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VMware vSphere Big Data Extensions
Administrator's and User's Guide
vSphere Big Data Extensions 2.2
This document supports the version of each product listed and
supports all subsequent versions until the document is
replaced by a new edition. To check for more recent editions
of this document, see http://www.vmware.com/support/pubs.
EN-001701-00
VMware vSphere Big Data Extensions Administrator's and User's Guide
2 VMware, Inc.
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Contents
About This Book 7
1About VMware vSphere Big Data Extensions 9
Getting Started with Big Data Extensions 9
Big Data Extensions and Project Serengeti 10
About Big Data Extensions Architecture 12
About Application Managers 12
Big Data Extensions Support for MapReduce Distribution Features 15
Feature Support By Hadoop Distribution 17
2Installing Big Data Extensions 19
System Requirements for Big Data Extensions 19
Unicode UTF-8 and Special Character Support 22
The Customer Experience Improvement Program 23
Deploy the Big Data Extensions vApp in the vSphere Web Client 24
Install RPMs in the Serengeti Management Server Yum Repository 27
Install the Big Data Extensions Plug-In 28
Configure vCenter Single Sign-On Settings for the Serengeti Management Server 30
Connect to a Serengeti Management Server 30
Install the Serengeti Remote Command-Line Interface Client 31
Access the Serengeti CLI By Using the Remote CLI Client 32
3Upgrading Big Data Extensions 35
Prepare to Upgrade Big Data Extensions 35
Upgrade Big Data Extensions Virtual Appliance 37
Upgrade the Big Data Extensions Plug-in 40
Upgrade the Serengeti CLI 41
Upgrade Big Data Extensions Virtual Machine Components by Using the Serengeti Command-Line
Interface 41
Add a Remote Syslog Server 42
4Managing Application Managers 45
Add an Application Manager by Using the vSphere Web Client 45
Modify an Application Manager by Using the Web Client 46
Delete an Application Manager by Using the vSphere Web Client 46
View Application Managers and Distributions by Using the Web Client 46
View Roles for Application Manager and Distribution by Using the Web Client 46
5Managing Hadoop Distributions 49
Hadoop Distribution Deployment Types 50
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Configure a Tarball-Deployed Hadoop Distribution by Using the Serengeti Command-Line
Interface 50
Configuring Yum and Yum Repositories 52
Create a Hadoop Template Virtual Machine using RHEL Server 6.x and VMware Tools 69
Maintain a Customized Hadoop Template Virtual Machine 73
6Managing the Big Data Extensions Environment 75
Add Specific User Names to Connect to the Serengeti Management Server 75
Change the Password for the Serengeti Management Server 76
Create a User Name and Password for the Serengeti Command-Line Interface 77
Specify a Group of Users in Active Directory or LDAP to Use a Hadoop Cluster 77
Stop and Start Serengeti Services 78
Ports Used for Communication between Big Data Extensions and the vCenter Server 79
Verify the Operational Status of the Big Data Extensions Environment 80
7Managing vSphere Resources for Clusters 91
Add a Resource Pool with the Serengeti Command-Line Interface 91
Remove a Resource Pool with the Serengeti Command-Line Interface 92
Add a Datastore in the vSphere Web Client 92
Remove a Datastore in the vSphere Web Client 93
Add a Network in the vSphere Web Client 93
Modify the DNS Type in the vSphere Web Client 94
Reconfigure a Static IP Network in the vSphere Web Client 95
Remove a Network in the vSphere Web Client 95
8Creating Hadoop and HBase Clusters 97
About Hadoop and HBase Cluster Deployment Types 99
Hadoop Distributions Supporting MapReduce v1 and MapReduce v2 (YARN) 99
About Cluster Topology 100
About HBase Database Access 100
Create a Big Data Cluster in the vSphere Web Client 101
Create an HBase Only Cluster in Big Data Extensions 104
Create a Cluster with an Application Manager by Using the vSphere Web Client 106
Create a Compute-Only Cluster with a Third Party Application Manager by Using vSphere Web
Client 106
Create a Compute Workers Only Cluster by Using the vSphere Web Client 107
9Managing Hadoop and HBase Clusters 109
Stop and Start a Cluster in the vSphere Web Client 109
Delete a Cluster in the vSphere Web Client 110
About Resource Usage and Elastic Scaling 110
Scale a Cluster in or out by using the vSphere Web Client 114
Scale CPU and RAM in the vSphere Web Client 115
Use Disk I/O Shares to Prioritize Cluster Virtual Machines in the vSphere Web Client 116
About vSphere High Availability and vSphere Fault Tolerance 116
Change the User Password on All of the Nodes of a Cluster 117
Reconfigure a Cluster with the Serengeti Command-Line Interface 117
Recover from Disk Failure with the Serengeti Command-Line Interface Client 119
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Enter Maintenance Mode to Perform Backup and Restore with the Serengeti Command-Line
Interface Client 120
Log in to Hadoop Nodes with the Serengeti Command-Line Interface Client 121
10 Monitoring the Big Data Extensions Environment 123
Enable the Big Data Extensions Data Collector 123
Disable the Big Data Extensions Data Collector 124
View Serengeti Management Server Initialization Status 124
View Provisioned Clusters in the vSphere Web Client 125
View Cluster Information in the vSphere Web Client 126
Monitor the HDFS Status in the vSphere Web Client 127
Monitor MapReduce Status in the vSphere Web Client 127
Monitor HBase Status in the vSphere Web Client 128
11 Accessing Hive Data with JDBC or ODBC 129
Configure Hive to Work with JDBC 129
Configure Hive to Work with ODBC 131
12 Big Data Extensions Security Reference 133
Services, Network Ports, and External Interfaces 133
Big Data Extensions Configuration Files 135
Big Data Extensions Public Key, Certificate, and Keystore 136
Big Data Extensions Log Files 136
Big Data Extensions User Accounts 137
Security Updates and Patches 137
13 Troubleshooting 139
Log Files for Troubleshooting 140
Configure Serengeti Logging Levels 140
Collect Log Files for Troubleshooting 141
Troubleshooting Cluster Creation Failures 142
Big Data Extensions Virtual Appliance Upgrade Fails 148
Upgrade Cluster Error When Using Cluster Created in Earlier Version of Big Data Extensions 149
Virtual Update Manager Does Not Upgrade the Hadoop Template Virtual Machine Under Big
Data Extensions vApp 149
Unable to Connect the Big Data Extensions Plug-In to the Serengeti Server 150
vCenter Server Connections Fail to Log In 151
Management Server Cannot Connect to vCenter Server 151
Cannot Perform Serengeti Operations after Deploying Big Data Extensions 151
SSL Certificate Error When Connecting to Non-Serengeti Server with the vSphere Console 152
Cannot Restart or Reconfigure a Cluster For Which the Time Is Not Synchronized 153
Cannot Restart or Reconfigure a Cluster After Changing Its Distribution 153
Virtual Machine Cannot Get IP Address and Command Fails 153
Cannot Change the Serengeti Server IP Address From the vSphere Web Client 154
A New Plug-In Instance with the Same or Earlier Version Number as a Previous Plug-In Instance
Does Not Load 155
Host Name and FQDN Do Not Match for Serengeti Management Server 155
Serengeti Operations Fail After You Rename a Resource in vSphere 156
Contents
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Big Data Extensions Server Does Not Accept Resource Names With Two or More Contiguous
White Spaces 156
Non-ASCII characters are not displayed correctly 156
MapReduce Job Fails to Run and Does Not Appear In the Job History 157
Cannot Submit MapReduce Jobs for Compute-Only Clusters with External Isilon HDFS 157
MapReduce Job Stops Responding on a PHD or CDH4 YARN Cluster 158
Cannot Download the Package When Using Downloadonly Plugin 158
Cannot Find Packages When You Use Yum Search 158
Remove the HBase Rootdir in HDFS Before You Delete the HBase Only Cluster 159
Index 161
VMware vSphere Big Data Extensions Administrator's and User's Guide
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About This Book
VMware vSphere Big Data Extensions Administrator's and User's Guide describes how to install VMware
vSphere Big Data Extensions™ within your vSphere environment, and how to manage and monitor Hadoop
and HBase clusters using the Big Data Extensions plug-in for vSphere Web Client.
VMware vSphere Big Data Extensions Administrator's and User's Guide also describes how to perform Hadoop
and HBase operations using the VMware Serengeti™ Command-Line Interface Client, which provides a
greater degree of control for certain system management and big data cluster creation tasks.
Intended Audience
This guide is for system administrators and developers who want to use Big Data Extensions to deploy and
manage Hadoop clusters. To successfully work with Big Data Extensions, you should be familiar with
VMware® vSphere® and Hadoop and HBase deployment and operation.
VMware Technical Publications Glossary
VMware Technical Publications provides a glossary of terms that might be unfamiliar to you. For definitions
of terms as they are used in VMware technical documentation, go to
http://www.vmware.com/support/pubs.
VMware, Inc. 7
VMware vSphere Big Data Extensions Administrator's and User's Guide
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About VMware vSphere Big Data
Extensions 1
VMware vSphere Big Data Extensions lets you deploy and centrally operate big data clusters running on
VMware vSphere. Big Data Extensions simplifies the Hadoop and HBase deployment and provisioning
process, and gives you a real time view of the running services and the status of their virtual hosts. It
provides a central place from which to manage and monitor your big data cluster, and incorporates a full
range of tools to help you optimize cluster performance and utilization.
This chapter includes the following topics:
n“Getting Started with Big Data Extensions,” on page 9
n“Big Data Extensions and Project Serengeti,” on page 10
n“About Big Data Extensions Architecture,” on page 12
n“About Application Managers,” on page 12
n“Big Data Extensions Support for MapReduce Distribution Features,” on page 15
n“Feature Support By Hadoop Distribution,” on page 17
Getting Started with Big Data Extensions
Big Data Extensions lets you deploy big data clusters. The tasks in this section describe how to set up
VMware vSphere® for use with Big Data Extensions, deploy the Big Data Extensions vApp, access the
VMware vCenter Server® and command-line interface (CLI) administrative consoles, and configure a
Hadoop distribution for use with Big Data Extensions.
Prerequisites
nUnderstand what Project Serengeti® and Big Data Extensions is so that you know how they fit into your
big data workflow and vSphere environment.
nVerify that the Big Data Extensions features that you want to use, such as data-compute separated
clusters and elastic scaling, are supported by Big Data Extensions for the Hadoop distribution that you
want to use.
nUnderstand which features are supported by your Hadoop distribution.
Procedure
1 Do one of the following.
nInstall Big Data Extensions for the first time. Review the system requirements, install vSphere, and
install the Big Data Extensions components: Big Data Extensions vApp, Big Data Extensions plug-
in for vCenter Server, and Serengeti CLI Client.
nUpgrade Big Data Extensions from a previous version. Perform the upgrade steps.
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2 (Optional) Install and configure a distribution other than Apache Bigtop for use with
Big Data Extensions.
Apache Bigtop is included in the Serengeti Management Server, but you can use any Hadoop
distribution that Big Data Extensions supports.
What to do next
After you have successfully installed and configured your Big Data Extensions environment, you can
perform the following additional tasks.
nStop and start the Serengeti services, create user accounts, manage passwords, and log in to cluster
nodes to perform troubleshooting.
nManage the vSphere resource pools, datastores, and networks that you use to create Hadoop and HBase
clusters.
nCreate, provision, and manage big data clusters.
nMonitor the status of the clusters that you create, including their datastores, networks, and resource
pools, through the vSphere Web Client and the Serengeti Command-Line Interface.
nOn your Big Data clusters, run HDFS commands, Hive and Pig scripts , and MapReduce jobs, and
access Hive data.
nIf you encounter any problems when using Big Data Extensions, see Chapter 13, “Troubleshooting,” on
page 139.
Big Data Extensions and Project Serengeti
Big Data Extensions runs on top of Project Serengeti, the open source project initiated by VMware to
automate the deployment and management of Hadoop and HBase clusters on virtual environments such as
vSphere.
Big Data Extensions and Project Serengeti provide the following components.
Project Serengeti An open source project initiated by VMware, Project Serengeti lets users
deploy and manage big data clusters in a vCenter Server managed
environment. The major components are the Serengeti Management Server,
which provides cluster provisioning, software configuration, and
management services; an elastic scaling framework; and command-line
interface. Project Serengeti is made available under the Apache 2.0 license,
under which anyone can modify and redistribute Project Serengeti according
to the terms of the license.
Serengeti Management
Server
Provides the framework and services to run Big Data clusters on vSphere.
The Serengeti Management Server performs resource management, policy-
based virtual machine placement, cluster provisioning, software
configuration management, and environment monitoring.
VMware vSphere Big Data Extensions Administrator's and User's Guide
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Serengeti Command-
Line Interface Client
The command-line interface (CLI) client provides a comprehensive set of
tools and utilities with which to monitor and manage your Big Data
deployment. If you are using the open source version of Serengeti without
Big Data Extensions, the CLI is the only interface through which you can
perform administrative tasks. For more information about the CLI, see the
VMware vSphere Big Data Extensions Command-Line Interface Guide.
Big Data Extensions The commercial version of the open source Project Serengeti from VMware,
Big Data Extensions, is delivered as a vCenter Server Appliance.
Big Data Extensions includes all the Project Serengeti functions and the
following additional features and components.
nEnterprise level support from VMware.
nBigtop distribution from the Apache community.
NOTE VMware provides the Hadoop distribution as a convenience but
does not provide enterprise-level support. The Apache Bigtop
distribution is supported by the open source community.
nThe Big Data Extensions plug-in, a graphical user interface integrated
with vSphere Web Client. This plug-in lets you perform common
Hadoop infrastructure and cluster management administrative tasks.
nElastic scaling lets you optimize cluster performance and utilization of
physical compute resources in a vSphere environment. Elasticity-
enabled clusters start and stop virtual machines, adjusting the number
of active compute nodes based on configuration settings that you
specify, to optimize resource consumption. Elasticity is ideal in a mixed
workload environment to ensure that workloads can efficiently share the
underlying physical resources while high-priority jobs are assigned
sufficient resources.
Chapter 1 About VMware vSphere Big Data Extensions
VMware, Inc. 11
About Big Data Extensions Architecture
The Serengeti Management Server and Hadoop Template virtual machine work together to configure and
provision big data clusters.
Figure 1‑1. Big Data Extensions Architecture
CLI GUI
Rest API
VM and Application
Provisioning Framework
Software Management SPI
Default
adapter
Cloudera
adapter
Ambari
adapter
Software
Management
Thrift Service
Cloudera
Manager
Server
Ambari
Server
Big Data Extensions performs the following steps to deploy a big data cluster.
1 The Serengeti Management Server searches for ESXi hosts with sufficient resources to operate the
cluster based on the configuration settings that you specify, and then selects the ESXi hosts on which to
place Hadoop virtual machines.
2 The Serengeti Management Server sends a request to the vCenter Server to clone and configure virtual
machines to use with the big data cluster.
3 The Serengeti Management Server configures the operating system and network parameters for the
new virtual machines.
4 Each virtual machine downloads the Hadoop software packages and installs them by applying the
distribution and installation information from the Serengeti Management Server.
5 The Serengeti Management Server configures the Hadoop parameters for the new virtual machines
based on the cluster configuration settings that you specify.
6 The Hadoop services are started on the new virtual machines, at which point you have a running
cluster based on your configuration settings.
About Application Managers
You can use Cloudera Manager, Apache Ambari, and the default application manager to provision and
manage clusters with VMware vSphere Big Data Extensions.
After you add a new Cloudera Manager or Ambari application manager to Big Data Extensions, you can
redirect your software management tasks, including monitoring and managing clusters, to that application
manager.
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You can use an application manager to perform the following tasks:
nList all available vendor instances, supported distributions, and configurations or roles for a specific
application manager and distribution.
nCreate clusters.
nMonitor and manage services from the application manager console.
Check the documentation for your application manager for tool-specific requirements.
Restrictions
The following restrictions apply to Cloudera Manager and Ambari application managers:
nTo add an application manager with HTTPS, use the FQDN instead of the URL.
nYou cannot rename a cluster that was created with a Cloudera Manager or Ambari application
manager.
nYou cannot change services for a big data cluster from Big Data Extensions if the cluster was created
with Ambari or Cloudera Manager application manager.
nTo change services, configurations, or both, you must make the changes from the application manager
on the nodes.
If you install new services, Big Data Extensions starts and stops the new services together with old
services.
nIf you use an application manager to change services and big data cluster configurations, those changes
cannot be synced from Big Data Extensions. The nodes that you create with Big Data Extensions do not
contain the new services or configurations.
Services and Operations Supported by the Application Managers
If you use Cloudera Manager or Apache Ambari with Big Data Extensions, there are several additional
services that are available for your use.
Supported Application Managers and Distributions
Big Data Extensions supports certain application managers and Hadoop distributions.
Table 1‑1. Supported application managers and Hadoop distributions
Application Managers Supported Version
Supported
Distributions Supported EMC Isilon OneFS
Cloudera Manager 5.3, 5.4 CDH 5.3, 5.4 Isilon OneFS 7.1, 7.2
Apache Ambari 1.6, 1.7 HDP 2.1, 2.2, PHD 3.0 Isilon OneFS 7.1, 7.2
Big Data Extensions does not
support the provisioning of
compute-only clusters with Ambari
Manager. However Ambari can
provision compute-only clusters
when using Isilon OneFS. Refer to
the EMC Isilon Hadoop Starter Kit
for Hortonworks documentation
for information on configuring
Ambari and Isilon OneFS.
Default Apache Bigtop 0.8, CDH
5.2, 5.3, HDP 2.1, PHD
2.0, 2.1 MapR 4.0, 4.1
Isilon OneFS 7.1, 7.2
Chapter 1 About VMware vSphere Big Data Extensions
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Supported Features and Operations
The following features and operations are available when you use the Ambari application manager 1.6 and
1.7 (with versions HDP 2.1, 2.2, or PHD 3.0) and the Cloudera Manager application manager 5.3 and 5.4
(with versions CDH 5.3 and 5.4) on Big Data Extensions.
nCreate Hadoop Cluster
nCreate HBase Cluster
nScale Cluster In/Out
nCluster Delete
nCluster Export (can only be performed with the Serengeti CLI)
nCluster List
nCluster Resume
nCluster Start/Stop
nNameNode High Availability (available only with Ambari and Cloudera Manager)
To use NameNode High Availability (HA) with Ambari, you must configure NameNode HA for use
with your Hadoop deployment. See NameNode High Availability for Hadoop in the Hortonworks
documentation.
nHadoop Topology Awareness (RACK_AS_RACK, HOST_AS_RACK or HVE)
nvSphere Fault Tolerance
nvSphere High Availability
Services supported on Cloudera Manager and Ambari
Table 1‑2. Services supported on Cloudera Manager and Ambari
Service Name Cloudera Manager 5.3, 5.4 Ambari 1.6, 1.7
Falcon X
Flume X X
Ganglia X
HBase X X
HCatalog X
HDFS X X
Hive X X
Hue X X
Impala X
MapReduce X X
Nagios X
Oozie X X
Pig X
Sentry
Solr X
Spark X
Sqoop X X
VMware vSphere Big Data Extensions Administrator's and User's Guide
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Table 1‑2. Services supported on Cloudera Manager and Ambari (Continued)
Service Name Cloudera Manager 5.3, 5.4 Ambari 1.6, 1.7
Storm X
TEZ X
WebHCAT X
YARN X X
Zookeeper X X
About Service Level vSphere High Availability for Ambari
Ambari supports NameNode HA, however, you must configure NameNode HA for use with your Hadoop
deployment. See NameNode High Availability for Hadoop in the Hortonworks documentation.
About Service Level vSphere High Availability for Cloudera
The Cloudera distributions offer the following support for Service Level vSphere HA.
nCloudera using MapReduce v1 provides service level vSphere HA support for JobTracker.
nCloudera provides its own service level HA support for NameNode through HDFS2.
For information about how to use an application manager with the CLI, see the VMware vSphere Big Data
Extensions Command-Line Interface Guide.
Big Data Extensions Support for MapReduce Distribution Features
Big Data Extensions provides different levels of feature support depending on the distribution and version
that you configure for use with the default application manager.
Support for Hadoop MapReduce v1 Distribution Features
Table 1-3 lists the supported Hadoop MapReduce v1 distributions and indicates which features are
supported when you use the distribution with the default application manager on Big Data Extensions.
Table 1‑3. Big Data Extensions Feature Support for Hadoop MapReduce v1 Distributions
Cloudera Hortonworks MapR
Version 5.3, 5.4 1.3 4.0, 4.1
Automatic Deployment Yes Yes Yes
Scale Out Yes Yes Yes
Create Cluster with
Multiple Networks
Yes Yes No
Data-Compute
Separation
Yes Yes Yes
Compute-only Yes Yes No
Elastic Scaling of
Compute Nodes
Yes when using
MapReduce v1
Yes No
Hadoop Configuration Yes Yes No
Hadoop Topology
Configuration
Yes Yes No
Hadoop Virtualization
Extensions (HVE)
No Yes No
Chapter 1 About VMware vSphere Big Data Extensions
VMware, Inc. 15
Table 1‑3. Big Data Extensions Feature Support for Hadoop MapReduce v1 Distributions (Continued)
Cloudera Hortonworks MapR
vSphere HA Yes Yes Yes
Service Level vSphere
HA
See “About Service Level
vSphere High Availability
for Cloudera,” on page 15
Yes No
vSphere FT Yes Yes Yes
Support for Hadoop MapReduce v2 (YARN) Distribution Features
Table 1-4 lists the supported Hadoop MapReduce v2 distributions and indicates which features are
supported when you use the distribution with the default application manager on Big Data Extensions.
Table 1‑4. Big Data Extensions Feature Support for Hadoop MapReduce v2 (YARN) Distributions
Apache
Bigtop Cloudera Hortonworks MapR Pivotal
Version 0.8 5.3, 5.4 2.1 4.0, 4.1 2.0, 2.1
Automatic
Deployment
Yes Yes Yes Yes Yes
Scale Out Yes Yes Yes Yes Yes
Create Cluster
with Multiple
Networks
Yes Yes Yes No Yes
Data-Compute
Separation
Yes Yes Yes Yes Yes
Compute-only Yes Yes Yes No Yes
Elastic Scaling of
Compute Nodes
No No when using
MapReduce 2
Yes No No
Hadoop
Configuration
Yes Yes Yes No Yes
Hadoop
Topology
Configuration
Yes Yes Yes No Yes
Hadoop
Virtualization
Extensions
(HVE)
Support only
for HDFS
Support only
for HDFS
Support only
for HDFS.
No Yes
vSphere HA No No No Yes No
Service Level
vSphere HA
No See “About
Service Level
vSphere High
Availability for
Cloudera,” on
page 15
No No No
vSphere FT No No No Yes No
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Feature Support By Hadoop Distribution
Each Hadoop distribution and version provides differing feature support. Learn which Hadoop
distributions support which features.
Hadoop Features
The table illustrates which Hadoop distributions support which features when you use the distributions
with the default application manager on Big Data Extensions.
Table 1‑5. Hadoop Feature Support
Apache Bigtop Cloudera Hortonworks MapR Pivotal
Version 0.8 5.3, 5.4 2.1, 2.2 4.0, 4.1 2.0 , 2.1
HDFS1 No Yes No No No
HDFS2 Yes Yes Yes No Yes
MapReduce v1 No Yes No Yes No
MapReduce v2
(YARN)
Yes Yes Yes Yes Yes
Pig Yes Yes Yes Yes Yes
Hive Yes Yes Yes Yes Yes
Hive Server Yes Yes Yes Yes Yes
HBase Yes Yes Yes Yes Yes
ZooKeeper Yes Yes Yes Yes Yes
Chapter 1 About VMware vSphere Big Data Extensions
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Installing Big Data Extensions 2
To install Big Data Extensions so that you can create and provision big data clusters, you must install the
Big Data Extensions components in the order described.
What to do next
If you want to create clusters on any Hadoop distribution other than Apache Bigtop, which is included in
theSerengeti Management Server, install and configure the distribution for use with Big Data Extensions.
This chapter includes the following topics:
n“System Requirements for Big Data Extensions,” on page 19
n“Unicode UTF-8 and Special Character Support,” on page 22
n“The Customer Experience Improvement Program,” on page 23
n“Deploy the Big Data Extensions vApp in the vSphere Web Client,” on page 24
n“Install RPMs in the Serengeti Management Server Yum Repository,” on page 27
n“Install the Big Data Extensions Plug-In,” on page 28
n“Configure vCenter Single Sign-On Settings for the Serengeti Management Server,” on page 30
n“Connect to a Serengeti Management Server,” on page 30
n“Install the Serengeti Remote Command-Line Interface Client,” on page 31
n“Access the Serengeti CLI By Using the Remote CLI Client,” on page 32
System Requirements for Big Data Extensions
Before you begin the Big Data Extensions deployment tasks, your system must meet all of the prerequisites
for vSphere, clusters, networks, storage, hardware, and licensing.
Big Data Extensions requires that you install and configure vSphere and that your environment meets
minimum resource requirements. Make sure that you have licenses for the VMware components of your
deployment.
vSphere Requirements Before you install Big Data Extensions, set up the following VMware
products.
nInstall vSphere 5.5 (or later) Enterprise or Enterprise Plus.
VMware, Inc. 19
nWhen you install Big Data Extensions on vSphere 5.5 or later, use
VMware® vCenter™ Single Sign-On to provide user authentication.
When logging in to vSphere 5.5 or later you pass authentication to the
vCenter Single Sign-On server, which you can configure with multiple
identity sources such as Active Directory and OpenLDAP. On successful
authentication, your user name and password is exchanged for a
security token that is used to access vSphere components such as
Big Data Extensions.
nConfigure all ESXi hosts to use the same Network Time Protocol (NTP)
server.
nOn each ESXi host, add the NTP server to the host configuration, and
from the host configuration's Startup Policy list, select Start and stop
with host. The NTP daemon ensures that time-dependent processes
occur in sync across hosts.
Cluster Settings Configure your cluster with the following settings.
nEnable vSphere HA and VMware vSphere® Distributed Resource
Scheduler™.
nEnable Host Monitoring.
nEnable admission control and set the policy you want. The default
policy is to tolerate one host failure.
nSet the virtual machine restart priority to high.
nSet the virtual machine monitoring to virtual machine and application
monitoring.
nSet the monitoring sensitivity to high.
nEnable vMotion and Fault Tolerance logging.
nAll hosts in the cluster have Hardware VT enabled in the BIOS.
nThe Management Network VMkernel Port has vMotion and Fault
Tolerance logging enabled.
Network Settings Big Data Extensions can deploy clusters on a single network or use multiple
networks. The environment determines how port groups that are attached to
NICs are configured and which network backs each port group.
You can use either a vSwitch or vSphere Distributed Switch (vDS) to provide
the port group backing a Serengeti cluster. vDS acts as a single virtual switch
across all attached hosts while a vSwitch is per-host and requires the port
group to be configured manually.
When you configure your networks to use with Big Data Extensions, verify
that the following ports are open as listening ports.
nPorts 8080 and 8443 are used by the Big Data Extensions plug-in user
interface and the Serengeti Command-Line Interface Client.
nPort 5480 is used by vCenter Single Sign-On for monitoring and
management.
nPort 22 is used by SSH clients.
nTo prevent having to open a network firewall port to access Hadoop
services, log into the Hadoop client node, and from that node you can
access your cluster.
VMware vSphere Big Data Extensions Administrator's and User's Guide
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nTo connect to the internet (for example, to create an internal yum
repository from which to install Hadoop distributions), you may use a
proxy.
nTo enable communications, be sure that firewalls and web filters do not
block the Serengeti Management Server or other Serengeti nodes.
Direct Attached Storage Attach and configure direct attached storage on the physical controller to
present each disk separately to the operating system. This configuration is
commonly described as Just A Bunch Of Disks (JBOD). Create VMFS
datastores on direct attached storage using the following disk drive
recommendations.
n8-12 disk drives per host. The more disk drives per host, the better the
performance.
n1-1.5 disk drives per processor core.
n7,200 RPM disk Serial ATA disk drives.
Do not use
Big Data Extensions in
conjunction with
vSphere Storage DRS
Big Data Extensions places virtual machines on hosts according to available
resources, Hadoop best practices, and user defined placement policies prior
to creating virtual machines. For this reason, you should not deploy
Big Data Extensions on vSphere environments in combination with Storage
DRS. Storage DRS continuously balances storage space usage and storage I/O
load to meet application service levels in specific environments. If Storage
DRS is used with Big Data Extensions, it will disrupt the placement policies
of your Big Data cluster virtual machines.
Migrating virtual
machines in vCenter
Server may disrupt the
virtual machine
placement policy
Big Data Extensions places virtual machines based on available resources,
Hadoop best practices, and user defined placement policies that you specify.
For this reason, DRS is disabled on all the virtual machines created within
the Big Data Extensions environment. While this prevents virtual machines
from being automatically migrated by vSphere, it does not prevent you from
inadvertently moving virtual machines using the vCenter Server user
interface. This may break the Big Data Extensions defined placement policy.
For example, this may disrupt the number of instances per host and group
associations.
Resource Requirements
for the vSphere
Management Server and
Templates
nResource pool with at least 27.5GB RAM.
n40GB or more (recommended) disk space for the management server
and Hadoop template virtual disks.
Resource Requirements
for the Hadoop Cluster
nDatastore free space is not less than the total size needed by the Hadoop
cluster, plus swap disks for each Hadoop node that is equal to the
memory size requested.
nNetwork configured across all relevant ESXi hosts, and has connectivity
with the network in use by the management server.
nvSphere HA is enabled for the master node if vSphere HA protection is
needed. To use vSphere HA or vSphere FT to protect the Hadoop master
node, you must use shared storage.
Chapter 2 Installing Big Data Extensions
VMware, Inc. 21
Hardware Requirements
for the vSphere and
Big Data Extensions
Environment
Host hardware is listed in the VMware Compatibility Guide. To run at optimal
performance, install your vSphere and Big Data Extensions environment on
the following hardware.
nDual Quad-core CPUs or greater that have Hyper-Threading enabled. If
you can estimate your computing workload, consider using a more
powerful CPU.
nUse High Availability (HA) and dual power supplies for the master
node's host machine.
n4-8 GBs of memory for each processor core, with 6% overhead for
virtualization.
nUse a 1GB Ethernet interface or greater to provide adequate network
bandwidth.
Tested Host and Virtual
Machine Support
The maximum host and virtual machine support that has been confirmed to
successfully run with Big Data Extensions is 256 physical hosts running a
total of 512 virtual machines.
vSphere Licensing You must use a vSphere Enterprise license or above to use VMware vSphere
HA and vSphere DRS.
Unicode UTF-8 and Special Character Support
Big Data Extensions supports internationalization (I18N) level 3. However, there are resources you specify
that do not provide UTF-8 support. You can use only ASCII attribute names consisting of alphanumeric
characters and underscores (_) for these resources.
Big Data Extensions Supports Unicode UTF-8
vCenter Server resources you specify using both the CLI and vSphere Web Client can be expressed with
underscore (_), hyphen (-), blank spaces, and all letters and numbers from any language. For example, you
can specify resources such as datastores labeled using non-English characters.
When using a Linux operating system, you should configure the system for use with UTF-8 encoding
specific to your locale. For example, to use U.S. English, specify the following locale encoding: en_US.UTF-8.
See your vendor's documentation for information on configuring UTF-8 encoding for your Linux
environment.
Special Character Support
The following vCenter Server resources can have a period (.) in their name, letting you select them using
both the CLI and vSphere Web Client.
nportgroup name
ncluster name
nresource pool name
ndatastore name
The use of a period is not allowed in the Serengeti resource name.
VMware vSphere Big Data Extensions Administrator's and User's Guide
22 VMware, Inc.
Resources Excluded From Unicode UTF-8 Support
The Serengeti cluster specification file, manifest file, and topology racks-hosts mapping file do not provide
UTF-8 support. When you create these files to define the nodes and resources for use by the cluster, use only
ASCII attribute names consisting of alphanumeric characters and underscores (_).
The following resource names are excluded from UTF-8 support:
ncluster name
nnodeGroup name
nnode name
nvirtual machine name
The following attributes in the Serengeti cluster specification file are excluded from UTF-8 support:
ndistro name
nrole
ncluster configuration
nstorage type
nhaFlag
ninstanceType
ngroupAssociationsType
The rack name in the topology racks-hosts mapping file, and the placementPolicies field of the Serengeti
cluster specification file is also excluded from UTF-8 support.
The Customer Experience Improvement Program
You can configure Big Data Extensions to collect data to help improve your user experience with VMware
products. The following section contains important information about the VMware Customer Experience
Improvement Program.
The goal of the Customer Experience Improvement Program is to quickly identify and address problems
that might be affecting your experience. If you choose to participate in the Customer Experience
Improvement Program,Big Data Extensions will regularly send anonymous data to VMware. You can use
this data for product development and troubleshooting purposes.
Before collecting the data, VMware makes anonymous all fields that contain information that is specific to
your organization. VMware sanitizes fields by generating a hash of the actual value. When a hash value is
collected, VMware cannot identify the actual value but can detect changes in the value when you change
your environment.
Chapter 2 Installing Big Data Extensions
VMware, Inc. 23
Categories of Information in Collected Data
When you choose to participate in VMware’s Customer Experience Improvement Program (CEIP), VMware
will receive the following categories of data:
Configuration Data Data about how you have configured VMware products and information
related to your IT environment. Examples of Configuration Data include:
version information for VMware products; details of the hardware and
software running in your environment; product configuration settings, and
information about your networking environment. Configuration Data may
include hashed versions of your device IDs and MAC and Internet Protocol
Addresses.
Feature Usage Data Data about how you use VMware products and services. Examples of
Feature Usage Data include: details about which product features are used;
metrics of user interface activity; and details about your API calls.
Performance Data Data about the performance of VMware products and services. Examples of
Performance Data include metrics of the performance and scale of VMware
products and services; response times for User Interfaces, and details about
your API calls.
Enabling and Disabling Data Collection
By default, enrollment in the Customer Experience Improvement Program is enabled during installation.
You have the option of disabling this service during installation. You can discontinue participation in the
Customer Experience Improvement Program at any time, and stop sending data to VMware. See“Disable
the Big Data Extensions Data Collector,” on page 124.
If you have any questions or concerns regarding the Customer Experience Improvement Program for Log
Insight, contact bde-info@vmware.com.
Deploy the Big Data Extensions vApp in the vSphere Web Client
Deploying the Big Data Extensions vApp is the first step in getting your cluster up and running with
Big Data Extensions.
Prerequisites
nInstall and configure vSphere.
nConfigure all ESXi hosts to use the same NTP server.
nOn each ESXi host, add the NTP server to the host configuration, and from the host configuration's
Startup Policy list, select Start and stop with host. The NTP daemon ensures that time-dependent
processes occur in sync across hosts.
nWhen installing Big Data Extensions on vSphere 5.1 or later, use vCenter Single Sign-On to provide
user authentication.
nVerify that you have one vSphere Enterprise license for each host on which you deploy virtual Hadoop
nodes. You manage your vSphere licenses in the vSphere Web Client or in vCenter Server.
nInstall the Client Integration plug-in for the vSphere Web Client. This plug-in enables OVF deployment
on your local file system.
NOTE Depending on the security settings of your browser, you might have to approve the plug-in
when you use it the first time.
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24 VMware, Inc.
nDownload the Big Data Extensions OVA from the VMware download site.
nVerify that you have at least 40GB disk space available for the OVA. You need additional resources for
the Hadoop cluster.
nEnsure that you know the vCenter Single Sign-On Look-up Service URL for your
vCenter Single Sign-On service.
If you are installing Big Data Extensions on vSphere 5.1 or later, ensure that your environment includes
vCenter Single Sign-On. Use vCenter Single Sign-On to provide user authentication on vSphere 5.1 or
later.
nReview the Customer Experience Improvement Program description, and determine if you wish to
collect data and send it to VMware help improve your user experience using Big Data Extensions. See
“The Customer Experience Improvement Program,” on page 23.
Procedure
1 In the vSphere Web Client vCenter Hosts and Clusters view, select Actions > All vCenter Actions >
Deploy OVF Template.
2 Choose the location where the Big Data Extensions OVA resides and click Next.
Option Description
Deploy from File Browse your file system for an OVF or OVA template.
Deploy from URL Type a URL to an OVF or OVA template located on the internet. For
example: http://vmware.com/VMTN/appliance.ovf.
3 View the OVF Template Details page and click Next.
4 Accept the license agreement and click Next.
5 Specify a name for the vApp, select a target datacenter for the OVA, and click Next.
The only valid characters for Big Data Extensions vApp names are alphanumeric and underscores. The
vApp name must be < 60 characters. When you choose the vApp name, also consider how you will
name your clusters. Together the vApp and cluster names must be < 80 characters.
6 Select a vSphere resource pool for the OVA and click Next.
Select a top-level resource pool. Child resource pools are not supported by Big Data Extensions even
though you can select a child resource pool. If you select a child resource pool, you will not be able to
create clusters from Big Data Extensions.
