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書名： Hadoop MapReduce v2 Cookbook（Second Edition）
作者名： Thilina Gunarathne
本章字?jǐn)?shù)： 513字
更新時間： 2021-07-23 20:32:53

Setting up Hadoop YARN in a distributed cluster environment using Hadoop v2

Hadoop v2 YARN deployment includes deploying the ResourceManager service on the master node and deploying NodeManager services in the slave nodes. YARN ResourceManager is the service that arbitrates all the resources of the cluster, and NodeManager is the service that manages the resources in a single node.

Hadoop MapReduce applications can run on YARN using a YARN ApplicationMaster to coordinate each job and a set of resource containers to run the Map and Reduce tasks.

Tip

Installing Hadoop directly using Hadoop release artifacts, as mentioned in this recipe, is recommended for development testing and for advanced use cases only. For regular production clusters, we recommend using a packaged Hadoop distribution as mentioned in the Setting up Hadoop ecosystem in a distributed cluster environment using a Hadoop distribution recipe. Packaged Hadoop distributions make it much easier to install, configure, maintain, and update the components of the Hadoop ecosystem.

Getting ready

You can follow this recipe either using a single machine as a pseudo-distributed installation or using a multiple machine cluster. If you are using multiple machines, you should choose one machine as the master node where you will run the HDFS NameNode and the YARN ResourceManager. If you are using a single machine, use it as both the master node as well as the slave node.

Set up HDFS by following the Setting up HDFS recipe.

How to do it...

Let's set up Hadoop YARN by setting up the YARN ResourceManager and the NodeManagers.

In each machine, create a directory named local inside {HADOOP_DATA_DIR}, which you created in the Setting up HDFS recipe. Change the directory permissions to 755.
Add the following to the {HADOOP_HOME}/etc/hadoop/mapred-site.xml template and save it as {HADOOP_HOME}/etc/hadoop/mapred-site.xml:
```
<property>
  <name>fs.default.name</name>
  <value>hdfs://localhost:9000</value>
</property>
```

Add the following to the {HADOOP_HOME}/etc/hadoop/yarn-site.xml file:

<property>
  <name>yarn.nodemanager.aux-services</name>
  <value>mapreduce_shuffle</value>
</property>
<property>
  <name>yarn.nodemanager.aux-services.mapreduce_shuffle.class</name>
  <value>org.apache.hadoop.mapred.ShuffleHandler</value>
</property>

Start HDFS using the following command:
```
$ $HADOOP_HOME/sbin/start-dfs.sh
```

Run the following command to start the YARN services:

$ $HADOOP_HOME/sbin/start-yarn.sh
starting yarn daemons
starting resourcemanager, logging to ………
xxx.xx.xxx.xxx: starting nodemanager, logging to ………

Run the following command to start the MapReduce JobHistoryServer. This enables the web console for MapReduce job histories:
```
$ $HADOOP_HOME/sbin/mr-jobhistory-daemon.sh start historyserver
```
Verify the installation by listing the processes through the jps command. The master node will list the NameNode, ResourceManager, and JobHistoryServer services. The slave nodes will list DataNode and NodeManager services:
```
$ jps
27084 NameNode
2073 JobHistoryServer
2106 Jps
2588
1536 ResourceManager
```
Visit the web-based monitoring pages for ResourceManager available at http://{MASTER_NODE}:8088/.

How it works...

As described in the introduction to the chapter, Hadoop v2 installation consists of HDFS nodes, YARN ResourceManager, and worker nodes. When we start the NameNode, it finds slaves through the HADOOP_HOME/slaves file and uses SSH to start the DataNodes in the remote server at the startup. Also, when we start ResourceManager, it finds slaves through the HADOOP_HOME/slaves file and starts NodeManagers.

官术网_书友最值得收藏!

Setting up Hadoop YARN in a distributed cluster environment using Hadoop v2

Tip

Getting ready

How to do it...

How it works...

See also

官术网_书友最值得收藏!

Hadoop MapReduce v2 Cookbook（Second Edition）

Setting up Hadoop YARN in a distributed cluster environment using Hadoop v2

Tip

Getting ready

How to do it...

How it works...

See also