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I am trying to understand how spark runs on YARN cluster/client. I have the following question in my mind.

Is it necessary that spark is installed on all the nodes in yarn cluster? I think it should because worker nodes in cluster execute a task and should be able to decode the code(spark APIs) in spark application sent to cluster by the driver?

It says in the documentation "Ensure that HADOOP_CONF_DIR or YARN_CONF_DIR points to the directory which contains the (client side) configuration files for the Hadoop cluster". Why does client node have to install Hadoop when it is sending the job to cluster?

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Coming to your second question first, Ensure that HADOOP_CONF_DIR or YARN_CONF_DIR points to the directory which contains the (client side) configuration files for the Hadoop cluster.

It is mandatory to do as these configs are used to write to HDFS and connect to the YARN ResourceManager. The configuration contained in this directory will be distributed to the YARN cluster so that the same configuration would be used by all containers used by the application. If the configuration references Java system properties or environment variables not managed by YARN, they should also be set in the Spark application’s configuration (driver, executors, and the AM when running in client mode).

Now,  to launch Spark applications on YARN, we have got two deploy modes.

  • Cluster mode

  • Client mode

In cluster mode, the Spark driver runs inside an application master process which is managed by YARN on the cluster, and the client can go away after initiating the application. In client mode, the driver runs in the client process, and the application master is only used for requesting resources from YARN.

Unlike other cluster managers supported by Spark in which the master’s address is specified in the --master parameter, in YARN mode the ResourceManager’s address is picked up from the Hadoop configuration. Thus, the --master parameter is yarn.

To launch a Spark application in cluster mode:

$ ./bin/spark-submit --class path.to.your.Class --master yarn --deploy-mode cluster [options] <app jar> [app options]

For example:

$ ./bin/spark-submit --class org.apache.spark.examples.SparkPi \

    --master yarn \

    --deploy-mode cluster \

    --driver-memory 4g \

    --executor-memory 2g \

    --executor-cores 1 \

    --queue thequeue \

    examples/jars/spark-examples*.jar \

The above starts a YARN client program which starts the default Application Master. Then SparkPi will be run as a child thread of Application Master. The client will periodically poll the Application Master for status updates and display them in the console. The client will exit once your application has finished running. Refer to the “Debugging your Application” section below for how to see driver and executor logs.

To launch a Spark application in client mode, do the same, but replace cluster with a client. The following shows how you can run spark-shell in client mode:

$ ./bin/spark-shell --master yarn --deploy-mode client

You can refer the following video if you want more information regarding the same:

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