7 Select shared storage for the OVA and click Next.
If shared storage is not available, local storage is acceptable.
8 For each network specified in the OVF template, select a network in the Destination Networks column
in your infrastructure to set up the network mapping.
The first network lets the Management Server communicate with your Hadoop cluster. The second
network lets the Management Server communicate with vCenter Server. If your vCenter Server
deployment does not use IPv6, you can specify the same IPv4 destination network for use by both
source networks.
Chapter 2 Installing Big Data Extensions
VMware, Inc. 25
9 Configure the network settings for your environment, and click Next.
a Enter the network settings that let the Management Server communicate with your Hadoop
cluster.
Use a static IPv4 (IP) network. An IPv4 address is four numbers separated by dots as in
aaa.bbb.ccc.ddd, where each number ranges from 0 to 255. You must enter a netmask, such as
255.255.255.0, and a gateway address, such as 192.168.1.253.
If the vCenter Server or any ESXi host or Hadoop distribution repository is resolved using a fully
qualified domain name (FQDN), you must enter a DNS address. Enter the DNS server IP address
as DNS Server 1. If there is a secondary DNS server, enter its IP address as DNS Server 2.
NOTE You cannot use a shared IP pool with Big Data Extensions.
b (Optional) If you are using IPv6 between the Management Server and vCenter Server, select the
Enable Ipv6 Connection checkbox.
Enter the IPv6 address, or FQDN, of the vCenter Server. The IPv6 address size is 128 bits. The
preferred IPv6 address representation is: xxxx:xxxx:xxxx:xxxx:xxxx:xxxx:xxxx:xxxx where each x is
a hexadecimal digit representing 4 bits. IPv6 addresses range from
0000:0000:0000:0000:0000:0000:0000:0000 to ffff:ffff:ffff:ffff:ffff:ffff:ffff:ffff. For convenience, an IPv6
address may be abbreviated to shorter notations by application of the following rules.
nRemove one or more leading zeroes from any groups of hexadecimal digits. This is usually
done to either all or none of the leading zeroes. For example, the group 0042 is converted to 42.
nReplace consecutive sections of zeroes with a double colon (::). You may only use the double
colon once in an address, as multiple uses would render the address indeterminate. RFC 5952
recommends that a double colon not be used to denote an omitted single section of zeroes.
The following example demonstrates applying these rules to the address
2001:0db8:0000:0000:0000:ff00:0042:8329.
nRemoving all leading zeroes results in the address 2001:db8:0:0:0:ff00:42:8329.
nOmitting consecutive sections of zeroes results in the address 2001:db8::ff00:42:8329.
See RFC 4291 for more information on IPv6 address notation.
10 Verify that the Initialize Resources check box is selected and click Next.
If the check box is unselected, the resource pool, data store, and network connection assigned to the
vApp will not be added to Big Data Extensions.
If you do not add the resource pool, datastore, and network when you deploy the vApp, use the
vSphere Web Client or the Serengeti CLI Client to specify the resource pool, datastore, and network
information before you create a Hadoop cluster.
11 Run the vCenter Single Sign-On Lookup Service URL to enable vCenter Single Sign-On.
nIf you use vCenter 5.x, use the following URL: https://FQDN_or_IP_of_SSO_SERVER:
7444/lookupservice/sdk
nIf you use vCenter 6.0, use the following URL: https://FQDN_of_SSO_SERVER:
443/lookupservice/sdk
If you don't input the URL, vCenter Single Sign-On is disabled.
12 To disable the Big Data Extensions data collector, uncheck the Customer Experience Improvement
Program checkbox.
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13 (Optional) To disable the Big Data Extensions Web plug-in from automatically registering, uncheck the
enable checkbox.
By default the checkbox to enable automatic registration of the Big Data Extensions Web plug-in is
selected. When you first login to the Big Data Extensions Web client, it automatically connects to the
Serengeti management server.
14 Specify a remote syslog server, such as VMware vRealize Log Insight, to which Big Data Extensions can
send logging information to across the network.
Retention, rotation and the splitting of logs received and managed by a syslog server are controlled by
that syslog server. Big Data Extensions cannot configure or control log management on a remote syslog
server. For more information on log management, see the documentation for the syslog server.
Regardless of the additional syslog configuration specified with this option, logs continue to be placed
in the default locations of the Big Data Extensions environment.
15 Verify the vService bindings and click Next.
16 Verify the installation information and click Finish.
vCenter Server deploys the Big Data Extensions vApp. When deployment finishes, two virtual
machines are available in the vApp.
nThe Management Server virtual machine, management-server (also called the
Serengeti Management Server), which is started as part of the OVA deployment.
nThe Hadoop Template virtual machine, hadoop-template, which is not started. Big Data Extensions
clones Hadoop nodes from this template when provisioning a cluster. Do not start or stop this
virtual machine without good reason. The template does not include a Hadoop distribution.
IMPORTANT Do not delete any files under the /opt/serengeti/.chef directory. If you delete any of these
files, such as the serengeti.pem file, subsequent upgrades to Big Data Extensions might fail without
displaying error notifications.
What to do next
Install the Big Data Extensions plug-in within the vSphere Web Client. See “Install the Big Data Extensions
Plug-In,” on page 28.
If the Initialize Resources check box is not selected, add resources to the Big Data Extensions server before
you create a Hadoop cluster.
Install RPMs in the Serengeti Management Server Yum Repository
Install the wsdl4j and mailx Red Hat Package Manager (RPM) packages within the internal Yum repository
of the Serengeti Management Server.
The wsdl4j and mailx RPM packages are not embedded within big Data Extension due to licensing
agreements. For this reason you must install them within the internal Yum repository of the Serengeti
Management Server.
Prerequisites
Deploy the Big Data Extensions vApp.
Procedure
1 Open a command shell, such as Bash or PuTTY, and log in to the Serengeti Management Server as the
user serengeti.
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2 Download and install the wsdl4j and mailx RPM packages.
nIf the Serengeti Management Server can connect to the Internet, run the commands as shown in the
example below to download the RPMs, copy the files to the required directory, and create a
repository.
cd /opt/serengeti/www/yum/repos/centos/6/base/RPMS/
wget http://mirror.centos.org/centos/6/os/x86_64/Packages/mailx-12.4-7.el6.x86_64.rpm
wget http://mirror.centos.org/centos/6/os/x86_64/Packages/wsdl4j-1.5.2-7.8.el6.noarch.rpm
createrepo ..
nIf the Serengeti Management Server cannot connect to the Internet, you must run the following
tasks manually.
a Download the RPM files as shown in the example below.
http://mirror.centos.org/centos/6/os/x86_64/Packages/mailx-12.4-7.el6.x86_64.rpm
http://mirror.centos.org/centos/6/os/x86_64/Packages/wsdl4j-1.5.2-7.8.el6.noarch.rpm
b Copy the RPM files to /opt/serengeti/www/yum/repos/centos/6/base/RPMS/.
c Run the createrepo command to create a repository from the RPMs you downloaded.
createrepo /opt/serengeti/www/yum/repos/centos/6/base/
Install the Big Data Extensions Plug-In
To enable the Big Data Extensions user interface for use with a vCenter Server Web Client, register the plug-
in with the vSphere Web Client. The Big Data Extensions graphical user interface is supported only when
you use vSphere Web Client 5.1 and later.
The Big Data Extensions plug-in provides a GUI that integrates with the vSphere Web Client. Using the
Big Data Extensions plug-in interface you can perform common Hadoop infrastructure and cluster
management tasks.
NOTE Use only the Big Data Extensions plug-in interface in the vSphere Web Client or the Serengeti CLI
Client to monitor and manage your Big Data Extensions environment. Performing management operations
in vCenter Server might cause the Big Data Extensions management tools to become unsynchronized and
unable to accurately report the operational status of your Big Data Extensions environment.
Prerequisites
nDeploy the Big Data Extensions vApp. See “Deploy the Big Data Extensions vApp in the vSphere Web
Client,” on page 24.
nBy default, the Big Data Extensions Web plug-in automatically installs and registers when you deploy
the Big Data Extensions vApp. To install the Big Data Extensions Web plug-in after deploying the
Big Data Extensions vApp, you must has opted not to enable automatic registration of the Web plug-in
during deployment. See “Deploy the Big Data Extensions vApp in the vSphere Web Client,” on
page 24.
nEnsure that you have login credentials with administrator privileges for the vCenter Server system with
which you are registering Big Data Extensions.
NOTE The user name and password you use to login cannot contain characters whose UTF-8 encoding
is greater than 0x8000.
nIf you want to use the vCenter Server IP address to access the vSphere Web Client, and your browser
uses a proxy, add the vCenter Server IP address to the list of proxy exceptions.
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Procedure
1 Open a Web browser and go to the URL of vSphere Web Client 5.1 or later.
https://hostname-or-ip-address:port/vsphere-client
The hostname-or-ip-address can be either the DNS hostname or IP address of vCenter Server. By default
the port is 9443, but this might have changed during installation of the vSphere Web Client.
2 Enter the user name and password with administrative privileges that has permissions on
vCenter Server, and click Login.
3 Using the vSphere Web Client Navigator pane, locate the ZIP file on the Serengeti Management Server
that contains the Big Data Extensions plug-in to register to the vCenter Server.
You can find the Serengeti Management Server under the datacenter and resource pool to which you
deployed it.
4 From the inventory tree, select management-server to display information about the
Serengeti Management Server in the center pane.
Click the Summary tab in the center pane to access additional information.
5 Note the IP address of the Serengeti Management Server virtual machine.
6 Open a Web browser and go to the URL of the management-server virtual machine.
https://management-server-ip-address:8443/register-plugin
The management-server-ip-address is the IP address you noted in Step 5.
7 Enter the information to register the plug-in.
Option Action
Register or Unregister Click Install to install the plug-in. Select Uninstall to uninstall the plug-in.
vCenter Server host name or IP
address
Enter the server host name or IP address of vCenter Server.
Do not include http:// or https:// when you enter the host name or IP
address.
User Name and Password Enter the user name and password with administrative privileges that you
use to connect to vCenter Server. The user name and password cannot
contain characters whose UTF-8 encoding is greater than 0x8000.
Big Data Extensions Package URL Enter the URL with the IP address of the management-server virtual
machine where the Big Data Extensions plug-in package is located:
https://management-server-ip-address/vcplugin/serengeti-
plugin.zip
8 Click Submit.
The Big Data Extensions plug-in registers with vCenter Server and with the vSphere Web Client.
9 Log out of the vSphere Web Client, and log back in using your vCenter Server user name and
password.
The Big Data Extensions icon appears in the list of objects in the inventory.
10 Click Big Data Extensions in the Inventory pane.
What to do next
Connect the Big Data Extensions plug-in to the Big Data Extensions instance that you want to manage by
connecting to the corresponding Serengeti Management Server. See “Connect to a Serengeti Management
Server,” on page 30.
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Configure vCenter Single Sign-On Settings for the Serengeti
Management Server
If the Big Data Extensions Single Sign-On (SSO) authentication settings are not configured or if they change
after you install the Big Data Extensions plug-in, you can use the Serengeti Management Server
Administration Portal to enable SSO, update the certificate, and register the plug-in so that you can connect
to the Serengeti Management Server and continue managing clusters.
The SSL certificate for the Big Data Extensions plug-in can change for many reasons. For example, you
install a custom certificate or replace an expired certificate.
Prerequisites
nEnsure that you know the IP address of the Serengeti Management Server to which you want to
connect.
nEnsure that you have login credentials for the Serengeti Management Server root user.
Procedure
1 Open a Web browser and go the URL of the Serengeti Management Server Administration Portal.
https://management-server-ip-address:5480
2 Type root for the user name, type the password, and click Login.
3 Select the SSO tab.
4 Do one of the following.
Option Description
Update the certificate Click Update Certificate.
Enable SSO for the first time Type the Lookup Service URL, and click Enable SSO.
The Big Data Extensions and vCenter SSO server certificates are synchronized.
What to do next
Reregister the Big Data Extensions plug-in with the Serengeti Management Server. See “Connect to a
Serengeti Management Server,” on page 30.
Connect to a Serengeti Management Server
To use the Big Data Extensions plug-in to manage and monitor big data clusters and Hadoop distributions,
you must connect the Big Data Extensions plug-in to the Serengeti Management Server in your
Big Data Extensions deployment.
You can deploy multiple instances of the Serengeti Management Server in your environment. However, you
can connect the Big Data Extensions plug-in with only one Serengeti Management Server instance at a time.
You can change which Serengeti Management Server instance the plug-in connects to, and use the
Big Data Extensions plug-in interface to manage and monitor multiple Hadoop and HBase distributions
deployed in your environment.
IMPORTANT The Serengeti Management Server that you connect to is shared by all users of the
Big Data Extensions plug-in interface in the vSphere Web Client. If a user connects to a different
Serengeti Management Server, all other users are affected by this change.
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Prerequisites
nVerify that the Big Data Extensions vApp deployment was successful and that the
Serengeti Management Server virtual machine is running.
nVerify that the version of the Serengeti Management Server and the Big Data Extensions plug-in is the
same.
nEnsure that vCenter Single Sign-On is enabled and configured for use by Big Data Extensions for
vSphere 5.1 and later.
nInstall theBig Data Extensions plug-in.
Procedure
1 Use the vSphere Web Client to log in to vCenter Server.
2 Select Big Data Extensions.
3 Click the Summary tab.
4 In the Connected Server pane, click the Connect Server link.
5 Navigate to the Serengeti Management Server virtual machine in the Big Data Extensions vApp to
which to connect, select it, and click OK.
The Big Data Extensions plug-in communicates using SSL with the Serengeti Management Server.
When you connect to a Serengeti server instance, the plug-in verifies that the SSL certificate in use by
the server is installed, valid, and trusted.
The Serengeti server instance appears as the connected server on the Summary tab of the
Big Data Extensions Home page.
What to do next
You can add resource pool, datastore, and network resources to your Big Data Extensions deployment, and
create big data clusters that you can provision for use.
Install the Serengeti Remote Command-Line Interface Client
Although theBig Data Extensions Plug-in for vSphere Web Client supports basic resource and cluster
management tasks, you can perform a greater number of the management tasks using the Serengeti CLI
Client.
Prerequisites
nVerify that the Big Data Extensions vApp deployment was successful and that the Management Server
is running.
nVerify that you have the correct user name and password to log into the Serengeti CLI Client. If you are
deploying on vSphere 5.1 or later, the Serengeti CLI Client uses your vCenter Single Sign-On
credentials.
nVerify that the Java Runtime Environment (JRE) is installed in your environment, and that its location is
in your PATH environment variable.
Procedure
1 Use the vSphere Web Client to log in to vCenter Server.
2 Select Big Data Extensions.
3 Click the Getting Started tab, and click the Download Serengeti CLI Console link.
A ZIP file containing the Serengeti CLI Client downloads to your computer.
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4 Unzip and examine the download, which includes the following components in the cli directory.
nThe serengeti-cli-version JAR file, which includes the Serengeti CLI Client.
nThe samples directory, which includes sample cluster configurations.
nLibraries in the lib directory.
5 Open a command shell, and navigate to the directory where you unzipped the Serengeti CLI Client
download package.
6 Change to the cli directory, and run the following command to open the Serengeti CLI Client:
java -jar serengeti-cli-version.jar
What to do next
To learn more about using the Serengeti CLI Client, see the VMware vSphere Big Data Extensions Command-
line Interface Guide.
Access the Serengeti CLI By Using the Remote CLI Client
You can access the Serengeti Command-Line Interface (CLI) to perform Serengeti administrative tasks with
the Serengeti Remote CLI Client.
Prerequisites
nUse the VMware vSphere Web Client to log in to the VMware vCenter Server® on which you deployed
the Serengeti vApp.
nVerify that the Serengeti vApp deployment was successful and that the Management Server is running.
nVerify that you have the correct password to log in to Serengeti CLI. See the VMware vSphere Big Data
Extensions Administrator's and User's Guide.
The Serengeti CLI uses its vCenter Server credentials.
nVerify that the Java Runtime Environment (JRE) is installed in your environment and that its location is
in your path environment variable.
Procedure
1 Download the Serengeti CLI package from the Serengeti Management Server.
Open a Web browser and navigate to the following URL: https://server_ip_address/cli/VMware-
Serengeti-CLI.zip
2 Download the ZIP file.
The filename is in the format VMware-Serengeti-cli-version_number-build_number.ZIP.
3 Unzip the download.
The download includes the following components.
nThe serengeti-cli-version_number JAR file, which includes the Serengeti Remote CLI Client.
nThe samples directory, which includes sample cluster configurations.
nLibraries in the lib directory.
4 Open a command shell, and change to the directory where you unzipped the package.
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5 Change to the cli directory, and run the following command to enter the Serengeti CLI.
nFor any language other than French or German, run the following command.
java -jar serengeti-cli-version_number.jar
nFor French or German languages, which use code page 850 (CP 850) language encoding when
running the Serengeti CLI from a Windows command console, run the following command.
java -Dfile.encoding=cp850 -jar serengeti-cli-version_number.jar
6 Connect to the Serengeti service.
You must run the connect host command every time you begin a CLI session, and again after the 30
minute session timeout. If you do not run this command, you cannot run any other commands.
a Run the connect command.
connect --host xx.xx.xx.xx:8443
b At the prompt, type your user name, which might be different from your login credentials for the
Serengeti Management Server.
NOTE If you do not create a user name and password for the
Serengeti Command-Line Interface Client, you can use the default vCenter Server administrator
credentials. The Serengeti Command-Line Interface Client uses the vCenter Server login credentials
with read permissions on the Serengeti Management Server.
c At the prompt, type your password.
A command shell opens, and the Serengeti CLI prompt appears. You can use the help command to get help
with Serengeti commands and command syntax.
nTo display a list of available commands, type help.
nTo get help for a specific command, append the name of the command to the help command.
help cluster create
nPress Tab to complete a command.
Chapter 2 Installing Big Data Extensions
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VMware vSphere Big Data Extensions Administrator's and User's Guide
34 VMware, Inc.
Upgrading Big Data Extensions 3
You can use VMware vSphere® Update Manager™ to upgrade Big Data Extensions from earlier versions.
This chapter includes the following topics:
n“Prepare to Upgrade Big Data Extensions,” on page 35
n“Upgrade Big Data Extensions Virtual Appliance,” on page 37
n“Upgrade the Big Data Extensions Plug-in,” on page 40
n“Upgrade the Serengeti CLI,” on page 41
n“Upgrade Big Data Extensions Virtual Machine Components by Using the Serengeti Command-Line
Interface,” on page 41
n“Add a Remote Syslog Server,” on page 42
Prepare to Upgrade Big Data Extensions
As a prerequisite to upgrading Big Data Extensions, you must prepare your system to ensure that you have
all necessary software installed and configured properly, and that all components are in the correct state.
Data from nonworking Big Data Extensions deployments is not migrated during the upgrade process. If
Big Data Extensions is not working and you cannot recover according to the troubleshooting procedures, do
not try to perform the upgrade. Instead, uninstall the previous Big Data Extensions components and install
the new version.
IMPORTANT Do not delete any files in the /opt/serengeti/.chef directory. If you delete any of these files,
such as the sernegeti.pem file, subsequent upgrades to Big Data Extensions might fail without displaying
error notifications.
Prerequisites
nInstall vSphere Update Manager. For more information, see the vSphere Update Manager
documentation.
nVerify that your previous Big Data Extensions deployment is working normally.
nVerify that you can create a default Hadoop cluster.
Procedure
1 Install vSphere Update Manager on a Windows Server.
nUse the same version of vSphere Update Manager as vCenter Server. For example, if you are using
vCenter Server 5.5, use vSphere Update Manager 5.5.
VMware, Inc. 35
nvSphere Update Manager requires network connectivity with vCenter Server. Each
vSphere Update Manager instance must be registered with a single vCenter Server instance.
2 Log in to vCenter Server with the vSphere Web Client.
3 Power on the Hadoop Template virtual machine.
4 If the Serengeti Management Server is configured to use a static IP network, make sure that the Hadoop
Template virtual machine receives a valid IP address.
You must have a valid IP address and be connected to the network for the Hadoop Template virtual
machine to connect to vSphere Update Manager.
5 Open a command shell and log in to the Serengeti Management Server as the user serengeti, and run
the following series of commands.
/usr/sbin/vmware-rpctool 'info-set guestinfo.vmware.vami.version 2010100'
/usr/sbin/vmware-rpctool 'info-set guestinfo.vmware.vami.features SUP'
/usr/sbin/vmware-rpctool 'info-set guestinfo.vmware.vami.https-port 5489'
/usr/sbin/vmware-rpctool 'info-set guestinfo.vmware.vami.http-port 5488'
/usr/sbin/vmware-rpctool 'info-set guestinfo.vmware.vami.https-
clientkeyfile /opt/vmware/etc/sfcb/file.pem'
/usr/sbin/vmware-rpctool 'info-set guestinfo.vmware.vami.https-
clienttruststorefile /opt/vmware/etc/sfcb/client.pem'
6 For each cluster that is in AUTO scaling mode, change the scaling mode to MANUAL.
a Open a command shell and log in to the Serengeti Management Server as the user serengeti.
b Set the scaling mode of the cluster to MANUAL and the --targetComputeNodeNum parameter value
to the number of provisioned compute nodes in the cluster.
cluster setParam --name cluster-name --elasticityMode manual --targetComputeNodeNum num-
provisioned-compute-nodes
7 Verify that all Hadoop clusters are in one of the following states:
nRUNNING
nSTOPPED
nCONFIGURE_ERROR
If the status of the cluster is PROVISIONING, wait for the process to finish and for the state of the
cluster to change to RUNNING.
8 Make sure that the host name of the Serengeti Management Server matches its fully qualified domain
name (FQDN).
9 Access the Admin view of vSphere Update Manager.
a Start vSphere Update Manager.
b On the Home page of the vSphere Web Client, select Hosts and Clusters.
c Click Update Manager.
d Open the Admin view.
Perform the upgrade tasks in the Admin view.
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Upgrade Big Data Extensions Virtual Appliance
You must perform several tasks to complete the upgrade of the Big Data Extensions virtual appliance.
Because the versions of the virtual appliance, the Serengeti CLI, and the Big Data Extensions plug-in must
all be the same, it is important that you upgrade all components to the new version.
Prerequisites
Complete the preparation steps for upgrading Big Data Extensions.
Procedure
1Configure Proxy Settings on page 37
You must have access to the Internet to upgrade your Big Data Extensions virtual appliance. If your
site uses a proxy server to access the Internet, you must configure vSphere Update Manager to use the
proxy server.
2Download the Upgrade Source and Accept the License Agreement on page 38
To start the Big Data Extensions upgrade process, you download the upgrade source and accept the
license agreement (EULA).
3Create an Upgrade Baseline on page 38
When you upgrade Big Data Extensions virtual appliances, you must create a custom virtual appliance
upgrade baseline.
4Specify Upgrade Compliance Settings on page 39
Upgrade compliance settings ensure that the upgrade baseline does not conflict with the current state
of your Big Data Extensions virtual appliance.
5Configure the Upgrade Remediation Task and Run the Upgrade Process on page 39
The upgrade remediation task is the process by which vSphere Update Manager applies patches,
extensions, and upgrades to the Big Data Extensions virtual appliance. You configure and run the
remediation task to finish the Big Data Extensions virtual appliance upgrade process.
6Replace the Hadoop Template Virtual Machine Under Big Data Extensions vApp on page 40
To complete upgrade process, you must manually replace the virtual machine template after you
upgrade the Big Data Extensions vApp.
Configure Proxy Settings
You must have access to the Internet to upgrade your Big Data Extensions virtual appliance. If your site uses
a proxy server to access the Internet, you must configure vSphere Update Manager to use the proxy server.
If you do not use a proxy server, continue to “Download the Upgrade Source and Accept the License
Agreement,” on page 38.
Prerequisites
Verify that you have obtained the values for the proxy server URL and port from your network
administrator.
Procedure
1 In the Admin view of vSphere Update Manager, click Configuration and then select Download
Settings.
2 In the Proxy Settings section, click Use proxy.
3 Enter the values for the proxy URL and port.
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4 Click Test Connection to ensure that the settings are correct.
5 If the settings are correct, click Apply.
vSphere Update Manager can now access the Web using the proxy server for your site.
Download the Upgrade Source and Accept the License Agreement
To start the Big Data Extensions upgrade process, you download the upgrade source and accept the license
agreement (EULA).
Prerequisites
Verify that you have the URL from which to download the upgrade source.
For information about the upgrade source and the URL where you can download the upgrade source, see
the VMware knowledge base article at http://kb.vmware.com/ and search on article number 1004543.
Procedure
1 In the Admin view of vSphere Update Manager, click Configuration and then select Download
Settings.
2 On the Download Settings page, click Add Download Source.
3 Enter the upgrade source URL in the Source URL text box.
4 Click Validate URL to verify connectivity to the upgrade URL.
5 Click OK to add the download source to vSphere Update Manager.
6 Click Apply.
7 Click Download Now.
8 On the VA Upgrades tab, select the upgrade.
9 Click EULA to accept the end user license agreement.
The upgrade source is downloaded.
Create an Upgrade Baseline
When you upgrade Big Data Extensions virtual appliances, you must create a custom virtual appliance
upgrade baseline.
Prerequisites
Verify that you are logged in to a vSphere Web Client as an administrator and that the vSphere Web Client
is connected to a vCenter Server system with which vSphere Update Manager is registered.
Procedure
1 On the Baselines and Groups tab, click VMs/Vas to review the existing baselines and groups.
2 Click Create.
3 Enter a meaningful name, such as Big Data Extensions VA Upgrade 1.5, and click Next.
4 Click Add Multiple Rules to create a set of rules that determine the target upgrade version for virtual
appliances.
5 Review the baseline settings and click Finish.
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Specify Upgrade Compliance Settings
Upgrade compliance settings ensure that the upgrade baseline does not conflict with the current state of
your Big Data Extensions virtual appliance.
Prerequisites
Verify that you are logged in to a vSphere Web Client as an administrator and that the vSphere Web Client
is connected to a vCenter Server system with which vSphere Update Manager is registered.
Procedure
1 In vSphere Web Client, navigate to VMs and Templates and click Upgrade Manager.
2 Open Compliance View and select the virtual appliance to upgrade.
3 Click Attach.
4 Select the upgrade baseline.
5 Click Attach again.
6 Verify that the virtual appliance needs to be updated.
a In the inventory list, right-click the baseline.
b Select Scan for Updates.
vSphere Update Manager scans the baseline against the virtual appliance and determines whether
the virtual appliance is up-to-date with the latest Big Data Extensions version. A
vSphere Update Manager scan result of 100 percent indicates that your Big Data Extensions version
is up-to-date.
What to do next
If the Big Data Extensions virtual appliance is up-to-date, discontinue the upgrade process. If the
Big Data Extensions virtual appliance is not up-to-date, continue the upgrade process.
Configure the Upgrade Remediation Task and Run the Upgrade Process
The upgrade remediation task is the process by which vSphere Update Manager applies patches, extensions,
and upgrades to the Big Data Extensions virtual appliance. You configure and run the remediation task to
finish the Big Data Extensions virtual appliance upgrade process.
Prerequisites
Verify that you logged in to a vSphere Web Client as an administrator and that the vSphere Web Client is
connected to a vCenter Server system with which vSphere Update Manager is registered.
NOTE The upgrade can take a few hours to finish.
Procedure
1 In the left pane of the VMs and Templates view, right-click the virtual appliance to upgrade and select
Remediate.
All virtual machines and appliances in the container are also remediated.
2 On the Remediation Selection page of the Remediate wizard, select the baseline group and upgrade
baselines to apply.
3 Select the virtual machines and appliances that you want to remediate and click Next.
4 On the Schedule page, enter a unique name and an optional description for the task.
Chapter 3 Upgrading Big Data Extensions
VMware, Inc. 39
5 Select Immediately to begin the upgrade process immediately after the configuration is finished and
click Next.
6 Configure the rollback options.
7 Specify the snapshot backup to roll back to and click Next.
8 Review the task definition and click Finish.
Big Data Extensions restarts when the upgrade remediation task finishes.
9 Verify that the Big Data Extensions virtual appliance upgrade was successful.
You can view the version of the Big Data Extensions virtual appliance in the vCenter client.
Some upgrade process errors are written to the Serengeti virtual appliance deployment logs in
vCenter Server rather than appearing as error messages.
Replace the Hadoop Template Virtual Machine Under Big Data Extensions vApp
To complete upgrade process, you must manually replace the virtual machine template after you upgrade
the Big Data Extensions vApp.
Procedure
1 Download the Big Data Extensions template OVA file from the Big Data Extensions download page.
2 Login to the vSphere Client.
3 Delete the original hadoop-template virtual machine under the Big Data Extensions vApp or move it
out of the Big Data Extensions vApp which has been upgraded.
4 Select File > Deploy OVA Template.
5 Enter the downloaded template OVA path in the Deploy OVA Template dialog.
6 Complete the steps in the OVA Deployment wizard.
On the step to configure the name and location, enter hadoop-template or any other valid name. On the
step to configure the resource pool, select the Big Data Extensions vApp which has been upgraded.
7 Login to the Big Data Extensions server via SSH.
8 Restart Big Data Extensions Web services: sudo service tomcat restart
Upgrade the Big Data Extensions Plug-in
You must use the same version of the Serengeti Management Server and the Big Data Extensions plug-in.
By default, the Big Data Extensions Web plug-in automatically installs and registers with the
Serengeti Management Server when you deploy the Big Data Extensions vApp. If you chose not to install
and register the Big Data Extensions Web plug-in when installing the Big Data Extensions vApp, you must
perform this task to upgrade the plug-in.
Procedure
1 Open a Web browser and go to the URL of the Serengeti Management Server plug-in manager service.
https://management-server-ip-address:8443/register-plugin
2 Select Uninstall and click Submit.
3 Select Install.
4 Enter the information to register the new plug-in, and click Submit.
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Upgrade the Serengeti CLI
The Serengeti CLI must be the same version as your Big Data Extensions deployment. If you run the CLI
remotely to connect to the management server, you must upgrade the Serengeti CLI.
Procedure
1 Log in to the vSphere Web Client.
2 Select Big Data Extensions from the navigation panel.
3 Click the Summary tab.
4 In the Connected Server panel, click Connect Server.
5 Select the Serengeti Management Server virtual machine in the Big Data Extensions vApp to which you
want to connect and click OK.
6 Click the Getting Started tab, and click Download Serengeti CLI Console.
A ZIP file containing the Serengeti CLI Client downloads to your computer.
7 Unzip and examine the ZIP file, which includes the following components in the CLI directory:
nThe serengeti-cli-version JAR file, which includes the Serengeti CLI Client.
nThe samples directory, which includes sample cluster configurations.
nLibraries in the lib directory.
8 Open a command shell and navigate to the directory where you unzipped the Serengeti CLI Client
download package.
9 Change to the CLI directory, and run the following command to open the Serengeti CLI Client:
java -jar serengeti-cli-version.jar
What to do next
1 If your clusters are deployed with a Hadoop Template virtual machine that has a customized version of
the CentOS 6.x operating system that includes VMware Tools, you must customize a new CentOS 6.x
template to use after you upgrade Big Data Extensions.
2 To enable the Serengeti Management Server to manage clusters that you created in a previous version
of Big Data Extensions, you must upgrade each cluster.
Upgrade Big Data Extensions Virtual Machine Components by Using
the Serengeti Command-Line Interface
To enable the Serengeti Management Server to manage clusters created in a previous version of Big Data
Extensions, you must upgrade the components in the virtual machines of each cluster. The Serengeti
Management Server uses these components to control the cluster nodes.
When you upgrade from an earlier version of Big Data Extensions, clusters that you need to upgrade are
shown with an alert icon next to the cluster name. When you click the alert icon the error message "Upgrade
the cluster to the latest version" displays as a tool tip. See “View Provisioned Clusters in the vSphere Web
Client,” on page 125.
You can also identify clusters you need to upgrade using the cluster list command. When you run the
cluster list command, the message "Earlier" displays where the cluster version normally appears.
Prerequisites
nYou must be upgrading a cluster that was created with a previous version of Big Data Extensions.
Chapter 3 Upgrading Big Data Extensions
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nEnsure that you have applied the security patch BDE-2.0.0-Patch1–bash.tar.
For more information on the security patch BDE-2.0.0-Patch1–bash.tar, see the VMware knowledge
base article at http://kb.vmware.com/kb/2091050.
Procedure
1 For each cluster that you created in a previous version of Big Data Extensions, make sure that all of the
nodes of a cluster are in the following states: RUNNING, STOPPED, ERROR, CONFIGURE_ERROR
and UPGRADE_ERROR.
If a node does not have a valid IP address, it cannot be upgraded to the new version of
Big Data Extensions virtual machine tools.
a Log into the vSphere Web Client that is connected to vCenter Server and navigate to Hosts and
Clusters.
b Select the resource pool of the cluster, select the Virtual Machines tab, and power on the cluster's
virtual machines.
IMPORTANT It might take up to five minutes for vCenter Server to assign valid IP addresses to the Big
Data cluster nodes. Do not perform the remaining upgrade steps until the nodes have received their IP
addresses.
2 Open a command shell, such as Bash or PuTTY, and log in to the Serengeti Management Server as user
serengeti.
3 Run the cluster upgrade command for each cluster that was created with a previous version of
Big Data Extensions.
4 If the upgrade fails for a node, make sure that the failed node has a valid IP address and then rerun the
cluster upgrade command.
You can rerun the command as many times as you need to upgrade all the nodes.
What to do next
Stop and restart your big data clusters.
Add a Remote Syslog Server
If you wish to use a remote syslog server after upgrading from earlier versions of Big Data Extensions, you
must manually specify the remote syslog server you wish to use.
The retention, rotation and splitting of logs received and managed by a syslog server are controlled by that
syslog server. Big Data Extensions cannot configure or control log management on a remote syslog server.
For more information on log management, see the documentation for your syslog server.
Prerequisites
nSuccessfully upgrade to the current release of Big Data Extensions.
nHave a remote syslog server within your environment that Big Data Extensions can send logging
information to.
Procedure
1 Open a command shell, such as Bash or PuTTY, and log in to the Serengeti Management Server as the
user serengeti.
2 Open the file /etc/rsyslog.d/20-base.conf in a text editor.
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42 VMware, Inc.
3 Edit the file to include the remote syslog service information.
*.* @syslog_ip_address:port_number
4 Restart the syslog service.
service rsyslog restart
Your upgraded Big Data Extensions deployment will send logging information to the remote syslog service
you specify.
NOTE Regardless of the additional syslog configuration specified with this procedure, logs continue to be
placed in the default locations of the Big Data Extensions environment. See “Log Files for Troubleshooting,”
on page 140.
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44 VMware, Inc.
Managing Application Managers 4
A key to managing your Hadoop clusters is understanding how to manage the different application
managers that you use in your Big Data Extensions environment.
This chapter includes the following topics:
n“Add an Application Manager by Using the vSphere Web Client,” on page 45
n“Modify an Application Manager by Using the Web Client,” on page 46
n“Delete an Application Manager by Using the vSphere Web Client,” on page 46
n“View Application Managers and Distributions by Using the Web Client,” on page 46
n“View Roles for Application Manager and Distribution by Using the Web Client,” on page 46
Add an Application Manager by Using the vSphere Web Client
To use either Cloudera Manager or Ambari application managers to manage clusters, you must add the
application manager and add server information to Big Data Extensions.
Application manager names can include only alphanumeric characters ([0-9, a-z, A-Z]) and the following
special characters; underscores, hyphens, and blank spaces.
Procedure
1 On the Big Data Extensions navigation pane, click Application Managers.
2 Click the Add Application Manager icon (+) at the top of the page to open the New Application
Manager wizard.
3 Follow the prompts to complete the installation of the application manager.
You can use either http or https.
Option Action
Use http Enter the server URL with http. The SSL certification text box is disabled.
Use https Enter the FQDN instead of the URL. The SSL certification text box is
enabled.
The vSphere Web UI refreshes the Application Manager list and displays it in the List view.
VMware, Inc. 45
Modify an Application Manager by Using the Web Client
You can modify the information for an application manager, for example, you can change the manager
server IP address if it is not a static IP, or you can upgrade the administrator account.
Prerequisites
Verify that you have at least one external application manager installed on your Big Data Extensions
environment.
Procedure
1 In the vSphere Web Client, click Application Managers in the navigation menu.
2 From the Application Managers list, right-click the application manager to modify and select edit
settings.
3 In the Edit Application Manager dialog box, make the changes to the application manager and click
OK.
Delete an Application Manager by Using the vSphere Web Client
You can delete an application manager with the vSphere Web Client when you no longer need it.
The process fails if the application manager you want to delete contains clusters.
Prerequisites
Verify that you have at least one external application manager installed in your Big Data Extensions
environment.
