Apache HBase ™ Reference Guide



User Manual:

Open the PDF directly: View PDF PDF.
Page Count: 729

DownloadApache HBase ™ Reference Guide
Open PDF In BrowserView PDF
Apache HBase ™ Reference Guide
Apache HBase Team
Version 2.1.0

Preface. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Getting Started. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2. Quick Start - Standalone HBase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
Apache HBase Configuration. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
3. Configuration Files . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
4. Basic Prerequisites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
5. HBase run modes: Standalone and Distributed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
6. Running and Confirming Your Installation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
7. Default Configuration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
8. Example Configurations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
9. The Important Configurations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
10. Dynamic Configuration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
Upgrading. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
11. HBase version number and compatibility. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86
12. Rollback . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
13. Upgrade Paths . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
The Apache HBase Shell . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106
14. Scripting with Ruby . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107
15. Running the Shell in Non-Interactive Mode . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108
16. HBase Shell in OS Scripts. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109
17. Read HBase Shell Commands from a Command File . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111
18. Passing VM Options to the Shell. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113
19. Shell Tricks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114
Data Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120
20. Conceptual View . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121
21. Physical View . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123
22. Namespace . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124
23. Table . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126
24. Row . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127
25. Column Family . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128
26. Cells. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129
27. Data Model Operations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130
28. Versions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132
29. Sort Order . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137
30. Column Metadata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138
31. Joins . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139
32. ACID . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140

HBase and Schema Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141
33. Schema Creation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142
34. Table Schema Rules Of Thumb . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143
RegionServer Sizing Rules of Thumb . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144
35. On the number of column families . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145
36. Rowkey Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146
37. Number of Versions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153
38. Supported Datatypes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154
39. Joins . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155
40. Time To Live (TTL) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 156
41. Keeping Deleted Cells . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157
42. Secondary Indexes and Alternate Query Paths . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161
43. Constraints . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163
44. Schema Design Case Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164
45. Operational and Performance Configuration Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174
46. Special Cases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177
HBase and MapReduce . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 178
47. HBase, MapReduce, and the CLASSPATH . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179
48. MapReduce Scan Caching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 184
49. Bundled HBase MapReduce Jobs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185
50. HBase as a MapReduce Job Data Source and Data Sink. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 186
51. Writing HFiles Directly During Bulk Import. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 187
52. RowCounter Example. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 188
53. Map-Task Splitting. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 189
54. HBase MapReduce Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 190
55. Accessing Other HBase Tables in a MapReduce Job . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 197
56. Speculative Execution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 198
57. Cascading. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 199
Securing Apache HBase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 200
58. Using Secure HTTP (HTTPS) for the Web UI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 201
59. Using SPNEGO for Kerberos authentication with Web UIs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 202
60. Secure Client Access to Apache HBase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 204
61. Simple User Access to Apache HBase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 211
62. Securing Access to HDFS and ZooKeeper . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 214
63. Securing Access To Your Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 216
64. Security Configuration Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 242
Architecture. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 245
65. Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 246
66. Catalog Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 248
67. Client. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 249
68. Client Request Filters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 253

69. Master . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 259
70. RegionServer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 260
71. Regions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 280
72. Bulk Loading. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 307
73. HDFS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 309
74. Timeline-consistent High Available Reads . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 310
75. Storing Medium-sized Objects (MOB). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 322
In-memory Compaction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 327
76. Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 328
77. Enabling. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 329
Apache HBase APIs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 331
78. Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 332
Apache HBase External APIs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 334
79. REST . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 335
80. Thrift. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 346
81. C/C++ Apache HBase Client . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 347
82. Using Java Data Objects (JDO) with HBase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 348
83. Scala . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 351
84. Jython . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 353
Thrift API and Filter Language . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 356
85. Filter Language . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 357
Apache HBase Coprocessors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 363
86. Coprocessor Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 364
87. Types of Coprocessors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 365
88. Loading Coprocessors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 367
89. Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 372
90. Guidelines For Deploying A Coprocessor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 378
91. Restricting Coprocessor Usage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 380
Apache HBase Performance Tuning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 381
92. Operating System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 382
93. Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 383
94. Java . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 385
95. HBase Configurations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 386
96. ZooKeeper . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 390
97. Schema Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 391
98. HBase General Patterns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 395
99. Writing to HBase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 396
100. Reading from HBase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 399
101. Deleting from HBase. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 404
102. HDFS. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 405
103. Amazon EC2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 407

104. Collocating HBase and MapReduce. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 408
105. Case Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 409
Troubleshooting and Debugging Apache HBase. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 410
106. General Guidelines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 411
107. Logs. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 412
108. Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 416
109. Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 417
110. Client . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 425
111. MapReduce . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 429
112. NameNode . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 431
113. Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 434
114. RegionServer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 435
115. Master . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 444
116. ZooKeeper . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 446
117. Amazon EC2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 447
118. HBase and Hadoop version issues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 448
119. HBase and HDFS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 449
120. Running unit or integration tests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 452
121. Case Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 453
122. Cryptographic Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 454
123. Operating System Specific Issues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 455
124. JDK Issues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 456
Apache HBase Case Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 457
125. Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 458
126. Schema Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 459
127. Performance/Troubleshooting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 460
Apache HBase Operational Management. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 464
128. HBase Tools and Utilities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 465
129. Region Management. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 484
130. Node Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 485
131. HBase Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 491
132. HBase Monitoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 496
133. Cluster Replication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 500
134. Running Multiple Workloads On a Single Cluster . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 513
135. HBase Backup. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 521
136. HBase Snapshots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 523
137. Storing Snapshots in Microsoft Azure Blob Storage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 527
138. Capacity Planning and Region Sizing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 528
139. Table Rename. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 532
140. RegionServer Grouping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 533
141. Region Normalizer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 537

Building and Developing Apache HBase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 542
142. Getting Involved . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 543
143. Apache HBase Repositories. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 546
144. IDEs. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 547
145. Building Apache HBase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 550
146. Releasing Apache HBase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 554
147. Voting on Release Candidates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 562
148. Announcing Releases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 563
149. Generating the HBase Reference Guide. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 564
150. Updating hbase.apache.org. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 565
151. Tests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 566
152. Developer Guidelines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 580
Unit Testing HBase Applications. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 595
153. JUnit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 596
154. Mockito . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 598
155. MRUnit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 600
156. Integration Testing with an HBase Mini-Cluster . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 602
Protobuf in HBase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 604
157. Protobuf. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 605
Procedure Framework (Pv2): HBASE-12439 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 607
158. Procedures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 608
159. Subprocedures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 611
160. ProcedureExecutor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 612
161. Nonces . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 613
162. Wait/Wake/Suspend/Yield . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 614
163. Locking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 615
164. Procedure Types . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 616
165. References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 617
AMv2 Description for Devs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 618
166. Background. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 619
167. New System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 620
168. Procedures Detail . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 621
169. UI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 623
170. Logging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 624
171. Implementation Notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 625
172. New Configs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 626
173. Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 627
ZooKeeper . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 628
174. Using existing ZooKeeper ensemble . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 630
175. SASL Authentication with ZooKeeper . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 631
Community . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 638

176. Decisions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 639
177. Community Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 640
178. Commit Message format . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 641
Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 642
Appendix A: Contributing to Documentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 643
Appendix B: FAQ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 654
Appendix C: hbck In Depth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 657
Appendix D: Access Control Matrix. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 661
Appendix E: Compression and Data Block Encoding In HBase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 667
Appendix F: SQL over HBase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 678
Appendix G: YCSB . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 679
Appendix H: HFile format . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 680
Appendix I: Other Information About HBase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 689
Appendix J: HBase History . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 690
Appendix K: HBase and the Apache Software Foundation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 691
Appendix L: Apache HBase Orca . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 692
Appendix M: Enabling Dapper-like Tracing in HBase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 693
179. Client Modifications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 695
180. Tracing from HBase Shell . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 696
Appendix N: 0.95 RPC Specification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 697
Appendix O: Known Incompatibilities Among HBase Versions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 701
181. HBase 2.0 Incompatible Changes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 702

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
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@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.


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.

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.
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.


Getting Started


Chapter 1. Introduction
Quickstart will get you up and running on a single-node, standalone instance of HBase.


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-2.1.0-bin.tar.gz
$ cd hbase-2.1.0/
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.

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  tags beneath the  tags, which
should be empty in a new HBase install.


Example 1. Example hbase-site.xml for Standalone HBase




Controls whether HBase will check for stream capabilities (hflush/hsync).
Disable this if you intend to run on LocalFileSystem, denoted by a
with the 'file://' scheme, but be mindful of the NOTE below.
WARNING: Setting this to false blinds you to potential data loss and
inconsistent system state in the event of process and/or node failures.
HBase is complaining of an inability to use hsync or hflush it's most
likely not a false positive.

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,


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 >

$ ./bin/hbase shell
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


hbase(main):002:0> list '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
{NAME => 'cf', VERSIONS => '1', EVICT_BLOCKS_ON_CLOSE => 'false',
alse', IN_MEMORY => 'false', CACHE_BLOOMS_ON_WRITE => 'false',
=> '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.


hbase(main):006:0> scan 'test'
3 row(s) in 0.0230 seconds

column=cf:a, timestamp=1421762485768,
column=cf:b, timestamp=1421762491785,
column=cf:c, timestamp=1421762496210,

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'
1 row(s) in 0.0350 seconds

timestamp=1421762485768, value=value1

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

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.


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.



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.


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
Found 7 items

fs -ls /hbase






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.


Running multiple HMaster instances on the same hardware does not make


sense in a production environment, in the same way that running a pseudodistributed 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 localmaster-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 localmaster-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.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


8. Stop HBase.
You can stop HBase the same way as in the quickstart procedure, using the bin/stop-hbase.sh

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
















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

$ 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


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.




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 pseudodistributed 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.


$ bin/start-hbase.sh
node-c.example.com: starting zookeeper, logging to /home/hbuser/hbase-0.98.3hadoop2/bin/../logs/hbase-hbuser-zookeeper-node-c.example.com.out
node-a.example.com: starting zookeeper, logging to /home/hbuser/hbase-0.98.3hadoop2/bin/../logs/hbase-hbuser-zookeeper-node-a.example.com.out
node-b.example.com: starting zookeeper, logging to /home/hbuser/hbase-0.98.3hadoop2/bin/../logs/hbase-hbuser-zookeeper-node-b.example.com.out
starting master, logging to /home/hbuser/hbase-0.98.3-hadoop2/bin/../logs/hbasehbuser-master-node-a.example.com.out
node-c.example.com: starting regionserver, logging to /home/hbuser/hbase-0.98.3hadoop2/bin/../logs/hbase-hbuser-regionserver-node-c.example.com.out
node-b.example.com: starting regionserver, logging to /home/hbuser/hbase-0.98.3hadoop2/bin/../logs/hbase-hbuser-regionserver-node-b.example.com.out
node-b.example.com: starting master, logging to /home/hbuser/hbase-0.98.3hadoop2/bin/../logs/hbase-hbuser-master-nodeb.example.com.out
ZooKeeper starts first, followed by the master, then the RegionServers, and finally the backup
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


node-c jps Output

$ jps
13901 Jps
13639 HQuorumPeer
13737 HRegionServer


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
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.


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.


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

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.
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.
The default policy configuration file used by RPC servers to make authorization decisions on
client requests. Only used if HBase security is enabled.
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.
Configuration file for HBase logging via log4j.
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.


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.


Chapter 4. Basic Prerequisites
This section lists required services and some required system configuration.

The following table summarizes the recommendation of the HBase community wrt deploying on
various Java versions. An entry of "yes" 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 "no" or "Not
Supported" 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. wether compiling / unit tests work, specific operational issues, etc) will also be
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 10


Not Supported


Not Supported

Not Supported




Not Supported

Not Supported




Not Supported

Not Supported


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

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.
HBase uses the local hostname to self-report its IP address.
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 timesynchronization 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

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
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.


Example 2. ulimit Settings on Ubuntu

To configure ulimit settings on Ubuntu, edit /etc/security/limits.conf, which is a spacedelimited 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.





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


Linux Shell
All of the shell scripts that come with HBase rely on the GNU Bash shell.
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:


Hadoop version support matrix

• "S" = supported
• "X" = not supported
• "NT" = Not tested



















































































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+.
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:


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
complaint about missing blocks. For example:

10/12/08 20:10:31 INFO hdfs.DFSClient: Could not obtain block
blk_XXXXXXXXXXXXXXXXXXXXXX_YYYYYYYY from any node: java.io.IOException: No
live nodes
contain current block. Will get new block locations from namenode and
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.


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:



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


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-installationdefinitive-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.


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.




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

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.


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:


a. Add a pointer to your HADOOP_CONF_DIR to the HBASE_CLASSPATH environment variable in hbaseenv.sh.
b. Add











${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.


Chapter 6. Running and Confirming Your
Make sure HDFS is running first. Start and stop the Hadoop HDFS daemons by running bin/starthdfs.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:

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.


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.

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.


The directory shared by region servers and into which HBase persists. The URL should be 'fullyqualified' 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









${hbase.tmp.dir} is set too — usually /tmp — so change this configuration or else all data will be
lost on machine restart.


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




Comma separated list of servers in the ZooKeeper ensemble (This config. should have been




"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.


Max sleep time before retry zookeeper operations in milliseconds, a max time is needed here so
that sleep time won’t grow unboundedly


Directory on the local filesystem to be used as a local storage.


The port the HBase Master should bind to.


The port for the HBase Master web UI. Set to -1 if you do not want a UI instance run.


The bind address for the HBase Master web UI


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.


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.


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.


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.


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).




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.


The port the HBase RegionServer binds to.


The port for the HBase RegionServer web UI Set to -1 if you do not want the RegionServer UI to


The address for the HBase RegionServer web UI

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.


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.




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.


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


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 longread 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.



Interval between messages from the RegionServer to Master in milliseconds.


Period at which we will roll the commit log regardless of how many edits it has.


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


The WAL file reader implementation.


The WAL file writer implementation.


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.


Maximum size of all memstores in a region server before flushes are forced. Defaults to 95% of

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.


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


Maximum amount of time an edit lives in memory before being automatically flushed. Default 1
hour. Set it to 0 to disable automatic flushing.


The name of the Network Interface from which a region server should report its IP address.


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.


A split policy determines when a region should be split. The various other split policies that are





DisabledRegionSplitPolicy, DelimitedKeyPrefixRegionSplitPolicy, KeyPrefixRegionSplitPolicy, and
SteppingSplitPolicy. DisabledRegionSplitPolicy blocks manual region splitting.




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.


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.


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.


Root ZNode for access control lists.


The name of the Network Interface from which a ZooKeeper server should report its IP address.




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.


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.


Port used by ZooKeeper for leader election. See http://hadoop.apache.org/zookeeper/docs/r3.1.1/
zookeeperStarted.html#sc_RunningReplicatedZooKeeper for more information.


Property from ZooKeeper’s config zoo.cfg. The number of ticks that the initial synchronization
phase can take.


Property from ZooKeeper’s config zoo.cfg. The number of ticks that can pass between sending a
request and getting an acknowledgment.


Property from ZooKeeper’s config zoo.cfg. The directory where the snapshot is stored.


Property from ZooKeeper’s config zoo.cfg. The port at which the clients will connect.




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 pseudodistributed.


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


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.


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


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.




The maximum number of concurrent mutation tasks a single HTable instance will send to the


The maximum number of concurrent mutation tasks a single HTable instance will send to a
single region server.


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.


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/HBASE16388 for details.


Number of rows that we try to fetch when calling next on a scanner if it is not served from








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


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.


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.


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.


Client scanner lease period in milliseconds.



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.


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


transition when balancing. Then the cluster’s availability is at least 99% when balancing.


Period at which the region balancer runs in the Master.


Period at which the region normalizer runs in the Master.


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).


Time to sleep in between searches for work (in milliseconds). Used as sleep interval by service
threads such as log roller.


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.


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.



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).


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.









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.


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.


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.




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.


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.


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.


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.


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.




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.


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.


The maximum number of StoreFiles which will be selected for a single minor compaction,











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.


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.




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.


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.


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.offpeak.end.hour are also enabled.


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.




The start of off-peak hours, expressed as an integer between 0 and 23, inclusive. Set to -1 to
disable off-peak.


The end of off-peak hours, expressed as an integer between 0 and 23, inclusive. Set to -1 to
disable off-peak.


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,










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.


Specifies whether to drop pages read/written into the system page cache by major compactions.
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.


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.



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.


Enables StoreFileScanner parallel-seeking in StoreScanner, a feature which can reduce response
latency under special conditions.


The default thread pool size if parallel-seeking feature enabled.


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.


This allows to put non-root multi-level index blocks into the block cache at the time the index is
being written.


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.




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


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


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).


The HFile format version to use for new files. Version 3 adds support for tags in hfiles (See







Enables cache-on-write for inline blocks of a compound Bloom filter.


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.


Whether an HFile block should be added to the block cache when the block is finished.


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


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.


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 clientserver 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


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


Set no delay on rpc socket connections. See http://docs.oracle.com/javase/1.5.0/docs/api/java/net/


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.


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.


Full path to the kerberos keytab file to use for logging in the configured HMaster server


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.




Full path to the kerberos keytab file to use for logging in the configured HRegionServer server


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


The policy configuration file used by RPC servers to make authorization decisions on client
requests. Only used when HBase security is enabled.


List of users or groups (comma-separated), who are allowed full privileges, regardless of stored
ACLs, across the cluster. Only used when HBase security is enabled.


The update interval for master key for authentication tokens in servers in milliseconds. Only
used when HBase security is enabled.


The maximum lifetime in milliseconds after which an authentication token expires. Only used

when HBase security is enabled.


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.


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.


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.


Enables or disables coprocessor loading. If 'false' (disabled), any other coprocessor related
configuration will be ignored.


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.




A comma-separated list of 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, just put it in HBase’s classpath and add the fully qualified class name here. A
coprocessor can also be loaded on demand by setting HTableDescriptor.


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.


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.


The port for the HBase REST server.


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.




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.


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.


Enables running the REST server to support proxy-user mode.


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"


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.


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.



The "core size" of the thread pool. New threads are created on every connection until this many
threads are created.


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


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.


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.


Default frame size when using framed transport, in MB


Use Thrift TCompactProtocol binary serialization protocol.




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.


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.


Enable, if true, that file permissions should be assigned to the files written by the regionserver


File permissions that should be used to write data files when hbase.data.umask.enable is true


Set to true to allow snapshots to be taken / restored / cloned.


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


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



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.


How long we wait on dfs lease recovery in total before giving up.


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


New column family descriptors will use this value as the default number of versions to keep.


If set to true, this configuration parameter enables short-circuit local reads.



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.


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.


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.


Number of bytes in a newly created checksum chunk for HBase-level checksums in hfile blocks.


Name of an algorithm that is used to compute checksums. Possible values are NULL, CRC32,




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


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.


This setting activates the publication by the master of the status of the region server. When a
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.


Implementation of the status publication with a multicast message.


Implementation of the status listener with a multicast message.


Multicast address to use for the status publication by multicast.



Multicast port to use for the status publication by multicast.


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.


Controls whether or not secure authentication is enabled for HBase. Possible values are 'simple'
(no authentication), and 'kerberos'.


Servlet filters for REST service.


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).


Factor Table name when the balancer runs. Default: false.



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/


Set to true to enable protection against cross-site request forgery (CSRF)


A comma-separated list of regular expressions used to match against an HTTP request’s UserAgent 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
enforced. This helps achieve backwards-compatibility with existing automation that has not
been updated to send the CSRF prevention header.


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


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










RegionServerProcedureManager, just put it in HBase’s classpath and add the fully qualified class
name here.







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










MasterProcedureManager, just put it in HBase’s classpath and add the fully qualified class name


Fully qualified name of class implementing coordinated state manager.


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


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.


















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.


This property if enabled, will check whether the labels in the visibility expression are associated
with the user issuing the mutation


The maximum number of threads that the HTTP Server will create in its ThreadPool.


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.


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.


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).


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.


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.


The amount of time in seconds before the mob cache evicts cached mob files. The default value
is 3600 seconds.


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.


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.




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.


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.


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
mob compaction. The default value is 100.


The period that MobCompactionChore runs. The unit is second. The default value is one week.


Implementation of mob compactor, the default one is PartitionedMobCompactor.


The max number of threads used in MobCompactor.




Timeout for master for the snapshot procedure execution.


Timeout for regionservers to keep threads in snapshot request pool waiting.


Number of rows in a batch operation above which a warning will be logged.


Default is 5 minutes. Make it 30 seconds for tests. See HBASE-19794 for some context.


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 hbasesite.xml used by tests).
For Java applications using Maven, including the hbase-shaded-client module is the recommended
dependency when connecting to a cluster:


A basic example hbase-site.xml for client only may look as follows:

The directory shared by region servers.

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


Configuration config = HBaseConfiguration.create();
config.set("hbase.zookeeper.quorum", "localhost"); // Here we are running zookeeper
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 many timeout settings to limit the execution time of different remote operations.
The hbase.rpc.timeout property limits how long an RPC call can run before it times out. You can also



















hbase.rpc.timeout will be used. A higher-level timeout is hbase.client.operation.timeout which is
valid for each client call. Timeout for scan operations is controlled differently. To set it you can use
hbase.client.scanner.timeout.period property.


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 hbaseenv.sh — found in the HBase conf directory might look like.

8.1.1. hbase-site.xml

The directory shared by RegionServers.

Property from ZooKeeper config zoo.cfg.
The directory where the snapshot is stored.

