily basis. See regions.arch.
•Decreased by compression and data block encoding, depending on data. See also this thread.
You might want to test what compression and encoding (if any) make sense for your data.
•Increased by size of region server wal (usually fixed and negligible - less than half of RS
memory size, per RS).
•Increased by HDFS replication - usually x3.
Aside from the disk space necessary to store the data, one RS may not be able to serve arbitrarily
large amounts of data due to some practical limits on region count and size (see
ops.capacity.regions).
160.1.2. Read/Write throughput
Number of nodes can also be driven by required throughput for reads and/or writes. The
throughput one can get per node depends a lot on data (esp. key/value sizes) and request patterns,
as well as node and system configuration. Planning should be done for peak load if it is likely that
the load would be the main driver of the increase of the node count. PerformanceEvaluation and
ycsb tools can be used to test single node or a test cluster.
For write, usually 5-15Mb/s per RS can be expected, since every region server has only one active
WAL. There’s no good estimate for reads, as it depends vastly on data, requests, and cache hit rate.
perf.casestudy might be helpful.
160.1.3. JVM GC limitations
RS cannot currently utilize very large heap due to cost of GC. There’s also no good way of running
multiple RS-es per server (other than running several VMs per machine). Thus, ~20-24Gb or less
memory dedicated to one RS is recommended. GC tuning is required for large heap sizes. See
gcpause, trouble.log.gc and elsewhere (TODO: where?)
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160.2. Determining region count and size
Generally less regions makes for a smoother running cluster (you can always manually split the big
regions later (if necessary) to spread the data, or request load, over the cluster); 20-200 regions per
RS is a reasonable range. The number of regions cannot be configured directly (unless you go for
fully disable.splitting); adjust the region size to achieve the target region size given table size.
When configuring regions for multiple tables, note that most region settings can be set on a per-
table basis via HTableDescriptor, as well as shell commands. These settings will override the ones
in hbase-site.xml. That is useful if your tables have different workloads/use cases.
Also note that in the discussion of region sizes here, HDFS replication factor is not (and should not
be) taken into account, whereas other factors ops.capacity.nodes.datasize should be. So, if your data is
compressed and replicated 3 ways by HDFS, "9 Gb region" means 9 Gb of compressed data. HDFS
replication factor only affects your disk usage and is invisible to most HBase code.
160.2.1. Viewing the Current Number of Regions
You can view the current number of regions for a given table using the HMaster UI. In the Tables
section, the number of online regions for each table is listed in the Online Regions column. This
total only includes the in-memory state and does not include disabled or offline regions.
160.2.2. Number of regions per RS - upper bound
In production scenarios, where you have a lot of data, you are normally concerned with the
maximum number of regions you can have per server. too many regions has technical discussion
on the subject. Basically, the maximum number of regions is mostly determined by memstore
memory usage. Each region has its own memstores; these grow up to a configurable size; usually in
128-256 MB range, see hbase.hregion.memstore.flush.size. One memstore exists per column family
(so there’s only one per region if there’s one CF in the table). The RS dedicates some fraction of total
memory to its memstores (see hbase.regionserver.global.memstore.size). If this memory is
exceeded (too much memstore usage), it can cause undesirable consequences such as unresponsive
server or compaction storms. A good starting point for the number of regions per RS (assuming one
table) is:
((RS memory) * (total memstore fraction)) / ((memstore size)*(# column families))
This formula is pseudo-code. Here are two formulas using the actual tunable parameters, first for
HBase 0.98+ and second for HBase 0.94.x.
HBase 0.98.x
((RS Xmx) * hbase.regionserver.global.memstore.size) /
(hbase.hregion.memstore.flush.size * (# column families))
HBase 0.94.x
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((RS Xmx) * hbase.regionserver.global.memstore.upperLimit) /
(hbase.hregion.memstore.flush.size * (# column families))+
If a given RegionServer has 16 GB of RAM, with default settings, the formula works out to
16384*0.4/128 ~ 51 regions per RS is a starting point. The formula can be extended to multiple
tables; if they all have the same configuration, just use the total number of families.
This number can be adjusted; the formula above assumes all your regions are filled at
approximately the same rate. If only a fraction of your regions are going to be actively written to,
you can divide the result by that fraction to get a larger region count. Then, even if all regions are
written to, all region memstores are not filled evenly, and eventually jitter appears even if they are
(due to limited number of concurrent flushes). Thus, one can have as many as 2-3 times more
regions than the starting point; however, increased numbers carry increased risk.
For write-heavy workload, memstore fraction can be increased in configuration at the expense of
block cache; this will also allow one to have more regions.
160.2.3. Number of regions per RS - lower bound
HBase scales by having regions across many servers. Thus if you have 2 regions for 16GB data, on a
20 node machine your data will be concentrated on just a few machines - nearly the entire cluster
will be idle. This really can’t be stressed enough, since a common problem is loading 200MB data
into HBase and then wondering why your awesome 10 node cluster isn’t doing anything.
On the other hand, if you have a very large amount of data, you may also want to go for a larger
number of regions to avoid having regions that are too large.
160.2.4. Maximum region size
For large tables in production scenarios, maximum region size is mostly limited by compactions -
very large compactions, esp. major, can degrade cluster performance. Currently, the recommended
maximum region size is 10-20Gb, and 5-10Gb is optimal. For older 0.90.x codebase, the upper-bound
of regionsize is about 4Gb, with a default of 256Mb.
The size at which the region is split into two is generally configured via hbase.hregion.max.filesize;
for details, see arch.region.splits.
If you cannot estimate the size of your tables well, when starting off, it’s probably best to stick to
the default region size, perhaps going smaller for hot tables (or manually split hot regions to spread
the load over the cluster), or go with larger region sizes if your cell sizes tend to be largish (100k
and up).
In HBase 0.98, experimental stripe compactions feature was added that would allow for larger
regions, especially for log data. See ops.stripe.
160.2.5. Total data size per region server
According to above numbers for region size and number of regions per region server, in an
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optimistic estimate 10 GB x 100 regions per RS will give up to 1TB served per region server, which is
in line with some of the reported multi-PB use cases. However, it is important to think about the
data vs cache size ratio at the RS level. With 1TB of data per server and 10 GB block cache, only 1%
of the data will be cached, which may barely cover all block indices.
160.3. Initial configuration and tuning
First, see important configurations. Note that some configurations, more than others, depend on
specific scenarios. Pay special attention to:
•hbase.regionserver.handler.count - request handler thread count, vital for high-throughput
workloads.
•config.wals - the blocking number of WAL files depends on your memstore configuration and
should be set accordingly to prevent potential blocking when doing high volume of writes.
Then, there are some considerations when setting up your cluster and tables.
160.3.1. Compactions
Depending on read/write volume and latency requirements, optimal compaction settings may be
different. See compaction for some details.
When provisioning for large data sizes, however, it’s good to keep in mind that compactions can
affect write throughput. Thus, for write-intensive workloads, you may opt for less frequent
compactions and more store files per regions. Minimum number of files for compactions
(hbase.hstore.compaction.min) can be set to higher value; hbase.hstore.blockingStoreFiles should
also be increased, as more files might accumulate in such case. You may also consider manually
managing compactions: managed.compactions
160.3.2. Pre-splitting the table
Based on the target number of the regions per RS (see ops.capacity.regions.count) and number of
RSes, one can pre-split the table at creation time. This would both avoid some costly splitting as the
table starts to fill up, and ensure that the table starts out already distributed across many servers.
If the table is expected to grow large enough to justify that, at least one region per RS should be
created. It is not recommended to split immediately into the full target number of regions (e.g. 50 *
number of RSes), but a low intermediate value can be chosen. For multiple tables, it is
recommended to be conservative with presplitting (e.g. pre-split 1 region per RS at most), especially
if you don’t know how much each table will grow. If you split too much, you may end up with too
many regions, with some tables having too many small regions.
For pre-splitting howto, see manual region splitting decisions and precreate.regions.
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Chapter 161. Table Rename
In versions 0.90.x of hbase and earlier, we had a simple script that would rename the hdfs table
directory and then do an edit of the hbase:meta table replacing all mentions of the old table name
with the new. The script was called ./bin/rename_table.rb. The script was deprecated and removed
mostly because it was unmaintained and the operation performed by the script was brutal.
As of hbase 0.94.x, you can use the snapshot facility renaming a table. Here is how you would do it
using the hbase shell:
hbase shell> disable 'tableName'
hbase shell> snapshot 'tableName', 'tableSnapshot'
hbase shell> clone_snapshot 'tableSnapshot', 'newTableName'
hbase shell> delete_snapshot 'tableSnapshot'
hbase shell> drop 'tableName'
or in code it would be as follows:
void rename(Admin admin, String oldTableName, TableName newTableName) {
Ê String snapshotName = randomName();
Ê admin.disableTable(oldTableName);
Ê admin.snapshot(snapshotName, oldTableName);
Ê admin.cloneSnapshot(snapshotName, newTableName);
Ê admin.deleteSnapshot(snapshotName);
Ê admin.deleteTable(oldTableName);
}
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Chapter 162. RegionServer Grouping
RegionServer Grouping (A.K.A rsgroup) is an advanced feature for partitioning regionservers into
distinctive groups for strict isolation. It should only be used by users who are sophisticated enough
to understand the full implications and have a sufficient background in managing HBase clusters. It
was developed by Yahoo! and they run it at scale on their large grid cluster. See HBase at Yahoo!
Scale.
RSGroups are defined and managed with shell commands. The shell drives a Coprocessor Endpoint
whose API is marked private given this is an evolving feature; the Coprocessor API is not for public
consumption. A server can be added to a group with hostname and port pair and tables can be
moved to this group so that only regionservers in the same rsgroup can host the regions of the
table. RegionServers and tables can only belong to one rsgroup at a time. By default, all tables and
regionservers belong to the default rsgroup. System tables can also be put into a rsgroup using the
regular APIs. A custom balancer implementation tracks assignments per rsgroup and makes sure to
move regions to the relevant regionservers in that rsgroup. The rsgroup information is stored in a
regular HBase table, and a zookeeper-based read-only cache is used at cluster bootstrap time.
To enable, add the following to your hbase-site.xml and restart your Master:
Ê<property>
Ê <name>hbase.coprocessor.master.classes</name>
Ê <value>org.apache.hadoop.hbase.rsgroup.RSGroupAdminEndpoint</value>
Ê</property>
Ê<property>
Ê <name>hbase.master.loadbalancer.class</name>
Ê <value>org.apache.hadoop.hbase.rsgroup.RSGroupBasedLoadBalancer</value>
Ê</property>
Then use the shell rsgroup commands to create and manipulate RegionServer groups: e.g. to add a
rsgroup and then add a server to it. To see the list of rsgroup commands available in the hbase shell
type:
Êhbase(main):008:0> help ‘rsgroup’
ÊTook 0.5610 seconds
High level, you create a rsgroup that is other than the default group using add_rsgroup command.
You then add servers and tables to this group with the move_servers_rsgroup and
move_tables_rsgroup commands. If necessary, run a balance for the group if tables are slow to
migrate to the groups dedicated server with the balance_rsgroup command (Usually this is not
needed). To monitor effect of the commands, see the Tables tab toward the end of the Master UI
home page. If you click on a table, you can see what servers it is deployed across. You should see
here a reflection of the grouping done with your shell commands. View the master log if issues.
Here is example using a few of the rsgroup commands. To add a group, do as follows:
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Êhbase(main):008:0> add_rsgroup 'my_group'
ÊTook 0.5610 seconds
RegionServer Groups must be Enabled
If you have not enabled the rsgroup Coprocessor Endpoint in the master and you
run the any of the rsgroup shell commands, you will see an error message like the
below:
ERROR: org.apache.hadoop.hbase.exceptions.UnknownProtocolException: No
registered master coprocessor service found for name
RSGroupAdminService
Ê at org.apache.hadoop.hbase.master.MasterRpcServices
.execMasterService(MasterRpcServices.java:604)
Ê at org.apache.hadoop.hbase.shaded.protobuf.generated.MasterProtos
$MasterService$2.callBlockingMethod(MasterProtos.java)
Ê at org.apache.hadoop.hbase.ipc.RpcServer.call(RpcServer.java:1140)
Ê at org.apache.hadoop.hbase.ipc.CallRunner.run(CallRunner.java:133)
Ê at org.apache.hadoop.hbase.ipc.RpcExecutor$Handler.run(RpcExecutor
.java:277)
Ê at org.apache.hadoop.hbase.ipc.RpcExecutor$Handler.run(RpcExecutor
.java:257)
Add a server (specified by hostname + port) to the just-made group using the move_servers_rsgroup
command as follows:
Êhbase(main):010:0> move_servers_rsgroup 'my_group',['k.att.net:51129']
Hostname and Port vs ServerName
The rsgroup feature refers to servers in a cluster with hostname and port only. It
does not make use of the HBase ServerName type identifying RegionServers; i.e.
hostname + port + starttime to distinguish RegionServer instances. The rsgroup
feature keeps working across RegionServer restarts so the starttime of
ServerName — and hence the ServerName type — is not appropriate.
Administration
Servers come and go over the lifetime of a Cluster. Currently, you must manually align the servers
referenced in rsgroups with the actual state of nodes in the running cluster. What we mean by this
is that if you decommission a server, then you must update rsgroups as part of your server
decommission process removing references.
But, there is no _remove_offline_servers_rsgroup_command you say!
The way to remove a server is to move it to the default group. The default group is special. All
rsgroups, but the default rsgroup, are static in that edits via the shell commands are persisted to
the system hbase:rsgroup table. If they reference a decommissioned server, then they need to be
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updated to undo the reference.
The default group is not like other rsgroups in that it is dynamic. Its server list mirrors the current
state of the cluster; i.e. if you shutdown a server that was part of the default rsgroup, and then do a
get_rsgroup default to list its content in the shell, the server will no longer be listed. For non-default
groups, though a mode may be offline, it will persist in the non-default group’s list of servers. But if
you move the offline server from the non-default rsgroup to default, it will not show in the default
list. It will just be dropped.
162.1. Best Practice
The authors of the rsgroup feature, the Yahoo! HBase Engineering team, have been running it on
their grid for a good while now and have come up with a few best practices informed by their
experience.
162.1.1. Isolate System Tables
Either have a system rsgroup where all the system tables are or just leave the system tables in
default rsgroup and have all user-space tables are in non-default rsgroups.
162.1.2. Dead Nodes
Yahoo! Have found it useful at their scale to keep a special rsgroup of dead or questionable nodes;
this is one means of keeping them out of the running until repair.
Be careful replacing dead nodes in an rsgroup. Ensure there are enough live nodes before you start
moving out the dead. Move in good live nodes first if you have to.
162.2. Troubleshooting
Viewing the Master log will give you insight on rsgroup operation.
If it appears stuck, restart the Master process.
162.3. Remove RegionServer Grouping
Removing RegionServer Grouping feature from a cluster on which it was enabled involves more
steps in addition to removing the relevant properties from hbase-site.xml. This is to clean the
RegionServer grouping related meta data so that if the feature is re-enabled in the future, the old
meta data will not affect the functioning of the cluster.
•Move all tables in non-default rsgroups to default regionserver group
#Reassigning table t1 from non default group - hbase shell
hbase(main):005:0> move_tables_rsgroup 'default',['t1']
•Move all regionservers in non-default rsgroups to default regionserver group
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#Reassigning all the servers in the non-default rsgroup to default - hbase shell
hbase(main):008:0> move_servers_rsgroup
'default',['rs1.xxx.com:16206','rs2.xxx.com:16202','rs3.xxx.com:16204']
•Remove all non-default rsgroups. default rsgroup created implicitly doesn’t have to be removed
#removing non default rsgroup - hbase shell
hbase(main):009:0> remove_rsgroup 'group2'
•Remove the changes made in hbase-site.xml and restart the cluster
•Drop the table hbase:rsgroup from hbase
#Through hbase shell drop table hbase:rsgroup
hbase(main):001:0> disable 'hbase:rsgroup'
0 row(s) in 2.6270 seconds
hbase(main):002:0> drop 'hbase:rsgroup'
0 row(s) in 1.2730 seconds
•Remove znode rsgroup from the cluster ZooKeeper using zkCli.sh
#From ZK remove the node /hbase/rsgroup through zkCli.sh
rmr /hbase/rsgroup
162.4. ACL
To enable ACL, add the following to your hbase-site.xml and restart your Master:
<property>
Ê <name>hbase.security.authorization</name>
Ê <value>true</value>
<property>
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Chapter 163. Region Normalizer
The Region Normalizer tries to make Regions all in a table about the same in size. It does this by
finding a rough average. Any region that is larger than twice this size is split. Any region that is
much smaller is merged into an adjacent region. It is good to run the Normalizer on occasion on a
down time after the cluster has been running a while or say after a burst of activity such as a large
delete.
(The bulk of the below detail was copied wholesale from the blog by Romil Choksi at HBase Region
Normalizer)
The Region Normalizer is feature available since HBase-1.2. It runs a set of pre-calculated
merge/split actions to resize regions that are either too large or too small compared to the average
region size for a given table. Region Normalizer when invoked computes a normalization 'plan' for
all of the tables in HBase. System tables (such as hbase:meta, hbase:namespace, Phoenix system
tables etc) and user tables with normalization disabled are ignored while computing the plan. For
normalization enabled tables, normalization plan is carried out in parallel across multiple tables.
Normalizer can be enabled or disabled globally for the entire cluster using the ‘normalizer_switch’
command in the HBase shell. Normalization can also be controlled on a per table basis, which is
disabled by default when a table is created. Normalization for a table can be enabled or disabled by
setting the NORMALIZATION_ENABLED table attribute to true or false.
To check normalizer status and enable/disable normalizer
hbase(main):001:0> normalizer_enabled
true
0 row(s) in 0.4870 seconds
hbase(main):002:0> normalizer_switch false
true
0 row(s) in 0.0640 seconds
hbase(main):003:0> normalizer_enabled
false
0 row(s) in 0.0120 seconds
hbase(main):004:0> normalizer_switch true
false
0 row(s) in 0.0200 seconds
hbase(main):005:0> normalizer_enabled
true
0 row(s) in 0.0090 seconds
When enabled, Normalizer is invoked in the background every 5 mins (by default), which can be
configured using hbase.normalization.period in hbase-site.xml. Normalizer can also be invoked
manually/programmatically at will using HBase shell’s normalize command. HBase by default uses
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SimpleRegionNormalizer, but users can design their own normalizer as long as they implement the
RegionNormalizer Interface. Details about the logic used by SimpleRegionNormalizer to compute its
normalization plan can be found here.
The below example shows a normalization plan being computed for an user table, and merge
action being taken as a result of the normalization plan computed by SimpleRegionNormalizer.
Consider an user table with some pre-split regions having 3 equally large regions (about 100K rows)
and 1 relatively small region (about 25K rows). Following is the snippet from an hbase meta table
scan showing each of the pre-split regions for the user table.
table_p8ddpd6q5z,,1469494305548.68b9892220865cb6048 column=info:regioninfo,
timestamp=1469494306375, value={ENCODED => 68b9892220865cb604809c950d1adf48, NAME =>
'table_p8ddpd6q5z,,1469494305548.68b989222 09c950d1adf48. 0865cb604809c950d1adf48.',
STARTKEY => '', ENDKEY => '1'}
....
table_p8ddpd6q5z,1,1469494317178.867b77333bdc75a028 column=info:regioninfo,
timestamp=1469494317848, value={ENCODED => 867b77333bdc75a028bb4c5e4b235f48, NAME =>
'table_p8ddpd6q5z,1,1469494317178.867b7733 bb4c5e4b235f48.
3bdc75a028bb4c5e4b235f48.', STARTKEY => '1', ENDKEY => '3'}
....
table_p8ddpd6q5z,3,1469494328323.98f019a753425e7977 column=info:regioninfo,
timestamp=1469494328486, value={ENCODED => 98f019a753425e7977ab8636e32deeeb, NAME =>
'table_p8ddpd6q5z,3,1469494328323.98f019a7 ab8636e32deeeb.
53425e7977ab8636e32deeeb.', STARTKEY => '3', ENDKEY => '7'}
....
table_p8ddpd6q5z,7,1469494339662.94c64e748979ecbb16 column=info:regioninfo,
timestamp=1469494339859, value={ENCODED => 94c64e748979ecbb166f6cc6550e25c6, NAME =>
'table_p8ddpd6q5z,7,1469494339662.94c64e74 6f6cc6550e25c6.
8979ecbb166f6cc6550e25c6.', STARTKEY => '7', ENDKEY => '8'}
....
table_p8ddpd6q5z,8,1469494339662.6d2b3f5fd1595ab8e7 column=info:regioninfo,
timestamp=1469494339859, value={ENCODED => 6d2b3f5fd1595ab8e7c031876057b1ee, NAME =>
'table_p8ddpd6q5z,8,1469494339662.6d2b3f5f c031876057b1ee.
d1595ab8e7c031876057b1ee.', STARTKEY => '8', ENDKEY => ''}
Invoking the normalizer using ‘normalize’ int the HBase shell, the below log snippet from HMaster
log shows the normalization plan computed as per the logic defined for SimpleRegionNormalizer.
Since the total region size (in MB) for the adjacent smallest regions in the table is less than the
average region size, the normalizer computes a plan to merge these two regions.
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2016-07-26 07:08:26,928 DEBUG [B.fifo.QRpcServer.handler=20,queue=2,port=20000]
master.HMaster: Skipping normalization for table: hbase:namespace, as it's either
system table or doesn't have auto
normalization turned on
2016-07-26 07:08:26,928 DEBUG [B.fifo.QRpcServer.handler=20,queue=2,port=20000]
master.HMaster: Skipping normalization for table: hbase:backup, as it's either system
table or doesn't have auto normalization turned on
2016-07-26 07:08:26,928 DEBUG [B.fifo.QRpcServer.handler=20,queue=2,port=20000]
master.HMaster: Skipping normalization for table: hbase:meta, as it's either system
table or doesn't have auto normalization turned on
2016-07-26 07:08:26,928 DEBUG [B.fifo.QRpcServer.handler=20,queue=2,port=20000]
master.HMaster: Skipping normalization for table: table_h2osxu3wat, as it's either
system table or doesn't have autonormalization turned on
2016-07-26 07:08:26,928 DEBUG [B.fifo.QRpcServer.handler=20,queue=2,port=20000]
normalizer.SimpleRegionNormalizer: Computing normalization plan for table:
table_p8ddpd6q5z, number of regions: 5
2016-07-26 07:08:26,929 DEBUG [B.fifo.QRpcServer.handler=20,queue=2,port=20000]
normalizer.SimpleRegionNormalizer: Table table_p8ddpd6q5z, total aggregated regions
size: 12
2016-07-26 07:08:26,929 DEBUG [B.fifo.QRpcServer.handler=20,queue=2,port=20000]
normalizer.SimpleRegionNormalizer: Table table_p8ddpd6q5z, average region size: 2.4
2016-07-26 07:08:26,929 INFO [B.fifo.QRpcServer.handler=20,queue=2,port=20000]
normalizer.SimpleRegionNormalizer: Table table_p8ddpd6q5z, small region size: 0 plus
its neighbor size: 0, less thanthe avg size 2.4, merging them
2016-07-26 07:08:26,971 INFO [B.fifo.QRpcServer.handler=20,queue=2,port=20000]
normalizer.MergeNormalizationPlan: Executing merging normalization plan:
MergeNormalizationPlan{firstRegion={ENCODED=> d51df2c58e9b525206b1325fd925a971, NAME
=> 'table_p8ddpd6q5z,,1469514755237.d51df2c58e9b525206b1325fd925a971.', STARTKEY =>
'', ENDKEY => '1'}, secondRegion={ENCODED => e69c6b25c7b9562d078d9ad3994f5330, NAME =>
'table_p8ddpd6q5z,1,1469514767669.e69c6b25c7b9562d078d9ad3994f5330.',
STARTKEY => '1', ENDKEY => '3'}}
Region normalizer as per it’s computed plan, merged the region with start key as ‘’ and end key as
‘1’, with another region having start key as ‘1’ and end key as ‘3’. Now, that these regions have been
merged we see a single new region with start key as ‘’ and end key as ‘3’
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table_p8ddpd6q5z,,1469516907210.e06c9b83c4a252b130e column=info:mergeA,
timestamp=1469516907431,
value=PBUF\x08\xA5\xD9\x9E\xAF\xE2*\x12\x1B\x0A\x07default\x12\x10table_p8ddpd6q5z\x1A
\x00"\x011(\x000\x00 ea74d246741ba. 8\x00
table_p8ddpd6q5z,,1469516907210.e06c9b83c4a252b130e column=info:mergeB,
timestamp=1469516907431,
value=PBUF\x08\xB5\xBA\x9F\xAF\xE2*\x12\x1B\x0A\x07default\x12\x10table_p8ddpd6q5z\x1A
\x011"\x013(\x000\x0 ea74d246741ba. 08\x00
table_p8ddpd6q5z,,1469516907210.e06c9b83c4a252b130e column=info:regioninfo,
timestamp=1469516907431, value={ENCODED => e06c9b83c4a252b130eea74d246741ba, NAME =>
'table_p8ddpd6q5z,,1469516907210.e06c9b83c ea74d246741ba. 4a252b130eea74d246741ba.',
STARTKEY => '', ENDKEY => '3'}
....
table_p8ddpd6q5z,3,1469514778736.bf024670a847c0adff column=info:regioninfo,
timestamp=1469514779417, value={ENCODED => bf024670a847c0adffb74b2e13408b32, NAME =>
'table_p8ddpd6q5z,3,1469514778736.bf024670 b74b2e13408b32. a847c0adffb74b2e13408b32.'
STARTKEY => '3', ENDKEY => '7'}
....
table_p8ddpd6q5z,7,1469514790152.7c5a67bc755e649db2 column=info:regioninfo,
timestamp=1469514790312, value={ENCODED => 7c5a67bc755e649db22f49af6270f1e1, NAME =>
'table_p8ddpd6q5z,7,1469514790152.7c5a67bc 2f49af6270f1e1.
755e649db22f49af6270f1e1.', STARTKEY => '7', ENDKEY => '8'}
....
table_p8ddpd6q5z,8,1469514790152.58e7503cda69f98f47 column=info:regioninfo,
timestamp=1469514790312, value={ENCODED => 58e7503cda69f98f4755178e74288c3a, NAME =>
'table_p8ddpd6q5z,8,1469514790152.58e7503c 55178e74288c3a.
da69f98f4755178e74288c3a.', STARTKEY => '8', ENDKEY => ''}
A similar example can be seen for an user table with 3 smaller regions and 1 relatively large region.
For this example, we have an user table with 1 large region containing 100K rows, and 3 relatively
smaller regions with about 33K rows each. As seen from the normalization plan, since the larger
region is more than twice the average region size it ends being split into two regions – one with
start key as ‘1’ and end key as ‘154717’ and the other region with start key as '154717' and end key
as ‘3’
2016-07-26 07:39:45,636 DEBUG [B.fifo.QRpcServer.handler=7,queue=1,port=20000]
master.HMaster: Skipping normalization for table: hbase:backup, as it's either system
table or doesn't have auto normalization turned on
2016-07-26 07:39:45,636 DEBUG [B.fifo.QRpcServer.handler=7,queue=1,port=20000]
normalizer.SimpleRegionNormalizer: Computing normalization plan for table:
table_p8ddpd6q5z, number of regions: 4
2016-07-26 07:39:45,636 DEBUG [B.fifo.QRpcServer.handler=7,queue=1,port=20000]
normalizer.SimpleRegionNormalizer: Table table_p8ddpd6q5z, total aggregated regions
size: 12
2016-07-26 07:39:45,636 DEBUG [B.fifo.QRpcServer.handler=7,queue=1,port=20000]
normalizer.SimpleRegionNormalizer: Table table_p8ddpd6q5z, average region size: 3.0
2016-07-26 07:39:45,636 DEBUG [B.fifo.QRpcServer.handler=7,queue=1,port=20000]
normalizer.SimpleRegionNormalizer: No normalization needed, regions look good for
table: table_p8ddpd6q5z
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2016-07-26 07:39:45,636 DEBUG [B.fifo.QRpcServer.handler=7,queue=1,port=20000]
normalizer.SimpleRegionNormalizer: Computing normalization plan for table:
table_h2osxu3wat, number of regions: 5
2016-07-26 07:39:45,636 DEBUG [B.fifo.QRpcServer.handler=7,queue=1,port=20000]
normalizer.SimpleRegionNormalizer: Table table_h2osxu3wat, total aggregated regions
size: 7
2016-07-26 07:39:45,636 DEBUG [B.fifo.QRpcServer.handler=7,queue=1,port=20000]
normalizer.SimpleRegionNormalizer: Table table_h2osxu3wat, average region size: 1.4
2016-07-26 07:39:45,636 INFO [B.fifo.QRpcServer.handler=7,queue=1,port=20000]
normalizer.SimpleRegionNormalizer: Table table_h2osxu3wat, large region
table_h2osxu3wat,1,1469515926544.27f2fdbb2b6612ea163eb6b40753c3db. has size 4, more
than twice avg size, splitting
2016-07-26 07:39:45,640 INFO [B.fifo.QRpcServer.handler=7,queue=1,port=20000]
normalizer.SplitNormalizationPlan: Executing splitting normalization plan:
SplitNormalizationPlan{regionInfo={ENCODED => 27f2fdbb2b6612ea163eb6b40753c3db, NAME
=> 'table_h2osxu3wat,1,1469515926544.27f2fdbb2b6612ea163eb6b40753c3db.', STARTKEY =>
'1', ENDKEY => '3'}, splitPoint=null}
2016-07-26 07:39:45,656 DEBUG [B.fifo.QRpcServer.handler=7,queue=1,port=20000]
master.HMaster: Skipping normalization for table: hbase:namespace, as it's either
system table or doesn't have auto normalization turned on
2016-07-26 07:39:45,656 DEBUG [B.fifo.QRpcServer.handler=7,queue=1,port=20000]
master.HMaster: Skipping normalization for table: hbase:meta, as it's either system
table or doesn't
have auto normalization turned on …..…..….
2016-07-26 07:39:46,246 DEBUG [AM.ZK.Worker-pool2-t278] master.RegionStates: Onlined
54de97dae764b864504704c1c8d3674a on hbase-test-rc-5.openstacklocal,16020,1469419333913
{ENCODED => 54de97dae764b864504704c1c8d3674a, NAME =>
'table_h2osxu3wat,1,1469518785661.54de97dae764b864504704c1c8d3674a.', STARTKEY => '1',
ENDKEY => '154717'}
2016-07-26 07:39:46,246 INFO [AM.ZK.Worker-pool2-t278] master.RegionStates:
Transition {d6b5625df331cfec84dce4f1122c567f state=SPLITTING_NEW, ts=1469518786246,
server=hbase-test-rc-5.openstacklocal,16020,1469419333913} to
{d6b5625df331cfec84dce4f1122c567f state=OPEN, ts=1469518786246,
server=hbase-test-rc-5.openstacklocal,16020,1469419333913}
2016-07-26 07:39:46,246 DEBUG [AM.ZK.Worker-pool2-t278] master.RegionStates: Onlined
d6b5625df331cfec84dce4f1122c567f on hbase-test-rc-5.openstacklocal,16020,1469419333913
{ENCODED => d6b5625df331cfec84dce4f1122c567f, NAME =>
'table_h2osxu3wat,154717,1469518785661.d6b5625df331cfec84dce4f1122c567f.', STARTKEY =>
'154717', ENDKEY => '3'}
608
Building and Developing Apache
HBase
This chapter contains information and guidelines for building and releasing HBase code and
documentation. Being familiar with these guidelines will help the HBase committers to use your
contributions more easily.
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Chapter 164. Getting Involved
Apache HBase gets better only when people contribute! If you are looking to contribute to Apache
HBase, look for issues in JIRA tagged with the label 'beginner'. These are issues HBase contributors
have deemed worthy but not of immediate priority and a good way to ramp on HBase internals. See
What label is used for issues that are good on ramps for new contributors? from the dev mailing list
for background.
Before you get started submitting code to HBase, please refer to developing.
As Apache HBase is an Apache Software Foundation project, see asf for more information about
how the ASF functions.
164.1. Mailing Lists
Sign up for the dev-list and the user-list. See the mailing lists page. Posing questions - and helping to
answer other people’s questions - is encouraged! There are varying levels of experience on both
lists so patience and politeness are encouraged (and please stay on topic.)
164.2. Slack
The Apache HBase project has its own link: Slack Channel for real-time questions and discussion.
Mail dev@hbase.apache.org to request an invite.
164.3. Internet Relay Chat (IRC)
(NOTE: Our IRC channel seems to have been deprecated in favor of the above Slack channel)
For real-time questions and discussions, use the #hbase IRC channel on the FreeNode IRC network.
FreeNode offers a web-based client, but most people prefer a native client, and several clients are
available for each operating system.
164.4. Jira
Check for existing issues in Jira. If it’s either a new feature request, enhancement, or a bug, file a
ticket.
We track multiple types of work in JIRA:
•Bug: Something is broken in HBase itself.
•Test: A test is needed, or a test is broken.
•New feature: You have an idea for new functionality. It’s often best to bring these up on the
mailing lists first, and then write up a design specification that you add to the feature request
JIRA.
•Improvement: A feature exists, but could be tweaked or augmented. It’s often best to bring these
up on the mailing lists first and have a discussion, then summarize or link to the discussion if
610
others seem interested in the improvement.
•Wish: This is like a new feature, but for something you may not have the background to flesh
out yourself.
Bugs and tests have the highest priority and should be actionable.
164.4.1. Guidelines for reporting effective issues
•Search for duplicates: Your issue may have already been reported. Have a look, realizing that
someone else might have worded the summary differently.
Also search the mailing lists, which may have information about your problem and how to
work around it. Don’t file an issue for something that has already been discussed and resolved
on a mailing list, unless you strongly disagree with the resolution and are willing to help take
the issue forward.
◦Discuss in public: Use the mailing lists to discuss what you’ve discovered and see if there is
something you’ve missed. Avoid using back channels, so that you benefit from the
experience and expertise of the project as a whole.
◦Don’t file on behalf of others: You might not have all the context, and you don’t have as
much motivation to see it through as the person who is actually experiencing the bug. It’s
more helpful in the long term to encourage others to file their own issues. Point them to this
material and offer to help out the first time or two.
◦Write a good summary: A good summary includes information about the problem, the
impact on the user or developer, and the area of the code.
▪Good: Address new license dependencies from hadoop3-alpha4
▪Room for improvement: Canary is broken
If you write a bad title, someone else will rewrite it for you. This is time they could have
spent working on the issue instead.
◦Give context in the description: It can be good to think of this in multiple parts:
▪What happens or doesn’t happen?
▪How does it impact you?
▪How can someone else reproduce it?
▪What would "fixed" look like?
You don’t need to know the answers for all of these, but give as much information as you
can. If you can provide technical information, such as a Git commit SHA that you think
might have caused the issue or a build failure on builds.apache.org where you think the
issue first showed up, share that info.
◦Fill in all relevant fields: These fields help us filter, categorize, and find things.
◦One bug, one issue, one patch: To help with back-porting, don’t split issues or fixes among
multiple bugs.
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◦Add value if you can: Filing issues is great, even if you don’t know how to fix them. But
providing as much information as possible, being willing to triage and answer questions,
and being willing to test potential fixes is even better! We want to fix your issue as quickly
as you want it to be fixed.
◦Don’t be upset if we don’t fix it: Time and resources are finite. In some cases, we may not
be able to (or might choose not to) fix an issue, especially if it is an edge case or there is a
workaround. Even if it doesn’t get fixed, the JIRA is a public record of it, and will help others
out if they run into a similar issue in the future.
164.4.2. Working on an issue
To check for existing issues which you can tackle as a beginner, search for issues in JIRA tagged
with the label 'beginner'.
JIRA Priorites
•Blocker: Should only be used if the issue WILL cause data loss or cluster instability reliably.
•Critical: The issue described can cause data loss or cluster instability in some cases.
•Major: Important but not tragic issues, like updates to the client API that will add a lot of much-
needed functionality or significant bugs that need to be fixed but that don’t cause data loss.
•Minor: Useful enhancements and annoying but not damaging bugs.
•Trivial: Useful enhancements but generally cosmetic.
Example 41. Code Blocks in Jira Comments
A commonly used macro in Jira is {code}. Everything inside the tags is preformatted, as in this
example.
{code}
code snippet
{code}
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Chapter 165. Apache HBase Repositories
Apache HBase consists of multiple repositories which are hosted on Apache GitBox. These are the
following:
•hbase - main Apache HBase repository
•hbase-connectors - connectors to Apache Kafka and Apache Spark
•hbase-operator-tools - operability and supportability tools, such as HBase HBCK2
•hbase-site - hbase.apache.org website
•hbase-thirdparty - relocated versions of popular third-party libraries
613
Chapter 166. IDEs
166.1. Eclipse
166.1.1. Code Formatting
Under the dev-support/ folder, you will find hbase_eclipse_formatter.xml. We encourage you to have
this formatter in place in eclipse when editing HBase code.