Procedure
1 In the vSphere Web Client, click Application Managers in the navigation pane.
2 Right-click the application manager to delete and select Delete.
The application manager is removed from the Application Managers list panel.
View Application Managers and Distributions by Using the Web Client
You can view a list of the application managers and distributions that are currently being used in your
Big Data Extensions environment.
Procedure
uFrom Big Data Extensions, click Application Managers from the Inventory Lists.
A list opens that contains the distributions, descriptions, application managers, and how many clusters
are managed by your Big Data Extensions environment.
View Roles for Application Manager and Distribution by Using the
Web Client
You can use the Application Managers pane to view a list and the details of the Hadoop roles for a specific
application manager and distribution.
Procedure
1 From Big Data Extensions, click Inventory Lists > Application Managers.
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2 Select the application manager for which you want to view details.
The details pane opens that contains a list of supported distributions with the name, vendor, version
and roles of the distribution.
Chapter 4 Managing Application Managers
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48 VMware, Inc.
Managing Hadoop Distributions 5
The Serengeti Management Server includes the Apache Bigtop distribution, but you can add any supported
Hadoop distribution to your Big Data Extensions environment.
Procedure
1Hadoop Distribution Deployment Types on page 50
You can choose which Hadoop distribution to use when you deploy a cluster. The type of distribution
you choose determines how you configure it for use with Big Data Extensions. When you deploy the
Big Data Extensions vApp, the Bigtop 0.8.0 distribution is included in the OVA that you download
and deploy.
2Configure a Tarball-Deployed Hadoop Distribution by Using the Serengeti Command-Line Interface
on page 50
You can add and configure Hadoop distributions other than those included with the Big Data
Extensions vApp using the command line. You can configure multiple Hadoop distributions from
different vendors.
3Configuring Yum and Yum Repositories on page 52
You can deploy Cloudera CDH4 and CDH5, Apache Bigtop, MapR, and Pivotal PHD Hadoop
distributions using Yellowdog Updater, Modified (yum). Yum enables automatic updates and
package management of RPM-based software distributions. To deploy a Hadoop distribution using
yum, you must create and configure a yum repository.
4Create a Hadoop Template Virtual Machine using RHEL Server 6.x and VMware Tools on page 69
You can create a Hadoop Template virtual machine that has a customized version of the Red Hat
Enterprise Linux (RHEL) Server 6.x operating system that includes VMware Tools. Although only a
few Hadoop distributions require a custom version of RHEL Server 6.x, you can customize RHEL
Server 6.x for any Hadoop distribution.
5Maintain a Customized Hadoop Template Virtual Machine on page 73
You can modify or update the Hadoop Template virtual machine operating system. When you make
updates, you must remove the snapshot that is created by the virtual machine.
VMware, Inc. 49
Hadoop Distribution Deployment Types
You can choose which Hadoop distribution to use when you deploy a cluster. The type of distribution you
choose determines how you configure it for use with Big Data Extensions. When you deploy the
Big Data Extensions vApp, the Bigtop 0.8.0 distribution is included in the OVA that you download and
deploy.
Depending on which Hadoop distribution you want to configure to use with Big Data Extensions, use either
a tarball or yum repository to install your distribution. The table lists the supported Hadoop distributions,
the distribution name, vendor abbreviation, and version number to use as input parameters when you
configure the distribution for use with Big Data Extensions.
Table 5‑1. Hadoop Deployment Types
Hadoop Distribution Version Number
Vendor
Abbreviation Deployment Type HVE Support?
Bigtop 0.8 BIGTOP Yum No
Pivotal HD 3.0 PHD Yum Yes
Hortonworks Data Platform 2.1.1 - 2.2 HDP Yum No
Cloudera 5.3, 5.4 CDH Yum No
MapR 4.0, 4.1 MAPR Yum No
About Hadoop
Virtualization
Extensions
Hadoop Virtualization Extensions (HVE), developed by VMware, improves
Hadoop performance in virtual environments by enhancing Hadoop’s
topology awareness mechanism to account for the virtualization layer.
Configure Hadoop 2.x
and Later Distributions
with DNS Name
Resolution
When you create clusters using Hadoop distributions based on Hadoop 2.0
and later, the DNS server in your network must provide forward and reverse
FQDN/IP resolution. Without valid DNS and FQDN settings, the cluster
creation process might fail, or the cluster is created but does not function.
Hadoop distributions based on Hadoop 2.x and later include Apache Bigtop,
Cloudera CDH4 and CDH5, Hortonworks HDP 2.x, and Pivotal PHD 1.1 and
later releases.
Configure a Tarball-Deployed Hadoop Distribution by Using the
Serengeti Command-Line Interface
You can add and configure Hadoop distributions other than those included with the Big Data Extensions
vApp using the command line. You can configure multiple Hadoop distributions from different vendors.
Refer to your Hadoop distribution vendor's Web site to obtain the download URLs to use for the
components that you want to install. If you are behind a firewall, you might need to modify your proxy
settings to allow the download. Before you install and configure tarball-based deployments, ensure that you
have the vendor's URLs from which to download the different Hadoop components. Use these URLs as
input parameters to the config-distro.rb configuration utility.
If you have a local Hadoop distribution and your server does not have access to the Internet, you can
manually upload the distribution.
Prerequisites
nDeploy the Big Data Extensions vApp.
nReview the different Hadoop distributions so you know which distribution name abbreviation, vendor
name abbreviation, and version number to use as an input parameter, and whether the distribution
supports Hadoop Virtualization Extension (HVE).
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n(Optional) Set the password for the Serengeti Management Server.
Procedure
1 Open a command shell, such as Bash or PuTTY, and log in to the Serengeti Management Server as user
serengeti.
2 Run the /opt/serengeti/sbin/config-distro.rb Ruby script.
config-distro.rb --name distro_name --vendor vendor_name --version version_number
--hadoop hadoop_package_url --pig pig_package_url --hive hive_package_url
--hbase hbase_package_url --zookeeper zookeeper_package_URL --hve {true | false} --yes
Option Description
--name Name to identify the Hadoop distribution that you are downloading. For
example, hdp for Hortonworks. This name can include alphanumeric
characters ([a-z], [A-Z], [0-9]) and underscores ("_").
--vendor Vendor name whose Hadoop distribution you want to use. For example,
HDP for Hortonworks.
--version Version of the Hadoop distribution that you want to use. For example,
1.3.
--hadoop URL from which to download the Hadoop distribution tarball package
from the Hadoop vendor's Web site.
--pig URL from which to download the Pig distribution tarball package from the
Hadoop vendor's Web site.
--hive URL from which to download the Hive distribution tarball package from
the Hadoop vendor's Web site.
--hbase (Optional) URL from which to download the HBase distribution tarball
package from the Hadoop vendor's Web site.
--zookeeper (Optional) URL from which to download the ZooKeeper distribution
tarball package from the Hadoop vendor's Web site.
--hve {true | false} (Optional) Specifies whether the Hadoop distribution supports HVE
--yes (Optional) Specifies that all confirmation prompts from the config-
distro.rb script are answered with a "yes" response.
The example downloads the tarball version of Hortonworks Data Platform (HDP), which consists of
Hortonworks Hadoop, Hive, HBase, Pig, and ZooKeeper distributions. Note that you must provide the
download URL for each of the software components you wish to configure for use with
Big Data Extensions.
config-distro.rb --name hdp --vendor HDP --version 1.3.2
--hadoop http://public-repo-1.hortonworks.com/HDP/centos6/1.x/updates/1.3.2.0/tars/
hadoop-1.2.0.1.3.2.0-111.tar.gz
--pig http://public-repo-1.hortonworks.com/HDP/centos6/1.x/updates/1.3.2.0/tars/
pig-0.11.1.1.3.2.0-111.tar.gz
--hive http://public-repo-1.hortonworks.com/HDP/centos6/1.x/updates/1.3.2.0/tars/
hive-0.11.0.1.3.2.0-111.tar.gz
--hbase http://public-repo-1.hortonworks.com/HDP/centos6/1.x/updates/1.3.2.0/tars/
hbase-0.94.6.1.3.2.0-111-security.tar.gz
--zookeeper http://public-repo-1.hortonworks.com/HDP/centos6/1.x/updates/1.3.2.0/tars/
zookeeper-3.4.5.1.3.2.0-111.tar.gz
--hve true
The script downloads the files.
Chapter 5 Managing Hadoop Distributions
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3 When the download finishes, explore the /opt/serengeti/www/distros directory, which includes the
following directories and files.
Item Description
name Directory that is named after the distribution. For example, apache.
manifest The manifest file that is generated by config-distro.rb that is used to
download the Hadoop distribution.
manifest.example Example manifest file. This file is available before you perform the
download. The manifest file is a JSON file with three sections: name,
version, and packages.
4 To enable Big Data Extensions to use the added distribution, restart the tomcat service.
sudo /sbin/service tomcat restart
The Serengeti Management Server reads the revised manifest file and adds the distribution to those
from which you can create a cluster.
5 Return to the Big Data Extensions Plug-in for vSphere Web Client, and click Hadoop Distributions to
verify that the Hadoop distribution is available to use to create a cluster.
The distribution and the corresponding role appear.
The distribution is added to the Serengeti Management Server, but is not installed in the Hadoop Template
virtual machine. The agent is preinstalled on each virtual machine that copies the distribution components
that you specify from the Serengeti Management Server to the nodes during the Hadoop cluster creation
process.
What to do next
You can add datastore and network resources for the Hadoop clusters that you create.
You can create and deploy big data clusters using your chosen Hadoop distribution.
Configuring Yum and Yum Repositories
You can deploy Cloudera CDH4 and CDH5, Apache Bigtop, MapR, and Pivotal PHD Hadoop distributions
using Yellowdog Updater, Modified (yum). Yum enables automatic updates and package management of
RPM-based software distributions. To deploy a Hadoop distribution using yum, you must create and
configure a yum repository.
nYum Repository Configuration Values on page 53
To create a local yum repository, you create a configuration file that identifies the file and package
names of a distribution to download and deploy. When you create the configuration file, you replace a
set of placeholder values with values that correspond to your Hadoop distribution. The yum
repositories are used to install or update Hadoop software on CentOS and other operating systems
that use Red Hat Package Manager (RPM).
nSetup a Local Yum Repository for Apache Bigtop, Cloudera, Hortonworks, and MapR Hadoop
Distributions on page 56
Although publicly available yum repositories exist for Ambari, Apache Bigtop, Cloudera,
Hortonworks, and MapReduce distributions, creating your own yum repository can result in faster
download times and greater control over the repository.
VMware vSphere Big Data Extensions Administrator's and User's Guide
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nSetup a Local Yum Repository for the Pivotal Hadoop Distribution on page 58
Pivotal does not provide a publicly accessible yum repository from which you can deploy and
upgrade the Pivotal Hadoop software distribution. Therefore, you might want to download the
Pivotal software tarballs and create your own yum repository for Pivotal which provides you with
better access and control over installing and updating your Pivotal HD distribution software.
nConfigure a Yum-Deployed Hadoop Distribution on page 60
You can install Hadoop distributions that use yum repositories (as opposed to tarballs) for use with
Big Data Extensions. When you create a cluster for a yum-deployed Hadoop distribution, the Hadoop
nodes download and install Red Hat Package Manager (RPM) packages from the official yum
repositories for a particular distribution or your local yum repositories.
nSet Up a Local Yum Repository for Cloudera Manager Application Manager on page 61
When you create a new cluster with an external application manager, you must install agents and
distribution packages on each cluster node. If the installation downloads the agents and packages
from the Internet, the process might be slow. If you do not have an Internet connection, the cluster
creation process is not possible. To avoid these problems, you can create a local yum repository.
nSet Up a Local Yum Repository for Ambari Application Manager on page 64
When you create a new cluster with an external application manager, you must install agents and
distribution packages on each cluster node. If the installation downloads the agents and packages
from the Internet, the process might be slow. If you do not have an Internet connection, the cluster
creation process is impossible. To avoid these problems, you can create a local yum repository.
Yum Repository Configuration Values
To create a local yum repository, you create a configuration file that identifies the file and package names of
a distribution to download and deploy. When you create the configuration file, you replace a set of
placeholder values with values that correspond to your Hadoop distribution. The yum repositories are used
to install or update Hadoop software on CentOS and other operating systems that use Red Hat Package
Manager (RPM).
The following tables list the values to use for the Ambari, Apache Bigtop, Cloudera, Hortonworks, MapR,
and Pivotal distributions.
NOTE If you copy-and-paste values from the table, be sure to include all required information. Some values
appear on two lines in the table, for example, "maprtech maprecosystem", and they must be combined into a
single line when you use them.
Apache Bigtop Yum Repository Configuration Values
Table 5‑2. Apache Bigtop Yum Repository Placeholder Values
Placeholder Value
repo_file_name bigtop.repo
package_info [bigtop]
name=Bigtop
enabled=1
gpgcheck=1
type=NONE
baseurl=http://bigtop.s3.amazonaws.com/releases/0.7.0/redhat/6/x86_64
gpgkey=http://archive.apache.org/dist/bigtop/KEYS
NOTE If you use a version other than 0.7.0, use the exact version number of your Apache
Bigtop distribution in the pathname.
mirror_cmds reposync -r bigtop
Chapter 5 Managing Hadoop Distributions
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Table 5‑2. Apache Bigtop Yum Repository Placeholder Values (Continued)
Placeholder Value
default_rpm_dir bigtop
target_rpm_dir bigtop
local_repo_info [bigtop]
name=Apache Bigtop
baseurl=http://ip_of_yum_repo_webserver/bigtop/
enabled=1
gpgcheck=0
Cloudera Yum Repository Configuration Values
Table 5‑3. Cloudera Yum Repository Placeholder Values
Placeholder Value
repo_file_name cloudera-cdh.repo
package_info If you use CDH4, use the values below.
[cloudera-cdh]
name=Cloudera's Distribution for Hadoop
http://archive.cloudera.com/cdh4/redhat/6/x86_64/cdh/4/
gpkey=http://archive.cloudera.com/cdh4/redhat/6/x86_64/cdh/RPM-GPG-KEY-cloudera
gpgcheck=1
If you use CDH5, use the values below.
[cloudera-cdh]
name=Cloudera's Distribution for Hadoop
baseurl=http://archive.cloudera.com/cdh5/redhat/6/x86_64/cdh/5/
gpgkey=http://archive.cloudera.com/cdh5/redhat/6/x86_64/cdh/RPM-GPG-KEY-cloudera
gpgcheck=1
mirror_cmds reposync -r cloudera-cdh4
default_rpm_dir cloudera-cdh/RPMS
target_rpm_dir cdh/version_number
local_repo_info [cloudera-cdh]
name=Cloudera's Distribution for Hadoop
baseurl=http://ip_of_yum_repo_webserver/cdh/version_number/
enabled=1
gpgcheck=0
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Hortonworks Yum Repository Configuration Values
Table 5‑4. Hortonworks Yum Repository Placeholder Values
Placeholder Value
repo_file_name hdp.repo
package_info [hdp]
name=Hortonworks Data Platform Version - HDP-2.1.1.0
baseurl=http://public-repo-1.hortonworks.com/HDP/centos6/2.x/GA/2.1.1.0
gpgcheck=1
gpgkey=http://public-repo-1.hortonworks.com/HDP/centos6/2.x/GA/2.1.1.0/RPM-GPG-
KEY/RPM-GPG-KEY-Jenkins
enabled=1
priority=1
NOTE If you use a version other than HDP 2.1.1.0, use the exact version number of your
Hortonworks distribution in the pathname.
mirror_cmds reposync -r hdp
default_rpm_dir hdp
target_rpm_dir hdp/2
local_repo_info [hdp]
name=Hortonworks Data Platform Version -HDP-2.1.1.0
baseurl=http://ip_of_yum_repo_webserver/hdp/2/
enabled=1
gpgcheck=0
MapR Yum Repository Configuration Values
Table 5‑5. MapR Yum Repository Placeholder Values
Placeholder Value
repo_file_name mapr.repo
package_info [maprtech]
name=MapR Technologies
baseurl=http://package.mapr.com/releases/3.1.0/redhat/
enabled=1
gpgcheck=0
protect=1
[maprecosystem]
name=MapR Technologies
baseurl=http://package.mapr.com/releases/ecosystem/redhat
enabled=1
gpgcheck=0
protect=1
NOTE If you use a version other than 3.1.0, use the exact version number of your MapR
distribution in the pathname.
mirror_cmds reposync -r maprtech
reposync -r maprecosystem
default_rpm_dir maprtech maprecosystem
Chapter 5 Managing Hadoop Distributions
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Table 5‑5. MapR Yum Repository Placeholder Values (Continued)
Placeholder Value
target_rpm_dir mapr/3
local_repo_info [mapr]
name=MapR Version 3
baseurl=http://ip_of_yum_repo_webserver/mapr/3/
enabled=1
gpgcheck=0
protect=1
Pivotal Yum Repository Configuration Values
Table 5‑6. Pivotal Yum Repository Placeholder Values
Placeholder Value
repo_file_name phd.repo
package_info Not Applicable
mirror_cmds Not Applicable
default_rpm_dir pivotal
target_rpm_dir phd/1
local_repo_info [pivotalhd]
name=PHD Version 1.0
baseurl=http://ip_of_yum_repo_webserver/phd/1/
enabled=1
gpgcheck=0
Setup a Local Yum Repository for Apache Bigtop, Cloudera, Hortonworks, and
MapR Hadoop Distributions
Although publicly available yum repositories exist for Ambari, Apache Bigtop, Cloudera, Hortonworks, and
MapReduce distributions, creating your own yum repository can result in faster download times and
greater control over the repository.
Prerequisites
nHigh-speed Internet access.
nCentOS 6.x 64-bit or Red Hat Enterprise Linux (RHEL) 6.x 64-bit.
The hadoop-template virtual machine in the Serengeti vApp contains CentOS 6.5 64-bit. You can clone
the hadoop-template virtual machine to a new virtual machine and create the yum repository on it.
nAn HTTP server with which to create the yum repository. For example, Apache HTTP server.
nIf there is a firewall on your system, ensure that the firewall does not block the network port number
used by your HTTP server proxy. Typically, this is port 80.
nRefer to the yum repository placeholder values to populate the variables required in the steps. See
“Yum Repository Configuration Values,” on page 53.
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Procedure
1 If your yum repository server requires an HTTP proxy server, open a command shell, such as Bash or
PuTTY, log in to the yum repository server, and run the following commands to export the http_proxy
environment variable.
# switch to root user
sudo su
export http_proxy=http://host:port
Option Description
host The hostname or the IP address of the proxy server.
port The network port number to use with the proxy server.
2 Install the HTTP server that you want to use as a yum server.
This example installs the Apache HTTP Server and enables the httpd server to start whenever the
machine is restarted.
yum install -y httpd
/sbin/service httpd start
/sbin/chkconfig httpd on
3 Install the yum-utils and createrepo packages.
The yum-utils package contains the reposync command.
yum install -y yum-utils createrepo
4 Synchronize the yum server with the official yum repository of your preferred Hadoop vendor.
a Using a text editor, create the file /etc/yum.repos.d/$repo_file_name.
b Add the package_info content to the new file.
c Mirror the remote yum repository to the local machine by running the mirror_cmds for your
distribution packages.
It might take several minutes to download the RPMs from the remote repository. The RPMs are
placed in the $default_rpm_dir directories.
5 Create the local yum repository.
a Move the RPMs to a new directory under the Apache HTTP Server document root.
The default document root is /var/www/html/.
doc_root=/var/www/html
mkdir -p $doc_root/$target_rpm_dir
mv $default_rpm_dir $doc_root/$target_rpm_dir/
For example, the mv command for the MapR Hadoop distribution is the following:
mv maprtech maprecosystem $doc_root/mapr/3/
b Create a yum repository for the RPMs.
cd $doc_root/$target_rpm_dir
createrepo .
c Create a new file, $doc_root/$target_rpm_dir/$repo_file_name, and include the local_repo_info.
d From a different machine, ensure that you can download the repository file from
http://ip_of_webserver target_rpm_dir//repo_file_name.
Chapter 5 Managing Hadoop Distributions
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6 (Optional) Configure HTTP proxy.
If the virtual machines created by the Serengeti Management Server do not need an HTTP proxy to
connect to the local yum repository, skip this step.
On the Serengeti Management Server, edit the /opt/serengeti/conf/serengeti.properties file and add
the following content anywhere in the file or replace existing items:
# set http proxy server
serengeti.http_proxy = http://<proxy_server:port>
# set the FQDNs (or IPs if no FQDN) of the Serengeti Management Server and the
local yum repository servers for 'serengeti.no_proxy'.
The wildcard for matching multi IPs doesn't work.
serengeti.no_proxy = serengeti_server_fqdn_or_ip.
yourdomain.com, yum_server_fqdn_or_ip.
yourdomain.com
What to do next
Configure your Apache Bigtop, Cloudera, Hortonworks, or MapR deployment for use with Big Data
Extensions. See “Configure a Yum-Deployed Hadoop Distribution,” on page 60.
Setup a Local Yum Repository for the Pivotal Hadoop Distribution
Pivotal does not provide a publicly accessible yum repository from which you can deploy and upgrade the
Pivotal Hadoop software distribution. Therefore, you might want to download the Pivotal software tarballs
and create your own yum repository for Pivotal which provides you with better access and control over
installing and updating your Pivotal HD distribution software.
Pivotal does not provide a publicly accessible yum repository from which you can deploy and upgrade the
Pivotal Hadoop software distribution. You might want to download the Pivotal software tarballs, and create
your own yum repository from which to deploy and configure the Pivotal Hadoop software.
Prerequisites
nHigh-speed Internet access.
nCentOS 6.x 64-bit or Red Hat Enterprise Linux (RHEL) 6.x 64-bit.
The hadoop-template virtual machine in the Big Data Extensions vApp contains CentOS 6.5 64-bit. You
can clone the hadoop-template virtual machine to a new virtual machine and create the yum repository
on it.
NOTE Because the Pivotal Hadoop distribution requires CentOS 6.2 64-bit version or 6.4 64-bit version
(x86_64), the yum server that you create to deploy the distribution must also use a CentOS 6.x 64-bit
operating system.
nAn HTTP server with which to create the yum repository. For example, Apache HTTP server.
nIf there is a firewall on your system, ensure that the firewall does not block the network port number
used by your HTTP server proxy. Typically, this is port 80.
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Procedure
1 If your yum repository server requires an HTTP proxy server, open a command shell, such as Bash or
PuTTY, log in to the yum repository server, and run the following commands to export the http_proxy
environment variable.
# switch to root user
sudo su
export http_proxy=http://host:port
Option Description
host The hostname or the IP address of the proxy server.
port The network port number to use with the proxy server.
2 Install the HTTP server that you want to use with a yum server.
This example installs the Apache HTTP Server and enables the httpd server to start whenever the
machine is restarted.
yum install -y httpd
/sbin/service httpd start
/sbin/chkconfig httpd on
3 Install the yum-utils and createrepo packages.
The yum-utils package includes the reposync command.
yum install -y yum-utils createrepo
4 Download the Pivotal HD 1.0 or 2.0 tarball from the Pivotal Web site.
5 Extract the tarball that you downloaded.
The tarball name might vary if you download a different version of Pivotal HD.
tar -xf phd_1.0.1.0-19_community.tar
6 Extract PHD_1.0.1_CE/PHD-1.0.1.0-19.tar to the default_rpm_dir directory.
For Pivotal Hadoop the default_rpm_dir directory is pivotal.
The version numbers of the tar that you extract might be different from those used in the example if an
update has occurred.
tar -xf PHD_1.0.1_CE/PHD-1.0.1.0-19.tar -C pivotal
7 Create and configure the local yum repository.
a Move the RPMs to a new directory under the Apache HTTP Server document root.
The default document root is /var/www/html/.
doc_root=/var/www/html
mkdir -p $doc_root/$target_rpm_dir
mv $default_rpm_dir $doc_root/$target_rpm_dir/
This example moves the RPMs for the Pivotal Hadoop distribution.
mv pivotal $doc_root/phd/1/
b Create a yum repository for the RPMs.
cd $doc_root/$target_rpm_dir
createrepo .
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c Create a file, $doc_root/$target_rpm_dir/$repo_file_name, and include the local_repo_info.
d From a different machine, ensure that you can download the repository file from
http://ip_of_webserver/$target_rpm_dir/$repo_file_name.
8 (Optional) Configure an HTTP proxy.
If the virtual machines created by the Serengeti Management Server do not need an HTTP proxy to
connect to the local yum repository, skip this step.
On the Serengeti Management Server, edit the file/opt/serengeti/conf/serengeti.properties, and add
the following content anywhere in the file or replace existing items:
# set http proxy server
serengeti.http_proxy = http://<proxy_server:port>
# set the FQDNs (or IPs if no FQDN) of the Serengeti Management Server and the
local yum repository servers for 'serengeti.no_proxy'.
The wildcard for matching multi IPs doesn't work.
serengeti.no_proxy = serengeti_server_fqdn_or_ip.
yourdomain.com, yum_server_fqdn_or_ip.yourdomain.com
Configure a Yum-Deployed Hadoop Distribution
You can install Hadoop distributions that use yum repositories (as opposed to tarballs) for use with
Big Data Extensions. When you create a cluster for a yum-deployed Hadoop distribution, the Hadoop nodes
download and install Red Hat Package Manager (RPM) packages from the official yum repositories for a
particular distribution or your local yum repositories.
Prerequisites
nReview the different Hadoop distributions so that you know which distribution name, vendor
abbreviation, and version number to use as an input parameter, and whether the distribution supports
Hadoop Virtualization Extensions.
nCreate a local yum repository for your Hadoop distribution. Creating your own repository can result in
better access and more control over the repository.
Procedure
1 Open a command shell, such as Bash or PuTTY, and log in to the Serengeti Management Server as user
serengeti.
2 Run the /opt/serengeti/sbin/config-distro.rb Ruby script.
config-distro.rb --name distro_name --vendor vendor_abbreviation --version ver_number
--repos http://url_to_yum_repo/name.repo
Option Description
--name Name to identify the Hadoop distribution that you are downloading. For
example, chd4 for Cloudera CDH4. This name can include alphanumeric
characters ([a-z], [A-Z], [0-9]) and underscores ("_").
--vendor Abbreviation of vendor name whose Hadoop distribution you want to use.
For example, CDH.
--version Version of the Hadoop distribution that you want to use. For example,
4.6.0.
--repos URL from which to download the Hadoop distribution yum package. This
URL can be a local yum repository that you create or a publicly accessible
yum repository hosted by the software vendor.
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This example adds the Apache Bigtop Hadoop Distribution to Big Data Extensions.
config-distro.rb --name bigtop --vendor BIGTOP --version 0.8.0
--repos http://url_to_yum_repo/bigtop.repo
The example adds the Cloudera CDH4 Hadoop distribution to Big Data Extensions.
config-distro.rb --name cdh4 --vendor CDH --version 4.6.0 --repos
http://url_to_yum_repo/cloudera-cdh4.repo
NOTE The config-distro.rb script downloads files only for tarball-deployed distributions. No files are
downloaded for yum-deployed distributions.
This example adds the Hortonworks Hadoop Distribution to Big Data Extensions.
config-distro.rb --name hdp --vendor HDP --version 2.1.1
--repos http://url_to_yum_repo/hdp.repo
The example adds the MapR Hadoop distribution to Big Data Extensions.
config-distro.rb --name mapr --vendor MAPR --version 3.1.0 --repos
http://url_to_yum_repo/mapr.repo
This example adds the Pivotal Hadoop Distribution to Big Data Extensions.
config-distro.rb --name phd --vendor PHD --version 2.0
--repos http://url_to_yum_repo/phd.repo
3 To enable Big Data Extensions to use the new distribution, restart the Tomcat service.
sudo /sbin/service tomcat restart
The Serengeti Management Server reads the revised manifest file and adds the distribution to those
from which you can create a cluster.
4 Return to the Big Data Extensions Plug-in for vSphere Web Client, and click Hadoop Distributions to
verify that the Hadoop distribution is available.
What to do next
You can create Hadoop and HBase clusters.
Set Up a Local Yum Repository for Cloudera Manager Application Manager
When you create a new cluster with an external application manager, you must install agents and
distribution packages on each cluster node. If the installation downloads the agents and packages from the
Internet, the process might be slow. If you do not have an Internet connection, the cluster creation process is
not possible. To avoid these problems, you can create a local yum repository.
Prepare the Software Environment for the Local Repository for Cloudera Manager
The first step to create a local yum repository for Cloudera Manager is to prepare the software environment
by setting up necessary servers and directories.
Prerequisites
Verify that you have the following conditions in place.
nHigh-speed Internet access.
nCentOS 6.x 64-bit or Red Hat Enterprise Linux (RHEL) 6.x 64-bit.
The hadoop-template virtual machine in the Serengeti vApp contains CentOS 6.5 64-bit. You can clone
the hadoop-template virtual machine to a new virtual machine and create the yum repository on it.
Chapter 5 Managing Hadoop Distributions
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nAn HTTP server with which to create the yum repository. For example, Apache HTTP server.
nIf your system has a firewall, ensure that the firewall does not block the network port number that your
HTTP server proxy uses. Typically, this is port 80.
nFor more information about the yum repository placeholder values, see “Yum Repository
Configuration Values,” on page 53.
Procedure
1 If your yum repository server requires an HTTP proxy server, perform the steps:
a Open a command shell, such as Bash or PuTTY.
b Log in to the yum repository server.
c Export the http_proxy environment variable.
# switch to root user
sudo su
export http_proxy=http://host:port
Option Description
host The hostname or the IP address of the proxy server.
port The network port number to use with the proxy server.
2 Install the HTTP server to use as a yum server.
This example installs the Apache HTTP Server and enables the httpd server to start whenever the
machine restarts.
yum install -y httpd
/sbin/service httpd start
/sbin/chkconfig httpd on
3 Make the CentOS directory.
mkdir -p /var/www/html/yum/centos6
4 Make the Cloudera Manager directory.
mkdir -p /var/www/html/yum/cm
5 Install the createrepo RPM.
yum install -y createrepo
Set Up the Local CentOS Yum Repository
You must copy all the RPM packages from the CentOS 6 DVD ISO images to set up the local CentOS yum
repository.
Prerequisites
Verify that you prepared the software environment for the CentOS yum repository creation, including the
directories for CentOS and the application manager. Refer to your CentOS documentation.
Procedure
1 Download the CentOS-6.5-x86_64-bin-DVD1.iso and CentOS-6.5-x86_64-bin-DVD2.iso CentOS 6 DVD
ISO images from the CentOS official website.
2 Download the ISO images to the virtual machine servers.
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3 Copy all of the CentOS RPM packages to /var/www/html/yum/centos6.
mkdir /mnt/centos6-1
mount -o loop CentOS-6.5-x86_64-bin-DVD1.iso /mnt/centos6-1
cp /mnt/centos6-1/Packages/* /var/www/html/yum/centos6
mkdir /mnt/centos6-2
mount -o loop CentOS-6.5-x86_64-bin-DVD2.iso /mnt/centos6-2
cp /mnt/centos6-2/Packages/* /var/www/html/yum/centos6
4 Create the CentOS 6 yum repository.
createrepo /var/www/html/yum/centos6
Download Packages for Cloudera Manager
After you set up the local CentOS yum repository, you must download the packages for Cloudera Manager.
Procedure
1 Download the cm5.0.1-centos6.tar.gz file.
wget http://archive-primary.cloudera.com/cm5/repo-as-tarball/5.0.1
/cm5.0.1-centos6.tar.gz
For other versions of Cloudera Manager, the URLs used in the example might vary.
2 Extract the tarball.
tar xzf cm5.0.1-centos6.tar.gz
For other versions of Cloudera Manager, the URLs used in the example might vary.
3 Copy all of the files in cm/5.0.1/RPMS/x86_64/ to /var/www/html/yum/cm/
cp cm/5.0.1/RPMS/x86_64/* /var/www/html/yum/cm/
Configure the Yum Repository Server and the Local Parcel Repository
You must configure the yum repository server and the local parcel repository before you can distribute the
parcels file.
Procedure
1 Create the yum repository.
The repodata directory is created in /var/www/html/yum/.
createrepo /var/www/html/yum
2 Ensure that you can access the URL http://yum_repo_server_ip/yum from a browser.
3 Create the Parcels directory.
mkdir -p /var/www/html/parcels
4 Change to the Parcels directory.
cd /var/www/html/parcels
5 Download the Parcels file.
wget http://archive-primary.cloudera.com/cdh5/parcels/5.0.1/
CDH-5.0.1-1.cdh5.0.1.p0.47-el6.parcel
6 Download the manifest.json file.
wget http://archive-primary.cloudera.com/cdh5/parcels/5.0.1/manifest.json
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7 In the manifest.json file, remove all items except for CDH-5.0.1-1.cdh5.0.1.p0.47-el6.parcel
8 Open a browser, go to http://your_cloudera_manager_server:7180/cmf/parcel/status and click Edit
Settings.
9 Select one minute in the Parcel Update Frequency text box.
10 Remove the remote parcel repository URL that was replaced by the target parcel URL.
11 Add the URL http://yum_repo_server_ip/parcels.
You can now create clusters for the Cloudera Manager by using the local yum repository.
Set Up a Local Yum Repository for Ambari Application Manager
When you create a new cluster with an external application manager, you must install agents and
distribution packages on each cluster node. If the installation downloads the agents and packages from the
Internet, the process might be slow. If you do not have an Internet connection, the cluster creation process is
impossible. To avoid these problems, you can create a local yum repository.
Prepare the Software Environment for the Local Repository for Ambari
The first step to create a local yum repository for Ambari is to prepare the software environment.
Prerequisites
Verify that you have the following conditions in place.
nHigh-speed Internet access.
nCentOS 6.x 64-bit or Red Hat Enterprise Linux (RHEL) 6.x 64-bit.
The hadoop-template virtual machine in the Serengeti vApp contains CentOS 6.5 64-bit. You can clone
the hadoop-template virtual machine to a new virtual machine and create the yum repository on it.
nAn HTTP server with which to create the yum repository. For example, Apache HTTP server.
nIf your system has a firewall, ensure that the firewall does not block the network port number that your
HTTP server proxy uses. Typically, this is port 80.
nFor more information about the yum repository placeholder values, see “Yum Repository
Configuration Values,” on page 53.
Procedure
1 If your yum repository server requires an HTTP proxy server, open a command shell, such as Bash or
PuTTY, log in to the yum repository server, and export the http_proxy environment variable.
# switch to root user
sudo su
export http_proxy=http://host:port
Option Description
host The hostname or the IP address of the proxy server.
port The network port number to use with the proxy server.
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2 Install the HTTP server to use as a yum server.
This example installs the Apache HTTP Server and enables the httpd server to start whenever the
machine restarts.
yum install -y httpd
/sbin/service httpd start
/sbin/chkconfig httpd on
3 Make the CentOS directory.
mkdir -p /var/www/html/yum/centos6
4 Make the Ambari directory.
mkdir -p /var/www/html/yum/ambari
5 Install the createrepo RPM.
yum install -y createrepo
Set Up the Local CentOS Yum Repository
You must copy all the RPM packages from the CentOS 6 DVD ISO images to set up the local CentOS yum
repository.
Prerequisites
Verify that you prepared the software environment for the CentOS yum repository creation, including the
directories for CentOS and the application manager. Refer to your CentOS documentation.
Procedure
1 Download the CentOS-6.5-x86_64-bin-DVD1.iso and CentOS-6.5-x86_64-bin-DVD2.iso CentOS 6 DVD
ISO images from the CentOS official website.
2 Download the ISO images to the virtual machine servers.
3 Copy all of the CentOS RPM packages to /var/www/html/yum/centos6.
mkdir /mnt/centos6-1
mount -o loop CentOS-6.5-x86_64-bin-DVD1.iso /mnt/centos6-1
cp /mnt/centos6-1/Packages/* /var/www/html/yum/centos6
mkdir /mnt/centos6-2
mount -o loop CentOS-6.5-x86_64-bin-DVD2.iso /mnt/centos6-2
cp /mnt/centos6-2/Packages/* /var/www/html/yum/centos6
4 Create the CentOS 6 yum repository.
createrepo /var/www/html/yum/centos6
Download Packages for Ambari
After you set up the local CentOS yum repository, you must download the packages for the Ambari
application manager.
Procedure
1 Go to /var/www/html/yum/ambari.
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2 Download the Ambari agent.
wget http://s3.amazonaws.com/public-repo-1.hortonworks.com/ambari/centos6/
ambari-1.6.0-centos6.tar.gz
If you use other versions of Ambari, for example Ambari 1.6.1, the URL that you use might vary.
3 Download the HDP packages.
wget http://s3.amazonaws.com/public-repo-1.hortonworks.com/HDP/
centos6/HDP-2.1.2.0-centos6-rpm.tar.gz
If you use other versions of HDP, for example HDP 1.3.2 or HDP 2.0, the URL that you use might vary.
