YARN-5980. Update documentation for single node hbase deploy. Contributed by Vrushali C.
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@ -158,18 +158,64 @@ For more configurations used for cross-origin support, refer to [HttpAuthenticat
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### <a name="Enabling_Timeline_Service_v2"></a>Enabling Timeline Service v.2
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#### Preparing Apache HBase cluster for storage
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There are a few steps to be done for preparing the storage for Timeline Service v.2:
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Step 1) [Set up the HBase cluster](#Set_up_the_HBase_cluster)
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Step 2) [Enable the coprocessor](#Enable_the_coprocessor)
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Step 3) [Create the schema for Timeline Service v.2](#Create_schema)
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Each step is explained in more detail below.
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##### <a name="Set_up_the_HBase_cluster"> </a>Step 1) Set up the HBase cluster
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The first part is to set up or pick an Apache HBase cluster to use as the storage cluster. The
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version of Apache HBase that is supported with Timeline Service v.2 is 1.1.x. The 1.0.x versions
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do not work with Timeline Service v.2. The 1.2.x versions have not been tested.
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version of Apache HBase that is supported with Timeline Service v.2 is 1.2.4. The 1.0.x versions
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do not work with Timeline Service v.2. Later versions of HBase have not been tested with
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Timeline Service.
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Once you have an Apache HBase cluster ready to use for this purpose, perform the following steps.
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HBase has different deployment modes. Refer to the HBase book for understanding them and pick a
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mode that is suitable for your setup.
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(http://hbase.apache.org/book.html#standalone_dist)
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First, add the timeline service jar to the HBase classpath in all HBase machines in the cluster. It
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##### Simple deployment for HBase
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If you are intent on a simple deploy profile for the Apache HBase cluster
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where the data loading is light but the data needs to persist across node
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comings and goings, you could consider the "Standalone HBase over HDFS" deploy mode.
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This is a useful variation on the standalone HBase setup and has all HBase daemons running inside
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one JVM but rather than persisting to the local filesystem, it persists to an HDFS instance.
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Writing to HDFS where data is replicated ensures that data is persisted across node
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comings and goings. To configure this standalone variant, edit your `hbase-site.xml` setting
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the `hbase.rootdir` to point at a directory in your HDFS instance but then set
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`hbase.cluster.distributed` to false. For example:
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```
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<configuration>
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<property>
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<name>hbase.rootdir</name>
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<value>hdfs://namenode.example.org:8020/hbase</value>
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</property>
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<property>
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<name>hbase.cluster.distributed</name>
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<value>false</value>
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</property>
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</configuration>
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```
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For more details on this mode, refer to
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http://hbase.apache.org/book.html#standalone.over.hdfs .
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Once you have an Apache HBase cluster ready to use, perform the following steps.
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##### <a name="Enable_the_coprocessor"> </a>Step 2) Enable the coprocessor
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Step 2.1) Add the timeline service jar to the HBase classpath in all HBase machines in the cluster. It
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is needed for the coprocessor as well as the schema creator. For example,
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cp hadoop-yarn-server-timelineservice-hbase-3.0.0-alpha1-SNAPSHOT.jar /usr/hbase/lib/
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Then, enable the coprocessor that handles the aggregation. To enable it, add the following entry in
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Step 2.2) Enable the coprocessor that handles the aggregation. To enable it, add the following entry in
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region servers' `hbase-site.xml` file (generally located in the `conf` directory) as follows:
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```
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@ -179,10 +225,11 @@ region servers' `hbase-site.xml` file (generally located in the `conf` directory
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</property>
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```
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Restart the region servers and the master to pick up the timeline service jar as well as the config
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change. In this version, the coprocessor is loaded statically (i.e. system coprocessor) as opposed
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to a dynamically (table coprocessor).
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Step 2.3) Restart the region servers and the master to pick up the timeline service jar as well
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as the config change. In this version, the coprocessor is loaded statically
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(i.e. system coprocessor) as opposed to a dynamically (table coprocessor).
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##### <a name="Create_schema"> </a>Step 3) Create the timeline service schema
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Finally, run the schema creator tool to create the necessary tables:
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bin/hadoop org.apache.hadoop.yarn.server.timelineservice.storage.TimelineSchemaCreator -create
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