Go to Preferences→Java→Code Style→Formatter→Import to load the xml file. Go to Preferences
→Java→Editor→Save Actions, and make sure 'Format source code' and 'Format edited lines' is
selected.
In addition to the automatic formatting, make sure you follow the style guidelines explained in
common.patch.feedback.
166.1.2. Eclipse Git Plugin
If you cloned the project via git, download and install the Git plugin (EGit). Attach to your local git
repo (via the Git Repositories window) and you’ll be able to see file revision history, generate
patches, etc.
166.1.3. HBase Project Setup in Eclipse using m2eclipse
The easiest way is to use the m2eclipse plugin for Eclipse. Eclipse Indigo or newer includes
m2eclipse, or you can download it from http://www.eclipse.org/m2e/. It provides Maven integration
for Eclipse, and even lets you use the direct Maven commands from within Eclipse to compile and
test your project.
To import the project, click and select the HBase root directory. m2eclipse locates all the hbase
modules for you.
If you install m2eclipse and import HBase in your workspace, do the following to fix your eclipse
Build Path.
1. Remove target folder
2. Add target/generated-jamon and target/generated-sources/java folders.
3. Remove from your Build Path the exclusions on the src/main/resources and src/test/resources to
avoid error message in the console, such as the following:
Failed to execute goal
org.apache.maven.plugins:maven-antrun-plugin:1.6:run (default) on project hbase:
'An Ant BuildException has occurred: Replace: source file .../target/classes/hbase-
default.xml
doesn't exist
614
This will also reduce the eclipse build cycles and make your life easier when developing.
166.1.4. HBase Project Setup in Eclipse Using the Command Line
Instead of using m2eclipse, you can generate the Eclipse files from the command line.
1. First, run the following command, which builds HBase. You only need to do this once.
mvn clean install -DskipTests
2. Close Eclipse, and execute the following command from the terminal, in your local HBase
project directory, to generate new .project and .classpath files.
mvn eclipse:eclipse
3. Reopen Eclipse and import the .project file in the HBase directory to a workspace.
166.1.5. Maven Classpath Variable
The $M2_REPO classpath variable needs to be set up for the project. This needs to be set to your local
Maven repository, which is usually ~/.m2/repository
If this classpath variable is not configured, you will see compile errors in Eclipse like this:
Description Resource Path Location Type
The project cannot be built until build path errors are resolved hbase
Unknown Java Problem
Unbound classpath variable: 'M2_REPO/asm/asm/3.1/asm-3.1.jar' in project 'hbase'
hbase Build path Build Path Problem
Unbound classpath variable: 'M2_REPO/com/google/guava/guava/r09/guava-r09.jar' in
project 'hbase' hbase Build path Build Path Problem
Unbound classpath variable: 'M2_REPO/com/google/protobuf/protobuf-java/2.3.0/protobuf-
java-2.3.0.jar' in project 'hbase' hbase Build path Build Path Problem
Unbound classpath variable:
166.1.6. Eclipse Known Issues
Eclipse will currently complain about Bytes.java. It is not possible to turn these errors off.
615
Description Resource Path Location Type
Access restriction: The method arrayBaseOffset(Class) from the type Unsafe is not
accessible due to restriction on required library
/System/Library/Java/JavaVirtualMachines/1.6.0.jdk/Contents/Classes/classes.jar
Bytes.java /hbase/src/main/java/org/apache/hadoop/hbase/util line 1061 Java
Problem
Access restriction: The method arrayIndexScale(Class) from the type Unsafe is not
accessible due to restriction on required library
/System/Library/Java/JavaVirtualMachines/1.6.0.jdk/Contents/Classes/classes.jar
Bytes.java /hbase/src/main/java/org/apache/hadoop/hbase/util line 1064 Java
Problem
Access restriction: The method getLong(Object, long) from the type Unsafe is not
accessible due to restriction on required library
/System/Library/Java/JavaVirtualMachines/1.6.0.jdk/Contents/Classes/classes.jar
Bytes.java /hbase/src/main/java/org/apache/hadoop/hbase/util line 1111 Java
Problem
166.1.7. Eclipse - More Information
For additional information on setting up Eclipse for HBase development on Windows, see Michael
Morello’s blog on the topic.
166.2. IntelliJ IDEA
You can set up IntelliJ IDEA for similar functionality as Eclipse. Follow these steps.
1. Select
2. You do not need to select a profile. Be sure Maven project required is selected, and click [ Next ].
3. Select the location for the JDK.
Using the HBase Formatter in IntelliJ IDEA
Using the Eclipse Code Formatter plugin for IntelliJ IDEA, you can import the HBase code formatter
described in eclipse.code.formatting.
166.3. Other IDEs
It would be useful to mirror the eclipse set-up instructions for other IDEs. If you would like to assist,
please have a look at HBASE-11704.
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Chapter 167. Building Apache HBase
167.1. Basic Compile
HBase is compiled using Maven. You must use at least Maven 3.0.4. To check your Maven version,
run the command mvn -version.
JDK Version Requirements
Starting with HBase 1.0 you must use Java 7 or later to build from source code. See
java for more complete information about supported JDK versions.
167.1.1. Maven Build Commands
All commands are executed from the local HBase project directory.
Package
The simplest command to compile HBase from its java source code is to use the package target,
which builds JARs with the compiled files.
mvn package -DskipTests
Or, to clean up before compiling:
mvn clean package -DskipTests
With Eclipse set up as explained above in eclipse, you can also use the Build command in Eclipse.
To create the full installable HBase package takes a little bit more work, so read on.
Compile
The compile target does not create the JARs with the compiled files.
mvn compile
mvn clean compile
Install
To install the JARs in your ~/.m2/ directory, use the install target.
mvn install
617
mvn clean install
mvn clean install -DskipTests
167.1.2. Running all or individual Unit Tests
See the hbase.unittests.cmds section in hbase.unittests
167.1.3. Building against various hadoop versions.
HBase supports building against Apache Hadoop versions: 2.y and 3.y (early release artifacts). By
default we build against Hadoop 2.x.
To build against a specific release from the Hadoop 2.y line, set e.g. -Dhadoop-two.version=2.6.3.
mvn -Dhadoop-two.version=2.6.3 ...
To change the major release line of Hadoop we build against, add a hadoop.profile property when
you invoke mvn:
mvn -Dhadoop.profile=3.0 ...
The above will build against whatever explicit hadoop 3.y version we have in our pom.xml as our
'3.0' version. Tests may not all pass so you may need to pass -DskipTests unless you are inclined to
fix the failing tests.
To pick a particular Hadoop 3.y release, you’d set hadoop-three.version property e.g. -Dhadoop
-three.version=3.0.0.
167.1.4. Build Protobuf
You may need to change the protobuf definitions that reside in the hbase-protocol module or other
modules.
Previous to hbase-2.0.0, protobuf definition files were sprinkled across all hbase modules but now
all to do with protobuf must reside in the hbase-protocol module; we are trying to contain our
protobuf use so we can freely change versions without upsetting any downstream project use of
protobuf.
The protobuf files are located in hbase-protocol/src/main/protobuf. For the change to be effective,
you will need to regenerate the classes.
mvn package -pl hbase-protocol -am
618
Similarly, protobuf definitions for internal use are located in the hbase-protocol-shaded module.
mvn package -pl hbase-protocol-shaded -am
Typically, protobuf code generation is done using the native protoc binary. In our build we use a
maven plugin for convenience; however, the plugin may not be able to retrieve appropriate
binaries for all platforms. If you find yourself on a platform where protoc fails, you will have to
compile protoc from source, and run it independent of our maven build. You can disable the inline
code generation by specifying -Dprotoc.skip in your maven arguments, allowing your build to
proceed further.
If you need to manually generate your protobuf files, you should not use clean in
subsequent maven calls, as that will delete the newly generated files.
Read the hbase-protocol/README.txt for more details
167.1.5. Build Thrift
You may need to change the thrift definitions that reside in the hbase-thrift module or other
modules.
The thrift files are located in hbase-thrift/src/main/resources. For the change to be effective, you will
need to regenerate the classes. You can use maven profile compile-thrift to do this.
mvn compile -Pcompile-thrift
You may also want to define thrift.path for the thrift binary, using the following command:
Ê mvn compile -Pcompile-thrift -Dthrift.path=/opt/local/bin/thrift
167.1.6. Build a Tarball
You can build a tarball without going through the release process described in releasing, by
running the following command:
mvn -DskipTests clean install && mvn -DskipTests package assembly:single
The distribution tarball is built in hbase-assembly/target/hbase-<version>-bin.tar.gz.
You can install or deploy the tarball by having the assembly:single goal before install or deploy in
the maven command:
mvn -DskipTests package assembly:single install
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mvn -DskipTests package assembly:single deploy
167.1.7. Build Gotchas
Maven Site failure
If you see Unable to find resource 'VM_global_library.vm', ignore it. It’s not an error. It is officially
ugly though.
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Chapter 168. Releasing Apache HBase
Building against HBase 1.x
HBase 1.x requires Java 7 to build. See java for Java requirements per HBase
release.
621
Example 42. Example ~/.m2/settings.xml File
Publishing to maven requires you sign the artifacts you want to upload. For the build to sign
them for you, you a properly configured settings.xml in your local repository under .m2, such
as the following.
<settings xmlns="http://maven.apache.org/SETTINGS/1.0.0"
Ê xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
Ê xsi:schemaLocation="http://maven.apache.org/SETTINGS/1.0.0
Ê http://maven.apache.org/xsd/settings-1.0.0.xsd">
Ê <servers>
Ê <!- To publish a snapshot of some part of Maven -->
Ê <server>
Ê <id>apache.snapshots.https</id>
Ê <username>YOUR_APACHE_ID
Ê </username>
Ê <password>YOUR_APACHE_PASSWORD
Ê </password>
Ê </server>
Ê <!-- To publish a website using Maven -->
Ê <!-- To stage a release of some part of Maven -->
Ê <server>
Ê <id>apache.releases.https</id>
Ê <username>YOUR_APACHE_ID
Ê </username>
Ê <password>YOUR_APACHE_PASSWORD
Ê </password>
Ê </server>
Ê </servers>
Ê <profiles>
Ê <profile>
Ê <id>apache-release</id>
Ê <properties>
Ê <gpg.keyname>YOUR_KEYNAME</gpg.keyname>
Ê <!--Keyname is something like this ... 00A5F21E... do gpg --list-keys to find
it-->
Ê <gpg.passphrase>YOUR_KEY_PASSWORD
Ê </gpg.passphrase>
Ê </properties>
Ê </profile>
Ê </profiles>
</settings>
168.1. Making a Release Candidate
Only committers may make releases of hbase artifacts.
Before You Begin
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Make sure your environment is properly set up. Maven and Git are the main tooling used in the
below. You’ll need a properly configured settings.xml file in your local ~/.m2 maven repository with
logins for apache repos (See Example ~/.m2/settings.xml File). You will also need to have a published
signing key. Browse the Hadoop How To Release wiki page on how to release. It is a model for most
of the instructions below. It often has more detail on particular steps, for example, on adding your
code signing key to the project KEYS file up in Apache or on how to update JIRA in preparation for
release.
Before you make a release candidate, do a practice run by deploying a SNAPSHOT. Check to be sure
recent builds have been passing for the branch from where you are going to take your release. You
should also have tried recent branch tips out on a cluster under load, perhaps by running the
hbase-it integration test suite for a few hours to 'burn in' the near-candidate bits.
Specifying the Heap Space for Maven
You may run into OutOfMemoryErrors building, particularly building the site and
documentation. Up the heap for Maven by setting the MAVEN_OPTS variable. You can
prefix the variable to the Maven command, as in the following example:
MAVEN_OPTS="-Xmx4g -XX:MaxPermSize=256m" mvn package
You could also set this in an environment variable or alias in your shell.
The script dev-support/make_rc.sh automates many of the below steps. It will
checkout a tag, clean the checkout, build src and bin tarballs, and deploy the built
jars to repository.apache.org. It does NOT do the modification of the CHANGES.txt
for the release, the checking of the produced artifacts to ensure they are
'good' — e.g. extracting the produced tarballs, verifying that they look right, then
starting HBase and checking that everything is running correctly — or the signing
and pushing of the tarballs to people.apache.org. Take a look. Modify/improve as
you see fit.
Procedure: Release Procedure
1. Update the CHANGES.txt file and the POM files.
Update CHANGES.txt with the changes since the last release. Make sure the URL to the JIRA
points to the proper location which lists fixes for this release. Adjust the version in all the POM
files appropriately. If you are making a release candidate, you must remove the -SNAPSHOT label
from all versions in all pom.xml files. If you are running this receipe to publish a snapshot, you
must keep the -SNAPSHOT suffix on the hbase version. The Versions Maven Plugin can be of use
here. To set a version in all the many poms of the hbase multi-module project, use a command
like the following:
$ mvn clean org.codehaus.mojo:versions-maven-plugin:2.5:set -DnewVersion=2.1.0
-SNAPSHOT
Make sure all versions in poms are changed! Checkin the CHANGES.txt and any maven version
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changes.
2. Update the documentation.
Update the documentation under src/main/asciidoc. This usually involves copying the latest
from master branch and making version-particular adjustments to suit this release candidate
version.
3. Clean the checkout dir
$ mvn clean
$ git clean -f -x -d
4. Run Apache-Rat Check licenses are good
$ mvn apache-rat
If the above fails, check the rat log.
$ grep 'Rat check' patchprocess/mvn_apache_rat.log
5. Create a release tag. Presuming you have run basic tests, the rat check, passes and all is looking
good, now is the time to tag the release candidate (You always remove the tag if you need to
redo). To tag, do what follows substituting in the version appropriate to your build. All tags
should be signed tags; i.e. pass the -s option (See Signing Your Work for how to set up your git
environment for signing).
$ git tag -s 2.0.0-alpha4-RC0 -m "Tagging the 2.0.0-alpha4 first Releae Candidate
(Candidates start at zero)"
Or, if you are making a release, tags should have a rel/ prefix to ensure they are preserved in the
Apache repo as in:
+$ git tag -s rel/2.0.0-alpha4 -m "Tagging the 2.0.0-alpha4 Release"
Push the (specific) tag (only) so others have access.
+
$ git push origin 2.0.0-alpha4-RC0
+ For how to delete tags, see How to Delete a Tag. Covers deleting tags that have not yet been
pushed to the remote Apache repo as well as delete of tags pushed to Apache.
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1. Build the source tarball.
Now, build the source tarball. Lets presume we are building the source tarball for the tag 2.0.0-
alpha4-RC0 into /tmp/hbase-2.0.0-alpha4-RC0/ (This step requires that the mvn and git clean steps
described above have just been done).
$ git archive --format=tar.gz --output="/tmp/hbase-2.0.0-alpha4-RC0/hbase-2.0.0
-alpha4-src.tar.gz" --prefix="hbase-2.0.0-alpha4/" $git_tag
Above we generate the hbase-2.0.0-alpha4-src.tar.gz tarball into the /tmp/hbase-2.0.0-alpha4-RC0
build output directory (We don’t want the RC0 in the name or prefix. These bits are currently a
release candidate but if the VOTE passes, they will become the release so we do not taint the artifact
names with RCX).
1. Build the binary tarball. Next, build the binary tarball. Add the -Prelease profile when building.
It runs the license apache-rat check among other rules that help ensure all is wholesome. Do it
in two steps.
First install into the local repository
$ mvn clean install -DskipTests -Prelease
Next, generate documentation and assemble the tarball. Be warned, this next step can take a good
while, a couple of hours generating site documentation.
$ mvn install -DskipTests site assembly:single -Prelease
+ Otherwise, the build complains that hbase modules are not in the maven repository when you try
to do it all in one step, especially on a fresh repository. It seems that you need the install goal in
both steps.
+ Extract the generated tarball — you’ll find it under hbase-assembly/target and check it out. Look at
the documentation, see if it runs, etc. If good, copy the tarball beside the source tarball in the build
output directory.
1. Deploy to the Maven Repository.
Next, deploy HBase to the Apache Maven repository. Add the apache-release` profile when
running the mvn deploy command. This profile comes from the Apache parent pom referenced
by our pom files. It does signing of your artifacts published to Maven, as long as the settings.xml
is configured correctly, as described in Example ~/.m2/settings.xml File. This step depends on the
local repository having been populate by the just-previous bin tarball build.
$ mvn deploy -DskipTests -Papache-release -Prelease
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This command copies all artifacts up to a temporary staging Apache mvn repository in an 'open'
state. More work needs to be done on these maven artifacts to make them generally available.
We do not release HBase tarball to the Apache Maven repository. To avoid deploying the tarball,
do not include the assembly:single goal in your mvn deploy command. Check the deployed
artifacts as described in the next section.
make_rc.sh
If you run the dev-support/make_rc.sh script, this is as far as it takes you. To finish
the release, take up the script from here on out.
1. Make the Release Candidate available.
The artifacts are in the maven repository in the staging area in the 'open' state. While in this
'open' state you can check out what you’ve published to make sure all is good. To do this, log in
to Apache’s Nexus at repository.apache.org using your Apache ID. Find your artifacts in the
staging repository. Click on 'Staging Repositories' and look for a new one ending in "hbase" with
a status of 'Open', select it. Use the tree view to expand the list of repository contents and inspect
if the artifacts you expect are present. Check the POMs. As long as the staging repo is open you
can re-upload if something is missing or built incorrectly.
If something is seriously wrong and you would like to back out the upload, you can use the
'Drop' button to drop and delete the staging repository. Sometimes the upload fails in the
middle. This is another reason you might have to 'Drop' the upload from the staging repository.
If it checks out, close the repo using the 'Close' button. The repository must be closed before a
public URL to it becomes available. It may take a few minutes for the repository to close. Once
complete you’ll see a public URL to the repository in the Nexus UI. You may also receive an
email with the URL. Provide the URL to the temporary staging repository in the email that
announces the release candidate. (Folks will need to add this repo URL to their local poms or to
their local settings.xml file to pull the published release candidate artifacts.)
When the release vote concludes successfully, return here and click the 'Release' button to
release the artifacts to central. The release process will automatically drop and delete the
staging repository.
hbase-downstreamer
See the hbase-downstreamer test for a simple example of a project that is
downstream of HBase an depends on it. Check it out and run its simple test to
make sure maven artifacts are properly deployed to the maven repository. Be
sure to edit the pom to point to the proper staging repository. Make sure you
are pulling from the repository when tests run and that you are not getting
from your local repository, by either passing the -U flag or deleting your local
repo content and check maven is pulling from remote out of the staging
repository.
See Publishing Maven Artifacts for some pointers on this maven staging process.
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+ If the HBase version ends in -SNAPSHOT, the artifacts go elsewhere. They are put into the Apache
snapshots repository directly and are immediately available. Making a SNAPSHOT release, this is
what you want to happen.
+ At this stage, you have two tarballs in your 'build output directory' and a set of artifacts in a
staging area of the maven repository, in the 'closed' state. Next sign, fingerprint and then 'stage'
your release candiate build output directory via svnpubsub by committing your directory to The
dev distribution directory (See comments on HBASE-10554 Please delete old releases from
mirroring system but in essence it is an svn checkout of dev/hbase — releases are at release/hbase).
In the version directory run the following commands:
$ for i in *.tar.gz; do echo $i; gpg --print-md MD5 $i > $i.md5 ; done
$ for i in *.tar.gz; do echo $i; gpg --print-md SHA512 $i > $i.sha ; done
$ for i in *.tar.gz; do echo $i; gpg --armor --output $i.asc --detach-sig $i ; done
$ cd ..
# Presuming our 'build output directory' is named 0.96.0RC0, copy it to the svn
checkout of the dist dev dir
# in this case named hbase.dist.dev.svn
$ cd /Users/stack/checkouts/hbase.dist.dev.svn
$ svn info
Path: .
Working Copy Root Path: /Users/stack/checkouts/hbase.dist.dev.svn
URL: https://dist.apache.org/repos/dist/dev/hbase
Repository Root: https://dist.apache.org/repos/dist
Repository UUID: 0d268c88-bc11-4956-87df-91683dc98e59
Revision: 15087
Node Kind: directory
Schedule: normal
Last Changed Author: ndimiduk
Last Changed Rev: 15045
Last Changed Date: 2016-08-28 11:13:36 -0700 (Sun, 28 Aug 2016)
$ mv 0.96.0RC0 /Users/stack/checkouts/hbase.dist.dev.svn
$ svn add 0.96.0RC0
$ svn commit ...
+ Ensure it actually gets published by checking https://dist.apache.org/repos/dist/dev/hbase/.
Announce the release candidate on the mailing list and call a vote.
168.2. Publishing a SNAPSHOT to maven
Make sure your settings.xml is set up properly (see Example ~/.m2/settings.xml File). Make sure the
hbase version includes -SNAPSHOT as a suffix. Following is an example of publishing SNAPSHOTS of a
release that had an hbase version of 0.96.0 in its poms.
Ê$ mvn clean install -DskipTests javadoc:aggregate site assembly:single -Prelease
Ê$ mvn -DskipTests deploy -Papache-release
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The make_rc.sh script mentioned above (see maven.release) can help you publish SNAPSHOTS. Make
sure your hbase.version has a -SNAPSHOT suffix before running the script. It will put a snapshot up
into the apache snapshot repository for you.
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Chapter 169. Voting on Release Candidates
Everyone is encouraged to try and vote on HBase release candidates. Only the votes of PMC
members are binding. PMC members, please read this WIP doc on policy voting for a release
candidate, Release Policy. [quote]_Before casting 1 binding votes, individuals are required to
download the signed source code package onto their own hardware, compile it as provided, and
test the resulting executable on their own platform, along with also validating cryptographic
signatures and verifying that the package meets the requirements of the ASF policy on releases._
Regards the latter, run +mvn apache-rat:check to verify all files are suitably licensed. See HBase,
mail # dev - On recent discussion clarifying ASF release policy. for how we arrived at this process.
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Chapter 170. Announcing Releases
Once an RC has passed successfully and the needed artifacts have been staged for disribution, you’ll
need to let everyone know about our shiny new release. It’s not a requirement, but to make things
easier for release managers we have a template you can start with. Be sure you replace _version_
and other markers with the relevant version numbers. You should manually verify all links before
sending.
The HBase team is happy to announce the immediate availability of HBase _version_.
Apache HBase™ is an open-source, distributed, versioned, non-relational database.
Apache HBase gives you low latency random access to billions of rows with
millions of columns atop non-specialized hardware. To learn more about HBase,
see https://hbase.apache.org/.
HBase _version_ is the _nth_ minor release in the HBase _major_.x line, which aims to
improve the stability and reliability of HBase. This release includes roughly
XXX resolved issues not covered by previous _major_.x releases.
Notable new features include:
- List text descriptions of features that fit on one line
- Including if JDK or Hadoop support versions changes
- If the "stable" pointer changes, call that out
- For those with obvious JIRA IDs, include them (HBASE-YYYYY)
The full list of issues can be found in the included CHANGES.md and RELEASENOTES.md,
or via our issue tracker:
Ê https://s.apache.org/hbase-_version_-jira
To download please follow the links and instructions on our website:
Ê https://hbase.apache.org/downloads.html
Question, comments, and problems are always welcome at: dev@hbase.apache.org.
Thanks to all who contributed and made this release possible.
Cheers,
The HBase Dev Team
You should sent this message to the following lists: dev@hbase.apache.org, user@hbase.apache.org,
announce@apache.org. If you’d like a spot check before sending, feel free to ask via jira or the dev
list.
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Chapter 171. Generating the HBase
Reference Guide
The manual is marked up using Asciidoc. We then use the Asciidoctor maven plugin to transform
the markup to html. This plugin is run when you specify the site goal as in when you run mvn site.
See appendix contributing to documentation for more information on building the documentation.
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Chapter 172. Updating hbase.apache.org
172.1. Contributing to hbase.apache.org
See appendix contributing to documentation for more information on contributing to the
documentation or website.
172.2. Publishing hbase.apache.org
See Publishing the HBase Website and Documentation for instructions on publishing the website
and documentation.
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Chapter 173. Tests
Developers, at a minimum, should familiarize themselves with the unit test detail; unit tests in
HBase have a character not usually seen in other projects.
This information is about unit tests for HBase itself. For developing unit tests for your HBase
applications, see unit.tests.
173.1. Apache HBase Modules
As of 0.96, Apache HBase is split into multiple modules. This creates "interesting" rules for how and
where tests are written. If you are writing code for hbase-server, see hbase.unittests for how to
write your tests. These tests can spin up a minicluster and will need to be categorized. For any other
module, for example hbase-common, the tests must be strict unit tests and just test the class under test
- no use of the HBaseTestingUtility or minicluster is allowed (or even possible given the dependency
tree).
173.1.1. Testing the HBase Shell
The HBase shell and its tests are predominantly written in jruby.
In order to make these tests run as a part of the standard build, there are a few JUnit test classes
that take care of loading the jruby implemented tests and running them. The tests were split into
separate classes to accomodate class level timeouts (see Unit Tests for specifics). You can run all of
these tests from the top level with:
Ê mvn clean test -Dtest=Test*Shell
If you have previously done a mvn install, then you can instruct maven to run only the tests in the
hbase-shell module with:
Ê mvn clean test -pl hbase-shell
Alternatively, you may limit the shell tests that run using the system variable shell.test. This value
should specify the ruby literal equivalent of a particular test case by name. For example, the tests
that cover the shell commands for altering tables are contained in the test case AdminAlterTableTest
and you can run them with:
Ê mvn clean test -pl hbase-shell -Dshell.test=/AdminAlterTableTest/
You may also use a Ruby Regular Expression literal (in the /pattern/ style) to select a set of test
cases. You can run all of the HBase admin related tests, including both the normal administration
and the security administration, with the command:
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Ê mvn clean test -pl hbase-shell -Dshell.test=/.*Admin.*Test/
In the event of a test failure, you can see details by examining the XML version of the surefire
report results
Ê vim hbase-shell/target/surefire-reports/TEST-
org.apache.hadoop.hbase.client.TestShell.xml
173.1.2. Running Tests in other Modules
If the module you are developing in has no other dependencies on other HBase modules, then you
can cd into that module and just run:
mvn test
which will just run the tests IN THAT MODULE. If there are other dependencies on other modules,
then you will have run the command from the ROOT HBASE DIRECTORY. This will run the tests in
the other modules, unless you specify to skip the tests in that module. For instance, to skip the tests
in the hbase-server module, you would run:
mvn clean test -PskipServerTests
from the top level directory to run all the tests in modules other than hbase-server. Note that you
can specify to skip tests in multiple modules as well as just for a single module. For example, to skip
the tests in hbase-server and hbase-common, you would run:
mvn clean test -PskipServerTests -PskipCommonTests
Also, keep in mind that if you are running tests in the hbase-server module you will need to apply
the maven profiles discussed in hbase.unittests.cmds to get the tests to run properly.
173.2. Unit Tests
Apache HBase unit tests must carry a Category annotation and as of hbase-2.0.0, must be stamped
with the HBase ClassRule. Here is an example of what a Test Class looks like with a Category and
ClassRule included:
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...
@Category(SmallTests.class)
public class TestHRegionInfo {
Ê @ClassRule
Ê public static final HBaseClassTestRule CLASS_RULE =
Ê HBaseClassTestRule.forClass(TestHRegionInfo.class);
Ê @Test
Ê public void testCreateHRegionInfoName() throws Exception {
Ê // ...
Ê }
}
Here the Test Class is TestHRegionInfo. The CLASS_RULE has the same form in every test class only the
.class you pass is that of the local test; i.e. in the TestTimeout Test Class, you’d pass
TestTimeout.class to the CLASS_RULE instead of the TestHRegionInfo.class we have above. The
CLASS_RULE is where we’ll enforce timeouts (currently set at a hard-limit of thirteen! minutes for all
tests — 780 seconds) and other cross-unit test facility. The test is in the SmallTest Category.
Categories can be arbitrary and provided as a list but each test MUST carry one from the following
list of sizings: small, medium, large, and integration. The test sizing is designated using the JUnit
categories: SmallTests, MediumTests, LargeTests, IntegrationTests. JUnit Categories are denoted using
java annotations (a special unit test looks for the presence of the @Category annotation in all unit
tess and will fail if it finds a test suite missing a sizing marking).
The first three categories, small, medium, and large, are for test cases which run when you type $ mvn
test. In other words, these three categorizations are for HBase unit tests. The integration category
is not for unit tests, but for integration tests. These are normally run when you invoke $ mvn verify.
Integration tests are described in integration.tests.
Keep reading to figure which annotation of the set small, medium, and large to put on your new
HBase test case.
Categorizing Tests
Small Tests
Small test cases are executed in a shared JVM and each test suite/test class should run in 15
seconds or less; i.e. a junit test fixture, a java object made up of test methods, should finish in
under 15 seconds, no matter how many or how few test methods it has. These test cases should
not use a minicluster.
Medium Tests
Medium test cases are executed in separate JVM and individual test suites or test classes or in
junit parlance, test fixture, should run in 50 seconds or less. These test cases can use a mini
cluster.
Large Tests
Large test cases are everything else. They are typically large-scale tests, regression tests for
specific bugs, timeout tests, or performance tests. No large test suite can take longer than ten
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minutes. It will be killed as timed out. Cast your test as an Integration Test if it needs to run
longer.
Integration Tests
Integration tests are system level tests. See integration.tests for more info. If you invoke $ mvn
test on integration tests, there is no timeout for the test.
173.3. Running tests
173.3.1. Default: small and medium category tests
Running mvn test will execute all small tests in a single JVM (no fork) and then medium tests in a
separate JVM for each test instance. Medium tests are NOT executed if there is an error in a small
test. Large tests are NOT executed.
173.3.2. Running all tests
Running mvn test -P runAllTests will execute small tests in a single JVM then medium and large
tests in a separate JVM for each test. Medium and large tests are NOT executed if there is an error in
a small test.
173.3.3. Running a single test or all tests in a package
To run an individual test, e.g. MyTest, rum mvn test -Dtest=MyTest You can also pass multiple,
individual tests as a comma-delimited list:
mvn test -Dtest=MyTest1,MyTest2,MyTest3
You can also pass a package, which will run all tests under the package:
mvn test '-Dtest=org.apache.hadoop.hbase.client.*'
When -Dtest is specified, the localTests profile will be used. Each junit test is executed in a
separate JVM (A fork per test class). There is no parallelization when tests are running in this mode.
You will see a new message at the end of the -report: "[INFO] Tests are skipped". It’s harmless.
However, you need to make sure the sum of Tests run: in the Results: section of test reports
matching the number of tests you specified because no error will be reported when a non-existent
test case is specified.
173.3.4. Other test invocation permutations
Running mvn test -P runSmallTests will execute "small" tests only, using a single JVM.
Running mvn test -P runMediumTests will execute "medium" tests only, launching a new JVM for
each test-class.
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Running mvn test -P runLargeTests will execute "large" tests only, launching a new JVM for each
test-class.
For convenience, you can run mvn test -P runDevTests to execute both small and medium tests,
using a single JVM.
173.3.5. Running tests faster
By default, $ mvn test -P runAllTests runs 5 tests in parallel. It can be increased on a developer’s
machine. Allowing that you can have 2 tests in parallel per core, and you need about 2GB of
memory per test (at the extreme), if you have an 8 core, 24GB box, you can have 16 tests in parallel.
but the memory available limits it to 12 (24/2), To run all tests with 12 tests in parallel, do this: mvn
test -P runAllTests -Dsurefire.secondPartForkCount=12. If using a version earlier than 2.0, do: +mvn
test -P runAllTests -Dsurefire.secondPartThreadCount=12 +. To increase the speed, you can as well
use a ramdisk. You will need 2GB of memory to run all tests. You will also need to delete the files
between two test run. The typical way to configure a ramdisk on Linux is:
$ sudo mkdir /ram2G
sudo mount -t tmpfs -o size=2048M tmpfs /ram2G
You can then use it to run all HBase tests on 2.0 with the command:
mvn test
Ê -P runAllTests -Dsurefire.secondPartForkCount=12
Ê -Dtest.build.data.basedirectory=/ram2G
On earlier versions, use:
mvn test
Ê -P runAllTests -Dsurefire.secondPartThreadCount=12
Ê -Dtest.build.data.basedirectory=/ram2G
173.3.6. hbasetests.sh
It’s also possible to use the script hbasetests.sh. This script runs the medium and large tests in
parallel with two maven instances, and provides a single report. This script does not use the hbase
version of surefire so no parallelization is being done other than the two maven instances the script
sets up. It must be executed from the directory which contains the pom.xml.
For example running ./dev-support/hbasetests.sh will execute small and medium tests. Running
./dev-support/hbasetests.sh runAllTests will execute all tests. Running ./dev-support/hbasetests.sh
replayFailed will rerun the failed tests a second time, in a separate jvm and without parallelisation.
173.3.7. Test Timeouts
The HBase unit test sizing Categorization timeouts are not strictly enforced.
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Any test that runs longer than ten minutes will be timedout/killed.
As of hbase-2.0.0, we have purged all per-test-method timeouts: i.e.
...
Ê @Test(timeout=30000)
Ê public void testCreateHRegionInfoName() throws Exception {
Ê // ...
Ê }
They are discouraged and don’t make much sense given we are timing base of how long the whole
Test Fixture/Class/Suite takes and that the variance in how long a test method takes varies wildly
dependent upon context (loaded Apache Infrastructure versus developer machine with nothing
else running on it).
173.3.8. Test Resource Checker
A custom Maven SureFire plugin listener checks a number of resources before and after each
HBase unit test runs and logs its findings at the end of the test output files which can be found in
target/surefire-reports per Maven module (Tests write test reports named for the test class into this
directory. Check the *-out.txt files). The resources counted are the number of threads, the number
of file descriptors, etc. If the number has increased, it adds a LEAK? comment in the logs. As you
can have an HBase instance running in the background, some threads can be deleted/created
without any specific action in the test. However, if the test does not work as expected, or if the test
should not impact these resources, it’s worth checking these log lines
...hbase.ResourceChecker(157): before... and ...hbase.ResourceChecker(157): after.... For example:
2012-09-26 09:22:15,315 INFO [pool-1-thread-1]
hbase.ResourceChecker(157): after:
regionserver.TestColumnSeeking#testReseeking Thread=65 (was 65),
OpenFileDescriptor=107 (was 107), MaxFileDescriptor=10240 (was 10240),
ConnectionCount=1 (was 1)
173.4. Writing Tests
173.4.1. General rules
•As much as possible, tests should be written as category small tests.
•All tests must be written to support parallel execution on the same machine, hence they should
not use shared resources as fixed ports or fixed file names.
•Tests should not overlog. More than 100 lines/second makes the logs complex to read and use i/o
that are hence not available for the other tests.
•Tests can be written with HBaseTestingUtility. This class offers helper functions to create a
temp directory and do the cleanup, or to start a cluster.
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173.4.2. Categories and execution time
•All tests must be categorized, if not they could be skipped.
•All tests should be written to be as fast as possible.
•See <<hbase.unittests,hbase.unittests> for test case categories and corresponding timeouts. This
should ensure a good parallelization for people using it, and ease the analysis when the test
fails.
173.4.3. Sleeps in tests
Whenever possible, tests should not use Thread.sleep, but rather waiting for the real event they
need. This is faster and clearer for the reader. Tests should not do a Thread.sleep without testing an
ending condition. This allows understanding what the test is waiting for. Moreover, the test will
work whatever the machine performance is. Sleep should be minimal to be as fast as possible.
Waiting for a variable should be done in a 40ms sleep loop. Waiting for a socket operation should
be done in a 200 ms sleep loop.
173.4.4. Tests using a cluster
Tests using a HRegion do not have to start a cluster: A region can use the local file system.
Start/stopping a cluster cost around 10 seconds. They should not be started per test method but per
test class. Started cluster must be shutdown using HBaseTestingUtility#shutdownMiniCluster,
which cleans the directories. As most as possible, tests should use the default settings for the
cluster. When they don’t, they should document it. This will allow to share the cluster later.
173.4.5. Tests Skeleton Code
Here is a test skeleton code with Categorization and a Category-based timeout rule to copy and
paste and use as basis for test contribution.
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/**
Ê* Describe what this testcase tests. Talk about resources initialized in @BeforeClass
(before
Ê* any test is run) and before each test is run, etc.
Ê*/
// Specify the category as explained in <<hbase.unittests,hbase.unittests>>.
@Category(SmallTests.class)
public class TestExample {
Ê // Replace the TestExample.class in the below with the name of your test fixture
class.
Ê private static final Log LOG = LogFactory.getLog(TestExample.class);
Ê // Handy test rule that allows you subsequently get the name of the current method.
See
Ê // down in 'testExampleFoo()' where we use it to log current test's name.