4 Download the HDP-UTILS packages.
wget http://s3.amazonaws.com/public-repo-1.hortonworks.com/HDP-UTILS-1.1.0.16/
repos/centos6/HDP-UTILS-1.1.0.16-centos6.tar.gz
wget http://s3.amazonaws.com/public-repo-1.hortonworks.com/HDP-UTILS-1.1.0.17/
repos/centos6/HDP-UTILS-1.1.0.17-centos6.tar.gz
5 Extract all of the tarball files and copy them to /var/www/html/yum/ambari.
Configure the Ambari Repository File on the Ambari Server
To set up the local yum repository, you must configure the Ambari repository file.
Procedure
1 Stop the Ambari server..
ambari-server stop
2 Download the ambari.repo file.
wget http://s3.amazonaws.com/public-repo-1.hortonworks.com/ambari/centos6/1.x/updates/
1.6.0/ambari.repo
3 Move the ambari.repo file to the /etc/yum.repos.d/ directory.
4 Edit the ambari.repo file.
a Replace the URLs with the yum repository server address.
b Remove the group check.
c Add a new section for CentOS.
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Example: Configuring the Ambari Repository File on the Ambari Server
[centos]
name=centos6
baseurl=http://<yum_repo_server_ip>/yum/centos6/
gpgcheck=0
enabled=1
[ambari-1.x]
name=Ambari 1.x
baseurl=http://<yum_repo_server_ip>/yum/ambari/ambari/centos6/1.x/updates/1.6.0/
gpgcheck=0
enabled=1
priority=1
[HDP-UTILS-1.1.0.16]
name=Hortonworks Data Platform Utils Version - HDP-UTILS-1.1.0.16
baseurl=http://<yum_repo_server_ip>/yum/ambari/HDP-UTILS-1.1.0.16/repos/centos6/
gpgcheck=0
enabled=1
priority=1
[HDP-UTILS-1.1.0.17]
name=Hortonworks Data Platform Utils Version - HDP-UTILS-1.1.0.17
baseurl=http://<yum_repo_server_ip>/yum/ambari/HDP-UTILS-1.1.0.17/repos/centos6/
gpgcheck=0
enabled=1
priority=1
Configure the HDP Repository URL on the Local Yum Server
After you configure the Ambari repository on the Ambari server, you must configure the HDP repository
URL on the local yum server.
Prerequisites
Verify that you configured the Ambari repository on the Ambari server.
Procedure
1 Get the hdp_urlinfo.json file from http://public-repo-1.hortonworks.com/HDP/hdp_urlinfo.json.
2 Move the hdp_urlinfo.json file to /var/www/html/yum/ambari/HDP/
3 Edit the hdp_urlinfo.json file by replacing the URL for HDP-2.1 and centos6 with your local HDP
repository URL.
Set the URL in the correct section based on your HDP and Linux OS version and type.
{
"HDP-2.1": {
"latest": {
"centos5": "http://public-
repo-1.hortonworks.com/HDP/centos5/2.x/updates/2.1.3.0/hdp.repo",
"centos6":
"http://<yum_repo_server_ip>/yum/ambari/HDP/centos6/2.x/updates/2.1.2.0/hdp.repo",
"suse11": "http://public-
repo-1.hortonworks.com/HDP/suse11/2.x/updates/2.1.3.0/hdp.repo"
}
},
"HDP-2.0": {
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"latest": {
"centos5": "http://public-
repo-1.hortonworks.com/HDP/centos5/2.x/updates/2.0.12.0/hdp.repo",
"centos6": "http://public-
repo-1.hortonworks.com/HDP/centos6/2.x/updates/2.0.12.0/hdp.repo",
"suse11": "http://public-
repo-1.hortonworks.com/HDP/suse11/2.x/updates/2.0.12.0/hdp.repo"
}
},
"HDP-1.3": {
"latest": {
"centos5": "http://public-
repo-1.hortonworks.com/HDP/centos5/1.x/updates/1.3.8.0/hdp.repo",
"centos6": "http://public-
repo-1.hortonworks.com/HDP/centos6/1.x/updates/1.3.8.0/hdp.repo",
"suse11": "http://public-
repo-1.hortonworks.com/HDP/suse11/1.x/updates/1.3.8.0/hdp.repo"
}
}
}
Configure the HDP Repository on the Ambari Server
After you configure the Ambari repository on the Ambari server, you must configure the HDP repository on
the Ambari server.
Prerequisites
Verify that you have configured the ambari.repository on the Ambari server.
Procedure
1 Edit the following file:
/var/lib/ambari-server/resources/stacks/HDP/2.1/repos/repoinfo.xml
a Replace the version number 2.1 with your version number.
b Replace the URL of the latest element to the location of our hdp_urlinfo.json:
http://<yum_repo_server_ip>/yum/ambari/HDP/hdp_urlinfo.json
c Replace the baseurl in the os type="redhat6" with your local HDP repository URL, as shown in the
following example:
<?xml version="1.0"?>
<!--
License section(not displayed here).
-->
<reposinfo>
<latest>http://<yum_repo_server_ip>/yum/ambari/HDP/hdp_urlinfo.json</latest>
<os type="redhat6">
<repo>
<baseurl>http://<yum_repo_server_ip>/yum/ambari/HDP/centos6/2.x/updates/2.1.2.0/</baseurl>
<repoid>HDP-2.1</repoid>
<reponame>HDP</reponame>
</repo>
</os>
<os type="redhat5">
<repo>
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<baseurl>http://public-repo-1.hortonworks.com/HDP/centos5/2.x/updates/2.1.2.0/</baseurl>
<repoid>HDP-2.1</repoid>
<reponame>HDP<?reponame>
</repo>
</os>
<os type="suse11">
<repo>
<baseurl>http://public-repo-1.hortonworks.come/HDP/suse11/2.x/updates/2.1.2.0/<baseurl>
<repoid>HDP-2.1</repoid>
<reponame>HDP</reponame>
</repo>
</os>
<os type="debian 12">
<repo>
<baseurl>http://public-repo-1.hortonworks.com/HDP/ubuntu 12/2.x/updates/2.1.2.0/</baseurl>
<repoid>HDP-2.1</repoid>
<reponame>HDP</reponame>
</repo>
</os>
</reposinfo>
2 Start the Ambari server.
ambari-server start
You are ready to create clusters for the Ambari server by using the local yum repository.
Create a Hadoop Template Virtual Machine using RHEL Server 6.x and
VMware Tools
You can create a Hadoop Template virtual machine that has a customized version of the Red Hat Enterprise
Linux (RHEL) Server 6.x operating system that includes VMware Tools. Although only a few Hadoop
distributions require a custom version of RHEL Server 6.x, you can customize RHEL Server 6.x for any
Hadoop distribution.
Before You Create a Hadoop Template Virtual Machine using RHEL Server 6.x
and VMware Tools
Before you create a Hadoop template virtual machine using the RHEL server 6.x and VMware tools, you
must perform some prerequisite tasks and be familiar with some important information on the RHEL Server
6.1, hostnames, disk partitioning, and creating Hadoop Template virtual machines with multiple cores per
socket.
You can create a Hadoop Template virtual machine that uses RHEL Server 6.1 or later as the guest operating
system into which you can install VMware Tools for RHEL 6.x in combination with a supported Hadoop
distribution. This allows you to create a Hadoop Template virtual machine that uses your organization's
operating system configuration. When you provision Big Data clusters using the customized Hadoop
template, the VMware Tools for RHEL 6.x will be in the virtual machines that are created from the Hadoop
Template virtual machine.
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If you create Hadoop Template virtual machines with multiple cores per socket, when you specify the CPU
settings for the virtual machine you must specify a multiple of cores per socket. For example, if the virtual
machine uses two cores per socket, the vCPU settings must be an even number. For example: 4, 8, or 12. If
you specify an odd number, the cluster provisioning or CPU resizing will fail.
IMPORTANT
nYou must use localhost.localdomain as the hostname when you install the RHEL template otherwise
the FQDN of the virtual machine cloned from the template may not be set correctly.
nIf you are performing disk partitioning, do not use the Linux Volume Manager (LVM).
Prerequisites
nDeploy theBig Data Extensions vApp. See “Deploy the Big Data Extensions vApp in the vSphere Web
Client,” on page 24.
nObtain the IP address of the Serengeti Management Server.
nLocate the VMware Tools version that corresponds to the ESXi version in your data center.
Create a Virtual Machine Template with a 20GB Thin Provisioned Disk and
Install RHEL 6.x
For more information on this procedure, see the Red Hat Enterprise Linux Installation Guide, available on the
Red Hat website.
Procedure
1 Download the RHEL Server 6.x installation ISO from www.redhat.com to a datastore.
2 In vSphere Client, create a new virtual machine with a 20GB thin provision disk and select Red Hat
Enterprise Linux 6 (64-bit) as the Guest OS.
3 Right-click the virtual machine and click Edit Settings.
4 Select CD/DVD Device 0, and select the datastore ISO file for the RHEL ISO file.
5 Select SCSI controller 0 > Change Type > LSI Logic Parallel and click OK.
6 Under Device Status, select connected and connect at power on, and click OK.
7 From the console window of the virtual machine, install the RHEL Server 6.x operating system using
the default settings for all settings except the following items:
nYou can select the language and time zone you want the operating system to use
nYou can specify that the swap partition use a smaller size to save disk space (for example, 500MB)
nYou can reduce the size of the swap partition because it is not used by Big Data Extensions.
nSelect Minimal in the Package Installation Defaults screen.
Ensure the Virtual Machine has a Valid IP and Internet Connectivity
The Hadoop template virtual machine requires a valid IP address and an Internet connection.
Prerequisites
n
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Procedure
uRun the ifconfig command to ensure that the virtual machine has a valid IP and Internet connectivity.
This task assumes the use of Dynamic Host Configuration Protocol (DHCP).
nIf IP address information appears in the output of the ifconfig command , see “Configure the
Network for the Hadoop Template Virtual Machine to use DHCP,” on page 71.
nIf no IP address information appears, see “Configure the Network for the Hadoop Template
Virtual Machine to use DHCP,” on page 71.
Configure the Network for the Hadoop Template Virtual Machine to use DHCP
Procedure
1 Using a text editor open the /etc/sysconfig/network-scripts/ifcfg-eth0 file.
2 Locate the following parameters and specify the following configuration.
ONBOOT=yes
DEVICE=eth0
BOOTPROTO=dhcp
3 Save your changes and close the file.
4 Restart the network service.
sudo /sbin/service network restart
5 Run the ifconfig command to ensure that the virtual machine has a valid IP and Internet connectivity.
Install the JDK 7 RPM
Procedure
1 From the Oracle® Java SE 7 Downloads page, download the latest JDK 7 Linux x64 RPM and copy it to
the root folder of the virtual machine template.
2 Install the RPM.
rpm -Uvh jdk-7u80-linux-x64.rpm
3 Delete the RPM file.
rm -f jdk-7u80-linux-x64.rpm
4 Edit /etc/environment and add the following line: JAVA_HOME=/usr/java/default
Customize the Virtual Machine
Run the installation scripts to customize the virtual machine.
Procedure
1 Register the RHEL operating system to enable the RHEL yum repositories. This allows the installation
script to download packages from the yum repository. See "Registering from the Command Line" in the
Red Hat Enterprise Linux 6 Deployment Guide, available on the Red Hat website.
2 Download the scripts from https://deployed_serengeti_server_IP/custos/custos.tar.gz.
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3 Create the directory /tmp/custos, make this your working directory, and run tar xf to uncompress the
tar file.
mkdir /tmp/custos
cd /tmp/custos
tar xf /tmp/custos/custos.tar.gz
4 Run the installer.sh script specifying the /usr/java/default directory path.
./installer.sh /usr/java/default
You must use the same version of the installer.sh script as your Big Data Extensions deployment.
5 Remove the /etc/udev/rules.d/70-persistent-net.rules file to prevent increasing the eth number
during the clone operation.
If you do not remove the file, virtual machines that are cloned from the template cannot get IP
addresses. If you power on the Hadoop template virtual machine to make changes, remove the file
before you shut down this virtual machine.
Install VMware Tools for RHEL 6.x
Procedure
1 Right-click the RHEL 6 virtual machine in vSphere Client, then select Guest > Install/Upgrade VMware
Tools.
2 Log in to the virtual machine and mount the CD-ROM to access the VMware Tools installation package.
mkdir /mnt/cdrom
mount /dev/cdrom /mnt/cdrom
mkdir /tmp/vmtools
cd /tmp/vmtools
3 Run the tar xf command to extract the VMware Tools package tar file.
tar xf /mnt/cdrom/VMwareTools-*.tar.gz
4 Make vmware-tools-distrib your working directory, and run the vmware-install.pl script.
cd vmware-tools-distrib
./vmware-install.pl
Press Enter to finish the installation.
5 Remove the vmtools temporary (temp) file that is created as an artifact of the installation process.
rm -rf /tmp/vmtools
6 Shut down virtual machine.
Synchronize the Time on the Hadoop Template Virtual Machine
Synchronize the time on the Hadoop template virtual machine with vCenter Server.
Procedure
1 In the vSphere Web Client, right-click the Hadoop Template virtual machine and select Edit Settings.
2 On the VM Options tab, click VMware Tools > Synchronize guest time with host.
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Complete the Process of Creating a Hadoop Template Virtual Machine
To use the customized Hadoop Template virtual machine you replace the original Hadoop Template virtual
machine and restart the Tomcat service to enable the custom RHEL virtual machine template.
Procedure
1 On the Virtual Hardware tab of the Edit Settings dialog, uncheck the Connected checkbox. If the
CD/DVD Device is connected to the ISO file, the clone virtual machine process fails.
2 Replace the original Hadoop Template virtual machine with the customized virtual machine that you
created.
a Move the original Hadoop Template virtual machine out of the vApp.
b Drag the new template virtual machine that you just created into the vApp.
3 Log in to the Serengeti Management Server as the user serengeti, and restart the Tomcat service.
sudo /sbin/service tomcat restart
Restarting the Tomcat service enables the custom RHEL virtual machine template, making it your
Hadoop Template virtual machine.
Maintain a Customized Hadoop Template Virtual Machine
You can modify or update the Hadoop Template virtual machine operating system. When you make
updates, you must remove the snapshot that is created by the virtual machine.
If you create a custom Hadoop Template virtual machine that uses a version of RHEL 6.x, or modify the
operating system, you must remove the serengeti-snapshot that Big Data Extensions creates. If you do not
remove the serengeti-snapshot, changes you made to the Hadoop Template virtual machine will not take
effect.
Prerequisites
nDeploy the Big Data Extensions vApp. See “Deploy the Big Data Extensions vApp in the vSphere Web
Client,” on page 24.
nCreate a customized Hadoop Template virtual machine using RHEL 6.x. See “Create a Hadoop
Template Virtual Machine using RHEL Server 6.x and VMware Tools,” on page 69
.
Procedure
1 Use the vSphere Web Client to log in to vCenter Server.
2 Power on the Hadoop Template virtual machine and apply changes or updates.
3 Remove the /etc/udev/rules.d/70-persistent-net.rules file to prevent increasing the eth number
during the clone operation.
If you do not remove the file, virtual machines that are cloned from the template cannot get IP
addresses. If you power on the Hadoop template virtual machine to make changes, remove the file
before you shut down this virtual machine.
4 From the vSphere Web Client, shut down the Hadoop Template virtual machine.
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5 Delete the snapshot labeled serengeti-snapshot from the customized Hadoop Template virtual machine.
a In the vSphere Web Client, right-click the Hadoop Template virtual machine and select Snapshot >
Snapshot Manager
b Select the serengeti-snapshot, and click Delete.
The generated snapshot is removed.
6 Synchronize the time on the Hadoop template virtual machine with vCenter Server.
a In the vSphere Web Client, right-click the Hadoop template virtual machine and select Edit
Settings.
b On the VM Options tab, click VMware Tools > Synchronize guest time with host.
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Managing the Big Data Extensions
Environment 6
After you install Big Data Extensions, you can stop and start the Serengeti services, create user accounts,
manage passwords, update SSL certificates, and log in to cluster nodes to perform troubleshooting.
This chapter includes the following topics:
n“Add Specific User Names to Connect to the Serengeti Management Server,” on page 75
n“Change the Password for the Serengeti Management Server,” on page 76
n“Create a User Name and Password for the Serengeti Command-Line Interface,” on page 77
n“Specify a Group of Users in Active Directory or LDAP to Use a Hadoop Cluster,” on page 77
n“Stop and Start Serengeti Services,” on page 78
n“Ports Used for Communication between Big Data Extensions and the vCenter Server,” on page 79
n“Verify the Operational Status of the Big Data Extensions Environment,” on page 80
Add Specific User Names to Connect to the Serengeti Management
Server
You can add specific user names with which to login to the Serengeti Management Server. The user names
you add are the only users who can connect to the Serengeti Management Server using the Serengeti CLI or
the Big Data Extensions user interface for use with vSphere Web Client.
Passwords must be from 8 to 20 characters, use only visible lowerASCII characters (no spaces), and must
contain at least one uppercase alphabetic character (A - Z), at least one lowercase alphabetic character (a - z),
at least one digit (0 - 9), and at least one of the following special characters: _, @, #, $, %, ^, &, *
Prerequisites
nDeploy the Serengeti vApp.
nUse the vSphere Web Client to log in to vCenter Server, and verify that the
Serengeti Management Server virtual machine is running.
Procedure
1 Right-click the Serengeti Management Server virtual machine and select Open Console.
The password for the Serengeti Management Server appears.
NOTE If the password scrolls off the console screen, press Ctrl+D to return to the command prompt.
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2 Open a command shell, such as Bash or PuTTY, and log in to the Serengeti Management Server as user
serengeti.
Use the IP address that appears in the Summary tab and the current password.
3 Edit the /opt/serengeti/conf/Users.xml file to add additional user names.
vi /opt/serengeti/conf/Users.xml
4 Edit the <user name="*" /> attribute by replacing the asterisk (*) wildcard character with the user name
you wish to use. You can add multiple user names by adding a new <user name="name" /> attribute on
its own line. The User.xml file supports multiple lines.
<user name="jsmith" />
<user name="sjones" />
<user name="jlydon" />
5 Restart the Tomcat service.
/sbin/service tomcat restart
Only the user names you add to the User.xml file can be used to login to the Serengeti Management Server
using the Serengeti CLI or the Big Data Extensions user interface for use with vSphere Web Client.
Change the Password for the Serengeti Management Server
When you power on the Serengeti Management Server for the first time, it generates a random password
that is used for the root and serengeti users. If you want an easier to remember password, you can use the
virtual machine console to change the random password for the root and serengeti users.
NOTE You can change the password for the virtual machine of any node by using this procedure.
Passwords must be from 8 to 20 characters, use only visible lowerASCII characters (no spaces), and must
contain at least one uppercase alphabetic character (A - Z), at least one lowercase alphabetic character (a - z),
at least one digit (0 - 9), and at least one of the following special characters: _, @, #, $, %, ^, &, *
Prerequisites
nDeploy the Serengeti vApp.
nUse the vSphere Web Client to log in to vCenter Server, and verify that the Serengeti Management
Server virtual machine is running.
Procedure
1 Right-click the Serengeti Management Server virtual machine and select Open Console.
The password for the Serengeti Management Server appears.
NOTE If the password scrolls off the console screen, press Ctrl+D to return to the command prompt.
2 Open a command shell, such as Bash or PuTTY, and log in to the Serengeti Management Server as user
serengeti.
Use the IP address that appears in the Summary tab and the current password.
3 Use the /opt/serengeti/sbin/set-password command to change the password for the root user and the
serengeti user.
sudo /opt/serengeti/sbin/set-password -u
4 Enter a new password, and enter it again to confirm.
The next time you log in to the Serengeti Management Server, use the new password.
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What to do next
You can create a new user name and password for the Serengeti Command-Line Interface Client.
Create a User Name and Password for the Serengeti Command-Line
Interface
The Serengeti Command-Line Interface Client uses the vCenter Server login credentials with read
permissions on the Serengeti Management Server. If you do not create a user name and password for the
Serengeti Command-Line Interface Client, it will use the default vCenter Server administrator credentials.
However, for security reasons, it's best to create a user account specifically for use with the Serengeti
Command-Line Interface Client.
Passwords must be from 8 to 20 characters, use only visible lowerASCII characters (no spaces), and must
contain at least one uppercase alphabetic character (A - Z), at least one lowercase alphabetic character (a - z),
at least one digit (0 - 9), and at least one of the following special characters: _, @, #, $, %, ^, &, *
Prerequisites
nDeploy the Big Data Extensions vApp. See “Deploy the Big Data Extensions vApp in the vSphere Web
Client,” on page 24.
nInstall the Serengeti Command-Line Interface Client. See “Install the Serengeti Remote Command-Line
Interface Client,” on page 31.
Procedure
1 Open a Web browser and go to: https://vc-hostname:port/vsphere-client.
The vc-hostname can be either the DNS host name or IP address of vCenter Server. By default the port is
9443, but this can change during the installation of the vSphere Web Client.
2 Type the user name and password that has administrative privileges on vCenter Server, and click
Login.
NOTE vCenter Server 5.5 users must use a local domain to perform SSO related operations.
3 From the vSphere Web Client Navigator panel, select Administration, SSO Users and Groups.
4 Change the login credentials.
The login credentials are updated. The next time you access the Serengeti Command-Line Interface use the
new login credentials.
What to do next
You can change the password of the Serengeti Management Server. See “Change the Password for the
Serengeti Management Server,” on page 76.
Specify a Group of Users in Active Directory or LDAP to Use a
Hadoop Cluster
You can specify an Active Directory or LDAP server for user authentication. This lets you manage users
from a central point.
By defaultBig Data Extensions is installed with authentication only for local user accounts. If you want to
use LDAP or Active Directory to authenticate users, you must configure Big Data Extensions for use with
your LDAP or Active Directory service.
Big Data Extensions lets you authenticate local users, those managed by LDAP or Active Directory server, or
a combination of these authentication methods.
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Prerequisites
nDeploy the Big Data Extensions vApp. See “Deploy the Big Data Extensions vApp in the vSphere Web
Client,” on page 24.
nUse the Serengeti Management Server Administration Portal to enable SSO and update the certificate.
See “Configure vCenter Single Sign-On Settings for the Serengeti Management Server,” on page 30.
Procedure
1 Use the vSphere Web Clientto log in to vCenter Server.
2 Select Big Data Extensions and click the Manage tab.
3 Select User Mode and click Edit.
The Configure User dialog box appears.
4 Choose the user authentication mode you wish to use for your Big Data Extensions environment.
Table 6‑1. User Authentication Modes
User Mode Description
Local Select Local to create and manage users and groups that are stored locally in your
Big Data Extensions environment. Local is the default user management solution.
LDAP user Select LDAP user to create and manage users and groups that are stored in your
organization's identity source, such as Active Directory or LDAP. If you choose LDAP user
you must configure Big Data Extensions to use an LDAP or Active Directory service.
Mixed mode Select Mixed mode to use a combination of both local users and users stored in an external
identity source. If you choose mixed mode you must configure Big Data Extensions to use
AD as LDAP mode.
5 If you choose to use LDAP or Mixed mode, configure Big Data Extensions to use an LDAP or Active
Directory service.
Table 6‑2. LDAP Connection Information
Base user DN Specify the base user DN.
Base group DN Specify the base group DN.
Primary server URL Specify the primary server URL of your Active Directory or LDAP server.
Secondary server URL Specify the secondary server URL of your Active Directory or LDAP server.
Username Type the username of the Active Directory or LDAP server administrator account.
Password Type the password of the Active Directory or LDAP server administrator account.
6 (Optional) Click Test to verify that user accounts are found.
Stop and Start Serengeti Services
You can stop and start Serengeti services to make a reconfiguration take effect, or to recover from an
operational anomaly.
Procedure
1 Open a command shell, such as Bash or PuTTY, and log in to the Serengeti Management Server as user
serengeti.
2 Run the serengeti-stop-services.sh script to stop the Serengeti services.
serengeti-stop-services.sh
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3 Run the serengeti-start-services.sh script to start the Serengeti services.
serengeti-start-services.sh
Ports Used for Communication between Big Data Extensions and the
vCenter Server
Big Data Extensions queries information from the vCenter Server, and uses the vCenter Server Single Sign-
On service.
Big Data Extensions Management Server
The table below shows the published port for the management server.
Service Port Comments
Serengeti Rest API 8080, 8443 Open for Serengeti client and for BDE plugin
registration called by VC
SSHD 22 Open for Serengeti client connection
Hadoop Ports
Serengeti deploys Hadoop and Hbase clusters using all default ports. The following lists all ports that are
used by the Hadoop or HBase service, the production network.
Daemon Default Port
HDFS Namenode Webpage 50070
Namenode RPC 8020
Datanode 50075
50010
50020
MapReduce JobTracker Webpage 50030
JobTracker RPC 8021
TaskTracker 50060
Yarn Resource Manager Webpage 8088
Resource Manager RPC 8030, 8031, 8032, 8033
Node Manager 8040, 8042
Hive N/A 1000
Hbase Ports
The table below shows the ports used by HBase clusters, along with the default port numbers.
Service Property Name Port
ZooKeeper hbase.zookeeper.property.clientPort 2181
Master hbase.master.port 60000
Master hbase.master.info.port 60010
Region server hbase.regionserver.port 60020
Region server hbase.regionserver.info.port 60030
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Service Property Name Port
REST server hbase.rest.port 8080
REST server hbase.rest.info.port 8085
Thrift server hbase.thrift.port 9090
Thrift server hbase.thrift.info.port 9095
MapR Ports
The table below defines the ports used by a MapR cluster, along with the default port numbers.
Service Port
CLDB 7222
CLDB JMX monitor port 7220
CLDB web port 7221
HBase Master 60000
HBase Master (for GUI) 60010
HBase RegionServer 60020
Hive Metastore 9083
JobTracker Webpage 50030
JobTracker RPC 8021
MFS server 5660
MySQL 3306
NFS 2049
NFS monitor (for HA) 9997
NFS management 9998
Port mapper 111
TaskTracker 50060
Web UI HTTPS 8443
ZooKeeper 5181
Verify the Operational Status of the Big Data Extensions Environment
To successfully provision a Hadoop cluster, your Big Data Extensions environment must meet certain
criteria. You can verify that your environment meets these criteria prior to creating Hadoop clusters, as well
as troubleshoot cluster creation issues you may encounter.
Operational Status of Big Data Extensions Services
Big Data Extensions consists of several services that you can verify are running.
Big Data Extensions consists of the following services: Tomcat service, Yum server, Chef server, and
PostgreSQL server. You can verify that these services are running prior to creating Hadoop clusters.
Prerequisites
nDeploy the Serengeti vApp.
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nUse the vSphere Web Client to log in to vCenter Server, and verify that the
Serengeti Management Server virtual machine is running.
Procedure
1 Open a command shell, such as Bash or PuTTY, and log in to the Serengeti Management Server as user
serengeti.
2 Verify that the Tomcat service is running.
a Run the command pgrep -f org.apache.catalina.startup.Bootstrap -l.
pgrep -f org.apache.catalina.startup.Bootstrap -l
b Run the command wget https://bde_server_ip:8443 --no-check-certificate
wget https://bde_server_ip:8443 --no-check-certificate
3 Verify that the Yum server is running.
Run the command /sbin/service httpd status.
/sbin/service httpd status
If the Yum server is operating properly it responds with the status message running.
4 Verify that the Chef server is running.
Run the command sudo /usr/bin/chef-server-ctl status. The status subcommand displays the
status of all services available to the Chef server.
sudo /usr/bin/chef-server-ctl status
5 Verify that the PostgreSQL server is running.
a Run the command pgrep -f /opt/chef-server/embedded/bin/postgres -l to verify that the
postgres process is running. The -l option lists the available databases.
pgrep -f /opt/chef-server/embedded/bin/postgres -l
b Run the command echo "\dt" | psql -U serengeti to display the database tables created for
Big Data Extensions. The -dt option specifies the name of the database to connect to, and turns off
the display of the database column names in the resulting output. The -U option specifies the
username with which to connect to the database.
echo "\dt" | psql -U serengeti
If the databases available to PostgreSQL and the tables owned by the serengeti user display, your
PostgreSQL server is running as expected.
What to do next
If any of the above services is not running, you can view the initialization status of the
Serengeti Management Server services, view error messages to help troubleshoot problems, and recover
services that may not have successfully started using the Serengeti Management Server Administration
Portal. See “View Serengeti Management Server Initialization Status,” on page 124.
Verify Network Connectivity with vCenter Server
You can verify if your Big Data Extensions deployment can connect to vCenter Server, and identify possible
causes that may be preventing a successful network connection.
Prerequisites
nDeploy the Serengeti vApp.
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nUse the vSphere Web Client to log in to vCenter Server, and verify that the
Serengeti Management Server virtual machine is running.
Procedure
1 Open a command shell, such as Bash or PuTTY, and log in to the Serengeti Management Server as user
serengeti.
2 Run the command wget https://vcenter_server_ip:9443 --no-check-certificate.
wget https://vcenter_server_ip:9443 --no-check-certificate
If this command retrieves the index.html file whose title is vSphere Web Client, vCenter Server is
running, and there is connectivity between Big Data Extensions and vCenter Server.
If running this command fails to retrieve the index.html file, see Step. 3.
3 If the command returns the error message Connecting to vcenter_server_ip:vcenter_server_port...
failed: Connection refused, the vCenter Server IP address you specified is reachable, but the vCenter
Server network port number is incorrect.
4 If the vCenter Server IP address and port number are correct, check your Big Data Extensions
deployment's network configuration and ensure that it is properly configured. For example, is
Big Data Extensions using a valid IP address and gateway?
What to do next
If you are unable to verify a network connection between Big Data Extensions and vCenter Server, and
cannot identify the cause of the problem, the troubleshooting topics provide solutions to problems you
might encounter when using Big Data Extensions. See Chapter 13, “Troubleshooting,” on page 139
Verify vCenter Server User Authentication
You can verify if your vCenter Server user authentication is working properly, and identify possible causes
that may be preventing a successful cluster creation.
Prerequisites
nDeploy the Serengeti vApp.
nUse the vSphere Web Client to log in to vCenter Server, and verify that the
Serengeti Management Server virtual machine is running.
Procedure
1 Open a command shell, such as Bash or PuTTY, and log in to the Serengeti Management Server as the
user serengeti.
2 Type serengeti to start the Serengeti Command-Line Interface.
3 Run the command connect –host localhost:8443, and at the prompt type your username and
password, which might be different from your login credentials for the Serengeti Management Server.
If you can log into Big Data Extensions your vCenter Server user authentication is working correctly.
What to do next
Before creating new virtual machines on hosts, the time on the target hosts is checked against the time on
the Serengeti Management Server. If the time between the Serengeti Management Server and the hosts is not
synchronized, the virtual machine creation will fail. See “Check Time Synchronization Between Serengeti
Management Server and Hosts,” on page 83.
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Check Time Synchronization Between Serengeti Management Server and Hosts
When you run the cluster create or cluster create ... --resume command, the command can fail if
there are time discrepancies in the environment. You can verify that the time is within allowable tolerances
and synchronize the time between the Serengeti Management Server and the other hosts within your
environment.
Before creating new virtual machines on hosts, the time on the target hosts is checked against the time on
the Serengeti Management Server. If the time between the Serengeti Management Server and the hosts is not
synchronized, the cluster creation might fail.
Prerequisites
nDeploy the Serengeti vApp.
nUse the vSphere Web Client to log in to vCenter Server, and verify that the
Serengeti Management Server virtual machine is running.
Procedure
1 Open a command shell, such as Bash or PuTTY, and log in to the Serengeti Management Server as user
serengeti.
2 Run the command date +%T to see the time on the Serengeti Management Server.
date +%T
3 From the vSphere Web Client, record the time of each host in the datacenter.
4 Compare the date and time from the Serengeti Management Server and each host to see if they are
greater than the Maximum-Threshold. If there is HBase service in the cluster, the Maximum-Threshold
is 20 seconds. Otherwise, the Maximum-Threshold is 4 minutes.
If the times between hosts are not synchronized, login to each host and view the /etc/ntp.conf file to
verify if the NTP configuration is correct.
5 From the vSphere Web Client, configure all ESXi hosts to synchronize their clocks with the same NTP
server.
What to do next
After you synchronize the time between the Serengeti Management Server and the other ESXi hosts within
your environment, try to create a cluster.
Verify Network Connectivity Between Compute Nodes and Isilon HDFS
If you are using EMC Isilon OneFS for your HDFS, you can verify the network connectivity from the
compute nodes to the Isilon OneFS filesystem.
Procedure
1 Open a command shell, such as Bash or PuTTY, and log in to the Serengeti Management Server as user
serengeti.
2 For each compute node (TaskTracker or NodeManager), login and run the command hadoop dfsadmin
-report to verify that the HDFS is running correctly. If the command returns the Configured Capacity
and Present Capacity, the worker node can successfully access the HDFS.
If the HDFS does not respond, see Step 3.
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3 Ensure that the HDFS IP address and network port number is correct. Login to the Isilon Namenode
(which may require a different username and password) and verify that the HDFS service is listening
on port 8020.
If the HDFS is listening on the correct network port, see Step 4.
4 Check the fs.defaultFS entry in the Hadoop configuration file core-site.xml. Ensure that the IP
address, FQDN, and network port are configured to use the correct HDFS service.
Check Which Users and User Groups Exist in the Isilon OneFS
If you use EMC Isilon OneFS as the external HDFS cluster, you must create and configure users and user
groups and prepare your Isilon OneFS environment. You can verify that you have created the correct users
and user groups, and check which users and groups exist in your Isilon OneFS environment.
Prerequisites
Prepare the Isilon OneFS for use as an external HDFS cluster. See “Prepare the EMC Isilon OneFS as the
External HDFS Cluster,” on page 104.
Procedure
1 Open a command shell, such as Bash or PuTTY, and SSH to the Isilon OneFS node.
2 Run the command isi auth users/groups list to list the existing Isilon OneFS users and user groups.
3 Run the command ls -al HDFS_ROOT_DIR to verify which users and user groups are using the HDFS.
When running the ls command in the Isilon filesystem, the -al option must come before the
HDFS_ROOT_DIR directory name. Otherwise, the -al option will be regarded as a directory name by the ls
command.
ls -al HDFS_ROOT_DIR
NOTE In the HDFS subdirectory there may be files and directories with permissions and ownership
assigned to users or groups other than those using Big Data Extensions.
Check Storage Capacity
To successfully deploy a cluster you must have enough storage capacity in your Big Data Extensions
environment.
The datastores you add to your Big Data Extensions environment are made available to the clusters you
create within Big Data Extensions. If you do not add enough storage capacity cluster creation will fail.
In addition to overall storage capacity, you must ensure that you have enough Shared and Local storage.
Shared stroage is recommended for master nodes, and enables you to use vMotion, HA, and Fault
Tolerance. Local storage is recommended for worker nodes.
Prerequisites
You must have added a datastore to your Big Data Extensions environment. See “Add a Datastore in the
vSphere Web Client,” on page 92
Procedure
1 Open a command shell, such as Bash or PuTTY, and log in to the Serengeti Management Server as user
serengeti.
2 Run the command datastore list --detail to see which vCenter Server datastores are in use by
Big Data Extensions.
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3 Using the configuration values specified in the cluster specification file, calculate how much storage
capacity the cluster will require.
4 Use the vSphere Web Client to log in to vCenter Server, and verify that the datastores you identified as
belonging to Big Data Extensions have enough storage capacity for the clusters you want to create.
Additionally, ensure that the datastores are in an active state.
What to do next
If your Big Data Extensions environment does not have adequate storage capacity to create clusters, add
additional datastores. See “Add a Datastore in the vSphere Web Client,” on page 92.
Verify the Ambari Application Manager Installation
If you use Apache Ambari to manage your Hadoop cluster, you can verify that the Ambari service is
running, has a network connection, and valid user credentials with which to connect to your cluster.
Prerequisites
nDeploy the Big Data Extensions vApp. See “Deploy the Big Data Extensions vApp in the vSphere Web
Client,” on page 24
nUse the vSphere Web Client to log in to vCenter Server, and verify that the
Serengeti Management Server virtual machine is running.
nAdd the Ambari application manager to your Big Data Extensions environment. See “Add an
Application Manager by Using the vSphere Web Client,” on page 45.
Procedure
1 Open a command shell, such as Bash or PuTTY, and log in to the Serengeti Management Server as the
user serengeti.
2 Run the curl command with the -u option to specify the username and password in use by the Ambari
service, and the -G option to specify the URL of the Ambari system check service:
http://ambari_server_ip:8080/api/v1/check
curl -u username:password -G http://ambari_server_ip:8080/api/v1/check
nIf the system returns RUNNING, the Ambari server is running. If you receive a system message
indicating that your Ambari service is not running, investigate the issue and confirm that you can
successfully start Ambari before proceeding.
nIf the system returns Bad credentials, the username and password are incorrect. Obtain the correct
username and password for your Ambari installation.
nIf the curl command hangs for 30 or more seconds, and the system returns the error message curl:
(7) Failed to connect to ambari_server_ip port port_number: Connection refused, the
IP/FQDN or port number is incorrect. Obtain the correct network address for your Ambari
installation.