The directory shared by RegionServers.

The mode the cluster will be in. Possible values are
false: standalone and pseudo-distributed setups with managed ZooKeeper
true: fully-distributed with unmanaged ZooKeeper Quorum (see hbase-env.sh)


8.1.2. regionservers
In this file you list the nodes that will run RegionServers. In our case, these nodes are example1example9.


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.
Use rsync to copy the content of the conf directory to all nodes of the cluster.


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 HBASE6389 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
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
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


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.
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,














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 nearfrictionless 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).


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









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

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




org.apache.hadoop.hbase.util.RegionSplitter utility also provides a network-IO-safe rolling split of


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










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

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


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 lateversion 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.

How much time we allow elapse between calls to recover lease.
Should be larger than the dfs timeout.

Down the DFS timeout from 60 to 10 seconds.

And on the NameNode/DataNode side, set the following to enable 'staleness' introduced in HDFS3703, HDFS-3912.


Down the DFS timeout from 60 to 10 seconds.

Down the DFS timeout from 8 * 60 to 10 seconds.

Down from 60 seconds to 3.

Down from 45 seconds to 3 (2 == 3 retries).

Enable stale state in hdfs

Down from default 30 seconds

Enable stale state in hdfs

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
As an alternative, you can use the coprocessor-based JMX implementation provided by HBase. To
enable it, add below property in hbase-site.xml:




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:



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
See example password/access file under $JRE_HOME/lib/management.
To enable SSL communication with password authentication, follow below steps:


#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
-Dcom.sun.management.jmxremote.password.file=your_password file
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:


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)


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
Table 3. Configurations support dynamically change







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.


Chapter 11. HBase version number and
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
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
• 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.


• 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
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


• Metric changes
• Behavioral changes of services
• JMX APIs exposed via the /jmx/ endpoint

• 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/jls13.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.]




Client-Server wire








File Format

N [3:
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.]


Client API




Client Binary




Stable N



Evolving N



Unstable N











Server-Side Limited API Compatibility


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











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














InterfaceAudience.Public interface. All main classes in hbase-client and dependent modules





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


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
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.


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.

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'.


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.

• 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
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.

• 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.


Clean HBase information out of ZooKeeper

[hpnewton@gateway_node.example.com ~]$ zookeeper-client -server
Welcome to ZooKeeper!
JLine support is disabled
rmr /hbase
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.

• 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-upgradebackup
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

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
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
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
Welcome to ZooKeeper!
JLine support is disabled
rmr /hbase
6. Start HBase
7. Verify HBase contents–use the HBase shell to list tables and scan some known values.


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 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.
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
• 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.hregion.percolumnfamilyflush.size.lower. hbase.hregion.percolumnfamilyflush.size.lower.
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
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







• 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
(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
• 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
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 preHBase 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
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..
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 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

public Optional getRegionObserver() {
return Optional.of(this);
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





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:hbaseshaded-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:hbaseshaded-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
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.

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).

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
2. Design refactor so implementers need to fill out less boilerplate and so we can do more compiletime checking (HBASE-17732)
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
3. getTable() has been removed from the CoprocessorEnvrionment, coprocessors should selfmanage 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.

• 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.

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
◦ 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.

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.
• 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 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.


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  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
See shell exercises for example basic shell operation.
Here is a nicely formatted listing of all shell commands by Rajeshbabu Chintaguntla.


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


Chapter 15. Running the Shell in NonInteractive 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.


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













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'
'test1', {NAME => 'cf', DATA_BLOCK_ENCODING => 'NON true
'false', BLOCKSIZE => '65536', IN_MEMORY => 'false'
, 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


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.

echo "describe 'test'" | ./hbase shell -n > /dev/null 2>&1
echo "The status was " $status
if ($status == 0); then
echo "The command succeeded"
echo "The command may have failed."
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.


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',
put 'test', 'row2',
put 'test', 'row3',
put 'test', 'row4',
scan 'test'
get 'test', 'row1'
disable 'test'
enable 'test'




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
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
4 row(s) in 0.0420 seconds


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



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 \
-XX:+PrintGCDetails -Xloggc:$HBASE_HOME/logs/gc-hbase.log" ./bin/hbase shell


Chapter 19. Shell Tricks
19.1. 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:

0 row(s) in 1.0970
0 row(s) in 0.0080

create 't', 'f'
put 't', 'rold', 'f', 'v'

hbase(main):002:0> scan 't'
1 row(s) in 0.0130 seconds

column=f:, timestamp=1378473207660, value=v

hbase(main):003:0> describe 't'
SCOPE => '0', VERSIONS => '1', COMPRESSION => 'NONE', MIN_VERSIONS => '0', TTL => '2
147483647', KEEP_DELETED_CELLS => 'false', BLOCKSIZE => '65536', IN_MEMORY => 'false
', 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
Now you can assign the table to a variable and use the results in jruby shell code.


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
column=f:, timestamp=1331865816290, value=v
1 row(s) in 0.0110 seconds
hbase(main):010:0> t.describe
SCOPE => '0', VERSIONS => '1', COMPRESSION => 'NONE', MIN_VERSIONS => '0', TTL => '2
147483647', KEEP_DELETED_CELLS => 'false', BLOCKSIZE => '65536', IN_MEMORY => 'false
', 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

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
column=f:, timestamp=1378473876949, value=v
1 row(s) in 0.0240 seconds
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.


hbase(main):016 > tables = list('t.*')
1 row(s) in 0.1040 seconds
=> #<#:0x21d377a4>
hbase(main):017:0> tables.map { |t| disable t ; drop
0 row(s) in 2.2510 seconds


=> [nil]

19.2. 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.

19.3. 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


To output in a format that is exactly like that of the HBase log format will take a little messing with

19.4. 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"

19.5. 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
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



# 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)
for i in 1 .. num_regions-1
return results
hbase(main):078:1> end
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.

19.6. Debug
19.6.1. 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 

19.6.2. DEBUG log level
To enable DEBUG level logging in the shell, launch it with the -d option.


$ ./bin/hbase shell -d

19.7. Commands
19.7.1. count
Count command returns the number of rows in a table. It’s quite fast when configured with the
right CACHE

hbase> count '', 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.


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

An HBase table consists of multiple rows.
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.
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.
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.
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.


Chapter 20. 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





= "CNN"



= "CNN.com"



contents:html =



contents:html =



contents:html =

"com.example.ww t5

contents: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.


"com.cnn.www": {
contents: {
t6: contents:html: "..."
t5: contents:html: "..."
t3: contents:html: "..."
anchor: {
t9: anchor:cnnsi.com = "CNN"
t8: anchor:my.look.ca = "CNN.com"
people: {}
"com.example.www": {
contents: {
t5: contents:html: "..."
anchor: {}
people: {
t5: people:author: "John Doe"


Chapter 21. 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



anchor:cnnsi.com = "CNN"



anchor:my.look.ca = "CNN.com"

Row Key

Time Stamp

ColumnFamily contents:



contents:html = "…"



contents:html = "…"



contents:html = "…"

Table 8. ColumnFamily contents

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.


Chapter 22. 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.

22.1. 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:

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'} 22.2. 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 124 Example 8. Examples #namespace=foo and table qualifier=bar create 'foo:bar', 'fam' #namespace=default and table qualifier=bar create 'bar', 'fam' 125 Chapter 23. Table Tables are declared up front at schema definition time. 126 Chapter 24. 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. 127 Chapter 25. 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. 128 Chapter 26. Cells A {row, column, version} tuple exactly specifies a cell in HBase. Cell content is uninterpreted bytes 129 Chapter 27. Data Model Operations The four primary data model operations are Get, Put, Scan, and Delete. Operations are applied via Table instances. 27.1. Get Get returns attributes for a specified row. Gets are executed via Table.get 27.2. 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) 27.3. 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 { for (Result r = rs.next(); r != null; r = rs.next()) { // process result... } } finally { 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. 130 27.4. 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. 131 Chapter 28. 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. 28.1. 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 => 5 132 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. 28.2. Versions and HBase Operations In this section we look at the behavior of the version dimension for each of the core HBase operations. 28.2.1. 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. 28.2.2. 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 133 28.2.3. 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 kv = r.getColumn(CF, ATTR); // returns all versions of this column 28.2.4. 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. 134 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. 28.2.5. 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. 28.3. 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 135 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 hbase2.x release) as now the order in which Mutations arrive is a factor. 28.4. 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. 28.4.1. 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. 28.4.2. 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.) 136 Chapter 29. 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). 137 Chapter 30. 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. 138 Chapter 31. 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. 139 Chapter 32. ACID See ACID Semantics. Lars Hofhansl has also written a note on ACID in HBase. 140 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 NonRelational 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. 141 Chapter 33. 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); HColumnDescriptor cf2 = ...; admin.modifyColumn(table, cf2); // adding new ColumnFamily // 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. 33.1. 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. 142 Chapter 34. 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. 143 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 readheavy 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 144 Chapter 35. 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. 35.1. 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. 145 Chapter 36. Rowkey Design 36.1. 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. 146 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. 147 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/discussionon-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. 36.2. 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. 36.3. 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 148 (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. 36.3.1. 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. 36.3.2. 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. 36.3.3. 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. 36.3.4. 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. 149 // 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); 26 // returns 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 f:q \x00\x00\x00\x00\x00\x01 1 row(s) in 0.0310 seconds CELL timestamp=1369163040570, value=\x00\x00 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. 36.4. 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 150 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 rowkeys 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. 36.5. Rowkeys and ColumnFamilies Rowkeys are scoped to ColumnFamilies. Thus, the same rowkey could exist in each ColumnFamily that exists in a table without collision. 36.6. 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). 36.7. 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 54 -10 -10 -10 -10 -10 -10 -10 -10 -10 -10 -10 -10 -10 -10 -10 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 75 75 75 75 75 75 75 75 75 75 75 75 75 75 75 72 82 18 18 18 18 18 18 18 18 18 18 18 18 18 18 14 88 -40 -40 -40 -40 -40 -40 -40 -40 -40 -40 -40 -40 -40 -40 -44 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 // // // // // // // // // 0 6 = D K R X _ 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 151 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 { try { admin.createTable( table, splits ); return true; } catch (TableExistsException e) { logger.info("table " + table.getNameAsString() + " already exists"); // the table already exists... return false; } } public static byte[][] getHexSplits(String startKey, String endKey, int numRegions) { byte[][] splits = new byte[numRegions-1][]; BigInteger lowestKey = new BigInteger(startKey, 16); BigInteger highestKey = new BigInteger(endKey, 16); BigInteger range = highestKey.subtract(lowestKey); BigInteger regionIncrement = range.divide(BigInteger.valueOf(numRegions)); lowestKey = lowestKey.add(regionIncrement); for(int i=0; i < numRegions-1;i++) { BigInteger key = lowestKey.add(regionIncrement.multiply(BigInteger.valueOf(i))); byte[] b = String.format("%016x", key).getBytes(); splits[i] = b; } return splits; } 152 Chapter 37. Number of Versions 37.1. 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. 37.2. 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 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: 157 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 r1 column=e:c1, =value r1 column=e:c1, =value r1 column=e:c1, =DeleteColumn r1 column=e:c1, =value 1 row(s) in 0.0120 seconds timestamp=14, value timestamp=12, value timestamp=11, type timestamp=10, value 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 r1 column=e:c1, timestamp=14, value =value r1 column=e:c1, timestamp=12, value =value r1 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 r1 column=e:c1, timestamp=14, value =value r1 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 percolumn-family): hbase(main):005:0> create 'test', {NAME=>'e', VERSIONS=>2147483647, KEEP_DELETED_CELLS => true} 158 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', 0 row(s) in 0.0290 seconds 11 hbase(main):010:0> scan 'test', {RAW=>true, VERSIONS=>1000} ROW COLUMN+CELL r1 column=e:c1, timestamp=14, value=value r1 column=e:c1, timestamp=12, value=value r1 column=e:c1, timestamp=11, type=DeleteColumn r1 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 r1 column=e:c1, timestamp=14, value=value r1 column=e:c1, timestamp=12, value=value r1 column=e:c1, timestamp=11, type=DeleteColumn r1 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 r1 159 column=e:c1, timestamp=14, r1 column=e:c1, timestamp=12, r1 column=e:c1, timestamp=11, r1 column=e:c1, timestamp=10, 1 row(s) in 0.0650 seconds value=value value=value type=DeleteColumn value=value 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. 160 Chapter 42. 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 "usertimestamp" 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 42.1. 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). 42.2. 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. 161 42.3. 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). 42.4. 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. 42.5. Coprocessor Secondary Index Coprocessors act like RDBMS triggers. These were added in 0.92. For more information, see coprocessors 162 Chapter 43. 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. 163 Chapter 44. 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 44.1. 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? 44.1.1. 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: 164 [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. 44.1.2. 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. 44.1.3. 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. 44.1.4. 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: 165 • [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. 44.2. 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. 44.3. Case Study - Customer/Order Assume that HBase is used to store customer and order information. There are two core recordtypes 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 166 • 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 44.3.1. 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] 167 • [type] = type indicating `2' for order record type • [order] The advantage of this particular CUSTOMER++ approach is that organizes many different recordtypes 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. 44.3.2. 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. 168 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. 169 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. 44.4. 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. 44.4.1. 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). 44.4.2. 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." 44.4.3. Rows as Columns The middle path between Rows vs. Columns is packing data that would be a separate row into 170 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. 44.5. 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: :"" (no value) :"" (no value) :"" (no value) The other option we had was to do this entirely using: :... :... 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 171 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). 172 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! :) 173 Chapter 45. Operational and Performance Configuration Options 45.1. 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 45.2. 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 45.3. 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 174 (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 45.4. 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). 175 45.5. JVM Tuning 45.5.1. 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 45.5.2. 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 = 0 176 Chapter 46. Special Cases 46.1. 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). 46.2. 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 46.3. 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. 177 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/hadoopmapreduce-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 o.a.h.h.mapreduce but replace with o.a.h.h.mapred if that is what you are using. 178 to Chapter 47. 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/hadoopenv.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 BASHcompatible shell. $ HADOOP_CLASSPATH=`${HBASE_HOME}/bin/hbase classpath` \ ${HADOOP_HOME}/bin/hadoop jar ${HBASE_HOME}/lib/hbase-mapreduce-VERSION.jar \ 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: 179 $ HADOOP_CLASSPATH=`${HBASE_HOME}/bin/hbase classpath` \ ${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: CellCounter: Count cells in HBase table. WALPlayer: Replay WAL files. completebulkload: Complete a bulk data load. copytable: Export a table from local cluster to peer cluster. export: Write table data to HDFS. exportsnapshot: Export the specific snapshot to a given FileSystem. import: Import data written by Export. importtsv: Import data in TSV format. rowcounter: Count rows in HBase table. 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` \ ${HADOOP_HOME}/bin/hadoop jar ${HBASE_HOME}/lib/hbase-mapreduce-2.0.0-SNAPSHOT.jar \ 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 ':' ',') ... 180 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=${HBASE_BUILD_HOME}/hbase-mapreduce/target/hbasemapreduce-VERSION-SNAPSHOT.jar:`${HBASE_BUILD_HOME}/bin/hbase classpath` ${HADOOP_HOME}/bin/hadoop jar ${HBASE_BUILD_HOME}/hbasemapreduce/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: 181 Exception in thread "main" java.lang.IllegalAccessError: class com.google.protobuf.ZeroCopyLiteralByteString cannot access its superclass com.google.protobuf.LiteralByteString at java.lang.ClassLoader.defineClass1(Native Method) at java.lang.ClassLoader.defineClass(ClassLoader.java:792) at java.security.SecureClassLoader.defineClass(SecureClassLoader.java:142) at java.net.URLClassLoader.defineClass(URLClassLoader.java:449) at java.net.URLClassLoader.access$100(URLClassLoader.java:71) at java.net.URLClassLoader$1.run(URLClassLoader.java:361) at java.net.URLClassLoader$1.run(URLClassLoader.java:355) at java.security.AccessController.doPrivileged(Native Method) at java.net.URLClassLoader.findClass(URLClassLoader.java:354) at java.lang.ClassLoader.loadClass(ClassLoader.java:424) at java.lang.ClassLoader.loadClass(ClassLoader.java:357) at org.apache.hadoop.hbase.protobuf.ProtobufUtil.toScan(ProtobufUtil.java: 818) at org.apache.hadoop.hbase.mapreduce.TableMapReduceUtil.convertScanToStrin g(TableMapReduceUtil.java:433) at org.apache.hadoop.hbase.mapreduce.TableMapReduceUtil.initTableMapperJob (TableMapReduceUtil.java:186) at org.apache.hadoop.hbase.mapreduce.TableMapReduceUtil.initTableMapperJob (TableMapReduceUtil.java:147) at org.apache.hadoop.hbase.mapreduce.TableMapReduceUtil.initTableMapperJob (TableMapReduceUtil.java:270) at 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 182 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 hbaseprotocol.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. 183 Chapter 48. 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. 184 Chapter 49. 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: copytable: Export a table from local cluster to peer cluster completebulkload: Complete a bulk data load. export: Write table data to HDFS. import: Import data written by Export. importtsv: Import data in TSV format. 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 185 Chapter 50. 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. 186 Chapter 51. 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. 187 Chapter 52. 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. 188 Chapter 53. Map-Task Splitting 53.1. 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. 53.2. 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. 189 Chapter 54. HBase MapReduce Examples 54.1. 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); MapReduce jobs scan.setCacheBlocks(false); // set other scan attrs ... // 1 is the default in Scan, which will be bad for // don't set to true for MR jobs TableMapReduceUtil.initTableMapperJob( tableName, // input HBase table name scan, // Scan instance to control CF and attribute selection MyMapper.class, // mapper null, // mapper output key null, // mapper output value job); job.setOutputFormatClass(NullOutputFormat.class); // because we aren't emitting anything from mapper boolean b = job.waitForCompletion(true); if (!b) { throw new IOException("error with job!"); } …and the mapper instance would extend TableMapper… public static class MyMapper extends TableMapper { public void map(ImmutableBytesWritable row, Result value, Context context) throws InterruptedException, IOException { // process data for the row from the Result instance. } } 190 54.2. 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); MapReduce jobs scan.setCacheBlocks(false); // set other scan attrs // 1 is the default in Scan, which will be bad for // don't set to true for MR jobs TableMapReduceUtil.initTableMapperJob( sourceTable, // input table scan, // Scan instance to control CF and attribute selection MyMapper.class, // mapper class null, // mapper output key null, // mapper output value job); TableMapReduceUtil.initTableReducerJob( targetTable, // output table null, // reducer class job); job.setNumReduceTasks(0); boolean b = job.waitForCompletion(true); if (!b) { 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. 191 public static class MyMapper extends TableMapper { public void map(ImmutableBytesWritable row, Result value, Context context) throws IOException, InterruptedException { // this example is just copying the data from the source table... context.write(row, resultToPut(row,value)); } private static Put resultToPut(ImmutableBytesWritable key, Result result) throws IOException { Put put = new Put(key.get()); for (KeyValue kv : result.raw()) { put.add(kv); } return put; } } 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. 54.3. HBase MapReduce Read/Write Example With Multi-Table Output TODO: example for MultiTableOutputFormat. 54.4. 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. 192 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); MapReduce jobs scan.setCacheBlocks(false); // set other scan attrs // 1 is the default in Scan, which will be bad for // don't set to true for MR jobs TableMapReduceUtil.initTableMapperJob( sourceTable, // input table scan, // Scan instance to control CF and attribute selection MyMapper.class, // mapper class Text.class, // mapper output key IntWritable.class, // mapper output value job); TableMapReduceUtil.initTableReducerJob( targetTable, // output table MyTableReducer.class, // reducer class job); job.setNumReduceTasks(1); // at least one, adjust as required boolean b = job.waitForCompletion(true); if (!b) { 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 public static final byte[] CF = "cf".getBytes(); public static final byte[] ATTR1 = "attr1".getBytes(); { private final IntWritable ONE = new IntWritable(1); private Text text = new Text(); public void map(ImmutableBytesWritable row, Result value, Context context) throws IOException, InterruptedException { String val = new String(value.getValue(CF, ATTR1)); text.set(val); // we can only emit Writables... context.write(text, ONE); } } In the reducer, the "ones" are counted (just like any other MR example that does this), and then 193 emits a Put. public static class MyTableReducer extends TableReducer { public static final byte[] CF = "cf".getBytes(); public static final byte[] COUNT = "count".getBytes(); public void reduce(Text key, Iterable values, Context context) throws IOException, InterruptedException { int i = 0; for (IntWritable val : values) { i += val.get(); } Put put = new Put(Bytes.toBytes(key.toString())); put.add(CF, COUNT, Bytes.toBytes(i)); context.write(null, put); } } 54.5. 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. 194 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); MapReduce jobs scan.setCacheBlocks(false); // set other scan attrs // 1 is the default in Scan, which will be bad for // don't set to true for MR jobs TableMapReduceUtil.initTableMapperJob( sourceTable, // input table scan, // Scan instance to control CF and attribute selection MyMapper.class, // mapper class Text.class, // mapper output key IntWritable.class, // mapper output value 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) { 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 { public void reduce(Text key, Iterable values, Context context) throws IOException, InterruptedException { int i = 0; for (IntWritable val : values) { i += val.get(); } context.write(key, new IntWritable(i)); } } 54.6. HBase MapReduce Summary to HBase Without Reducer It is also possible to perform summaries without a reducer - if you use HBase as the reducer. 195 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. 54.7. 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 { private Connection c = null; public void setup(Context context) { // create DB connection... } public void reduce(Text key, Iterable values, Context context) throws IOException, InterruptedException { // do summarization // in this example the keys are Text, but this is just an example } public void cleanup(Context context) { // close db connection } } In the end, the summary results are written to your RDBMS table/s. 196 Chapter 55. 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 { private Table myOtherTable; public void setup(Context context) { // In here create a Connection to the cluster and save it or use the Connection // from the existing table myOtherTable = connection.getTable("myOtherTable"); } public void map(ImmutableBytesWritable row, Result value, Context context) throws IOException, InterruptedException { // process Result... // use 'myOtherTable' for lookups } 197 Chapter 56. 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. 198 Chapter 57. 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()) { // print out each tuple from HBase System.out.println( "iterator.next() = " + iterator.next() ); } iterator.close(); 199 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@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. 200 Chapter 58. 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 hbasesite.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. 201 Chapter 59. 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 hbase.security.authentication = kerberos). hbase.security.authentication.ui kerberos Controls what kind of authentication should be used for the HBase web UIs. hbase.security.authentication kerberos The Kerberos keytab file to use for SPNEGO authentication by the web server. A number of properties exist to configure SPNEGO authentication for the web server: 202 (e.g hbase.security.authentication.spnego.kerberos.principal HTTP/_HOST@EXAMPLE.COM Required for SPNEGO, the Kerberos principal to use for SPNEGO authentication by the web server. The _HOST keyword will be automatically substituted with the node's hostname. hbase.security.authentication.spnego.kerberos.keytab /etc/security/keytabs/spnego.service.keytab Required for SPNEGO, the Kerberos keytab file to use for SPNEGO authentication by the web server. hbase.security.authentication.spnego.kerberos.name.rules Optional, Hadoop-style `auth_to_local` rules which will be parsed and used in the handling of Kerberos principals hbase.security.authentication.signature.secret.file Optional, a file whose contents will be used as a secret to sign the HTTP cookies as a part of the SPNEGO authentication handshake. If this is not provided, Java's `Random` library will be used for the secret. 203 Chapter 60. 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. 60.1. 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 coresite.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. 60.2. 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: hbase.security.authentication kerberos hbase.security.authorization true hbase.coprocessor.region.classes org.apache.hadoop.hbase.security.token.TokenProvider A full shutdown and restart of HBase service is required when deploying these configuration changes. 60.3. Client-side Configuration for Secure Operation First, refer to Prerequisites and ensure that your underlying HDFS configuration is secure. 204 Add the following to the hbase-site.xml file on every client: hbase.security.authentication kerberos 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. 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: hbase.rpc.protection privacy This configuration property can also be set on a per-connection basis. Set it in the Configuration supplied to Table: Configuration conf = HBaseConfiguration.create(); Connection connection = ConnectionFactory.createConnection(conf); conf.set("hbase.rpc.protection", "privacy"); try (Connection connection = ConnectionFactory.createConnection(conf); Table table = connection.getTable(TableName.valueOf(tablename))) { .... do your stuff } Expect a ~10% performance penalty for encrypted communication. 60.4. Client-side Configuration for Secure Operation Thrift Gateway Add the following to the hbase-site.xml file for every Thrift gateway: 205 hbase.thrift.keytab.file /etc/hbase/conf/hbase.keytab hbase.thrift.kerberos.principal $USER/_HOST@HADOOP.LOCALDOMAIN hbase.thrift.dns.interface default hbase.thrift.dns.nameserver default 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 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. 60.5. 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.  206 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. 60.6. 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: hbase.regionserver.thrift.http true hbase.thrift.support.proxyuser true/value> To allow proxy users when using doAs impersonation, add the following to the hbase-site.xml file for every HBase node: 207 hadoop.security.authorization true hadoop.proxyuser.$USER.groups $GROUPS hadoop.proxyuser.$USER.hosts $GROUPS Take a look at the demo client to get an overall idea of how to use this feature in your client. 60.7. Client-side Configuration for Secure Operation REST Gateway Add the following to the hbase-site.xml file for every REST gateway: hbase.rest.keytab.file $KEYTAB hbase.rest.kerberos.principal $USER/_HOST@HADOOP.LOCALDOMAIN 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 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 hbasesite.xml file for every REST gateway. 208 hbase.rest.support.proxyuser true hbase.rest.authentication.type kerberos hbase.rest.authentication.kerberos.principal HTTP/_HOST@HADOOP.LOCALDOMAIN hbase.rest.authentication.kerberos.keytab $KEYTAB hbase.rest.dns.interface default hbase.rest.dns.nameserver default Substitute the keytab for HTTP for $KEYTAB. HBase REST gateway supports different 'hbase.rest.authentication.type': simple, kerberos. You can also implement a custom authentication by implementing Hadoop AuthenticationHandler, then specify the full class name as 'hbase.rest.authentication.type' value. For more information, refer to SPNEGO HTTP authentication. 60.8. REST Gateway Impersonation Configuration By default, the REST gateway doesn’t support impersonation. It accesses the HBase on behalf of clients as the user configured as in the previous section. To the HBase server, all requests are from the REST gateway user. The actual users are unknown. You can turn on the impersonation support. With impersonation, the REST gateway user is a proxy user. The HBase server knows the actual/real user of each request. So it can apply proper authorizations. To turn on REST gateway impersonation, we need to configure HBase servers (masters and region servers) to allow proxy users; configure REST gateway to enable impersonation. To allow proxy users, add the following to the hbase-site.xml file for every HBase server: 209 hadoop.security.authorization true hadoop.proxyuser.$USER.groups $GROUPS hadoop.proxyuser.$USER.hosts $GROUPS 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. hbase.rest.authentication.type kerberos hbase.rest.authentication.kerberos.principal HTTP/_HOST@HADOOP.LOCALDOMAIN hbase.rest.authentication.kerberos.keytab $KEYTAB Substitute the keytab for HTTP for $KEYTAB. 210 Chapter 61. 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. 61.1. 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. 61.2. Prerequisites None 61.3. Server-side Configuration for Simple User Access Operation Add the following to the hbase-site.xml file on every server machine in the cluster: 211 hbase.security.authentication simple hbase.security.authorization true hbase.coprocessor.master.classes org.apache.hadoop.hbase.security.access.AccessController hbase.coprocessor.region.classes org.apache.hadoop.hbase.security.access.AccessController hbase.coprocessor.regionserver.classes org.apache.hadoop.hbase.security.access.AccessController For 0.94, add the following to the hbase-site.xml file on every server machine in the cluster: hbase.rpc.engine org.apache.hadoop.hbase.ipc.SecureRpcEngine hbase.coprocessor.master.classes org.apache.hadoop.hbase.security.access.AccessController hbase.coprocessor.region.classes org.apache.hadoop.hbase.security.access.AccessController A full shutdown and restart of HBase service is required when deploying these configuration changes. 61.4. Client-side Configuration for Simple User Access Operation Add the following to the hbase-site.xml file on every client: 212 hbase.security.authentication simple For 0.94, add the following to the hbase-site.xml file on every server machine in the cluster: hbase.rpc.engine org.apache.hadoop.hbase.ipc.SecureRpcEngine 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. 61.4.1. 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. 61.4.2. 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. 213 Chapter 62. 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). 62.1. 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. 62.2. 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 214 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/hbasestaging) 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. 215 Chapter 63. 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. hfile.format.version 3 2. Enable SASL and Kerberos authentication for RPC and ZooKeeper, as described in security.prerequisites and SASL Authentication with ZooKeeper. 216 63.1. 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. 63.1.1. 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. 63.2. Access Control Labels (ACLs) 63.2.1. 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. 217 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 218 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. 219 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. 220 hbase.security.authorization true hbase.coprocessor.region.classes org.apache.hadoop.hbase.security.access.AccessController, org.apache.hadoop.hbase.security.token.TokenProvider hbase.coprocessor.master.classes org.apache.hadoop.hbase.security.access.AccessController hbase.coprocessor.regionserver.classes org.apache.hadoop.hbase.security.access.AccessController hbase.security.exec.permission.checks true 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. 221 hadoop.security.group.mapping org.apache.hadoop.security.LdapGroupsMapping hadoop.security.group.mapping.ldap.url ldap://server hadoop.security.group.mapping.ldap.bind.user Administrator@example-ad.local hadoop.security.group.mapping.ldap.bind.password **** hadoop.security.group.mapping.ldap.base dc=example-ad,dc=local hadoop.security.group.mapping.ldap.search.filter.user (&(objectClass=user)(sAMAccountName={0})) hadoop.security.group.mapping.ldap.search.filter.group (objectClass=group) hadoop.security.group.mapping.ldap.search.attr.member member hadoop.security.group.mapping.ldap.search.attr.group.name cn 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 222 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) 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 hbaseserver/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. 223 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