Ê @Rule public TestName testName = new TestName();
Ê // The below rule does two things. It decides the timeout based on the category
Ê // (small/medium/large) of the testcase. This @Rule requires that the full testcase
runs
Ê // within this timeout irrespective of individual test methods' times. The second
Ê // feature is we'll dump in the log when the test is done a count of threads still
Ê // running.
Ê @Rule public static TestRule timeout = CategoryBasedTimeout.builder().
Ê withTimeout(this.getClass()).withLookingForStuckThread(true).build();
Ê @Before
Ê public void setUp() throws Exception {
Ê }
Ê @After
Ê public void tearDown() throws Exception {
Ê }
Ê @Test
Ê public void testExampleFoo() {
Ê LOG.info("Running test " + testName.getMethodName());
Ê }
}
173.5. Integration Tests
HBase integration/system tests are tests that are beyond HBase unit tests. They are generally long-
lasting, sizeable (the test can be asked to 1M rows or 1B rows), targetable (they can take
configuration that will point them at the ready-made cluster they are to run against; integration
tests do not include cluster start/stop code), and verifying success, integration tests rely on public
APIs only; they do not attempt to examine server internals asserting success/fail. Integration tests
are what you would run when you need to more elaborate proofing of a release candidate beyond
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what unit tests can do. They are not generally run on the Apache Continuous Integration build
server, however, some sites opt to run integration tests as a part of their continuous testing on an
actual cluster.
Integration tests currently live under the src/test directory in the hbase-it submodule and will
match the regex: */IntegrationTest.java. All integration tests are also annotated with
@Category(IntegrationTests.class).
Integration tests can be run in two modes: using a mini cluster, or against an actual distributed
cluster. Maven failsafe is used to run the tests using the mini cluster. IntegrationTestsDriver class is
used for executing the tests against a distributed cluster. Integration tests SHOULD NOT assume
that they are running against a mini cluster, and SHOULD NOT use private API’s to access cluster
state. To interact with the distributed or mini cluster uniformly, IntegrationTestingUtility, and
HBaseCluster classes, and public client API’s can be used.
On a distributed cluster, integration tests that use ChaosMonkey or otherwise manipulate services
thru cluster manager (e.g. restart regionservers) use SSH to do it. To run these, test process should
be able to run commands on remote end, so ssh should be configured accordingly (for example, if
HBase runs under hbase user in your cluster, you can set up passwordless ssh for that user and run
the test also under it). To facilitate that, hbase.it.clustermanager.ssh.user,
hbase.it.clustermanager.ssh.opts and hbase.it.clustermanager.ssh.cmd configuration settings can
be used. "User" is the remote user that cluster manager should use to perform ssh commands.
"Opts" contains additional options that are passed to SSH (for example, "-i /tmp/my-key"). Finally, if
you have some custom environment setup, "cmd" is the override format for the entire tunnel (ssh)
command. The default string is {/usr/bin/ssh %1$s %2$s%3$s%4$s "%5$s"} and is a good starting
point. This is a standard Java format string with 5 arguments that is used to execute the remote
command. The argument 1 (%1$s) is SSH options set the via opts setting or via environment
variable, 2 is SSH user name, 3 is "@" if username is set or "" otherwise, 4 is the target host name,
and 5 is the logical command to execute (that may include single quotes, so don’t use them). For
example, if you run the tests under non-hbase user and want to ssh as that user and change to
hbase on remote machine, you can use:
/usr/bin/ssh %1$s %2$s%3$s%4$s "su hbase - -c \"%5$s\""
That way, to kill RS (for example) integration tests may run:
{/usr/bin/ssh some-hostname "su hbase - -c \"ps aux | ... | kill ...\""}
The command is logged in the test logs, so you can verify it is correct for your environment.
To disable the running of Integration Tests, pass the following profile on the command line
-PskipIntegrationTests. For example,
$ mvn clean install test -Dtest=TestZooKeeper -PskipIntegrationTests
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173.5.1. Running integration tests against mini cluster
HBase 0.92 added a verify maven target. Invoking it, for example by doing mvn verify, will run all
the phases up to and including the verify phase via the maven failsafe plugin, running all the above
mentioned HBase unit tests as well as tests that are in the HBase integration test group. After you
have completed mvn install -DskipTests You can run just the integration tests by invoking:
cd hbase-it
mvn verify
If you just want to run the integration tests in top-level, you need to run two commands. First: mvn
failsafe:integration-test This actually runs ALL the integration tests.
This command will always output BUILD SUCCESS even if there are test failures.
At this point, you could grep the output by hand looking for failed tests. However, maven will do
this for us; just use: mvn failsafe:verify The above command basically looks at all the test results (so
don’t remove the 'target' directory) for test failures and reports the results.
Running a subset of Integration tests
This is very similar to how you specify running a subset of unit tests (see above), but use the
property it.test instead of test. To just run IntegrationTestClassXYZ.java, use: mvn
failsafe:integration-test -Dit.test=IntegrationTestClassXYZ The next thing you might want to do is
run groups of integration tests, say all integration tests that are named IntegrationTestClassX*.java:
mvn failsafe:integration-test -Dit.test=*ClassX* This runs everything that is an integration test that
matches ClassX. This means anything matching: "*/IntegrationTest*ClassX". You can also run
multiple groups of integration tests using comma-delimited lists (similar to unit tests). Using a list of
matches still supports full regex matching for each of the groups. This would look something like:
mvn failsafe:integration-test -Dit.test=*ClassX*, *ClassY
173.5.2. Running integration tests against distributed cluster
If you have an already-setup HBase cluster, you can launch the integration tests by invoking the
class IntegrationTestsDriver. You may have to run test-compile first. The configuration will be
picked by the bin/hbase script.
mvn test-compile
Then launch the tests with:
bin/hbase [--config config_dir] org.apache.hadoop.hbase.IntegrationTestsDriver
Pass -h to get usage on this sweet tool. Running the IntegrationTestsDriver without any argument
will launch tests found under hbase-it/src/test, having @Category(IntegrationTests.class)
annotation, and a name starting with IntegrationTests. See the usage, by passing -h, to see how to
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filter test classes. You can pass a regex which is checked against the full class name; so, part of class
name can be used. IntegrationTestsDriver uses Junit to run the tests. Currently there is no support
for running integration tests against a distributed cluster using maven (see HBASE-6201).
The tests interact with the distributed cluster by using the methods in the DistributedHBaseCluster
(implementing HBaseCluster) class, which in turn uses a pluggable ClusterManager. Concrete
implementations provide actual functionality for carrying out deployment-specific and
environment-dependent tasks (SSH, etc). The default ClusterManager is HBaseClusterManager, which
uses SSH to remotely execute start/stop/kill/signal commands, and assumes some posix commands
(ps, etc). Also assumes the user running the test has enough "power" to start/stop servers on the
remote machines. By default, it picks up HBASE_SSH_OPTS, HBASE_HOME, HBASE_CONF_DIR from the env,
and uses bin/hbase-daemon.sh to carry out the actions. Currently tarball deployments, deployments
which uses hbase-daemons.sh, and Apache Ambari deployments are supported. /etc/init.d/ scripts
are not supported for now, but it can be easily added. For other deployment options, a
ClusterManager can be implemented and plugged in.
173.5.3. Destructive integration / system tests (ChaosMonkey)
HBase 0.96 introduced a tool named ChaosMonkey, modeled after same-named tool by Netflix’s Chaos
Monkey tool. ChaosMonkey simulates real-world faults in a running cluster by killing or
disconnecting random servers, or injecting other failures into the environment. You can use
ChaosMonkey as a stand-alone tool to run a policy while other tests are running. In some
environments, ChaosMonkey is always running, in order to constantly check that high availability
and fault tolerance are working as expected.
ChaosMonkey defines Actions and Policies.
Actions
Actions are predefined sequences of events, such as the following:
•Restart active master (sleep 5 sec)
•Restart random regionserver (sleep 5 sec)
•Restart random regionserver (sleep 60 sec)
•Restart META regionserver (sleep 5 sec)
•Restart ROOT regionserver (sleep 5 sec)
•Batch restart of 50% of regionservers (sleep 5 sec)
•Rolling restart of 100% of regionservers (sleep 5 sec)
Policies
A policy is a strategy for executing one or more actions. The default policy executes a random
action every minute based on predefined action weights. A given policy will be executed until
ChaosMonkey is interrupted.
Most ChaosMonkey actions are configured to have reasonable defaults, so you can run
ChaosMonkey against an existing cluster without any additional configuration. The following
example runs ChaosMonkey with the default configuration:
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$ bin/hbase org.apache.hadoop.hbase.util.ChaosMonkey
12/11/19 23:21:57 INFO util.ChaosMonkey: Using ChaosMonkey Policy: class
org.apache.hadoop.hbase.util.ChaosMonkey$PeriodicRandomActionPolicy, period:60000
12/11/19 23:21:57 INFO util.ChaosMonkey: Sleeping for 26953 to add jitter
12/11/19 23:22:24 INFO util.ChaosMonkey: Performing action: Restart active master
12/11/19 23:22:24 INFO util.ChaosMonkey: Killing
master:master.example.com,60000,1353367210440
12/11/19 23:22:24 INFO hbase.HBaseCluster: Aborting Master:
master.example.com,60000,1353367210440
12/11/19 23:22:24 INFO hbase.ClusterManager: Executing remote command: ps aux | grep
master | grep -v grep | tr -s ' ' | cut -d ' ' -f2 | xargs kill -s SIGKILL ,
hostname:master.example.com
12/11/19 23:22:25 INFO hbase.ClusterManager: Executed remote command, exit code:0 ,
output:
12/11/19 23:22:25 INFO hbase.HBaseCluster: Waiting service:master to stop:
master.example.com,60000,1353367210440
12/11/19 23:22:25 INFO hbase.ClusterManager: Executing remote command: ps aux | grep
master | grep -v grep | tr -s ' ' | cut -d ' ' -f2 , hostname:master.example.com
12/11/19 23:22:25 INFO hbase.ClusterManager: Executed remote command, exit code:0 ,
output:
12/11/19 23:22:25 INFO util.ChaosMonkey: Killed master
server:master.example.com,60000,1353367210440
12/11/19 23:22:25 INFO util.ChaosMonkey: Sleeping for:5000
12/11/19 23:22:30 INFO util.ChaosMonkey: Starting master:master.example.com
12/11/19 23:22:30 INFO hbase.HBaseCluster: Starting Master on: master.example.com
12/11/19 23:22:30 INFO hbase.ClusterManager: Executing remote command:
/homes/enis/code/hbase-0.94/bin/../bin/hbase-daemon.sh --config
/homes/enis/code/hbase-0.94/bin/../conf start master , hostname:master.example.com
12/11/19 23:22:31 INFO hbase.ClusterManager: Executed remote command, exit code:0 ,
output:starting master, logging to /homes/enis/code/hbase-0.94/bin/../logs/hbase-enis-
master-master.example.com.out
....
12/11/19 23:22:33 INFO util.ChaosMonkey: Started master:
master.example.com,60000,1353367210440
12/11/19 23:22:33 INFO util.ChaosMonkey: Sleeping for:51321
12/11/19 23:23:24 INFO util.ChaosMonkey: Performing action: Restart random region
server
12/11/19 23:23:24 INFO util.ChaosMonkey: Killing region
server:rs3.example.com,60020,1353367027826
12/11/19 23:23:24 INFO hbase.HBaseCluster: Aborting RS:
rs3.example.com,60020,1353367027826
12/11/19 23:23:24 INFO hbase.ClusterManager: Executing remote command: ps aux | grep
regionserver | grep -v grep | tr -s ' ' | cut -d ' ' -f2 | xargs kill -s SIGKILL ,
hostname:rs3.example.com
12/11/19 23:23:25 INFO hbase.ClusterManager: Executed remote command, exit code:0 ,
output:
12/11/19 23:23:25 INFO hbase.HBaseCluster: Waiting service:regionserver to stop:
rs3.example.com,60020,1353367027826
12/11/19 23:23:25 INFO hbase.ClusterManager: Executing remote command: ps aux | grep
regionserver | grep -v grep | tr -s ' ' | cut -d ' ' -f2 , hostname:rs3.example.com
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12/11/19 23:23:25 INFO hbase.ClusterManager: Executed remote command, exit code:0 ,
output:
12/11/19 23:23:25 INFO util.ChaosMonkey: Killed region
server:rs3.example.com,60020,1353367027826. Reported num of rs:6
12/11/19 23:23:25 INFO util.ChaosMonkey: Sleeping for:60000
12/11/19 23:24:25 INFO util.ChaosMonkey: Starting region server:rs3.example.com
12/11/19 23:24:25 INFO hbase.HBaseCluster: Starting RS on: rs3.example.com
12/11/19 23:24:25 INFO hbase.ClusterManager: Executing remote command:
/homes/enis/code/hbase-0.94/bin/../bin/hbase-daemon.sh --config
/homes/enis/code/hbase-0.94/bin/../conf start regionserver , hostname:rs3.example.com
12/11/19 23:24:26 INFO hbase.ClusterManager: Executed remote command, exit code:0 ,
output:starting regionserver, logging to /homes/enis/code/hbase-
0.94/bin/../logs/hbase-enis-regionserver-rs3.example.com.out
12/11/19 23:24:27 INFO util.ChaosMonkey: Started region
server:rs3.example.com,60020,1353367027826. Reported num of rs:6
The output indicates that ChaosMonkey started the default PeriodicRandomActionPolicy policy,
which is configured with all the available actions. It chose to run RestartActiveMaster and
RestartRandomRs actions.
173.5.4. Available Policies
HBase ships with several ChaosMonkey policies, available in the hbase/hbase-
it/src/test/java/org/apache/hadoop/hbase/chaos/policies/ directory.
173.5.5. Configuring Individual ChaosMonkey Actions
ChaosMonkey integration tests can be configured per test run. Create a Java properties file in the
HBase CLASSPATH and pass it to ChaosMonkey using the -monkeyProps configuration flag.
Configurable properties, along with their default values if applicable, are listed in the
org.apache.hadoop.hbase.chaos.factories.MonkeyConstants class. For properties that have defaults,
you can override them by including them in your properties file.
The following example uses a properties file called monkey.properties.
$ bin/hbase org.apache.hadoop.hbase.IntegrationTestIngest -m slowDeterministic
-monkeyProps monkey.properties
The above command will start the integration tests and chaos monkey. It will look for the properties
file monkey.properties on the HBase CLASSPATH; e.g. inside the HBASE conf dir.
Here is an example chaos monkey file:
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Example ChaosMonkey Properties File
sdm.action1.period=120000
sdm.action2.period=40000
move.regions.sleep.time=80000
move.regions.max.time=1000000
move.regions.sleep.time=80000
batch.restart.rs.ratio=0.4f
Periods/time are expressed in milliseconds.
HBase 1.0.2 and newer adds the ability to restart HBase’s underlying ZooKeeper quorum or HDFS
nodes. To use these actions, you need to configure some new properties, which have no reasonable
defaults because they are deployment-specific, in your ChaosMonkey properties file, which may be
hbase-site.xml or a different properties file.
<property>
Ê <name>hbase.it.clustermanager.hadoop.home</name>
Ê <value>$HADOOP_HOME</value>
</property>
<property>
Ê <name>hbase.it.clustermanager.zookeeper.home</name>
Ê <value>$ZOOKEEPER_HOME</value>
</property>
<property>
Ê <name>hbase.it.clustermanager.hbase.user</name>
Ê <value>hbase</value>
</property>
<property>
Ê <name>hbase.it.clustermanager.hadoop.hdfs.user</name>
Ê <value>hdfs</value>
</property>
<property>
Ê <name>hbase.it.clustermanager.zookeeper.user</name>
Ê <value>zookeeper</value>
</property>
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Chapter 174. Developer Guidelines
174.1. Branches
We use Git for source code management and latest development happens on master branch. There
are branches for past major/minor/maintenance releases and important features and bug fixes are
often back-ported to them.
174.2. Code Standards
174.2.1. Interface Classifications
Interfaces are classified both by audience and by stability level. These labels appear at the head of a
class. The conventions followed by HBase are inherited by its parent project, Hadoop.
The following interface classifications are commonly used:
InterfaceAudience
@InterfaceAudience.Public
APIs for users and HBase applications. These APIs will be deprecated through major versions of
HBase.
@InterfaceAudience.Private
APIs for HBase internals developers. No guarantees on compatibility or availability in future
versions. Private interfaces do not need an @InterfaceStability classification.
@InterfaceAudience.LimitedPrivate(HBaseInterfaceAudience.COPROC)
APIs for HBase coprocessor writers.
No @InterfaceAudience Classification
Packages without an @InterfaceAudience label are considered private. Mark your new packages if
publicly accessible.
Excluding Non-Public Interfaces from API Documentation
Only interfaces classified @InterfaceAudience.Public should be included in API
documentation (Javadoc). Committers must add new package excludes
ExcludePackageNames section of the pom.xml for new packages which do not contain
public classes.
@InterfaceStability
@InterfaceStability is important for packages marked @InterfaceAudience.Public.
@InterfaceStability.Stable
Public packages marked as stable cannot be changed without a deprecation path or a very good
reason.
@InterfaceStability.Unstable
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Public packages marked as unstable can be changed without a deprecation path.
@InterfaceStability.Evolving
Public packages marked as evolving may be changed, but it is discouraged.
No @InterfaceStability Label
Public classes with no @InterfaceStability label are discouraged, and should be considered
implicitly unstable.
If you are unclear about how to mark packages, ask on the development list.
174.2.2. Code Formatting Conventions
Please adhere to the following guidelines so that your patches can be reviewed more quickly. These
guidelines have been developed based upon common feedback on patches from new contributors.
See the Code Conventions for the Java Programming Language for more information on coding
conventions in Java. See eclipse.code.formatting to setup Eclipse to check for some of these
guidelines automatically.
Space Invaders
Do not use extra spaces around brackets. Use the second style, rather than the first.
if ( foo.equals( bar ) ) { // don't do this
if (foo.equals(bar)) {
foo = barArray[ i ]; // don't do this
foo = barArray[i];
Auto Generated Code
Auto-generated code in Eclipse often uses bad variable names such as arg0. Use more informative
variable names. Use code like the second example here.
Êpublic void readFields(DataInput arg0) throws IOException { // don't do this
Ê foo = arg0.readUTF(); // don't do this
Êpublic void readFields(DataInput di) throws IOException {
Ê foo = di.readUTF();
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Long Lines
Keep lines less than 100 characters. You can configure your IDE to do this automatically.
Bar bar = foo.veryLongMethodWithManyArguments(argument1, argument2, argument3,
argument4, argument5, argument6, argument7, argument8, argument9); // don't do this
Bar bar = foo.veryLongMethodWithManyArguments(
Êargument1, argument2, argument3,argument4, argument5, argument6, argument7,
argument8, argument9);
Trailing Spaces
Be sure there is a line break after the end of your code, and avoid lines with nothing but
whitespace. This makes diffs more meaningful. You can configure your IDE to help with this.
Bar bar = foo.getBar(); <--- imagine there is an extra space(s) after the
semicolon.
API Documentation (Javadoc)
Don’t forget Javadoc!
Javadoc warnings are checked during precommit. If the precommit tool gives you a '-1', please fix
the javadoc issue. Your patch won’t be committed if it adds such warnings.
Also, no @author tags - that’s a rule.
Findbugs
Findbugs is used to detect common bugs pattern. It is checked during the precommit build. If errors
are found, please fix them. You can run findbugs locally with mvn findbugs:findbugs, which will
generate the findbugs files locally. Sometimes, you may have to write code smarter than findbugs.
You can annotate your code to tell findbugs you know what you’re doing, by annotating your class
with the following annotation:
@edu.umd.cs.findbugs.annotations.SuppressWarnings(
value="HE_EQUALS_USE_HASHCODE",
justification="I know what I'm doing")
It is important to use the Apache-licensed version of the annotations. That generally means using
annotations in the edu.umd.cs.findbugs.annotations package so that we can rely on the cleanroom
reimplementation rather than annotations in the javax.annotations package.
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Javadoc - Useless Defaults
Don’t just leave javadoc tags the way IDE generates them, or fill redundant information in them.
Ê /**
Ê * @param table <---- don't leave them empty!
Ê * @param region An HRegion object. <---- don't fill redundant information!
Ê * @return Foo Object foo just created. <---- Not useful information
Ê * @throws SomeException <---- Not useful. Function declarations
already tell that!
Ê * @throws BarException when something went wrong <---- really?
Ê */
Ê public Foo createFoo(Bar bar);
Either add something descriptive to the tags, or just remove them. The preference is to add
something descriptive and useful.
One Thing At A Time, Folks
If you submit a patch for one thing, don’t do auto-reformatting or unrelated reformatting of code
on a completely different area of code.
Likewise, don’t add unrelated cleanup or refactorings outside the scope of your Jira.
Ambiguous Unit Tests
Make sure that you’re clear about what you are testing in your unit tests and why.
Implementing Writable
Applies pre-0.96 only
In 0.96, HBase moved to protocol buffers (protobufs). The below section on
Writables applies to 0.94.x and previous, not to 0.96 and beyond.
Every class returned by RegionServers must implement the Writable interface. If you are creating a
new class that needs to implement this interface, do not forget the default constructor.
174.2.3. Garbage-Collection Conserving Guidelines
The following guidelines were borrowed from http://engineering.linkedin.com/performance/
linkedin-feed-faster-less-jvm-garbage. Keep them in mind to keep preventable garbage collection to
a minimum. Have a look at the blog post for some great examples of how to refactor your code
according to these guidelines.
•Be careful with Iterators
•Estimate the size of a collection when initializing
•Defer expression evaluation
•Compile the regex patterns in advance
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•Cache it if you can
•String Interns are useful but dangerous
174.3. Invariants
We don’t have many but what we have we list below. All are subject to challenge of course but until
then, please hold to the rules of the road.
174.3.1. No permanent state in ZooKeeper
ZooKeeper state should transient (treat it like memory). If ZooKeeper state is deleted, hbase should
be able to recover and essentially be in the same state.
•.Exceptions: There are currently a few exceptions that we need to fix around whether a table is
enabled or disabled.
•Replication data is currently stored only in ZooKeeper. Deleting ZooKeeper data related to
replication may cause replication to be disabled. Do not delete the replication tree,
/hbase/replication/.
Replication may be disrupted and data loss may occur if you delete the
replication tree (/hbase/replication/) from ZooKeeper. Follow progress on this
issue at HBASE-10295.
174.4. Running In-Situ
If you are developing Apache HBase, frequently it is useful to test your changes against a more-real
cluster than what you find in unit tests. In this case, HBase can be run directly from the source in
local-mode. All you need to do is run:
${HBASE_HOME}/bin/start-hbase.sh
This will spin up a full local-cluster, just as if you had packaged up HBase and installed it on your
machine.
Keep in mind that you will need to have installed HBase into your local maven repository for the in-
situ cluster to work properly. That is, you will need to run:
mvn clean install -DskipTests
to ensure that maven can find the correct classpath and dependencies. Generally, the above
command is just a good thing to try running first, if maven is acting oddly.
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174.5. Adding Metrics
After adding a new feature a developer might want to add metrics. HBase exposes metrics using the
Hadoop Metrics 2 system, so adding a new metric involves exposing that metric to the hadoop
system. Unfortunately the API of metrics2 changed from hadoop 1 to hadoop 2. In order to get
around this a set of interfaces and implementations have to be loaded at runtime. To get an in-
depth look at the reasoning and structure of these classes you can read the blog post located here.
To add a metric to an existing MBean follow the short guide below:
174.5.1. Add Metric name and Function to Hadoop Compat Interface.
Inside of the source interface the corresponds to where the metrics are generated (eg
MetricsMasterSource for things coming from HMaster) create new static strings for metric name
and description. Then add a new method that will be called to add new reading.
174.5.2. Add the Implementation to Both Hadoop 1 and Hadoop 2 Compat
modules.
Inside of the implementation of the source (eg. MetricsMasterSourceImpl in the above example)
create a new histogram, counter, gauge, or stat in the init method. Then in the method that was
added to the interface wire up the parameter passed in to the histogram.
Now add tests that make sure the data is correctly exported to the metrics 2 system. For this the
MetricsAssertHelper is provided.
174.6. Git Best Practices
Avoid git merges.
Use git pull --rebase or git fetch followed by git rebase.
Do not use git push --force.
If the push does not work, fix the problem or ask for help.
Please contribute to this document if you think of other Git best practices.
174.6.1. rebase_all_git_branches.sh
The dev-support/rebase_all_git_branches.sh script is provided to help keep your Git repository clean.
Use the -h parameter to get usage instructions. The script automatically refreshes your tracking
branches, attempts an automatic rebase of each local branch against its remote branch, and gives
you the option to delete any branch which represents a closed HBASE- JIRA. The script has one
optional configuration option, the location of your Git directory. You can set a default by editing the
script. Otherwise, you can pass the git directory manually by using the -d parameter, followed by an
absolute or relative directory name, or even '.' for the current working directory. The script checks
the directory for sub-directory called .git/, before proceeding.
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174.7. Submitting Patches
If you are new to submitting patches to open source or new to submitting patches to Apache, start
by reading the On Contributing Patches page from Apache Commons Project. It provides a nice
overview that applies equally to the Apache HBase Project.
174.7.1. Create Patch
Make sure you review common.patch.feedback for code style. If your patch was generated
incorrectly or your code does not adhere to the code formatting guidelines, you may be asked to
redo some work.
Using submit-patch.py (recommended)
$ dev-support/submit-patch.py -jid HBASE-xxxxx
Use this script to create patches, upload to jira and optionally create/update reviews on Review
Board. Patch name is automatically formatted as (JIRA).(branch name).(patch number).patch to
follow Yetus' naming rules. Use -h flag to know detailed usage information. Most useful options are:
•-b BRANCH, --branch BRANCH : Specify base branch for generating the diff. If not specified,
tracking branch is used. If there is no tracking branch, error will be thrown.
•-jid JIRA_ID, --jira-id JIRA_ID : If used, deduces next patch version from attachments in the
jira and uploads the new patch. Script will ask for jira username/password for authentication. If
not set, patch is named <branch>.patch.
By default, it’ll also create/update review board. To skip that action, use -srb option. It uses 'Issue
Links' in the jira to figure out if a review request already exists. If no review request is present,
then creates a new one and populates all required fields using jira summary, patch description, etc.
Also adds this review’s link to the jira.
Save authentication credentials (optional)
Since attaching patches on JIRA and creating/changing review request on ReviewBoard requires
valid user authentication, the script will prompt you for username and password. To avoid the
hassle every time, set up ~/.apache-creds with login details and encrypt it by following the steps
in footer of script’s help message.
Python dependencies
To install required python dependencies, execute pip install -r dev-support/python-
requirements.txt from the master branch.
Manually
1. Use git rebase -i first, to combine (squash) smaller commits into a single larger one.
2. Create patch using IDE or Git commands. git format-patch is preferred since it preserves patch
author’s name and commit message. Also, it handles binary files by default, whereas git diff
ignores them unless you use the --binary option.
3. Patch name should be as follows to adhere to Yetus' naming convention:
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(JIRA).(branch name).(patch number).patch
For eg. HBASE-11625.master.001.patch, HBASE-XXXXX.branch-1.2.0005.patch, etc.
4. Attach the patch to the JIRA using More→Attach Files then click on [ Submit Patch ] button,
which’ll trigger Hudson job to check patch for validity.
5. If your patch is longer than a single screen, also create a review on Review Board and add the
link to JIRA. See reviewboard.
Few general guidelines
•Always patch against the master branch first, even if you want to patch in another branch.
HBase committers always apply patches first to the master branch, and backport if necessary.
•Submit one single patch for a fix. If necessary, squash local commits to merge local commits into
a single one first. See this Stack Overflow question for more information about squashing
commits.
•Please understand that not every patch may get committed, and that feedback will likely be
provided on the patch.
•If you need to revise your patch, leave the previous patch file(s) attached to the JIRA, and upload
a new one with incremented patch number.
Click on [ Cancel Patch ] and then on [ Submit Patch ] to trigger the presubmit run.
174.7.2. Unit Tests
Always add and/or update relevant unit tests when making the changes. Make sure that
new/changed unit tests pass locally before submitting the patch because it is faster than waiting for
presubmit result which runs full test suite. This will save your own time and effort. Use mockito to
make mocks which are very useful for testing failure scenarios by injecting appropriate failures.
If you are creating a new unit test class, notice how other unit test classes have classification/sizing
annotations before class name and a static methods for setup/teardown of testing environment. Be
sure to include annotations in any new unit test files. See hbase.tests for more information on tests.
174.7.3. Integration Tests
Significant new features should provide an integration test in addition to unit tests, suitable for
exercising the new feature at different points in its configuration space.
174.7.4. ReviewBoard
Patches larger than one screen, or patches that will be tricky to review, should go through
ReviewBoard.
Procedure: Use ReviewBoard
1. Register for an account if you don’t already have one. It does not use the credentials from
issues.apache.org. Log in.
2. Click New Review Request.
3. Choose the hbase-git repository. Click Choose File to select the diff and optionally a parent diff.
Click [ Create Review Request ].
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4. Fill in the fields as required. At the minimum, fill in the Summary and choose hbase as the
Review Group. If you fill in the Bugs field, the review board links back to the relevant JIRA. The
more fields you fill in, the better. Click [ Publish ] to make your review request public. An email
will be sent to everyone in the hbase group, to review the patch.
5. Back in your JIRA, click , and paste in the URL of your ReviewBoard request. This attaches the
ReviewBoard to the JIRA, for easy access.
6. To cancel the request, click .
For more information on how to use ReviewBoard, see the ReviewBoard documentation.
174.7.5. Guide for HBase Committers
Becoming a committer
Committers are responsible for reviewing and integrating code changes, testing and voting on
release candidates, weighing in on design discussions, as well as other types of project
contributions. The PMC votes to make a contributor a committer based on an assessment of their
contributions to the project. It is expected that committers demonstrate a sustained history of high-
quality contributions to the project and community involvement.
Contributions can be made in many ways. There is no single path to becoming a committer, nor any
expected timeline. Submitting features, improvements, and bug fixes is the most common avenue,
but other methods are both recognized and encouraged (and may be even more important to the
health of HBase as a project and a community). A non-exhaustive list of potential contributions (in
no particular order):
•Update the documentation for new changes, best practices, recipes, and other improvements.
•Keep the website up to date.
•Perform testing and report the results. For instance, scale testing and testing non-standard
configurations is always appreciated.
•Maintain the shared Jenkins testing environment and other testing infrastructure.
•Vote on release candidates after performing validation, even if non-binding. A non-binding vote
is a vote by a non-committer.
•Provide input for discussion threads on the mailing lists (which usually have [DISCUSS] in the
subject line).
•Answer questions questions on the user or developer mailing lists and on Slack.
•Make sure the HBase community is a welcoming one and that we adhere to our Code of
conduct. Alert the PMC if you have concerns.
•Review other people’s work (both code and non-code) and provide public feedback.
•Report bugs that are found, or file new feature requests.
•Triage issues and keep JIRA organized. This includes closing stale issues, labeling new issues,
updating metadata, and other tasks as needed.
•Mentor new contributors of all sorts.
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•Give talks and write blogs about HBase. Add these to the News section of the website.
•Provide UX feedback about HBase, the web UI, the CLI, APIs, and the website.
•Write demo applications and scripts.
•Help attract and retain a diverse community.
•Interact with other projects in ways that benefit HBase and those other projects.
Not every individual is able to do all (or even any) of the items on this list. If you think of other
ways to contribute, go for it (and add them to the list). A pleasant demeanor and willingness to
contribute are all you need to make a positive impact on the HBase project. Invitations to become a
committer are the result of steady interaction with the community over the long term, which builds
trust and recognition.
New committers
New committers are encouraged to first read Apache’s generic committer documentation:
•Apache New Committer Guide
•Apache Committer FAQ
Review
HBase committers should, as often as possible, attempt to review patches submitted by others.
Ideally every submitted patch will get reviewed by a committer within a few days. If a committer
reviews a patch they have not authored, and believe it to be of sufficient quality, then they can
commit the patch. Otherwise the patch should be cancelled with a clear explanation for why it was
rejected.
The list of submitted patches is in the HBase Review Queue, which is ordered by time of last
modification. Committers should scan the list from top to bottom, looking for patches that they feel
qualified to review and possibly commit. If you see a patch you think someone else is better
qualified to review, you can mention them by username in the JIRA.
For non-trivial changes, it is required that another committer review your patches before commit.
Self-commits of non-trivial patches are not allowed. Use the [ Submit Patch ] button in JIRA, just
like other contributors, and then wait for a +1 response from another committer before committing.
Reject
Patches which do not adhere to the guidelines in HowToContribute and to the code review checklist
should be rejected. Committers should always be polite to contributors and try to instruct and
encourage them to contribute better patches. If a committer wishes to improve an unacceptable
patch, then it should first be rejected, and a new patch should be attached by the committer for
further review.
Commit
Committers commit patches to the Apache HBase GIT repository.
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Before you commit!!!!
Make sure your local configuration is correct, especially your identity and email.
Examine the output of the $ git config --list command and be sure it is correct. See
Set Up Git if you need pointers.
When you commit a patch:
1. Include the Jira issue ID in the commit message along with a short description of the change.
Try to add something more than just the Jira title so that someone looking at git log output
doesn’t have to go to Jira to discern what the change is about. Be sure to get the issue ID right,
because this causes Jira to link to the change in Git (use the issue’s "All" tab to see these
automatic links).
2. Commit the patch to a new branch based off master or the other intended branch. It’s a good
idea to include the JIRA ID in the name of this branch. Check out the relevant target branch
where you want to commit, and make sure your local branch has all remote changes, by doing a
git pull --rebase or another similar command. Next, cherry-pick the change into each relevant
branch (such as master), and push the changes to the remote branch using a command such as
git push <remote-server> <remote-branch>.
If you do not have all remote changes, the push will fail. If the push fails for
any reason, fix the problem or ask for help. Do not do a git push --force.
Before you can commit a patch, you need to determine how the patch was created. The
instructions and preferences around the way to create patches have changed, and there will be
a transition period.
Determine How a Patch Was Created
◦If the first few lines of the patch look like the headers of an email, with a From, Date, and
Subject, it was created using git format-patch. This is the preferred way, because you can
reuse the submitter’s commit message. If the commit message is not appropriate, you can
still use the commit, then run git commit --amend and reword as appropriate.
◦If the first line of the patch looks similar to the following, it was created using git diff
without --no-prefix. This is acceptable too. Notice the a and b in front of the file names. This
is the indication that the patch was not created with --no-prefix.
diff --git a/src/main/asciidoc/_chapters/developer.adoc
b/src/main/asciidoc/_chapters/developer.adoc
◦If the first line of the patch looks similar to the following (without the a and b), the patch was
created with git diff --no-prefix and you need to add -p0 to the git apply command below.
diff --git src/main/asciidoc/_chapters/developer.adoc
src/main/asciidoc/_chapters/developer.adoc
Example 43. Example of committing a Patch
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One thing you will notice with these examples is that there are a lot of git pull commands.
The only command that actually writes anything to the remote repository is git push, and
you need to make absolutely sure you have the correct versions of everything and don’t
have any conflicts before pushing. The extra git pull commands are usually redundant, but
better safe than sorry.
The first example shows how to apply a patch that was generated with git format-patch
and apply it to the master and branch-1 branches.
The directive to use git format-patch rather than git diff, and not to use --no-prefix, is a
new one. See the second example for how to apply a patch created with git diff, and
educate the person who created the patch.
$ git checkout -b HBASE-XXXX
$ git am ~/Downloads/HBASE-XXXX-v2.patch --signoff # If you are committing
someone else's patch.
$ git checkout master
$ git pull --rebase
$ git cherry-pick <sha-from-commit>
# Resolve conflicts if necessary or ask the submitter to do it
$ git pull --rebase # Better safe than sorry
$ git push origin master
# Backport to branch-1
$ git checkout branch-1
$ git pull --rebase
$ git cherry-pick <sha-from-commit>
# Resolve conflicts if necessary
$ git pull --rebase # Better safe than sorry
$ git push origin branch-1
$ git branch -D HBASE-XXXX
This example shows how to commit a patch that was created using git diff without --no
-prefix. If the patch was created with --no-prefix, add -p0 to the git apply command.
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$ git apply ~/Downloads/HBASE-XXXX-v2.patch
$ git commit -m "HBASE-XXXX Really Good Code Fix (Joe Schmo)"
--author=<contributor> -a # This and next command is needed for patches
created with 'git diff'
$ git commit --amend --signoff
$ git checkout master
$ git pull --rebase
$ git cherry-pick <sha-from-commit>
# Resolve conflicts if necessary or ask the submitter to do it
$ git pull --rebase # Better safe than sorry
$ git push origin master
# Backport to branch-1
$ git checkout branch-1
$ git pull --rebase
$ git cherry-pick <sha-from-commit>
# Resolve conflicts if necessary or ask the submitter to do it
$ git pull --rebase # Better safe than sorry
$ git push origin branch-1
$ git branch -D HBASE-XXXX
3. Resolve the issue as fixed, thanking the contributor. Always set the "Fix Version" at this point,
but only set a single fix version for each branch where the change was committed, the earliest
release in that branch in which the change will appear.