This error message may also indicate that the Ambari server virtual machine in powered off. Verify
that the Ambari virtual machine is powered on, and that the Ambari server is running.
What to do next
If your Ambari installation is not responding, confirm that it is properly installed and configured. See
“Modify an Application Manager by Using the Web Client,” on page 46.
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Verify Cloudera Manager Installation
If you use Cloudera Manager to manage your Hadoop cluster, you can verify that Cloudera Manager is
running, has a network connection, and valid user credentials with which to connect to your cluster.
Prerequisites
nDeploy the Big Data Extensions vApp. See “Deploy the Big Data Extensions vApp in the vSphere Web
Client,” on page 24
nUse the vSphere Web Client to log in to vCenter Server, and verify that the
Serengeti Management Server virtual machine is running.
nAdd the Cloudera Manager application to your Big Data Extensions environment. See “Add an
Application Manager by Using the vSphere Web Client,” on page 45.
Procedure
1 Open a command shell, such as Bash or PuTTY, and log in to the Serengeti Management Server as the
user serengeti.
2 Run the curl command with the -u option to specify the username and password in use by
Cloudera Manager, and the -G option to specify the URL of the Cloudera ManagerAPI version number :
http://cloudera_manager_server_ip:7180/api/version
curl -u username:password -G http://cloudera_manager_server_ip:7180/api/version
Record the API version number returned by Cloudera Manager.
3 Run the curl command with the -u option to specify the username and password in use by
Cloudera Manager, and the -G option to specify the URL of the Cloudera Manager /tools/echo query:
http://cloudera_manager_server_ip:7180/api/cloudera_manager_api_version/tools/echo
curl -u username:password -G http://cloudera_manager_server_ip:
7180/api/cloudera_manager_api_version/tools/echo
This example specifies a Cloudera Manager installation using the username and password cloudera,
whose network address is 192.168.1.1 using API version v5.
curl -u cloudera:cloudera -G http://192.168.1.1:7180/api/v5/tools/echo
nIf the system returns Hello world!, Cloudera Manager is running. If you receive a system message
indicating that your Cloudera Manager is not running, investigate the issue and confirm that you
can successfully start Cloudera Manager before proceeding.
nIf the system returns Error 401 Bad credentials, the username and password are incorrect.
Obtain the correct username and password for your Cloudera Manager installation.
nIf the system returns the error message curl: (7) Failed to connect to
cloudera_manager_server_ip port 7180: No route to host, the IP address or FQDN is incorrect.
Obtain the correct network address for your Cloudera Manager installation.
This error message may also indicate that the Cloudera Manager virtual machine in powered off.
Verify that the Cloudera Manager virtual machine is powered on, and that Cloudera Manager is
running.
What to do next
If your Cloudera Manager installation is not responding, confirm that it is properly installed and
configured. See “Modify an Application Manager by Using the Web Client,” on page 46.
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Check DNS Forward and Reverse Lookup
Big Data Extensions requires a properly configured network environment. You can verify that you have a
properly configured forward and reverse address lookup for you DNS.
Reverse DNS lookup determines the hostname associated with a given IP address. Forward DNS lookup
determines the determines the IP address associated with a given hostname.
Prerequisites
nDeploy the Big Data Extensions vApp. See “Deploy the Big Data Extensions vApp in the vSphere Web
Client,” on page 24
nUse the vSphere Web Client to log in to vCenter Server, and verify that the
Serengeti Management Server virtual machine is running.
Procedure
1 Open a command shell, such as Bash or PuTTY, and log in to the Serengeti Management Server as the
user serengeti.
2 Run the echo command to retrieve the IP addresses in use by the cluster.
echo ipv4_address_from_network_interface | psql
Record the IP addresses for each network interface card in use by the cluster.
3 For each IP address you recorded in the previous step, run the host command to verify that DNS
reverse lookup returns the fully qualified domain name (FQDN). If the system responds with a FQDN
for each IP address, DNS reverse lookup is working.
host IP_address
Record the FQDN for each network address you check.
4 For each FQDN you recorded in the previous step, run the host command to verify that DNS forward
lookup returns the IP address associated with th FQDN. If the system responds with an IP address for
each FQDN, DNS forward lookup is working.
5 (Optional) If you are unable to resolve the IP addresses and FQDNs, open the file /etc/resolv.conf,
and confirm that a DNS name server has been configured for use with your environment.
nIf there is no name server configured for use with your environment, ask you administrator for the
correct DNS server name to use.
nIf a name server is configured, but your DNS does not provide forward or reverse lookup,
investigate the cause and configure your DNS as required. Possible causes preventing your DNS
from functioning correctly may include:
nThe name server cannot be reached due to an incorrect IP address.
nThe DNS service on that virtual machine may be shutdown, or unresponsive.
nThe virtual machine containing the DNS service may be shutdown.
What to do next
If your DNS is not functioning as expected, investigate the cause and make the necessary configuration or
operational changes until you are able to verify that you have a properly configured forward and reverse
address lookup for you DNS. See “Modify the DNS Type in the vSphere Web Client,” on page 94.
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Verify the Network Connection Between Big Data Extensions and the Cluster
Nodes
The Serengeti Management Server must be able to connect to each of the nodes in a Hadoop cluster. You can
verify that the Serengeti Management Server can contact each cluster node.
Prerequisites
nDeploy the Big Data Extensions vApp. See “Deploy the Big Data Extensions vApp in the vSphere Web
Client,” on page 24
nUse the vSphere Web Client to log in to vCenter Server, and verify that the
Serengeti Management Server virtual machine is running.
nAdd a network for use by Big Data Extensions. See “Add a Network in the vSphere Web Client,” on
page 93.
Procedure
1 Open a command shell, such as Bash or PuTTY, and log in to the Serengeti Management Server as the
user serengeti.
2 Run the echo command to retrieve the IP addresses in use by the cluster.
echo "select ipv4_address_from_network_interface" | psql
Record the IP addresses for each network interface card in use by the cluster.
3 Run the ping command to contact each IP address and verify that the Serengeti Management Server can
contact each of the cluster nodes.
What to do next
If you are unable to establish a connection between the Serengeti Management Server and the Hadoop
cluster nodes, investigate the cause and make the necessary changes until you are able to verify that you
have a properly configured network.
Verify the Local Yum Repository
If you created a local yum repository from which to deploy your Hadoop distributions, you can verify that
the repository is working properly.
Prerequisites
nDeploy the Big Data Extensions vApp. See “Deploy the Big Data Extensions vApp in the vSphere Web
Client,” on page 24
nUse the vSphere Web Client to log in to vCenter Server, and verify that the
Serengeti Management Server virtual machine is running.
nYou created a local Yum repository from which to deploy your Hadoop distributions. See “Configuring
Yum and Yum Repositories,” on page 52.
Procedure
1 Open a command shell, such as Bash or PuTTY, and log in to the Serengeti Management Server as the
user serengeti.
2 Run the command wget local_repository_url to download the local repository Web page.
3 You can open and view the local repository Web page with a Web browser inside your network to
verify that the local repository works.
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What to do next
You can successfully create Hadoop clusters in your Big Data Extensions environment. See Chapter 8,
“Creating Hadoop and HBase Clusters,” on page 97
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Managing vSphere Resources for
Clusters 7
Big Data Extensions lets you manage the resource pools, datastores, and networks that you use in the
clusters that you create.
This chapter includes the following topics:
n“Add a Resource Pool with the Serengeti Command-Line Interface,” on page 91
n“Remove a Resource Pool with the Serengeti Command-Line Interface,” on page 92
n“Add a Datastore in the vSphere Web Client,” on page 92
n“Remove a Datastore in the vSphere Web Client,” on page 93
n“Add a Network in the vSphere Web Client,” on page 93
n“Modify the DNS Type in the vSphere Web Client,” on page 94
n“Reconfigure a Static IP Network in the vSphere Web Client,” on page 95
n“Remove a Network in the vSphere Web Client,” on page 95
Add a Resource Pool with the Serengeti Command-Line Interface
You add resource pools to make them available for use by Hadoop clusters. Resource pools must be located
at the top level of a cluster. Nested resource pools are not supported.
When you add a resource pool to Big Data Extensions it symbolically represents the actual vSphere resource
pool as recognized by vCenter Server. This symbolic representation lets you use the Big Data Extensions
resource pool name, instead of the full path of the resource pool in vCenter Server, in cluster specification
files.
NOTE After you add a resource pool to Big Data Extensions, do not rename the resource pool in vSphere. If
you rename it, you cannot perform Serengeti operations on clusters that use that resource pool.
Procedure
1 Access the Serengeti Command-Line Interface client.
2 Run the resourcepool add command.
The --vcrp parameter is optional.
This example adds a Serengeti resource pool named myRP to the vSphere rp1 resource pool that is
contained by the cluster1 vSphere cluster.
resourcepool add --name myRP --vccluster cluster1 --vcrp rp1
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Remove a Resource Pool with the Serengeti Command-Line Interface
You can remove resource pools from Serengeti that are not in use by a Hadoop cluster. You remove resource
pools when you do not need them or if you want the Hadoop clusters you create in the Serengeti
Management Server to be deployed under a different resource pool. Removing a resource pool removes its
reference in vSphere. The resource pool is not deleted.
Procedure
1 Access the Serengeti Command-Line Interface client.
2 Run the resourcepool delete command.
If the command fails because the resource pool is referenced by a Hadoop cluster, you can use the
resourcepool list command to see which cluster is referencing the resource pool.
This example deletes the resource pool named myRP.
resourcepool delete --name myRP
Add a Datastore in the vSphere Web Client
You can add datastores to Big Data Extensions to make them available to big data clusters.
Big Data Extensions supports both shared datastores and local datastores.
Procedure
1 Use the vSphere Web Client to log in to vCenter Server.
2 Select Big Data Extensions.
3 From the Inventory Lists, select Resources.
4 Expand the Inventory Lists, and select Datastores.
5 Click the Add (+) icon.
6 In the Name text box, type a name with which to identify the datastore in Big Data Extensions.
Passwords must be from 8 to 20 characters, use only visible lowerASCII characters (no spaces), and
must contain at least one uppercase alphabetic character (A - Z), at least one lowercase alphabetic
character (a - z), at least one digit (0 - 9), and at least one of the following special characters: _, @, #, $, %,
^, &, *
7 From the Type list, select the datastore type in vSphere.
Type Description
Shared Recommended for master nodes. Enables you to leverage vMotion, HA,
and Fault Tolerance.
NOTE If you do not specify shared storage and try to provision a cluster
using vMotion, HA, or Fault Tolerance, the provisioning fails.
Local Recommended for worker nodes. Throughput is scalable and the cost of
storage is lower.
8 Select one or more vSphere datastores to make available to the Big Data Extensions datastore that you
are adding.
9 Click OK to save your changes.
The vSphere datastores are available for use by big data clusters deployed within Big Data Extensions.
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Remove a Datastore in the vSphere Web Client
You remove a datastore from Big Data Extensions when you no longer want the Hadoop clusters you create
to use that datastore.
Prerequisites
Remove all Hadoop clusters associated with the datastore. See “Delete a Cluster in the vSphere Web Client,”
on page 110.
Procedure
1 Use the vSphere Web Client to log in to vCenter Server.
2 Select Big Data Extensions.
3 From the Inventory Lists, select Resources.
4 Expand Resources, select Inventory Lists, and select Datastores.
5 Select the datastore that you want to remove, right-click, and select Remove.
6 Click Yes to confirm.
If you did not remove the cluster that uses the datastore, you receive an error message indicating that
the datastore cannot be removed because it is currently in use.
The datastore is removed from Big Data Extensions.
Add a Network in the vSphere Web Client
You add networks to Big Data Extensions to make the IP addresses contained by those networks available to
big data clusters.
Procedure
1 Use the vSphere Web Client to log in to vCenter Server.
2 Select Big Data Extensions.
3 From the Inventory Lists, select Resources.
4 Expand Resources, click Inventory Lists > Inventory Lists and select Networks.
5 Click the Add (+) icon.
6 In the Name text box, type a name with which to identify the network resource in Big Data Extensions.
7 From the Port group name list, select the vSphere port group that you want to add to Big Data
Extensions.
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8 Select a DNS type.
Option Description
Normal The DNS server provides both forward and reverse FQDN to IP resolution.
Reverse DNS is IP address to domain name mapping. The opposite of
forward (normal) DNS which maps domain names to IP addresses.
Normal is the default DNS type.
Dynamic Dynamic DNS (DDNS or DynDNS) is a method of automatically updating
a name server in the Domain Name System (DNS) with the active DNS
configuration of its configured hostnames, addresses or other information.
Big Data Extensions integrates with a Dynamic DNS server in its network
through which it provides meaningful host names to the nodes in a
Hadoop cluster. . The cluster will then automatically register with the DNS
server.
Others There is no DNS server in the VLAN, or the DNS server doesn't provide
normal DNS resolution or Dynamic DNS services. In this case, you must
add FQDN/IP mapping for all nodes in the /etc/hosts file for each node
in the cluster. Through this mapping of hostnames to IP addresses each
node can contact another node in the cluster.
9 Choose the type of addressing to use for the network: Use DHCP to obtain IP addresses or Use static
IP addresses.
10 (Optional) If you chose Use static IP addresses in Step 9, enter one or more IP address ranges.
11 Click OK to save your changes.
The IP addresses of the network are available to big data clusters that you create within Big Data Extensions.
Modify the DNS Type in the vSphere Web Client
DHCP selects the IP address for the IP pool randomly. The FQDN and the IP address of the nodes in a
cluster are random. The Hadoop user or application cannot identify where the master nodes are unless they
do a query to Big Data Extensions. Even if the user knows the original address, the address might change
when the cluster is restarted. Therefore, it is difficult for the Hadoop user or application to access the cluster.
Procedure
1 Use the vSphere Web Client to log in to vCenter Server.
2 Select Big Data Extensions.
3 From the Inventory Lists, select Resources.
4 Expand Resources, select Inventory Lists > Networks.
5 Select a single network to modify, right-click, and select Modify DNS Type.
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6 Select a DNS type.
Option Description
Normal The DNS server provides both forward and reverse FQDN to IP resolution.
Reverse DNS is IP address to domain name mapping. The opposite of
forward (normal) DNS which maps domain names to IP addresses.
Normal is the default DNS type.
Dynamic Dynamic DNS (DDNS or DynDNS) is a method of automatically updating
a name server in the Domain Name System (DNS) with the active DNS
configuration of its configured hostnames, addresses or other information.
Big Data Extensions integrates with a Dynamic DNS server in its network
through which it provides meaningful host names to the nodes in a
Hadoop cluster. . The cluster will then automatically register with the DNS
server.
Others There is no DNS server in the VLAN, or the DNS server doesn't provide
normal DNS resolution or Dynamic DNS services. In this case, you must
add FQDN/IP mapping for all nodes in the /etc/hosts file for each node
in the cluster. Through this mapping of hostnames to IP addresses each
node can contact another node in the cluster.
7 Click OK to save your changes.
Reconfigure a Static IP Network in the vSphere Web Client
You can reconfigure a Big Data Extensions static IP network by adding IP address segments to it. You might
need to add IP address segments so that there is enough capacity for a cluster that you want to create.
Prerequisites
If your network uses static IP addresses, be sure that the addresses are not occupied before you add the
network.
Procedure
1 Use the vSphere Web Client to log in to vCenter Server.
2 Select Big Data Extensions.
3 From the Inventory Lists, select Resources.
4 Expand Resources, select Inventory Lists > Networks.
5 Select the static IP network to reconfigure, right-click, and select Add IP Range.
6 Click Add IP range, and enter the IP address information.
7 Click OK to save your changes.
IP address segments are added to the network.
Remove a Network in the vSphere Web Client
You can remove an existing network from Big Data Extensions when you no longer need it. Removing an
unused network frees the IP addresses for use by other services.
Prerequisites
Remove clusters assigned to the network. See “Delete a Cluster in the vSphere Web Client,” on page 110.
Procedure
1 Use the vSphere Web Client to log in to vCenter Server.
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2 Select Big Data Extensions.
3 From the Inventory Lists, click Resources.
4 Expand Resources, select Inventory Lists > Networks.
5 Select the network to remove, right-click, and select Remove.
6 Click Yes to confirm.
If you have not removed the cluster that uses the network, you receive an error message indicating that
the network cannot be removed because it is currently in use.
The network is removed, and the IP addresses are available for use.
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Creating Hadoop and HBase Clusters 8
Big Data Extensions you can create and deploy Hadoop and HBase clusters. A big data cluster is a type of
computational cluster designed for storing and analyzing large amounts of unstructured data in a
distributed computing environment.
Restrictions
nWhen you create an HBase only cluster, you must use the default application manager because the
other application managers do not support HBase only clusters.
nYou cannot rename a cluster that was created with Cloudera Manager or Ambari application manager.
nTemporarily powering off hosts will cause Big Data clusters to fail during cluster creation.
When creating Big Data clusters, Big Data Extensions calculates virtual machine placement according to
available resources, Hadoop best practices, and user defined placement policies prior to creating the
virtual machines. When performing placement calculations, if some hosts are powered off or set to
stand-by, either manually, or automatically by VMware Distributed Power Management (VMware
DPM), those hosts will not be considered as available resources when Big Data Extensions calculates
virtual machine placement for use with a Big Data cluster.
If a host is powered off or set to stand-by after Big Data Extensions calculates virtual machine
placement, but before it creates the virtual machines, the cluster fails to create until you power on those
hosts. The following workarounds can help you both prevent and recover from this issue.
nDisable VMware DPM on those vSphere clusters where you deploy and run Big Data Extensions.
nPut hosts in maintenance mode before you power them off.
nIf a Big Data cluster fails to create due to its assigned hosts being temporarily unavailable, resume
the cluster creation after you power-on the hosts.
Requirements
The resource requirements are different for clusters created with the Serengeti Command-Line Interface and
the Big Data Extensions plug-in for the vSphere Web Client because the clusters use different default
templates. The default clusters created by using the Serengeti CLI are targeted for Project Serengeti users
and proof-of-concept applications, and are smaller than the Big Data Extensions plug-in templates, which
are targeted for larger deployments for commercial use.
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Some deployment configurations require more resources than other configurations. For example, if you
create a Greenplum HD 1.2 cluster, you cannot use the small size virtual machine. If you create a default
MapR or Greenplum HD cluster by using the Serengeti CLI, at least 550 GB of storage and 55 GB of memory
are recommended. For other Hadoop distributions, at least 350 GB of storage and 35 GB of memory are
recommended.
CAUTION When you create a cluster with Big Data Extensions, Big Data Extensions disables the virtual
machine automatic migration on the cluster. Although this prevents vSphere from automatically migrating
the virtual machines, it does not prevent you from inadvertently migrating cluster nodes to other hosts by
using the vCenter Server user interface. Do not use the vCenter Server user interface to migrate clusters.
Performing such management functions outside of the Big Data Extensions environment can make it
impossible for you to perform some Big Data Extensions operations, such as disk failure recovery.
Passwords must be from 8 to 20 characters, use only visible lowerASCII characters (no spaces), and must
contain at least one uppercase alphabetic character (A - Z), at least one lowercase alphabetic character (a - z),
at least one digit (0 - 9), and at least one of the following special characters: _, @, #, $, %, ^, &, *
This chapter includes the following topics:
n“About Hadoop and HBase Cluster Deployment Types,” on page 99
n“Hadoop Distributions Supporting MapReduce v1 and MapReduce v2 (YARN),” on page 99
n“About Cluster Topology,” on page 100
n“About HBase Database Access,” on page 100
n“Create a Big Data Cluster in the vSphere Web Client,” on page 101
n“Create an HBase Only Cluster in Big Data Extensions,” on page 104
n“Create a Cluster with an Application Manager by Using the vSphere Web Client,” on page 106
n“Create a Compute-Only Cluster with a Third Party Application Manager by Using vSphere Web
Client,” on page 106
n“Create a Compute Workers Only Cluster by Using the vSphere Web Client,” on page 107
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About Hadoop and HBase Cluster Deployment Types
With Big Data Extensions, you can create and use several types of big data clusters.
Basic Hadoop Cluster Simple Hadoop deployment for proof of concept projects and other small-
scale data processing tasks. The Basic Hadoop cluster contains HDFS and the
MapReduce framework. The MapReduce framework processes problems in
parallel across huge datasets in the HDFS.
HBase Cluster Runs on top of HDFS and provides a fault-tolerant way of storing large
quantities of sparse data.
Data and Compute
Separation Cluster
Separates the data and compute nodes, or clusters that contain compute
nodes only. In this type of cluster, the data node and compute node are not
on the same virtual machine.
Compute Only Cluster You can create a cluster that contain only compute nodes, for example
Jobtracker, Tasktracker, ResourceManager and NodeManager nodes, but not
Namenode and Datanodes. A compute only cluster is used to run
MapReduce jobs on an external HDFS cluster.
Compute Workers Only
Cluster
Contains only compute worker nodes, for example, Tasktracker and
NodeManager nodes, but not Namenodes and Datanodes. A compute
workers only cluster is used to add more compute worker nodes to an
existing Hadoop cluster.
HBase Only Cluster Contains HBase Master, HBase RegionServer, and Zookeeper nodes, but not
Namenodes or Datanodes. Multiple HBase only clusters can use the same
external HDFS cluster.
Customized Cluster Uses a cluster specification file to create clusters using the same
configuration as your previously created clusters. You can edit the cluster
specification file to customize the cluster configuration.
Hadoop Distributions Supporting MapReduce v1 and MapReduce v2
(YARN)
If you use either Cloudera CDH4 or CDH5 Hadoop distributions, which support both MapReduce v1 and
MapReduce v2 (YARN), the default Hadoop cluster configurations are different. The default hadoop cluster
configuration for CDH4 is a MapReduce v1 cluster. The default hadoop cluster configuration for CDH5 is a
MapReduce v2 cluster. All other distributions support either MapReduce v1 or MapReduce v2 (YARN), but
not both.
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About Cluster Topology
You can improve workload balance across your cluster nodes, and improve performance and throughput,
by specifying how Hadoop virtual machines are placed using topology awareness. For example, you can
have separate data and compute nodes, and improve performance and throughput by placing the nodes on
the same set of physical hosts.
To get maximum performance out of your big data cluster, configure your cluster so that it has awareness of
the topology of your environment's host and network information. Hadoop performs better when it uses
within-rack transfers, where more bandwidth is available, to off-rack transfers when assigning MapReduce
tasks to nodes. HDFS can place replicas more intelligently to trade off performance and resilience. For
example, if you have separate data and compute nodes, you can improve performance and throughput by
placing the nodes on the same set of physical hosts.
CAUTION When you create a cluster with Big Data Extensions, Big Data Extensions disables the virtual
machine automatic migration of the cluster. Although this prevents vSphere from migrating the virtual
machines, it does not prevent you from inadvertently migrating cluster nodes to other hosts by using the
vCenter Server user interface. Do not use the vCenter Server user interface to migrate clusters. Performing
such management functions outside of the Big Data Extensions environment might break the placement
policy of the cluster, such as the number of instances per host and the group associations. Even if you do not
specify a placement policy, using vCenter Server to migrate clusters can break the default ROUNDROBIN
placement policy constraints.
You can specify the following topology awareness configurations.
Hadoop Virtualization
Extensions (HVE)
Enhanced cluster reliability and performance provided by refined Hadoop
replica placement, task scheduling, and balancer policies. Hadoop clusters
implemented on a virtualized infrastructure have full awareness of the
topology on which they are running when using HVE.
To use HVE, your Hadoop distribution must support HVE and you must
create and upload a topology rack-hosts mapping file.
RACK_AS_RACK Standard topology for Apache Hadoop distributions. Only rack and host
information are exposed to Hadoop. To use RACK_AS_RACK, create and
upload a server topology file.
HOST_AS_RACK Simplified topology for Apache Hadoop distributions. To avoid placing all
HDFS data block replicas on the same physical host, each physical host is
treated as a rack. Because data block replicas are never placed on a rack, this
avoids the worst case scenario of a single host failure causing the complete
loss of any data block.
Use HOST_AS_RACK if your cluster uses a single rack, or if you do not have
rack information with which to decide about topology configuration options.
None No topology is specified.
About HBase Database Access
Serengeti supports several methods of HBase database access.
nLog in to the client node virtual machine and run hbase shell commands.
nLog in to the client node virtual machine and run HBase jobs by using the hbase command.
hbase org.apache.hadoop.hbase.PerformanceEvaluation –-nomapred randomWrite 3
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The default Serengeti-deployed HBase cluster does not contain Hadoop JobTracker or Hadoop
TaskTracker daemons. To run an HBase MapReduce job, you must deploy a customized cluster that
includes JobTracker and TaskTracker nodes.
nUse the client node Rest-ful Web Services, which listen on port 8080, by using the curl command.
curl –I http://client_node_ip:8080/status/cluster
nUse the client node Thrift gateway, which listens on port 9090.
Create a Big Data Cluster in the vSphere Web Client
After you complete deployment of the Hadoop distribution, you can create big data clusters to process data.
You can create multiple clusters in your Big Data Extensions environment but your environment must meet
all prerequisites and have adequate resources.
Prerequisites
nStart the Big Data Extensions vApp.
nInstall theBig Data Extensions plug-in.
nConnect to a Serengeti Management Server.
nConfigure one or more Hadoop distributions.
nUnderstand the topology configuration options that you want to use with your cluster.
Procedure
1 Use the vSphere Web Client to log in to vCenter Server.
2 Select Big Data Extensions > Big Data Clusters.
3 In the Objects tab, click New Big Data Cluster.
4 Follow the prompts to create the new cluster. The table describes the information to enter for the cluster
that you want to create.
Option Description
Hadoop cluster name Type a name to identify the cluster.
The only valid characters for cluster names are alphanumeric and
underscores. When you choose the cluster name, also consider the
applicable vApp name. Together, the vApp and cluster names must be < 80
characters.
Application manager Select an application manager. The list contains the default application
manager and the application managers that you added to your Big Data
Extensions environment. For example, Cloudera Manager and Ambari.
Hadoop distro Select the Hadoop distribution. The list contains the default Apache Bigtop
distribution for Big Data Extensions and the distributions that you added
to your Big Data Extensions environment. The distribution names match
the value of the --name parameter that was passed to the config-
distro.rb script when the Hadoop distribution was configured. For
example, cdh5 and mapr.
NOTE To create an Apache Bigtop, Cloudera CDH4 and CDH5,
Hortonworks HDP 2.x, or Pivotal PHD 1.1 or later cluster, you must
configure a valid DNS and FQDN for the cluster's HDFS and MapReduce
network traffic. If the DNS server cannot provide valid forward and
reverse FQDN/IP resolution, the cluster creation process might fail or the
cluster is created but does not function.
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Option Description
Local repository URL Type a local repository URL. This is an optional item for all of application
managers. If you specify a local repository URL, the Cloudera Manager or
Ambari application manager downloads the required Red Hat Package
Managers (RPMs) from the local repository that you specify instead of
from a remote repository, which could affect your system performance.
Deployment type Select the type of cluster you want to create.
nBasic Hadoop Cluster
nBasic HBase Cluster
nCompute Only Hadoop Cluster
nCompute Workers Only Cluster
nHBase Only Cluster
nData/Compute Separation Hadoop Cluster
nCustomized
The type of cluster you create determines the available node group
selections.
If you select Customize, you can load an existing cluster specification file.
DataMaster Node Group The DataMaster node is a virtual machine that runs the Hadoop
NameNode service. This node manages HDFS data and assigns tasks to
Hadoop TaskTracker services deployed in the worker node group.
Select a resource template from the drop-down menu, or select Customize
to customize a resource template.
For the master node, use shared storage so that you protect this virtual
machine with vSphere HA and vSphere FT.
ComputeMaster Node Group The ComputeMaster node is a virtual machine that runs the Hadoop
JobTracker service. This node assigns tasks to Hadoop TaskTracker
services deployed in the worker node group.
Select a resource template from the drop-down menu, or select Customize
to customize a resource template.
For the master node, use shared storage so that you protect this virtual
machine with vSphere HA and vSphere FT.
HBaseMaster Node Group (HBase
cluster only)
The HBaseMaster node is a virtual machine that runs the HBase master
service. This node orchestrates a cluster of one or more RegionServer slave
nodes.
Select a resource template from the drop-down menu, or select Customize
to customize a resource template.
For the master node, use shared storage so that you protect this virtual
machine with vSphere HA and vSphere FT.
Worker Node Group Worker nodes are virtual machines that run the Hadoop DataNode,
TaskTracker, and HBase HRegionServer services. These nodes store HDFS
data and execute tasks.
Select the number of nodes and the resource template from the drop-down
menu, or select Customize to customize a resource template.
For worker nodes, use local storage.
NOTE You can add nodes to the worker node group by using Scale Out
Cluster. You cannot reduce the number of nodes.
Client Node Group A client node is a virtual machine that contains Hadoop client components.
From this virtual machine you can access HDFS, submit MapReduce jobs,
run Pig scripts, run Hive queries, and HBase commands.
Select the number of nodes and a resource template from the drop-down
menu, or select Customize to customize a resource template.
NOTE You can add nodes to the client node group by using Scale Out
Cluster. You cannot reduce the number of nodes.
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Option Description
Hadoop Topology Select the topology configuration that you want the cluster to use.
nRACK_AS_RACK
nHOST_AS_RACK
nHVE
nNONE
If you do not see the topology configuration that you want, define it in a
topology rack-hosts mapping file, and use the Serengeti Command-Line
Interface to upload the file to the Serengeti Management Server. See
“About Cluster Topology,” on page 100
Network Select one or more networks for the cluster to use.
For optimal performance, use the same network for HDFS and MapReduce
traffic in Hadoop and Hadoop+HBase clusters. HBase clusters use the
HDFS network for traffic related to the HBase Master and HBase
RegionServer services.
IMPORTANT You cannot configure multiple networks for clusters that use
the MapR Hadoop distribution.
nTo use one network for all traffic, select the network from the Network
list.
nTo use separate networks for the management, HDFS, and MapReduce
traffic, select Customize the HDFS network and MapReduce
network, and select a network from each network list.
Resource Pools Select one or more resource pools that you want the cluster to use.
VM Password Choose how initial administrator passwords are assigned to the virtual
machine nodes of the cluster.
nUse random password.
nSet password.
To assign a custom initial administrator password to all the nodes in the
cluster, choose Set password, and type and confirm the initial password.
Passwords must be from 8 to 20 characters, use only visible lowerASCII
characters (no spaces), and must contain at least one uppercase alphabetic
character (A - Z), at least one lowercase alphabetic character (a - z), at least
one digit (0 - 9), and at least one of the following special characters: _, @, #,
$, %, ^, &, *
IMPORTANT If you set an initial administrator password, it is used for
nodes that are created by future scaling and disk failure recovery
operations. If you use the random password, nodes that are created by
future scaling and disk failure recovery operations will use new, random
passwords.
Local repository URL Type a local repository URL.
This is an optional item for all application managers. If you specify a local
repository URL, the Cloudera Manager or Ambari application manager
downloads the required Red Hat Package Managers (RPMs) from the local
repository that you specify instead of from a remote repository, which
could affect your system performance.
The Serengeti Management Server clones the template virtual machine to create the nodes in the cluster.
When each virtual machine starts, the agent on that virtual machine pulls the appropriate
Big Data Extensions software components to that node and deploys the software.
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Create an HBase Only Cluster in Big Data Extensions
With Big Data Extensions, you can create an HBase only cluster, which contain only HBase Master, HBase
RegionServer, and Zookeeper nodes, but not Namenodes and Datanodes. The advantage of having an
HBase only cluster is that multiple HBase clusters can use the same external HDFS.
Procedure
1Prerequisites for Creating an HBase Only Cluster on page 104
Before you can create an HBase only cluster, you must verify that your system meets all of the
prerequisites.
2Prepare the EMC Isilon OneFS as the External HDFS Cluster on page 104
If you use EMC Isilon OneFS as the external HDFS cluster to the HBase only cluster, you must create
and configure users and user groups, and prepare your Isilon OneFS environment.
3Create an HBase Only Cluster by Using the vSphere Web Client on page 105
You can use the vSphere Web Client to create an HBase only cluster.
Prerequisites for Creating an HBase Only Cluster
Before you can create an HBase only cluster, you must verify that your system meets all of the prerequisites.
Prerequisites
nVerify that you started the Serengeti vApp.
nVerify that you have more than one distribution if you want to use a distribution other than the default
distribution.
nVerify that you have an existing HDFS cluster to use as the external HDFS cluster.
To avoid conflicts between the HBase only cluster and the external HDFS cluster, the clusters should
use the same Hadoop distribution and version.
nIf the external HDFS cluster was not created using Big Data Extensions, verify that the HDFS
directory /hadoop/hbase, the group hadoop, and the following users exist in the external HDFS cluster:
nhdfs
nhbase
nserengeti
nIf you use the EMC Isilon OneFS as the external HDFS cluster, verify that your Isilon environment is
prepared.
For information about how to prepare your environment, see “Prepare the EMC Isilon OneFS as the
External HDFS Cluster,” on page 104.
Prepare the EMC Isilon OneFS as the External HDFS Cluster
If you use EMC Isilon OneFS as the external HDFS cluster to the HBase only cluster, you must create and
configure users and user groups, and prepare your Isilon OneFS environment.
Procedure
1 Log in to one of the Isilon HDFS nodes as user root.
2 Create the users.
nhdfs
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nhbase
nserengeti
nmapred
The yarn and mapred users should have write, read, and execute permissions to the entire exported
HDFS directory.
3 Create the user group hadoop.
4 Create the directory tmp under the root HDFS directory.
5 Set the owner as hdfs:hadoop with the read and write permissions set as 777.
6 Create the directory hadoop under the root HDFS directory.
7 Set the owner as hdfs:hadoop with the read and write permissions set as 775.
8 Create the directory hbase under the directory hadoop.
9 Set the owner as hbase:hadoop with the read and write permissions set as 775.
10 Set the owner of the root HDFS directory as hdfs:hadoop.
Example: Configuring the EMC Isilon OneFS Environment
isi auth users create --name="hdfs"
isi auth users create --name="hbase"
isi auth users create --name="serengeti"
isi auth groups create --name="hadoop"
pw useradd mapred -G wheel
pw useradd yarn -G wheel
chown hdfs:hadoop /ifs
mkdir /ifs/tmp
chmod 777 /ifs/tmp
chown hdfs:hadoop /ifs/tmp
mkdir -p /ifs/hadoop/hbase
chmod -R 775 /ifs/hadoop
chown hdfs:hadoop /ifs/hadoop
chown hbase:hadoop /ifs/hadoop/hbase
What to do next
You are now ready to create the HBase only cluster with the EMC Isilon OneFS as the external cluster.
Create an HBase Only Cluster by Using the vSphere Web Client
You can use the vSphere Web Client to create an HBase only cluster.
You must use the default application manager because the other application managers do not support
HBase only clusters.
Procedure
1 In the Big Data Clusters page, click New Big Data Cluster.
2 On the General page, enter a name for the cluster.
3 Select Default from the Application Manager drop-down menu.
4 Select a distribution from the Hadoop Distribution drop-down menu.
5 On the Set Node Groups page, select HBase Only Cluster from the Deployment Type drop-down
menu.
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6 In the NameNode URI text box, enter the external HDFS NameNode URI.
The NameNode URI is the URI of the NameNode, for example hdfs://namenode_hostname:8020.
7 Follow the prompts to complete the HBase cluster creation process.
Create a Cluster with an Application Manager by Using the vSphere
Web Client
To create and manage a cluster with an application manager other than the default application manager,
you must specify the application manager to use before you create the cluster.
NOTE If you want to use a local yum repository, after you select either Cloudera Manager or Ambari for
your application manager, a text box appears where you can enter the URL of the local repository you want
to use. It is important that you have created the repository before you create the cluster. For more
information about setting up a yum repository, see “Set Up a Local Yum Repository for Ambari Application
Manager,” on page 64 or “Set Up a Local Yum Repository for Cloudera Manager Application Manager,” on
page 61.
Prerequisites
nConnect to an application manager.
nEnsure that you have adequate resources allocated to run the Hadoop cluster. For information about
resource requirements, see the documentation for your application manager.
nConfigure one or more Hadoop distributions.
Procedure
1 In the Big Data Clusters page, click New Big Data Cluster.
2 Follow the prompts to create the new cluster.
What to do next
To view the new cluster, from theBig Data Extensions navigation pane, under Inventory Lists, click Big
Data Clusters.
If you do not specify an application manager, the default application manager is used.
Create a Compute-Only Cluster with a Third Party Application
Manager by Using vSphere Web Client
You can create compute-only clusters to run MapReduce jobs on existing HDFS clusters, including storage
solutions that serve as an external HDFS.