, \ { '' => \ '', ... }, \ { } ◦ is the user or group name, prefixed with @ in the case of a group. 224 ◦ is a string containing any or all of "RWXCA", though only R and W are meaningful at cell scope. ◦ 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', \ { '@developers' => 'RW', 'testuser' => 'R' }, \ { 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. 225 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, final TableName table, final byte[] family, final byte[] qualifier, final Permission.Action... actions) throws Exception { SecureTestUtil.updateACLs(util, new Callable() { @Override public Void call() throws Exception { try (Connection connection = ConnectionFactory.createConnection(util .getConfiguration()); Table acl = connection.getTable(AccessControlLists.ACL_TABLE_NAME)) { BlockingRpcChannel service = acl.coprocessorService(HConstants .EMPTY_START_ROW); AccessControlService.BlockingInterface protocol = AccessControlService.newBlockingStub(service); AccessControlUtil.grant(null, protocol, user, table, family, qualifier, false, actions); } return null; } }); } To grant permissions at the cell level, you can use the Mutation.setACL method: Mutation.setACL(String user, Permission perms) Mutation.setACL(Map 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. 226 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, final TableName table, final byte[] family, final byte[] qualifier, final Permission.Action... actions) throws Exception { SecureTestUtil.updateACLs(util, new Callable() { @Override public Void call() throws Exception { Configuration conf = HBaseConfiguration.create(); Connection connection = ConnectionFactory.createConnection(conf); Table acl = connection.getTable(util.getConfiguration(), AccessControlLists.ACL_TABLE_NAME); try { BlockingRpcChannel service = acl.coprocessorService(HConstants .EMPTY_START_ROW); AccessControlService.BlockingInterface protocol = AccessControlService.newBlockingStub(service); ProtobufUtil.revoke(protocol, user, table, family, qualifier, actions); } finally { acl.close(); } return null; } }); } 4. Showing a User’s Effective Permissions HBase Shell hbase> user_permission 'user' hbase> user_permission '.*' hbase> user_permission JAVA_REGEX 227 Example 17. API public static void verifyAllowed(User user, AccessTestAction action, int count) throws Exception { try { Object obj = user.runAs(action); if (obj != null && obj instanceof List<?>) { List<?> results = (List<?>) obj; if (results != null && results.isEmpty()) { fail("Empty non null results from action for user '" ` user.getShortName() ` "'"); } assertEquals(count, results.size()); } } catch (AccessDeniedException ade) { fail("Expected action to pass for user '" ` user.getShortName() ` "' but was denied"); } } 63.3. 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) org.apache.hadoop.hbase.security.visibility.VisibilityClient method class and provided by the 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 228 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 and Get#setAuthorizations(Authorizations(String,…)) 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.  There is currently no way to determine which labels have been applied to a cell.  Visibility labels are not currently applied for superusers. See below for an example of how this can be done. See HBASE-12470 for details. 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. 229 63.3.1. 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. hbase.security.authorization true hbase.coprocessor.region.classes org.apache.hadoop.hbase.security.visibility.VisibilityController hbase.coprocessor.master.classes org.apache.hadoop.hbase.security.visibility.VisibilityController 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. 63.3.2. 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. 230 API Examples Many of the Java API examples in this section are taken from the source file hbaseserver/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 { PrivilegedExceptionAction action = new PrivilegedExceptionAction() { public VisibilityLabelsResponse run() throws Exception { String[] labels = { SECRET, TOPSECRET, CONFIDENTIAL, PUBLIC, PRIVATE, COPYRIGHT, ACCENT, UNICODE_VIS_TAG, UC1, UC2 }; try { VisibilityClient.addLabels(conf, labels); } catch (Throwable t) { throw new IOException(t); } return null; } }; 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' ] 231 hbase> set_auths '@qagroup', [ 'test' ] + .Java API public void testSetAndGetUserAuths() throws Throwable { final String user = "user1"; PrivilegedExceptionAction action = new PrivilegedExceptionAction() { public Void run() throws Exception { String[] auths = { SECRET, CONFIDENTIAL }; try { VisibilityClient.setAuths(conf, auths, user); } catch (Throwable e) { } return null; } ... 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 232 ... auths = new String[] { SECRET, PUBLIC, CONFIDENTIAL }; VisibilityLabelsResponse response = null; try { response = VisibilityClient.clearAuths(conf, auths, user); } catch (Throwable e) { fail("Should not have failed"); ... } 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 233 static Table createTableAndWriteDataWithLabels(TableName tableName, String... labelExps) throws Exception { Configuration conf = HBaseConfiguration.create(); Connection connection = ConnectionFactory.createConnection(conf); Table table = NULL; try { table = TEST_UTIL.createTable(tableName, fam); int i = 1; List puts = new ArrayList(); for (String labelExp : labelExps) { Put put = new Put(Bytes.toBytes("row" + i)); put.add(fam, qual, HConstants.LATEST_TIMESTAMP, value); put.setCellVisibility(new CellVisibility(labelExp)); puts.add(put); i++; } table.put(puts); } finally { if (table != null) { table.flushCommits(); } } 63.3.3. 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'] 234 Example 19. Java API ... public Void run() throws Exception { String[] auths1 = { SECRET, CONFIDENTIAL }; GetAuthsResponse authsResponse = null; try { VisibilityClient.setAuths(conf, auths1, user); try { authsResponse = VisibilityClient.getAuths(conf, user); } catch (Throwable e) { fail("Should not have failed"); } } catch (Throwable e) { } List authsList = new ArrayList(); for (ByteString authBS : authsResponse.getAuthList()) { authsList.add(Bytes.toString(authBS.toByteArray())); } assertEquals(2, authsList.size()); assertTrue(authsList.contains(SECRET)); assertTrue(authsList.contains(CONFIDENTIAL)); return null; } ... 63.3.4. 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 hbase.regionserver.scan.visibility.label.generator.class. The by using output the for property 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. 63.3.5. 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 235 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. hbase.security.authorization true hbase.coprocessor.regionserver.classes org.apache.hadoop.hbase.security.visibility.VisibilityController$VisibilityRepl ication 63.4. 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 nonintrusive 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. 63.4.1. 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 236 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. 63.4.2. 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 \ -storetype jceks -storepass **** \ -genseckey -keyalg AES -keysize 128 \ -alias Replace **** with the password for the keystore file and 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. 237 hbase.crypto.keyprovider org.apache.hadoop.hbase.io.crypto.KeyStoreKeyProvider hbase.crypto.keyprovider.parameters jceks:///path/to/hbase/conf/hbase.jks?password=**** 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. hbase.crypto.master.key.name my-alias 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. hfile.format.version 3 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 238 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. hbase.regionserver.hlog.reader.impl org.apache.hadoop.hbase.regionserver.wal.SecureProtobufLogReader hbase.regionserver.hlog.writer.impl org.apache.hadoop.hbase.regionserver.wal.SecureProtobufLogWriter hbase.regionserver.wal.encryption true 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. 63.4.3. 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. 239 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. 63.5. 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. 240 hbase.security.authorization true hbase.bulkload.staging.dir /tmp/hbase-staging hbase.coprocessor.region.classes org.apache.hadoop.hbase.security.token.TokenProvider, org.apache.hadoop.hbase.security.access.AccessController,org.apache.hadoop.hbase.secur ity.access.SecureBulkLoadEndpoint 63.6. 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; hbase.security.authorization true 241 Chapter 64. 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.format.version 3 hbase.superuser hbase, admin hbase.security.authorization true hbase.coprocessor.region.classes org.apache.hadoop.hbase.security.access.AccessController, org.apache.hadoop.hbase.security.visibility.VisibilityController, org.apache.hadoop.hbase.security.token.TokenProvider hbase.coprocessor.master.classes org.apache.hadoop.hbase.security.access.AccessController, org.apache.hadoop.hbase.security.visibility.VisibilityController hbase.coprocessor.regionserver.classes org.apache.hadoop/hbase.security.access.AccessController, org.apache.hadoop.hbase.security.access.VisibilityController hbase.security.exec.permission.checks true hbase.security.visibility.mutations.checkauth false 242 hbase.rpc.protection privacy hbase.crypto.keyprovider org.apache.hadoop.hbase.io.crypto.KeyStoreKeyProvider hbase.crypto.keyprovider.parameters jceks:///path/to/hbase/conf/hbase.jks?password=*** hbase.crypto.master.key.name hbase hbase.regionserver.hlog.reader.impl org.apache.hadoop.hbase.regionserver.wal.SecureProtobufLogReader hbase.regionserver.hlog.writer.impl org.apache.hadoop.hbase.regionserver.wal.SecureProtobufLogWriter hbase.regionserver.wal.encryption true hbase.crypto.master.alternate.key.name hbase.old hbase.bulkload.staging.dir /tmp/hbase-staging hbase.coprocessor.region.classes org.apache.hadoop.hbase.security.token.TokenProvider, org.apache.hadoop.hbase.security.access.AccessController,org.apache.hadoop.hbase.s ecurity.access.SecureBulkLoadEndpoint 243 Example 21. Example Group Mapper in Hadoop core-site.xml Adjust these settings to suit your environment. hadoop.security.group.mapping org.apache.hadoop.security.LdapGroupsMapping hadoop.security.group.mapping.ldap.url ldap://server hadoop.security.group.mapping.ldap.bind.user Administrator@example-ad.local hadoop.security.group.mapping.ldap.bind.password **** hadoop.security.group.mapping.ldap.base dc=example-ad,dc=local hadoop.security.group.mapping.ldap.search.filter.user (&(objectClass=user)(sAMAccountName={0})) hadoop.security.group.mapping.ldap.search.filter.group (objectClass=group) hadoop.security.group.mapping.ldap.search.attr.member member hadoop.security.group.mapping.ldap.search.attr.group.name cn 244 Architecture 245 Chapter 65. Overview 65.1. 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. 65.2. 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 246 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. 65.3. 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. 247 Chapter 66. 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. 66.1. 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. 66.2. 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. 248 Chapter 67. 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 67.1. Cluster Connections The API changed in HBase 1.0. For connection configuration information, see Client configuration and dependencies connecting to an HBase cluster. 67.1.1. 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. 67.1.2. 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: 249 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); Table table = connection.getTable(TableName.valueOf(tablename))) { // 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. 67.2. 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. 67.3. Asynchronous Client It is a new API introduced in HBase 2.0 which aims to provide the ability to access HBase asynchronously. 250 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. 67.4. 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. 251 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. 67.5. External Clients Information on non-Java clients and custom protocols is covered in Apache HBase External APIs 252 Chapter 68. 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. 68.1. Structural Structural Filters contain other Filters. 68.1.1. 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( cf, column, CompareOperator.EQUAL, Bytes.toBytes("my value") ); list.add(filter1); SingleColumnValueFilter filter2 = new SingleColumnValueFilter( cf, column, CompareOperator.EQUAL, Bytes.toBytes("my other value") ); list.add(filter2); scan.setFilter(list); 68.2. Column Value 68.2.1. SingleColumnValueFilter A SingleColumnValueFilter (see: https://hbase.apache.org/apidocs/org/apache/hadoop/hbase/filter/ SingleColumnValueFilter.html) (CompareOperaor.EQUAL), can inequality be used to test column (CompareOperaor.NOT_EQUAL), values or for equivalence ranges (e.g., CompareOperaor.GREATER). The following is an example of testing equivalence of a column to a String value "my value"… 253 SingleColumnValueFilter filter = new SingleColumnValueFilter( cf, column, CompareOperaor.EQUAL, Bytes.toBytes("my value") ); scan.setFilter(filter); 68.2.2. 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( cf, column, CompareOperaor.EQUAL, Bytes.toBytes("my value") ); 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, 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. 68.3. 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. 254 68.3.1. RegexStringComparator RegexStringComparator supports regular expressions for value comparisons. RegexStringComparator comp = new RegexStringComparator("my."); starts with 'my' SingleColumnValueFilter filter = new SingleColumnValueFilter( cf, column, CompareOperaor.EQUAL, comp ); scan.setFilter(filter); // any value that See the Oracle JavaDoc for supported RegEx patterns in Java. 68.3.2. 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"); value' SingleColumnValueFilter filter = new SingleColumnValueFilter( cf, column, CompareOperaor.EQUAL, comp ); scan.setFilter(filter); // looking for 'my 68.3.3. BinaryPrefixComparator See BinaryPrefixComparator. 68.3.4. BinaryComparator See BinaryComparator. 68.4. 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. 68.4.1. FamilyFilter FamilyFilter can be used to filter on the ColumnFamily. It is generally a better idea to select 255 ColumnFamilies in the Scan than to do it with a Filter. 68.4.2. QualifierFilter QualifierFilter can be used to filter based on Column (aka Qualifier) name. 68.4.3. 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()) { for (KeyValue kv : r.raw()) { // each kv represents a column } } rs.close(); 68.4.4. 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" 256 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()) { for (KeyValue kv : r.raw()) { // each kv represents a column } } rs.close(); 68.4.5. 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()) { for (KeyValue kv : r.raw()) { // each kv represents a column } } rs.close(); 257 Note: Introduced in HBase 0.92 68.5. RowKey 68.5.1. RowFilter It is generally a better idea to use the startRow/stopRow methods on Scan for row selection, however RowFilter can also be used. 68.6. Utility 68.6.1. FirstKeyOnlyFilter This is primarily used for rowcount jobs. See FirstKeyOnlyFilter. 258 Chapter 69. 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 . 69.1. 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. 69.2. 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. 69.3. 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. 69.4. Processes The Master runs several background threads: 69.4.1. 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. 69.4.2. CatalogJanitor Periodically checks and cleans up the hbase:meta table. See hbase:meta for more information on the meta table. 259 Chapter 70. RegionServer HRegionServer is the RegionServer implementation. It is responsible for serving and managing regions. In a distributed cluster, a RegionServer runs on a DataNode. 70.1. Interface The methods exposed by HRegionRegionInterface contain both data-oriented and regionmaintenance 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. 70.2. Processes The RegionServer runs a variety of background threads: 70.2.1. CompactSplitThread Checks for splits and handle minor compactions. 70.2.2. MajorCompactionChecker Checks for major compactions. 70.2.3. MemStoreFlusher Periodically flushes in-memory writes in the MemStore to StoreFiles. 70.2.4. LogRoller Periodically checks the RegionServer’s WAL. 70.3. 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. 70.4. 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, 260 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. 70.4.1. 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. 70.4.2. 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. 70.4.3. LruBlockCache Design The LruBlockCache is an LRU cache that contains three levels of block priority to allow for scanresistance 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); 261 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 70.4.4. 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 262 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. 70.4.5. 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. 263 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 264 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. hbase.bucketcache.ioengine offheap hfile.block.cache.size 0.2 hbase.bucketcache.size 4196 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). 265 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 hfile.block.cache.sizes (instead of hfile.block.cache.size) 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. hfile.block.cache.sizes 4096,8192 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 * where hbase.dfs.client.read.shortcircuit.buffer.size 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. 266 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-ofbucketcache  and the BucketCache hbase.bucketcache.percentage.in.combinedcache size-of-bucket-cache itself is EITHER the size was size-of-bucket-cache. where * 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. 70.4.6. 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. 70.5. RegionServer Offheap Read/Write Path 70.5.1. 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 267 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 268 required fields of the entire Cell as per the use cases. [ See CellUtil#cloneXXX(Cell) APIs ] 70.5.2. Offheap write-path TODO 70.6. 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. 269 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. 270 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. 70.7. Write Ahead Log (WAL) 70.7.1. 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 WriteAhead Log article. 271 70.7.2. 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). • 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. 70.7.3. 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. HBase 1.0 introduces support MultiWal in HBASE-5699. MultiWAL allows a RegionServer to write 272 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: hbase.wal.provider multiwal Restart the RegionServer for the changes to take effect. To disable MultiWAL for a RegionServer, unset the property and restart the RegionServer. 70.7.4. WAL Flushing TODO (describe). 70.7.5. 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/,, 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 RegionServer failure. Renaming the logs directory ensures that existing, valid WAL files which 273 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/,,-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///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 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. 274 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: 275 [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%2C1340474893287splitting%2Fhost6.sample.com%253A57020.1340474893945 unassigned host2.sample.com:57000 cZxid = 0×7115 ctime = Sat Jun 23 11:13:40 PDT 2012 ... 276 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. 277 ▪ 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. 70.7.6. WAL Compression The content of the WAL can be compressed using LRU Dictionary compression. This can be used to 15 speed up WAL replication to different datanodes. The dictionary can store up to 2 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. 70.7.7. 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 278 70.7.8. 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. 279 Chapter 71. 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 Region Store MemStore StoreFile Block the table) (HBase table) (Regions for the table) (Store per ColumnFamily for each Region for the table) (MemStore for each Store for each Region for the table) (StoreFiles for each Store for each Region for the table) (Blocks within a StoreFile within a Store for each Region for For a description of what HBase files look like when written to HDFS, see Browsing HDFS for HBase Objects. 71.1. 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: 71.1.1. 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 280 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. 71.2. Region-RegionServer Assignment This section describes how Regions are assigned to RegionServers. 71.2.1. 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. 71.2.2. 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 71.2.3. Region Load Balancing Regions can be periodically moved by the LoadBalancer. 281 71.2.4. 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 282 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 283 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 284 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. 71.3. 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. 71.4. 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). 71.4.1. 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. 285 Configuring the Split Policy Globally in hbase-site.xml hbase.regionserver.region.split.policy org.apache.hadoop.hbase.regionserver.IncreasingToUpperBoundRegionSplitPolicy 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. 71.5. 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. 286 • 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. 71.5.1. 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. 71.6. 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 287 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. 71.7. 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. 71.7.1. 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. 71.7.2. 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. 71.7.3. 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 288 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. 71.7.4. 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 hdfs://, the type file the following: $ ${HBASE_HOME}/bin/hbase hfile -v -f hdfs:// 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. 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. 289 71.7.5. 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. 71.7.6. 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 • 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: 290 • 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. 71.7.7. 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 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 291 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 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. 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. 292 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 bulkloading 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. 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. 293 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 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 hbasesite.xml file. To switch back to the ExploringCompactionPolicy, remove the setting from the hbasesite.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 294 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 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 295 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 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 296 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 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) 297 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: * * older ----> newer * _ * | | _ * | | | | _ * --|-|- |-|- |-|---_-------_------* | | | | | | | | _ | | * | | | | | | | | | | | | * | | | | | | | | | | | | */ minCompactSize Important knobs: • hbase.hstore.compaction.ratio Ratio used in compaction file selection algorithm (default 1.2f). • 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). 298 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) • 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 3 299 • 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 maxnumber 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. Date Tiered Compaction Date tiered compaction is a date-aware store file compaction strategy that is beneficial for timerange 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 300 • 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. 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. 301 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 Setting Notes hbase.hstore.compaction.date.tiered.max.storef Files with max-timestamp smaller than this will ile.age.millis no longer be compacted.Default at Long.MAX_VALUE. hbase.hstore.compaction.date.tiered.base.windo Base window size in milliseconds. Default at 6 w.millis hours. hbase.hstore.compaction.date.tiered.windows.pe Number of windows per tier. Default at 4. r.tier hbase.hstore.compaction.date.tiered.incoming.w Minimal number of files to compact in the indow.min 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 The policy to select store files within the same icy.class 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. 302  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. 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. 303 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'} 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 304 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 representative of early data overall is row not 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. 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, 305 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.maxFiles, hbase.hstore.compaction.max. 306 hbase.store.stripe.compaction.minFiles rather than hbase.hstore.compaction.min and and Chapter 72. Bulk Loading 72.1. 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. 72.2. Bulk Load Limitations As bulk loading bypasses the write path, the WAL doesn’t get written to as part of the process. Replication works by reading the WAL files so it won’t see the bulk loaded data – and the same goes for the edits that use Put.setDurability(SKIP_WAL). One way to handle that is to ship the raw files or the HFiles to the other cluster and do the other processing there. 72.3. Bulk Load Architecture The HBase bulk load process consists of two main steps. 72.3.1. 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. 72.3.2. 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 307 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. $ 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. 72.4. 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. 72.5. 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. 308 Chapter 73. 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. 73.1. NameNode The NameNode is responsible for maintaining the filesystem metadata. See the above HDFS Architecture link for more information. 73.2. DataNode The DataNodes are responsible for storing HDFS blocks. See the above HDFS Architecture link for more information. 