Commit Message Format
The commit message should contain the JIRA ID and a description of what the patch does. The
preferred commit message format is:
<jira-id> <jira-title> (<contributor-name-if-not-commit-author>)
HBASE-12345 Fix All The Things (jane@example.com)
If the contributor used git format-patch to generate the patch, their commit message is in their
patch and you can use that, but be sure the JIRA ID is at the front of the commit message, even if the
contributor left it out.
Add Amending-Author when a conflict cherrypick backporting
We’ve established the practice of committing to master and then cherry picking back to branches
whenever possible, unless
•it’s breaking compat: In which case, if it can go in minor releases, backport to branch-1 and
branch-2.
•it’s a new feature: No for maintenance releases, For minor releases, discuss and arrive at
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consensus.
When there is a minor conflict we can fix it up and just proceed with the commit. The resulting
commit retains the original author. When the amending author is different from the original
committer, add notice of this at the end of the commit message as: Amending-Author: Author
<committer&apache> See discussion at HBase, mail # dev - [DISCUSSION Best practice when amending
commits cherry picked from master to branch].
Close related GitHub PRs
As a project we work to ensure there’s a JIRA associated with each change, but we don’t mandate
any particular tool be used for reviews. Due to implementation details of the ASF’s integration
between hosted git repositories and GitHub, the PMC has no ability to directly close PRs on our
GitHub repo. In the event that a contributor makes a Pull Request on GitHub, either because the
contributor finds that easier than attaching a patch to JIRA or because a reviewer prefers that UI
for examining changes, it’s important to make note of the PR in the commit that goes to the master
branch so that PRs are kept up to date.
To read more about the details of what kinds of commit messages will work with the GitHub "close
via keyword in commit" mechanism see the GitHub documentation for "Closing issues using
keywords". In summary, you should include a line with the phrase "closes #XXX", where the XXX is
the pull request id. The pull request id is usually given in the GitHub UI in grey at the end of the
subject heading.
Committers are responsible for making sure commits do not break the build or tests
If a committer commits a patch, it is their responsibility to make sure it passes the test suite. It is
helpful if contributors keep an eye out that their patch does not break the hbase build and/or tests,
but ultimately, a contributor cannot be expected to be aware of all the particular vagaries and
interconnections that occur in a project like HBase. A committer should.
Patching Etiquette
In the thread HBase, mail # dev - ANNOUNCEMENT: Git Migration In Progress (WAS ⇒ Re: Git
Migration), it was agreed on the following patch flow
1. Develop and commit the patch against master first.
2. Try to cherry-pick the patch when backporting if possible.
3. If this does not work, manually commit the patch to the branch.
Merge Commits
Avoid merge commits, as they create problems in the git history.
Committing Documentation
See appendix contributing to documentation.
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174.7.6. Dialog
Committers should hang out in the #hbase room on irc.freenode.net for real-time discussions.
However any substantive discussion (as with any off-list project-related discussion) should be re-
iterated in Jira or on the developer list.
174.7.7. Do not edit JIRA comments
Misspellings and/or bad grammar is preferable to the disruption a JIRA comment edit causes: See
the discussion at Re:(HBASE-451) Remove HTableDescriptor from HRegionInfo
174.8. The hbase-thirdparty dependency and
shading/relocation
A new project was created for the release of hbase-2.0.0. It was called hbase-thirdparty. This project
exists only to provide the main hbase project with relocated — or shaded — versions of popular
thirdparty libraries such as guava, netty, and protobuf. The mainline HBase project relies on the
relocated versions of these libraries gotten from hbase-thirdparty rather than on finding these
classes in their usual locations. We do this so we can specify whatever the version we wish. If we
don’t relocate, we must harmonize our version to match that which hadoop, spark, and other
projects use.
For developers, this means you need to be careful referring to classes from netty, guava, protobuf,
gson, etc. (see the hbase-thirdparty pom.xml for what it provides). Devs must refer to the hbase-
thirdparty provided classes. In practice, this is usually not an issue (though it can be a bit of a pain).
You will have to hunt for the relocated version of your particular class. You’ll find it by prepending
the general relocation prefix of org.apache.hbase.thirdparty.. For example if you are looking for
com.google.protobuf.Message, the relocated version used by HBase internals can be found at
org.apache.hbase.thirdparty.com.google.protobuf.Message.
For a few thirdparty libs, like protobuf (see the protobuf chapter in this book for the why), your IDE
may give you both options — the com.google.protobuf. and the
org.apache.hbase.thirdparty.com.google.protobuf. — because both classes are on your CLASSPATH.
Unless you are doing the particular juggling required in Coprocessor Endpoint development (again
see above cited protobuf chapter), you’ll want to use the shaded version, always.
The hbase-thirdparty project has groupid of org.apache.hbase.thirdparty. As of this writing, it
provides three jars; one for netty with an artifactid of hbase-thirdparty-netty, one for protobuf at
hbase-thirdparty-protobuf and then a jar for all else — gson, guava — at hbase-thirdpaty-
miscellaneous.
The hbase-thirdparty artifacts are a product produced by the Apache HBase project under the aegis
of the HBase Project Management Committee. Releases are done via the usual voting project on the
hbase dev mailing list. If issue in the hbase-thirdparty, use the hbase JIRA and mailing lists to post
notice.
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174.9. Development of HBase-related Maven
archetypes
The development of HBase-related Maven archetypes was begun with HBASE-14876. For an
overview of the hbase-archetypes infrastructure and instructions for developing new HBase-related
Maven archetypes, please see hbase/hbase-archetypes/README.md.
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Unit Testing HBase Applications
This chapter discusses unit testing your HBase application using JUnit, Mockito, MRUnit, and
HBaseTestingUtility. Much of the information comes from a community blog post about testing
HBase applications. For information on unit tests for HBase itself, see hbase.tests.
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Chapter 175. JUnit
HBase uses JUnit for unit tests
This example will add unit tests to the following example class:
public class MyHBaseDAO {
Ê public static void insertRecord(Table.getTable(table), HBaseTestObj obj)
Ê throws Exception {
Ê Put put = createPut(obj);
Ê table.put(put);
Ê }
Ê private static Put createPut(HBaseTestObj obj) {
Ê Put put = new Put(Bytes.toBytes(obj.getRowKey()));
Ê put.add(Bytes.toBytes("CF"), Bytes.toBytes("CQ-1"),
Ê Bytes.toBytes(obj.getData1()));
Ê put.add(Bytes.toBytes("CF"), Bytes.toBytes("CQ-2"),
Ê Bytes.toBytes(obj.getData2()));
Ê return put;
Ê }
}
The first step is to add JUnit dependencies to your Maven POM file:
<dependency>
Ê <groupId>junit</groupId>
Ê <artifactId>junit</artifactId>
Ê <version>4.11</version>
Ê <scope>test</scope>
</dependency>
Next, add some unit tests to your code. Tests are annotated with @Test. Here, the unit tests are in
bold.
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public class TestMyHbaseDAOData {
Ê @Test
Ê public void testCreatePut() throws Exception {
Ê HBaseTestObj obj = new HBaseTestObj();
Ê obj.setRowKey("ROWKEY-1");
Ê obj.setData1("DATA-1");
Ê obj.setData2("DATA-2");
Ê Put put = MyHBaseDAO.createPut(obj);
Ê assertEquals(obj.getRowKey(), Bytes.toString(put.getRow()));
Ê assertEquals(obj.getData1(), Bytes.toString(put.get(Bytes.toBytes("CF"), Bytes
.toBytes("CQ-1")).get(0).getValue()));
Ê assertEquals(obj.getData2(), Bytes.toString(put.get(Bytes.toBytes("CF"), Bytes
.toBytes("CQ-2")).get(0).getValue()));
Ê }
}
These tests ensure that your createPut method creates, populates, and returns a Put object with
expected values. Of course, JUnit can do much more than this. For an introduction to JUnit, see
https://github.com/junit-team/junit/wiki/Getting-started.
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Chapter 176. Mockito
Mockito is a mocking framework. It goes further than JUnit by allowing you to test the interactions
between objects without having to replicate the entire environment. You can read more about
Mockito at its project site, https://code.google.com/p/mockito/.
You can use Mockito to do unit testing on smaller units. For instance, you can mock a
org.apache.hadoop.hbase.Server instance or a org.apache.hadoop.hbase.master.MasterServices
interface reference rather than a full-blown org.apache.hadoop.hbase.master.HMaster.
This example builds upon the example code in unit.tests, to test the insertRecord method.
First, add a dependency for Mockito to your Maven POM file.
<dependency>
Ê <groupId>org.mockito</groupId>
Ê <artifactId>mockito-core</artifactId>
Ê <version>2.1.0</version>
Ê <scope>test</scope>
</dependency>
Next, add a @RunWith annotation to your test class, to direct it to use Mockito.
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@RunWith(MockitoJUnitRunner.class)
public class TestMyHBaseDAO{
Ê @Mock
Ê Configuration config = HBaseConfiguration.create();
Ê @Mock
Ê Connection connection = ConnectionFactory.createConnection(config);
Ê @Mock
Ê private Table table;
Ê @Captor
Ê private ArgumentCaptor putCaptor;
Ê @Test
Ê public void testInsertRecord() throws Exception {
Ê //return mock table when getTable is called
Ê when(connection.getTable(TableName.valueOf("tablename")).thenReturn(table);
Ê //create test object and make a call to the DAO that needs testing
Ê HBaseTestObj obj = new HBaseTestObj();
Ê obj.setRowKey("ROWKEY-1");
Ê obj.setData1("DATA-1");
Ê obj.setData2("DATA-2");
Ê MyHBaseDAO.insertRecord(table, obj);
Ê verify(table).put(putCaptor.capture());
Ê Put put = putCaptor.getValue();
Ê assertEquals(Bytes.toString(put.getRow()), obj.getRowKey());
Ê assert(put.has(Bytes.toBytes("CF"), Bytes.toBytes("CQ-1")));
Ê assert(put.has(Bytes.toBytes("CF"), Bytes.toBytes("CQ-2")));
Ê assertEquals(Bytes.toString(put.get(Bytes.toBytes("CF"),Bytes.toBytes("CQ-1")).
get(0).getValue()), "DATA-1");
Ê assertEquals(Bytes.toString(put.get(Bytes.toBytes("CF"),Bytes.toBytes("CQ-2")).
get(0).getValue()), "DATA-2");
Ê }
}
This code populates HBaseTestObj with ROWKEY-1'', DATA-1'', ``DATA-2'' as values. It then inserts the
record into the mocked table. The Put that the DAO would have inserted is captured, and values are
tested to verify that they are what you expected them to be.
The key here is to manage Connection and Table instance creation outside the DAO. This allows you
to mock them cleanly and test Puts as shown above. Similarly, you can now expand into other
operations such as Get, Scan, or Delete.
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Chapter 177. MRUnit
Apache MRUnit is a library that allows you to unit-test MapReduce jobs. You can use it to test HBase
jobs in the same way as other MapReduce jobs.
Given a MapReduce job that writes to an HBase table called MyTest, which has one column family
called CF, the reducer of such a job could look like the following:
public class MyReducer extends TableReducer<Text, Text, ImmutableBytesWritable> {
Ê public static final byte[] CF = "CF".getBytes();
Ê public static final byte[] QUALIFIER = "CQ-1".getBytes();
Ê public void reduce(Text key, Iterable<Text> values, Context context) throws
IOException, InterruptedException {
Ê //bunch of processing to extract data to be inserted, in our case, let's say we
are simply
Ê //appending all the records we receive from the mapper for this particular
Ê //key and insert one record into HBase
Ê StringBuffer data = new StringBuffer();
Ê Put put = new Put(Bytes.toBytes(key.toString()));
Ê for (Text val : values) {
Ê data = data.append(val);
Ê }
Ê put.add(CF, QUALIFIER, Bytes.toBytes(data.toString()));
Ê //write to HBase
Ê context.write(new ImmutableBytesWritable(Bytes.toBytes(key.toString())), put);
Ê }
Ê}
To test this code, the first step is to add a dependency to MRUnit to your Maven POM file.
<dependency>
Ê <groupId>org.apache.mrunit</groupId>
Ê <artifactId>mrunit</artifactId>
Ê <version>1.0.0 </version>
Ê <scope>test</scope>
</dependency>
Next, use the ReducerDriver provided by MRUnit, in your Reducer job.
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public class MyReducerTest {
Ê ReduceDriver<Text, Text, ImmutableBytesWritable, Writable> reduceDriver;
Ê byte[] CF = "CF".getBytes();
Ê byte[] QUALIFIER = "CQ-1".getBytes();
Ê @Before
Ê public void setUp() {
Ê MyReducer reducer = new MyReducer();
Ê reduceDriver = ReduceDriver.newReduceDriver(reducer);
Ê }
Ê @Test
Ê public void testHBaseInsert() throws IOException {
Ê String strKey = "RowKey-1", strValue = "DATA", strValue1 = "DATA1",
strValue2 = "DATA2";
Ê List<Text> list = new ArrayList<Text>();
Ê list.add(new Text(strValue));
Ê list.add(new Text(strValue1));
Ê list.add(new Text(strValue2));
Ê //since in our case all that the reducer is doing is appending the records that
the mapper
Ê //sends it, we should get the following back
Ê String expectedOutput = strValue + strValue1 + strValue2;
Ê //Setup Input, mimic what mapper would have passed
Ê //to the reducer and run test
Ê reduceDriver.withInput(new Text(strKey), list);
Ê //run the reducer and get its output
Ê List<Pair<ImmutableBytesWritable, Writable>> result = reduceDriver.run();
Ê //extract key from result and verify
Ê assertEquals(Bytes.toString(result.get(0).getFirst().get()), strKey);
Ê //extract value for CF/QUALIFIER and verify
Ê Put a = (Put)result.get(0).getSecond();
Ê String c = Bytes.toString(a.get(CF, QUALIFIER).get(0).getValue());
Ê assertEquals(expectedOutput,c );
Ê }
}
Your MRUnit test verifies that the output is as expected, the Put that is inserted into HBase has the
correct value, and the ColumnFamily and ColumnQualifier have the correct values.
MRUnit includes a MapperDriver to test mapping jobs, and you can use MRUnit to test other
operations, including reading from HBase, processing data, or writing to HDFS,
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Chapter 178. Integration Testing with an
HBase Mini-Cluster
HBase ships with HBaseTestingUtility, which makes it easy to write integration tests using a mini-
cluster. The first step is to add some dependencies to your Maven POM file. Check the versions to be
sure they are appropriate.
<properties>
Ê <hbase.version>2.0.0-SNAPSHOT</hbase.version>
</properties>
<dependencies>
Ê <dependency>
Ê <groupId>org.apache.hbase</groupId>
Ê <artifactId>hbase-testing-util</artifactId>
Ê <version>${hbase.version}</version>
Ê <scope>test</scope>
Ê </dependency>
</dependencies>
This code represents an integration test for the MyDAO insert shown in unit.tests.
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public class MyHBaseIntegrationTest {
Ê private static HBaseTestingUtility utility;
Ê byte[] CF = "CF".getBytes();
Ê byte[] CQ1 = "CQ-1".getBytes();
Ê byte[] CQ2 = "CQ-2".getBytes();
Ê @Before
Ê public void setup() throws Exception {
Ê utility = new HBaseTestingUtility();
Ê utility.startMiniCluster();
Ê }
Ê @Test
Ê public void testInsert() throws Exception {
Ê Table table = utility.createTable(Bytes.toBytes("MyTest"), CF);
Ê HBaseTestObj obj = new HBaseTestObj();
Ê obj.setRowKey("ROWKEY-1");
Ê obj.setData1("DATA-1");
Ê obj.setData2("DATA-2");
Ê MyHBaseDAO.insertRecord(table, obj);
Ê Get get1 = new Get(Bytes.toBytes(obj.getRowKey()));
Ê get1.addColumn(CF, CQ1);
Ê Result result1 = table.get(get1);
Ê assertEquals(Bytes.toString(result1.getRow()), obj.getRowKey());
Ê assertEquals(Bytes.toString(result1.value()), obj.getData1());
Ê Get get2 = new Get(Bytes.toBytes(obj.getRowKey()));
Ê get2.addColumn(CF, CQ2);
Ê Result result2 = table.get(get2);
Ê assertEquals(Bytes.toString(result2.getRow()), obj.getRowKey());
Ê assertEquals(Bytes.toString(result2.value()), obj.getData2());
Ê }
}
This code creates an HBase mini-cluster and starts it. Next, it creates a table called MyTest with one
column family, CF. A record is inserted, a Get is performed from the same table, and the insertion is
verified.
Starting the mini-cluster takes about 20-30 seconds, but that should be appropriate
for integration testing.
See the paper at HBase Case-Study: Using HBaseTestingUtility for Local Testing and Development
(2010) for more information about HBaseTestingUtility.
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Protobuf in HBase
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Chapter 179. Protobuf
HBase uses Google’s protobufs wherever it persists metadata — in the tail of hfiles or Cells written
by HBase into the system hbase:meta table or when HBase writes znodes to zookeeper, etc. — and
when it passes objects over the wire making RPCs. HBase uses protobufs to describe the RPC
Interfaces (Services) we expose to clients, for example the Admin and Client Interfaces that the
RegionServer fields, or specifying the arbitrary extensions added by developers via our
Coprocessor Endpoint mechanism.
In this chapter we go into detail for developers who are looking to understand better how it all
works. This chapter is of particular use to those who would amend or extend HBase functionality.
With protobuf, you describe serializations and services in a .protos file. You then feed these
descriptors to a protobuf tool, the protoc binary, to generate classes that can marshall and
unmarshall the described serializations and field the specified Services.
See the README.txt in the HBase sub-modules for details on how to run the class generation on a
per-module basis; e.g. see hbase-protocol/README.txt for how to generate protobuf classes in the
hbase-protocol module.
In HBase, .proto files are either in the hbase-protocol module; a module dedicated to hosting the
common proto files and the protoc generated classes that HBase uses internally serializing
metadata. For extensions to hbase such as REST or Coprocessor Endpoints that need their own
descriptors; their protos are located inside the function’s hosting module: e.g. hbase-rest is home to
the REST proto files and the hbase-rsgroup table grouping Coprocessor Endpoint has all protos that
have to do with table grouping.
Protos are hosted by the module that makes use of them. While this makes it so generation of
protobuf classes is distributed, done per module, we do it this way so modules encapsulate all to do
with the functionality they bring to hbase.
Extensions whether REST or Coprocessor Endpoints will make use of core HBase protos found back
in the hbase-protocol module. They’ll use these core protos when they want to serialize a Cell or a
Put or refer to a particular node via ServerName, etc., as part of providing the CPEP Service. Going
forward, after the release of hbase-2.0.0, this practice needs to whither. We’ll explain why in the
later hbase-2.0.0 section.
179.1. hbase-2.0.0 and the shading of protobufs
(HBASE-15638)
As of hbase-2.0.0, our protobuf usage gets a little more involved. HBase core protobuf references
are offset so as to refer to a private, bundled protobuf. Core stops referring to protobuf classes at
com.google.protobuf.* and instead references protobuf at the HBase-specific offset
org.apache.hadoop.hbase.shaded.com.google.protobuf.*. We do this indirection so hbase core can
evolve its protobuf version independent of whatever our dependencies rely on. For instance, HDFS
serializes using protobuf. HDFS is on our CLASSPATH. Without the above described indirection, our
protobuf versions would have to align. HBase would be stuck on the HDFS protobuf version until
HDFS decided to upgrade. HBase and HDFS versions would be tied.
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We had to move on from protobuf-2.5.0 because we need facilities added in protobuf-3.1.0; in
particular being able to save on copies and avoiding bringing protobufs onheap for
serialization/deserialization.
In hbase-2.0.0, we introduced a new module, hbase-protocol-shaded inside which we contained all
to do with protobuf and its subsequent relocation/shading. This module is in essence a copy of
much of the old hbase-protocol but with an extra shading/relocation step. Core was moved to
depend on this new module.
That said, a complication arises around Coprocessor Endpoints (CPEPs). CPEPs depend on public
HBase APIs that reference protobuf classes at com.google.protobuf.* explicitly. For example, in our
Table Interface we have the below as the means by which you obtain a CPEP Service to make
invocations against:
...
Ê <T extends com.google.protobuf.Service,R> Map<byte[],R> coprocessorService(
Ê Class<T> service, byte[] startKey, byte[] endKey,
Ê org.apache.hadoop.hbase.client.coprocessor.Batch.Call<T,R> callable)
Ê throws com.google.protobuf.ServiceException, Throwable
Existing CPEPs will have made reference to core HBase protobufs specifying ServerNames or
carrying Mutations. So as to continue being able to service CPEPs and their references to
com.google.protobuf. across the upgrade to hbase-2.0.0 and beyond, HBase needs to be able to
deal with both com.google.protobuf. references and its internal offset
org.apache.hadoop.hbase.shaded.com.google.protobuf.* protobufs.
The hbase-protocol-shaded module hosts all protobufs used by HBase core.
But for the vestigial CPEP references to the (non-shaded) content of hbase-protocol, we keep around
most of this module going forward just so it is available to CPEPs. Retaining the most of hbase-
protocol makes for overlapping, 'duplicated' proto instances where some exist as non-shaded/non-
relocated here in their old module location but also in the new location, shaded under hbase-
protocol-shaded. In other words, there is an instance of the generated protobuf class
org.apache.hadoop.hbase.protobuf.generated.ServerName in hbase-protocol and another generated
instance that is the same in all regards except its protobuf references are to the internal shaded
version at org.apache.hadoop.hbase.shaded.protobuf.generated.ServerName (note the 'shaded'
addition in the middle of the package name).
If you extend a proto in hbase-protocol-shaded for internal use, consider extending it also in hbase-
protocol (and regenerating).
Going forward, we will provide a new module of common types for use by CPEPs that will have the
same guarantees against change as does our public API. TODO.
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Procedure Framework (Pv2):
HBASE-12439
Procedure v2 …aims to provide a unified way to build…multi-step procedures with a rollback/roll-
forward ability in case of failure (e.g. create/delete table) — Matteo Bertozzi, the author of Pv2.
With Pv2 you can build and run state machines. It was built by Matteo to make distributed state
transitions in HBase resilient in the face of process failures. Previous to Pv2, state transition
handling was spread about the codebase with implementation varying by transition-type and
context. Pv2 was inspired by FATE, of Apache Accumulo.
Early Pv2 aspects have been shipping in HBase with a good while now but it has continued to
evolve as it takes on more involved scenarios. What we have now is powerful but intricate in
operation and incomplete, in need of cleanup and hardening. In this doc we have given overview
on the system so you can make use of it (and help with its polishing).
This system has the awkward name of Pv2 because HBase already had the notion of a Procedure
used in snapshots (see hbase-server org.apache.hadoop.hbase.procedure as opposed to hbase-
procedure org.apache.hadoop.hbase.procedure2). Pv2 supercedes and is to replace Procedure.
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Chapter 180. Procedures
A Procedure is a transform made on an HBase entity. Examples of HBase entities would be Regions
and Tables.
Procedures are run by a ProcedureExecutor instance. Procedure current state is kept in the
ProcedureStore.
The ProcedureExecutor has but a primitive view on what goes on inside a Procedure. From its PoV,
Procedures are submitted and then the ProcedureExecutor keeps calling #execute(Object) until the
Procedure is done. Execute may be called multiple times in the case of failure or restart, so
Procedure code must be idempotent yielding the same result each time it run. Procedure code can
also implement rollback so steps can be undone if failure. A call to execute() can result in one of
following possibilities:
•execute() returns
◦null: indicates we are done.
◦this: indicates there is more to do so, persist current procedure state and re-execute().
◦Array of sub-procedures: indicates a set of procedures needed to be run to completion
before we can proceed (after which we expect the framework to call our execute again).
•execute() throws exception
◦suspend: indicates execution of procedure is suspended and can be resumed due to some
external event. The procedure state is persisted.
◦yield: procedure is added back to scheduler. The procedure state is not persisted.
◦interrupted: currently same as yield.
◦Any exception not listed above: Procedure state is changed to FAILED (after which we expect
the framework will attempt rollback).
The ProcedureExecutor stamps the frameworks notions of Procedure State into the Procedure
itself; e.g. it marks Procedures as INITIALIZING on submit. It moves the state to RUNNABLE when it
goes to execute. When done, a Procedure gets marked FAILED or SUCCESS depending. Here is the
list of all states as of this writing:
•INITIALIZING Procedure in construction, not yet added to the executor
•RUNNABLE Procedure added to the executor, and ready to be executed.
•WAITING The procedure is waiting on children (subprocedures) to be completed
•WAITING_TIMEOUT The procedure is waiting a timeout or an external event
•ROLLEDBACK The procedure failed and was rolledback.
•SUCCESS The procedure execution completed successfully.
•FAILED The procedure execution failed, may need to rollback.
After each execute, the Procedure state is persisted to the ProcedureStore. Hooks are invoked on
Procedures so they can preserve custom state. Post-fault, the ProcedureExecutor re-hydrates its pre-
crash state by replaying the content of the ProcedureStore. This makes the Procedure Framework
resilient against process failure.
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180.1. Implementation
In implementation, Procedures tend to divide transforms into finer-grained tasks and while some
of these work items are handed off to sub-procedures, the bulk are done as processing steps in-
Procedure; each invocation of the execute is used to perform a single step, and then the Procedure
relinquishes returning to the framework. The Procedure does its own tracking of where it is in the
processing.
What comprises a sub-task, or step in the execution is up to the Procedure author but generally it is
a small piece of work that cannot be further decomposed and that moves the processing forward
toward its end state. Having procedures made of many small steps rather than a few large ones
allows the Procedure framework give out insight on where we are in the processing. It also allows
the framework be more fair in its execution. As stated per above, each step may be called multiple
times (failure/restart) so steps must be implemented idempotent.
It is easy to confuse the state that the Procedure itself is keeping with that of the Framework itself.
Try to keep them distinct.
180.2. Rollback
Rollback is called when the procedure or one of the sub-procedures has failed. The rollback step is
supposed to cleanup the resources created during the execute() step. In case of failure and restart,
rollback() may be called multiple times, so again the code must be idempotent.
180.3. Metrics
There are hooks for collecting metrics on submit of the procedure and on finish.
•updateMetricsOnSubmit()
•updateMetricsOnFinish()
Individual procedures can override these methods to collect procedure specific metrics. The default
implementations of these methods try to get an object implementing an interface ProcedureMetrics
which encapsulates following set of generic metrics:
•SubmittedCount (Counter): Total number of procedure instances submitted of a type.
•Time (Histogram): Histogram of runtime for procedure instances.
•FailedCount (Counter): Total number of failed procedure instances.
Individual procedures can implement this object and define these generic set of metrics.
180.4. Baggage
Procedures can carry baggage. One example is the step the procedure last attained (see previous
section); procedures persist the enum that marks where they are currently. Other examples might
be the Region or Server name the Procedure is currently working. After each call to execute, the
Procedure#serializeStateData is called. Procedures can persist whatever.
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180.5. Result/State and Queries
(From Matteo’s ProcedureV2 and Notification Bus doc)
In the case of asynchronous operations, the result must be kept around until the client asks for it.
Once we receive a “get” of the result we can schedule the delete of the record. For some operations
the result may be “unnecessary” especially in case of failure (e.g. if the create table fail, we can
query the operation result or we can just do a list table to see if it was created) so in some cases we
can schedule the delete after a timeout. On the client side the operation will return a “Procedure
ID”, this ID can be used to wait until the procedure is completed and get the result/exception.
Admin.doOperation() { longprocId=master.doOperation(); master.waitCompletion(procId);
} +
If the master goes down while performing the operation the backup master will pickup the half in(
progress operation and complete it. The client will not notice the failure.
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Chapter 181. Subprocedures
Subprocedures are Procedure instances created and returned by #execute(Object) method of a
procedure instance (parent procedure). As subprocedures are of type Procedure, they can
instantiate their own subprocedures. As its a recursive, procedure stack is maintained by the
framework. The framework makes sure that the parent procedure does not proceed till all sub-
procedures and their subprocedures in a procedure stack are successfully finished.
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Chapter 182. ProcedureExecutor
ProcedureExecutor uses ProcedureStore and ProcedureScheduler and executes procedures
submitted to it. Some of the basic operations supported are:
•abort(procId): aborts specified procedure if its not finished
•submit(Procedure): submits procedure for execution
•retrieve: list of get methods to get Procedure instances and results
•register/ unregister listeners: for listening on Procedure related notifications
When ProcedureExecutor starts it loads procedure instances persisted in ProcedureStore from
previous run. All unfinished procedures are resumed from the last stored state.
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Chapter 183. Nonces
You can pass the nonce that came in with the RPC to the Procedure on submit at the executor. This
nonce will then be serialized along w/ the Procedure on persist. If a crash, on reload, the nonce will
be put back into a map of nonces to pid in case a client tries to run same procedure for a second
time (it will be rejected). See the base Procedure and how nonce is a base data member.
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Chapter 184. Wait/Wake/Suspend/Yield
‘suspend’ means stop processing a procedure because we can make no more progress until a
condition changes; i.e. we sent RPC and need to wait on response. The way this works is that a
Procedure throws a suspend exception from down in its guts as a GOTO the end-of-the-current-
processing step. Suspend also puts the Procedure back on the scheduler. Problematic is we do some
accounting on our way out even on suspend making it so it can take time exiting (We have to
update state in the WAL).
RegionTransitionProcedure#reportTransition is called on receipt of a report from a RS. For Assign
and Unassign, this event response from the server we sent an RPC wakes up suspended
Assign/Unassigns.
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Chapter 185. Locking
Procedure Locks are not about concurrency! They are about giving a Procedure read/write access
to an HBase Entity such as a Table or Region so that is possible to shut out other Procedures from
making modifications to an HBase Entity state while the current one is running.
Locking is optional, up to the Procedure implementor but if an entity is being operated on by a
Procedure, all transforms need to be done via Procedures using the same locking scheme else
havoc.
Two ProcedureExecutor Worker threads can actually end up both processing the same Procedure
instance. If it happens, the threads are meant to be running different parts of the one
Procedure — changes that do not stamp on each other (This gets awkward around the procedure
frameworks notion of ‘suspend’. More on this below).
Locks optionally may be held for the life of a Procedure. For example, if moving a Region, you
probably want to have exclusive access to the HBase Region until the Region completes (or fails).
This is used in conjunction with {@link #holdLock(Object)}. If {@link #holdLock(Object)} returns
true, the procedure executor will call acquireLock() once and thereafter not call {@link
#releaseLock(Object)} until the Procedure is done (Normally, it calls release/acquire around each
invocation of {@link #execute(Object)}.
Locks also may live the life of a procedure; i.e. once an Assign Procedure starts, we do not want
another procedure meddling w/ the region under assignment. Procedures that hold the lock for the
life of the procedure set Procedure#holdLock to true. AssignProcedure does this as do Split and
Move (If in the middle of a Region move, you do not want it Splitting).
Locking can be for life of Procedure.
Some locks have a hierarchy. For example, taking a region lock also takes (read) lock on its
containing table and namespace to prevent another Procedure obtaining an exclusive lock on the
hosting table (or namespace).
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Chapter 186. Procedure Types
186.1. StateMachineProcedure
One can consider each call to #execute(Object) method as transitioning from one state to another in
a state machine. Abstract class StateMachineProcedure is wrapper around base Procedure class
which provides constructs for implementing a state machine as a Procedure. After each state
transition current state is persisted so that, in case of crash/ restart, the state transition can be
resumed from the previous state of a procedure before crash/ restart. Individual procedures need
to define initial and terminus states and hooks executeFromState() and setNextState() are provided
for state transitions.
186.2. RemoteProcedureDispatcher
A new RemoteProcedureDispatcher (+ subclass RSProcedureDispatcher) primitive takes care of
running the Procedure-based Assignments ‘remote’ component. This dispatcher knows about
‘servers’. It does aggregation of assignments by time on a time/count basis so can send procedures
in batches rather than one per RPC. Procedure status comes back on the back of the RegionServer
heartbeat reporting online/offline regions (No more notifications via ZK). The response is passed to
the AMv2 to ‘process’. It will check against the in-memory state. If there is a mismatch, it fences out
the RegionServer on the assumption that something went wrong on the RS side. Timeouts trigger
retries (Not Yet Implemented!). The Procedure machine ensures only one operation at a time on any
one Region/Table using entity locking and smarts about what is serial and what can be run
concurrently (Locking was zk-based — you’d put a znode in zk for a table — but now has been
converted to be procedure-based as part of this project).
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Chapter 187. References
•Matteo had a slide deck on what it the Procedure Framework would look like and the problems
it addresses initially attached to the Pv2 issue.
•A good doc by Matteo on problem and how Pv2 addresses it w/ roadmap (from the Pv2 JIRA). We
should go back to the roadmap to do the Notification Bus, convertion of log splitting to Pv2, etc.
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AMv2 Description for Devs
The AssignmentManager (AM) in HBase Master manages assignment of Regions over a cluster of
RegionServers.
The AMv2 project is a redo of Assignment in an attempt at addressing the root cause of many of our
operational issues in production, namely slow assignment and problematic accounting such that
Regions are misplaced stuck offline in the notorious Regions-In-Transition (RIT) limbo state.
Below are notes for devs on key aspects of AMv2 in no particular order.
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Chapter 188. Background
Assignment in HBase 1.x has been problematic in operation. It is not hard to see why. Region state is
kept at the other end of an RPC in ZooKeeper (Terminal states — i.e. OPEN or CLOSED — are
published to the hbase:meta table). In HBase-1.x.x, state has multiple writers with Master and
RegionServers all able to make state edits concurrently (in hbase:meta table and out on ZooKeeper).
If clocks are awry or watchers missed, state changes can be skipped or overwritten. Locking of
HBase Entities — tables, regions — is not comprehensive so a table
operation — disable/enable — could clash with a region-level operation; a split or merge. Region
state is distributed and hard to reason about and test. Assignment is slow in operation because each
assign involves moving remote znodes through transitions. Cluster size tends to top out at a couple
of hundred thousand regions; beyond this, cluster start/stop takes hours and is prone to corruption.
AMv2 (AssignmentManager Version 2) is a refactor (HBASE-14350) of the hbase-1.x
AssignmentManager putting it up on a ProcedureV2 (HBASE-12439) basis. ProcedureV2 (Pv2), is an
awkwardly named system that allows describing and running multi-step state machines. It is
performant and persists all state to a Store which is recoverable post crash. See the companion
chapter on Procedure Framework (Pv2): HBASE-12439, to learn more about the ProcedureV2
system.
In AMv2, all assignment, crash handling, splits and merges are recast as Procedures(v2). ZooKeeper
is purged from the mix. As before, the final assignment state gets published to hbase:meta for non-
Master participants to read (all-clients) with intermediate state kept in the local Pv2 WAL-based
‘store’ but only the active Master, a single-writer, evolves state. The Master’s in-memory cluster
image is the authority and if disagreement, RegionServers are forced to comply. Pv2 adds
shared/exclusive locking of all core HBase Entities — namespace, tables, and regions — to ensure
one actor at a time access and to prevent operations contending over resources (move/split,
disable/assign, etc.).
This redo of AM atop of a purposed, performant state machine with all operations taking on the
common Procedure form with a single state writer only moves our AM to a new level of resilience
and scale.
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Chapter 189. New System
Each Region Assign or Unassign of a Region is now a Procedure. A Move (Region) Procedure is a
compound of Procedures; it is the running of an Unassign Procedure followed by an Assign
Procedure. The Move Procedure spawns the Assign and Unassign in series and then waits on their
completions.
And so on. ServerCrashProcedure spawns the WAL splitting tasks and then the reassign of all
regions that were hosted on the crashed server as subprocedures.
AMv2 Procedures are run by the Master in a ProcedureExecutor instance. All Procedures make use
of utility provided by the Pv2 framework.
For example, Procedures persist each state transition to the frameworks’ Procedure Store. The
default implementation is done as a WAL kept on HDFS. On crash, we reopen the Store and rerun
all WALs of Procedure transitions to put the Assignment State Machine back into the attitude it had
just before crash. We then continue Procedure execution.
In the new system, the Master is the Authority on all things Assign. Previous we were ambiguous;
e.g. the RegionServer was in charge of Split operations. Master keeps an in-memory image of Region
states and servers. If disagreement, the Master always prevails; at an extreme it will kill the
RegionServer that is in disagreement.
A new RegionStateStore class takes care of publishing the terminal Region state, whether OPEN or
CLOSED, out to the _hbase:meta _table.
RegionServers now report their run version on Connection. This version is available inside the AM
for use running migrating rolling restarts.