If you use EMC Isilon OneFS as the external HDFS cluster to the HBase only cluster, you must create and
configure users and user groups, and prepare your Isilon OneFS environment. See “Prepare the EMC Isilon
OneFS as the External HDFS Cluster,” on page 104.
Prerequisites
nDeploy the Serengeti vApp.
nEnsure that you have adequate resources allocated to run the Hadoop cluster.
nTo use any Hadoop distribution other than the default distribution, add one or more Hadoop
distributions. See the VMware vSphere Big Data Extensions Administrator's and User's Guide.
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Procedure
1 From Big Data Extensions, select New Big Data Cluster.
2 On the General panel, select from the pull-down list the application manager you want to use to
manage the cluster.
3 To customize the cluster for the Cloudera Manager or Ambari application managers, select Customize
from the pull down list.
4 Click Load to select the specification file.
5 Complete the steps of the wizard to finish the cluster creation process.
Create a Compute Workers Only Cluster by Using the vSphere Web
Client
If you already have a physical Hadoop cluster and want to do more CPU or memory intensive operations,
you can increase the compute capacity by provisioning a workers only cluster. The workers only cluster is a
part of the physical Hadoop cluster and can be scaled out elastically.
With the compute workers only clusters, you can "burst out to virtual." It is a temporary operation that
involves borrowing resources when you need them and then returning the resources when you no longer
need them. With "burst out to virtual," you spin up compute only workers nodes and add them to either an
existing physical or virtual Hadoop cluster.
Worker only clusters are not supported on Ambari and Cloudera Manager application managers.
Prerequisites
nEnsure that you have an existing Hadoop cluster.
nVerify that you have the IP addresses of the NameNode and ResourceManager node.
Procedure
1 Click Create Big Data Cluster on the objects pane.
2 In the Create Big Data Cluster wizard, choose the same distribution as the Hadoop cluster.
3 Set the DataMaster URL HDFS:namenode ip or fqdn:8020.
4 Set the ComputeMaster URL nodeManager ip or fqdn.
5 Follow the steps in the wizard and add the other resources.
There will be three node managers in the cluster. The three new node managers are registered to the
resource manager.
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Managing Hadoop and HBase
Clusters 9
You can use the vSphere Web Client to start and stop your big data cluster and modify the cluster
configuration. You can also manage a cluster using the Serengeti Command-Line Interface.
CAUTION Do not use vSphere management functions such as migrating cluster nodes to other hosts for
clusters that you create with Big Data Extensions. Performing such management functions outside of the
Big Data Extensions environment can make it impossible for you to perform some Big Data Extensions
operations, such as disk failure recovery.
This chapter includes the following topics:
n“Stop and Start a Cluster in the vSphere Web Client,” on page 109
n“Delete a Cluster in the vSphere Web Client,” on page 110
n“About Resource Usage and Elastic Scaling,” on page 110
n“Scale a Cluster in or out by using the vSphere Web Client,” on page 114
n“Scale CPU and RAM in the vSphere Web Client,” on page 115
n“Use Disk I/O Shares to Prioritize Cluster Virtual Machines in the vSphere Web Client,” on page 116
n“About vSphere High Availability and vSphere Fault Tolerance,” on page 116
n“Change the User Password on All of the Nodes of a Cluster,” on page 117
n“Reconfigure a Cluster with the Serengeti Command-Line Interface,” on page 117
n“Recover from Disk Failure with the Serengeti Command-Line Interface Client,” on page 119
n“Enter Maintenance Mode to Perform Backup and Restore with the Serengeti Command-Line Interface
Client,” on page 120
n“Log in to Hadoop Nodes with the Serengeti Command-Line Interface Client,” on page 121
Stop and Start a Cluster in the vSphere Web Client
You can stop a running Hadoop cluster and start a stopped Hadoop cluster from the vSphere Web Client.
Prerequisites
nTo stop a cluster it must be running.
nTo start a cluster it must be stopped.
Procedure
1 Use the vSphere Web Client to log in to vCenter Server.
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2 Select Big Data Extensions.
3 From the Inventory Lists, click Big Data Clusters.
4 Select the cluster to stop or start from the Hadoop Cluster Name column, and right-click to display the
Actions menu.
5 Select Shut Down Big Data Cluster to stop a running cluster, or select Start Big Data Cluster to start a
cluster.
Delete a Cluster in the vSphere Web Client
You can delete a cluster by using the vSphere Web Client. When you delete a cluster, it is removed from the
inventory and the datastore.
When you create a cluster, Big Data Extensions creates a folder and a resource pool for each cluster, and
resource pools for each node group in the cluster. When you delete a cluster all of these organizational
folders and resource pools are also removed.
When you delete a cluster, it is removed from the inventory and the datastore.
You can delete a running cluster, a stopped cluster, or a cluster that is in an error state.
Procedure
1 Use the vSphere Web Client to log in to vCenter Server.
2 In the object navigator, select Big Data Extensions.
3 In Inventory Lists, select Big Data Clusters.
4 From the Objects Name column, select the cluster to delete.
5 Click the All Actions icon, and select Delete Big Data Cluster.
The cluster and all the virtual machines it contains are removed from your Big Data Extensions
environment.
About Resource Usage and Elastic Scaling
Scaling lets you adjust the compute capacity of Hadoop data-compute separated clusters. When you enable
elastic scaling for a Hadoop cluster, the Serengeti Management Server can stop and start compute nodes to
match resource requirements to available resources. You can use manual scaling for more explicit cluster
control.
Manual scaling is appropriate for static environments where capacity planning can predict resource
availability for workloads. Elastic scaling is best suited for mixed workload environments where resource
requirements and availability fluctuate.
When you select manual scaling, Big Data Extensions disables elastic scaling. You can configure the target
number of compute nodes for manual scaling. If you do not configure the target number of compute nodes,
Big Data Extensions sets the number of active compute nodes to the current number of active compute
nodes. If nodes become unresponsive, they remain in the cluster and the cluster operates with fewer
functional nodes. In contrast, when you enable elastic scaling, Big Data Extensions manages the number of
active TaskTracker nodes according to the range that you specify, replacing unresponsive or faulty nodes
with live, responsive nodes.
For both manual and elastic scaling, Big Data Extensions, not vCenter Server, controls the number of active
nodes. However, vCenter Server applies the usual reservations, shares, and limits to the resource pool of a
cluster according to the vSphere configuration of the cluster. vSphere DRS operates as usual, allocating
resources between competing workloads, which in turn influences how Big Data Extensions dynamically
adjusts the number of active nodes in competing Hadoop clusters while elastic scaling is in effect.
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Big Data Extensions also lets you adjust the access priority for the datastores of cluster nodes by using the
vSphere Storage I/O Control feature. Clusters configured for HIGH I/O shares receive higher priority access
than clusters with NORMAL priority. Clusters configured for NORMAL I/O shares receive higher priority
access than clusters with LOW priority. In general, higher priority provides better disk I/O performance.
Scaling Modes
To change between manual and elastic scaling, you change the scaling mode.
nMANUAL. Big Data Extensions disables elastic scaling. When you change to manual scaling, you can
configure the target number of compute nodes. If you do not configure the target number of compute
nodes, Big Data Extensions sets the number of active compute nodes to the current number of active
compute nodes.
nAUTO. Enables elastic scaling. Big Data Extensions manages the number of active compute nodes,
maintaining the number of compute nodes in the range from the configured minimum to the
configured maximum number of compute nodes in the cluster. If the minimum number of compute
nodes is undefined, the lower limit is 0. If the maximum number of compute nodes is undefined, the
upper limit is the number of available compute nodes.
Elastic scaling operates on a per-host basis, at a node-level granularity. That is, the more compute nodes
a Hadoop cluster has on a host, the finer the control that Big Data Extensions elasticity can exercise. The
tradeoff is that the more compute nodes you have, the higher the overhead in terms of runtime resource
cost, disk footprint, I/O requirements, and so on.
When resources are overcommitted, elastic scaling reduces the number of powered on compute nodes.
Conversely, if the cluster receives all the resources it requested from vSphere, and Big Data Extensions
determines that the cluster can make use of additional capacity, elastic scaling powers on additional
compute nodes.
Resources can become overcommitted for many reasons, such as:
nThe compute nodes have lower resource entitlements than a competing workload, according to
how vCenter Server applies the usual reservations, shares, and limits as configured for the cluster.
nPhysical resources are configured to be available, but another workload is consuming those
resources.
In elastic scaling, Big Data Extensions has two different behaviors for deciding how many active
compute nodes to maintain. In both behaviors, Big Data Extensions replaces unresponsive or faulty
nodes with live, responsive nodes.
nVariable. The number of active, healthy TaskTracker compute nodes is maintained from the
configured minimum number of compute nodes to the configured maximum number of compute
nodes. The number of active compute nodes varies as resource availability fluctuates.
nFixed. The number of active, healthy TaskTracker compute nodes is maintained at a fixed number
when the same value is configured for the minimum and maximum number of compute nodes.
Default Cluster Scaling Parameter Values
When you create a cluster, the scaling configuration of the cluster is as follows.
nThe scaling mode is MANUAL, for manual scaling.
nThe minimum number of compute nodes is -1. It appears as "Unset" in the Serengeti CLI displays. Big
Data Extensions elastic scaling treats a minComputeNodeNum value of -1 as if it were zero (0).
nThe maximum number of compute nodes is -1. It appears as "Unset" in the Serengeti CLI displays. Big
Data Extensions elastic scaling treats a maxComputeNodeNum value of -1 as if it were unlimited.
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nThe target number of nodes is not applicable. Its value is -1. Big Data Extensions manual scaling
operations treat a targetComputeNodeNum value of -1 as if it were unspecified upon a change to manual
scaling.
Interactions Between Scaling and Other Cluster Operations
Some cluster operations cannot be performed while Big Data Extensions is actively scaling a cluster.
If you try to perform the following operations while Big Data Extensions is scaling a cluster in MANUAL
mode, Big Data Extensions warns you that in the current state of the cluster, the operation cannot be
performed.
nConcurrent attempt at manual scaling
nSwitch to AUTO mode while manual scaling operations are in progress
If a cluster is in AUTO mode for elastic scaling when you perform the following cluster operations on it, Big
Data Extensions changes the scaling mode to MANUAL and changes the cluster to manual scaling. You can
re-enable the AUTO mode for elastic scaling after the cluster operation finishes, except if you delete the
cluster.
nDelete the cluster
nRepair the cluster
nStop the cluster
If a cluster is in AUTO mode for elastic scaling when you perform the following cluster operations on it, Big
Data Extensions temporarily switches the cluster to MANUAL mode. When the cluster operation finishes,
Big Data Extensions returns the scaling mode to AUTO, which re-enables elastic scaling.
nResize the cluster
nReconfigure the cluster
If Big Data Extensions is scaling a cluster when you perform an operation that changes the scaling mode to
MANUAL, your requested operation waits until the scaling finishes, and then the requested operation
begins.
Optimize Cluster Resource Usage with Elastic Scaling in the
vSphere Web Client
You can specify the scaling mode of a cluster. Scaling lets you specify the number of nodes that the cluster
can use, and whether it adds nodes or uses nodes within a targeted range.
When you enable elastic scaling for a cluster, Big Data Extensions optimizes cluster performance and use of
nodes that have a Hadoop TaskTracker role.
When you set a cluster's scaling mode to AUTO, configure the minimum number of compute nodes. If you
do not configure the minimum and maximum number of compute nodes, the previous settings are retained.
When you set a cluster's scaling mode to MANUAL, configure the target number of compute nodes. If you
do not configure the target number of compute nodes, Big Data Extensions sets the number of active
compute nodes to the current number of active compute nodes.
In elastic scaling, Big Data Extensions has two different behaviors for deciding how many active compute
nodes to maintain. In both behaviors, Big Data Extensions replaces unresponsive or faulty nodes with live,
responsive nodes.
nVariable. The number of active, healthy TaskTracker compute nodes is maintained from the configured
minimum number of compute nodes to the configured maximum number of compute nodes. The
number of active compute nodes varies as resource availability fluctuates.
nFixed. The number of active, healthy TaskTracker compute nodes is maintained at a fixed number when
the same value is configured for the minimum and maximum number of compute nodes.
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Prerequisites
nUnderstand how elastic scaling and resource usage work. See “About Resource Usage and Elastic
Scaling,” on page 110.
nVerify that the cluster you want to optimize is data-compute separated. See “About Hadoop and HBase
Cluster Deployment Types,” on page 99
Procedure
1 Use the vSphere Web Client to log in to vCenter Server.
2 In the object navigator select Big Data Extensions.
3 Under Inventory Lists click Big Data Clusters.
4 Select the cluster whose elasticity mode you want to set from the Hadoop Cluster Name column.
5 Click the All Actions icon, and select Set Elasticity Mode.
6 Specify the elasticity settings for the cluster that you want to modify.
Option Description
Elasticity mode Select the type of elasticity mode you want to use. You can choose manual
or automatic.
Target compute nodes Specify the number of compute nodes the cluster should target for use.
This option is applicable only to manual scaling (manual elasticity mode).
If you do not specify the target number of compute nodes, the node setting
remains unconfigured, and Big Data Extensions sets the number of active
compute nodes to the current number of active compute nodes.
NOTE A value of "Unset" or "-1" means that the node setting has not been
configured and is not applicable.
Min compute nodes Specify the minimum number (the lower limit) of active compute nodes to
maintain in the cluster. This option is applicable only to elastic scaling
(automatic elasticity mode).
To ensure that under contention elasticity keeps a cluster operating with
more than a cluster’s initial default setting of zero compute nodes,
configure the minimum number of compute nodes to a nonzero number.
Max compute nodes Specify the maximum number (the upper limit) of active compute nodes to
maintain in the cluster. This option is applicable only to elastic scaling
(automatic elasticity mode).
What to do next
Specify the cluster's access priority for datastores. See “Use Disk I/O Shares to Prioritize Cluster Virtual
Machines in the vSphere Web Client,” on page 116.
Schedule Fixed Elastic Scaling for a Hadoop Cluster
You can enable fixed, elastic scaling according to a preconfigured schedule. Scheduled fixed, elastic scaling
provides more control than variable, elastic scaling while still improving efficiency, allowing explicit
changes in the number of active compute nodes during periods of predictable usage.
For example, in an office with typical workday hours, there is likely a reduced load on a VMware View
resource pool after the office staff goes home. You could configure scheduled fixed, elastic scaling to specify
a greater number of compute nodes from 8 PM to 4 AM, when you know that the workload would
otherwise be very light.
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Prerequisites
From the Serengeti Command-Line Interface, enable the cluster for elastic scaling, and set the
minComputeNodeNum and MaxComputeNodeNum parameters to the same value: the number of active TaskTracker
nodes that you want during the period of scheduled fixed elasticity.
Procedure
1 Open a command shell, such as Bash or PuTTY, and log in to the Serengeti Management Server as user
serengeti.
2 Use any scheduling mechanism that you want to call
the /opt/serengeti/sbin/set_compute_node_num.sh script to set the number of active TaskTracker
compute nodes that you want.
/opt/serengeti/sbin/set_compute_node_num.sh --name cluster_name
--computeNodeNum num_TT_to_maintain
After the scheduling mechanism calls the set_compute_node_num.sh script, fixed, elastic scaling remains
in effect with the configured number of active TaskTracker compute nodes until the next scheduling
mechanism change or until a user changes the scaling mode or parameters in either the vSphere Web
Client or the Serengeti Command-Line Interface.
This example shows how to use a crontab file on the Serengeti Management Server to schedule specific
numbers of active TaskTracker compute nodes.
# cluster_A: use 20 active TaskTracker compute nodes from 11:00 to 16:00, and 30 compute
nodes the rest of the day
00 11 * * * /opt/serengeti/sbin/set_compute_node_num.sh --name cluster_A
--computeNodeNum 20 >> $HOME/schedule_elasticity.log 2>&1
00 16 * * * /opt/serengeti/sbin/set_compute_node_num.sh --name cluster_A
--computeNodeNum 30 >> $HOME/schedule_elasticity.log 2>&1
# cluster_B: use 3 active TaskTracker compute nodes beginning at 10:00 every weekday
0 10 * * 1-5 /opt/serengeti/sbin/set_compute_node_num.sh --name cluster_B
--computeNodeNum 3 >> $HOME/schedule_elasticity.log 2>&1
# cluster_C: reset the number of active TaskTracker compute nodes every 6 hours to 15
0 */6 * * * /opt/serengeti/sbin/set_compute_node_num.sh --name cluster_B
--computeNodeNum 15 >> $HOME/schedule_elasticity.log 2>&1
Scale a Cluster in or out by using the vSphere Web Client
When you create Hadoop clusters you must specify the number of nodes to use. After the cluster is created,
you can resize the cluster by changing the number of worker nodes and client nodes. You can increase the
number of nodes to scale out a node group. You can also decrease the number of nodes to scale in a
compute-only node group. A node group is considered to be a compute-only node group if it only contains
compute roles such as tasktracker or nodemanger.
You can resize the cluster using the vSphere Web Client or the Serengeti CLI Client. However, the CLI
provides more configuration options than the vSphere Web Client. See the VMware vSphere Big Data
Extensions Command-Line Interface Guide.
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By default you can only scale in compute nodes. To scale in node groups containing other roles (for example
role A and role B), you need to login to the Big Data Extensions server and remove role A and role B in
related blacklist files. The blacklist file name is scale_in_roles_blacklist.json, and is located in the
directory /opt/serengeti/conf/application_manager_type. The application_manager_type can be Ambari,
Cloudera Manager, or Default.
IMPORTANT Even if you changed the user password on the nodes, the changed password is not used for the
new nodes that are created when you resize a cluster. If you set the initial administrator password when you
created the cluster, that initial administrator password is used for the new nodes. If you did not set the
initial administrator password when you created the cluster, new random passwords are used for the new
nodes.
Prerequisites
nVerify that the cluster is running. See “Stop and Start a Cluster in the vSphere Web Client,” on page 109.
Procedure
1 Use the vSphere Web Client to log in to vCenter Server.
2 Select Big Data Extensions.
3 From the Inventory List, select Big Data Clusters.
4 From the Hadoop Cluster Name column, select the cluster to resize.
5 Click the All Actions icon, and select Scale Out/In.
6 From the Node Group list, select the worker or client node group to scale out or to scale in.
If a node group does not have any nodes, it does not appear in the Node group list.
7 In the Instance number text box, type the target number of node instances to add, and click OK.
The cluster is scaled to the specified number of nodes.
Scale CPU and RAM in the vSphere Web Client
You can increase or decrease the compute capacity of a cluster to prevent CPU or memory resource
contention among running jobs.
You can adjust compute resources without increasing the workload on the Master node. If increasing or
decreasing the CPU or RAM of a cluster is unsuccessful for a node, which is commonly because of
insufficient resources being available, the node is returned to its original CPU or RAM setting.
All node types support CPU and RAM scaling, but do not scale the master node CPU or RAM of a cluster
because Big Data Extensions powers down the virtual machine during the scaling process.
When you scale the CPU or RAM of a cluster, the number of CPUs must be a multiple of the number of
cores per socket, and you must scale the amount of RAM as a multiple of 4, allowing a minimum of 3748
MB.
Prerequisites
nVerify that the cluster that you want to scale is running. See “Stop and Start a Cluster in the vSphere
Web Client,” on page 109.
Procedure
1 Use the vSphere Web Client to log in to vCenter Server.
2 Select Big Data Extensions.
3 From the Inventory Lists, select Big Data Clusters.
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4 From the Hadoop Cluster Name column, select the cluster that you want to scale up or down.
5 Click the All Actions icon, and select Scale Up/Down.
6 From the Node group drop-down menu, select the ComputeMaster, DataMaster, Worker, Client, or
Customized node group whose CPU or RAM you want to scale up or down.
7 Enter the number of vCPUs to use and the amount of RAM and click OK.
After applying new values for CPU and RAM, the cluster is placed into Maintenance mode as it applies the
new values. You can monitor the status of the cluster as the new values are applied.
Use Disk I/O Shares to Prioritize Cluster Virtual Machines in the
vSphere Web Client
You can set the disk I/O shares for the virtual machines running a cluster. Disk shares distinguish high-
priority virtual machines from low-priority virtual machines.
Disk shares is a value that represents the relative metric for controlling disk bandwidth to all virtual
machines. The values are compared to the sum of all shares of all virtual machines on the server and, on an
ESXi host, the service console. Big Data Extensions can adjust disk shares for all virtual machines in a
cluster. Using disk shares you can change a cluster's I/O bandwidth to improve the cluster's I/O
performance.
For more information about using disk shares to prioritize virtual machines, see the VMware vSphere ESXi
and vCenter Server documentation.
Procedure
1 Use the vSphere Web Client to log in to vCenter Server.
2 In the object navigator select Big Data Extensions.
3 In the Inventory Lists click Big Data Clusters.
4 Select the cluster whose disk IO shares you want to set from the Hadoop Cluster Name column.
5 Click the Actions icon, and select Set Disk IO Share.
6 Specify a value to allocate a number of shares of disk bandwidth to the virtual machine running the
cluster.
Clusters configured for HIGH I/O shares receive higher priority access than those with NORMAL and
LOW priorities, which provides better disk I/O performance. Disk shares are commonly set LOW for
compute virtual machines and NORMAL for data virtual machines. The master node virtual machine is
commonly set to NORMAL.
7 Click OK to save your changes.
About vSphere High Availability and vSphere Fault Tolerance
The Serengeti Management Server leverages vSphere HA to protect the Hadoop master node virtual
machine, which can be monitored by vSphere.
When a Hadoop NameNode or JobTracker service stops unexpectedly, vSphere restarts the Hadoop virtual
machine in another host, reducing unplanned downtime. If vsphere Fault Tolerance is configured and the
master node virtual machine stops unexpectedly because of host failover or loss of network connectivity, the
secondary node is used, without downtime.
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Change the User Password on All of the Nodes of a Cluster
You can change the user password for all nodes in a cluster. The user password that you can change
includes the serengeti and root users.
Passwords must be from 8 to 20 characters, use only visible lowerASCII characters (no spaces), and must
contain at least one uppercase alphabetic character (A - Z), at least one lowercase alphabetic character (a - z),
at least one digit (0 - 9), and at least one of the following special characters: _, @, #, $, %, ^, &, *
IMPORTANT If you scale out or perform disk recovery operations on a cluster after you change the user
password for the cluster's original nodes, the changed password is not used for the new cluster nodes that
are created by the scale out or disk recovery operation. If you set the cluster's initial administrator password
when you created the cluster, that initial administrator password is used for the new nodes. If you did not
set the cluster's initial administrator password when you created the cluster, new random passwords are
used for the new nodes.
Prerequisites
nDeploy the Big Data Extensions vApp. See “Deploy the Big Data Extensions vApp in the vSphere Web
Client,” on page 24 .
nConfigure a Hadoop distribution to use with Big Data Extensions.
nCreate a cluster.
Procedure
1 Open a command shell, such as Bash or PuTTY, and log in to the Serengeti Management Server as user
serengeti.
2 Run the serengeti-ssh.sh script..
serengeti-ssh.sh cluster_name 'echo new_password | sudo passwd username --stdin'
This example changes the password for all nodes in the cluster labeled mycluster for the user serengeti
to mypassword.
serengeti-ssh.sh mycluster 'echo mypassword | sudo passwd serengeti --stdin'
The password for the user account that you specify changes on all the nodes in the cluster.
Reconfigure a Cluster with the Serengeti Command-Line Interface
You can reconfigure any big data cluster that you create with Big Data Extensions.
The cluster configuration is specified by attributes in Hadoop distribution XML configuration files such as:
core-site.xml, hdfs-site.xml, mapred-site.xml, hadoop-env.sh, yarn-env.sh, yarn-site.sh, and hadoop-
metrics.properties.
NOTE Always use the cluster config command to change the parameters specified by the configuration
files. If you manually modify these files, your changes will be erased if the virtual machine is rebooted, or
you use the cluster config, cluster start, cluster stop, or cluster resize commands.
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Procedure
1 Use the cluster export command to export the cluster specification file for the cluster that you want to
reconfigure.
cluster export --name cluster_name --specFile file_path/cluster_spec_file_name
Option Description
cluster_name Name of the cluster that you want to reconfigure.
file_path The file system path to which to export the specification file.
cluster_spec_file_name The name with which to label the exported cluster specification file.
2 Edit the configuration information located near the end of the exported cluster specification file.
If you are modeling your configuration file on existing Hadoop XML configuration files, use the
convert-hadoop-conf.rb conversion tool to convert Hadoop XML configuration files to the required
JSON format.
…
"configuration": {
"hadoop": {
"core-site.xml": {
// check for all settings at http://hadoop.apache.org/common/docs/stable/core-
default.html
// note: any value (int, float, boolean, string) must be enclosed in double quotes
and here is a sample:
// "io.file.buffer.size": "4096"
},
"hdfs-site.xml": {
// check for all settings at http://hadoop.apache.org/common/docs/stable/hdfs-
default.html
},
"mapred-site.xml": {
// check for all settings at http://hadoop.apache.org/common/docs/stable/mapred-
default.html
},
"hadoop-env.sh": {
// "HADOOP_HEAPSIZE": "",
// "HADOOP_NAMENODE_OPTS": "",
// "HADOOP_DATANODE_OPTS": "",
// "HADOOP_SECONDARYNAMENODE_OPTS": "",
// "HADOOP_JOBTRACKER_OPTS": "",
// "HADOOP_TASKTRACKER_OPTS": "",
// "HADOOP_CLASSPATH": "",
// "JAVA_HOME": "",
// "PATH": "",
},
"log4j.properties": {
// "hadoop.root.logger": "DEBUG, DRFA ",
// "hadoop.security.logger": "DEBUG, DRFA ",
},
"fair-scheduler.xml": {
// check for all settings at
http://hadoop.apache.org/docs/stable/fair_scheduler.html
// "text": "the full content of fair-scheduler.xml in one line"
},
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"capacity-scheduler.xml": {
// check for all settings at
http://hadoop.apache.org/docs/stable/capacity_scheduler.html
}
}
}
…
3 (Optional) If the JAR files of your Hadoop distribution are not in the $HADOOP_HOME/lib directory, add
the full path of the JAR file in $HADOOP_CLASSPATH to the cluster specification file.
This action lets the Hadoop daemons locate the distribution JAR files.
For example, the Cloudera CDH3 Hadoop Fair Scheduler JAR files are
in /usr/lib/hadoop/contrib/fairscheduler/. Add the following to the cluster specification file to
enable Hadoop to use the JAR files.
…
"configuration": {
"hadoop": {
"hadoop-env.sh": {
"HADOOP_CLASSPATH": "/usr/lib/hadoop/contrib/fairscheduler/*:$HADOOP_CLASSPATH"
},
"mapred-site.xml": {
"mapred.jobtracker.taskScheduler": "org.apache.hadoop.mapred.FairScheduler"
…
},
"fair-scheduler.xml": {
…
}
}
}
…
4 Access the Serengeti CLI.
5 Run the cluster config command to apply the new Hadoop configuration.
cluster config --name cluster_name --specFile file_path/cluster_spec_file_name
6 (Optional) Reset an existing configuration attribute to its default value.
a Remove the attribute from the configuration section of the cluster configuration file or comment
out the attribute using double back slashes (//).
b Re-run the cluster config command.
Recover from Disk Failure with the Serengeti Command-Line Interface
Client
If there is a disk failure in a cluster, and the disk does not perform management roles such as NameNode,
JobTracker, ResourceManager, HMaster, or ZooKeeper, you can recover by running the Serengeti cluster
fix command.
Big Data Extensions uses a large number of inexpensive disk drives for data storage (configured as JBOD). If
several disks fail, the Hadoop data node might shutdown. Big Data Extensions enables you to recover from
disk failures.
Serengeti supports recovery from swap and data disk failure on all supported Hadoop distributions. Disks
are recovered and started in sequence to avoid the temporary loss of multiple nodes at once. A new disk
matches the storage type and placement policies of the corresponding failed disk.
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The MapR distribution does not support recovery from disk failure by using the cluster fix command.
IMPORTANT Even if you changed the user password on the nodes of the cluster, the changed password is not
used for the new nodes that are created by the disk recovery operation. If you set the initial administrator
password of the cluster when you created the cluster, that initial administrator password is used for the new
nodes. If you did not set the initial administrator password of the cluster when you created the cluster, new
random passwords are used for the new nodes.
Procedure
1 Access the Serengeti CLI.
2 Run the cluster fix command.
The nodeGroup parameter is optional.
cluster fix --name cluster_name --disk [--nodeGroup nodegroup_name]
Enter Maintenance Mode to Perform Backup and Restore with the
Serengeti Command-Line Interface Client
Before performing backup and restore operations, or other maintenance tasks, you must place
Big Data Extensions into maintenance mode.
Prerequisites
nDeploy the Serengeti vApp.
nEnsure that you have adequate resources allocated to run the Hadoop cluster.
nTo use any Hadoop distribution other than the default distribution, add one or more Hadoop
distributions. See the VMware vSphere Big Data Extensions Administrator's and User's Guide.
Procedure
1 Log into the Serengeti Management Server.
2 Run the script /opt/serengeti/sbin/serengeti-maintenance.sh to place Big Data Extensions into
maintenance mode, or check maintenance status.
serengeti-maintenance.sh on | off | status
Option Description
on Turns on maintenance mode. Upon entering maintenance mode,
Big Data Extensions continues executing jobs that have already been
started, but will not respond to any new requests.
off Turn off maintenance mode, and returns Big Data Extensions to its normal
operating state.
status Displays the maintenance status of Big Data Extensions.
nA status of safe means it is safe to backup or perform other
maintenance tasks on your Big Data Extensions deployment.
nA status of off means maintenance mode has been turned off, and it is
not safe to perform maintenance tasks such as backup and restore.
nA status of on means Big Data Extensions has entered maintenance
mode, but it is not yet safe to perform back and restore operations. You
must wait until the system returns the safe status message.
To place your Big Data Extensions deployment into maintenance mode, run the serengeti-
maintenance.sh script with the on option.
serengeti-maintenance.sh on
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3 Verify that Big Data Extensions is in maintenance mode.
When Big Data Extensions completes all jobs that have been submitted, the maintenance status will
enter safe mode. Run the serengeti-maintenance.sh with the status parameter repeatedly until it
returns the safe system status message.
serengeti-maintenance.sh status
safe
4 Perform the necessary system maintenance tasks.
5 Once you have completed the necessary system maintenance tasks, return Big Data Extensions to its
normal operating state by manually exiting maintenance mode.
serengeti-maintenance.sh off
Log in to Hadoop Nodes with the Serengeti Command-Line Interface
Client
To perform troubleshooting or to run your management automation scripts, log in to Hadoop master,
worker, and client nodes with SSH from the Serengeti Management Server using SSH client tools such as
SSH, PDSH, ClusterSSH, and Mussh, which do not require password authentication.
To connect to Hadoop cluster nodes over SSH, you can use a user name and password authenticated login.
All deployed nodes are password-protected with either a random password or a user-specified password
that was assigned when the cluster was created.
Prerequisites
Use the vSphere Web Client to log in to vCenter Server, and verify that the Serengeti Management Server
virtual machine is running.
Procedure
1 Right-click the Serengeti Management Server virtual machine and select Open Console.
The password for the Serengeti Management Server appears.
NOTE If the password scrolls off the console screen, press Ctrl+D to return to the command prompt.
2 Use the vSphere Web Client to log in to the Hadoop node.
The password for the root user appears on the virtual machine console in the vSphere Web Client.
3 Change the password of the Hadoop node by running the set-password -u command.
sudo /opt/serengeti/sbin/set-password -u
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Monitoring the Big Data Extensions
Environment 10
You can monitor the status of Serengeti-deployed clusters, including their datastores, networks, and
resource pools through the Serengeti Command-Line Interface. You can also view a list of available Hadoop
distributions. Monitoring capabilities are also available in the vSphere Web Client.
This chapter includes the following topics:
n“Enable the Big Data Extensions Data Collector,” on page 123
n“Disable the Big Data Extensions Data Collector,” on page 124
n“View Serengeti Management Server Initialization Status,” on page 124
n“View Provisioned Clusters in the vSphere Web Client,” on page 125
n“View Cluster Information in the vSphere Web Client,” on page 126
n“Monitor the HDFS Status in the vSphere Web Client,” on page 127
n“Monitor MapReduce Status in the vSphere Web Client,” on page 127
n“Monitor HBase Status in the vSphere Web Client,” on page 128
Enable the Big Data Extensions Data Collector
If you did not enable the Big Data Extensions data collector during installation, you can enable it at a later
time. The Customer Experience Improvement Program collects product usage data from your
Big Data Extensions environment for analysis and troubleshooting.
The data collector collects four types of data including the Big Data Extensions footprint, operations
information, environmental information, and cluster snapshots.
Prerequisites
nReview the Customer Experience Improvement Program description, and determine if you wish to
collect data and send it to VMware help improve your user experience using Big Data Extensions. See
“The Customer Experience Improvement Program,” on page 23.
nInstall Big Data Extensions. See Chapter 2, “Installing Big Data Extensions,” on page 19
Procedure
1 Use the vSphere Web Client to log in to vCenter Server.
2 Select Big Data Extensions and click the Manage tab.
3 In the Customer Experience Improvement Program pane click Edit.
The Customer Experience Improvement Program dialog box appears.
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4 Select the Enable Customer Experience Improvement Program check box.
What to do next
You can disable the data collector at a later time if you wish to discontinue your use of the Customer
Experience Improvement Program. See “Disable the Big Data Extensions Data Collector,” on page 124.
Disable the Big Data Extensions Data Collector
The Customer Experience Improvement Program collects product usage data from your Big Data Extensions
environment for analysis and troubleshooting if necessary. If you do not want to use the Customer
Experience Improvement Program feature, you can disable the Big Data Extensions data collector.
The data collector collects four types of data including the Big Data Extensions footprint, operations
information, environmental information, and cluster snapshots. If you disable the Customer Experience
Improvement Program, this data is not available for use in troubleshooting and problem resolution.
Procedure
1 Use the vSphere Web Client to log in to Big Data Extensions.
2 Select Big Data Extensions and click the Manage tab.
3 In the Customer Experience Improvement Program pane click Edit.
The Customer Experience Improvement Program dialog box appears.
4 Deselect the Enable Customer Experience Improvement Program check box.
What to do next
You can enable the data collector at a later time if you choose to use of the Customer Experience
Improvement Program. See “Enable the Big Data Extensions Data Collector,” on page 123.
View Serengeti Management Server Initialization Status
You can you view the initialization status of the Serengeti Management Server services, view error messages
to help troubleshoot problems, and recover services that may not have successfully started.
Big Data Extensions may not successfully start for many reasons. The Serengeti Management Server
Administration Portal lets you view the initialization status of the Serengeti services, view error messages
for individual services to help troubleshoot problems, and recover services that may not have successfully
started.
Prerequisites
nEnsure that you know the IP address of the Serengeti Management Server to which you want to
connect.
nEnsure that you have login credentials for the Serengeti Management Server root user.
Procedure
1 Open a Web browser and go the URL of the Serengeti Management Server Administration Portal.
https://management-server-ip-address:5480
2 Type root for the user name, type the password, and click Login.
3 Click the Summary tab.
The Serengeti Management Server services and their operational status is displayed in the Summary
page.
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4 Do one of the following.
Option Description
View Initialize Status Click Details. The Serengeti Server Setup dialog box lets you view the
initialization status of the Serengeti Management Server. If the Serengeti
Management Server fails to initialize, an error message with
troubleshooting information displays. Once you resolve the error, a Retry
button lets you restart the failed service.
View Chef Server Services Click the Chef Server tree control to expand the list of Chef services.
Recover a Stopped or Failed
Service
Click Recover to restart a stopped or failed service. If a service fails due to
a configuration error, you must first resolve the problem that caused the
service to fail before you can successfully recover the failed service.
Refresh Click Refresh to update the information displayed in the Summary page.
What to do next
If there is an error that you need to resolve, the troubleshooting topics provide solutions to problems you
might encounter when using Big Data Extensions. See Chapter 13, “Troubleshooting,” on page 139.
View Provisioned Clusters in the vSphere Web Client
You can view the clusters deployed within Big Data Extensions, including information about whether the
cluster is running, the type of Hadoop distribution used by a cluster, and the number and type of nodes in
the cluster.
Prerequisites
nCreate one or more clusters whose information you can view.
Procedure
1 Use the vSphere Web Client to log in to vCenter Server.
2 Select Big Data Extensions.
3 In the Inventory Lists, select Big Data Clusters.
4 Select Big Data Clusters.
Information about all provisioned clusters appears in the right pane.
Table 10‑1. Cluster Information
Option Description
Name Name of the cluster.
Status Status of the cluster.
Distribution Hadoop distribution in use by the cluster.
Elasticity Mode The elasticity mode in use by the cluster.
Disk IO Shares The disk I/O shares in use by the cluster.