309 Chapter 74. 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. 74.1. 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). 74.2. Timeline Consistency With this feature, HBase introduces a Consistency definition, which can be provided per read operation (get or scan). 310 public enum Consistency { STRONG, 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 peroperation 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. 311 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. 74.3. 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 312 • 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. 74.4. Where is the code This feature is delivered in two phases, Phase 1 and 2. The first phase is done in time for HBase1.0.0 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. 74.5. 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. 74.5.1. 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. 313 74.5.2. 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 multidatacenter 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: hbase> disable_peer 'region_replica_replication' 74.6. 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. 74.7. 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 314 hbase.regionserver.storefile.refresh.period. 74.8. 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. 74.9. 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. 74.10. 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. 74.10.1. Server side properties 315 hbase.regionserver.storefile.refresh.period 0 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. hbase.regionserver.meta.storefile.refresh.period 300000 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). hbase.region.replica.replication.enabled true 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. hbase.region.replica.replication.memstore.enabled true If you set this to `false`, replicas do not receive memstore updates from the primary RegionServer. If you set this to `true`, you can still disable memstore replication on a per-table basis, by setting the table's `REGION_MEMSTORE_REPLICATION` configuration property to `false`. If memstore replication is disabled, the secondaries will only receive 316 updates for events like flushes and bulkloads, and will not have access to data which the primary has not yet flushed. This preserves the guarantee of row-level consistency, even when the read requests `Consistency.TIMELINE`. hbase.master.hfilecleaner.ttl 3600000 The period (in milliseconds) to keep store files in the archive folder before deleting them from the file system. hbase.meta.replica.count 3 Region replication count for the meta regions. Defaults to 1. hbase.region.replica.storefile.refresh.memstore.multiplier 4 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). hbase.region.replica.wait.for.primary.flush true 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. 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. 317 74.10.2. Client side properties Ensure to set the following for all clients (and servers) that will use region replicas. hbase.ipc.client.specificThreadForWriting true Whether to enable interruption of RPC threads at the client side. This is required for region replicas with fallback RPC’s to secondary regions. hbase.client.primaryCallTimeout.get 10000 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. hbase.client.primaryCallTimeout.multiget 10000 The timeout (in microseconds), before secondary fallback RPC’s are submitted for multi-get requests (Table.get(List)) 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. hbase.client.replicaCallTimeout.scan 1000000 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. hbase.meta.replicas.use true Whether to use meta table replicas or not. Default is false. 318 Note HBase-1.0.x users should use hbase.ipc.client.allowsInterrupt rather than hbase.ipc.client.specificThreadForWriting. 74.11. 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. 74.12. 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. 74.12.1. Shell create 't1', 'f1', {REGION_REPLICATION => 2} describe 't1' for i in 1..100 put 't1', "r#{i}", 'f1:c1', i end flush 't1' 74.12.2. 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. 74.13. Read API and Usage 74.13.1. Shell You can do reads in shell using a the Consistency.TIMELINE semantics as follows hbase(main):001:0> get 't1','r6', {CONSISTENCY => "TIMELINE"} 319 You can simulate a region server pausing or becoming unavailable and do a read from the secondary replica: $ kill -STOP hbase(main):001:0> get 't1','r6', {CONSISTENCY => "TIMELINE"} Using scans is also similar hbase> scan 't1', {CONSISTENCY => 'TIMELINE'} 74.13.2. 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 gets = new ArrayList(); 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: 320 Result result = table.get(get); if (result.isStale()) { ... } 74.14. Resources 1. More information about the design and implementation can be found at the jira issue: HBASE10070 2. HBaseCon 2014 talk: HBase Read High Availability Using Timeline-Consistent Region Replicas also contains some details and slides. 321 Chapter 75. 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). 75.1. 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); ... 322 75.2. 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 => MOB_COMPACT_PARTITION_POLICY hbase> create 't1', {NAME => MOB_COMPACT_PARTITION_POLICY hbase> create 't1', {NAME => MOB_COMPACT_PARTITION_POLICY 'f1', IS_MOB => true, MOB_THRESHOLD => 102400, => 'daily'} 'f1', IS_MOB => true, MOB_THRESHOLD => 102400, => 'weekly'} 'f1', IS_MOB => true, MOB_THRESHOLD => 102400, => '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'} 75.3. 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. hbase.mob.compaction.mergeable.threshold 10000000000 75.4. 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 \ -threshold 1024 \ -minMobDataSize 512 \ -maxMobDataSize 5120 323 • 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. 75.5. 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. 324 Example 24. Example MOB Cache Configuration hbase.mob.file.cache.size 1000 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 handers" The default value is 1000. hbase.mob.cache.evict.period 3600 The amount of time in seconds after which an unused file is evicted from the MOB cache. The default value is 3600 seconds. hbase.mob.cache.evict.remain.ratio 0.5f A multiplier (between 0.0 and 1.0), which determines how many files remain cached after the threshold of files that remains cached after a cache eviction occurs which is triggered by reaching the `hbase.mob.file.cache.size` threshold. The default value is 0.5f, which means that half the files (the leastrecently-used ones) are evicted. 75.6. MOB Optimization Tasks 75.6.1. 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. 325 hbase> compact 't1', 'c1’, ‘MOB’ hbase> major_compact 't1', 'c1’, ‘MOB’ These commands are also available via Admin.compact and Admin.majorCompact methods. 326 In-memory Compaction 327 Chapter 76. 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. 328 Chapter 77. 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 329 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 IN_MEMORY_COMPACTION first to setting, the column and family if none, configuration we looking then for consult the 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 (org.apache.hadoop.hbase.regionserver.CompactingMemStore) to see detail on its operation. 330 class 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. 331 Chapter 78. Examples Example 25. Create, modify and delete a Table Using Java package com.example.hbase.admin; import java.io.IOException; import import import import import import import import import import import org.apache.hadoop.conf.Configuration; org.apache.hadoop.fs.Path; org.apache.hadoop.hbase.HBaseConfiguration; org.apache.hadoop.hbase.HColumnDescriptor; org.apache.hadoop.hbase.HConstants; org.apache.hadoop.hbase.HTableDescriptor; org.apache.hadoop.hbase.TableName; org.apache.hadoop.hbase.client.Admin; org.apache.hadoop.hbase.client.Connection; org.apache.hadoop.hbase.client.ConnectionFactory; org.apache.hadoop.hbase.io.compress.Compression.Algorithm; public class Example { private static final String TABLE_NAME = "MY_TABLE_NAME_TOO"; private static final String CF_DEFAULT = "DEFAULT_COLUMN_FAMILY"; public static void createOrOverwrite(Admin admin, HTableDescriptor table) throws IOException { if (admin.tableExists(table.getTableName())) { admin.disableTable(table.getTableName()); admin.deleteTable(table.getTableName()); } admin.createTable(table); } public static void createSchemaTables(Configuration config) throws IOException { try (Connection connection = ConnectionFactory.createConnection(config); Admin admin = connection.getAdmin()) { HTableDescriptor table = new HTableDescriptor(TableName.valueOf(TABLE_NAME) ); table.addFamily(new HColumnDescriptor(CF_DEFAULT).setCompressionType (Algorithm.NONE)); System.out.print("Creating table. "); createOrOverwrite(admin, table); System.out.println(" Done."); } } 332 public static void modifySchema (Configuration config) throws IOException { try (Connection connection = ConnectionFactory.createConnection(config); Admin admin = connection.getAdmin()) { TableName tableName = TableName.valueOf(TABLE_NAME); if (!admin.tableExists(tableName)) { System.out.println("Table does not exist."); System.exit(-1); } HTableDescriptor table = admin.getTableDescriptor(tableName); // Update existing table HColumnDescriptor newColumn = new HColumnDescriptor("NEWCF"); newColumn.setCompactionCompressionType(Algorithm.GZ); newColumn.setMaxVersions(HConstants.ALL_VERSIONS); admin.addColumn(tableName, newColumn); // Update existing column family HColumnDescriptor existingColumn = new HColumnDescriptor(CF_DEFAULT); existingColumn.setCompactionCompressionType(Algorithm.GZ); existingColumn.setMaxVersions(HConstants.ALL_VERSIONS); table.modifyFamily(existingColumn); admin.modifyTable(tableName, table); // Disable an existing table admin.disableTable(tableName); // Delete an existing column family admin.deleteColumn(tableName, CF_DEFAULT.getBytes("UTF-8")); // Delete a table (Need to be disabled first) admin.deleteTable(tableName); } } public static void main(String... args) throws IOException { Configuration config = HBaseConfiguration.create(); //Add any necessary configuration files (hbase-site.xml, core-site.xml) config.addResource(new Path(System.getenv("HBASE_CONF_DIR"), "hbase-site.xml" )); config.addResource(new Path(System.getenv("HADOOP_CONF_DIR"), "core-site.xml" )); createSchemaTables(config); modifySchema(config); } } 333 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. 334 Chapter 79. 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. 79.1. 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 # Background, logging to a file in $HBASE_LOGS_DIR $ bin/hbase-daemon.sh start rest -p 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 79.2. 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. 79.3. 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 335 Endpoint HTTP Verb Description Example /version/clust GET er curl -vi -X GET \ Version of HBase -H "Accept: text/xml" \ running on this cluster "http://example.com:8000/version/cluster" /status/cluste GET r Cluster status curl -vi -X GET \ -H "Accept: text/xml" \ "http://example.com:8000/status/cluster" / List of all non-system tables curl -vi -X GET \ -H "Accept: text/xml" \ "http://example.com:8000/" GET Table 14. Namespace Endpoints Endpoint HTTP Verb Description Example /namespaces GET List all namespaces curl -vi -X GET \ -H "Accept: text/xml" \ "http://example.com:8000/namespaces/" Describe a specific namespace curl -vi -X GET \ -H "Accept: text/xml" \ /namespaces/na GET mespace "http://example.com:8000/namespaces/special_ ns" /namespaces/na POST mespace Create a new namespace curl -vi -X POST \ -H "Accept: text/xml" \ "example.com:8000/namespaces/special_ns" /namespaces/na GET mespace/tables List all tables in a specific namespace curl -vi -X GET \ -H "Accept: text/xml" \ "http://example.com:8000/namespaces/special_ ns/tables" /namespaces/na PUT mespace Alter an existing namespace. Currently not used. /namespaces/na DELETE Delete a namespace. mespace The namespace must be empty. curl -vi -X PUT \ -H "Accept: text/xml" \ "http://example.com:8000/namespaces/special_ ns curl -vi -X DELETE \ -H "Accept: text/xml" \ "example.com:8000/namespaces/special_ns" Table 15. Table Endpoints Endpoint HTTP Verb Description /table/schema GET Describe the schema of curl -vi -X GET \ -H "Accept: text/xml" \ the specified table. "http://example.com:8000/users/schema" 336 Example Endpoint HTTP Verb Description /table/schema POST curl -vi -X POST \ Update an existing -H "Accept: text/xml" \ table with the provided -H "Content-Type: text/xml" \ schema fragment -d '<?xml version="1.0" encoding="UTF8"?><TableSchema name="users"><ColumnSchema name="cf" KEEP_DELETED_CELLS="true" /></TableSchema>' \ "http://example.com:8000/users/schema" /table/schema PUT Create a new table, or replace an existing table’s schema /table/schema DELETE Delete the table. You must use the / table/schema endpoint, not just /table/. /table/regions GET List the table regions Example curl -vi -X PUT \ -H "Accept: text/xml" \ -H "Content-Type: text/xml" \ -d '<?xml version="1.0" encoding="UTF8"?><TableSchema name="users"><ColumnSchema name="cf" /></TableSchema>' \ "http://example.com:8000/users/schema" curl -vi -X DELETE \ -H "Accept: text/xml" \ "http://example.com:8000/users/schema" curl -vi -X GET \ -H "Accept: text/xml" \ "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 \ -H "Accept: text/xml" \ "http://example.com:8000/users/row1" /table/row/col GET umn:qualifier/ timestamp Get the value of a single curl -vi -X GET \ -H "Accept: text/xml" \ column. Values are Base-64 encoded. "http://example.com:8000/users/row1/cf:a/145 8586888395" /table/row/col GET umn:qualifier Get the value of a single curl -vi -X GET \ -H "Accept: text/xml" \ column. Values are "http://example.com:8000/users/row1/cf:a" Base-64 encoded. curl -vi -X GET \ -H "Accept: text/xml" \ "http://example.com:8000/users/row1/cf:a/" 337 Endpoint HTTP Verb /table/row/col GET umn:qualifier/ ?v=number_of_v ersions Description Example curl -vi -X GET \ Multi-Get a specified -H "Accept: text/xml" \ number of versions of a given cell. Values are "http://example.com:8000/users/row1/cf:a?v=2 Base-64 encoded. " Table 17. Endpoints for Scan Operations Endpoint HTTP Verb /table/scanner PUT / 338 Description Example curl -vi -X PUT \ Get a Scanner object. -H "Accept: text/xml" \ Required by all other -H "Content-Type: text/xml" \ Scan operations. Adjust -d '' \ the batch parameter to "http://example.com:8000/users/scanner/" 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. Endpoint HTTP Verb /table/scanner PUT / Description Example To supply filters to the curl -vi -X PUT \ -H "Accept: text/xml" \ Scanner object or -H "Content-Type:text/xml" \ configure the Scanner -d @filter.txt \ in any other way, you "http://example.com:8000/users/scanner/" can create a text file and add your filter to the file. For example, to return only rows for which keys start with u123 and use a batch size of 100, the filter file would look like this: [source,xml] --- { "type": "PrefixFilter", "value": "u123" } ---Pass the file to the -d argument of the curl request. /table/scanner GET /scanner-id Get the next batch from curl -vi -X GET \ -H "Accept: text/xml" \ the scanner. Cell values are byte-encoded. If the "http://example.com:8000/users/scanner/14586 scanner has been 9072824375522207" exhausted, HTTP status 204 is returned. table/scanner/ DELETE Deletes the scanner and curl -vi -X DELETE \ scanner-id -H "Accept: text/xml" \ frees the resources it used. "http://example.com:8000/users/scanner/14586 9072824375522207" Table 18. Endpoints for Put Operations 339 Endpoint HTTP Verb /table/row_key PUT Description Example 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 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 \ -H "Accept: text/xml" \ -H "Content-Type: text/xml" \ -d 'dmFsdWU1Cg==< /CellSet>' \ "http://example.com:8000/users/fakerow" curl -vi -X PUT \ -H "Accept: text/json" \ -H "Content-Type: text/json" \ -d '{"Row":[{"key":"cm93NQo=", "Cell": [{"column":"Y2Y6ZQo=", "$":"dmFsdWU1Cg=="}]}]}'' \ "example.com:8000/users/fakerow" 79.4. REST XML Schema 340 341 79.5. REST Protobufs Schema message Version { optional string optional string optional string optional string optional string } restVersion = 1; jvmVersion = 2; osVersion = 3; serverVersion = 4; jerseyVersion = 5; message StorageClusterStatus { message Region { required bytes name = 1; optional int32 stores = 2; optional int32 storefiles = 3; optional int32 storefileSizeMB = 4; optional int32 memstoreSizeMB = 5; 343 optional int32 storefileIndexSizeMB = 6; } message Node { required string name = 1; // name:port optional int64 startCode = 2; optional int32 requests = 3; optional int32 heapSizeMB = 4; optional int32 maxHeapSizeMB = 5; repeated Region regions = 6; } // node status repeated Node liveNodes = 1; repeated string deadNodes = 2; // summary statistics optional int32 regions = 3; optional int32 requests = 4; optional double averageLoad = 5; } message TableList { repeated string name = 1; } message TableInfo { required string name = 1; message Region { required string name = 1; optional bytes startKey = 2; optional bytes endKey = 3; optional int64 id = 4; optional string location = 5; } repeated Region regions = 2; } message TableSchema { optional string name = 1; message Attribute { required string name = 1; required string value = 2; } repeated Attribute attrs = 2; repeated ColumnSchema columns = 3; // optional helpful encodings of commonly used attributes optional bool inMemory = 4; optional bool readOnly = 5; } message ColumnSchema { optional string name = 1; message Attribute { 344 required string name = 1; required string value = 2; } repeated Attribute attrs = 2; // optional helpful encodings of commonly used attributes optional int32 ttl = 3; optional int32 maxVersions = 4; optional string compression = 5; } message Cell { optional bytes optional bytes optional int64 optional bytes } row = 1; // unused if Cell is in a CellSet column = 2; timestamp = 3; data = 4; message CellSet { message Row { required bytes key = 1; repeated Cell values = 2; } repeated Row rows = 1; } message Scanner { optional bytes startRow = 1; optional bytes endRow = 2; repeated bytes columns = 3; optional int32 batch = 4; optional int64 startTime = 5; optional int64 endTime = 6; } 345 Chapter 80. Thrift Documentation about Thrift has moved to Thrift. 346 Chapter 81. 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. 347 Chapter 82. 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-transactionaltableindexed) 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 import import import java.io.File; java.io.FileInputStream; java.io.InputStream; java.util.Hashtable; import org.apache.hadoop.fs.Path; import org.apache.hadoop.hbase.client.tableindexed.IndexedTable; import import import import import import import import import import com.apache.hadoop.hbase.client.jdo.AbstractHBaseDBO; com.apache.hadoop.hbase.client.jdo.HBaseBigFile; com.apache.hadoop.hbase.client.jdo.HBaseDBOImpl; com.apache.hadoop.hbase.client.jdo.query.DeleteQuery; com.apache.hadoop.hbase.client.jdo.query.HBaseOrder; com.apache.hadoop.hbase.client.jdo.query.HBaseParam; com.apache.hadoop.hbase.client.jdo.query.InsertQuery; com.apache.hadoop.hbase.client.jdo.query.QSearch; com.apache.hadoop.hbase.client.jdo.query.SelectQuery; com.apache.hadoop.hbase.client.jdo.query.UpdateQuery; /** * Hbase JDO Example. * 348 * dependency library. * - commons-beanutils.jar * - commons-pool-1.5.5.jar * - hbase0.90.0-transactionl.jar * * you can expand Delete,Select,Update,Insert Query classes. * */ public class HBaseExample { public static void main(String[] args) throws Exception { AbstractHBaseDBO dbo = new HBaseDBOImpl(); //*drop if table is already exist.* if(dbo.isTableExist("user")){ dbo.deleteTable("user"); } //*create table* dbo.createTableIfNotExist("user",HBaseOrder.DESC,"account"); //dbo.createTableIfNotExist("user",HBaseOrder.ASC,"account"); //create index. String[] cols={"id","name"}; dbo.addIndexExistingTable("user","account",cols); //insert InsertQuery insert = dbo.createInsertQuery("user"); UserBean bean = new UserBean(); bean.setFamily("account"); bean.setAge(20); bean.setEmail("ncanis@gmail.com"); bean.setId("ncanis"); bean.setName("ncanis"); bean.setPassword("1111"); insert.insert(bean); //select 1 row SelectQuery select = dbo.createSelectQuery("user"); UserBean resultBean = (UserBean)select.select(bean.getRow(),UserBean.class); // select column value. String value = (String)select.selectColumn(bean.getRow(),"account","id", String.class); // search with option (QSearch has EQUAL, NOT_EQUAL, LIKE) // select id,password,name,email from account where id='ncanis' limit startRow,20 HBaseParam param = new HBaseParam(); param.setPage(bean.getRow(),20); param.addColumn("id","password","name","email"); param.addSearchOption("id","ncanis",QSearch.EQUAL); 349 select.search("account", param, UserBean.class); // search column value is existing. boolean isExist = select.existColumnValue("account","id","ncanis".getBytes()); // update password. UpdateQuery update = dbo.createUpdateQuery("user"); Hashtable colsTable = new Hashtable(); colsTable.put("password","2222".getBytes()); update.update(bean.getRow(),"account",colsTable); //delete DeleteQuery delete = dbo.createDeleteQuery("user"); delete.deleteRow(resultBean.getRow()); //////////////////////////////////// // etc // HTable pool with apache commons pool // borrow and release. HBasePoolManager(maxActive, minIdle etc..) IndexedTable table = dbo.getPool().borrow("user"); dbo.getPool().release(table); // upload bigFile by hadoop directly. HBaseBigFile bigFile = new HBaseBigFile(); File file = new File("doc/movie.avi"); FileInputStream fis = new FileInputStream(file); Path rootPath = new Path("/files/"); String filename = "movie.avi"; bigFile.uploadFile(rootPath,filename,fis,true); // receive file stream from hadoop. Path p = new Path(rootPath,filename); InputStream is = bigFile.path2Stream(p,4096); } } 350 Chapter 83. Scala 83.1. 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-amd6464:/path/to/scala-library.jar 83.2. 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( "org.apache.hadoop" % "hadoop-core" % "0.20.2", "org.apache.hbase" % "hbase" % "0.90.4" ) 83.3. Example Scala Code This example lists HBase tables, creates a new table, and adds a row to it. 351 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)) 352 Chapter 84. Jython 84.1. 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 84.2. Jython Code Examples 353 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): admin.disableTable(tableName) 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 354 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: result = scanner.next() if not result: break print java.lang.String(result.row), java.lang.String(result.getValue(cf, attr)) 355 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. 356 Chapter 85. 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. 85.1. 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. 85.2. 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. 357 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) 85.3. 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. 85.4. 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. 358 85.5. 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" 85.6. Example PHP Client Program that uses the Filter Language 359 ', ); $hbase->open(); $client = $hbase->getClient(); $result = $client->scannerOpenWithFilterString('table_name', "(PrefixFilter ('row2') AND (QualifierFilter (>=, 'binary:xyz'))) AND (TimestampsFilter ( 123, 456))"); $to_print = $client->scannerGetList($result,1); while ($to_print) { print_r($to_print); $to_print = $client->scannerGetList($result,1); } $client->scannerClose($result); ?> 85.7. 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 < xyz • "SKIP ValueFilter (0)" will skip the entire row if any of the values in the row is not 0 85.8. 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. 360 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 361 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 keyvalue. 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. 362 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. 363 Chapter 86. 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. 86.1. 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). 86.2. 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. 364 Chapter 87. Types of Coprocessors 87.1. 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. 87.1.1. 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. 87.1.2. 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. 365 87.2. 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. 366 Chapter 88. 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). 88.1. 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 element with a and a sub-element. The 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. 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): hbase.coprocessor.region.classes org.myname.hbase.coprocessor.endpoint.SumEndPoint 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. 367 88.2. Static Unloading 1. Delete the coprocessor’s 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. 88.3. 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://:/user//coprocessor.jar. 88.3.1. 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://:/ 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 368 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://:/user// or hdfs://:/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 subdirectories. 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. 88.3.2. 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. 369 TableName tableName = TableName.valueOf("users"); String path = "hdfs://:/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); 88.3.3. 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://:/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. 370 88.4. Dynamic Unloading 88.4.1. 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' 88.4.2. 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://:/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. 371 Chapter 89. 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 89.1. 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 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: 372 public class RegionObserverExample implements RegionObserver { private private private private static static static static final final final final byte[] byte[] byte[] byte[] ADMIN = Bytes.toBytes("admin"); COLUMN_FAMILY = Bytes.toBytes("details"); COLUMN = Bytes.toBytes("Admin_det"); VALUE = Bytes.toBytes("You can't see Admin details"); @Override public void preGetOp(final ObserverContext e, final Get get, final List results) throws IOException { if (Bytes.equals(get.getRow(),ADMIN)) { Cell c = CellUtil.createCell(get.getRow(),COLUMN_FAMILY, COLUMN, System.currentTimeMillis(), (byte)4, VALUE); results.add(c); e.bypass(); } } } 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 { Filter filter = new RowFilter(CompareOp.NOT_EQUAL, new BinaryComparator(ADMIN)); scan.setFilter(filter); 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: 373 @Override public boolean postScannerNext(final ObserverContext e, final InternalScanner s, final List results, final int limit, final boolean hasMore) throws IOException { Result result = null; Iterator iterator = results.iterator(); while (iterator.hasNext()) { result = iterator.next(); if (Bytes.equals(result.getRow(), ROWKEY)) { iterator.remove(); break; } } return hasMore; } 89.2. 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 { required string family = 1; required string column = 2; } message SumResponse { required int64 sum = 1 [default = 0]; } service SumService { rpc getSum(SumRequest) returns (SumResponse); } 2. Execute the protoc command to generate the Java code from the above .proto' file. 374 $ 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 { private RegionCoprocessorEnvironment env; @Override public Service getService() { return this; } @Override public void start(CoprocessorEnvironment env) throws IOException { if (env instanceof RegionCoprocessorEnvironment) { this.env = (RegionCoprocessorEnvironment)env; } else { throw new CoprocessorException("Must be loaded on a table region!"); } } @Override public void stop(CoprocessorEnvironment env) throws IOException { // do nothing } @Override public void getSum(RpcController controller, Sum.SumRequest request, RpcCallback done) { Scan scan = new Scan(); scan.addFamily(Bytes.toBytes(request.getFamily())); scan.addColumn(Bytes.toBytes(request.getFamily()), Bytes.toBytes(request .getColumn())); Sum.SumResponse response = null; InternalScanner scanner = null; 375 try { scanner = env.getRegion().getScanner(scan); List results = new ArrayList<>(); boolean hasMore = false; long sum = 0L; do { hasMore = scanner.next(results); for (Cell cell : results) { sum = sum + Bytes.toLong(CellUtil.cloneValue(cell)); } results.clear(); } while (hasMore); response = Sum.SumResponse.newBuilder().setSum(sum).build(); } catch (IOException ioe) { ResponseConverter.setControllerException(controller, ioe); } finally { if (scanner != null) { try { scanner.close(); } catch (IOException ignored) {} } } done.run(response); } } 376 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 { Map results = table.coprocessorService( Sum.SumService.class, null, /* start key */ null, /* end key */ new Batch.Call() { @Override public Long call(Sum.SumService aggregate) throws IOException { BlockingRpcCallback rpcCallback = new BlockingRpcCallback<>(); aggregate.getSum(null, request, rpcCallback); Sum.SumResponse response = rpcCallback.get(); return response.hasSum() ? response.getSum() : 0L; } } ); for (Long sum : results.values()) { System.out.println("Sum = " + sum); } } catch (ServiceException e) { e.printStackTrace(); } catch (Throwable e) { e.printStackTrace(); } 4. Load the Coprocessor. 