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Chapter 190. Procedures Detail
190.1. Assign/Unassign
Assign and Unassign subclass a common RegionTransitionProcedure. There can only be one
RegionTransitionProcedure per region running at a time since the RTP instance takes a lock on the
region. The RTP base Procedure has three steps; a store the procedure step
(REGION_TRANSITION_QUEUE); a dispatch of the procedure open or close followed by a suspend
waiting on the remote regionserver to report successful open or fail
(REGION_TRANSITION_DISPATCH) or notification that the server fielding the request crashed; and
finally registration of the successful open/close in hbase:meta (REGION_TRANSITION_FINISH).
Here is how the assign of a region 56f985a727afe80a184dac75fbf6860c looks in the logs. The assign
was provoked by a Server Crash (Process ID 1176 or pid=1176 which when it is the parent of a
procedure, it is identified as ppid=1176). The assign is pid=1179, the second region of the two being
assigned by this Server Crash.
2017-05-23 12:04:24,175 INFO [ProcExecWrkr-30] procedure2.ProcedureExecutor:
Initialized subprocedures=[{pid=1178, ppid=1176, state=RUNNABLE
:REGION_TRANSITION_QUEUE; AssignProcedure table=IntegrationTestBigLinkedList, region
=bfd57f0b72fd3ca77e9d3c5e3ae48d76, target=ve0540.halxg.example.org,16020,
1495525111232}, {pid=1179, ppid=1176, state=RUNNABLE:REGION_TRANSITION_QUEUE;
AssignProcedure table=IntegrationTestBigLinkedList, region=56f
985a727afe80a184dac75fbf6860c, target=ve0540.halxg.example.org,16020,1495525111232}]
Next we start the assign by queuing (‘registering’) the Procedure with the framework.
2017-05-23 12:04:24,241 INFO [ProcExecWrkr-30] assignment.AssignProcedure: Start pid
=1179, ppid=1176, state=RUNNABLE:REGION_TRANSITION_QUEUE; AssignProcedure table
=IntegrationTestBigLinkedList, region=56f985a727afe80a184dac75fbf6860c, target=ve0540
.halxg.example.org,16020,1495525111232; rit=OFFLINE, location=ve0540.halxg.example.
org,16020,1495525111232; forceNewPlan=false, retain=false
Track the running of Procedures in logs by tracing their process id — here pid=1179.
Next we move to the dispatch phase where we update hbase:meta table setting the region state as
OPENING on server ve540. We then dispatch an rpc to ve540 asking it to open the region.
Thereafter we suspend the Assign until we get a message back from ve540 on whether it has
opened the region successfully (or not).
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2017-05-23 12:04:24,494 INFO [ProcExecWrkr-38] assignment.RegionStateStore: pid=1179
updating hbase:meta row=IntegrationTestBigLinkedList,H\xE3@\x8D\x964\x9D\xDF\x8F@9\
x0F\xC8\xCC\xC2,1495566261066.56f985a727afe80a184dac75fbf6860c., regionState=OPENING,
regionLocation=ve0540.halxg.example.org,16020,1495525111232
2017-05-23 12:04:24,498 INFO [ProcExecWrkr-38] assignment.RegionTransitionProcedure:
Dispatch pid=1179, ppid=1176, state=RUNNABLE:REGION_TRANSITION_DISPATCH;
AssignProcedure table=IntegrationTestBigLinkedList, region=56f
985a727afe80a184dac75fbf6860c, target=ve0540.halxg.example.org,16020,1495525111232;
rit=OPENING, location=ve0540.halxg.example.org,16020,1495525111232
Below we log the incoming report that the region opened successfully on ve540. The Procedure is
woken up (you can tell it the procedure is running by the name of the thread, its a
ProcedureExecutor thread, ProcExecWrkr-9). The woken up Procedure updates state in hbase:meta
to denote the region as open on ve0540. It then reports finished and exits.
2017-05-23 12:04:26,643 DEBUG [RpcServer.default.FPBQ.Fifo.handler=46,queue=1,port
=16000] assignment.RegionTransitionProcedure: Received report OPENED seqId=11984985,
pid=1179, ppid=1176, state=RUNNABLE:REGION_TRANSITION_DISPATCH; AssignProcedure table
=IntegrationTestBigLinkedList, region=56f985a727afe80a184dac75fbf6860c, target=ve0540
.halxg.example.org,16020,1495525111232; rit=OPENING, location=ve0540.halxg.example.
org,16020,1495525111232
2017-05-23 12:04:26,643 INFO [ProcExecWrkr-9] assignment.RegionStateStore: pid=1179
updating hbase:meta row=IntegrationTestBigLinkedList,H\xE3@\x8D\x964\x9D\xDF\x8F@9\
x0F\xC8\xCC\xC2,1495566261066.56f985a727afe80a184dac75fbf6860c., regionState=OPEN,
openSeqNum=11984985, regionLocation=ve0540.halxg.example.org,16020,1495525111232
2017-05-23 12:04:26,836 INFO [ProcExecWrkr-9] procedure2.ProcedureExecutor: Finish
suprocedure pid=1179, ppid=1176, state=SUCCESS; AssignProcedure table
=IntegrationTestBigLinkedList, region=56f985a727afe80a184dac75fbf6860c, target=ve0540
.halxg.example.org,16020,1495525111232
Unassign looks similar given it is based on the base RegionTransitionProcedure. It has the same
state transitions and does basically the same steps but with different state name (CLOSING,
CLOSED).
Most other procedures are subclasses of a Pv2 StateMachine implementation. We have both Table
and Region focused StateMachines types.
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Chapter 191. UI
Along the top-bar on the Master, you can now find a ‘Procedures&Locks’ tab which takes you to a
page that is ugly but useful. It dumps currently running procedures and framework locks. Look at
this when you can’t figure what stuff is stuck; it will at least identify problematic procedures (take
the pid and grep the logs…). Look for ROLLEDBACK or pids that have been RUNNING for a long
time.
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Chapter 192. Logging
Procedures log their process ids as pid= and their parent ids (ppid=) everywhere. Work has been
done so you can grep the pid and see history of a procedure operation.
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Chapter 193. Implementation Notes
In this section we note some idiosyncrasies of operation as an attempt at saving you some head-
scratching.
193.1. Region Transition RPC and RS Heartbeat can
arrive at ~same time on Master
Reporting Region Transition on a RegionServer is now a RPC distinct from RS heartbeating
(‘RegionServerServices’ Service). An heartbeat and a status update can arrive at the Master at about
the same time. The Master will update its internal state for a Region but this same state is checked
when heartbeat processing. We may find the unexpected; i.e. a Region just reported as CLOSED so
heartbeat is surprised to find region OPEN on the back of the RS report. In the new system, all
slaves must cow to the Masters’ understanding of cluster state; the Master will kill/close any
misaligned entities.
To address the above, we added a lastUpdate for in-memory Master state. Let a region state have
some vintage before we act on it (one second currently).
193.2. Master as RegionServer or as RegionServer that
just does system tables
AMv2 enforces current master branch default of HMaster carrying system tables only; i.e. the
Master in an HBase cluster acts also as a RegionServer only it is the exclusive host for tables such as
hbase:meta, hbase:namespace, etc., the core system tables. This is causing a couple of test failures as
AMv1, though it is not supposed to, allows moving hbase:meta off Master while AMv2 does not.
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Chapter 194. New Configs
These configs all need doc on when you’d change them.
194.1.
hbase.procedure.remote.dispatcher.threadpool.size
Defaults 128
194.2. hbase.procedure.remote.dispatcher.delay.msec
Default 150ms
194.3.
hbase.procedure.remote.dispatcher.max.queue.size
Default 32
194.4. hbase.regionserver.rpc.startup.waittime
Default 60 seconds.
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Chapter 195. Tools
HBASE-15592 Print Procedure WAL Content
Patch in HBASE-18152 [AMv2] Corrupt Procedure WAL file; procedure data stored out of order
https://issues.apache.org/jira/secure/attachment/12871066/reading_bad_wal.patch
195.1.
MasterProcedureSchedulerPerformanceEvaluation
Tool to test performance of locks and queues in procedure scheduler independently from other
framework components. Run this after any substantial changes in proc system. Prints nice output:
******************************************
Time - addBack : 5.0600sec
Ops/sec - addBack : 1.9M
Time - poll : 19.4590sec
Ops/sec - poll : 501.9K
Num Operations : 10000000
Completed : 10000006
Yield : 22025876
Num Tables : 5
Regions per table : 10
Operations type : both
Threads : 10
******************************************
Raw format for scripts
RESULT [num_ops=10000000, ops_type=both, num_table=5, regions_per_table=10,
threads=10, num_yield=22025876, time_addback_ms=5060, time_poll_ms=19459]
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ZooKeeper
A distributed Apache HBase installation depends on a running ZooKeeper cluster. All participating
nodes and clients need to be able to access the running ZooKeeper ensemble. Apache HBase by
default manages a ZooKeeper "cluster" for you. It will start and stop the ZooKeeper ensemble as
part of the HBase start/stop process. You can also manage the ZooKeeper ensemble independent of
HBase and just point HBase at the cluster it should use. To toggle HBase management of ZooKeeper,
use the HBASE_MANAGES_ZK variable in conf/hbase-env.sh. This variable, which defaults to true, tells
HBase whether to start/stop the ZooKeeper ensemble servers as part of HBase start/stop.
When HBase manages the ZooKeeper ensemble, you can specify ZooKeeper configuration directly
in conf/hbase-site.xml. A ZooKeeper configuration option can be set as a property in the HBase
hbase-site.xml XML configuration file by prefacing the ZooKeeper option name with
hbase.zookeeper.property. For example, the clientPort setting in ZooKeeper can be changed by
setting the hbase.zookeeper.property.clientPort property. For all default values used by HBase,
including ZooKeeper configuration, see hbase default configurations. Look for the
hbase.zookeeper.property prefix. For the full list of ZooKeeper configurations, see ZooKeeper’s
zoo.cfg. HBase does not ship with a zoo.cfg so you will need to browse the conf directory in an
appropriate ZooKeeper download.
You must at least list the ensemble servers in hbase-site.xml using the hbase.zookeeper.quorum
property. This property defaults to a single ensemble member at localhost which is not suitable for
a fully distributed HBase. (It binds to the local machine only and remote clients will not be able to
connect).
How many ZooKeepers should I run?
You can run a ZooKeeper ensemble that comprises 1 node only but in production it
is recommended that you run a ZooKeeper ensemble of 3, 5 or 7 machines; the
more members an ensemble has, the more tolerant the ensemble is of host
failures. Also, run an odd number of machines. In ZooKeeper, an even number of
peers is supported, but it is normally not used because an even sized ensemble
requires, proportionally, more peers to form a quorum than an odd sized
ensemble requires. For example, an ensemble with 4 peers requires 3 to form a
quorum, while an ensemble with 5 also requires 3 to form a quorum. Thus, an
ensemble of 5 allows 2 peers to fail, and thus is more fault tolerant than the
ensemble of 4, which allows only 1 down peer.
Give each ZooKeeper server around 1GB of RAM, and if possible, its own dedicated
disk (A dedicated disk is the best thing you can do to ensure a performant
ZooKeeper ensemble). For very heavily loaded clusters, run ZooKeeper servers on
separate machines from RegionServers (DataNodes and TaskTrackers).
For example, to have HBase manage a ZooKeeper quorum on nodes rs{1,2,3,4,5}.example.com,
bound to port 2222 (the default is 2181) ensure HBASE_MANAGE_ZK is commented out or set to true in
conf/hbase-env.sh and then edit conf/hbase-site.xml and set hbase.zookeeper.property.clientPort and
hbase.zookeeper.quorum. You should also set hbase.zookeeper.property.dataDir to other than the
default as the default has ZooKeeper persist data under /tmp which is often cleared on system
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restart. In the example below we have ZooKeeper persist to /user/local/zookeeper.
Ê <configuration>
Ê ...
Ê <property>
Ê <name>hbase.zookeeper.property.clientPort</name>
Ê <value>2222</value>
Ê <description>Property from ZooKeeper's config zoo.cfg.
Ê The port at which the clients will connect.
Ê </description>
Ê </property>
Ê <property>
Ê <name>hbase.zookeeper.quorum</name>
Ê
<value>rs1.example.com,rs2.example.com,rs3.example.com,rs4.example.com,rs5.example.com
</value>
Ê <description>Comma separated list of servers in the ZooKeeper Quorum.
Ê For example, "host1.mydomain.com,host2.mydomain.com,host3.mydomain.com".
Ê By default this is set to localhost for local and pseudo-distributed modes
Ê of operation. For a fully-distributed setup, this should be set to a full
Ê list of ZooKeeper quorum servers. If HBASE_MANAGES_ZK is set in hbase-env.sh
Ê this is the list of servers which we will start/stop ZooKeeper on.
Ê </description>
Ê </property>
Ê <property>
Ê <name>hbase.zookeeper.property.dataDir</name>
Ê <value>/usr/local/zookeeper</value>
Ê <description>Property from ZooKeeper's config zoo.cfg.
Ê The directory where the snapshot is stored.
Ê </description>
Ê </property>
Ê ...
Ê </configuration>
What version of ZooKeeper should I use?
The newer version, the better. ZooKeeper 3.4.x is required as of HBase 1.0.0
ZooKeeper Maintenance
Be sure to set up the data dir cleaner described under ZooKeeper Maintenance else
you could have 'interesting' problems a couple of months in; i.e. zookeeper could
start dropping sessions if it has to run through a directory of hundreds of
thousands of logs which is wont to do around leader reelection time — a process
rare but run on occasion whether because a machine is dropped or happens to
hiccup.
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Chapter 196. Using existing ZooKeeper
ensemble
To point HBase at an existing ZooKeeper cluster, one that is not managed by HBase, set
HBASE_MANAGES_ZK in conf/hbase-env.sh to false
Ê ...
Ê # Tell HBase whether it should manage its own instance of ZooKeeper or not.
Ê export HBASE_MANAGES_ZK=false
Next set ensemble locations and client port, if non-standard, in hbase-site.xml.
When HBase manages ZooKeeper, it will start/stop the ZooKeeper servers as a part of the regular
start/stop scripts. If you would like to run ZooKeeper yourself, independent of HBase start/stop, you
would do the following
${HBASE_HOME}/bin/hbase-daemons.sh {start,stop} zookeeper
Note that you can use HBase in this manner to spin up a ZooKeeper cluster, unrelated to HBase. Just
make sure to set HBASE_MANAGES_ZK to false if you want it to stay up across HBase restarts so that
when HBase shuts down, it doesn’t take ZooKeeper down with it.
For more information about running a distinct ZooKeeper cluster, see the ZooKeeper Getting
Started Guide. Additionally, see the ZooKeeper Wiki or the ZooKeeper documentation for more
information on ZooKeeper sizing.
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Chapter 197. SASL Authentication with
ZooKeeper
Newer releases of Apache HBase (>= 0.92) will support connecting to a ZooKeeper Quorum that
supports SASL authentication (which is available in ZooKeeper versions 3.4.0 or later).
This describes how to set up HBase to mutually authenticate with a ZooKeeper Quorum.
ZooKeeper/HBase mutual authentication (HBASE-2418) is required as part of a complete secure
HBase configuration (HBASE-3025). For simplicity of explication, this section ignores additional
configuration required (Secure HDFS and Coprocessor configuration). It’s recommended to begin
with an HBase-managed ZooKeeper configuration (as opposed to a standalone ZooKeeper quorum)
for ease of learning.
197.1. Operating System Prerequisites
You need to have a working Kerberos KDC setup. For each $HOST that will run a ZooKeeper server,
you should have a principle zookeeper/$HOST. For each such host, add a service key (using the kadmin
or kadmin.local tool’s ktadd command) for zookeeper/$HOST and copy this file to $HOST, and make it
readable only to the user that will run zookeeper on $HOST. Note the location of this file, which we
will use below as $PATH_TO_ZOOKEEPER_KEYTAB.
Similarly, for each $HOST that will run an HBase server (master or regionserver), you should have a
principle: hbase/$HOST. For each host, add a keytab file called hbase.keytab containing a service key
for hbase/$HOST, copy this file to $HOST, and make it readable only to the user that will run an HBase
service on $HOST. Note the location of this file, which we will use below as
$PATH_TO_HBASE_KEYTAB.
Each user who will be an HBase client should also be given a Kerberos principal. This principal
should usually have a password assigned to it (as opposed to, as with the HBase servers, a keytab
file) which only this user knows. The client’s principal’s maxrenewlife should be set so that it can be
renewed enough so that the user can complete their HBase client processes. For example, if a user
runs a long-running HBase client process that takes at most 3 days, we might create this user’s
principal within kadmin with: addprinc -maxrenewlife 3days. The ZooKeeper client and server
libraries manage their own ticket refreshment by running threads that wake up periodically to do
the refreshment.
On each host that will run an HBase client (e.g. hbase shell), add the following file to the HBase
home directory’s conf directory:
Client {
Ê com.sun.security.auth.module.Krb5LoginModule required
Ê useKeyTab=false
Ê useTicketCache=true;
};
We’ll refer to this JAAS configuration file as $CLIENT_CONF below.
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197.2. HBase-managed ZooKeeper Configuration
On each node that will run a zookeeper, a master, or a regionserver, create a JAAS configuration
file in the conf directory of the node’s HBASE_HOME directory that looks like the following:
Server {
Ê com.sun.security.auth.module.Krb5LoginModule required
Ê useKeyTab=true
Ê keyTab="$PATH_TO_ZOOKEEPER_KEYTAB"
Ê storeKey=true
Ê useTicketCache=false
Ê principal="zookeeper/$HOST";
};
Client {
Ê com.sun.security.auth.module.Krb5LoginModule required
Ê useKeyTab=true
Ê useTicketCache=false
Ê keyTab="$PATH_TO_HBASE_KEYTAB"
Ê principal="hbase/$HOST";
};
where the $PATH_TO_HBASE_KEYTAB and $PATH_TO_ZOOKEEPER_KEYTAB files are what you
created above, and $HOST is the hostname for that node.
The Server section will be used by the ZooKeeper quorum server, while the Client section will be
used by the HBase master and regionservers. The path to this file should be substituted for the text
$HBASE_SERVER_CONF in the hbase-env.sh listing below.
The path to this file should be substituted for the text $CLIENT_CONF in the hbase-env.sh listing
below.
Modify your hbase-env.sh to include the following:
export HBASE_OPTS="-Djava.security.auth.login.config=$CLIENT_CONF"
export HBASE_MANAGES_ZK=true
export HBASE_ZOOKEEPER_OPTS="-Djava.security.auth.login.config=$HBASE_SERVER_CONF"
export HBASE_MASTER_OPTS="-Djava.security.auth.login.config=$HBASE_SERVER_CONF"
export HBASE_REGIONSERVER_OPTS="-Djava.security.auth.login.config=$HBASE_SERVER_CONF"
where $HBASE_SERVER_CONF and $CLIENT_CONF are the full paths to the JAAS configuration files
created above.
Modify your hbase-site.xml on each node that will run zookeeper, master or regionserver to
contain:
700
<configuration>
Ê <property>
Ê <name>hbase.zookeeper.quorum</name>
Ê <value>$ZK_NODES</value>
Ê </property>
Ê <property>
Ê <name>hbase.cluster.distributed</name>
Ê <value>true</value>
Ê </property>
Ê <property>
Ê <name>hbase.zookeeper.property.authProvider.1</name>
Ê <value>org.apache.zookeeper.server.auth.SASLAuthenticationProvider</value>
Ê </property>
Ê <property>
Ê <name>hbase.zookeeper.property.kerberos.removeHostFromPrincipal</name>
Ê <value>true</value>
Ê </property>
Ê <property>
Ê <name>hbase.zookeeper.property.kerberos.removeRealmFromPrincipal</name>
Ê <value>true</value>
Ê </property>
</configuration>
where $ZK_NODES is the comma-separated list of hostnames of the ZooKeeper Quorum hosts.
Start your hbase cluster by running one or more of the following set of commands on the
appropriate hosts:
bin/hbase zookeeper start
bin/hbase master start
bin/hbase regionserver start
197.3. External ZooKeeper Configuration
Add a JAAS configuration file that looks like:
Client {
Ê com.sun.security.auth.module.Krb5LoginModule required
Ê useKeyTab=true
Ê useTicketCache=false
Ê keyTab="$PATH_TO_HBASE_KEYTAB"
Ê principal="hbase/$HOST";
};
where the $PATH_TO_HBASE_KEYTAB is the keytab created above for HBase services to run on this
host, and $HOST is the hostname for that node. Put this in the HBase home’s configuration directory.
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We’ll refer to this file’s full pathname as $HBASE_SERVER_CONF below.
Modify your hbase-env.sh to include the following:
export HBASE_OPTS="-Djava.security.auth.login.config=$CLIENT_CONF"
export HBASE_MANAGES_ZK=false
export HBASE_MASTER_OPTS="-Djava.security.auth.login.config=$HBASE_SERVER_CONF"
export HBASE_REGIONSERVER_OPTS="-Djava.security.auth.login.config=$HBASE_SERVER_CONF"
Modify your hbase-site.xml on each node that will run a master or regionserver to contain:
<configuration>
Ê <property>
Ê <name>hbase.zookeeper.quorum</name>
Ê <value>$ZK_NODES</value>
Ê </property>
Ê <property>
Ê <name>hbase.cluster.distributed</name>
Ê <value>true</value>
Ê </property>
Ê <property>
Ê <name>hbase.zookeeper.property.authProvider.1</name>
Ê <value>org.apache.zookeeper.server.auth.SASLAuthenticationProvider</value>
Ê </property>
Ê <property>
Ê <name>hbase.zookeeper.property.kerberos.removeHostFromPrincipal</name>
Ê <value>true</value>
Ê </property>
Ê <property>
Ê <name>hbase.zookeeper.property.kerberos.removeRealmFromPrincipal</name>
Ê <value>true</value>
Ê </property>
</configuration>
where $ZK_NODES is the comma-separated list of hostnames of the ZooKeeper Quorum hosts.
Also on each of these hosts, create a JAAS configuration file containing:
Server {
Ê com.sun.security.auth.module.Krb5LoginModule required
Ê useKeyTab=true
Ê keyTab="$PATH_TO_ZOOKEEPER_KEYTAB"
Ê storeKey=true
Ê useTicketCache=false
Ê principal="zookeeper/$HOST";
};
where $HOST is the hostname of each Quorum host. We will refer to the full pathname of this file as
702
$ZK_SERVER_CONF below.
Start your ZooKeepers on each ZooKeeper Quorum host with:
SERVER_JVMFLAGS="-Djava.security.auth.login.config=$ZK_SERVER_CONF" bin/zkServer start
Start your HBase cluster by running one or more of the following set of commands on the
appropriate nodes:
bin/hbase master start
bin/hbase regionserver start
197.4. ZooKeeper Server Authentication Log Output
If the configuration above is successful, you should see something similar to the following in your
ZooKeeper server logs:
11/12/05 22:43:39 INFO zookeeper.Login: successfully logged in.
11/12/05 22:43:39 INFO server.NIOServerCnxnFactory: binding to port
0.0.0.0/0.0.0.0:2181
11/12/05 22:43:39 INFO zookeeper.Login: TGT refresh thread started.
11/12/05 22:43:39 INFO zookeeper.Login: TGT valid starting at: Mon Dec 05
22:43:39 UTC 2011
11/12/05 22:43:39 INFO zookeeper.Login: TGT expires: Tue Dec 06
22:43:39 UTC 2011
11/12/05 22:43:39 INFO zookeeper.Login: TGT refresh sleeping until: Tue Dec 06
18:36:42 UTC 2011
..
11/12/05 22:43:59 INFO auth.SaslServerCallbackHandler:
Ê Successfully authenticated client: authenticationID=hbase/ip-10-166-175-249.us-west-
1.compute.internal@HADOOP.LOCALDOMAIN;
Ê authorizationID=hbase/ip-10-166-175-249.us-west-
1.compute.internal@HADOOP.LOCALDOMAIN.
11/12/05 22:43:59 INFO auth.SaslServerCallbackHandler: Setting authorizedID: hbase
11/12/05 22:43:59 INFO server.ZooKeeperServer: adding SASL authorization for
authorizationID: hbase
197.5. ZooKeeper Client Authentication Log Output
On the ZooKeeper client side (HBase master or regionserver), you should see something similar to
the following:
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11/12/05 22:43:59 INFO zookeeper.ZooKeeper: Initiating client connection,
connectString=ip-10-166-175-249.us-west-1.compute.internal:2181 sessionTimeout=180000
watcher=master:60000
11/12/05 22:43:59 INFO zookeeper.ClientCnxn: Opening socket connection to server
/10.166.175.249:2181
11/12/05 22:43:59 INFO zookeeper.RecoverableZooKeeper: The identifier of this process
is 14851@ip-10-166-175-249
11/12/05 22:43:59 INFO zookeeper.Login: successfully logged in.
11/12/05 22:43:59 INFO client.ZooKeeperSaslClient: Client will use GSSAPI as SASL
mechanism.
11/12/05 22:43:59 INFO zookeeper.Login: TGT refresh thread started.
11/12/05 22:43:59 INFO zookeeper.ClientCnxn: Socket connection established to ip-10-
166-175-249.us-west-1.compute.internal/10.166.175.249:2181, initiating session
11/12/05 22:43:59 INFO zookeeper.Login: TGT valid starting at: Mon Dec 05
22:43:59 UTC 2011
11/12/05 22:43:59 INFO zookeeper.Login: TGT expires: Tue Dec 06
22:43:59 UTC 2011
11/12/05 22:43:59 INFO zookeeper.Login: TGT refresh sleeping until: Tue Dec 06
18:30:37 UTC 2011
11/12/05 22:43:59 INFO zookeeper.ClientCnxn: Session establishment complete on server
ip-10-166-175-249.us-west-1.compute.internal/10.166.175.249:2181, sessionid =
0x134106594320000, negotiated timeout = 180000
197.6. Configuration from Scratch
This has been tested on the current standard Amazon Linux AMI. First setup KDC and principals as
described above. Next checkout code and run a sanity check.
git clone https://gitbox.apache.org/repos/asf/hbase.git
cd hbase
mvn clean test -Dtest=TestZooKeeperACL
Then configure HBase as described above. Manually edit target/cached_classpath.txt (see below):
bin/hbase zookeeper &
bin/hbase master &
bin/hbase regionserver &
197.7. Future improvements
197.7.1. Fix target/cached_classpath.txt
You must override the standard hadoop-core jar file from the target/cached_classpath.txt file with
the version containing the HADOOP-7070 fix. You can use the following script to do this:
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echo `find ~/.m2 -name "*hadoop-core*7070*SNAPSHOT.jar"` ':' `cat
target/cached_classpath.txt` | sed 's/ //g' > target/tmp.txt
mv target/tmp.txt target/cached_classpath.txt
197.7.2. Set JAAS configuration programmatically
This would avoid the need for a separate Hadoop jar that fixes HADOOP-7070.
197.7.3. Elimination of kerberos.removeHostFromPrincipal
and`kerberos.removeRealmFromPrincipal`
705
Community
706
Chapter 198. Decisions
Feature Branches
Feature Branches are easy to make. You do not have to be a committer to make one. Just request the
name of your branch be added to JIRA up on the developer’s mailing list and a committer will add it
for you. Thereafter you can file issues against your feature branch in Apache HBase JIRA. Your code
you keep elsewhere — it should be public so it can be observed — and you can update dev mailing
list on progress. When the feature is ready for commit, 3 +1s from committers will get your feature
merged. See HBase, mail # dev - Thoughts about large feature dev branches
How to set fix version in JIRA on issue resolve
Here is how we agreed to set versions in JIRA when we resolve an issue. If master is going to be
2.0.0, and branch-1 1.4.0 then:
•Commit only to master: Mark with 2.0.0
•Commit to branch-1 and master: Mark with 2.0.0, and 1.4.0
•Commit to branch-1.3, branch-1, and master: Mark with 2.0.0, 1.4.0, and 1.3.x
•Commit site fixes: no version
Policy on when to set a RESOLVED JIRA as CLOSED
We agreed that for issues that list multiple releases in their Fix Version/s field, CLOSE the issue on
the release of any of the versions listed; subsequent change to the issue must happen in a new JIRA.
Only transient state in ZooKeeper!
You should be able to kill the data in zookeeper and hbase should ride over it recreating the zk
content as it goes. This is an old adage around these parts. We just made note of it now. We also are
currently in violation of this basic tenet — replication at least keeps permanent state in zk — but we
are working to undo this breaking of a golden rule.
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Chapter 199. Community Roles
199.1. Release Managers
Each maintained release branch has a release manager, who volunteers to coordinate new features
and bug fixes are backported to that release. The release managers are committers. If you would
like your feature or bug fix to be included in a given release, communicate with that release
manager. If this list goes out of date or you can’t reach the listed person, reach out to someone else
on the list.
End-of-life releases are not included in this list.
Table 20. Release Managers
Release Release Manager
1.2 Sean Busbey
1.3 Mikhail Antonov
1.4 Andrew Purtell
2.0 Michael Stack
2.1 Duo Zhang
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Chapter 200. Commit Message format
We agreed to the following Git commit message format:
HBASE-xxxxx <title>. (<contributor>)
If the person making the commit is the contributor, leave off the '(<contributor>)' element.
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Appendix
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Appendix A: Contributing to Documentation
The Apache HBase project welcomes contributions to all aspects of the project, including the
documentation.
In HBase, documentation includes the following areas, and probably some others:
•The HBase Reference Guide (this book)
•The HBase website
•API documentation
•Command-line utility output and help text
•Web UI strings, explicit help text, context-sensitive strings, and others
•Log messages
•Comments in source files, configuration files, and others
•Localization of any of the above into target languages other than English
No matter which area you want to help out with, the first step is almost always to download
(typically by cloning the Git repository) and familiarize yourself with the HBase source code. For
information on downloading and building the source, see developer.
A.1. Contributing to Documentation or Other Strings
If you spot an error in a string in a UI, utility, script, log message, or elsewhere, or you think
something could be made more clear, or you think text needs to be added where it doesn’t
currently exist, the first step is to file a JIRA. Be sure to set the component to Documentation in
addition to any other involved components. Most components have one or more default owners,
who monitor new issues which come into those queues. Regardless of whether you feel able to fix
the bug, you should still file bugs where you see them.
If you want to try your hand at fixing your newly-filed bug, assign it to yourself. You will need to
clone the HBase Git repository to your local system and work on the issue there. When you have
developed a potential fix, submit it for review. If it addresses the issue and is seen as an
improvement, one of the HBase committers will commit it to one or more branches, as appropriate.
Procedure: Suggested Work flow for Submitting Patches
This procedure goes into more detail than Git pros will need, but is included in this appendix so
that people unfamiliar with Git can feel confident contributing to HBase while they learn.
1. If you have not already done so, clone the Git repository locally. You only need to do this once.
2. Fairly often, pull remote changes into your local repository by using the git pull command,
while your tracking branch is checked out.
3. For each issue you work on, create a new branch. One convention that works well for naming
the branches is to name a given branch the same as the JIRA it relates to:
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$ git checkout -b HBASE-123456
4. Make your suggested changes on your branch, committing your changes to your local repository
often. If you need to switch to working on a different issue, remember to check out the
appropriate branch.
5. When you are ready to submit your patch, first be sure that HBase builds cleanly and behaves
as expected in your modified branch.
6. If you have made documentation changes, be sure the documentation and website builds by
running mvn clean site.
7. If it takes you several days or weeks to implement your fix, or you know that the area of the
code you are working in has had a lot of changes lately, make sure you rebase your branch
against the remote master and take care of any conflicts before submitting your patch.
$ git checkout HBASE-123456
$ git rebase origin/master
8. Generate your patch against the remote master. Run the following command from the top level
of your git repository (usually called hbase):
$ git format-patch --stdout origin/master > HBASE-123456.patch
The name of the patch should contain the JIRA ID.
9. Look over the patch file to be sure that you did not change any additional files by accident and
that there are no other surprises.
10. When you are satisfied, attach the patch to the JIRA and click the [ Patch Available ] button. A
reviewer will review your patch.
11. If you need to submit a new version of the patch, leave the old one on the JIRA and add a
version number to the name of the new patch.
12. After a change has been committed, there is no need to keep your local branch around.
A.2. Editing the HBase Website
The source for the HBase website is in the HBase source, in the src/site/ directory. Within this
directory, source for the individual pages is in the xdocs/ directory, and images referenced in those
pages are in the resources/images/ directory. This directory also stores images used in the HBase
Reference Guide.
The website’s pages are written in an HTML-like XML dialect called xdoc, which has a reference
guide at https://maven.apache.org/archives/maven-1.x/plugins/xdoc/reference/xdocs.html. You can
edit these files in a plain-text editor, an IDE, or an XML editor such as XML Mind XML Editor (XXE)
or Oxygen XML Author.
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To preview your changes, build the website using the mvn clean site -DskipTests command. The
HTML output resides in the target/site/ directory. When you are satisfied with your changes, follow
the procedure in submit doc patch procedure to submit your patch.
A.3. Publishing the HBase Website and Documentation
HBase uses the ASF’s gitpubsub mechanism. A Jenkins job runs the dev-support/jenkins-
scripts/generate-hbase-website.sh script, which runs the mvn clean site site:stage against the
master branch of the hbase repository and commits the built artifacts to the asf-site branch of the
hbase-site repository. When the commit is pushed, the website is redeployed automatically. If the
script encounters an error, an email is sent to the developer mailing list. You can run the script
manually or examine it to see the steps involved.
A.4. Checking the HBase Website for Broken Links
A Jenkins job runs periodically to check HBase website for broken links, using the dev-
support/jenkins-scripts/check-website-links.sh script. This script uses a tool called linklint to
check for bad links and create a report. If broken links are found, an email is sent to the developer
mailing list. You can run the script manually or examine it to see the steps involved.
A.5. HBase Reference Guide Style Guide and Cheat
Sheet
The HBase Reference Guide is written in Asciidoc and built using AsciiDoctor. The following cheat
sheet is included for your reference. More nuanced and comprehensive documentation is available
at http://asciidoctor.org/docs/user-manual/.
Table 21. AsciiDoc Cheat Sheet
Element Type Desired Rendering How to do it
A paragraph a paragraph Just type some text with a blank
line at the top and bottom.
Add line breaks within a
paragraph without adding
blank lines
Manual line breaks This will break + at the plus
sign. Or prefix the whole
paragraph with a line
containing '[%hardbreaks]'
Give a title to anything Colored italic bold differently-
sized text
In-Line Code or commands monospace `text`
In-line literal content (things to
be typed exactly as shown)
bold mono *`typethis`*
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Element Type Desired Rendering How to do it
In-line replaceable content
(things to substitute with your
own values)
bold italic mono *_typesomething_*
Code blocks with highlighting monospace, highlighted,
preserve space [source,java]
----
Ê myAwesomeCode() {
}
----
Code block included from a
separate file
included just as though it were
part of the main file [source,ruby]
----
include\::path/to/app.rb[
]
----
Include only part of a separate
file
Similar to Javadoc See http://asciidoctor.org/docs/
user-manual/#by-tagged-
regions
Filenames, directory names,
new terms
italic _hbase-default.xml_
External naked URLs A link with the URL as link text
link:http://www.google.co
m
External URLs with text A link with arbitrary link text
link:http://www.google.co
m[Google]
Create an internal anchor to
cross-reference
not rendered
[[anchor_name]]
Cross-reference an existing
anchor using its default title
an internal hyperlink using the
element title if available,
otherwise using the anchor
name
<<anchor_name>>
Cross-reference an existing
anchor using custom text
an internal hyperlink using
arbitrary text <<anchor_name,Anchor
Text>>
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Element Type Desired Rendering How to do it
A block image The image with alt text
image::sunset.jpg[Alt
Text]
(put the image in the
src/site/resources/images
directory)
An inline image The image with alt text, as part
of the text flow image:sunset.jpg [Alt
Text]
(only one colon)
Link to a remote image show an image hosted
elsewhere image::http://inkscape.or
g/doc/examples/tux.svg[Tu
x,250,350]
(or image:)
Add dimensions or a URL to the
image
depends inside the brackets after the alt
text, specify width, height
and/or
link="http://my_link.com"
A footnote subscript link which takes you
to the footnote Some text.footnote:[The
footnote text.]
A note or warning with no title The admonition image followed
by the admonition NOTE:My note here
WARNING:My warning here
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Element Type Desired Rendering How to do it
A complex note The note has a title and/or
multiple paragraphs and/or
code blocks or lists, etc
.The Title
[NOTE]
====
Here is the note text.
Everything until the
second set of four equals
signs is part of the
note.