Resources The resource pool or vCenter Server cluster in use by the Big Data cluster.
Managed by The application manager that manages the cluster.
Information Number and type of nodes in the cluster.
Progress Status messages of actions being performed on the cluster.
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View Cluster Information in the vSphere Web Client
Use the vSphere Web Client to view virtual machines running each node, resource allocation, IP addresses,
and storage information for each node in the Hadoop cluster.
Prerequisites
nCreate one or more Hadoop clusters.
nStart the Hadoop cluster.
Procedure
1 Use the vSphere Web Client to log in to vCenter Server.
2 Select Big Data Extensions.
3 From the Inventory Lists, click Big Data Clusters.
4 Click a Big Data cluster.
Information about the cluster appears in the right pane, in the Nodes tab.
Table 10‑2. Cluster Information
Column Description
Node Group Lists all nodes by type in the cluster.
VM Name Name of the virtual machine on which a node is running.
Management
Network
IP address of the virtual machine.
Host Host name, IP address, or Fully Qualified Domain Name (FQDN) of the ESXi host on which
the virtual machine is running.
Status The virtual machine reports the following status types:
nNot Exist. Status before you create a virtual machine instance in vSphere.
nPowered On. The virtual machine is powered on after virtual disks and network are
configured.
nVM Ready. A virtual machine is started and IP is ready.
nService Ready. Services inside the virtual machine have been provisioned.
nBootstrap Failed. A service inside the virtual machine failed to provision.
nPowered Off. The virtual machine is powered off.
nService Alert. There is critical issue reported for the services inside of the virtual machine.*
nService Unhealthy. There is an unhealthy issue reported for the services inside of the
virtual machine.*
* Check the details from the corresponding application manager.
Task Status of in-progress Serengeti operations.
5 From the Nodes tab, select a node group.
Information about the node group appears in the Node details panel of the Nodes tab.
Table 10‑3. Cluster Node Details
Field Description
Node Group Name of the selected node group.
VM Name Name of the node group's virtual machine.
Management network Network used for management traffic.
HDFS Network Network used for HDFS traffic.
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Table 10‑3. Cluster Node Details (Continued)
Field Description
MapReduce Network Network used for MapReduce traffic.
Host Host name, IP address, or Fully Qualified Domain Name
(FQDN) of the ESXi host on which the virtual machine is
running.
vCPU Number of virtual CPUs assigned to the node.
RAM Amount of RAM used by the node.
NOTE The RAM size that appears for each node shows
the allocated RAM, not the RAM that is in use.
Storage The amount of storage allocated for use by the virtual
machine running the node.
Error Indicates a node failure.
Monitor the HDFS Status in the vSphere Web Client
When you configure a Hadoop distribution to use with Big Data Extensions, the Hadoop software includes
the Hadoop Distributed File System (HDFS). You can monitor the health and status of HDFS from the
vSphere Web Client. The HDFS page lets you browse the Hadoop file system, view NameNode logs, and
view cluster information including live, dead, and decommissioning nodes, and NameNode storage
information.
HDFS is the primary distributed storage used by Hadoop applications. A HDFS cluster consists of a
NameNode that manages the file system metadata and DataNodes that store the actual data.
Prerequisites
nCreate one or more Hadoop clusters.
Procedure
1 Use the vSphere Web Client to log in to vCenter Server.
2 Select Big Data Extensions.
3 In the Inventory Lists, select Big Data Clusters.
4 Select the cluster whose HDFS status you want to view from the Big Data Cluster List tab.
5 Select Open HDFS Status Page from the Actions menu.
The HDFS status information appears in a new Web page.
NOTE If you use Big Data Extensions in a vCenter Server environment using IPv6, the vSphere Web
Client is unable to access the HDFS Status Page, which uses an IPv4 address. To view the HDFS Status
Page, open a Web browser and go to the URL that displays in the error message when you attempt to
access the status page as instructed in this procedure.
Monitor MapReduce Status in the vSphere Web Client
The Hadoop software includes MapReduce, a software framework for distributed data processing. You can
monitor MapReduce status vSphere Web Client. The MapReduce Web page includes information about
scheduling, running jobs, retired jobs, and log files.
Prerequisites
nCreate one or more Hadoop clusters whose MapReduce status you can monitor.
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Procedure
1 Use the vSphere Web Client to log in to vCenter Server.
2 Select Big Data Extensions.
3 From the Inventory Lists, click Big Data Clusters.
4 Select the cluster whose MapReduce status you want to view from the Big Data Cluster List tab.
5 Select Open MapReduce Status Page from the Actions menu.
The MapReduce status information appears in a new Web page.
NOTE If you use Big Data Extensions in a vCenter Server environment using IPv6, the vSphere Web
Client is unable to access the MapReduce Status Page, which uses an IPv4 address. To view the
MapReduce Status Page, open a Web browser and go to the URL that displays in the error message
when you attempt to access the status page as instructed in this procedure.
Monitor HBase Status in the vSphere Web Client
HBase is the Hadoop database. You can monitor the health and status of your HBase cluster, as well as the
tables that it hosts, from the vSphere Web Client.
Prerequisites
Create one or more HBase clusters.
Procedure
1 Use the vSphere Web Client to log in to vCenter Server.
2 Select Big Data Extensions.
3 In the Inventory Lists, click Big Data Clusters.
4 In the Big Data Cluster List tab, select the cluster whose HBase status you want to view.
5 From the Actions menu, select Open HBase Status Page.
The HBase status information appears in a new Web page.
NOTE If you use Big Data Extensions in a vCenter Server environment using IPv6, the vSphere Web
Client is unable to access the HBase Status Page, which uses an IPv4 address. To view the HBase Status
Page, open a Web browser and go to the URL that displays in the error message when you attempt to
access the status page as instructed in this procedure.
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Accessing Hive Data with JDBC or
ODBC 11
You can run Hive queries from a Java Database Connectivity (JDBC) or Open Database Connectivity
(ODBC) application leveraging the Hive JDBC and ODBC drivers.
You can access data from Hive using either JDBC or ODBC.
Hive JDBC Driver
Hive provides a Type 4 (pure Java) JDBC driver, defined in the class
org.apache.hadoop.hive.jdbc.HiveDriver. When configured with a JDBC URI of the form
jdbc:hive://host:port/dbname, a Java application can connect to a Hive server running at the specified host
and port. The driver makes calls to an interface implemented by the Hive Thrift Client using the Java Thrift
bindings.
You can choose to connect to Hive through JDBC in embedded mode by using the URI jdbc:hive://. In
embedded mode, Hive runs in the same JVM as the application that invokes it. You do not have to launch it
as a standalone server, because it does not use the Thrift service or the Hive Thrift Client.
Hive ODBC Driver
The Hive ODBC driver allows applications that support the ODBC protocol to connect to Hive. Like the
JDBC driver, the ODBC driver uses Thrift to communicate with the Hive server.
This chapter includes the following topics:
n“Configure Hive to Work with JDBC,” on page 129
n“Configure Hive to Work with ODBC,” on page 131
Configure Hive to Work with JDBC
The Hive JDBC driver lets you access Hive from a Java program that you write, or from a Business
Intelligence or similar application that uses JDBC to communicate with database products.
The default JDBC 2.0 port is 21050. Hive accepts JDBC connections through port 21050 by default. Make sure
this port is available for communication with other hosts on your network. For example, ensure that the port
is not blocked by firewall software.
Prerequisites
You must have an application that can use the Hive JDBC driver to connect to a Hive server.
Procedure
1 Open a command shell, such as Bash or PuTTY, and log in to the Hive server node.
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2 Create the file HiveJdbcClient.java with the Java code to connect to the Hive Server.
import java.sql.SQLException;
import java.sql.Connection;
import java.sql.ResultSet;
import java.sql.Statement;
import java.sql.DriverManager;
public class HiveJdbcClient {
private static String driverName = "org.apache.hadoop.hive.jdbc.HiveDriver";
/**
* @param args
* @throws SQLException
**/
public static void main(String[] args) throws SQLException {
try {
Class.forName(driverName);
} catch (ClassNotFoundException e){
// TODO Auto-generated catch block
e.printStackTrace();
System.exit(1);
}
Connection con = DriverManager.getConnection("jdbc:hive://localhost:10000/default",
"", "");
Statement stmt = con.createStatement();
String tableName = "testHiveDriverTable";
stmt.executeQuery("drop table " + tableName);
ResultSet res = stmt.executeQuery("create table " + tableName + " (key int, value
string)");
// show tables
String sql = "show tables '" + tableName + "'";
System.out.println("Running: " + sql);
res = stmt.executeQuery(sql);
if (res.next()) {
System.out.println(res.getString(1));
}
// describe table
sql = "describe " + tableName;
System.out.println("Running: " + sql);
res = stmt.executeQuery(sql);
while (res.next()) {
System.out.println(res.getString(1) + "\t" + res.getString(2));
}
// load data into table
// NOTE: filepath has to be local to the hive server
// NOTE: /tmp/test_hive_server.txt is a ctrl-A separated file with two fields per
line
String filepath = "/tmp/test_hive_server.txt";
sql = "load data local inpath '" + filepath + "' into table " + tableName;
System.out.println("Running: " + sql);
res = stmt.executeQuery(sql);
// select * query
sql = "select * from " + tableName;
System.out.println("Running: " + sql);
res = stmt.executeQuery(sql);
while (res.next()){
System.out.println(String.valueOf(res.getInt(1)) + "\t" + res.getString(2));
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}
// regular hive query
sql = "select count(1) from " + tableName;
System.out.println("Running: " + sql);
res = stmt.executeQuery(sql);
while (res.next()){
System.out.println(res.getString(1));
}
}
}
3 Run the JDBC code using one of the following methods.
uRun the javac command identifying the Java code containing the JDBC code.javac
HiveJdbcClient.java
uRun a shell script to populate the data file, define the classpath, and invoke the JDBC client.
The example below uses Apache Hadoop 1.1.2 distribution. If you are using a different Hadoop
distribution, you must update the value of the HADOOP_CORE variable to correspond to the version of the
distribution you are using.
#!/bin/bash
HADOOP_HOME=/usr/lib/hadoop
HIVE_HOME=/usr/lib/hive
echo -e '1\x01foo' > /tmp/test_hive_server.txt
echo -e '2\x01bar' >> /tmp/test_hive_server.txt
HADOOP_CORE=`ls /usr/lib/hadoop-1.1.2/hadoop-core-*.jar`
CLASSPATH=.:$HADOOP_CORE:$HIVE_HOME/conf
for jar_file_name in ${HIVE_HOME}/lib/*.jar
do
CLASSPATH=$CLASSPATH:$jar_file_name
done
java -cp $CLASSPATH HiveJdbcClient
Either of these methods establishes a JDBC connection with the Hive server using the host and port
information that you specify in the Java application or shell script.
Configure Hive to Work with ODBC
The Hive ODBC driver allows you to access Hive from a program that you write, or a Business Intelligence
or similar application that uses ODBC to communicate with database products.
To access Hive data using ODBC, use the ODBC driver recommended for use with your Hadoop
distribution.
Prerequisites
nVerify that the Hive ODBC driver supports the application or the third-party product that you intend to
use.
nDownload an appropriate ODBC connector and configure it for use with your environment.
nConfigure a Data Source Name (DSN).
DSNs specify how an application connects to Hive or other database products. Refer to your particular
application's documentation to understand how it connects to Hive and other database products using
ODBC.
Procedure
1 Open the ODBC Data Source Administrator from the Windows Start menu.
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2 Click the System DSN tab, and click Add.
3 Select the ODBC driver that you want to use with your Hadoop distribution, and click Finish.
4 Enter values for the following fields.
Option Description
Data Source Name Type a name by which to identify the DSN.
Host Fully qualified hostname or IP address of the node running the Hive
service.
Port Port number for the Hive service. The default is 21000.
Hive Server Type Set to HiveServer1 or HiveServer2.
Authentication If you are using Hiveserver2, specify the following.
nMechanism. Set to User Name.
nUser Name. User name with which to run Hive queries.
5 Click OK.
6 Click Test to test the ODBC connection.
7 After you verify that the connection works, click Finish.
The new ODBC connector appears in the User Data Sources list.
What to do next
Configure the application to work with your Hadoop distribution's Hive service. See your particular
application's documentation to understand how it connects to Hive and other database products that use
ODBC.
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Big Data Extensions Security
Reference 12
Use the Security Reference to learn about the security features of your Big Data Extensions installation and
the measures that you can take to safeguard your environment from attack.
nServices, Network Ports, and External Interfaces on page 133
The operation of Big Data Extensions depends on certain services, ports, and external interfaces.
nBig Data Extensions Configuration Files on page 135
Some Big Data Extensions configuration files contain settings that may affect your environment's
security.
nBig Data Extensions Public Key, Certificate, and Keystore on page 136
The Big Data Extensions public key, certificate, and keystore are located on the
Serengeti Management Server.
nBig Data Extensions Log Files on page 136
The files that contain system messages are located on the Serengeti Management Server.
nBig Data Extensions User Accounts on page 137
You must set up an administrative user and a root user account to administer Big Data Extensions.
nSecurity Updates and Patches on page 137
You can apply security updates and patches as they are made available by either VMware, or the
vendors of operating systems and Hadoop distributions.
Services, Network Ports, and External Interfaces
The operation of Big Data Extensions depends on certain services, ports, and external interfaces.
Big Data Extensions Services
The operation of Big Data Extensions depends on several services that run on the Big Data Extensions vApp.
Table 12‑1. Big Data Extensions Services
Service Names Startup Type Description
http Automatic Apache Web Server Secure remote
console access.
sshd Automatic Secure remote console access.
rsyslog Automatic The rsyslog service is an enhanced,
multi-threaded syslog daemon
Tomcat Automatic Tomcat Server which runs the
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Table 12‑1. Big Data Extensions Services (Continued)
Service Names Startup Type Description
Thrift Service Automatic The communication broker between
Big Data Extensions Web Service and
Chef Server's knife process.
Chef Server Automatic Chef is an open source configuration
management framework and tool. The
Chef Server is the primary component
of the Chef framework.
Nginx Automatic Nginx is part of the Chef Server, and
acts as the proxy for handling all
requests to Chef Server API.
Postgres Automatic The database server is use by the
Chef Server and Big Data Extensions
Web Service.
Big Data Extensions Communication Ports
Big Data Extensions uses several communication ports and protocols.
The table below shows the ports listening on the Serengeti Management Server (also called the
Big Data Extensions Management Server) for all local and external network addresses.
Table 12‑2. Serengeti Management Server Services and Network Ports
Service Name Ports Protocol Listen on Local Port?
httpd 433/TCP HTTP No
sshd 22/TCP SSH No
Tomcat 8080/TCP, 8443/TCP HTTP, HTTPS No
nginx 9080/TCP, 9443/TCP HTTP, HTTPS No
Thrift Service 9090 TCP Yes
postgres 5432 Postgres Yes
Big Data Extensions Hadoop and HBase Node Communication Ports
Big Data Extensions deploys Hadoop and HBase clusters which use their default ports for the cluster nodes
they deploy.
Table 12‑3. Ports in use by Hadoop clusters created with Big Data Extensions
Service Name Daemon Name Ports Protocol
HDFS Namenode Web page 50070/TCP HTTP
Namenode RPC 8020/TCP RPC
Datanode 50075/TCP, 50010/TCP,
50020/TCP
RPC
MapReduce JobTracker Web page 50030/TCP HTTP
JobTracker RPC 8021/TCP RPC
TaskTracker 50060/TCP RPC
Yarn Resource Manager Web
page
8088/TCP HTTP
Resource Manager RPC 8030/TCP, 8031/TCP,
8032/TCP, 8033/TCP
RPC
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Table 12‑3. Ports in use by Hadoop clusters created with Big Data Extensions (Continued)
Service Name Daemon Name Ports Protocol
NodeManager 8040/TCP, 8042/TCP RPC
Hive Hive Server 10000/TCP RPC
Table 12‑4. Ports in use by HBase clusters created with Big Data Extensions
Service Name Ports Protocol
Zookeeper 2181/TCP Zookeeper
HBase Master 60000/TCP, 60010/TCP RPC
HBase RegionServer 60020/TCP, 60030/TCP RPC
HBase Thrift Service 9090/TCP, 9095/TCP RPC
HBase REST Service 8080/TCP, 8085/TCP HTTP
Table 12‑5. Ports in use by MapR clusters created with Big Data Extensions
Service Name Ports Protocol
CLDB 7222
CLDB JMX monitor port 7220
CLDB web port 7221
HBase Master 60000
HBase Master (for GUI) 60010
HBase RegionServer 60020
Hive Metastore 9083
JobTracker Webpage 50030
JobTracker RPC 8021 RPC
MFS server 5660
MySQL 3306
NFS 2049
NFS monitor (for HA) 9997
NFS management 9998
Port mapper 111
TaskTracker 50060
Web UI HTTPS 8443
Zookeeper 5180
Big Data Extensions Configuration Files
Some Big Data Extensions configuration files contain settings that may affect your environment's security.
Big Data Extensions Configuration Files Containing Security-Related
Resources
All security-related resources are accessible by the serengeti and root user accounts. Protecting these user
accounts is critical to the security of Big Data Extensions.
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Table 12‑6. Configuration Files Containing Security-Related Resources
File Description
/opt/serengeti/tomcat/conf/server.xml Configuration file for theTomcat server, which includes
network ports and SSL key store file locations and
passwords.
/opt/serengeti/conf/vc.properties Key store configuration file for Big Data Extensions Web
Service.
/var/opt//chef-server/nginx/etc/nginx.conf Configuration file for the Nginx Web server, which
includes network ports and certificate information.
/etc/httpd/conf.d/ssl.conf Configuration file for thehttpd Web server.
Big Data Extensions Public Key, Certificate, and Keystore
The Big Data Extensions public key, certificate, and keystore are located on the
Serengeti Management Server.
All security-related resources are accessible by the serengeti and root user accounts. Protecting these user
accounts is critical to the security of Big Data Extensions.
Table 12‑7. Big Data Extensions Public Key, Certificate, and Keystore
File Location Service
/opt/serengeti/.certs/ Tomcat
/var/opt/chef-server/nginx/ca/ Nginx
/etc/pki/tls/private/ httpd
/etc/pki/tls/certs/ httpd
Big Data Extensions Log Files
The files that contain system messages are located on the Serengeti Management Server.
Big Data Extensions uses the following log files to track and record system messages and events. The log
files are located on the Serengeti Management Server and Chef Server.
Table 12‑8. Big Data Extensions Log Files
File Description
/opt/serengeti/logs/serengeti.log Tracks and records events for the Big Data Extensions Web
Service
/opt/serengeti/logs/ironfan.log Tracks and records events when provisioning new clusters
using the default application manager.
/opt/serengeti/logs/serengeti-boot.log Tracks and records events when the Big Data Extensions
Server boots up.
/opt/serengeti/logs/serengeti-upgrade.log Tracks and records events when upgrading
Big Data Extensions and cluster nodes.
/opt/serengeti/logs/provision-hook.log Tracks and records events when executing hooks during
cluster provisioning.
sudo chef-server-ctl tail To track the Chef Server log files run the tail command
on the chef-server-ctl service.
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Security-Related Log Messages
Big Data Extensions does not provide any security-related log messages.
Big Data Extensions User Accounts
You must set up an administrative user and a root user account to administer Big Data Extensions.
Big Data Extensions Root User Account
The root password of Serengeti Management Server is a random password generated when powering on the
Big Data Extensions vApp for the first time. You can see the password in the virtual machine console for
Big Data Extensions in the vSphere Web Client.
The root password of Big Data Extensions nodes in a cluster is a random password generated when creating
the cluster, or specified by a user before creating the cluster.
Passwords must be from 8 to 20 characters, use only visible lowerASCII characters (no spaces), and must
contain at least one uppercase alphabetic character (A - Z), at least one lowercase alphabetic character (a - z),
at least one digit (0 - 9), and at least one of the following special characters: _, @, #, $, %, ^, &, *
Only visible lower ASCII characters (No spaces)
Big Data Extensions Administrative User Account
Big Data Extensions administrative user is the user account serengeti, which has sudo privileges. The
serengeti user password is the same as that of the root user. You can change the password by running the
command sudo /opt/serengeti/sbin/set-password -u on the Serengeti Management Server.
You can specify a password for the serengeti user by running the command passwd serengeti. The
password for the serengeti user can be a different password from that assigned to the root user.
To manage Big Data Extensions you must login to the Serengeti Management Server as the serengeti user.
Once logged in a the serengeti user you can change to the root user account if necessary.
Support for Active Directory and OpenLDAP
Big Data Extensions supports integration with Active Directory and OpenLDAP. When configured to work
with Active Directory or OpenLDAP, the Serengeti Management Server and cluster nodes can authenticate
or authorize users against your Active Directory or OpenLDAP user directory.
Security Updates and Patches
You can apply security updates and patches as they are made available by either VMware, or the vendors of
operating systems and Hadoop distributions.
Big Data Extensions Operating System Versions
Big Data Extensions uses the following operating systems and versions.
nThe Big Data Extensions virtual appliance uses CentOS 5.11 (x86_64) and CentOS 6.6 (x86_64) as guest
operating systems.
nThe Serengeti Management Server uses CentOS 5.11.
nThe Big Data Extensions cluster nodes use CentOS 6.6.
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Applying Patches and Security Updates
You apply security patches and updates using conventional upgrade procedures. For example, using yum
or rpm upgrade. See Chapter 3, “Upgrading Big Data Extensions,” on page 35.
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Troubleshooting 13
The troubleshooting topics provide solutions to problems that you might encounter when using Big Data
Extensions.
This chapter includes the following topics:
n“Log Files for Troubleshooting,” on page 140
n“Configure Serengeti Logging Levels,” on page 140
n“Collect Log Files for Troubleshooting,” on page 141
n“Troubleshooting Cluster Creation Failures,” on page 142
n“Big Data Extensions Virtual Appliance Upgrade Fails,” on page 148
n“Upgrade Cluster Error When Using Cluster Created in Earlier Version of Big Data Extensions,” on
page 149
n“Virtual Update Manager Does Not Upgrade the Hadoop Template Virtual Machine Under Big Data
Extensions vApp,” on page 149
n“Unable to Connect the Big Data Extensions Plug-In to the Serengeti Server,” on page 150
n“vCenter Server Connections Fail to Log In,” on page 151
n“Management Server Cannot Connect to vCenter Server,” on page 151
n“Cannot Perform Serengeti Operations after Deploying Big Data Extensions,” on page 151
n“SSL Certificate Error When Connecting to Non-Serengeti Server with the vSphere Console,” on
page 152
n“Cannot Restart or Reconfigure a Cluster For Which the Time Is Not Synchronized,” on page 153
n“Cannot Restart or Reconfigure a Cluster After Changing Its Distribution,” on page 153
n“Virtual Machine Cannot Get IP Address and Command Fails,” on page 153
n“Cannot Change the Serengeti Server IP Address From the vSphere Web Client,” on page 154
n“A New Plug-In Instance with the Same or Earlier Version Number as a Previous Plug-In Instance
Does Not Load,” on page 155
n“Host Name and FQDN Do Not Match for Serengeti Management Server,” on page 155
n“Serengeti Operations Fail After You Rename a Resource in vSphere,” on page 156
n“Big Data Extensions Server Does Not Accept Resource Names With Two or More Contiguous White
Spaces,” on page 156
n“Non-ASCII characters are not displayed correctly,” on page 156
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n“MapReduce Job Fails to Run and Does Not Appear In the Job History,” on page 157
n“Cannot Submit MapReduce Jobs for Compute-Only Clusters with External Isilon HDFS,” on
page 157
n“MapReduce Job Stops Responding on a PHD or CDH4 YARN Cluster,” on page 158
n“Cannot Download the Package When Using Downloadonly Plugin,” on page 158
n“Cannot Find Packages When You Use Yum Search,” on page 158
n“Remove the HBase Rootdir in HDFS Before You Delete the HBase Only Cluster,” on page 159
Log Files for Troubleshooting
Big Data Extensions and Serengeti create log files that provide system and status information that you can
use to troubleshoot deployment and operation problems.
Table 13‑1. Log Files
Category File Name Information Location
Serengeti vApp boot-up log nserengeti-boot.log Deployment time
messages, which you can
use to troubleshoot an
unsuccessful deployment.
/opt/serengeti/logs
Serengeti server service log nserengeti.log Web service component
logs.
/opt/serengeti/logs
Serengeti server installation
and configuration log
nironfan.log Software installation and
configuration information.
/opt/serengeti/logs
Serengeti server elastic
scaling log
nvhm.log Elastic scaling logs. /opt/serengeti/logs
VMware vSphere ESXi and vCenter Server Log Files
In addition to the Big Data Extensions and Serengeti log files, vSphere ESXi and vCenter Server also create
log files that provide system and status information that you can use to troubleshoot deployment and
operation problems.
If you encounter error messages which begin with the statement Failed to execute vCenter Server
command:, check your vSphere ESXi and vCenter Server log files for additional troubleshooting information.
There are a number of ways in which you can view log files depending on whether they are for vCenter
Server or an ESXi host. See the VMware vSphere ESXi and vCenter Server documentation for your ESXi and
vCenter Server product version.
Configure Serengeti Logging Levels
The Serengeti system and back-end tasks use Apache log4j, with the default logging level INFO, to log
messages. You can configure the logging level to customize the amount and type of information shown in
the system and event logs.
Enabling logging at a given level also enables logging at all higher levels.
The levels in descending order are:
nSEVERE (highest value)
nWARNING
nINFO
nCONFIG
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nFINE
nFINER
nFINEST (lowest value)
In addition there is a level OFF that can be used to turn off logging, and a level ALL that can be used to
enable logging of all messages.
Procedure
1 Open the /opt/serengeti/conf/log4j.properties file for editing.
2 Change the logging level.
3 Save your changes and close the file.
4 Stop and restart the Serengeti services.
Collect Log Files for Troubleshooting
You can collect log files from the Serengeti Management Server or from the nodes of a cluster to help you
and the VMware support team with troubleshooting.
If you include a cluster name when you run the command, the following log files are collected from each
node in the specified cluster.
n/var/log/hadoop
n/var/log/hbase
n/var/log/zookeeper
n/var/log/gphd
n/opt/mapr/logs
n/opt/mapr/hadoop/hadoop/logs
n/var/chef/cache/chef-stacktrace.out
If you do not include a cluster name when you run the command, the following log files are collected from
the Serengeti Management Server.
n/opt/serengeti/logs
n/opt/serengeti/conf
n/var/log/messages
NOTE The log files that are collected from each node or the Serengeti Management Server are configured in
the /opt/serengeti/etc/support/cluster.files and /opt/serengeti/etc/support/serengeti.files files,
respectively. To change which log files are collected, edit the applicable FILES file.
Procedure
1 Open a command shell, such as Bash or PuTTY, and log in to the Serengeti Management Server as user
serengeti.
2 Change to the directory where you want the log files stored.
3 Run the serengeti-support.sh script.
serengeti-support.sh cluster_name
Big Data Extensions collects the log files and saves them in a single tarball in the
Serengeti Management Server directory from which the command was run.
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Troubleshooting Cluster Creation Failures
The cluster creation process can fail for many reasons. If cluster creation fails, try to resume the process.
You can use one of these methods to resume the cluster creation process.
nIf you created the cluster with the Serengeti Command-Line Interface, run the cluster create ... --
resume command.
nIf you created the cluster with the vSphere Web Client, select the cluster, right-click, and select Resume.
If you cannot resume the process and successfully create the cluster, see the troubleshooting topics in this
section.
Bootstrap Failed 401 Unauthorized Error
When you run the cluster create or cluster create ... --resume command, the command can fail. The
reason it failed is logged to the associated Serengeti server installation and configuration log
file, /opt/serengeti/logs/ironfan.log.
Problem
The cluster create or cluster create ... --resume command fails.
nOn the Command-Line Interface, an error message appears:
Bootstrap Failed
nIn the Serengeti server installation and configuration log file, /opt/seregeti/logs/ironfan.log, an error
message appears:
[Fri, 09 Aug 2013 01:24:01 +0000] INFO: *** Chef 11.X.X *** [Fri, 09 Aug 2013 01:24:01
+0000] INFO: Client key /home/ubuntu/chef-repo/client.pem is not present - registering [Fri,
09 Aug 2013 01:24:01 +0000] INFO: HTTP Request Returned 401 Unauthorized: Failed to
authenticate. Please synchronize the clock on your client [Fri, 09 Aug 2013 01:24:01 +0000]
FATAL: Stacktrace dumped to /var/chef/cache/chef-stacktrace.out [Fri, 09 Aug 2013 01:24:01
+0000] FATAL: Net::HTTPServerException: 401 "Unauthorized"
Cause
This error occurs if the Serengeti Management Server and the failed virtual machine clocks are not
synchronized.
Solution
From the vSphere Client, configure all ESXi hosts to synchronize their clocks with the same NTP server.
After you correct the clocks, you can run the cluster create ... --resume command to complete the
cluster provisioning process.
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Cannot Create a Cluster with the hdfs-hbase-template-spec.json File
If you use the /opt/serengeti/conf/hdfs-hbase-template-spec.json from the Serengeti server virtual
machine to create a cluster, cluster creation fails.
Problem
The cluster create or cluster create ... --resume command fails, and the Command-Line Interface
displays an error message:
cluster cluster_name create failed: Unrecognized field "groups" (Class
com.vmware.bdd.apitypes.ClusterCreate), not marked as ignorable at [Source:
java.io.StringReader@7563a320; line: 3, column: 13] (through reference chain:
com.vmware.bdd.apitypes.ClusterCreate["groups"])
Cause
The /opt/serengeti/conf/hdfs-hbase-template-spec.json file is for Serengeti Management Server internal
use only. It is not a valid cluster specification file.
Solution
Create your own cluster specification file.
Sample cluster specification files are in the /opt/serengeti/samples directory.
Insufficient Storage Space
If sufficient storage resources are not available when you run the cluster create or cluster create ... --
resume command, cluster creation fails.
Problem
The cluster create or cluster create ... --resume command fails, and the Command-Line Interface or
Big Data Extensions plug-in interface displays the following error message:
cluster $CLUSTER_NAME create failed: Cannot find a host with enough storage to place base nodes
[$NODE_NAME].
Node $NODE_NAME placed on host $HOST_NAME. Node $NODE_NAME placed on host $HOST_NAME. You must
add datastores on these hosts [$HOST_NAMES] to use them with the node group [$GROUP_NAME].
Cause
This error occurs if sufficient datastore space is not available.
Solution
1 Review the /opt/serengeti/logs/serengeti.log file, and search for the phrase cannot find host with
enough.
This information shows the Serengeti server snapshot of the vCenter Server cluster environment
immediately after the placement failure.
You can also find information about the datastore name and its capacity. Additionally, you can find the
cluster specification file that you used, and information for the nodes that have been successfully
placed.
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2 Review your cluster specification file.
The cluster specification file defines the cluster’s datastore requirements and determines the available
space on the datastore that you added to Serengeti. Use this information to determine which storage is
insufficient.
For example, if there is insufficient LOCAL datastore capacity for worker nodes, you must add
additional LOCAL datastores to the Serengeti server and assign them to the cluster.
Distribution Download Failure
If the server for the Hadoop distribution is down when you run the cluster create or cluster create ...
--resume command, cluster creation fails.
Problem
The reason the command failed is logged.
nFor tarball-deployed distributions, the following error message appears on the Command-Line
Interface or the Big Data Extensions plug-in interface:
Unable to run command 'execute[install hadoop-1.2.1 from tarball]' on node xftest-client-0.
SSH to this node and run the command 'sudo chef-client' to view error messages.
nFor Yum-deployed distributions, the following error message appears on the Command-Line Interface
or the Big Data Extensions plug-in interface:
Cannot bootstrap node xfbigtop-master-0.
remote_file[/etc/yum.repos.d/bigtop2.repo] (hadoop_common::add_repo line 85) had an error:
Net::HTTPServerException: 404 "Not Found"
SSH to this node and view the log file /var/chef/cache/chef-stacktrace.out, or run the
command 'sudo chef-client' to view error messages.
Cause
The package server is down.
nFor tarball-deployed distributions, the package server is the Serengeti Management Server.
nFor Yum-deployed distributions, the package server is the source of the Yum-deployed distribution:
either the official Yum repository or your local Yum server.
Solution
1 Ensure that the package is reachable.
Distribution Type Requirement
tarball-deployed Ensure that the httpd service on the Serengeti Management Server is
running.
Yum-deployed Ensure that the Yum repository file URLs are correctly configured in the
manifest file.
2 Ensure that you can download the necessary file from the failed node.
Distribution Type Necessary File
tarball-deployed tarball
Yum-deployed Yum repository file
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Serengeti Management Server IP Address Unexpectedly Changes
The IP address of the Serengeti Management Server changes unexpectedly.
Problem
When you create a cluster after the Serengeti Management Server IP address changes, the cluster creation
process fails with a bootstrap failure.
Cause
The network setting is DHCP.
Solution
Restart the Serengeti Management Server virtual machine.
After Disconnecting a Host from vCenter Server the Cluster Resume Process
Fails
If you disconnect a host from vCenter Server after a failed cluster creation attempt, you cannot successfully
resume the cluster creation.
Problem
If cluster creation fails, and then you disconnect the affected host from vCenter Server and try to resume the
cluster creation process, it fails and you receive the following error message: cluster cluster-name resume
failed: Failed to create virtual machine cluster cluster-name.
Cause
When you disconnect the host from vCenter Server, the host's virtual machines become unavailable. When
you try to resume the cluster creation, the Serengeti Management Server cannot remove the unavailable
virtual machines from the disconnected host.
Solution
1 Manually remove the affected hosts from vCenter Server.
2 Repeat the cluster create resume process.
Cluster Provisioning Stops Responding if Virtual Machines are Powered Off or
Reset During Bootstrapping
When you create, configure, or resume creating or configuring a cluster, the process stops responding.
Problem
If you create, configure, or resume creating or configuring a cluster, and then power off or reset a virtual
machine while it is bootstrapping, the cluster provisioning process stops responding.
Cause
When a virtual machine is powered off or reset during bootstrapping, its SSH connection stops responding.
Solution
1 Do one of the following:
nIf you are using the Serengeti Command-Line Interface, press Ctrl+C.
nIf you are using the vSphere Web Client, no action is required.
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2 Open a command shell, such as Bash or PuTTY, and log in to the Serengeti Management Server as user
serengeti.
3 Kill the failed cluster provisioning process.
ps ax | grep knife | grep cluster-name | head -1 | awk '{print $1}' | xargs kill -9
4 Force the cluster's status to PROVISION_ERROR.
set-cluster-status.sh cluster-name PROVISION_ERROR
5 Use the vSphere Web Client to log in to vCenter Server.
6 Power on any virtual machines in the cluster that are powered off.
7 Resume the cluster creation process.
nIf you created the cluster with the Serengeti Command-Line Interface, run the cluster create ...
--resume command.
nIf you created the cluster with the vSphere Web Client, select the cluster, right-click, and select
Resume.
HBase Cluster Creation Job Fails When Time Difference Among Nodes is More
Than 20 Seconds
If the time difference among nodes is more than 20 seconds, you must synchronize the times before you can
create an HBase cluster or run jobs.
Problem
If you attempt to create an HBase cluster with nodes whose times are more than 20 seconds apart, the cluster
creation might fail. If it succeeds, any HBase jobs that you run will fail.
Cause
HBase requires that the time difference between its master-server and region-server nodes be 20 seconds or
less.
Solution
1 Make sure that the NTP server is running on all ESXi hosts and that the time difference among all ESXi
hosts is less than 20 seconds.
Wait a few minutes to let the nodes synchronize their time with their ESXi hosts.
2 Make sure that the time difference among nodes in the cluster is less than 20 seconds.
a Open a command shell, such as Bash or PuTTY, and log in to the Serengeti Management Server as
user serengeti.
b Run the serengeti-ssh.sh script.
serengeti-ssh.sh hbase_cluster_name date
c If the times are more than 20 seconds apart, repeat steps 1 and 2.
3 Start the failed process or services.
nIf the original cluster creation failed, try to resume the cluster creation process.
nIf you created the cluster with the Serengeti Command-Line Interface, run the cluster
create ... --resume command.
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nIf you created the cluster with the vSphere Web Client, select the cluster, right-click, and select
Resume.
nIf the cluster resume process failed, try again to resume it.
nIf the cluster creation succeeded but running a job failed, start the failed services.
nIf you are using the Serengeti Command-Line Interface, run the following commands.
cluster export --name cluster_name --specFile /tmp/1
cluster config --name cluster_name --specFile /tmp/1 --yes
nIf you are using the vSphere Web Client, stop and restart the cluster.
Creating a Large Scale Cluster in Big Data Extensions Results In a Bootstrap
Failed Error
When you create a large scale cluster, for example, of 300 or more nodes per cluster, in Big Data Extensions,
you might get a Bootstrap failed error.