5. Write a client code to call the Coprocessor. 377 Chapter 90. 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: arg1 1 arg2 2 Then you can read the configuration using code like the following: 378 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()) { System.out.println(Bytes.toString(CellUtil.cloneRow(c)) + "==> " + Bytes.toString(CellUtil.cloneFamily(c)) + "{" + Bytes.toString(CellUtil.cloneQualifier(c)) + ":" + Bytes.toLong(CellUtil.cloneValue(c)) + "}"); } Scan scan = new Scan(); ResultScanner scanner = table.getScanner(scan); for (Result res : scanner) { for (Cell c : res.rawCells()) { System.out.println(Bytes.toString(CellUtil.cloneRow(c)) + " ==> " + Bytes.toString(CellUtil.cloneFamily(c)) + " {" + Bytes.toString(CellUtil.cloneQualifier(c)) + ":" + Bytes.toLong(CellUtil.cloneValue(c)) + "}"); } } 379 Chapter 91. 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) 380 Apache HBase Performance Tuning 381 Chapter 92. Operating System 92.1. Memory RAM, RAM, RAM. Don’t starve HBase. 92.2. 64-bit Use a 64-bit platform (and 64-bit JVM). 92.3. Swapping Watch out for swapping. Set swappiness to 0. 92.4. CPU Make sure you have set up your Hadoop to use native, hardware checksumming. See link:[hadoop.native.lib]. 382 Chapter 93. 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 93.1. 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. 93.2. 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. 93.3. 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 383 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. 93.4. 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). 93.5. 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-sacrificepartition-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. 384 Chapter 94. Java 94.1. The Garbage Collector and Apache HBase 94.1.1. 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 385 Chapter 95. HBase Configurations See Recommended Configurations. 95.1. Improving the 99th Percentile Try link:[hedged_reads]. 95.2. Managing Compactions For larger systems, managing link:[compactions and splits] may be something you want to consider. 95.3. hbase.regionserver.handler.count See [hbase.regionserver.handler.count]. 95.4. hfile.block.cache.size See [hfile.block.cache.size]. A memory setting for the RegionServer process. 95.5. 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 inmemory 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 31. Enable Prefetch Using the API // ... HTableDescriptor tableDesc = new HTableDescriptor("myTable"); HColumnDescriptor cfDesc = new HColumnDescriptor("myCF"); cfDesc.setPrefetchBlocksOnOpen(true); tableDesc.addFamily(cfDesc); // ... 386 See the API documentation for CacheConfig. To see prefetch in operation, enable TRACE in org.apache.hadoop.hbase.io.hfile.HFileReaderImpl level hbase-2.0+ logging on or on org.apache.hadoop.hbase.io.hfile.HFileReaderV2 in earlier versions, hbase-1.x, of HBase. 95.6. hbase.regionserver.global.memstore.size See [hbase.regionserver.global.memstore.size]. This memory setting is often adjusted for the RegionServer process depending on needs. 95.7. 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. 95.8. hbase.hstore.blockingStoreFiles See [hbase.hstore.blockingStoreFiles]. If there is blocking in the RegionServer logs, increasing this can help. 95.9. hbase.hregion.memstore.block.multiplier See [hbase.hregion.memstore.block.multiplier]. If there is enough RAM, increasing this can help. 95.10. 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. 95.11. 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 387 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 388 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. 389 Chapter 96. ZooKeeper See ZooKeeper for information on configuring ZooKeeper, and see the part about having a dedicated disk. 390 Chapter 97. Schema Design 97.1. Number of Column Families See On the number of column families. 97.2. Key and Attribute Lengths See Try to minimize row and column sizes. See also However… for compression caveats. 97.3. 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. 97.4. 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?. 97.4.1. When To Use Bloom Filters Since HBase 0.96, row-based Bloom filters are enabled by default. You may choose to disable them 391 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. 97.4.2. 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'} 97.4.3. 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. 392 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. true enabled Master switch to enable Delete Family Bloom filters and store them in the StoreFile. io.storefile.bloom.block.size Target Bloom block size. Bloom filter blocks of approximately this size are interleaved with data blocks. 131072 hfile.block.bloom.cacheonwrite false Enables cache-on-write for inline blocks of a compound Bloom filter. 97.5. 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. 97.6. 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. 97.7. Compression Production systems should use compression with their ColumnFamily definitions. See Compression and Data Block Encoding In HBase for more information. 97.7.1. 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. 393 See Try to minimize row and column sizes on for schema design tips, and KeyValue for more information on HBase stores data internally. 394 Chapter 98. HBase General Patterns 98.1. 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")); version of value // returns current But especially when inside loops (and MapReduce jobs), converting the columnFamily and columnnames to byte-arrays repeatedly is surprisingly expensive. It’s better to use constants for the bytearrays, 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 395 Chapter 99. Writing to HBase 99.1. Batch Loading Use the bulk load tool if you can. See Bulk Loading. Otherwise, pay attention to the below. 99.2. 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. 396 99.3. 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. 99.4. 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. 99.5. 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. 99.6. 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. 99.7. 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. 397 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 398 Chapter 100. 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 100.1. 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. 100.1.1. 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. 100.2. 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. 100.3. 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 510 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: 399 Scan scan = new Scan(); scan.addColumn(...); scan.setAttribute(Scan.HINT_LOOKAHEAD, Bytes.toBytes(2)); table.getScanner(scan); 100.4. 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). 100.5. 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 { for (Result r = rs.next(); r != null; r = rs.next()) { // process result... } finally { rs.close(); // always close the ResultScanner! } table.close(); 100.6. 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 100.7. 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. 400 100.8. 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 100.9. 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. 100.9.1. 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). 100.9.2. 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. 401 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. 100.10. 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. 402 Example 32. Hedged Reads Configuration Example dfs.client.hedged.read.threadpool.size 20 dfs.client.hedged.read.threshold.millis 10 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. 403 Chapter 101. Deleting from HBase 101.1. 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. 101.2. 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 404 Chapter 102. HDFS Because HBase runs on HDFS it is important to understand how it works and how it affects HBase. 102.1. 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. 102.2. 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: dfs.client.read.shortcircuit true This configuration parameter turns on short-circuit local reads. dfs.domain.socket.path /home/stack/sockets/short_circuit_read_socket_PORT Optional. This is a path to a UNIX domain socket that will be used for communication between the DataNode and local HDFS clients. 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 than the hbase user. 405 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. 102.3. 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 usecase. 406 Chapter 103. 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 upfront that because EC2 issues are practically a separate class of performance issues. 407 Chapter 104. 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). 408 Chapter 105. Case Studies For Performance and Troubleshooting Case Studies, see Apache HBase Case Studies. 409 Troubleshooting and Debugging Apache HBase 410 Chapter 106. 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. 411 Chapter 107. Logs The key process logs are as follows… (replace with the user that started the service, and for the machine name) NameNode: $HADOOP_HOME/logs/hadoop--namenode-.log DataNode: $HADOOP_HOME/logs/hadoop--datanode-.log JobTracker: $HADOOP_HOME/logs/hadoop--jobtracker-.log TaskTracker: $HADOOP_HOME/logs/hadoop--tasktracker-.log HMaster: $HBASE_HOME/logs/hbase--master-.log RegionServer: $HBASE_HOME/logs/hbase--regionserver-.log ZooKeeper: TODO 107.1. 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. 107.1.1. 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. 107.1.2. 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. 107.2. Log Levels 107.2.1. 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. 412 To disable, set the logging level back to INFO level. 107.3. 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:" # 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: -XX:+UseGCLogFileRotation -XX:NumberOfGCLogFiles=1 -XX:GCLogFileSize=512M" # If 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: 413 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: 414 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 hbaseenv.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:" # 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: -XX:+UseGCLogFileRotation -XX:NumberOfGCLogFiles=1 -XX:GCLogFileSize=512M" # If 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. 415 Chapter 108. Resources 108.1. 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. 108.2. 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. 108.3. Slack See http://apache-hbase.slack.com Channel on Slack 108.4. IRC (You will probably get a more prompt response on the Slack channel) #hbase on irc.freenode.net 108.5. JIRA JIRA is also really helpful when looking for Hadoop/HBase-specific issues. 416 Chapter 109. Tools 109.1. Builtin Tools 109.1.1. 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. 109.1.2. 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. 109.1.3. zkcli zkcli is a very useful tool for investigating ZooKeeper-related issues. To invoke: ./hbase zkcli -server host:port The commands (and arguments) are: 417 connect host:port get path [watch] ls path [watch] set path data [version] delquota [-n|-b] path quit printwatches on|off create [-s] [-e] path data acl stat path [watch] close ls2 path [watch] history listquota path setAcl path acl getAcl path sync path redo cmdno addauth scheme auth delete path [version] setquota -n|-b val path 109.2. External Tools 109.2.1. 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). 109.2.2. 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 PID 15558 13268 8895 … 418 USER hadoop hadoop hadoop PR 18 18 18 NI VIRT RES SHR -2 3292m 2.4g 3556 -2 8967m 8.2g 4104 -2 1581m 497m 3420 S %CPU %MEM S 79 10.4 S 21 35.1 S 11 2.1 TIME+ COMMAND 6523:52 java 5170:30 java 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. 109.2.3. 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 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. 419 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<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 109.2.4. 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: "regionserver60020" prio=10 tid=0x0000000040ab4000 nid=0x45cf waiting on condition [0x00007f16b6a96000..0x00007f16b6a96a70] java.lang.Thread.State: TIMED_WAITING (parking) at sun.misc.Unsafe.park(Native Method) - parking to wait for <0x00007f16cd5c2f30> (a java.util.concurrent.locks .AbstractQueuedSynchronizer$ConditionObject) at java.util.concurrent.locks.LockSupport.parkNanos(LockSupport.java:198) at java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject .awaitNanos(AbstractQueuedSynchronizer.java:1963) at java.util.concurrent.LinkedBlockingQueue.poll(LinkedBlockingQueue.java:395) at org.apache.hadoop.hbase.regionserver.HRegionServer.run(HRegionServer.java:647) at java.lang.Thread.run(Thread.java:619) The MemStore flusher thread that is currently flushing to a file: 420 "regionserver60020.cacheFlusher" daemon prio=10 tid=0x0000000040f4e000 nid=0x45eb in Object.wait() [0x00007f16b5b86000..0x00007f16b5b87af0] java.lang.Thread.State: WAITING (on object monitor) at java.lang.Object.wait(Native Method) at java.lang.Object.wait(Object.java:485) at org.apache.hadoop.ipc.Client.call(Client.java:803) - locked <0x00007f16cb14b3a8> (a org.apache.hadoop.ipc.Client$Call) at org.apache.hadoop.ipc.RPC$Invoker.invoke(RPC.java:221) at $Proxy1.complete(Unknown Source) at sun.reflect.GeneratedMethodAccessor38.invoke(Unknown Source) at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl .java:25) at java.lang.reflect.Method.invoke(Method.java:597) at org.apache.hadoop.io.retry.RetryInvocationHandler.invokeMethod (RetryInvocationHandler.java:82) at org.apache.hadoop.io.retry.RetryInvocationHandler.invoke( RetryInvocationHandler.java:59) at $Proxy1.complete(Unknown Source) at org.apache.hadoop.hdfs.DFSClient$DFSOutputStream.closeInternal(DFSClient.java :3390) - locked <0x00007f16cb14b470> (a org.apache.hadoop.hdfs.DFSClient$DFSOutputStream) at org.apache.hadoop.hdfs.DFSClient$DFSOutputStream.close(DFSClient.java:3304) at org.apache.hadoop.fs.FSDataOutputStream$PositionCache.close(FSDataOutputStream .java:61) at org.apache.hadoop.fs.FSDataOutputStream.close(FSDataOutputStream.java:86) at org.apache.hadoop.hbase.io.hfile.HFile$Writer.close(HFile.java:650) at org.apache.hadoop.hbase.regionserver.StoreFile$Writer.close(StoreFile.java:853) at org.apache.hadoop.hbase.regionserver.Store.internalFlushCache(Store.java:467) - locked <0x00007f16d00e6f08> (a java.lang.Object) at org.apache.hadoop.hbase.regionserver.Store.flushCache(Store.java:427) at org.apache.hadoop.hbase.regionserver.Store.access$100(Store.java:80) at org.apache.hadoop.hbase.regionserver.Store$StoreFlusherImpl.flushCache(Store .java:1359) at org.apache.hadoop.hbase.regionserver.HRegion.internalFlushcache(HRegion.java :907) at org.apache.hadoop.hbase.regionserver.HRegion.internalFlushcache(HRegion.java :834) at org.apache.hadoop.hbase.regionserver.HRegion.flushcache(HRegion.java:786) at org.apache.hadoop.hbase.regionserver.MemStoreFlusher.flushRegion (MemStoreFlusher.java:250) at org.apache.hadoop.hbase.regionserver.MemStoreFlusher.flushRegion (MemStoreFlusher.java:224) 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.): 421 "IPC Server handler 16 on 60020" daemon prio=10 tid=0x00007f16b011d800 nid=0x4a5e waiting on condition [0x00007f16afefd000..0x00007f16afefd9f0] java.lang.Thread.State: WAITING (parking) at sun.misc.Unsafe.park(Native Method) - parking to wait for <0x00007f16cd3f8dd8> (a java.util.concurrent.locks .AbstractQueuedSynchronizer$ConditionObject) at java.util.concurrent.locks.LockSupport.park(LockSupport.java:158) at java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject .await(AbstractQueuedSynchronizer.java:1925) at java.util.concurrent.LinkedBlockingQueue.take(LinkedBlockingQueue.java :358) at org.apache.hadoop.hbase.ipc.HBaseServer$Handler.run(HBaseServer.java: 1013) And one that’s busy doing an increment of a counter (it’s in the phase where it’s trying to create a scanner in order to read the last value): "IPC Server handler 66 on 60020" daemon prio=10 tid=0x00007f16b006e800 nid=0x4a90 runnable [0x00007f16acb77000..0x00007f16acb77cf0] java.lang.Thread.State: RUNNABLE at org.apache.hadoop.hbase.regionserver.KeyValueHeap.(KeyValueHeap .java:56) at org.apache.hadoop.hbase.regionserver.StoreScanner.(StoreScanner .java:79) at org.apache.hadoop.hbase.regionserver.Store.getScanner(Store.java:1202) at org.apache.hadoop.hbase.regionserver.HRegion$RegionScanner.( HRegion.java:2209) at org.apache.hadoop.hbase.regionserver.HRegion.instantiateInternalScanner (HRegion.java:1063) at org.apache.hadoop.hbase.regionserver.HRegion.getScanner(HRegion.java: 1055) at org.apache.hadoop.hbase.regionserver.HRegion.getScanner(HRegion.java: 1039) at org.apache.hadoop.hbase.regionserver.HRegion.getLastIncrement(HRegion .java:2875) at org.apache.hadoop.hbase.regionserver.HRegion.incrementColumnValue( HRegion.java:2978) at org.apache.hadoop.hbase.regionserver.HRegionServer.incrementColumnValue (HRegionServer.java:2433) at sun.reflect.GeneratedMethodAccessor20.invoke(Unknown Source) at sun.reflect.DelegatingMethodAccessorImpl.invoke (DelegatingMethodAccessorImpl.java:25) at java.lang.reflect.Method.invoke(Method.java:597) at org.apache.hadoop.hbase.ipc.HBaseRPC$Server.call(HBaseRPC.java:560) at org.apache.hadoop.hbase.ipc.HBaseServer$Handler.run(HBaseServer.java: 1027) A thread that receives data from HDFS: 422 "IPC Client (47) connection to sv4borg9/ from hadoop" daemon prio=10 tid=0x00007f16a02d0000 nid=0x4fa3 runnable [0x00007f16b517d000..0x00007f16b517dbf0] java.lang.Thread.State: RUNNABLE at sun.nio.ch.EPollArrayWrapper.epollWait(Native Method) at sun.nio.ch.EPollArrayWrapper.poll(EPollArrayWrapper.java:215) at sun.nio.ch.EPollSelectorImpl.doSelect(EPollSelectorImpl.java:65) at sun.nio.ch.SelectorImpl.lockAndDoSelect(SelectorImpl.java:69) - locked <0x00007f17d5b68c00> (a sun.nio.ch.Util$1) - locked <0x00007f17d5b68be8> (a java.util.Collections$UnmodifiableSet) - locked <0x00007f1877959b50> (a sun.nio.ch.EPollSelectorImpl) at sun.nio.ch.SelectorImpl.select(SelectorImpl.java:80) at org.apache.hadoop.net.SocketIOWithTimeout$SelectorPool.select (SocketIOWithTimeout.java:332) at org.apache.hadoop.net.SocketIOWithTimeout.doIO(SocketIOWithTimeout.java :157) at org.apache.hadoop.net.SocketInputStream.read(SocketInputStream.java:155) at org.apache.hadoop.net.SocketInputStream.read(SocketInputStream.java:128) at java.io.FilterInputStream.read(FilterInputStream.java:116) at org.apache.hadoop.ipc.Client$Connection$PingInputStream.read(Client.java :304) at java.io.BufferedInputStream.fill(BufferedInputStream.java:218) at java.io.BufferedInputStream.read(BufferedInputStream.java:237) - locked <0x00007f1808539178> (a java.io.BufferedInputStream) at java.io.DataInputStream.readInt(DataInputStream.java:370) at org.apache.hadoop.ipc.Client$Connection.receiveResponse(Client.java:569) at org.apache.hadoop.ipc.Client$Connection.run(Client.java:477) And here is a master trying to recover a lease after a RegionServer died: 423 "LeaseChecker" daemon prio=10 tid=0x00000000407ef800 nid=0x76cd waiting on condition [0x00007f6d0eae2000..0x00007f6d0eae2a70] -java.lang.Thread.State: WAITING (on object monitor) at java.lang.Object.wait(Native Method) at java.lang.Object.wait(Object.java:485) at org.apache.hadoop.ipc.Client.call(Client.java:726) - locked <0x00007f6d1cd28f80> (a org.apache.hadoop.ipc.Client$Call) at org.apache.hadoop.ipc.RPC$Invoker.invoke(RPC.java:220) at $Proxy1.recoverBlock(Unknown Source) at org.apache.hadoop.hdfs.DFSClient$DFSOutputStream.processDatanodeError (DFSClient.java:2636) at org.apache.hadoop.hdfs.DFSClient$DFSOutputStream.(DFSClient.java :2832) at org.apache.hadoop.hdfs.DFSClient.append(DFSClient.java:529) at org.apache.hadoop.hdfs.DistributedFileSystem.append( DistributedFileSystem.java:186) at org.apache.hadoop.fs.FileSystem.append(FileSystem.java:530) at org.apache.hadoop.hbase.util.FSUtils.recoverFileLease(FSUtils.java:619) at org.apache.hadoop.hbase.regionserver.wal.HLog.splitLog(HLog.java:1322) at org.apache.hadoop.hbase.regionserver.wal.HLog.splitLog(HLog.java:1210) at org.apache.hadoop.hbase.master.HMaster.splitLogAfterStartup(HMaster.java :648) at org.apache.hadoop.hbase.master.HMaster.joinCluster(HMaster.java:572) at org.apache.hadoop.hbase.master.HMaster.run(HMaster.java:503) 109.2.5. 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. 109.2.6. 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. 424 Chapter 110. Client For more information on the HBase client, see client. 110.1. 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. 110.2. 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. 110.3. 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.regionserver.lease.period. Harsh J investigated the issue as part of the mailing list thread HBase, mail # user - Lease does not exist exceptions 110.4. 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. 425 110.5. 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. 110.6. 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 (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. 426 110.7. ZooKeeper Client Connection Errors Errors like this… 11/07/05 11:26:41 WARN zookeeper.ClientCnxn: Session 0x0 for server null, unexpected error, closing socket connection and attempting reconnect java.net.ConnectException: Connection refused: no further information at sun.nio.ch.SocketChannelImpl.checkConnect(Native Method) at sun.nio.ch.SocketChannelImpl.finishConnect(Unknown Source) at org.apache.zookeeper.ClientCnxn$SendThread.run(ClientCnxn.java:1078) 11/07/05 11:26:43 INFO zookeeper.ClientCnxn: Opening socket connection to server localhost/ 11/07/05 11:26:44 WARN zookeeper.ClientCnxn: Session 0x0 for server null, unexpected error, closing socket connection and attempting reconnect java.net.ConnectException: Connection refused: no further information at sun.nio.ch.SocketChannelImpl.checkConnect(Native Method) at sun.nio.ch.SocketChannelImpl.finishConnect(Unknown Source) at org.apache.zookeeper.ClientCnxn$SendThread.run(ClientCnxn.java:1078) 11/07/05 11:26:45 INFO zookeeper.ClientCnxn: Opening socket connection to server localhost/ …are either due to ZooKeeper being down, or unreachable due to network issues. The utility zkcli may help investigate ZooKeeper issues. 110.8. 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). 427 110.9. 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 /lib/security. 428 Chapter 111. MapReduce 111.1. 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. WARN mapred.LocalJobRunner: job_local_0001 java.lang.IllegalArgumentException: Can't read partitions file at org.apache.hadoop.hbase.mapreduce.hadoopbackport.TotalOrderPartitioner.setConf(TotalOr derPartitioner.java:111) at org.apache.hadoop.util.ReflectionUtils.setConf(ReflectionUtils.java:62) at org.apache.hadoop.util.ReflectionUtils.newInstance(ReflectionUtils.java:117) at org.apache.hadoop.mapred.MapTask$NewOutputCollector.(MapTask.java:560) at org.apache.hadoop.mapred.MapTask.runNewMapper(MapTask.java:639) at org.apache.hadoop.mapred.MapTask.run(MapTask.java:323) at org.apache.hadoop.mapred.LocalJobRunner$Job.run(LocalJobRunner.java:210) Caused by: java.io.FileNotFoundException: File _partition.lst does not exist. at org.apache.hadoop.fs.RawLocalFileSystem.getFileStatus(RawLocalFileSystem.java:383) at org.apache.hadoop.fs.FilterFileSystem.getFileStatus(FilterFileSystem.java:251) at org.apache.hadoop.fs.FileSystem.getLength(FileSystem.java:776) at org.apache.hadoop.io.SequenceFile$Reader.(SequenceFile.java:1424) at org.apache.hadoop.io.SequenceFile$Reader.(SequenceFile.java:1419) at org.apache.hadoop.hbase.mapreduce.hadoopbackport.TotalOrderPartitioner.readPartitions( TotalOrderPartitioner.java:296) …see the critical portion of the stack? It’s… at org.apache.hadoop.mapred.LocalJobRunner$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 429 classpaths. 111.2. 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 com.google.protobuf.ZeroCopyLiteralByteString job cannot jar: IllegalAccessError: access its class 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. 430 Chapter 112. NameNode For more information on the NameNode, see HDFS. 112.1. 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. 112.2. 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 /data / /
/ / / Regions for the table) (Namespaces in the cluster) (Tables in the cluster) (Regions for the table) (ColumnFamilies for the Region for the table) (StoreFiles for the ColumnFamily for the The HDFS directory structure of HBase WAL is.. 431 /hbase /WALs / / (RegionServers) (WAL files for the RegionServer) See the HDFS User Guide for other non-shell diagnostic utilities like fsck. 112.2.1. 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 112.2.2. 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. 112.3. 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/.snapshots, 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 <>. 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 432 WALs are saved when replication is disabled, as long as there are peers. Do not manage WALs manually via HDFS. 433 Chapter 113. Network 113.1. 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. 113.2. Loopback IP HBase expects the loopback IP Address to be 113.3. Network Interfaces Are all the network interfaces functioning correctly? Are you sure? See the Troubleshooting Case Study in Case Studies. 434 Chapter 114. RegionServer For more information on the RegionServers, see RegionServer. 114.1. Startup Errors 114.1.1. Master Starts, But RegionServers Do Not The Master believes the RegionServers have the IP of - which is localhost and resolves to the master’s own localhost. The RegionServers are erroneously informing the Master that their IP addresses are Modify /etc/hosts on the region servers, from… # Do not remove the following line, or various programs # that require network functionality will fail. fully.qualified.regionservername regionservername .localdomain localhost ::1 localhost6.localdomain6 localhost6 localhost ... to (removing the master node’s name from localhost)… # Do not remove the following line, or various programs # that require network functionality will fail. localhost.localdomain localhost ::1 localhost6.localdomain6 localhost6 114.1.2. 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 at java.lang.ClassLoader.loadLibrary(ClassLoader.java:1734) at java.lang.Runtime.loadLibrary0(Runtime.java:823) 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]. 114.1.3. 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 435 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/] 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/] regionserver.HRegionServer: ***** ABORTING region server,16020,1522946074234: Unhandled: cannot get log writer ***** java.io.IOException: cannot get log writer at org.apache.hadoop.hbase.wal.AsyncFSWALProvider.createAsyncWriter(AsyncFSWALProvider.ja va:112) at org.apache.hadoop.hbase.regionserver.wal.AsyncFSWAL.createWriterInstance(AsyncFSWAL.ja va:612) at org.apache.hadoop.hbase.regionserver.wal.AsyncFSWAL.createWriterInstance(AsyncFSWAL.ja va:124) at org.apache.hadoop.hbase.regionserver.wal.AbstractFSWAL.rollWriter(AbstractFSWAL.java:7 59) at org.apache.hadoop.hbase.regionserver.wal.AbstractFSWAL.rollWriter(AbstractFSWAL.java:4 89) at org.apache.hadoop.hbase.regionserver.wal.AsyncFSWAL.(AsyncFSWAL.java:251) at org.apache.hadoop.hbase.wal.AsyncFSWALProvider.createWAL(AsyncFSWALProvider.java:69) at org.apache.hadoop.hbase.wal.AsyncFSWALProvider.createWAL(AsyncFSWALProvider.java:44) at org.apache.hadoop.hbase.wal.AbstractFSWALProvider.getWAL(AbstractFSWALProvider.java:13 8) at org.apache.hadoop.hbase.wal.AbstractFSWALProvider.getWAL(AbstractFSWALProvider.java:57 ) at org.apache.hadoop.hbase.wal.WALFactory.getWAL(WALFactory.java:252) at org.apache.hadoop.hbase.regionserver.HRegionServer.getWAL(HRegionServer.java:2105) at org.apache.hadoop.hbase.regionserver.HRegionServer.buildServerLoad(HRegionServer.java: 1326) at 436 org.apache.hadoop.hbase.regionserver.HRegionServer.tryRegionServerReport(HRegionServer .java:1191) at org.apache.hadoop.hbase.regionserver.HRegionServer.run(HRegionServer.java:1007) at java.lang.Thread.run(Thread.java:745) Caused by: org.apache.hadoop.hbase.util.CommonFSUtils$StreamLacksCapabilityException: hflush and hsync at org.apache.hadoop.hbase.io.asyncfs.AsyncFSOutputHelper.createOutput(AsyncFSOutputHelpe r.java:69) at org.apache.hadoop.hbase.regionserver.wal.AsyncProtobufLogWriter.initOutput(AsyncProtob ufLogWriter.java:168) at org.apache.hadoop.hbase.regionserver.wal.AbstractProtobufLogWriter.init(AbstractProtob ufLogWriter.java:167) at org.apache.hadoop.hbase.wal.AsyncFSWALProvider.createAsyncWriter(AsyncFSWALProvider.ja va:99) ... 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. 114.1.4. 