----
some source code
----
====
Bullet lists bullet lists
* list item 1
(see http://asciidoctor.org/docs/
user-manual/#unordered-lists)
Numbered lists numbered list
. list item 2
(see http://asciidoctor.org/docs/
user-manual/#ordered-lists)
Checklists Checked or unchecked boxes Checked:
- [*]
Unchecked:
- [ ]
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Element Type Desired Rendering How to do it
Multiple levels of lists bulleted or numbered or combo
. Numbered (1), at top
level
* Bullet (2), nested
under 1
* Bullet (3), nested
under 1
. Numbered (4), at top
level
* Bullet (5), nested
under 4
** Bullet (6), nested
under 5
- [x] Checked (7), at top
level
Labelled lists / variablelists a list item title or summary
followed by content Title:: content
Title::
Ê content
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Element Type Desired Rendering How to do it
Sidebars, quotes, or other
blocks of text
a block of text, formatted
differently from the default
Delimited using different
delimiters, see
http://asciidoctor.org/docs/user-
manual/#built-in-blocks-
summary. Some of the
examples above use delimiters
like ...., ----,====.
[example]
====
This is an example block.
====
[source]
----
This is a source block.
----
[note]
====
This is a note block.
====
[quote]
____
This is a quote block.
____
If you want to insert literal
Asciidoc content that keeps
being interpreted, when in
doubt, use eight dots as the
delimiter at the top and bottom.
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Element Type Desired Rendering How to do it
Nested Sections chapter, section, sub-section, etc
= Book (or chapter if the
chapter can be built
alone, see the
leveloffset info below)
== Chapter (or section if
the chapter is
standalone)
=== Section (or
subsection, etc)
==== Subsection
and so on up to 6 levels (think
carefully about going deeper
than 4 levels, maybe you can
just titled paragraphs or lists
instead). Note that you can
include a book inside another
book by adding the
:leveloffset:+1 macro
directive directly before your
include, and resetting it to 0
directly after. See the book.adoc
source for examples, as this is
how this guide handles
chapters. Don’t do it for
prefaces, glossaries,
appendixes, or other special
types of chapters.
Include one file from another Content is included as though it
were inline include::[/path/to/file.a
doc]
For plenty of examples. see
book.adoc.
A table a table See http://asciidoctor.org/docs/
user-manual/#tables. Generally
rows are separated by newlines
and columns by pipes
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Element Type Desired Rendering How to do it
Comment out a single line A line is skipped during
rendering
// This line won’t show up
Comment out a block A section of the file is skipped
during rendering ////
Nothing between the
slashes will show up.
////
Highlight text for review text shows up with yellow
background Test between #hash marks#
is highlighted yellow.
A.6. Auto-Generated Content
Some parts of the HBase Reference Guide, most notably config.files, are generated automatically, so
that this area of the documentation stays in sync with the code. This is done by means of an XSLT
transform, which you can examine in the source at
src/main/xslt/configuration_to_asciidoc_chapter.xsl. This transforms the hbase-
common/src/main/resources/hbase-default.xml file into an Asciidoc output which can be included in
the Reference Guide.
Sometimes, it is necessary to add configuration parameters or modify their descriptions. Make the
modifications to the source file, and they will be included in the Reference Guide when it is rebuilt.
It is possible that other types of content can and will be automatically generated from HBase source
files in the future.
A.7. Images in the HBase Reference Guide
You can include images in the HBase Reference Guide. It is important to include an image title if
possible, and alternate text always. This allows screen readers to navigate to the image and also
provides alternative text for the image. The following is an example of an image with a title and
alternate text. Notice the double colon.
.My Image Title
image::sunset.jpg[Alt Text]
Here is an example of an inline image with alternate text. Notice the single colon. Inline images
cannot have titles. They are generally small images like GUI buttons.
image:sunset.jpg[Alt Text]
When doing a local build, save the image to the src/site/resources/images/ directory. When you link
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to the image, do not include the directory portion of the path. The image will be copied to the
appropriate target location during the build of the output.
When you submit a patch which includes adding an image to the HBase Reference Guide, attach the
image to the JIRA. If the committer asks where the image should be committed, it should go into the
above directory.
A.8. Adding a New Chapter to the HBase Reference
Guide
If you want to add a new chapter to the HBase Reference Guide, the easiest way is to copy an
existing chapter file, rename it, and change the ID (in double brackets) and title. Chapters are
located in the src/main/asciidoc/_chapters/ directory.
Delete the existing content and create the new content. Then open the src/main/asciidoc/book.adoc
file, which is the main file for the HBase Reference Guide, and copy an existing include element to
include your new chapter in the appropriate location. Be sure to add your new file to your Git
repository before creating your patch.
When in doubt, check to see how other files have been included.
A.9. Common Documentation Issues
The following documentation issues come up often. Some of these are preferences, but others can
create mysterious build errors or other problems.
Isolate Changes for Easy Diff Review.
Be careful with pretty-printing or re-formatting an entire XML file, even if the formatting has
degraded over time. If you need to reformat a file, do that in a separate JIRA where you do not
change any content. Be careful because some XML editors do a bulk-reformat when you open a
new file, especially if you use GUI mode in the editor.
Syntax Highlighting
The HBase Reference Guide uses coderay for syntax highlighting. To enable syntax highlighting
for a given code listing, use the following type of syntax:
[source,xml]
----
<name>My Name</name>
----
Several syntax types are supported. The most interesting ones for the HBase Reference Guide are
java, xml, sql, and bash.
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Appendix B: FAQ
B.1. General
When should I use HBase?
See Overview in the Architecture chapter.
Are there other HBase FAQs?
See the FAQ that is up on the wiki, HBase Wiki FAQ.
Does HBase support SQL?
Not really. SQL-ish support for HBase via Hive is in development, however Hive is based on
MapReduce which is not generally suitable for low-latency requests. See the Data Model section
for examples on the HBase client.
How can I find examples of NoSQL/HBase?
See the link to the BigTable paper in Other Information About HBase, as well as the other papers.
What is the history of HBase?
See hbase.history.
Why are the cells above 10MB not recommended for HBase?
Large cells don’t fit well into HBase’s approach to buffering data. First, the large cells bypass the
MemStoreLAB when they are written. Then, they cannot be cached in the L2 block cache during
read operations. Instead, HBase has to allocate on-heap memory for them each time. This can
have a significant impact on the garbage collector within the RegionServer process.
B.2. Upgrading
How do I upgrade Maven-managed projects from HBase 0.94 to HBase 0.96+?
In HBase 0.96, the project moved to a modular structure. Adjust your project’s dependencies to
rely upon the hbase-client module or another module as appropriate, rather than a single JAR.
You can model your Maven dependency after one of the following, depending on your targeted
version of HBase. See Section 3.5, “Upgrading from 0.94.x to 0.96.x” or Section 3.3, “Upgrading
from 0.96.x to 0.98.x” for more information.
Maven Dependency for HBase 0.98
<dependency>
Ê <groupId>org.apache.hbase</groupId>
Ê <artifactId>hbase-client</artifactId>
Ê <version>0.98.5-hadoop2</version>
</dependency>
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Maven Dependency for HBase 0.96
<dependency>
Ê <groupId>org.apache.hbase</groupId>
Ê <artifactId>hbase-client</artifactId>
Ê <version>0.96.2-hadoop2</version>
</dependency>
Maven Dependency for HBase 0.94
<dependency>
Ê <groupId>org.apache.hbase</groupId>
Ê <artifactId>hbase</artifactId>
Ê <version>0.94.3</version>
</dependency>
B.3. Architecture
How does HBase handle Region-RegionServer assignment and locality?
See Regions.
B.4. Configuration
How can I get started with my first cluster?
See Quick Start - Standalone HBase.
Where can I learn about the rest of the configuration options?
See Apache HBase Configuration.
B.5. Schema Design / Data Access
How should I design my schema in HBase?
See Data Model and HBase and Schema Design.
How can I store (fill in the blank) in HBase?
See Supported Datatypes.
How can I handle secondary indexes in HBase?
See Secondary Indexes and Alternate Query Paths.
Can I change a table’s rowkeys?
This is a very common question. You can’t. See Immutability of Rowkeys.
What APIs does HBase support?
See Data Model, Client, and Apache HBase External APIs.
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B.6. MapReduce
How can I use MapReduce with HBase?
See HBase and MapReduce.
B.7. Performance and Troubleshooting
How can I improve HBase cluster performance?
See Apache HBase Performance Tuning.
How can I troubleshoot my HBase cluster?
See Troubleshooting and Debugging Apache HBase.
B.8. Amazon EC2
I am running HBase on Amazon EC2 and…
EC2 issues are a special case. See Amazon EC2 and Amazon EC2.
B.9. Operations
How do I manage my HBase cluster?
See Apache HBase Operational Management.
How do I back up my HBase cluster?
See HBase Backup.
B.10. HBase in Action
Where can I find interesting videos and presentations on HBase?
See Other Information About HBase.
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Appendix C: Access Control Matrix
The following matrix shows the permission set required to perform operations in HBase. Before
using the table, read through the information about how to interpret it.
Interpreting the ACL Matrix Table
The following conventions are used in the ACL Matrix table:
C.1. Scopes
Permissions are evaluated starting at the widest scope and working to the narrowest scope.
A scope corresponds to a level of the data model. From broadest to narrowest, the scopes are as
follows:
Scopes
•Global
•Namespace (NS)
•Table
•Column Family (CF)
•Column Qualifier (CQ)
•Cell
For instance, a permission granted at table level dominates any grants done at the Column Family,
Column Qualifier, or cell level. The user can do what that grant implies at any location in the table.
A permission granted at global scope dominates all: the user is always allowed to take that action
everywhere.
C.2. Permissions
Possible permissions include the following:
Permissions
•Superuser - a special user that belongs to group "supergroup" and has unlimited access
•Admin (A)
•Create (C)
•Write (W)
•Read (R)
•Execute (X)
For the most part, permissions work in an expected way, with the following caveats:
Having Write permission does not imply Read permission.
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It is possible and sometimes desirable for a user to be able to write data that same user cannot
read. One such example is a log-writing process.
The hbase:meta table is readable by every user, regardless of the user’s other grants or
restrictions.
This is a requirement for HBase to function correctly.
CheckAndPut and CheckAndDelete operations will fail if the user does not have both Write and
Read permission.
Increment and Append operations do not require Read access.
The superuser, as the name suggests has permissions to perform all possible operations.
And for the operations marked with *, the checks are done in post hook and only subset of
results satisfying access checks are returned back to the user.
The following table is sorted by the interface that provides each operation. In case the table goes
out of date, the unit tests which check for accuracy of permissions can be found in hbase-
server/src/test/java/org/apache/hadoop/hbase/security/access/TestAccessController.java, and the
access controls themselves can be examined in hbase-
server/src/main/java/org/apache/hadoop/hbase/security/access/AccessController.java.
Table 22. ACL Matrix
Interface Operation Permissions
Master createTable superuser|global(C)|NS(C)
modifyTable superuser|global(A)|global(C)|
NS(A)|NS(C)|TableOwner|table
(A)|table(C)
deleteTable superuser|global(A)|global(C)|
NS(A)|NS(C)|TableOwner|table
(A)|table(C)
truncateTable superuser|global(A)|global(C)|
NS(A)|NS(C)|TableOwner|table
(A)|table(C)
addColumn superuser|global(A)|global(C)|
NS(A)|NS(C)|TableOwner|table
(A)|table(C)
modifyColumn superuser|global(A)|global(C)|
NS(A)|NS(C)|TableOwner|table
(A)|table(C)|column(A)|column
(C)
deleteColumn superuser|global(A)|global(C)|
NS(A)|NS(C)|TableOwner|table
(A)|table(C)|column(A)|column
(C)
enableTable superuser|global(A)|global(C)|
NS(A)|NS(C)|TableOwner|table
(A)|table(C)
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Interface Operation Permissions
disableTable superuser|global(A)|global(C)|
NS(A)|NS(C)|TableOwner|table
(A)|table(C)
disableAclTable Not allowed
move superuser|global(A)|NS(A)|Tab
leOwner|table(A)
assign superuser|global(A)|NS(A)|Tab
leOwner|table(A)
unassign superuser|global(A)|NS(A)|Tab
leOwner|table(A)
regionOffline superuser|global(A)|NS(A)|Tab
leOwner|table(A)
balance superuser|global(A)
balanceSwitch superuser|global(A)
shutdown superuser|global(A)
stopMaster superuser|global(A)
snapshot superuser|global(A)|NS(A)|Tab
leOwner|table(A)
listSnapshot superuser|global(A)|SnapshotO
wner
cloneSnapshot superuser|global(A)|(Snapshot
Owner & TableName matches)
restoreSnapshot superuser|global(A)|SnapshotO
wner &
(NS(A)|TableOwner|table(A))
deleteSnapshot superuser|global(A)|SnapshotO
wner
createNamespace superuser|global(A)
deleteNamespace superuser|global(A)
modifyNamespace superuser|global(A)
getNamespaceDescriptor superuser|global(A)|NS(A)
listNamespaceDescriptors* superuser|global(A)|NS(A)
flushTable superuser|global(A)|global(C)|
NS(A)|NS(C)|TableOwner|table
(A)|table(C)
getTableDescriptors* superuser|global(A)|global(C)|
NS(A)|NS(C)|TableOwner|table
(A)|table(C)
getTableNames* superuser|TableOwner|Any
global or table perm
setUserQuota(global level) superuser|global(A)
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Interface Operation Permissions
setUserQuota(namespace level) superuser|global(A)
setUserQuota(Table level) superuser|global(A)|NS(A)|Tab
leOwner|table(A)
setTableQuota superuser|global(A)|NS(A)|Tab
leOwner|table(A)
setNamespaceQuota superuser|global(A)
addReplicationPeer superuser|global(A)
removeReplicationPeer superuser|global(A)
enableReplicationPeer superuser|global(A)
disableReplicationPeer superuser|global(A)
getReplicationPeerConfig superuser|global(A)
updateReplicationPeerConfig superuser|global(A)
listReplicationPeers superuser|global(A)
getClusterStatus any user
Region openRegion superuser|global(A)
closeRegion superuser|global(A)
flush superuser|global(A)|global(C)|
TableOwner|table(A)|table(C)
split superuser|global(A)|TableOwn
er|TableOwner|table(A)
compact superuser|global(A)|global(C)|
TableOwner|table(A)|table(C)
getClosestRowBefore superuser|global(R)|NS(R)|Tab
leOwner|table(R)|CF(R)|CQ(R)
getOp superuser|global(R)|NS(R)|Tab
leOwner|table(R)|CF(R)|CQ(R)
exists superuser|global(R)|NS(R)|Tab
leOwner|table(R)|CF(R)|CQ(R)
put superuser|global(W)|NS(W)|ta
ble(W)|TableOwner|CF(W)|CQ
(W)
delete superuser|global(W)|NS(W)|ta
ble(W)|TableOwner|CF(W)|CQ
(W)
batchMutate superuser|global(W)|NS(W)|Ta
bleOwner|table(W)|CF(W)|CQ(
W)
checkAndPut superuser|global(RW)|NS(RW)
|TableOwner|table(RW)|CF(R
W)|CQ(RW)
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Interface Operation Permissions
checkAndPutAfterRowLock superuser|global(R)|NS(R)|Tab
leOwner|Table(R)|CF(R)|CQ(R)
checkAndDelete superuser|global(RW)|NS(RW)
|TableOwner|table(RW)|CF(R
W)|CQ(RW)
checkAndDeleteAfterRowLock superuser|global(R)|NS(R)|Tab
leOwner|table(R)|CF(R)|CQ(R)
incrementColumnValue superuser|global(W)|NS(W)|Ta
bleOwner|table(W)|CF(W)|CQ(
W)
append superuser|global(W)|NS(W)|Ta
bleOwner|table(W)|CF(W)|CQ(
W)
appendAfterRowLock superuser|global(W)|NS(W)|Ta
bleOwner|table(W)|CF(W)|CQ(
W)
increment superuser|global(W)|NS(W)|Ta
bleOwner|table(W)|CF(W)|CQ(
W)
incrementAfterRowLock superuser|global(W)|NS(W)|Ta
bleOwner|table(W)|CF(W)|CQ(
W)
scannerOpen superuser|global(R)|NS(R)|Tab
leOwner|table(R)|CF(R)|CQ(R)
scannerNext superuser|global(R)|NS(R)|Tab
leOwner|table(R)|CF(R)|CQ(R)
scannerClose superuser|global(R)|NS(R)|Tab
leOwner|table(R)|CF(R)|CQ(R)
bulkLoadHFile superuser|global(C)|TableOwn
er|table(C)|CF(C)
prepareBulkLoad superuser|global(C)|TableOwn
er|table(C)|CF(C)
cleanupBulkLoad superuser|global(C)|TableOwn
er|table(C)|CF(C)
Endpoint invoke superuser|global(X)|NS(X)|Tab
leOwner|table(X)
AccessController grant(global level) global(A)
grant(namespace level) global(A)|NS(A)
grant(table level) global(A)|NS(A)|TableOwner|t
able(A)|CF(A)|CQ(A)
revoke(global level) global(A)
revoke(namespace level) global(A)|NS(A)
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Interface Operation Permissions
revoke(table level) global(A)|NS(A)|TableOwner|t
able(A)|CF(A)|CQ(A)
getUserPermissions(global
level)
global(A)
getUserPermissions(namespace
level)
global(A)|NS(A)
getUserPermissions(table level) global(A)|NS(A)|TableOwner|t
able(A)|CF(A)|CQ(A)
hasPermission(table level) global(A)|SelfUserCheck
RegionServer stopRegionServer superuser|global(A)
mergeRegions superuser|global(A)
rollWALWriterRequest superuser|global(A)
replicateLogEntries superuser|global(W)
RSGroup addRSGroup superuser|global(A)
balanceRSGroup superuser|global(A)
getRSGroupInfo superuser|global(A)
getRSGroupInfoOfTable superuser|global(A)
getRSGroupOfServer superuser|global(A)
listRSGroups superuser|global(A)
moveServers superuser|global(A)
moveServersAndTables superuser|global(A)
moveTables superuser|global(A)
removeRSGroup superuser|global(A)
removeServers superuser|global(A)
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Appendix D: Compression and Data Block
Encoding In HBase
Codecs mentioned in this section are for encoding and decoding data blocks or row
keys. For information about replication codecs, see
cluster.replication.preserving.tags.
Some of the information in this section is pulled from a discussion on the HBase Development
mailing list.
HBase supports several different compression algorithms which can be enabled on a
ColumnFamily. Data block encoding attempts to limit duplication of information in keys, taking
advantage of some of the fundamental designs and patterns of HBase, such as sorted row keys and
the schema of a given table. Compressors reduce the size of large, opaque byte arrays in cells, and
can significantly reduce the storage space needed to store uncompressed data.
Compressors and data block encoding can be used together on the same ColumnFamily.
Changes Take Effect Upon Compaction
If you change compression or encoding for a ColumnFamily, the changes take effect during
compaction.
Some codecs take advantage of capabilities built into Java, such as GZip compression. Others rely on
native libraries. Native libraries may be available as part of Hadoop, such as LZ4. In this case,
HBase only needs access to the appropriate shared library.
Other codecs, such as Google Snappy, need to be installed first. Some codecs are licensed in ways
that conflict with HBase’s license and cannot be shipped as part of HBase.
This section discusses common codecs that are used and tested with HBase. No matter what codec
you use, be sure to test that it is installed correctly and is available on all nodes in your cluster.
Extra operational steps may be necessary to be sure that codecs are available on newly-deployed
nodes. You can use the compression.test utility to check that a given codec is correctly installed.
To configure HBase to use a compressor, see compressor.install. To enable a compressor for a
ColumnFamily, see changing.compression. To enable data block encoding for a ColumnFamily, see
data.block.encoding.enable.
Block Compressors
•none
•Snappy
•LZO
•LZ4
•GZ
Data Block Encoding Types
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Prefix
Often, keys are very similar. Specifically, keys often share a common prefix and only differ near
the end. For instance, one key might be RowKey:Family:Qualifier0 and the next key might be
RowKey:Family:Qualifier1.
In Prefix encoding, an extra column is added which holds the length of the prefix shared
between the current key and the previous key. Assuming the first key here is totally different
from the key before, its prefix length is 0.
The second key’s prefix length is 23, since they have the first 23 characters in common.
Obviously if the keys tend to have nothing in common, Prefix will not provide much benefit.
The following image shows a hypothetical ColumnFamily with no data block encoding.
Figure 18. ColumnFamily with No Encoding
Here is the same data with prefix data encoding.
Figure 19. ColumnFamily with Prefix Encoding
Diff
Diff encoding expands upon Prefix encoding. Instead of considering the key sequentially as a
monolithic series of bytes, each key field is split so that each part of the key can be compressed
more efficiently.
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Two new fields are added: timestamp and type.
If the ColumnFamily is the same as the previous row, it is omitted from the current row.
If the key length, value length or type are the same as the previous row, the field is omitted.
In addition, for increased compression, the timestamp is stored as a Diff from the previous row’s
timestamp, rather than being stored in full. Given the two row keys in the Prefix example, and
given an exact match on timestamp and the same type, neither the value length, or type needs to
be stored for the second row, and the timestamp value for the second row is just 0, rather than a
full timestamp.
Diff encoding is disabled by default because writing and scanning are slower but more data is
cached.
This image shows the same ColumnFamily from the previous images, with Diff encoding.
Figure 20. ColumnFamily with Diff Encoding
Fast Diff
Fast Diff works similar to Diff, but uses a faster implementation. It also adds another field which
stores a single bit to track whether the data itself is the same as the previous row. If it is, the data
is not stored again.
Fast Diff is the recommended codec to use if you have long keys or many columns.
The data format is nearly identical to Diff encoding, so there is not an image to illustrate it.
Prefix Tree
Prefix tree encoding was introduced as an experimental feature in HBase 0.96. It provides
similar memory savings to the Prefix, Diff, and Fast Diff encoder, but provides faster random
access at a cost of slower encoding speed. It was removed in hbase-2.0.0. It was a good idea but
little uptake. If interested in reviving this effort, write the hbase dev list.
D.1. Which Compressor or Data Block Encoder To Use
The compression or codec type to use depends on the characteristics of your data. Choosing the
wrong type could cause your data to take more space rather than less, and can have performance
implications.
In general, you need to weigh your options between smaller size and faster
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compression/decompression. Following are some general guidelines, expanded from a discussion at
Documenting Guidance on compression and codecs.
•If you have long keys (compared to the values) or many columns, use a prefix encoder.
FAST_DIFF is recommended.
•If the values are large (and not precompressed, such as images), use a data block compressor.
•Use GZIP for cold data, which is accessed infrequently. GZIP compression uses more CPU
resources than Snappy or LZO, but provides a higher compression ratio.
•Use Snappy or LZO for hot data, which is accessed frequently. Snappy and LZO use fewer CPU
resources than GZIP, but do not provide as high of a compression ratio.
•In most cases, enabling Snappy or LZO by default is a good choice, because they have a low
performance overhead and provide space savings.
•Before Snappy became available by Google in 2011, LZO was the default. Snappy has similar
qualities as LZO but has been shown to perform better.
D.2. Making use of Hadoop Native Libraries in HBase
The Hadoop shared library has a bunch of facility including compression libraries and fast
crc’ing — hardware crc’ing if your chipset supports it. To make this facility available to HBase, do
the following. HBase/Hadoop will fall back to use alternatives if it cannot find the native library
versions — or fail outright if you asking for an explicit compressor and there is no alternative
available.
First make sure of your Hadoop. Fix this message if you are seeing it starting Hadoop processes:
16/02/09 22:40:24 WARN util.NativeCodeLoader: Unable to load native-hadoop library for
your platform... using builtin-java classes where applicable
It means is not properly pointing at its native libraries or the native libs were compiled for another
platform. Fix this first.
Then if you see the following in your HBase logs, you know that HBase was unable to locate the
Hadoop native libraries:
2014-08-07 09:26:20,139 WARN [main] util.NativeCodeLoader: Unable to load native-
hadoop library for your platform... using builtin-java classes where applicable
If the libraries loaded successfully, the WARN message does not show. Usually this means you are
good to go but read on.
Let’s presume your Hadoop shipped with a native library that suits the platform you are running
HBase on. To check if the Hadoop native library is available to HBase, run the following tool
(available in Hadoop 2.1 and greater):
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$ ./bin/hbase --config ~/conf_hbase org.apache.hadoop.util.NativeLibraryChecker
2014-08-26 13:15:38,717 WARN [main] util.NativeCodeLoader: Unable to load native-
hadoop library for your platform... using builtin-java classes where applicable
Native library checking:
hadoop: false
zlib: false
snappy: false
lz4: false
bzip2: false
2014-08-26 13:15:38,863 INFO [main] util.ExitUtil: Exiting with status 1
Above shows that the native hadoop library is not available in HBase context.
The above NativeLibraryChecker tool may come back saying all is hunky-dory — i.e. all libs show
'true', that they are available — but follow the below presecription anyways to ensure the native
libs are available in HBase context, when it goes to use them.
To fix the above, either copy the Hadoop native libraries local or symlink to them if the Hadoop and
HBase stalls are adjacent in the filesystem. You could also point at their location by setting the
LD_LIBRARY_PATH environment variable in your hbase-env.sh.
Where the JVM looks to find native libraries is "system dependent" (See
java.lang.System#loadLibrary(name)). On linux, by default, is going to look in lib/native/PLATFORM
where PLATFORM is the label for the platform your HBase is installed on. On a local linux machine, it
seems to be the concatenation of the java properties os.name and os.arch followed by whether 32 or
64 bit. HBase on startup prints out all of the java system properties so find the os.name and os.arch
in the log. For example:
...
2014-08-06 15:27:22,853 INFO [main] zookeeper.ZooKeeper: Client environment:os.name
=Linux
2014-08-06 15:27:22,853 INFO [main] zookeeper.ZooKeeper: Client environment:os.arch
=amd64
...
So in this case, the PLATFORM string is Linux-amd64-64. Copying the Hadoop native libraries or
symlinking at lib/native/Linux-amd64-64 will ensure they are found. Rolling restart after you have
made this change.
Here is an example of how you would set up the symlinks. Let the hadoop and hbase installs be in
your home directory. Assume your hadoop native libs are at ~/hadoop/lib/native. Assume you are
on a Linux-amd64-64 platform. In this case, you would do the following to link the hadoop native
lib so hbase could find them.
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...
$ mkdir -p ~/hbaseLinux-amd64-64 -> /home/stack/hadoop/lib/native/lib/native/
$ cd ~/hbase/lib/native/
$ ln -s ~/hadoop/lib/native Linux-amd64-64
$ ls -la
# Linux-amd64-64 -> /home/USER/hadoop/lib/native
...
If you see PureJavaCrc32C in a stack track or if you see something like the below in a perf trace,
then native is not working; you are using the java CRC functions rather than native:
Ê 5.02% perf-53601.map [.] Lorg/apache/hadoop/util/PureJavaCrc32C;.update
See HBASE-11927 Use Native Hadoop Library for HFile checksum (And flip default from CRC32 to
CRC32C), for more on native checksumming support. See in particular the release note for how to
check if your hardware to see if your processor has support for hardware CRCs. Or checkout the
Apache Checksums in HBase blog post.
Here is example of how to point at the Hadoop libs with LD_LIBRARY_PATH environment variable:
$ LD_LIBRARY_PATH=~/hadoop-2.5.0-SNAPSHOT/lib/native ./bin/hbase --config ~/conf_hbase
org.apache.hadoop.util.NativeLibraryChecker
2014-08-26 13:42:49,332 INFO [main] bzip2.Bzip2Factory: Successfully loaded &
initialized native-bzip2 library system-native
2014-08-26 13:42:49,337 INFO [main] zlib.ZlibFactory: Successfully loaded &
initialized native-zlib library
Native library checking:
hadoop: true /home/stack/hadoop-2.5.0-SNAPSHOT/lib/native/libhadoop.so.1.0.0
zlib: true /lib64/libz.so.1
snappy: true /usr/lib64/libsnappy.so.1
lz4: true revision:99
bzip2: true /lib64/libbz2.so.1
Set in hbase-env.sh the LD_LIBRARY_PATH environment variable when starting your HBase.
D.3. Compressor Configuration, Installation, and Use
D.3.1. Configure HBase For Compressors
Before HBase can use a given compressor, its libraries need to be available. Due to licensing issues,
only GZ compression is available to HBase (via native Java libraries) in a default installation. Other
compression libraries are available via the shared library bundled with your hadoop. The hadoop
native library needs to be findable when HBase starts. See
Compressor Support On the Master
A new configuration setting was introduced in HBase 0.95, to check the Master to determine which
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data block encoders are installed and configured on it, and assume that the entire cluster is
configured the same. This option, hbase.master.check.compression, defaults to true. This prevents
the situation described in HBASE-6370, where a table is created or modified to support a codec that
a region server does not support, leading to failures that take a long time to occur and are difficult
to debug.
If hbase.master.check.compression is enabled, libraries for all desired compressors need to be
installed and configured on the Master, even if the Master does not run a region server.
Install GZ Support Via Native Libraries
HBase uses Java’s built-in GZip support unless the native Hadoop libraries are available on the
CLASSPATH. The recommended way to add libraries to the CLASSPATH is to set the environment
variable HBASE_LIBRARY_PATH for the user running HBase. If native libraries are not available and
Java’s GZIP is used, Got brand-new compressor reports will be present in the logs. See
brand.new.compressor).
Install LZO Support
HBase cannot ship with LZO because of incompatibility between HBase, which uses an Apache
Software License (ASL) and LZO, which uses a GPL license. See the Hadoop-LZO at Twitter for
information on configuring LZO support for HBase.
If you depend upon LZO compression, consider configuring your RegionServers to fail to start if
LZO is not available. See hbase.regionserver.codecs.
Configure LZ4 Support
LZ4 support is bundled with Hadoop. Make sure the hadoop shared library (libhadoop.so) is
accessible when you start HBase. After configuring your platform (see hadoop.native.lib), you can
make a symbolic link from HBase to the native Hadoop libraries. This assumes the two software
installs are colocated. For example, if my 'platform' is Linux-amd64-64:
$ cd $HBASE_HOME
$ mkdir lib/native
$ ln -s $HADOOP_HOME/lib/native lib/native/Linux-amd64-64
Use the compression tool to check that LZ4 is installed on all nodes. Start up (or restart) HBase.
Afterward, you can create and alter tables to enable LZ4 as a compression codec.:
hbase(main):003:0> alter 'TestTable', {NAME => 'info', COMPRESSION => 'LZ4'}
Install Snappy Support
HBase does not ship with Snappy support because of licensing issues. You can install Snappy
binaries (for instance, by using yum install snappy on CentOS) or build Snappy from source. After
installing Snappy, search for the shared library, which will be called libsnappy.so.X where X is a
number. If you built from source, copy the shared library to a known location on your system, such
as /opt/snappy/lib/.
In addition to the Snappy library, HBase also needs access to the Hadoop shared library, which will
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be called something like libhadoop.so.X.Y, where X and Y are both numbers. Make note of the
location of the Hadoop library, or copy it to the same location as the Snappy library.
The Snappy and Hadoop libraries need to be available on each node of your
cluster. See compression.test to find out how to test that this is the case.
See hbase.regionserver.codecs to configure your RegionServers to fail to start if a
given compressor is not available.
Each of these library locations need to be added to the environment variable HBASE_LIBRARY_PATH for
the operating system user that runs HBase. You need to restart the RegionServer for the changes to
take effect.
CompressionTest
You can use the CompressionTest tool to verify that your compressor is available to HBase:
Ê$ hbase org.apache.hadoop.hbase.util.CompressionTest hdfs://host/path/to/hbase snappy
Enforce Compression Settings On a RegionServer
You can configure a RegionServer so that it will fail to restart if compression is configured
incorrectly, by adding the option hbase.regionserver.codecs to the hbase-site.xml, and setting its
value to a comma-separated list of codecs that need to be available. For example, if you set this
property to lzo,gz, the RegionServer would fail to start if both compressors were not available. This
would prevent a new server from being added to the cluster without having codecs configured
properly.
D.3.2. Enable Compression On a ColumnFamily
To enable compression for a ColumnFamily, use an alter command. You do not need to re-create
the table or copy data. If you are changing codecs, be sure the old codec is still available until all the
old StoreFiles have been compacted.
Enabling Compression on a ColumnFamily of an Existing Table using HBaseShell
hbase> disable 'test'
hbase> alter 'test', {NAME => 'cf', COMPRESSION => 'GZ'}
hbase> enable 'test'
Creating a New Table with Compression On a ColumnFamily
hbase> create 'test2', { NAME => 'cf2', COMPRESSION => 'SNAPPY' }
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Verifying a ColumnFamily’s Compression Settings
hbase> describe 'test'
DESCRIPTION ENABLED
Ê'test', {NAME => 'cf', DATA_BLOCK_ENCODING => 'NONE false
Ê', BLOOMFILTER => 'ROW', REPLICATION_SCOPE => '0',
ÊVERSIONS => '1', COMPRESSION => 'GZ', MIN_VERSIONS
Ê=> '0', TTL => 'FOREVER', KEEP_DELETED_CELLS => 'fa
Êlse', BLOCKSIZE => '65536', IN_MEMORY => 'false', B
ÊLOCKCACHE => 'true'}
1 row(s) in 0.1070 seconds
D.3.3. Testing Compression Performance
HBase includes a tool called LoadTestTool which provides mechanisms to test your compression
performance. You must specify either -write or -update-read as your first parameter, and if you do
not specify another parameter, usage advice is printed for each option.
LoadTestTool Usage
$ bin/hbase org.apache.hadoop.hbase.util.LoadTestTool -h
usage: bin/hbase org.apache.hadoop.hbase.util.LoadTestTool <options>
Options:
Ê-batchupdate Whether to use batch as opposed to separate
Ê updates for every column in a row
Ê-bloom <arg> Bloom filter type, one of [NONE, ROW, ROWCOL]
Ê-compression <arg> Compression type, one of [LZO, GZ, NONE, SNAPPY,
Ê LZ4]
Ê-data_block_encoding <arg> Encoding algorithm (e.g. prefix compression) to
Ê use for data blocks in the test column family, one
Ê of [NONE, PREFIX, DIFF, FAST_DIFF, ROW_INDEX_V1].
Ê-encryption <arg> Enables transparent encryption on the test table,
Ê one of [AES]
Ê-generator <arg> The class which generates load for the tool. Any
Ê args for this class can be passed as colon
Ê separated after class name
Ê-h,--help Show usage
Ê-in_memory Tries to keep the HFiles of the CF inmemory as far
Ê as possible. Not guaranteed that reads are always
Ê served from inmemory
Ê-init_only Initialize the test table only, don't do any
Ê loading
Ê-key_window <arg> The 'key window' to maintain between reads and
Ê writes for concurrent write/read workload. The
Ê default is 0.
Ê-max_read_errors <arg> The maximum number of read errors to tolerate
Ê before terminating all reader threads. The default
Ê is 10.
Ê-multiput Whether to use multi-puts as opposed to separate
Ê puts for every column in a row
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Ê-num_keys <arg> The number of keys to read/write
Ê-num_tables <arg> A positive integer number. When a number n is
Ê speicfied, load test tool will load n table
Ê parallely. -tn parameter value becomes table name
Ê prefix. Each table name is in format
Ê <tn>_1...<tn>_n
Ê-read <arg> <verify_percent>[:<#threads=20>]
Ê-regions_per_server <arg> A positive integer number. When a number n is
Ê specified, load test tool will create the test
Ê table with n regions per server
Ê-skip_init Skip the initialization; assume test table already
Ê exists
Ê-start_key <arg> The first key to read/write (a 0-based index). The
Ê default value is 0.
Ê-tn <arg> The name of the table to read or write
Ê-update <arg> <update_percent>[:<#threads=20>][:<#whether to
Ê ignore nonce collisions=0>]
Ê-write <arg> <avg_cols_per_key>:<avg_data_size>[:<#threads=20>]
Ê-zk <arg> ZK quorum as comma-separated host names without
Ê port numbers
Ê-zk_root <arg> name of parent znode in zookeeper
Example Usage of LoadTestTool
$ hbase org.apache.hadoop.hbase.util.LoadTestTool -write 1:10:100 -num_keys 1000000
Ê -read 100:30 -num_tables 1 -data_block_encoding NONE -tn load_test_tool_NONE
D.4. Enable Data Block Encoding
Codecs are built into HBase so no extra configuration is needed. Codecs are enabled on a table by
setting the DATA_BLOCK_ENCODING property. Disable the table before altering its
DATA_BLOCK_ENCODING setting. Following is an example using HBase Shell:
Enable Data Block Encoding On a Table
hbase> disable 'test'
hbase> alter 'test', { NAME => 'cf', DATA_BLOCK_ENCODING => 'FAST_DIFF' }
Updating all regions with the new schema...
0/1 regions updated.
1/1 regions updated.
Done.