Problem
Generally, one database connection can serve two nodes at the same time, so for a cluster with 300 or more
nodes, 150 database connections are required. To avoid receiving a Bootstrap failed error, increase the size
of the database connection pool.
Cause
The size of the database connection pool was not large enough to handle creating a large scale cluster with
300 or more nodes.
Solution
1 After the Big Data Extensions vApp is deployed, log in to the Big Data Extensions Management Server
as user serengeti.
2 Increase the database connection pool size.
Option Description
/etc/chef-server/chef-server.rb Indicates the location on the Management Server to configure the database
connection pool size.
postgresql['max_connections'] Indicates the maximum number of connections for the postgresql database.
This value should usually be erchef['db_pool_size'] + 100.
erchef['db_pool_size'] Indicates the database connection pool size.
sudo sed -i -e "s|erchef\['db_pool_size'\] .*|erchef['db_pool_size'] = 150|"
/etc/chef-server/chef-server.rb
sudo sed -i -e "s|postgresql\['max_connections'\] .*|postgresql['max_connections'] = 250|"
/etc/chef-server/chef-server.rb
sudo chef-server-ctl reconfigure
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Cannot Create a Cluster for Which the Time Is Not Synchronized
When you run the cluster create or cluster create ... --resume command, the command can fail if
there are time discrepancies in the environment.
Problem
The cluster create or cluster create ... --resume command fails, and the Command-Line Interface or
Big Data Extensions plug-in interface displays the following error message:
You must synchronize the time of the following hosts [$HOST_NAMES] with the Serengeti Management
Server to use them.
Cause
Before creating new virtual machines on hosts, the time on the target hosts is checked against the time on
the Serengeti Management Server. If the time between the Serengeti Management Server and the hosts is not
synchronized, the virtual machine creation will fail.
Solution
uFrom the vSphere Web Client, configure all ESXi hosts to synchronize their clocks with the same NTP
server.
What to do next
After you synchronize the time between the Serengeti Management Server and the other ESXi hosts within
your environment, try to create a cluster.
Big Data Extensions Virtual Appliance Upgrade Fails
The upgrade of the Big Data Extensions virtual appliance might fail. If the upgrade process fails, you can try
the upgrade again.
Problem
The upgrade of the Big Data Extensions virtual appliance does not succeed.
Solution
1 Revert to the prior state for both of the virtual machines for the Big Data Extensions virtual appliance
based on the snapshots that vSphere Update Manager took.
Use the virtual machine's snapshot manager and select the snapshot created by vSphere Update
Manager.
2 Reboot the virtual appliance.
3 Resolve the blocking issue.
4 Restart the remediation task.
Click Remediate in the vSphere Update Manager user interface to redo the upgrade process.
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Upgrade Cluster Error When Using Cluster Created in Earlier Version
of Big Data Extensions
To enable the Serengeti Management Server to manage clusters that you created in a previous version of Big
Data Extensions, you must upgrade the components in each cluster's virtual machines. The Serengeti
Management Server uses these components to control the cluster nodes.
Problem
When you upgrade from an earlier version of Big Data Extensions, clusters that you need to upgrade are
shown with an alert icon next to the cluster name. When you click the alert icon the error message "Upgrade
the cluster to the latest version" displays as a tool tip. See “View Provisioned Clusters in the vSphere Web
Client,” on page 125.
You can also identify clusters you need to upgrade using the cluster list command. When you run the
cluster list command the message "Need Upgrade" displays where the cluster version normally appears.
Solution
1 For each cluster that you created in a previous version of Big Data Extensions, make sure that all of the
cluster's nodes are powered on and have valid IP addresses.
If a node does not have a valid IP address, it cannot be upgraded to the new version of Big Data
Extensions virtual machine tools.
a Log into the vSphere Web Client connected to vCenter Server and navigate to Hosts and Clusters.
b Select the cluster's resource pool, select the Virtual Machines tab, and power on the cluster's
virtual machines.
IMPORTANT It might take up to five minutes for vCenter Server to assign valid IP addresses to the big
data cluster nodes. Do not perform the remaining upgrade steps until the nodes have received their IP
addresses.
2 Open a command shell, such as Bash or PuTTY, and log in to the Serengeti Management Server as user
serengeti.
3 Run the cluster upgrade command for each cluster created in a previous version of Big Data
Extensions.
cluster upgrade --name cluster-name
4 If the upgrade fails for a node, make sure that the failed node has a valid IP address, and then rerun the
cluster upgrade command.
You can rerun the command as many times as you need to upgrade all the nodes.
5 Stop and restart your Hadoop and HBase clusters.
Virtual Update Manager Does Not Upgrade the Hadoop Template
Virtual Machine Under Big Data Extensions vApp
When you use the Virtual Update Manager (VUM) to upgrade the Big Data Extensions 2.0 vApp to
Big Data Extensions 2.1 vApp, VUM reports that the upgrade completed successfully, but VUM only
upgrades the Big Data Extensions Management Server, not the Big Data Extensions hadoop template.
Cause
The hadoop template virtual machine in Big Data Extensions 2.1 vApp contains CentOS 6.5 and the hadoop
template virtual machine in Big Data Extensions 2.0 vApp contains CentOS 6.4. The current versions of
Virtual Update Manager (versions 5.1 and 5.5) do not support upgrading from CentOS 6.4 to CentOS 6.5.
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Solution
To work around this issue, you must manually replace the hadoop template virtual machine after you
upgrade the Big Data Extensions vApp.
Procedure
1 Download the Big Data Extensions template OVA file from the Big Data Extensions download
page.
2 Login to the vSphere Client.
3 Delete the original hadoop-template virtual machine under the Big Data Extensions vApp or move
it out of the Big Data Extensions vApp which has been upgraded.
4 Select File > Deploy OVA Template.
5 Enter the downloaded template OVA path in the Deploy OVA Template dialog.
6 Complete the steps in the OVA Deployment wizard.
On the step to configure the name and location, enter hadoop-template or any other valid name. On
the step to configure the resource pool, select the Big Data Extensions vApp which has been
upgraded.
7 Login to the Big Data Extensions server via SSH.
8 Restart Big Data Extensions Web services: sudo service tomcat restart
Unable to Connect the Big Data Extensions Plug-In to the Serengeti
Server
When you install Big Data Extensions on vSphere 5.1 or later, the connection to the Serengeti Management
Server fails to authenticate.
Problem
The Big Data Extensions plug-in is unable to connect to the Serengeti server.
Cause
During the deployment, the Single Sign-On (SSO) link was not entered. The Serengeti Management Server
cannot authenticate the connection from the plug-in.
Solution
Use the Serengeti Management Server Administration Portal to configure the SSO settings. See “Configure
vCenter Single Sign-On Settings for the Serengeti Management Server,” on page 30.
If you still cannot connect the Big Data Extensions plug-in to the Serengeti Server, use the EnableSSOAuth
utility.
1 Open a command shell, such as Bash or PuTTY, and log in to the Serengeti Management Server as user
root.
2 Configure the SSO settings.
EnableSSOAuth https://vCenter-server-IP-address:7444/lookupservice/sdk
3 Restart the tomcat service.
/sbin/service tomcat restart
4 Connect the Big Data Extensions plug-in to the Serengeti Management Server.
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vCenter Server Connections Fail to Log In
The Serengeti Management Server tries but fails to connect to vCenter Server.
Problem
The Serengeti Management Server tries and fails to connect to vCenter Server.
Cause
vCenter Server is unreachable for any reason, such as network issues or too many running tasks.
Solution
Ensure that vCenter Server is reachable.
nConnect to vCenter Server with the vSphere Web Client or the VMware Infrastructure Client (VI
Client) .
nPing the vCenter Server IP address to verify that the Serengeti Management Server is connecting to the
correct IP address.
Management Server Cannot Connect to vCenter Server
If you enable an IPv6 connection with vCenter Server and then change the IP address, the management
server cannot connect to vCenter Server. You cannot fix the problem by restarting the management server.
Solution
1 Use the vSphere Web Client to log in to vCenter Server.
2 Power off the Management Server.
3 Navigate to the Management Server Network 2 Settings section.
4 Under vApp Options, select Edit Settings > Options > Properties.
5 Enter the new IPv6 address for vCenter Server to item in the vCenter's IPv6 Address to connect text
box.
6 Power on the Management Server.
Cannot Perform Serengeti Operations after Deploying Big Data
Extensions
If the Big Data Extensions plug-in cannot communicate with vCenter Server during initialization, internal
errors occur and you cannot perform Serengeti operations.
Problem
When you run a command, the Serengeti CLI or Big Data Extensions plug-in displays the following error.
Internal error: REST API transport layer error.
Cause
The Serengeti Management Server must communicate with the vCenter Extension vService through the
vCenter Server FQDN during initialization. The following scenarios can prevent you from performing
Serengeti operations.
n(vSphere 5.1) If the host name is not correctly set during vSphere installation, vCenter Server cannot
receive the correct host name or FQDN, nor provide the correct information to the Serengeti
Management Server. As a result, the Big Data Extensions plug-in cannot successfully communicate with
vCenter Server.
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n(vSphere 5.5) If the vCenter Server Appliance is deployed into an OVF environment that has a static IP
network configuration and a blank host name, the reverse lookup from the IP address cannot be
performed. Without the reverse lookup, the host name is incorrectly set for the vCenter Server
Appliance.
nIf vCenter Server is configured with an FQDN, but the Big Data Extensions vApp instance's DNS server
cannot communicate with vCenter Server or cannot resolve the vCenter Server FQDN, the host name
cannot be set.
Solution
For vSphere 5.1, set the host name.
1 Use the vSphere Web Client to log in to vCenter Server.
2 From the Inventory pane, click Big Data Extensions, and click the Summary tab.
3 In the Connected Server dialog, click the name of the connected vCenter Server.
4 Click the Manage tab, click Settings, and from the tab's left navigation pane, click Advanced Settings.
5 Find the keys related to the FQDN, and change their values from the incorrect FQDN to the correct
FQDN or IP address.
6 Delete the existing Big Data Extensions vApp instance and install a new Big Data Extensions vApp
instance.
For vSphere 5.5, reinstall your environment.
1 Delete and reinstall the vCenter Server Appliance. During reinstallation, set the vCenter Server
Appliance host name in the OVF properties.
2 Delete the existing Big Data Extensions vApp instance and install a new Big Data Extensions vApp
instance.
For DNS server or network problems, delete the existing Big Data Extensions vApp instance and install a
new Big Data Extensions vApp instance that includes network connectivity and a DNS server that can
resolve the vCenter Server FQDN.
SSL Certificate Error When Connecting to Non-Serengeti Server with
the vSphere Console
From the vSphere Web Client, you cannot connect to a non-Serengeti server.
Problem
When you use the Big Data Extensions plug-in to vCenter Server and try to connect to a non-Serengeti
server, you receive an error message:
SSL error:
Check certificate failed.
Please select a correct serengeti server.
Cause
When you use the Big Data Extensions plug-in, you can connect only to Serengeti servers.
Solution
Connect only to Serengeti servers. Do not perform certificate-related operations.
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Cannot Restart or Reconfigure a Cluster For Which the Time Is Not
Synchronized
When the time on the hosts and the Serengeti Management Server drifts apart, the cluster cannot be
restarted or reconfigured.
Problem
The cluster fails to start, and the Command-Line Interface or Big Data Extensions plug-in interface displays
the following error message:
Nodes in cluster $CLUSTER_NAME start failure: Synchronize the time of the host [$HOST_NAME(S)]
with the Serengeti Management Server running on $HOST_NAME.
Cause
This error occurs if the Serengeti Management Server and the failed virtual machine clocks are not
synchronized. The time on all hosts within a cluster is checked against the time on the
Serengeti Management Server. If the time between the Serengeti Management Server and the hosts is not
synchronized, the virtual machine fails to start.
Solution
uFrom the vSphere Web Client, configure all ESXi hosts to synchronize their clocks with the same NTP
server.
After you correct the clocks, you can try to start or reconfigure the cluster.
Cannot Restart or Reconfigure a Cluster After Changing Its
Distribution
After you change a cluster's distribution vendor or distribution version, but not the distribution name, the
cluster cannot be restarted or reconfigured.
Problem
When you try to restart or reconfigure a cluster after changing its distribution vendor or distribution version
in the manifest, you receive the following error message:
Bootstrap Failed
Cause
When you manually change an existing distribution's vendor or version in the manifest file and reuse a
distribution's name, the Serengeti server cannot start the node.
Solution
1 Revert the manifest file.
2 Use the config-distro.rb tool to add a new distribution, with a unique name, for the distribution
vendor and version that you want.
Virtual Machine Cannot Get IP Address and Command Fails
A Serengeti command fails, and the CLI displays the following error message: Virtual Machine Cannot Get
IP Address.
Cause
This error occurs when a network configuration error occurs.
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For static IP, the cause is typically an IP address conflict.
For DHCP, common causes include:
nThe number of virtual machines that require IPs exceeds the available DHCP addresses.
nThe DHCP server fails to allocate sufficient addresses.
nThe DHCP renew process failed after an IP address expires.
Solution
nVerify that the vSphere port group has enough available ports for the new virtual machine.
nIf the network is using static IP addressing, ensure that the IP address range is not used by another
virtual machine.
nIf the network is using DHCP addressing, ensure that an IP address is available to allocate for the new
virtual machine.
Cannot Change the Serengeti Server IP Address From the
vSphere Web Client
When you attempt to change the Serengeti server IP address from the vSphere Web Client, the procedure
fails.
Solution
Prerequisites
Get a static IP address.
Procedure
1 On the Serengeti Management Server, edit the following configuration
file /etc/sysconfig/network-scripts/ifcfg-eth0 by replacing the contents of the file with
following contents:
DEVICE=eth0
BOOTPROTO=static
ONBOOT=yes
TYPE=Ethernet
IPADDR=your_static_ip
PREFIX=your_prefix
GATEWAY=your_gateway
DNS1=your_dns1
DNS2=your_dns2
2 Reboot the Serengeti Management Server.
When the operating system starts, it configures the IP address according to the contents of the new
configuration file.
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A New Plug-In Instance with the Same or Earlier Version Number as a
Previous Plug-In Instance Does Not Load
When you install a new Big Data Extensions plug-in instance that has the same or earlier version as a
previous Big Data Extensions plug-in instance, the previous version is loaded instead of the new version.
Problem
When you install a new Big Data Extensions plug-in that has the same or lower version number as a
previous Big Data Extensions plug-in, the previous version is loaded instead of the new version. This
happens regardless of whether you uninstall the previous plug-in.
Cause
When you uninstall a plug-in instance, the vSphere Web client does not remove the plug-in instance
package from the Serengeti server.
After you install a plug-in instance with the same or earlier version number as the previous plug-in instance,
and try to load the new plug-in instance, vSphere finds the previous plug-in instance package in its local
directory. vSphere does not download the new plug-in instance package from the remote Serengeti server.
Solution
1 Uninstall the old plug-in instance.
2 Remove the old plug-in instance.
nFor vCenter Server Appliance instances, delete the /var/lib/vsphere-client/vc-
packages/vsphere-client-serenity/vsphere-bigdataextensions-version folder.
nFor vSphere Web client servers on Windows, delete the %ProgramData%/vmware/vSphere Web
Client/vc-packages/vsphere-client-serenity/vsphere-bigdataextensions-version folder.
3 Restart the vSphere Web Client.
nFor vCenter Server Appliance instances, restart the vSphere Web Client service at the vCenter
Server Appliance Web console, http://$vCenter-Server-Appliance-IP:5480
nFor vSphere Web Client servers on Windows, restart the vSphere Web Client service from the
services console.
4 Install the new plug-in instance.
Host Name and FQDN Do Not Match for Serengeti Management Server
The Serengeti Management Server host name and Fully Qualified Domain Name (FQDN) must match
before you can perform some Big Data Extensions operations, such as an upgrade.
Problem
The Serengeti Management Server's host name and FQDN are not the same.
Cause
Certain sequences of deployment steps can cause the Serengeti Management Server's host name and FQDN
to be different.
Solution
1 Open a command shell, such as Bash or PuTTY, and log in to the Serengeti Management Server as user
serengeti.
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2 Create a new file for the set_hostname.sh script.
touch /tmp/set_hostname.sh
3 Open the /tmp/set_hostname.sh file for editing, and add the following lines.
ETHIP=`/sbin/ifconfig eth0 | grep "inet addr" | awk '{print $2}' | sed 's/addr://'`
FQDN=$ETHIP
RET=`/bin/ipcalc --silent --hostname $ETHIP`
if [ "$?" = "0" ]; then
FQDN=`echo $RET | awk -F= '{print $2}'`
fi
echo "set hostname to ${FQDN}"
`hostname ${FQDN}`
4 Save your changes and close the file.
5 Run the set_hostname.sh script.
sudo bash /tmp/set_hostname.sh
Serengeti Operations Fail After You Rename a Resource in vSphere
After you use vSphere to rename a resource, Serengeti commands fail for all Serengeti clusters that use that
resource.
Problem
If you use vSphere to rename a Serengeti resource that is used by provisioned Serengeti clusters, Serengeti
operations fail for the clusters that use that resource. This problem occurs for vCenter Server resource pools,
datastores, and networks that you add to Serengeti, and their related hosts, vCenter Server clusters, and so
on. The error message depends on the type of resource, but generally indicates that the resource is
inaccessible.
Cause
The Serengeti resource mapping requires that resource names do not change.
Solution
Use vSphere to revert the resource to its original name.
Big Data Extensions Server Does Not Accept Resource Names With
Two or More Contiguous White Spaces
If you include two or more contiguous white space characters in the name for a Big Data Extensions
resource pool, datastore, or network, the add process fails.
Solution
No workarounds or patches are available for this issue.
Non-ASCII characters are not displayed correctly
When you work with the CLI on a Windows platform, if you run a script command on a file that contains
non-ASCII characters, it returns messages that are not displayed correctly.
Cause
It is a known issue that non-ASCII characters are not recognized on Windows platforms.
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Solution
No workarounds or patches are available for this issue
MapReduce Job Fails to Run and Does Not Appear In the Job History
A submitted MapReduce job fails to run and does not appear in the job history.
Problem
When you submit a MapReduce job and the workload is heavy, the MapReduce job does not run, and it
does not appear in the MapReduce job history.
Cause
During heavy workloads, the JobTracker or NameNode service might be too busy to respond to vSphere
HA monitoring within the configured timeout value. When a service does not respond to vSphere HA
request, vSphere restarts the affected service.
Solution
1 Stop the HMonitor service.
When you stop the HMonitor service, vSphere HA failover is disabled.
a Open a command shell, such as Bash or PuTTY, and log in to the affected cluster node.
b Stop the HMonitor service.
sudo /etc/init.d/hmonitor-*-monitor stop
2 Increase the JobTracker vSphere timeout value.
a Open the /user/lib/hadoop/monitor/vm-jobtracker.xml file for editing.
b Find the service.monitor.probe.connect.timeout property.
c Change the value of the <value> element.
d Save your changes and close the file.
3 Increase the NameNode vSphere timeout value.
a Open the /user/lib/hadoop/monitor/vm-namenode.xml file for editing.
b Find the service.monitor.portprobe.connect.timeout property.
c Change the value of the <value> element.
d Save your changes and close the file.
4 Start the HMonitor service.
sudo /etc/init.d/hmonitor-*-monitor start
Cannot Submit MapReduce Jobs for Compute-Only Clusters with
External Isilon HDFS
You cannot submit MapReduce Jobs for compute-only clusters that point to an external Isilon HDFS.
Problem
If you deploy a compute-only cluster with an external HDFS pointing to Isilon, the deployment appears to
be successful. However, the JobTracker is in safe mode, which does not let you submit MapReduce jobs.
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Cause
JobTracker requires a user named mapred.
Solution
1 SSH into the Isilon cluster.
2 Add the mapred user to the Isilon system's wheel group.
pw useradd mapred -G wheel
MapReduce Job Stops Responding on a PHD or CDH4 YARN Cluster
A MapReduce job stops responding on a PHD or CDH4 YARN cluster with one DataNode and one
NodeManager agent, each with 378MB of memory.
Problem
MapReduce jobs stop responding when you run them on a PHD or CDH4 YARN cluster with one data node
and one NodeManager agent.
Cause
Insufficient memory resources.
Solution
1 Create a PHD or CDH4 YARN cluster with two DataNodes and two NodeManagers.
2 Rerun the MapReduce job.
Cannot Download the Package When Using Downloadonly Plugin
When you try to set up a local yum repository, you might find that, when you use the downloadonly plugin,
you can find the package that you need but you cannot download the package.
Solution
1 Perform the following command to check if the package has been installed on the machine:
yum remove <package_name>
2 If the package has been installed on the machine, remove the package and try to download it again.
Cannot Find Packages When You Use Yum Search
When you try to set up a local yum repository, you must download packages for either the Cloudera
Manager or Ambari application manager. The packages have been put on the http server and can display in
a browser but, when you use yum search, you cannot find the specific packages that you need.
Cause
If the repo file is not set correctly or you have data in the yum cache on your system, you might encounter
this issue.
Solution
1 Make sure the yum repository server URL in the repo file is correct for the location and version.
2 Use the createrepo tool to make sure that you have created the repodata directory.
3 Use the yum clean all command to clean the yum cache.
4 Run the yum search again to locate the packages.
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Remove the HBase Rootdir in HDFS Before You Delete the HBase
Only Cluster
After you delete an HBase only cluster, the HBase data still exists on the external HDFS. It is important that
you remove the HBase rootdir in HDFS before you delete the HBase only cluster.
Cause
The HBase rootdir was not removed before the HBase only cluster was deleted.
Solution
You can keep the data or remove the data.
Procedure
1 Log in to the HBase master node in the HBase only cluster.
2 Open the hbase-site.xml file and find the value for the property hbase.rootdir.
/etc/hbase/conf/hbase-site.xml
3 Run the following command:
hadoop fs -rmr <value_of_hbase.rootdir>
4 Delete the HBase only cluster in Big Data Extensions.
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Index
Numerics
401 unauthorized error 142
A
accessing
Command-Line Interface 32
HBase databases 100
Hive data 129, 131
add a cluster, with an application manager 106
add process fails 156
adding
datastores 92
networks 93
resource pools 91
adding software management servers, with web
interface 45
Administration Portal, Serengeti Management
Server 30, 80, 124
Ambari, local repository 64
Apache Hadoop distribution
Big Data Extensions feature support 15
configuration values for Big Data
Extensions 50
Hadoop feature support 17
Apache log4j logs 140
application manager
delete 46
modify 46
application managers
add a cluster 106
and Big Data 12
distributions 46
managing 45
services and operations supported 13
appmanager add command 12
architecture 12
authenticating, troubleshooting 151
B
baseline, upgrade 38
basic Hadoop clusters 99
Big Data Extensions
installing 19
support for Hadoop features 15
system requirements 19
troubleshooting 151
upgrading 35
Big Data Extensions environment, managing 75
Big Data Extensions plug-in
cannot connect to the Serengeti Server 150
changing versions 155
connecting to Serengeti Management
Server 30
installing 28
loading 155
registering 28
upgrading 37, 40
Big Data Extensions vApp
deploying 24
upgrading 37
bootstrap failed 142, 147
burst out to virtual 107
C
CentOS 6.4 149
CentOS yum repository 62, 65
certificate 136
certificates, SSO 30
changing, cluster node passwords 117
Chef Server 136
CLI, accessing 32
CLI command-line interface, upgrading 41
CLI console 31
client nodes for Hadoop 99, 101
clock synchronization, and cluster creation
failure 142
Cloudera distribution
administrative commands with the Serengeti
CLI 31, 32
Big Data Extensions feature support 15
configuration values for Big Data
Extensions 50
DNS and FQDN for cluster traffic 101
Hadoop feature support 17
local yum repository 56, 60
Service Level vSphere HA 15, 17
yum repository for deployment 52
Cloudera Manager, local repository 61
cluster nodes, passwords 117
cluster upgrade command 41
cluster config command 117
cluster creation failure
401 unauthorized error 142
VMware, Inc. 161
Bootstrap Failed 142
distribution download failure 144
insufficient storage space 143
Serengeti server IP address changes 145
template-cluster-spec.json file 143
cluster export command 117
cluster fix command 119
cluster specification files
compute-only cluster 106
Hadoop distribution JARs 117
reconfiguring clusters 117
resource pool symbolic link 91
samples 143
clusters
add with an application manager 106
basic Hadoop 99
compute workers only 107
compute-only 99, 106
creating, See creating clusters
creation services 13
customized 99
data-compute separated 99
deleting 110
deploying under different resource pools 92
failover 116
HBase 99
HBase only 105
HBase status 128
managing 109
provisioning hangs 145
reconfiguration failure 153
reconfiguring 117
restart failure 153
restarting or reconfiguring 153
running MapReduce jobs on PHCD or
CDH4 158
scaling CPU and RAM 115
stopping and starting 109
topology 100
upgrade 41
user passwords 117
viewing 125, 126
communication with vCenter Server 79
compliance settings 39
compute workers only cluster 107
compute-only clusters 106
configuration files, converting Hadoop XML to
Serengeti JSON 117
configure the Ambari repository 66
configuring
Hive 129, 131
local yum repository 60
logging levels 140
proxy settings 37
yum and yum repositories 52
Yum repositories 53
yum repository for Cloudera distribution 60
yum repository for MapR distribution 60
connecting
Serengeti services 32
to Serengeti Management Server 30
to the Serengeti Management Server 75
connections
fail 150, 151
to vCenter Server 151
contact cluster nodes 88
convert-hadoop-conf.rb conversion tool 117
converting Hadoop XML to Serengeti JSON 117
CPU and RAM, increasing and decreasing 115
create local repository for Ambari
configure the HDP repository on the Ambari
Server 68
configure the HDP repository URL on the
Local Yum Server 67
download packages 65
prepare the software environment 64
create local repository for Cloudera Manager
download packages 63
prepare the software environment 61
creating
the Ambari repository 66
clusters, See creating clusters
local yum repository 56, 58
local repository for Cloudera Manager 63
Serengeti CLI users 77
yum repository for Cloudera distribution 56
yum repository for MapR distribution 56
yum repository for Pivotal distribution 58
creating clusters
compute-only 106
Hadoop or HBase 101
troubleshooting 142–146
creating a cluster, with an application
manager 106
creating CentOS yum repository, setting up the
CentOS yum repository 62, 65
Customer Experience Improvement Program
23, 123, 124
D
data collector, enabling 123
data-compute separated clusters 99
datastore add process fails 156
datastores
adding 92
removing 93
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delete an application manager 46
deleting
clusters 110
See also removing
Deleting HBase rootdir 159
Deleting HBase only cluster 159
deploying
Big Data Extensions vApp 24
OVA 24
deployment log files 140
Direct Attached Storage requirements 19
disconnecting, hosts 145
disk partitioning 69
disk failure, recovering from 119
disk I/O shares 116
distribution download failure 144
distributions
supported 12
viewing 46
DNS lookup, verify 87
DNS lookup, verify 87
DNS type, modify 94
Domain Name System 87
downloadonly plugin 158
E
elastic scaling, log file 140
EMC Isilon OneFS 83
enabling data collector 123
environment, managing 75
EULA (license agreement) 38
external interfaces 133
external HDFS cluster, preparing 104
F
features and operations supported, application
managers 13
fixed elastic scaling 112, 113
G
getting started 9
glossary 7
Greenplum distribution
Big Data Extensions feature support 15
configuration values for Big Data
Extensions 50
Hadoop feature support 17
H
Hadoop clusters
creating 101
See also clusters
Hadoop distributions
JAR files 117
managing 49
Hadoop nodes
logging in 121
passwords 121
Hadoop Distributed File System (HDFS),
monitoring 127
Hadoop distribution deployment types 50
Hadoop distributions supporting MapReduce
clusters 99
Hadoop feature support by distribution 17
Hadoop template virtual machines
creating 69
prerequisites 69
Hadoop Template virtual machines 73
Hadoop Virtualization Extensions (HVE) 50, 100
hadoop-template virtual machine 35
hardware requirements 19
HBase clusters
creating 101
See also clusters
HBase database access 100
HBase only cluster, using OneFS as external
HDFS cluster 104
HBase only cluster, prerequisites 104
HBase only clusters
creating, HBase only clusters 105
creating with the web client 105
HBase rootdir 159
HDFS, avoiding node role conflicts 106
HDFS status, See Hadoop Distributed File
System
Hive, configuring 129, 131
Hive data, accessing 129
Hortonworks distribution
Big Data Extensions feature support 15
configuration values for Big Data
Extensions 50
Hadoop feature support 17
HOST_AS_RACK 100
I
I/O shares 116
initialization status 80, 124
installing
Big Data Extensions plug-in 28
Big Data Extensions 19
remote CLI client 31
insufficient storage space 143
intended audience 7
internationalization and localization 22
iornfan.log 136
Index
VMware, Inc. 163
IP addresses
adding segments to networks 95
conflict 153
monitoring 126
IPv6 connection to vCenter 151
J
Java Database Connectivity, See JDBC
Java Runtime Environment (JRE) 32
JBOD, See Direct Attached Storage
JDBC 129
JDK 7 RPM, install 71
JDK 7 RPM 71
Just A Bunch Of Disks, See Direct Attached
Storage
K
keystore 136
L
large scale cluster create failure 147
LDAP and Active Directory 77
license agreement 38
listing roles, with the web client 46
loading, Big Data Extensions plug-in 155
local repositories, creating 61, 64
local repository for Ambari Server 66
local yum repository, troubleshooting 158
localization 22
log files, collecting 141
log4j.properties file 117, 140
logging levels, configuring 140
logging in to Hadoop nodes 121
M
maintenance mode 120
maintenance mode, enter 120
Management Server virtual machine, See
Serengeti Management Server
management server cannot connect to
vCenter 151
managing
clusters 109
vSphere resources 91
Managing Big Data Extensions environment 75
manual scaling 110
MapR distribution
administrative commands with the Serengeti
CLI 31, 32
Big Data Extensions feature support 15
configuration values for Big Data
Extensions 50
Hadoop feature support 17
local yum repository 56, 60
yum repository for deployment 52
MapReduce jobs
and compute-only clusters 157
cannot submit 157
fail to run 157
hanging 158
monitoring 127
MapReduce v1 clusters 99
MapReduce v2 (YARN) clusters 99
master nodes for Hadoop 99, 101
modify an application manager 46
modify DNS type 94
monitoring
Big Data Extensions environment 123
Hadoop Distributed File System (HDFS) 127
HBase clusters status 128
MapReduce jobs 127
nodes 126
resource allocation 126
N
network ports 133
network connectivity, verify 83
networks
add process fails 156
adding 93
adding IP addresses 95
removing 95
settings requirements 19
node groups, roles, avoiding conflict 106
nodes, monitoring 126
non-ASCII characters 156
O
ODBC 129, 131
Open Database Connectivity, See ODBC
operations fail 156
optimizing resource usage 112
overview, application managers 12
P
passwords
changing Serengeti Management Server 76
cluster nodes 117
Hadoop nodes 121
patches 137
PhoneHome, data collector 124
Pivotal distribution
administrative commands with the Serengeti
CLI 31, 32
Big Data Extensions feature support 15
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configuration values for Big Data
Extensions 50
DNS and FQDN for cluster traffic 101
Hadoop feature support 17
local yum repository 58
yum repository for deployment 52
plug-ins, See Big Data Extensions plug-in
port numbers
Hadoop 79
Hbase 79
MapReduce 79
port number, management server 79
preparing OneFS as external HDFS cluster 104
prerequisites for creating an HBase only
cluster 104
Project Serengeti 10
provision-hook.log 136
provisioning, hangs 145
proxy settings, configuring 37
public key 136
R
RACK_AS_RACK 100
reconfiguring, networks 95
recovering from disk failure 119
registering, Big Data Extensions plug-in 28
remote syslog, upgrading 42
remote syslog, upgrading 42
remote CLI Client, installing 31
Remote Command-Line Interface, upgrading 41
removing
datastores 93
networks 95
resource pools 92
serengeti-snapshot 73
See also deleting
Removing HBase rootdir 159
Removing HBase only cluster 159
renaming, vSphere resources 156
resource pools
add process fails 156
adding 91
removing 92
resource requirements
the Hadoop Cluster 19
vSphere Management Server and
templates 19
resource usage 110
resource names 156
resource requirements for vSphere Management
server 19
resourcepool add command 91
resourcepool delete command 92
resourcepool list command 92
resources, renaming in vSphere 156
REST API transport layer error 151
restarting or reconfiguring a cluster 153
RHEL 6.x template
customized virtual machines 73
customizing 69
RHEL template 69
roles, listing 46
root user 137
RPMs, installing 27
running, verify 85, 86
S
Scale Out Failed error message 114
scaling
CPU and RAM 115
elastic 112
manual 110
scaling out cluster 114
scheduling, fixed elastic scaling 113
script command 156
scripts
serengeti-start-services.sh 78
serengeti-stop-services.sh 78
set_hostname.sh 155
security 135, 136
security updates and patches 137
security reference 133
Serengeti servers
fail to connect to vCenter Server 151
IP address changes 145
service log file 140
Serengeti services
connecting 32
stopping and starting 78
Serengeti operations, troubleshooting 151
Serengeti Command-Line Interface
upgrading 37
user names and passwords 77
Serengeti Management Server
add user names 75
Administration Portal 30, 80, 124
changing passwords 76
connecting to 30, 37
FQDN 155
host name 155
IP address 145
Single Sign-On (SSO) 30
Serengeti operation failure 156
Serengeti server IP address, changing 154
serengeti user 137
serengeti-boot.log 136
Index
VMware, Inc. 165
serengeti-maintenance.sh 120
serengeti-maintenance.sh script 120
serengeti-snapshot
created during upgrades 73
creating 69
removing 69, 73
serengeti-start-services.sh script 78
serengeti-stop-services.sh script 78
serengeti-upgrade.log 136
serengeti.log 136
server information, adding 45
Service Level vSphere HA for Cloudera
CDH4 15
services 133
services available with Cloudera Manager or
Ambari 13
set_hostname.sh script 155
settings
clusters 19, 97, 114, 153
proxy 37
Single Sign-On (SSO) 30, 32, 150
snapshots, See serengeti-snapshot
software updates 137
software management servers, adding 45
source, downloading 38
special character support 22
SSL certificates, errors 152
starting
clusters 109
Serengeti services 78
stopping
clusters 109
Serengeti services 78
submitting, problems with MapReduce jobs 157
supported distributions 12, 13
synchronize time with Serengeti Management
Server 83, 148
synchronize time on hosts with Serengeti
Management Server 83, 148
system requirements, Big Data Extensions 19
T
tarball-deployed Hadoop distributions 50
template-cluster-spec.json file, and cluster
creation failure 143
tested host and virtual machine support 19
thin provisioned disk 70
time synchronization, restarting or
reconfiguring 153
topology, cluster 100
troubleshooting
cluster creation failures 142–146
collecting log files 141
data collector 124
log files for 140
overview 139
upgrade process, troubleshooting 148
troubleshooting script command 156
U
unicode UTF-8 22
unregistering, Big Data Extensions plug-in 28
upgrade
baseline 38
compliance settings 39
failure 148, 149
hadoop template 40
process 39
process, troubleshooting 148
remediation task 39
source, downloading 38
upgrade cluster error 149
upgrading Big Data Extensions
cluster upgrade command 41
clusters 35
connecting to Serengeti Management
Server 37
hadoop-template virtual machine 35
plug-in 37
preparing to upgrade 35
Serengeti Command-Line Interface 37
vApp 37
virtual machine components 41
vSphere Update Manager 35, 37
user authentication, verify 85
user passwords 77
user accounts 137
user authentication, verify, verify 86
user names 75, 77
V
vApp upgrade failure 149
vApp upgrade 40
variable elastic scaling 112
vCenter connection fails 151
vCenter Server, communication with 79
vCenter Server, connections fail to log in 151
Verifying IP address and connectivity 70
versions, Big Data Extensions plug-in 155
viewing
cluster information 126
provisioned clusters 125
virtual appliance, upgrade failure 148
virtual machines
cannot get IP address 153
disk shares 116
Hadoop Template 69
VMware vSphere Big Data Extensions Administrator's and User's Guide
166 VMware, Inc.
prioritizing 116
upgrading components 41
Virtual Update Manager, upgrade function
failure 149
vSphere Fault Tolerance (FT) 116
vSphere resources
managing 91
resource pools 91
vSphere High Availability (HA) 116
vSphere Update Manager 35
VUM, See Virtual Update Manager
W
white space characters 156
worker nodes for Hadoop 99, 101
Y
yum and yum repositories, configuring 52
Yum repository configuration values 53
yum repository, local
configuring 60
creating 56, 58
yum repository, cannot download the
package 158
yum repositories, creating 61, 64
Yum repository, installing RPMS 27
yum repository, verify 88
yum seaarch, cannot find packages 158
Yum-deployed Hadoop distributions 50
Index
VMware, Inc. 167
VMware vSphere Big Data Extensions Administrator's and User's Guide
168 VMware, Inc.

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