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. at org.apache.hadoop.hbase.io.asyncfs.FanOutOneBlockAsyncDFSOutputSaslHelper.(Fan OutOneBlockAsyncDFSOutputSaslHelper.java:268) at org.apache.hadoop.hbase.io.asyncfs.FanOutOneBlockAsyncDFSOutputHelper.initialize(FanOu tOneBlockAsyncDFSOutputHelper.java:661) at org.apache.hadoop.hbase.io.asyncfs.FanOutOneBlockAsyncDFSOutputHelper.access$300(FanOu 437 tOneBlockAsyncDFSOutputHelper.java:118) at org.apache.hadoop.hbase.io.asyncfs.FanOutOneBlockAsyncDFSOutputHelper$13.operationComp lete(FanOutOneBlockAsyncDFSOutputHelper.java:720) at org.apache.hadoop.hbase.io.asyncfs.FanOutOneBlockAsyncDFSOutputHelper$13.operationComp lete(FanOutOneBlockAsyncDFSOutputHelper.java:715) at org.apache.hbase.thirdparty.io.netty.util.concurrent.DefaultPromise.notifyListener0(De faultPromise.java:507) at org.apache.hbase.thirdparty.io.netty.util.concurrent.DefaultPromise.notifyListeners0(D efaultPromise.java:500) at org.apache.hbase.thirdparty.io.netty.util.concurrent.DefaultPromise.notifyListenersNow (DefaultPromise.java:479) at org.apache.hbase.thirdparty.io.netty.util.concurrent.DefaultPromise.notifyListeners(De faultPromise.java:420) at org.apache.hbase.thirdparty.io.netty.util.concurrent.DefaultPromise.trySuccess(Default Promise.java:104) at org.apache.hbase.thirdparty.io.netty.channel.DefaultChannelPromise.trySuccess(DefaultC hannelPromise.java:82) at org.apache.hbase.thirdparty.io.netty.channel.epoll.AbstractEpollChannel$AbstractEpollU nsafe.fulfillConnectPromise(AbstractEpollChannel.java:638) at org.apache.hbase.thirdparty.io.netty.channel.epoll.AbstractEpollChannel$AbstractEpollU nsafe.finishConnect(AbstractEpollChannel.java:676) at org.apache.hbase.thirdparty.io.netty.channel.epoll.AbstractEpollChannel$AbstractEpollU nsafe.epollOutReady(AbstractEpollChannel.java:552) at org.apache.hbase.thirdparty.io.netty.channel.epoll.EpollEventLoop.processReady(EpollEv entLoop.java:394) at org.apache.hbase.thirdparty.io.netty.channel.epoll.EpollEventLoop.run(EpollEventLoop.j ava:304) at org.apache.hbase.thirdparty.io.netty.util.concurrent.SingleThreadEventExecutor$5.run(S ingleThreadEventExecutor.java:858) at org.apache.hbase.thirdparty.io.netty.util.concurrent.DefaultThreadFactory$DefaultRunna bleDecorator.run(DefaultThreadFactory.java:138) at java.lang.Thread.run(Thread.java:748) Caused by: java.lang.NoSuchMethodException: org.apache.hadoop.hdfs.DFSClient.decryptEncryptedDataEncryptionKey(org.apache.hadoop.f s.FileEncryptionInfo) at java.lang.Class.getDeclaredMethod(Class.java:2130) 438 at org.apache.hadoop.hbase.io.asyncfs.FanOutOneBlockAsyncDFSOutputSaslHelper.createTransp arentCryptoHelper(FanOutOneBlockAsyncDFSOutputSaslHelper.java:232) at org.apache.hadoop.hbase.io.asyncfs.FanOutOneBlockAsyncDFSOutputSaslHelper.(Fan OutOneBlockAsyncDFSOutputSaslHelper.java:262) ... 18 more If you hit this error, please specify FSHLog, i.e, filesystem, explicitly in your config file. hbase.wal.provider filesystem 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. 114.2. Runtime Errors 114.2.1. 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. 114.2.2. 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 at java.io.UnixFileSystem.createFileExclusively(Native Method) at java.io.File.createNewFile(File.java:883) ... see the Getting Started section on link:[ulimit and nproc configuration]. 114.2.3. 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]. 439 114.2.4. 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. 114.2.5. 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. 114.2.6. "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. 114.2.7. ZooKeeper SessionExpired events Master or RegionServers shutting down with messages like those in the logs: 440 WARN org.apache.zookeeper.ClientCnxn: Exception closing session 0x278bd16a96000f to sun.nio.ch.SelectionKeyImpl@355811ec java.io.IOException: TIMED OUT at org.apache.zookeeper.ClientCnxn$SendThread.run(ClientCnxn.java:906) WARN org.apache.hadoop.hbase.util.Sleeper: We slept 79410ms, ten times longer than scheduled: 5000 INFO org.apache.zookeeper.ClientCnxn: Attempting connection to server hostname/IP:PORT INFO org.apache.zookeeper.ClientCnxn: Priming connection to java.nio.channels .SocketChannel[connected local=/IP:PORT remote=hostname/IP:PORT] INFO org.apache.zookeeper.ClientCnxn: Server connection successful WARN org.apache.zookeeper.ClientCnxn: Exception closing session 0x278bd16a96000d to sun.nio.ch.SelectionKeyImpl@3544d65e java.io.IOException: Session Expired at org.apache.zookeeper.ClientCnxn$SendThread.readConnectResult(ClientCnxn. java:589) at org.apache.zookeeper.ClientCnxn$SendThread.doIO(ClientCnxn.java:709) at org.apache.zookeeper.ClientCnxn$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. zookeeper.session.timeout 120000 hbase.zookeeper.property.tickTime 6000 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 over441 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. 114.2.8. 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. 114.2.9. 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. 114.2.10. 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. 114.3. 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. 442 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 114.4. Shutdown Errors 443 Chapter 115. Master For more information on the Master, see master. 115.1. Startup Errors 115.1.1. 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. 115.1.2. 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. 115.1.3. 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: 444 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. at org.apache.hadoop.hbase.procedure2.store.wal.WALProcedureStore.rollWriter(WALProcedure Store.java:1034) at org.apache.hadoop.hbase.procedure2.store.wal.WALProcedureStore.recoverLease(WALProcedu reStore.java:374) at org.apache.hadoop.hbase.procedure2.ProcedureExecutor.start(ProcedureExecutor.java:530) at org.apache.hadoop.hbase.master.HMaster.startProcedureExecutor(HMaster.java:1267) at org.apache.hadoop.hbase.master.HMaster.startServiceThreads(HMaster.java:1173) at org.apache.hadoop.hbase.master.HMaster.finishActiveMasterInitialization(HMaster.java:8 81) at org.apache.hadoop.hbase.master.HMaster.startActiveMasterManager(HMaster.java:2048) at org.apache.hadoop.hbase.master.HMaster.lambda$run$0(HMaster.java:568) 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. 115.2. Shutdown Errors 445 Chapter 116. ZooKeeper 116.1. Startup Errors 116.1.1. 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 hbasesite.xml to make sure it resolves to the correct FQDN. 116.2. 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. 446 Chapter 117. Amazon EC2 117.1. 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: 2009-10-19 11:52:27,030 INFO org.apache.zookeeper.ClientCnxn: Attempting connection to server ec2-174-129-15-236.compute-1.amazonaws.com/ 2009-10-19 11:52:27,032 WARN org.apache.zookeeper.ClientCnxn: Exception closing session 0x0 to sun.nio.ch.SelectionKeyImpl@656dc861 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. 117.2. 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 117.3. 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. 447 Chapter 118. HBase and Hadoop version issues 118.1. …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). 448 Chapter 119. 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. 449 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-server1:50010]:: All DataNodes for a block are dead, and recovery is not possible. Here is the sequence of events that leads to this error: 450 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. 451 Chapter 120. Running unit or integration tests 120.1. 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. at org.apache.hadoop.hbase.MiniHBaseCluster. 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. at org.apache.hadoop.hbase.MiniHBaseCluster. 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 HDFS2556 452 Chapter 121. Case Studies For Performance and Troubleshooting Case Studies, see Apache HBase Case Studies. 453 Chapter 122. Cryptographic Features 122.1. 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 at sun.security.pkcs11.wrapper.PKCS11.C_DecryptUpdate(Native Method) 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 ${java.home}/lib/security/nss.cfg Then renumber the remaining providers accordingly. 454 Chapter 123. Operating System Specific Issues 123.1. Page Allocation Failure  This issue is known to affect CentOS 6.2 and possibly CentOS 6.5. It may also affect some versions of Red Hat Enterprise Linux, according to https://bugzilla.redhat.com/show_bug.cgi?id=770545. Some users have reported seeing the following error: kernel: java: page allocation failure. order:4, mode:0x20 Raising the value of min_free_kbytes was reported to fix this problem. This parameter is set to a percentage of the amount of RAM on your system, and is described in more detail at http://www.centos.org/docs/5/html/5.1/Deployment_Guide/s3-proc-sys-vm.html. To find the current value on your system, run the following command: [user@host]# cat /proc/sys/vm/min_free_kbytes Next, raise the value. Try doubling, then quadrupling the value. Note that setting the value too low or too high could have detrimental effects on your system. Consult your operating system vendor for specific recommendations. Use the following command to modify the value of min_free_kbytes, substituting with your intended value: [user@host]# echo > /proc/sys/vm/min_free_kbytes 455 Chapter 124. JDK Issues 124.1. NoSuchMethodError: java.util.concurrent.ConcurrentHashMap.keySet If you see this in your logs: Caused by: java.lang.NoSuchMethodError: java.util.concurrent.ConcurrentHashMap.keySet ()Ljava/util/concurrent/ConcurrentHashMap$KeySetView; at org.apache.hadoop.hbase.master.ServerManager .findServerWithSameHostnamePortWithLock(ServerManager.java:393) at org.apache.hadoop.hbase.master.ServerManager.checkAndRecordNewServer (ServerManager.java:307) at org.apache.hadoop.hbase.master.ServerManager.regionServerStartup(ServerManager .java:244) at org.apache.hadoop.hbase.master.MasterRpcServices.regionServerStartup (MasterRpcServices.java:304) at org.apache.hadoop.hbase.protobuf.generated.RegionServerStatusProtos $RegionServerStatusService$2.callBlockingMethod(RegionServerStatusProtos.java:7910) at org.apache.hadoop.hbase.ipc.RpcServer.call(RpcServer.java:2020) ... 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 ConcurrentHashMap.keySet if running on JRE 7. 456 JDK8 NoSuchMethodError involving Apache HBase Case Studies 457 Chapter 125. 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. 458 Chapter 126. Schema Design See the schema design case studies here: Schema Design Case Studies 459 Chapter 127. Performance/Troubleshooting 127.1. Case Study #1 (Performance Issue On A Single Node) 127.1.1. 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. 127.1.2. 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. 127.1.3. 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 460 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: UP BROADCAST RUNNING MASTER MULTICAST MTU:1500 Metric:1 RX packets:2990700159 errors:12 dropped:0 overruns:1 frame:6 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) <--- Look Here! 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. 461 $ sudo ethtool eth0 Settings for eth0: Supported ports: [ TP ] Supported link modes: 10baseT/Half 10baseT/Full 100baseT/Half 100baseT/Full 1000baseT/Full Supports auto-negotiation: Yes Advertised link modes: 10baseT/Half 10baseT/Full 100baseT/Half 100baseT/Full 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! 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 Should say 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. 127.1.4. 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. 127.2. 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 462 127.3. 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/ 127.4. 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 . 463 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. 464 Chapter 128. HBase Tools and Utilities HBase provides several tools for administration, analysis, and debugging of your cluster. The entrypoint to most of these tools is the bin/hbase command, though some tools are available in the devsupport/ 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: --config DIR Configuration direction to use. Default: ./conf --hosts HOSTS Override the list in 'regionservers' file --auth-as-server Authenticate to ZooKeeper using servers configuration Commands: Some commands take arguments. Pass no args or -h for usage. shell Run the HBase shell hbck Run the hbase 'fsck' tool snapshot Tool for managing snapshots wal Write-ahead-log analyzer hfile Store file analyzer zkcli Run the ZooKeeper shell master Run an HBase HMaster node regionserver Run an HBase HRegionServer node zookeeper Run a ZooKeeper server rest Run an HBase REST server thrift Run the HBase Thrift server thrift2 Run the HBase Thrift2 server clean Run the HBase clean up script classpath Dump hbase CLASSPATH mapredcp Dump CLASSPATH entries required by mapreduce pe Run PerformanceEvaluation ltt Run LoadTestTool canary Run the Canary tool version Print the version backup Backup tables for recovery restore Restore tables from existing backup image regionsplitter Run RegionSplitter tool rowcounter Run RowCounter tool cellcounter Run CellCounter tool 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 elsewhere in this guide. 465 128.1. Canary There is a Canary class can help users to canary-test the HBase cluster status, with every columnfamily for every regions or RegionServer’s granularity. To see the usage, use the -help parameter. $ ${HBASE_HOME}/bin/hbase canary -help Usage: hbase canary [opts] [table1 [table2]...] | [regionserver1 [regionserver2]..] where [opts] are: -help Show this help and exit. -regionserver replace the table argument to regionserver, which means to enable regionserver mode -allRegions Tries all regions on a regionserver, only works in regionserver mode. -zookeeper Tries to grab zookeeper.znode.parent on each zookeeper instance -daemon Continuous check at defined intervals. -interval Interval between checks (sec) -e Use table/regionserver as regular expression which means the table/regionserver is regular expression pattern -f stop whole program if first error occurs, default is true -t timeout for a check, default is 600000 (millisecs) -writeTableTimeout write timeout for the writeTable, default is 600000 (millisecs) -readTableTimeouts =,=, ... comma-separated list of read timeouts per table (no spaces), default is 600000 (millisecs) -writeSniffing enable the write sniffing in canary -treatFailureAsError treats read / write failure as error -writeTable The table used for write sniffing. Default is hbase:canary -Dhbase.canary.read.raw.enabled= Use this flag to enable or disable raw scan during read canary test Default is false and raw is not enabled during scan -D= assigning or override the configuration params The Sink class is instantiated using the hbase.canary.sink.class configuration property which will also determine the used Monitor class. In the absence of this  property RegionServerStdOutSink will be used. You need to use the Sink according to the passed parameters to the canary command. As an example you have to set hbase.canary.sink.class property to org.apache.hadoop.hbase.tool.Canary$RegionStdOutSink for using table parameters. This tool will return non zero error codes to user for collaborating with other monitoring tools, such as Nagios. The error code definitions are: 466 private private private private private static static static static static final final final final final int int int int int USAGE_EXIT_CODE = 1; INIT_ERROR_EXIT_CODE = 2; TIMEOUT_ERROR_EXIT_CODE = 3; ERROR_EXIT_CODE = 4; FAILURE_EXIT_CODE = 5; Here are some examples based on the following given case. There are two Table objects called test01 and test-02, they have two column family cf1 and cf2 respectively, and deployed on the 3 RegionServers. see following table. RegionServer test-01 test-02 rs1 r1 r2 rs2 r2 rs3 r2 r1 Following are some examples based on the previous given case. 128.1.1. 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 test01,,1386230156732.0e3c7d77ffb6361ea1b996ac1042ca9a. column 13/12/09 03:26:32 INFO tool.Canary: read from region test01,,1386230156732.0e3c7d77ffb6361ea1b996ac1042ca9a. column 13/12/09 03:26:32 INFO tool.Canary: read from region test01,0004883,1386230156732.87b55e03dfeade00f441125159f8ca87. 13/12/09 03:26:32 INFO tool.Canary: read from region test01,0004883,1386230156732.87b55e03dfeade00f441125159f8ca87. ... 13/12/09 03:26:32 INFO tool.Canary: read from region test02,,1386559511167.aa2951a86289281beee480f107bb36ee. column 13/12/09 03:26:32 INFO tool.Canary: read from region test02,,1386559511167.aa2951a86289281beee480f107bb36ee. column 13/12/09 03:26:32 INFO tool.Canary: read from region test02,0004883,1386559511167.cbda32d5e2e276520712d84eaaa29d84. 13/12/09 03:26:32 INFO tool.Canary: read from region test02,0004883,1386559511167.cbda32d5e2e276520712d84eaaa29d84. family cf1 in 2ms family cf2 in 2ms column family cf1 in 4ms column family cf2 in 1ms family cf1 in 5ms family cf2 in 3ms column family cf1 in 31ms column family cf2 in 8ms So you can see, table test-01 has two regions and two column families, so the Canary tool will pick 4 small piece of data from 4 (2 region * 2 store) different stores. This is a default behavior of the this tool does. 467 128.1.2. Canary test for every column family (store) of every region of specific table(s) You can also test one or more specific tables. $ ${HBASE_HOME}/bin/hbase canary test-01 test-02 128.1.3. Canary test with RegionServer granularity This will pick one small piece of data from each RegionServer, and can also put your RegionServer name as input options for canary-test specific RegionServer. $ ${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 128.1.4. Canary test with regular expression pattern This will test both table test-01 and test-02. $ ${HBASE_HOME}/bin/hbase canary -e test-0[1-2] 128.1.5. Run canary test as daemon mode Run repeatedly with interval defined in option -interval whose default value is 60 seconds. This daemon will stop itself and return non-zero error code if any error occurs, due to the default value of option -f is true. $ ${HBASE_HOME}/bin/hbase canary -daemon Run repeatedly with 5 second intervals and will not stop itself even if errors occur in the test. $ ${HBASE_HOME}/bin/hbase canary -daemon -interval 5 -f false 128.1.6. 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 468 option to kill the canary test and return a non-zero error code. This run sets the timeout value to 60 seconds, the default value is 600 seconds. $ ${HBASE_HOME}/bin/hbase canary -t 60000 128.1.7. Enable write sniffing in canary By default, the canary tool only check the read operations, it’s hard to find the problem in the write path. To enable the write sniffing, you can run canary with the -writeSniffing option. When the write sniffing is enabled, the canary tool will create an hbase table and make sure the regions of the table distributed on 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 by the option -writeTable. $ ${HBASE_HOME}/bin/hbase canary -writeSniffing -writeTable ns:canary The default value size of each put is 10 bytes and you can set it by the config key: hbase.canary.write.value.size. 128.1.8. Treat read / write failure as error By default, the canary tool only logs read failure, due to e.g. RetriesExhaustedException, while returning normal exit code. To treat read / write failure as error, you can run canary with the -treatFailureAsError option. When enabled, read / write failure would result in error exit code. $ ${HBASE_HOME}/bin/hbase canary -treatFailureAsError 128.1.9. Running Canary in a Kerberos-enabled Cluster To run Canary in a Kerberos-enabled cluster, configure the following two properties in hbasesite.xml: • hbase.client.keytab.file • hbase.client.kerberos.principal Kerberos credentials are refreshed every 30 seconds when Canary runs in daemon mode. To configure the DNS interface for the client, configure the following optional properties in hbasesite.xml. • hbase.client.dns.interface • hbase.client.dns.nameserver 469 Example 33. Canary in a Kerberos-Enabled Cluster This example shows each of the properties with valid values. hbase.client.kerberos.principal hbase/_HOST@YOUR-REALM.COM hbase.client.keytab.file /etc/hbase/conf/keytab.krb5 hbase.client.dns.interface default hbase.client.dns.nameserver default 128.2. RegionSplitter usage: bin/hbase regionsplitter
SPLITALGORITHM is the java class name of a class implementing SplitAlgorithm, or one of the special strings HexStringSplit or DecimalStringSplit or UniformSplit, which are built-in split algorithms. HexStringSplit treats keys as hexadecimal ASCII, and DecimalStringSplit treats keys as decimal ASCII, and UniformSplit treats keys as arbitrary bytes. -c Create a new table with a pre-split number of regions -D Override HBase Configuration Settings -f Column Families to create with new table. Required with -c --firstrow First Row in Table for Split Algorithm -h Print this usage help --lastrow Last Row in Table for Split Algorithm -o Max outstanding splits that have unfinished major compactions -r Perform a rolling split of an existing region --risky Skip verification steps to complete quickly. STRONGLY DISCOURAGED for production systems. 470 For additional detail, see Manual Region Splitting. 128.3. 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. 128.4. 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. 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. 471 128.5. HBase hbck To run hbck against your HBase cluster run $./bin/hbase hbck. At the end of the command’s output it prints OK or INCONSISTENCY. If your cluster reports inconsistencies, pass -details to see more detail emitted. If inconsistencies, run hbck a few times because the inconsistency may be transient (e.g. cluster is starting up or a region is splitting). Passing -fix may correct the inconsistency (This is an experimental feature). For more information, see hbck In Depth. 128.6. HFile Tool See HFile Tool. 128.7. WAL Tools 128.7.1. 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: $ ./bin/hbase org.apache.hadoop.hbase.regionserver.wal.FSHLog --dump hdfs://example.org:8020/hbase/WALs/example.org,60020,1283516293161/ .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: $ ./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. $ ./bin/hbase wal hdfs://example.org:8020/hbase/WALs/example.org,60020,1283516293161/ .1283973724012 472 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. $ ./bin/hbase hlog hdfs://example.org:8020/hbase/.logs/example.org,60020,1283516293161/10. 10.21.10%3A60020.1283973724012 128.8. Compression Tool See compression.test. 128.9. 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: 473 $ ./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] Options: rs.class rs.impl startrow stoprow starttime endtime versions new.name peer.adr families all.cells Args: tablename hbase.regionserver.class of the peer cluster, specify if different from current cluster hbase.regionserver.impl of the peer cluster, the start row the stop row beginning of the time range (unixtime in millis) without endtime means from starttime to forever end of the time range. Ignored if no starttime specified. number of cell versions to copy new table's name Address of the peer cluster given in the format hbase.zookeeer.quorum:hbase.zookeeper.client.port:zookeeper.znode.parent comma-separated list of families to copy To copy from cf1 to cf2, give sourceCfName:destCfName. To keep the same name, just give "cfName" also copy delete markers and deleted cells Name of the table to copy Examples: To copy 'TestTable' to a cluster that uses replication for a 1 hour window: $ 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: It is recommended that you set the following to >=100. A higher value uses more memory but decreases the round trip time to the server and may increase performance. -Dhbase.client.scanner.caching=100 The following should always be set to false, to prevent writing data twice, which may produce inaccurate results. -Dmapred.map.tasks.speculative.execution=false  474 Scanner Caching Caching for the input Scan is configured via hbase.client.scanner.caching in the job configuration.  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. 128.10. 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 [ [ []]] endpoint-based Export $ bin/hbase org.apache.hadoop.hbase.coprocessor.Export [ [ []]] 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+), hbaseserver(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 normal, traditional RPC scan region Read Scalability depend on number of regions depend on number of mappers (see TableInputFormatBase#getSplit s) 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. 475 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 with the desired number of versions. Note: caching for the input Scan is configured via hbase.client.scanner.caching in the job configuration. 128.11. 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  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 128.12. 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 To generate StoreFiles for bulk-loading: $ bin/hbase org.apache.hadoop.hbase.mapreduce.ImportTsv -Dimporttsv.columns=a,b,c -Dimporttsv.bulk.output=hdfs://storefile-outputdir These generated StoreFiles can be loaded into HBase via completebulkload. 128.12.1. ImportTsv Options Running ImportTsv with no arguments prints brief usage information: 476 Usage: importtsv -Dimporttsv.columns=a,b,c 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: -Dimporttsv.bulk.output=/path/for/output 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: -Dimporttsv.skip.bad.lines=false - fail if encountering an invalid line '-Dimporttsv.separator=|' - eg separate on pipes instead of tabs -Dimporttsv.timestamp=currentTimeAsLong - use the specified timestamp for the import -Dimporttsv.mapper.class=my.Mapper - A user-defined Mapper to use instead of org.apache.hadoop.hbase.mapreduce.TsvImporterMapper 128.12.2. 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: row1 row2 row3 row4 row5 row6 row7 row8 row9 row10 c1 c1 c1 c1 c1 c1 c1 c1 c1 c1 c2 c2 c2 c2 c2 c2 c2 c2 c2 c2 For ImportTsv to use this input file, the command line needs to look like this: 477 HADOOP_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. 128.12.3. ImportTsv Warning If you have preparing a lot of data for bulk loading, make sure the target HBase table is pre-split appropriately. 128.12.4. See Also For more information about bulk-loading HFiles into HBase, see arch.bulk.load 128.13. 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 Driver HADOOP_CLASSPATH=`${HBASE_HOME}/bin/hbase classpath` ${HADOOP_HOME}/bin/hadoop jar ${HBASE_HOME}/hbase-server-VERSION.jar completebulkload 128.13.1. 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. 128.14. WALPlayer WALPlayer is a utility to replay WAL files into HBase. 478 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] []> 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. 128.14.1. WALPlayer Options Running WALPlayer with no arguments prints brief usage information: 479 Usage: WALPlayer [options] [] Replay all WAL files into HBase. 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 . is a comma separated list of target tables. If specified, each table in 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: -Dwal.bulk.output=/path/for/output (Only one table can be specified, and no mapping is allowed!) Time range options: -Dwal.start.time=[date|ms] -Dwal.end.time=[date|ms] (The start and the end date of timerange. The dates can be expressed in milliseconds since epoch or in yyyy-MM-dd'T'HH:mm:ss.SS format. E.g. 1234567890120 or 2009-02-13T23:32:30.12) Other options: -Dmapreduce.job.name=jobName Use the specified mapreduce job name for the wal player For performance also consider the following options: -Dmapreduce.map.speculative=false -Dmapreduce.reduce.speculative=false 128.15. 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] [--starttime= --endtime=] [-range=[startKey],[endKey][;[startKey],[endKey]...]] [ ...] RowCounter only counts one version per cell. For performance consider to use -Dhbase.client.scanner.caching=100 and -Dmapreduce.map.speculative=false options. 128.16. CellCounter HBase ships another diagnostic mapreduce job called CellCounter. Like RowCounter, it gathers 480 more fine-grained statistics about your table. The statistics gathered by CellCounter are more finegrained 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= and --endtime= flags. Use hbase.mapreduce.scan.column.family to specify scanning a single column family. $ bin/hbase cellcounter [reportSeparator] [regex or prefix] [--starttime= --endtime=] Note: just like RowCounter, caching for the input Scan is configured via hbase.client.scanner.caching in the job configuration. 128.17. 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. 128.18. Offline Compaction Tool See the usage for the CompactionTool. Run it like: $ ./bin/hbase org.apache.hadoop.hbase.regionserver.CompactionTool 128.19. 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. 481 $ bin/hbase clean Usage: hbase clean (--cleanZk|--cleanHdfs|--cleanAll) Options: --cleanZk cleans hbase related data from zookeeper. --cleanHdfs cleans hbase related data from hdfs. --cleanAll cleans hbase related data from both zookeeper and hdfs. 128.20. 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. 128.21. 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. 128.22. 