0 row(s) in 2.2820 seconds
hbase> enable 'test'
0 row(s) in 0.1580 seconds
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Verifying a ColumnFamily’s Data Block Encoding
hbase> describe 'test'
DESCRIPTION ENABLED
Ê'test', {NAME => 'cf', DATA_BLOCK_ENCODING => 'FAST true
Ê_DIFF', BLOOMFILTER => 'ROW', REPLICATION_SCOPE =>
Ê'0', VERSIONS => '1', COMPRESSION => 'GZ', MIN_VERS
ÊIONS => '0', TTL => 'FOREVER', KEEP_DELETED_CELLS =
Ê> 'false', BLOCKSIZE => '65536', IN_MEMORY => 'fals
Êe', BLOCKCACHE => 'true'}
1 row(s) in 0.0650 seconds
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Appendix E: SQL over HBase
The following projects offer some support for SQL over HBase.
E.1. Apache Phoenix
Apache Phoenix
E.2. Trafodion
Trafodion: Transactional SQL-on-HBase
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Appendix F: YCSB
YCSB: The Yahoo! Cloud Serving Benchmark and HBase
TODO: Describe how YCSB is poor for putting up a decent cluster load.
TODO: Describe setup of YCSB for HBase. In particular, presplit your tables before you start a run.
See HBASE-4163 Create Split Strategy for YCSB Benchmark for why and a little shell command for
how to do it.
Ted Dunning redid YCSB so it’s mavenized and added facility for verifying workloads. See Ted
Dunning’s YCSB.
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Appendix G: HFile format
This appendix describes the evolution of the HFile format.
G.1. HBase File Format (version 1)
As we will be discussing changes to the HFile format, it is useful to give a short overview of the
original (HFile version 1) format.
G.1.1. Overview of Version 1
An HFile in version 1 format is structured as follows:
Figure 21. HFile V1 Format
G.1.2. Block index format in version 1
The block index in version 1 is very straightforward. For each entry, it contains:
1. Offset (long)
2. Uncompressed size (int)
3. Key (a serialized byte array written using Bytes.writeByteArray)
a. Key length as a variable-length integer (VInt)
b. Key bytes
The number of entries in the block index is stored in the fixed file trailer, and has to be passed in to
the method that reads the block index. One of the limitations of the block index in version 1 is that
it does not provide the compressed size of a block, which turns out to be necessary for
decompression. Therefore, the HFile reader has to infer this compressed size from the offset
difference between blocks. We fix this limitation in version 2, where we store on-disk block size
instead of uncompressed size, and get uncompressed size from the block header.
G.2. HBase file format with inline blocks (version 2)
Note: this feature was introduced in HBase 0.92
G.2.1. Motivation
We found it necessary to revise the HFile format after encountering high memory usage and slow
startup times caused by large Bloom filters and block indexes in the region server. Bloom filters can
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get as large as 100 MB per HFile, which adds up to 2 GB when aggregated over 20 regions. Block
indexes can grow as large as 6 GB in aggregate size over the same set of regions. A region is not
considered opened until all of its block index data is loaded. Large Bloom filters produce a different
performance problem: the first get request that requires a Bloom filter lookup will incur the latency
of loading the entire Bloom filter bit array.
To speed up region server startup we break Bloom filters and block indexes into multiple blocks
and write those blocks out as they fill up, which also reduces the HFile writer’s memory footprint.
In the Bloom filter case, "filling up a block" means accumulating enough keys to efficiently utilize a
fixed-size bit array, and in the block index case we accumulate an "index block" of the desired size.
Bloom filter blocks and index blocks (we call these "inline blocks") become interspersed with data
blocks, and as a side effect we can no longer rely on the difference between block offsets to
determine data block length, as it was done in version 1.
HFile is a low-level file format by design, and it should not deal with application-specific details
such as Bloom filters, which are handled at StoreFile level. Therefore, we call Bloom filter blocks in
an HFile "inline" blocks. We also supply HFile with an interface to write those inline blocks.
Another format modification aimed at reducing the region server startup time is to use a
contiguous "load-on-open" section that has to be loaded in memory at the time an HFile is being
opened. Currently, as an HFile opens, there are separate seek operations to read the trailer,
data/meta indexes, and file info. To read the Bloom filter, there are two more seek operations for its
"data" and "meta" portions. In version 2, we seek once to read the trailer and seek again to read
everything else we need to open the file from a contiguous block.
G.2.2. Overview of Version 2
The version of HBase introducing the above features reads both version 1 and 2 HFiles, but only
writes version 2 HFiles. A version 2 HFile is structured as follows:
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Figure 22. HFile Version 2 Structure
G.2.3. Unified version 2 block format
In the version 2 every block in the data section contains the following fields:
1. 8 bytes: Block type, a sequence of bytes equivalent to version 1’s "magic records". Supported
block types are:
a. DATA – data blocks
b. LEAF_INDEX – leaf-level index blocks in a multi-level-block-index
c. BLOOM_CHUNK – Bloom filter chunks
d. META – meta blocks (not used for Bloom filters in version 2 anymore)
e. INTERMEDIATE_INDEX – intermediate-level index blocks in a multi-level blockindex
f. ROOT_INDEX – root-level index blocks in a multi-level block index
g. FILE_INFO – the ''file info'' block, a small key-value map of metadata
h. BLOOM_META – a Bloom filter metadata block in the load-on-open section
i. TRAILER – a fixed-size file trailer. As opposed to the above, this is not an HFile v2 block but a
fixed-size (for each HFile version) data structure
j. INDEX_V1 – this block type is only used for legacy HFile v1 block
2. Compressed size of the block’s data, not including the header (int).
Can be used for skipping the current data block when scanning HFile data.
3. Uncompressed size of the block’s data, not including the header (int)
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This is equal to the compressed size if the compression algorithm is NONE
4. File offset of the previous block of the same type (long)
Can be used for seeking to the previous data/index block
5. Compressed data (or uncompressed data if the compression algorithm is NONE).
The above format of blocks is used in the following HFile sections:
Scanned block section
The section is named so because it contains all data blocks that need to be read when an HFile is
scanned sequentially. Also contains Leaf index blocks and Bloom chunk blocks.
Non-scanned block section
This section still contains unified-format v2 blocks but it does not have to be read when doing a
sequential scan. This section contains "meta" blocks and intermediate-level index blocks.
We are supporting "meta" blocks in version 2 the same way they were supported in version 1, even
though we do not store Bloom filter data in these blocks anymore.
G.2.4. Block index in version 2
There are three types of block indexes in HFile version 2, stored in two different formats (root and
non-root):
1. Data index — version 2 multi-level block index, consisting of:
a. Version 2 root index, stored in the data block index section of the file
b. Optionally, version 2 intermediate levels, stored in the non-root format in the data index
section of the file. Intermediate levels can only be present if leaf level blocks are present
c. Optionally, version 2 leaf levels, stored in the non-root format inline with data blocks
2. Meta index — version 2 root index format only, stored in the meta index section of the file
3. Bloom index — version 2 root index format only, stored in the ''load-on-open'' section as part of
Bloom filter metadata.
G.2.5. Root block index format in version 2
This format applies to:
1. Root level of the version 2 data index
2. Entire meta and Bloom indexes in version 2, which are always single-level.
A version 2 root index block is a sequence of entries of the following format, similar to entries of a
version 1 block index, but storing on-disk size instead of uncompressed size.
1. Offset (long)
This offset may point to a data block or to a deeper-level index block.
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2. On-disk size (int)
3. Key (a serialized byte array stored using Bytes.writeByteArray)
4. Key (VInt)
5. Key bytes
A single-level version 2 block index consists of just a single root index block. To read a root index
block of version 2, one needs to know the number of entries. For the data index and the meta index
the number of entries is stored in the trailer, and for the Bloom index it is stored in the compound
Bloom filter metadata.
For a multi-level block index we also store the following fields in the root index block in the load-
on-open section of the HFile, in addition to the data structure described above:
1. Middle leaf index block offset
2. Middle leaf block on-disk size (meaning the leaf index block containing the reference to the
''middle'' data block of the file)
3. The index of the mid-key (defined below) in the middle leaf-level block.
These additional fields are used to efficiently retrieve the mid-key of the HFile used in HFile splits,
which we define as the first key of the block with a zero-based index of (n – 1) / 2, if the total
number of blocks in the HFile is n. This definition is consistent with how the mid-key was
determined in HFile version 1, and is reasonable in general, because blocks are likely to be the
same size on average, but we don’t have any estimates on individual key/value pair sizes.
When writing a version 2 HFile, the total number of data blocks pointed to by every leaf-level index
block is kept track of. When we finish writing and the total number of leaf-level blocks is
determined, it is clear which leaf-level block contains the mid-key, and the fields listed above are
computed. When reading the HFile and the mid-key is requested, we retrieve the middle leaf index
block (potentially from the block cache) and get the mid-key value from the appropriate position
inside that leaf block.
G.2.6. Non-root block index format in version 2
This format applies to intermediate-level and leaf index blocks of a version 2 multi-level data block
index. Every non-root index block is structured as follows.
1. numEntries: the number of entries (int).
2. entryOffsets: the "secondary index" of offsets of entries in the block, to facilitate a quick binary
search on the key (numEntries + 1 int values). The last value is the total length of all entries in
this index block. For example, in a non-root index block with entry sizes 60, 80, 50 the
"secondary index" will contain the following int array: {0, 60, 140, 190}.
3. Entries. Each entry contains:
a. Offset of the block referenced by this entry in the file (long)
b. On-disk size of the referenced block (int)
c. Key. The length can be calculated from entryOffsets.
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G.2.7. Bloom filters in version 2
In contrast with version 1, in a version 2 HFile Bloom filter metadata is stored in the load-on-open
section of the HFile for quick startup.
1. A compound Bloom filter.
2. Bloom filter version = 3 (int). There used to be a DynamicByteBloomFilter class that had the
Bloom filter version number 2
3. The total byte size of all compound Bloom filter chunks (long)
4. Number of hash functions (int)
5. Type of hash functions (int)
6. The total key count inserted into the Bloom filter (long)
7. The maximum total number of keys in the Bloom filter (long)
8. The number of chunks (int)
9. Comparator class used for Bloom filter keys, a UTF>8 encoded string stored using
Bytes.writeByteArray
10. Bloom block index in the version 2 root block index format
G.2.8. File Info format in versions 1 and 2
The file info block is a serialized map from byte arrays to byte arrays, with the following keys,
among others. StoreFile-level logic adds more keys to this.
hfile.LASTKEY The last key of the file (byte array)
hfile.AVG_KEY_LEN The average key length in the file (int)
hfile.AVG_VALUE_LEN The average value length in the file (int)
In version 2, we did not change the file format, but we moved the file info to the final section of the
file, which can be loaded as one block when the HFile is being opened.
Also, we do not store the comparator in the version 2 file info anymore. Instead, we store it in the
fixed file trailer. This is because we need to know the comparator at the time of parsing the load-on-
open section of the HFile.
G.2.9. Fixed file trailer format differences between versions 1 and 2
The following table shows common and different fields between fixed file trailers in versions 1 and
2. Note that the size of the trailer is different depending on the version, so it is ''fixed'' only within
one version. However, the version is always stored as the last four-byte integer in the file.
Table 23. Differences between HFile Versions 1 and 2
Version 1 Version 2
File info offset (long)
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Data index offset (long) loadOnOpenOffset (long) /The offset of the
section that we need to load when opening the
file./
Number of data index entries (int)
metaIndexOffset (long) /This field is not being
used by the version 1 reader, so we removed it
from version 2./
uncompressedDataIndexSize (long) /The total
uncompressed size of the whole data block
index, including root-level, intermediate-level,
and leaf-level blocks./
Number of meta index entries (int)
Total uncompressed bytes (long)
numEntries (int) numEntries (long)
Compression codec: 0 = LZO, 1 = GZ, 2 = NONE
(int)
Compression codec: 0 = LZO, 1 = GZ, 2 = NONE
(int)
The number of levels in the data block index
(int)
firstDataBlockOffset (long) /The offset of the first
data block. Used when scanning./
lastDataBlockEnd (long) /The offset of the first
byte after the last key/value data block. We don’t
need to go beyond this offset when scanning./
Version: 1 (int) Version: 2 (int)
G.2.10. getShortMidpointKey(an optimization for data index block)
Note: this optimization was introduced in HBase 0.95+
HFiles contain many blocks that contain a range of sorted Cells. Each cell has a key. To save IO when
reading Cells, the HFile also has an index that maps a Cell’s start key to the offset of the beginning
of a particular block. Prior to this optimization, HBase would use the key of the first cell in each
data block as the index key.
In HBASE-7845, we generate a new key that is lexicographically larger than the last key of the
previous block and lexicographically equal or smaller than the start key of the current block. While
actual keys can potentially be very long, this "fake key" or "virtual key" can be much shorter. For
example, if the stop key of previous block is "the quick brown fox", the start key of current block is
"the who", we could use "the r" as our virtual key in our hfile index.
There are two benefits to this:
•having shorter keys reduces the hfile index size, (allowing us to keep more indexes in memory),
and
•using something closer to the end key of the previous block allows us to avoid a potential extra
IO when the target key lives in between the "virtual key" and the key of the first element in the
target block.
This optimization (implemented by the getShortMidpointKey method) is inspired by LevelDB’s
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ByteWiseComparatorImpl::FindShortestSeparator() and FindShortSuccessor().
G.3. HBase File Format with Security Enhancements
(version 3)
Note: this feature was introduced in HBase 0.98
G.3.1. Motivation
Version 3 of HFile makes changes needed to ease management of encryption at rest and cell-level
metadata (which in turn is needed for cell-level ACLs and cell-level visibility labels). For more
information see hbase.encryption.server, hbase.tags, hbase.accesscontrol.configuration, and
hbase.visibility.labels.
G.3.2. Overview
The version of HBase introducing the above features reads HFiles in versions 1, 2, and 3 but only
writes version 3 HFiles. Version 3 HFiles are structured the same as version 2 HFiles. For more
information see hfilev2.overview.
G.3.3. File Info Block in Version 3
Version 3 added two additional pieces of information to the reserved keys in the file info block.
hfile.MAX_TAGS_LEN The maximum number of bytes needed to store
the serialized tags for any single cell in this hfile
(int)
hfile.TAGS_COMPRESSED Does the block encoder for this hfile compress
tags? (boolean). Should only be present if
hfile.MAX_TAGS_LEN is also present.
When reading a Version 3 HFile the presence of MAX_TAGS_LEN is used to determine how to
deserialize the cells within a data block. Therefore, consumers must read the file’s info block prior
to reading any data blocks.
When writing a Version 3 HFile, HBase will always include MAX_TAGS_LEN when flushing the
memstore to underlying filesystem.
When compacting extant files, the default writer will omit MAX_TAGS_LEN if all of the files selected do
not themselves contain any cells with tags.
See compaction for details on the compaction file selection algorithm.
G.3.4. Data Blocks in Version 3
Within an HFile, HBase cells are stored in data blocks as a sequence of KeyValues (see
hfilev1.overview, or Lars George’s excellent introduction to HBase Storage). In version 3, these
KeyValue optionally will include a set of 0 or more tags:
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Version 1 & 2, Version 3 without MAX_TAGS_LEN Version 3 with MAX_TAGS_LEN
Key Length (4 bytes)
Value Length (4 bytes)
Key bytes (variable)
Value bytes (variable)
Tags Length (2 bytes)
Tags bytes (variable)
If the info block for a given HFile contains an entry for MAX_TAGS_LEN each cell will have the length of
that cell’s tags included, even if that length is zero. The actual tags are stored as a sequence of tag
length (2 bytes), tag type (1 byte), tag bytes (variable). The format an individual tag’s bytes depends
on the tag type.
Note that the dependence on the contents of the info block implies that prior to reading any data
blocks you must first process a file’s info block. It also implies that prior to writing a data block you
must know if the file’s info block will include MAX_TAGS_LEN.
G.3.5. Fixed File Trailer in Version 3
The fixed file trailers written with HFile version 3 are always serialized with protocol buffers.
Additionally, it adds an optional field to the version 2 protocol buffer named encryption_key. If
HBase is configured to encrypt HFiles this field will store a data encryption key for this particular
HFile, encrypted with the current cluster master key using AES. For more information see
hbase.encryption.server.
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Appendix H: Other Information About
HBase
H.1. HBase Videos
Introduction to HBase
•Introduction to HBase by Todd Lipcon (Chicago Data Summit 2011).
•Building Real Time Services at Facebook with HBase by Jonathan Gray (Berlin buzzwords 2011)
•The Multiple Uses Of HBase by Jean-Daniel Cryans(Berlin buzzwords 2011).
H.2. HBase Presentations (Slides)
Advanced HBase Schema Design by Lars George (Hadoop World 2011).
Introduction to HBase by Todd Lipcon (Chicago Data Summit 2011).
Getting The Most From Your HBase Install by Ryan Rawson, Jonathan Gray (Hadoop World 2009).
H.3. HBase Papers
BigTable by Google (2006).
HBase and HDFS Locality by Lars George (2010).
No Relation: The Mixed Blessings of Non-Relational Databases by Ian Varley (2009).
H.4. HBase Sites
Cloudera’s HBase Blog has a lot of links to useful HBase information.
CAP Confusion is a relevant entry for background information on distributed storage systems.
HBase RefCard from DZone.
H.5. HBase Books
HBase: The Definitive Guide by Lars George.
H.6. Hadoop Books
Hadoop: The Definitive Guide by Tom White.
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Appendix I: HBase History
•2006: BigTable paper published by Google.
•2006 (end of year): HBase development starts.
•2008: HBase becomes Hadoop sub-project.
•2010: HBase becomes Apache top-level project.
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Appendix J: HBase and the Apache Software
Foundation
HBase is a project in the Apache Software Foundation and as such there are responsibilities to the
ASF to ensure a healthy project.
J.1. ASF Development Process
See the Apache Development Process page for all sorts of information on how the ASF is structured
(e.g., PMC, committers, contributors), to tips on contributing and getting involved, and how open-
source works at ASF.
J.2. ASF Board Reporting
Once a quarter, each project in the ASF portfolio submits a report to the ASF board. This is done by
the HBase project lead and the committers. See ASF board reporting for more information.
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Appendix K: Apache HBase Orca
Figure 23. Apache HBase Orca, HBase Colors, & Font
An Orca is the Apache HBase mascot. See NOTICES.txt. Our Orca logo we got here:
http://www.vectorfree.com/jumping-orca It is licensed Creative Commons Attribution 3.0. See
https://creativecommons.org/licenses/by/3.0/us/ We changed the logo by stripping the colored
background, inverting it and then rotating it some.
The 'official' HBase color is "International Orange (Engineering)", the color of the Golden Gate
bridge in San Francisco and for space suits used by NASA.
Our 'font' is Bitsumishi.
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Appendix L: Enabling Dapper-like Tracing in
HBase
HBase includes facilities for tracing requests using the open source tracing library, Apache HTrace.
Setting up tracing is quite simple, however it currently requires some very minor changes to your
client code (this requirement may be removed in the future).
Support for this feature using HTrace 3 in HBase was added in HBASE-6449. Starting with HBase
2.0, there was a non-compatible update to HTrace 4 via HBASE-18601. The examples provided in
this section will be using HTrace 4 package names, syntax, and conventions. For older examples,
please consult previous versions of this guide.
L.1. SpanReceivers
The tracing system works by collecting information in structures called 'Spans'. It is up to you to
choose how you want to receive this information by implementing the SpanReceiver interface,
which defines one method:
public void receiveSpan(Span span);
This method serves as a callback whenever a span is completed. HTrace allows you to use as many
SpanReceivers as you want so you can easily send trace information to multiple destinations.
Configure what SpanReceivers you’d like to us by putting a comma separated list of the fully-
qualified class name of classes implementing SpanReceiver in hbase-site.xml property:
hbase.trace.spanreceiver.classes.
HTrace includes a LocalFileSpanReceiver that writes all span information to local files in a JSON-
based format. The LocalFileSpanReceiver looks in hbase-site.xml for a hbase.local-file-span-
receiver.path property with a value describing the name of the file to which nodes should write
their span information.
<property>
Ê <name>hbase.trace.spanreceiver.classes</name>
Ê <value>org.apache.htrace.core.LocalFileSpanReceiver</value>
</property>
<property>
Ê <name>hbase.htrace.local-file-span-receiver.path</name>
Ê <value>/var/log/hbase/htrace.out</value>
</property>
HTrace also provides ZipkinSpanReceiver which converts spans to Zipkin span format and send
them to Zipkin server. In order to use this span receiver, you need to install the jar of htrace-zipkin
to your HBase’s classpath on all of the nodes in your cluster.
htrace-zipkin is published to the Maven central repository. You could get the latest version from
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there or just build it locally (see the HTrace homepage for information on how to do this) and then
copy it out to all nodes.
ZipkinSpanReceiver for properties called hbase.htrace.zipkin.collector-hostname and
hbase.htrace.zipkin.collector-port in hbase-site.xml with values describing the Zipkin collector
server to which span information are sent.
<property>
Ê <name>hbase.trace.spanreceiver.classes</name>
Ê <value>org.apache.htrace.core.ZipkinSpanReceiver</value>
</property>
<property>
Ê <name>hbase.htrace.zipkin.collector-hostname</name>
Ê <value>localhost</value>
</property>
<property>
Ê <name>hbase.htrace.zipkin.collector-port</name>
Ê <value>9410</value>
</property>
If you do not want to use the included span receivers, you are encouraged to write your own
receiver (take a look at LocalFileSpanReceiver for an example). If you think others would benefit
from your receiver, file a JIRA with the HTrace project.
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Chapter 201. Client Modifications
In order to turn on tracing in your client code, you must initialize the module sending spans to
receiver once per client process.
private SpanReceiverHost spanReceiverHost;
...
Ê Configuration conf = HBaseConfiguration.create();
Ê SpanReceiverHost spanReceiverHost = SpanReceiverHost.getInstance(conf);
Then you simply start tracing span before requests you think are interesting, and close it when the
request is done. For example, if you wanted to trace all of your get operations, you change this:
Configuration config = HBaseConfiguration.create();
Connection connection = ConnectionFactory.createConnection(config);
Table table = connection.getTable(TableName.valueOf("t1"));
Get get = new Get(Bytes.toBytes("r1"));
Result res = table.get(get);
into:
TraceScope ts = Trace.startSpan("Gets", Sampler.ALWAYS);
try {
Ê Table table = connection.getTable(TableName.valueOf("t1"));
Ê Get get = new Get(Bytes.toBytes("r1"));
Ê Result res = table.get(get);
} finally {
Ê ts.close();
}
If you wanted to trace half of your 'get' operations, you would pass in:
new ProbabilitySampler(0.5)
in lieu of Sampler.ALWAYS to Trace.startSpan(). See the HTrace README for more information on
Samplers.
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Chapter 202. Tracing from HBase Shell
You can use trace command for tracing requests from HBase Shell. trace 'start' command turns
on tracing and trace 'stop' command turns off tracing.
hbase(main):001:0> trace 'start'
hbase(main):002:0> put 'test', 'row1', 'f:', 'val1' # traced commands
hbase(main):003:0> trace 'stop'
trace 'start' and trace 'stop' always returns boolean value representing if or not there is
ongoing tracing. As a result, trace 'stop' returns false on success. trace 'status' just returns if or
not tracing is turned on.
hbase(main):001:0> trace 'start'
=> true
hbase(main):002:0> trace 'status'
=> true
hbase(main):003:0> trace 'stop'
=> false
hbase(main):004:0> trace 'status'
=> false
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Appendix M: 0.95 RPC Specification
In 0.95, all client/server communication is done with protobuf’ed Messages rather than with
Hadoop Writables. Our RPC wire format therefore changes. This document describes the
client/server request/response protocol and our new RPC wire-format.
For what RPC is like in 0.94 and previous, see Benoît/Tsuna’s Unofficial Hadoop / HBase RPC
protocol documentation. For more background on how we arrived at this spec., see HBase RPC: WIP
M.1. Goals
1. A wire-format we can evolve
2. A format that does not require our rewriting server core or radically changing its current
architecture (for later).
M.2. TODO
1. List of problems with currently specified format and where we would like to go in a version2,
etc. For example, what would we have to change if anything to move server async or to support
streaming/chunking?
2. Diagram on how it works
3. A grammar that succinctly describes the wire-format. Currently we have these words and the
content of the rpc protobuf idl but a grammar for the back and forth would help with groking
rpc. Also, a little state machine on client/server interactions would help with understanding
(and ensuring correct implementation).
M.3. RPC
The client will send setup information on connection establish. Thereafter, the client invokes
methods against the remote server sending a protobuf Message and receiving a protobuf Message
in response. Communication is synchronous. All back and forth is preceded by an int that has the
total length of the request/response. Optionally, Cells(KeyValues) can be passed outside of protobufs
in follow-behind Cell blocks (because we can’t protobuf megabytes of KeyValues or Cells). These
CellBlocks are encoded and optionally compressed.
For more detail on the protobufs involved, see the RPC.proto file in master.
M.3.1. Connection Setup
Client initiates connection.
Client
On connection setup, client sends a preamble followed by a connection header.
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<preamble>
<MAGIC 4 byte integer> <1 byte RPC Format Version> <1 byte auth type>
We need the auth method spec. here so the connection header is encoded if auth enabled.
E.g.: HBas0x000x50 — 4 bytes of MAGIC — `HBas' — plus one-byte of version, 0 in this case, and one
byte, 0x50 (SIMPLE). of an auth type.
<Protobuf ConnectionHeader Message>
Has user info, and ``protocol'', as well as the encoders and compression the client will use sending
CellBlocks. CellBlock encoders and compressors are for the life of the connection. CellBlock
encoders implement org.apache.hadoop.hbase.codec.Codec. CellBlocks may then also be
compressed. Compressors implement org.apache.hadoop.io.compress.CompressionCodec. This
protobuf is written using writeDelimited so is prefaced by a pb varint with its serialized length
Server
After client sends preamble and connection header, server does NOT respond if successful
connection setup. No response means server is READY to accept requests and to give out response.
If the version or authentication in the preamble is not agreeable or the server has trouble parsing
the preamble, it will throw a org.apache.hadoop.hbase.ipc.FatalConnectionException explaining the
error and will then disconnect. If the client in the connection header — i.e. the protobuf’d Message
that comes after the connection preamble — asks for a Service the server does not support or a
codec the server does not have, again we throw a FatalConnectionException with explanation.
M.3.2. Request
After a Connection has been set up, client makes requests. Server responds.
A request is made up of a protobuf RequestHeader followed by a protobuf Message parameter. The
header includes the method name and optionally, metadata on the optional CellBlock that may be
following. The parameter type suits the method being invoked: i.e. if we are doing a getRegionInfo
request, the protobuf Message param will be an instance of GetRegionInfoRequest. The response
will be a GetRegionInfoResponse. The CellBlock is optionally used ferrying the bulk of the RPC data:
i.e. Cells/KeyValues.
Request Parts
<Total Length>
The request is prefaced by an int that holds the total length of what follows.
<Protobuf RequestHeader Message>
Will have call.id, trace.id, and method name, etc. including optional Metadata on the Cell block IFF
one is following. Data is protobuf’d inline in this pb Message or optionally comes in the following
CellBlock
<Protobuf Param Message>
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If the method being invoked is getRegionInfo, if you study the Service descriptor for the client to
regionserver protocol, you will find that the request sends a GetRegionInfoRequest protobuf
Message param in this position.
<CellBlock>
An encoded and optionally compressed Cell block.
M.3.3. Response
Same as Request, it is a protobuf ResponseHeader followed by a protobuf Message response where
the Message response type suits the method invoked. Bulk of the data may come in a following
CellBlock.
Response Parts
<Total Length>
The response is prefaced by an int that holds the total length of what follows.
<Protobuf ResponseHeader Message>
Will have call.id, etc. Will include exception if failed processing. Optionally includes metadata on
optional, IFF there is a CellBlock following.
<Protobuf Response Message>
Return or may be nothing if exception. If the method being invoked is getRegionInfo, if you study
the Service descriptor for the client to regionserver protocol, you will find that the response sends a
GetRegionInfoResponse protobuf Message param in this position.
<CellBlock>
An encoded and optionally compressed Cell block.
M.3.4. Exceptions
There are two distinct types. There is the request failed which is encapsulated inside the response
header for the response. The connection stays open to receive new requests. The second type, the
FatalConnectionException, kills the connection.
Exceptions can carry extra information. See the ExceptionResponse protobuf type. It has a flag to
indicate do-no-retry as well as other miscellaneous payload to help improve client responsiveness.
M.3.5. CellBlocks
These are not versioned. Server can do the codec or it cannot. If new version of a codec with say,
tighter encoding, then give it a new class name. Codecs will live on the server for all time so old
clients can connect.
M.4. Notes
Constraints
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In some part, current wire-format — i.e. all requests and responses preceded by a length — has
been dictated by current server non-async architecture.
One fat pb request or header+param
We went with pb header followed by pb param making a request and a pb header followed by pb
response for now. Doing header+param rather than a single protobuf Message with both header
and param content:
1. Is closer to what we currently have
2. Having a single fat pb requires extra copying putting the already pb’d param into the body of
the fat request pb (and same making result)
3. We can decide whether to accept the request or not before we read the param; for example, the
request might be low priority. As is, we read header+param in one go as server is currently
implemented so this is a TODO.
The advantages are minor. If later, fat request has clear advantage, can roll out a v2 later.
M.4.1. RPC Configurations
CellBlock Codecs
To enable a codec other than the default KeyValueCodec, set hbase.client.rpc.codec to the name of
the Codec class to use. Codec must implement hbase’s Codec Interface. After connection setup, all
passed cellblocks will be sent with this codec. The server will return cellblocks using this same
codec as long as the codec is on the servers' CLASSPATH (else you will get
UnsupportedCellCodecException).
To change the default codec, set hbase.client.default.rpc.codec.
To disable cellblocks completely and to go pure protobuf, set the default to the empty String and do
not specify a codec in your Configuration. So, set hbase.client.default.rpc.codec to the empty
string and do not set hbase.client.rpc.codec. This will cause the client to connect to the server with
no codec specified. If a server sees no codec, it will return all responses in pure protobuf. Running
pure protobuf all the time will be slower than running with cellblocks.
Compression
Uses hadoop’s compression codecs. To enable compressing of passed CellBlocks, set
hbase.client.rpc.compressor to the name of the Compressor to use. Compressor must implement
Hadoop’s CompressionCodec Interface. After connection setup, all passed cellblocks will be sent
compressed. The server will return cellblocks compressed using this same compressor as long as
the compressor is on its CLASSPATH (else you will get UnsupportedCompressionCodecException).
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Appendix N: Known Incompatibilities
Among HBase Versions
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Chapter 203. HBase 2.0 Incompatible
Changes
This appendix describes incompatible changes from earlier versions of HBase against HBase 2.0.
This list is not meant to be wholly encompassing of all possible incompatibilities. Instead, this
content is intended to give insight into some obvious incompatibilities which most users will face
coming from HBase 1.x releases.
203.1. List of Major Changes for HBase 2.0
•HBASE-1912- HBCK is a HBase database checking tool for capturing the inconsistency. As an
HBase administrator, you should not use HBase version 1.0 hbck tool to check the HBase 2.0
database. Doing so will break the database and throw an exception error.
•HBASE-16189 and HBASE-18945- You cannot open the HBase 2.0 hfiles through HBase 1.0
version. If you are an admin or an HBase user who is using HBase version 1.x, you must first do
a rolling upgrade to the latest version of HBase 1.x and then upgrade to HBase 2.0.
•HBASE-18240 - Changed the ReplicationEndpoint Interface. It also introduces a new hbase-third
party 1.0 that packages all the third party utilities, which are expected to run in the hbase
cluster.
203.2. Coprocessor API changes
•HBASE-16769 - Deprecated PB references from MasterObserver and RegionServerObserver.
•HBASE-17312 - [JDK8] Use default method for Observer Coprocessors. The interface classes of
BaseMasterAndRegionObserver, BaseMasterObserver, BaseRegionObserver,
BaseRegionServerObserver and BaseWALObserver uses JDK8’s 'default' keyword to provide
empty and no-op implementations.
•Interface HTableInterface HBase 2.0 introduces following changes to the methods listed below:
203.2.1. [−] interface CoprocessorEnvironment changes (2)
Change Result
Abstract method getTable ( TableName ) has
been removed.
A client program may be interrupted by
NoSuchMethodError exception.
Abstract method getTable ( TableName,
ExecutorService ) has been removed.
A client program may be interrupted by
NoSuchMethodError exception.
•Public Audience
The following tables describes the coprocessor changes.
[−] class CoprocessorRpcChannel (1)
Change Result
766
This class has become interface. A client program may be interrupted by
IncompatibleClassChangeError or
InstantiationError exception depending on the
usage of this class.
Class CoprocessorHost<E>
Classes that were Audience Private but were removed.
Change Result
Type of field coprocessors has been changed
from java.util.SortedSet<E> to
org.apache.hadoop.hbase.util.SortedList<E>.
A client program may be interrupted by
NoSuchFieldError exception.
203.2.2. MasterObserver
HBase 2.0 introduces following changes to the MasterObserver interface.
[−] interface MasterObserver (14)
Change Result
Abstract method voidpostCloneSnapshot (
ObserverContext<MasterCoprocessorEnvironme
nt>, HBaseProtos.SnapshotDescription,
HTableDescriptor ) has been removed from this
interface.
A client program may be interrupted by
NoSuchMethodError exception.
Abstract method voidpostCreateTable (
ObserverContext<MasterCoprocessorEnvironme
nt>, HTableDescriptor, HRegionInfo[ ] ) has been
removed from this interface.
A client program may be interrupted by
NoSuchMethodErrorexception.
Abstract method voidpostDeleteSnapshot
(ObserverContext<MasterCoprocessorEnvironm
ent>, HBaseProtos.SnapshotDescription ) has
been removed from this interface.
A client program may be interrupted by
NoSuchMethodErrorexception.
Abstract method voidpostGetTableDescriptors (
ObserverContext<MasterCoprocessorEnvironme
nt>, List<HTableDescriptor> ) has been removed
from this interface.
A client program may be interrupted by
NoSuchMethodErrorexception.
Abstract method voidpostModifyTable (
ObserverContext<MasterCoprocessorEnvironme
nt>, TableName, HTableDescriptor ) has been
removed from this interface.
A client program may be interrupted by
NoSuchMethodErrorexception.
Abstract method voidpostRestoreSnapshot (
ObserverContext<MasterCoprocessorEnvironme
nt>, HBaseProtos.SnapshotDescription,
HTableDescriptor ) has been removed from this
interface.
A client program may be interrupted by
NoSuchMethodErrorexception.
767
Abstract method voidpostSnapshot (
ObserverContext<MasterCoprocessorEnvironme
nt>, HBaseProtos.SnapshotDescription,
HTableDescriptor ) has been removed from this
interface.
A client program may be interrupted by
NoSuchMethodErrorexception.
Abstract method voidpreCloneSnapshot (
ObserverContext<MasterCoprocessorEnvironme
nt>, HBaseProtos.SnapshotDescription,
HTableDescriptor ) has been removed from this
interface.
A client program may be interrupted by
NoSuchMethodErrorexception.
Abstract method voidpreCreateTable (
ObserverContext<MasterCoprocessorEnvironme
nt>, HTableDescriptor, HRegionInfo[ ] ) has been
removed from this interface.
A client program may be interrupted by
NoSuchMethodErrorexception.
Abstract method voidpreDeleteSnapshot (
ObserverContext<MasterCoprocessorEnvironme
nt>, HBaseProtos.SnapshotDescription ) has
been removed from this interface.
A client program may be interrupted by
NoSuchMethodErrorexception.
Abstract method voidpreGetTableDescriptors (
ObserverContext<MasterCoprocessorEnvironme
nt>, List<TableName>, List<HTableDescriptor> )
has been removed from this interface.
A client program may be interrupted by
NoSuchMethodErrorexception.
Abstract method voidpreModifyTable (
ObserverContext<MasterCoprocessorEnvironme
nt>, TableName, HTableDescriptor ) has been
removed from this interface.
A client program may be interrupted by
NoSuchMethodErrorexception.
Abstract method voidpreRestoreSnapshot (
ObserverContext<MasterCoprocessorEnvironme
nt>, HBaseProtos.SnapshotDescription,
HTableDescriptor ) has been removed from this
interface.
A client program may be interrupted by
NoSuchMethodErrorexception.
Abstract method voidpreSnapshot (
ObserverContext<MasterCoprocessorEnvironme
nt>, HBaseProtos.SnapshotDescription,
HTableDescriptor ) has been removed from this
interface.
A client program may be interrupted by
NoSuchMethodErrorexception.
203.2.3. RegionObserver
HBase 2.0 introduces following changes to the RegionObserver interface.
[−] interface RegionObserver (13)
Change Result
768
Abstract method voidpostCloseRegionOperation
(
ObserverContext<RegionCoprocessorEnvironme
nt>, HRegion.Operation ) has been removed
from this interface.
A client program may be interrupted by
NoSuchMethodError exception.
Abstract method voidpostCompactSelection (
ObserverContext<RegionCoprocessorEnvironme
nt>, Store, ImmutableList<StoreFile> ) has been
removed from this interface.