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 ... 128.22.1. 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. 482 $ bin/hbase pre-upgrade validate-cp [-jar ...] [-class ... | -table ... | -config] Options: -e Treat warnings as errors. -jar Jar file/directory of the coprocessor. -table Table coprocessor(s) to check. -class Coprocessor class(es) to check. -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: $ 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. 128.22.2. DataBlockEncoding validation HBase 2.0 removed PREFIX_TREE Data Block Encoding from column families. 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. To change PREFIX_TREE encoding to supported one check prefix-tree encoding removed. 483 Chapter 129. Region Management 129.1. 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. 129.2. 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 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. 484 Chapter 130. Node Management 130.1. 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> thrift If we should stop/start thrift before/after the hbase stop/start rest If we should stop/start rest before/after the hbase stop/start restart If we should restart after graceful stop reload Move offloaded regions back on to the stopped server debug Move offloaded regions back on to the stopped server hostname Hostname of server we are to stop 485 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. 130.1.1. 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 486 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. 130.1.2. 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. 130.2. 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. 130.2.1. 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. 487 rolling-restart.sh General Usage $ ./bin/rolling-restart.sh --help Usage: rolling-restart.sh [--config ] [--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. 130.2.2. 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. 130.2.3. 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. Use the hbck utility to ensure that the cluster is consistent. 488 $ ./bin/hbck Perform repairs if required. See hbck for details. 3. Restart the master first. You may need to modify these commands if your new HBase directory is different from the old one, such as for an upgrade. $ ./bin/hbase-daemon.sh stop master; ./bin/hbase-daemon.sh start master 4. 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 & 5. Restart the Master again, to clear out the dead servers list and re-enable the load balancer. 6. Run the hbck utility again, to be sure the cluster is consistent. 130.3. Adding a New Node Adding a new regionserver in HBase is essentially free, you simply start it like this: $ ./bin/hbasedaemon.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 489 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. 490 Chapter 131. 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. 131.1. 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. 131.2. 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. 131.3. 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. 491 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. 131.4. 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. 131.5. Most Important Master Metrics Note: Counts are usually over the last metrics reporting interval. hbase.master.numRegionServers Number of live regionservers 492 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 131.6. 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 493 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._ Operation latencies, where is one of Append, Delete, Mutate, Get, Replay, Increment; and where is one of min, max, mean, median, 75th_percentile, 95th_percentile, 99th_percentile hbase.regionserver.slowCount The number of operations we thought were slow, where 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 494 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 495 Chapter 132. HBase Monitoring 132.1. 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. 132.2. 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. 132.2.1. 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. 132.2.2. 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. 496 • hadoop.regionserver_rpc_methodName.aboveOneSec A metric reflecting the durations of all responses that lasted for more than one second. 132.2.3. 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. 132.2.4. 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":" 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. 132.3. 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. 497 Figure 4. Basic Info Figure 5. Config 498 Figure 6. Stats Figure 7. L1 and L2 This is not an exhaustive list of all the screens and reports available. Have a look in the Web UI. 499 Chapter 133. 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. 133.1. 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 reasons, it throws an error and the master retains the WAL and retries the replication at intervals. 500 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. 501 Figure 8. 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. 133.2. 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 502 the source cluster, in HBase Shell, enable the table replication, using the 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 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 Enable a previously-disabled replication relationship disable_peer 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 Disable and remove a replication relationship. HBase will no longer send edits to that peer cluster or keep track of WALs. enable_table_replication 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 Disable the table replication switch for all its column families. 133.3. Serial Replication Note: this feature is introduced in HBase 2.1 Function of serial replication 503 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 1. Set the serial flag to true for a repliation peer. You can either set it to true when creating a replication peer, or change it to true later. 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. 133.4. 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 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= --endtime= --families= + The VerifyReplication command prints out GOODROWS and BADROWS counters to indicate rows that did and did not replicate correctly. 504 133.5. Detailed Information About Cluster Replication Figure 9. Replication Architecture Overview 133.5.1. 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. 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 505 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 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. 133.5.2. 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 506 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. 133.5.3. 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. 133.5.4. 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 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 507 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. 133.5.5. 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. 133.5.6. 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.  WALs are saved when replication is enabled or disabled as long as peers exist. 133.5.7. 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 508 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/,60020,123456780/ 2/,60020.1234 (Contains a position),60020.1265,60020,123456790/ 2/,60020.1214 (Contains a position),60020.1248,60020.1312,60020, 123456630/ 2/,60020.1280 (Contains a position) Assume that loses its ZooKeeper session. The survivors will race to create a lock, and, arbitrarily, wins. It will then start transferring all the queues to its local peers znode by appending the name of the dead server. Right before is able to clean up the old znodes, the layout will look like the following: 509 /hbase/replication/rs/,60020,123456780/ 2/,60020.1234 (Contains a position),60020.1265,60020,123456790/ lock 2/,60020.1214 (Contains a position),60020.1248,60020.1312,60020,123456630/ 2/,60020.1280 (Contains a position) 2-,60020,123456790/,60020.1214 (Contains a position),60020.1248,60020.1312 Some time later, but before is able to finish replicating the last WAL from, it dies too. Some new logs were also created in the normal queues. The last region server will then try to lock’s znode and will begin transferring all the queues. The new layout will be: /hbase/replication/rs/,60020,123456780/ 2/,60020.1378 (Contains a position) 2-,60020,123456630/,60020.1325 (Contains a position),60020.1401 2-,60020,123456790-,60020,123456630/,60020.1312 (Contains a position),60020,123456630/ lock 2/,60020.1325 (Contains a position),60020.1401 2-,60020,123456790/,60020.1312 (Contains a position) 133.6. Replication Metrics The following metrics are exposed at the global region server level and at the peer level: 510 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 511 entries lines up with the expected end of the file. You should check Region Server log files for details of the failures. 133.7. 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 The name of the peer znode ers peers zookeeper.znode.replication.pe The name of peer-state znode ers.state peer-state zookeeper.znode.replication.rs rs The name of the rs znode 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 1 given region server should attempt to failover simultaneously. 133.8. 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. 512 Chapter 134. 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 134.1. 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. 513 # 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. 514 hbase> set_quota NAMESPACE => 'ns1', LIMIT => '100req/min' namespace request limit hbase> set_quota USER => 'u1', GLOBAL_BYPASS => true not affected by the limit # a per# user u1 is 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'} 134.2. 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 515 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 longrunning 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. 134.3. 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 longrunning 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 516 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. 134.4. 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. 517 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'. 134.5. 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- 518 site.xml. hbase.quota.remove.on.table.delete false The value is set to true by default. 134.6. 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. 519 hbase> list_snapshot_sizes SNAPSHOT t1.s1 520 SIZE 1159108 Chapter 135. 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. 135.1. 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: 135.1.1. Stop HBase 135.1.2. 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. 135.1.3. 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. 135.2. Live Cluster Backup - Replication This approach assumes that there is a second cluster. See the HBase page on replication for more information. 135.3. 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. 521 135.4. 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. 522 Chapter 136. HBase Snapshots HBase Snapshots allow you to take a snapshot of a table without too much impact on Region Servers. Snapshot, Clone and restore operations don’t involve data copying. Also, Exporting the snapshot to another cluster doesn’t have impact on the Region Servers. 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. 136.1. 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+) hbase.snapshot.enabled true 136.2. 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} 523 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. 136.3. Listing Snapshots List all snapshots taken (by printing the names and relative information). $ ./bin/hbase shell hbase> list_snapshots 136.4. 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' 136.5. 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' 136.6. 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' 524  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. 136.7. 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. 136.8. 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 200 136.9. 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 525 • 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 \ -snapshot MySnapshot \ -copy-from hdfs://srv2:8082/hbase \ -copy-to s3a:////hbase \ -chuser MyUser \ -chgroup MyGroup \ -chmod 700 \ -mappers 16 $ hbase org.apache.hadoop.hbase.snapshot.ExportSnapshot \ -snapshot MySnapshot -copy-from s3a:////hbase \ -copy-to hdfs://srv2:8082/hbase \ -chuser MyUser \ -chgroup MyGroup \ -chmod 700 \ -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 \ -remote-dir s3a:////hbase \ -list-snapshots 526 Chapter 137. 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://. 527 Chapter 138. 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. 138.1. Node count and hardware/VM configuration 138.1.1. 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). 138.1.2. 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. 138.1.3. 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?) 528 138.2. 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 pertable 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. 138.2.1. 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. If you do not want to use the HMaster UI, you can determine the number of regions by counting the number of subdirectories of the /hbase/
/ subdirectories in HDFS, or by running the bin/hbase hbck command. Each of these methods may return a slightly different number, depending on the status of each region. 138.2.2. 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 529 ((RS Xmx) * hbase.regionserver.global.memstore.size) / (hbase.hregion.memstore.flush.size * (# column families)) HBase 0.94.x ((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. 138.2.3. 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. 138.2.4. 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 530 regions, especially for log data. See ops.stripe. 138.2.5. Total data size per region server According to above numbers for region size and number of regions per region server, in an 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. 138.3. 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. 138.3.1. 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 138.3.2. 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. 531 Chapter 139. 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 hbase hbase hbase hbase shell> shell> shell> shell> shell> disable 'tableName' snapshot 'tableName', 'tableSnapshot' clone_snapshot 'tableSnapshot', 'newTableName' delete_snapshot 'tableSnapshot' drop 'tableName' or in code it would be as follows: void rename(Admin admin, String oldTableName, TableName newTableName) { String snapshotName = randomName(); admin.disableTable(oldTableName); admin.snapshot(snapshotName, oldTableName); admin.cloneSnapshot(snapshotName, newTableName); admin.deleteSnapshot(snapshotName); admin.deleteTable(oldTableName); } 532 Chapter 140. 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: hbase.coprocessor.master.classes org.apache.hadoop.hbase.rsgroup.RSGroupAdminEndpoint hbase.master.loadbalancer.class org.apache.hadoop.hbase.rsgroup.RSGroupBasedLoadBalancer 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: hbase(main):008:0> help ‘rsgroup’ Took 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: 533 hbase(main):008:0> add_rsgroup 'my_group' Took 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 at org.apache.hadoop.hbase.master.MasterRpcServices .execMasterService(MasterRpcServices.java:604) at org.apache.hadoop.hbase.shaded.protobuf.generated.MasterProtos $MasterService$2.callBlockingMethod(MasterProtos.java) at org.apache.hadoop.hbase.ipc.RpcServer.call(RpcServer.java:1140) at org.apache.hadoop.hbase.ipc.CallRunner.run(CallRunner.java:133) at org.apache.hadoop.hbase.ipc.RpcExecutor$Handler.run(RpcExecutor .java:277) 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: hbase(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 534 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. 140.1. 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. 140.1.1. 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. 140.1.2. 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. 140.2. Troubleshooting Viewing the Master log will give you insight on rsgroup operation. If it appears stuck, restart the Master process. 140.3. 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 535 #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 140.4. ACL To enable ACL, add the following to your hbase-site.xml and restart your Master: hbase.security.authorization true 536 Chapter 141. 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 537 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. 538 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’ 539 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 540 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'} 541 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. 542 Chapter 142. 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. 142.1. 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.) 142.2. 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. 142.3. 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. 142.4. 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 543 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. 142.4.1. 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. 544 ◦ 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. 142.4.2. 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 muchneeded 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 34. 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} 545 Chapter 143. Apache HBase Repositories There are two different repositories for Apache HBase: Subversion (SVN) and Git. GIT is our repository of record for all but the Apache HBase website. We used to be on SVN. We migrated. See Migrate Apache HBase SVN Repos to Git. See Source Code Management page for contributor and committer links or search for HBase on the Apache Git page. 546 Chapter 144. IDEs 144.1. Eclipse 144.1.1. 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. 144.1.2. 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. 144.1.3. 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/hbasedefault.xml doesn't exist 547 This will also reduce the eclipse build cycles and make your life easier when developing. 144.1.4. 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. 144.1.5. 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/protobufjava-2.3.0.jar' in project 'hbase' hbase Build path Build Path Problem Unbound classpath variable: 144.1.6. Eclipse Known Issues Eclipse will currently complain about Bytes.java. It is not possible to turn these errors off. 548 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 144.1.7. Eclipse - More Information For additional information on setting up Eclipse for HBase development on Windows, see Michael Morello’s blog on the topic. 144.2. 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. 144.3. 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. 549 Chapter 145. Building Apache HBase 145.1. 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. 145.1.1. 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 550 mvn clean install mvn clean install -DskipTests 145.1.2. Running all or individual Unit Tests See the hbase.unittests.cmds section in hbase.unittests 145.1.3. 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. 145.1.4. 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 551 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 145.1.5. 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: mvn compile -Pcompile-thrift -Dthrift.path=/opt/local/bin/thrift 145.1.6. 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--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 552 mvn -DskipTests package assembly:single deploy 145.1.7. Build Gotchas If you see Unable to find resource 'VM_global_library.vm', ignore it. It’s not an error. It is officially ugly though. 553 Chapter 146. Releasing Apache HBase  554 Building against HBase 1.x HBase 1.x requires Java 7 to build. See java for Java requirements per HBase release. Example 35. 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. apache.snapshots.https YOUR_APACHE_ID YOUR_APACHE_PASSWORD apache.releases.https YOUR_APACHE_ID YOUR_APACHE_PASSWORD apache-release YOUR_KEYNAME YOUR_KEY_PASSWORD 146.1. Making a Release Candidate Only committers may make releases of hbase artifacts. Before You Begin 555 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 556 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. 557 1. Build the source tarball. Now, build the source tarball. Lets presume we are building the source tarball for the tag 2.0.0alpha4-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 558 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. 559 + 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. 146.2. 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. $ mvn clean install -DskipTests javadoc:aggregate site assembly:single -Prelease $ mvn -DskipTests deploy -Papache-release 560 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. 561 Chapter 147. 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. 562 Chapter 148. 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: https://s.apache.org/hbase-_version_-jira To download please follow the links and instructions on our website: 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. 563 Chapter 149. 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. 564 Chapter 150. Updating hbase.apache.org 150.1. Contributing to hbase.apache.org See appendix contributing to documentation for more information on contributing to the documentation or website. 150.2. Publishing hbase.apache.org See Publishing the HBase Website and Documentation for instructions on publishing the website and documentation. 565 Chapter 151. 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. 151.1. 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). 151.1.1. 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: 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: 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: 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: 566 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 vim hbase-shell/target/surefire-reports/TESTorg.apache.hadoop.hbase.client.TestShell.xml 151.1.2. 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. 151.2. 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: 567 ... @Category(SmallTests.class) public class TestHRegionInfo { @ClassRule public static final HBaseClassTestRule CLASS_RULE = HBaseClassTestRule.forClass(TestHRegionInfo.class); @Test public void testCreateHRegionInfoName() throws Exception { // ... } } 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 568 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. 151.3. Running tests 151.3.1. 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. 151.3.2. 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. 151.3.3. 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. 151.3.4. 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. 569 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. 151.3.5. 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 -P runAllTests -Dsurefire.secondPartForkCount=12 -Dtest.build.data.basedirectory=/ram2G On earlier versions, use: mvn test -P runAllTests -Dsurefire.secondPartThreadCount=12 -Dtest.build.data.basedirectory=/ram2G 151.3.6. 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. 151.3.7. Test Timeouts The HBase unit test sizing Categorization timeouts are not strictly enforced. 570 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. ... @Test(timeout=30000) public void testCreateHRegionInfoName() throws Exception { // ... } 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). 151.3.8. 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) 151.4. Writing Tests 151.4.1. 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. 571 151.4.2. 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 < 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. 151.4.3. 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. 151.4.4. 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. 151.4.5. 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. 572 /** * Describe what this testcase tests. Talk about resources initialized in @BeforeClass (before * any test is run) and before each test is run, etc. */ // Specify the category as explained in <>. @Category(SmallTests.class) public class TestExample { // Replace the TestExample.class in the below with the name of your test fixture class. private static final Log LOG = LogFactory.getLog(TestExample.class); // Handy test rule that allows you subsequently get the name of the current method. See // down in 'testExampleFoo()' where we use it to log current test's name. @Rule public TestName testName = new TestName(); // The below rule does two things. It decides the timeout based on the category // (small/medium/large) of the testcase. This @Rule requires that the full testcase runs // within this timeout irrespective of individual test methods' times. The second // feature is we'll dump in the log when the test is done a count of threads still // running. @Rule public static TestRule timeout = CategoryBasedTimeout.builder(). withTimeout(this.getClass()).withLookingForStuckThread(true).build(); @Before public void setUp() throws Exception { } @After public void tearDown() throws Exception { } @Test public void testExampleFoo() { LOG.info("Running test " + testName.getMethodName()); } } 151.5. Integration Tests HBase integration/system tests are tests that are beyond HBase unit tests. They are generally longlasting, 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 573 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 574 -PskipIntegrationTests 151.5.1. 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 151.5.2. 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 575 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. 151.5.3. 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: 576 $ 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-enismaster-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 577 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/hbase0.94/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. 151.5.4. Available Policies HBase ships with several ChaosMonkey policies, available in the hbase/hbase- it/src/test/java/org/apache/hadoop/hbase/chaos/policies/ directory. 151.5.5. 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: 578 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. hbase.it.clustermanager.hadoop.home $HADOOP_HOME hbase.it.clustermanager.zookeeper.home $ZOOKEEPER_HOME hbase.it.clustermanager.hbase.user hbase hbase.it.clustermanager.hadoop.hdfs.user hdfs hbase.it.clustermanager.zookeeper.user zookeeper 579 Chapter 152. Developer Guidelines 152.1. 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. 152.2. Code Standards 152.2.1. 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 580 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. 152.2.2. 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. public void readFields(DataInput arg0) throws IOException { foo = arg0.readUTF(); // don't do this // don't do this public void readFields(DataInput di) throws IOException { foo = di.readUTF(); 581 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( argument1, 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(); semicolon. <--- imagine there is an extra space(s) after the 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. 582 Javadoc - Useless Defaults Don’t just leave javadoc tags the way IDE generates them, or fill redundant information in them. /** * @param table <---* @param region An HRegion object. <---* @return Foo Object foo just created. <---* @throws SomeException <---already tell that! * @throws BarException when something went wrong */ public Foo createFoo(Bar bar); don't leave them empty! don't fill redundant information! Not useful information Not useful. Function declarations <---- really? 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. Ambigious 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. 152.2.3. 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 583 • Cache it if you can • String Interns are useful but dangerous 152.3. 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. 152.3.1. 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. 152.4. 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 insitu 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. 584 152.5. 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 indepth 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: 152.5.1. 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. 152.5.2. 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. 152.6. 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. 152.6.1. 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. 585 152.7. 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. Accumulo doc on how to contribute and develop is also good read to understand development workflow. 152.7.1. 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 .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. 586 3. Patch name should be as follows to adhere to Yetus' naming convention: (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