A client program may be interrupted by
NoSuchMethodError exception.
Abstract method voidpostCompactSelection (
ObserverContext<RegionCoprocessorEnvironme
nt>, Store, ImmutableList<StoreFile>,
CompactionRequest ) has been removed from
this interface.
A client program may be interrupted by
NoSuchMethodError exception.
Abstract method voidpostGetClosestRowBefore (
ObserverContext<RegionCoprocessorEnvironme
nt>, byte[ ], byte[ ], Result ) has been removed
from this interface.
A client program may be interrupted by
NoSuchMethodError exception.
Abstract method
DeleteTrackerpostInstantiateDeleteTracker (
ObserverContext<RegionCoprocessorEnvironme
nt>, DeleteTracker ) has been removed from this
interface.
A client program may be interrupted by
NoSuchMethodError exception.
Abstract method voidpostSplit (
ObserverContext<RegionCoprocessorEnvironme
nt>, HRegion, HRegion ) has been removed from
this interface.
A client program may be interrupted by
NoSuchMethodError exception.
Abstract method voidpostStartRegionOperation (
ObserverContext<RegionCoprocessorEnvironme
nt>, HRegion.Operation ) has been removed
from this interface.
A client program may be interrupted by
NoSuchMethodError exception.
Abstract method
StoreFile.ReaderpostStoreFileReaderOpen (
ObserverContext<RegionCoprocessorEnvironme
nt>, FileSystem, Path,
FSDataInputStreamWrapper, long, CacheConfig,
Reference, StoreFile.Reader ) has been removed
from this interface.
A client program may be interrupted by
NoSuchMethodError exception.
Abstract method voidpostWALRestore (
ObserverContext<RegionCoprocessorEnvironme
nt>, HRegionInfo, HLogKey, WALEdit ) has been
removed from this interface.
A client program may be interrupted by
NoSuchMethodError exception.
Abstract method
InternalScannerpreFlushScannerOpen (
ObserverContext<RegionCoprocessorEnvironme
nt>, Store, KeyValueScanner, InternalScanner )
has been removed from this interface.
A client program may be interrupted by
NoSuchMethodError exception.
769
Abstract method voidpreGetClosestRowBefore (
ObserverContext<RegionCoprocessorEnvironme
nt>, byte[ ], byte[ ], Result ) has been removed
from this interface.
A client program may be interrupted by
NoSuchMethodError exception.
Abstract method
StoreFile.ReaderpreStoreFileReaderOpen (
ObserverContext<RegionCoprocessorEnvironme
nt>, FileSystem, Path,
FSDataInputStreamWrapper, long, CacheConfig,
Reference, StoreFile.Reader ) has been removed
from this interface.
A client program may be interrupted by
NoSuchMethodError exception.
Abstract method voidpreWALRestore (
ObserverContext<RegionCoprocessorEnvironme
nt>, HRegionInfo, HLogKey, WALEdit ) has been
removed from this interface.
A client program may be interrupted by
NoSuchMethodError exception.
203.2.4. WALObserver
HBase 2.0 introduces following changes to the WALObserver interface.
[−] interface WALObserver
Change Result
Abstract method voidpostWALWrite (
ObserverContext<WALCoprocessorEnvironment
>, HRegionInfo, HLogKey, WALEdit ) has been
removed from this interface.
A client program may be interrupted by
NoSuchMethodError exception.
Abstract method booleanpreWALWrite (
ObserverContext<WALCoprocessorEnvironment
>, HRegionInfo, HLogKey, WALEdit ) has been
removed from this interface.
A client program may be interrupted by
NoSuchMethodError exception.
203.2.5. Miscellaneous
HBase 2.0 introduces changes to the following classes:
hbase-server-1.0.0.jar, OnlineRegions.class package org.apache.hadoop.hbase.regionserver
[−] OnlineRegions.getFromOnlineRegions ( String p1 ) [abstract] : HRegion
org/apache/hadoop/hbase/regionserver/OnlineRegions.getFromOnlineRegions:(Ljava/lang/String;)L
org/apache/hadoop/hbase/regionserver/HRegion;
Change Result
Return value type has been changed from
Region to Region.
This method has been removed because the
return type is part of the method signature. A
client program may be interrupted by
NoSuchMethodError exception.
770
hbase-server-1.0.0.jar, RegionCoprocessorEnvironment.class package
org.apache.hadoop.hbase.coprocessor
[−] RegionCoprocessorEnvironment.getRegion ( ) [abstract] : HRegion
org/apache/hadoop/hbase/coprocessor/RegionCoprocessorEnvironment.getRegion:()Lorg/apache/ha
doop/hbase/regionserver/HRegion;
Change Result
Return value type has been changed from
org.apache.hadoop.hbase.regionserver.HRegion
to
org.apache.hadoop.hbase.regionserver.Region.
This method has been removed because the
return type is part of the method signature. A
client program may be interrupted by
NoSuchMethodError exception.
hbase-server-1.0.0.jar, RegionCoprocessorHost.class package org.apache.hadoop.hbase.regionserver
[−] RegionCoprocessorHost.postAppend ( Append append, Result result ) : void
org/apache/hadoop/hbase/regionserver/RegionCoprocessorHost.postAppend:(Lorg/apache/hadoop/h
base/client/Append;Lorg/apache/hadoop/hbase/client/Result;)V
Change Result
Return value type has been changed from void
to org.apache.hadoop.hbase.client.Result.
This method has been removed because the
return type is part of the method signature. A
client program may be interrupted by
NoSuchMethodError exception.
[−] RegionCoprocessorHost.preStoreFileReaderOpen ( FileSystem fs, Path p,
FSDataInputStreamWrapper in, long size,CacheConfig cacheConf, Reference r ) :
StoreFile.Reader
org/apache/hadoop/hbase/regionserver/RegionCoprocessorHost.preStoreFileReaderOpen:(Lorg/apa
che/hadoop/fs/FileSystem;Lorg/apache/hadoop/fs/Path;Lorg/apache/hadoop/hbase/io/FSDataInputSt
reamWrapper;JLorg/apache/hadoop/hbase/io/hfile/CacheConfig;Lorg/apache/hadoop/hbase/io/Refer
ence;)Lorg/apache/hadoop/hbase/regionserver/StoreFile$Reader;
Change Result
Return value type has been changed from
StoreFile.Reader to StoreFileReader.
This method has been removed because the
return type is part of the method signature. A
client program may be interrupted by
NoSuchMethodError exception.
203.2.6. IPC
203.2.7. Scheduler changes:
1. Following methods became abstract:
package org.apache.hadoop.hbase.ipc
771
[−]class RpcScheduler (1)
Change Result
Abstract method void dispatch ( CallRunner )
has been removed from this class.
A client program may be interrupted by
NoSuchMethodError exception.
hbase-server-1.0.0.jar, RpcScheduler.class package org.apache.hadoop.hbase.ipc
[−] RpcScheduler.dispatch ( CallRunner p1 ) [abstract] : void 1
org/apache/hadoop/hbase/ipc/RpcScheduler.dispatch:(Lorg/apache/hadoop/hbase/ipc/CallRunner;)V
Change Result
Return value type has been changed from void
to boolean.
This method has been removed because the
return type is part of the method signature. A
client program may be interrupted by
NoSuchMethodError exception.
1. Following abstract methods have been removed:
[−]interface PriorityFunction (2)
Change Result
Abstract method longgetDeadline (
RPCProtos.RequestHeader, Message ) has been
removed from this interface.
A client program may be interrupted by
NoSuchMethodError exception.
Abstract method int getPriority (
RPCProtos.RequestHeader, Message ) has been
removed from this interface.
A client program may be interrupted by
NoSuchMethodError exception.
203.2.8. Server API changes:
[−] class RpcServer (12)
Change Result
Type of field CurCall has been changed from
java.lang.ThreadLocal<RpcServer.Call> to
java.lang.ThreadLocal<RpcCall>.
A client program may be interrupted by
NoSuchFieldError exception.
This class became abstract. A client program may be interrupted by
InstantiationError exception.
Abstract method int getNumOpenConnections ( )
has been added to this class.
This class became abstract and a client program
may be interrupted by InstantiationError
exception.
Field callQueueSize of type
org.apache.hadoop.hbase.util.Counter has been
removed from this class.
A client program may be interrupted by
NoSuchFieldError exception.
772
Field connectionList of type
java.util.List<RpcServer.Connection> has been
removed from this class.
A client program may be interrupted by
NoSuchFieldError exception.
Field maxIdleTime of type int has been removed
from this class.
A client program may be interrupted by
NoSuchFieldError exception.
Field numConnections of type int has been
removed from this class.
A client program may be interrupted by
NoSuchFieldError exception.
Field port of type int has been removed from
this class.
A client program may be interrupted by
NoSuchFieldError exception.
Field purgeTimeout of type long has been
removed from this class.
A client program may be interrupted by
NoSuchFieldError exception.
Field responder of type RpcServer.Responder
has been removed from this class.
A client program may be interrupted by
NoSuchFieldError exception.
Field socketSendBufferSize of type int has been
removed from this class.
A client program may be interrupted by
NoSuchFieldError exception.
Field thresholdIdleConnections of type int has
been removed from this class.
A client program may be interrupted by
NoSuchFieldError exception.
Following abstract method has been removed:
Change Result
Abstract method Pair<Message,CellScanner>call
( BlockingService,
Descriptors.MethodDescriptor, Message,
CellScanner, long, MonitoredRPCHandler ) has
been removed from this interface.
A client program may be interrupted by
NoSuchMethodError exception.
203.2.9. Replication and WAL changes:
HBASE-18733: WALKey has been purged completely in HBase 2.0. Following are the changes to the
WALKey:
[−] classWALKey (8)
Change Result
Access level of field clusterIds has been changed
from protected to private.
A client program may be interrupted by
IllegalAccessError exception.
Access level of field compressionContext has
been changed from protected to private.
A client program may be interrupted by
IllegalAccessError exception.
Access level of field encodedRegionName has
been changed from protected to private.
A client program may be interrupted by
IllegalAccessError exception.
Access level of field tablename has been
changed from protectedto private.
A client program may be interrupted by
IllegalAccessError exception.
Access level of field writeTime has been changed
from protectedto private.
A client program may be interrupted by
IllegalAccessError exception.
773
Following fields have been removed:
Change Result
Field LOG of type
org.apache.commons.logging.Log has been
removed from this class.
A client program may be interrupted by
NoSuchFieldError exception.
Field VERSION of type WALKey.Version has been
removed from this class.
A client program may be interrupted by
NoSuchFieldError exception.
Field logSeqNum of type long has been removed
from this class.
A client program may be interrupted by
NoSuchFieldError exception.
Following are the changes to the WALEdit.class: hbase-server-1.0.0.jar, WALEdit.class package
org.apache.hadoop.hbase.regionserver.wal
WALEdit.getCompaction ( Cell kv ) [static] : WALProtos.CompactionDescriptor (1)
org/apache/hadoop/hbase/regionserver/wal/WALEdit.getCompaction:(Lorg/apache/hadoop/hbase/Ce
ll;)Lorg/apache/hadoop/hbase/protobuf/generated/WALProtos$CompactionDescriptor;
Change Result
Return value type has been changed from
org.apache.hadoop.hbase.protobuf.generated.W
ALProtos.CompactionDescriptor to
org.apache.hadoop.hbase.shaded.protobuf.gener
ated.WALProtos.CompactionDescriptor.
This method has been removed because the
return type is part of the method signature. A
client program may be interrupted by
NoSuchMethodError exception.
WALEdit.getFlushDescriptor ( Cell cell ) [static] : WALProtos.FlushDescriptor (1)
org/apache/hadoop/hbase/regionserver/wal/WALEdit.getFlushDescriptor:(Lorg/apache/hadoop/hbas
e/Cell;)Lorg/apache/hadoop/hbase/protobuf/generated/WALProtos$FlushDescriptor;
Change Result
Return value type has been changed from
org.apache.hadoop.hbase.protobuf.generated.W
ALProtos.FlushDescriptor to
org.apache.hadoop.hbase.shaded.protobuf.gener
ated.WALProtos.FlushDescriptor.
This method has been removed because the
return type is part of the method signature. A
client program may be interrupted by
NoSuchMethodError exception.
WALEdit.getRegionEventDescriptor ( Cell cell ) [static] : WALProtos.RegionEventDescriptor
(1)
org/apache/hadoop/hbase/regionserver/wal/WALEdit.getRegionEventDescriptor:(Lorg/apache/hado
op/hbase/Cell;)Lorg/apache/hadoop/hbase/protobuf/generated/WALProtos$RegionEventDescriptor;
Change Result
774
Return value type has been changed from
org.apache.hadoop.hbase.protobuf.generated.W
ALProtos.RegionEventDescriptor to
org.apache.hadoop.hbase.shaded.protobuf.gener
ated.WALProtos.RegionEventDescriptor.
This method has been removed because the
return type is part of the method signature. A
client program may be interrupted by
NoSuchMethodError exception.
Following is the change to the WALKey.class: package org.apache.hadoop.hbase.wal
WALKey.getBuilder ( WALCellCodec.ByteStringCompressor compressor ) :
WALProtos.WALKey.Builder 1
org/apache/hadoop/hbase/wal/WALKey.getBuilder:(Lorg/apache/hadoop/hbase/regionserver/wal/W
ALCellCodec$ByteStringCompressor;)Lorg/apache/hadoop/hbase/protobuf/generated/WALProtos$W
ALKey$Builder;
Change Result
Return value type has been changed from
org.apache.hadoop.hbase.protobuf.generated.W
ALProtos.WALKey.Builder to
org.apache.hadoop.hbase.shaded.protobuf.gener
ated.WALProtos.WALKey.Builder.
This method has been removed because the
return type is part of the method signature. A
client program may be interrupted by
NoSuchMethodError exception.
203.2.10. Deprecated APIs or coprocessor:
HBASE-16769 - PB references from MasterObserver and RegionServerObserver has been removed.
203.2.11. Admin Interface API changes:
You cannot administer an HBase 2.0 cluster with an HBase 1.0 client that includes RelicationAdmin,
ACC, Thrift and REST usage of Admin ops. Methods returning protobufs have been changed to
return POJOs instead. pb is not used in the APIs anymore. Returns have changed from void to
Future for async methods. HBASE-18106 - Admin.listProcedures and Admin.listLocks were renamed
to getProcedures and getLocks. MapReduce makes use of Admin doing following
admin.getClusterStatus() to calcluate Splits.
•Thrift usage of Admin API: compact(ByteBuffer) createTable(ByteBuffer,
List<ColumnDescriptor>) deleteTable(ByteBuffer) disableTable(ByteBuffer)
enableTable(ByteBuffer) getTableNames() majorCompact(ByteBuffer)
•REST usage of Admin API: hbase-rest org.apache.hadoop.hbase.rest RootResource getTableList()
TableName[] tableNames = servlet.getAdmin().listTableNames(); SchemaResource
delete(UriInfo) Admin admin = servlet.getAdmin(); update(TableSchemaModel, boolean,
UriInfo) Admin admin = servlet.getAdmin(); StorageClusterStatusResource get(UriInfo)
ClusterStatus status = servlet.getAdmin().getClusterStatus(); StorageClusterVersionResource
get(UriInfo) model.setVersion(servlet.getAdmin().getClusterStatus().getHBaseVersion());
TableResource exists() return servlet.getAdmin().tableExists(TableName.valueOf(table));
Following are the changes to the Admin interface:
775
[−] interface Admin (9)
Change Result
Abstract method createTableAsync (
HTableDescriptor, byte[ ][ ] ) has been removed
from this interface.
A client program may be interrupted by
NoSuchMethodError exception.
Abstract method disableTableAsync (
TableName ) has been removed from this
interface.
A client program may be interrupted by
NoSuchMethodError exception.
Abstract method enableTableAsync ( TableName
) has been removed from this interface.
A client program may be interrupted by
NoSuchMethodError exception.
Abstract method getCompactionState (
TableName ) has been removed from this
interface.
A client program may be interrupted by
NoSuchMethodError exception.
Abstract method getCompactionStateForRegion (
byte[ ] ) has been removed from this interface.
A client program may be interrupted by
NoSuchMethodError exception.
Abstract method isSnapshotFinished (
HBaseProtos.SnapshotDescription ) has been
removed from this interface.
A client program may be interrupted by
NoSuchMethodError exception.
Abstract method snapshot ( String, TableName,
HBaseProtos.SnapshotDescription.Type ) has
been removed from this interface.
A client program may be interrupted by
NoSuchMethodError exception.
Abstract method snapshot (
HBaseProtos.SnapshotDescription ) has been
removed from this interface.
A client program may be interrupted by
NoSuchMethodError exception.
Abstract method takeSnapshotAsync (
HBaseProtos.SnapshotDescription ) has been
removed from this interface.
A client program may be interrupted by
NoSuchMethodError exception.
Following are the changes to the Admin.class: hbase-client-1.0.0.jar, Admin.class package
org.apache.hadoop.hbase.client
[−] Admin.createTableAsync ( HTableDescriptor p1, byte[ ][ ] p2 ) [abstract] : void 1
org/apache/hadoop/hbase/client/Admin.createTableAsync:(Lorg/apache/hadoop/hbase/HTableDescr
iptor;[[B)V
Change Result
Return value type has been changed from void
to java.util.concurrent.Future<java.lang.Void>.
This method has been removed because the
return type is part of the method signature. A
client program may be interrupted by
NoSuchMethodError exception.
[−] Admin.disableTableAsync ( TableName p1 ) [abstract] : void 1
org/apache/hadoop/hbase/client/Admin.disableTableAsync:(Lorg/apache/hadoop/hbase/TableName;
)V
776
Change Result
Return value type has been changed from void
to java.util.concurrent.Future<java.lang.Void>.
This method has been removed because the
return type is part of the method signature. A
client program may be interrupted by
NoSuchMethodError exception.
Admin.enableTableAsync ( TableName p1 ) [abstract] : void 1
org/apache/hadoop/hbase/client/Admin.enableTableAsync:(Lorg/apache/hadoop/hbase/TableName;)
V
Change Result
Return value type has been changed from void
to java.util.concurrent.Future<java.lang.Void>.
This method has been removed because the
return type is part of the method signature. A
client program may be interrupted by
NoSuchMethodError exception.
[−] Admin.getCompactionState ( TableName p1 ) [abstract] :
AdminProtos.GetRegionInfoResponse.CompactionState 1
org/apache/hadoop/hbase/client/Admin.getCompactionState:(Lorg/apache/hadoop/hbase/TableNam
e;)Lorg/apache/hadoop/hbase/protobuf/generated/AdminProtos$GetRegionInfoResponse$Compacti
onState;
Change Result
Return value type has been changed from
org.apache.hadoop.hbase.protobuf.generated.Ad
minProtos.GetRegionInfoResponse.CompactionS
tate to CompactionState.
This method has been removed because the
return type is part of the method signature. A
client program may be interrupted by
NoSuchMethodError exception.
[−] Admin.getCompactionStateForRegion ( byte[ ] p1 ) [abstract] :
AdminProtos.GetRegionInfoResponse.CompactionState 1
org/apache/hadoop/hbase/client/Admin.getCompactionStateForRegion:([B)Lorg/apache/hadoop/hba
se/protobuf/generated/AdminProtos$GetRegionInfoResponse$CompactionState;
Change Result
Return value type has been changed from
org.apache.hadoop.hbase.protobuf.generated.Ad
minProtos.GetRegionInfoResponse.CompactionS
tate to CompactionState.
This method has been removed because the
return type is part of the method signature. A
client program may be interrupted by
NoSuchMethodError exception.
203.2.12. HTableDescriptor and HColumnDescriptor changes
HTableDescriptor and HColumnDescriptor has become interfaces and you can create it through
Builders. HCD has become CFD. It no longer implements writable interface. package
org.apache.hadoop.hbase
777
[−] class HColumnDescriptor (1)
Change Result
Removed super-interface
org.apache.hadoop.io.WritableComparable<HCo
lumnDescriptor>.
A client program may be interrupted by
NoSuchMethodError exception.
HColumnDescriptor in 1.0.0 {code} @InterfaceAudience.Public @InterfaceStability.Evolving public
class HColumnDescriptor implements WritableComparable<HColumnDescriptor> { {code}
HColumnDescriptor in 2.0 {code} @InterfaceAudience.Public @Deprecated // remove it in 3.0 public
class HColumnDescriptor implements ColumnFamilyDescriptor, Comparable<HColumnDescriptor>
{ {code}
For META_TABLEDESC, the maker method had been deprecated already in HTD in 1.0.0.
OWNER_KEY is still in HTD.
class HTableDescriptor (3)
Change Result
Removed super-interface
org.apache.hadoop.io.WritableComparable<HTa
bleDescriptor>.
A client program may be interrupted by
NoSuchMethodError exception.
Field META_TABLEDESC of type
HTableDescriptor has been removed from this
class.
A client program may be interrupted by
NoSuchFieldError exception.
hbase-client-1.0.0.jar, HTableDescriptor.class package org.apache.hadoop.hbase
[−] HTableDescriptor.getColumnFamilies ( ) : HColumnDescriptor[ ] (1)
org/apache/hadoop/hbase/HTableDescriptor.getColumnFamilies:()[Lorg/apache/hadoop/hbase/HCol
umnDescriptor;
[−] class HColumnDescriptor (1)
Change Result
Return value type has been changed from
HColumnDescriptor[]to
client.ColumnFamilyDescriptor[].
This method has been removed because the
return type is part of the method signature. A
client program may be interrupted by
NoSuchMethodError exception.
[−] HTableDescriptor.getCoprocessors ( ) : List<String> (1)
org/apache/hadoop/hbase/HTableDescriptor.getCoprocessors:()Ljava/util/List;
Change Result
778
Return value type has been changed from
java.util.List<java.lang.String> to
java.util.Collection.
This method has been removed because the
return type is part of the method signature. A
client program may be interrupted by
NoSuchMethodError exception.
•HBASE-12990 MetaScanner is removed and it is replaced by MetaTableAccessor.
HTableWrapper changes:
hbase-server-1.0.0.jar, HTableWrapper.class package org.apache.hadoop.hbase.client
[−] HTableWrapper.createWrapper ( List<HTableInterface> openTables, TableName
tableName, CoprocessorHost.Environment env, ExecutorService pool ) [static] :
HTableInterface 1
org/apache/hadoop/hbase/client/HTableWrapper.createWrapper:(Ljava/util/List;Lorg/apache/hadoo
p/hbase/TableName;Lorg/apache/hadoop/hbase/coprocessor/CoprocessorHost$Environment;Ljava/
util/concurrent/ExecutorService;)Lorg/apache/hadoop/hbase/client/HTableInterface;
Change Result
Return value type has been changed from
HTableInterface to Table.
This method has been removed because the
return type is part of the method signature. A
client program may be interrupted by
NoSuchMethodError exception.
•HBASE-12586: Delete all public HTable constructors and delete
ConnectionManager#{delete,get}Connection.
•HBASE-9117: Remove HTablePool and all HConnection pooling related APIs.
•HBASE-13214: Remove deprecated and unused methods from HTable class Following are the
changes to the Table interface:
[−] interface Table (4)
Change Result
Abstract method batch ( List<?> ) has been
removed from this interface.
A client program may be interrupted by
NoSuchMethodError exception.
Abstract method batchCallback ( List<?>,
Batch.Callback<R> )has been removed from this
interface.
A client program may be interrupted by
NoSuchMethodError exception.
Abstract method getWriteBufferSize ( ) has been
removed from this interface.
A client program may be interrupted by
NoSuchMethodError exception.
Abstract method setWriteBufferSize ( long ) has
been removed from this interface.
A client program may be interrupted by
NoSuchMethodError exception.
203.2.13. Deprecated buffer methods in Table (in 1.0.1) and removed in 2.0.0
•HBASE-13298- Clarify if Table.{set|get}WriteBufferSize() is deprecated or not.
779
•LockTimeoutException and OperationConflictException classes have been removed.
203.2.14. class OperationConflictException (1)
Change Result
This class has been removed. A client program may be interrupted by
NoClassDefFoundErrorexception.
203.2.15. class class LockTimeoutException (1)
Change Result
This class has been removed. A client program may be interrupted by
NoClassDefFoundErrorexception.
203.2.16. Filter API changes:
Following methods have been removed: package org.apache.hadoop.hbase.filter
[−] class Filter (2)
Change Result
Abstract method getNextKeyHint ( KeyValue )
has been removed from this class.
A client program may be interrupted by
NoSuchMethodError exception.
Abstract method transform ( KeyValue ) has
been removed from this class.
A client program may be interrupted by
NoSuchMethodError exception.
•HBASE-12296 Filters should work with ByteBufferedCell.
•HConnection is removed in HBase 2.0.
•RegionLoad and ServerLoad internally moved to shaded PB.
[−] class RegionLoad (1)
Change Result
Type of field regionLoadPB has been changed
from
protobuf.generated.ClusterStatusProtos.RegionL
oad to
shaded.protobuf.generated.ClusterStatusProtos.
RegionLoad.
A client program may be interrupted by
NoSuchFieldError exception.
•HBASE-15783:AccessControlConstants#OP_ATTRIBUTE_ACL_STRATEGY_CELL_FIRST is not used
any more. package org.apache.hadoop.hbase.security.access
[−] interface AccessControlConstants (3)
Change Result
780
Field OP_ATTRIBUTE_ACL_STRATEGY of type
java.lang.Stringhas been removed from this
interface.
A client program may be interrupted by
NoSuchFieldError exception.
Field
OP_ATTRIBUTE_ACL_STRATEGY_CELL_FIRST of
type byte[] has been removed from this
interface.
A client program may be interrupted by
NoSuchFieldError exception.
Field OP_ATTRIBUTE_ACL_STRATEGY_DEFAULT
of type byte[] has been removed from this
interface.
A client program may be interrupted by
NoSuchFieldError exception.
ServerLoad returns long instead of int 1
hbase-client-1.0.0.jar, ServerLoad.class package org.apache.hadoop.hbase
[−] ServerLoad.getNumberOfRequests ( ) : int 1
org/apache/hadoop/hbase/ServerLoad.getNumberOfRequests:()I
Change Result
Return value type has been changed from int to
long.
This method has been removed because the
return type is part of the method signature. A
client program may be interrupted by
NoSuchMethodError exception.
[−] ServerLoad.getReadRequestsCount ( ) : int 1
org/apache/hadoop/hbase/ServerLoad.getReadRequestsCount:()I
Change Result
Return value type has been changed from int to
long.
This method has been removed because the
return type is part of the method signature. A
client program may be interrupted by
NoSuchMethodError exception.
[−] ServerLoad.getTotalNumberOfRequests ( ) : int 1
org/apache/hadoop/hbase/ServerLoad.getTotalNumberOfRequests:()I
Change Result
Return value type has been changed from int to
long.
This method has been removed because the
return type is part of the method signature. A
client program may be interrupted by
NoSuchMethodError exception.
[−]ServerLoad.getWriteRequestsCount ( ) : int 1
org/apache/hadoop/hbase/ServerLoad.getWriteRequestsCount:()I
781
Change Result
Return value type has been changed from int to
long.
This method has been removed because the
return type is part of the method signature. A
client program may be interrupted by
NoSuchMethodError exception.
•HBASE-13636 Remove deprecation for HBASE-4072 (Reading of zoo.cfg)
•HConstants are removed. HBASE-16040 Remove configuration "hbase.replication"
[−]class HConstants (6)
Change Result
Field
DEFAULT_HBASE_CONFIG_READ_ZOOKEEPER_C
ONFIG of type boolean has been removed from
this class.
A client program may be interrupted by
NoSuchFieldError exception.
Field
HBASE_CONFIG_READ_ZOOKEEPER_CONFIG of
type java.lang.String has been removed from
this class.
A client program may be interrupted by
NoSuchFieldError exception.
Field REPLICATION_ENABLE_DEFAULT of type
boolean has been removed from this class.
A client program may be interrupted by
NoSuchFieldError exception.
Field REPLICATION_ENABLE_KEY of type
java.lang.String has been removed from this
class.
A client program may be interrupted by
NoSuchFieldError exception.
Field ZOOKEEPER_CONFIG_NAME of type
java.lang.String has been removed from this
class.
A client program may be interrupted by
NoSuchFieldError exception.
Field ZOOKEEPER_USEMULTI of type
java.lang.String has been removed from this
class.
A client program may be interrupted by
NoSuchFieldError exception.
•HBASE-18732: [compat 1-2] HBASE-14047 removed Cell methods without deprecation cycle.
[−]interface Cell 5
Change Result
Abstract method getFamily ( ) has been removed
from this interface.
A client program may be interrupted by
NoSuchMethodError exception.
Abstract method getMvccVersion ( ) has been
removed from this interface.
A client program may be interrupted by
NoSuchMethodError exception.
Abstract method getQualifier ( ) has been
removed from this interface.
A client program may be interrupted by
NoSuchMethodError exception.
Abstract method getRow ( ) has been removed
from this interface.
A client program may be interrupted by
NoSuchMethodError exception.
782
Abstract method getValue ( ) has been removed
from this interface.
A client program may be interrupted by
NoSuchMethodError exception.
•HBASE-18795:Expose KeyValue.getBuffer() for tests alone. Allows KV#getBuffer in tests only that
was deprecated previously.
203.2.17. Region scanner changes:
[−]interface RegionScanner (1)
Change Result
Abstract method boolean nextRaw ( List<Cell>,
int ) has been removed from this interface.
A client program may be interrupted by
NoSuchMethodError exception.
203.2.18. StoreFile changes:
[−] class StoreFile (1)
Change Result
This class became interface. A client program may be interrupted by
IncompatibleClassChangeError or
InstantiationError exception dependent on the
usage of this class.
203.2.19. Mapreduce changes:
HFile*Format has been removed in HBase 2.0.
203.2.20. ClusterStatus changes:
HBASE-15843: Replace RegionState.getRegionInTransition() Map with a Set hbase-client-1.0.0.jar,
ClusterStatus.class package org.apache.hadoop.hbase
[−] ClusterStatus.getRegionsInTransition ( ) : Map<String,RegionState> 1
org/apache/hadoop/hbase/ClusterStatus.getRegionsInTransition:()Ljava/util/Map;
Change Result
Return value type has been changed from
java.util.Map<java.lang.String,master.RegionStat
e> to java.util.List<master.RegionState>.
This method has been removed because the
return type is part of the method signature. A
client program may be interrupted by
NoSuchMethodError exception.
Other changes in ClusterStatus include removal of convert methods that were no longer necessary
after purge of PB from API.
783
203.2.21. Purge of PBs from API
PBs have been deprecated in APIs in HBase 2.0.
[−] HBaseSnapshotException.getSnapshotDescription ( ) : HBaseProtos.SnapshotDescription
1
org/apache/hadoop/hbase/snapshot/HBaseSnapshotException.getSnapshotDescription:()Lorg/apach
e/hadoop/hbase/protobuf/generated/HBaseProtos$SnapshotDescription;
Change Result
Return value type has been changed from
org.apache.hadoop.hbase.protobuf.generated.H
BaseProtos.SnapshotDescription to
org.apache.hadoop.hbase.client.SnapshotDescrip
tion.
This method has been removed because the
return type is part of the method signature. A
client program may be interrupted by
NoSuchMethodError exception.
•HBASE-15609: Remove PB references from Result, DoubleColumnInterpreter and any such
public facing class for 2.0. hbase-client-1.0.0.jar, Result.class package
org.apache.hadoop.hbase.client
[−] Result.getStats ( ) : ClientProtos.RegionLoadStats 1
org/apache/hadoop/hbase/client/Result.getStats:()Lorg/apache/hadoop/hbase/protobuf/generated/Cli
entProtos$RegionLoadStats;
Change Result
Return value type has been changed from
org.apache.hadoop.hbase.protobuf.generated.Cli
entProtos.RegionLoadStats to RegionLoadStats.
This method has been removed because the
return type is part of the method signature. A
client program may be interrupted by
NoSuchMethodError exception.
203.2.22. REST changes:
hbase-rest-1.0.0.jar, Client.class package org.apache.hadoop.hbase.rest.client
[−] Client.getHttpClient ( ) : HttpClient 1
org/apache/hadoop/hbase/rest/client/Client.getHttpClient:()Lorg/apache/commons/httpclient/HttpCli
ent
Change Result
Return value type has been changed from
org.apache.commons.httpclient.HttpClient to
org.apache.http.client.HttpClient.
This method has been removed because the
return type is part of the method signature. A
client program may be interrupted by
NoSuchMethodError exception.
hbase-rest-1.0.0.jar, Response.class package org.apache.hadoop.hbase.rest.client
784
[−] Response.getHeaders ( ) : Header[ ] 1
org/apache/hadoop/hbase/rest/client/Response.getHeaders:()[Lorg/apache/commons/httpclient/Head
er;
Change Result
Return value type has been changed from
org.apache.commons.httpclient.Header[] to
org.apache.http.Header[].
This method has been removed because the
return type is part of the method signature. A
client program may be interrupted by
NoSuchMethodError exception.
203.2.23. PrettyPrinter changes:
hbase-server-1.0.0.jar, HFilePrettyPrinter.class package org.apache.hadoop.hbase.io.hfile
[−]HFilePrettyPrinter.processFile ( Path file ) : void 1
org/apache/hadoop/hbase/io/hfile/HFilePrettyPrinter.processFile:(Lorg/apache/hadoop/fs/Path;)V
Change Result
Return value type has been changed from void
to int.
This method has been removed because the
return type is part of the method signature. A
client program may be interrupted by
NoSuchMethodError exception.
203.2.24. AccessControlClient changes:
HBASE-13171 Change AccessControlClient methods to accept connection object to reduce setup
time. Parameters have been changed in the following methods:
•hbase-client-1.2.7-SNAPSHOT.jar, AccessControlClient.class package
org.apache.hadoop.hbase.security.access AccessControlClient.getUserPermissions (
Configuration conf, String tableRegex ) [static] : List<UserPermission> DEPRECATED
org/apache/hadoop/hbase/security/access/AccessControlClient.getUserPermissions:(Lorg/apache/
hadoop/conf/Configuration;Ljava/lang/String;)Ljava/util/List;
•AccessControlClient.grant ( Configuration conf, String namespace, String userName,
Permission.Action… actions )[static] : void DEPRECATED
org/apache/hadoop/hbase/security/access/AccessControlClient.grant:(Lorg/apache/hadoop/conf/
Configuration;Ljava/lang/String;Ljava/lang/String;[Lorg/apache/hadoop/hbase/security/access/Pe
rmission$Action;)V
•AccessControlClient.grant ( Configuration conf, String userName, Permission.Action… actions )
[static] : void DEPRECATED
org/apache/hadoop/hbase/security/access/AccessControlClient.grant:(Lorg/apache/hadoop/conf/
Configuration;Ljava/lang/String;[Lorg/apache/hadoop/hbase/security/access/Permission$Action;)
V
•AccessControlClient.grant ( Configuration conf, TableName tableName, String userName, byte[ ]
family, byte[ ] qual,Permission.Action… actions ) [static] : void DEPRECATED
org/apache/hadoop/hbase/security/access/AccessControlClient.grant:(Lorg/apache/hadoop/conf/
785
Configuration;Lorg/apache/hadoop/hbase/TableName;Ljava/lang/String;[B[B[Lorg/apache/hadoo
p/hbase/security/access/Permission$Action;)V
•AccessControlClient.isAccessControllerRunning ( Configuration conf ) [static] : boolean
DEPRECATED
org/apache/hadoop/hbase/security/access/AccessControlClient.isAccessControllerRunning:(Lorg/
apache/hadoop/conf/Configuration;)Z
•AccessControlClient.revoke ( Configuration conf, String namespace, String userName,
Permission.Action… actions )[static] : void DEPRECATED
org/apache/hadoop/hbase/security/access/AccessControlClient.revoke:(Lorg/apache/hadoop/conf
/Configuration;Ljava/lang/String;Ljava/lang/String;[Lorg/apache/hadoop/hbase/security/access/P
ermission$Action;)V
•AccessControlClient.revoke ( Configuration conf, String userName, Permission.Action… actions )
[static] : void DEPRECATED
org/apache/hadoop/hbase/security/access/AccessControlClient.revoke:(Lorg/apache/hadoop/conf
/Configuration;Ljava/lang/String;[Lorg/apache/hadoop/hbase/security/access/Permission$Action;
)V
•AccessControlClient.revoke ( Configuration conf, TableName tableName, String username, byte[
] family, byte[ ] qualifier,Permission.Action… actions ) [static] : void DEPRECATED
org/apache/hadoop/hbase/security/access/AccessControlClient.revoke:(Lorg/apache/hadoop/conf
/Configuration;Lorg/apache/hadoop/hbase/TableName;Ljava/lang/String;[B[B[Lorg/apache/hado
op/hbase/security/access/Permission$Action;)V
•HBASE-18731: [compat 1-2] Mark protected methods of QuotaSettings that touch Protobuf
internals as IA.Private
786

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