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Difference Between Spark Cluster & Client Deployment Modes


Let's try to understand the Difference Between Spark Cluster & Client Deploy Modes. It is confusing concept for many. We will see if we can understand it and get clarified. But first some Basics. Spark has a notion of a Worker or Slave node(s) which is used for computation.

Each such nodes have N no. of Executor processes running on it. If Spark assigns a driver to be ran on such an arbitrary Worker or Slave node that doesn't mean that Worker\Slave node can't run additional Executor processes to do any computation task. But please note Spark doesn't select a Master node - A master node is fixed in the environment. What spark does is choose - where to run the driver, which is where the SparkContext will live for the lifetime of the app. For any Spark job, the Deployment mode is indicated by the flag deploy-mode which is used in spark-submit command. It determines whether the spark job will run in cluster or client mode. Let's see what these two modes mean -

  • -- deploy-mode cluster -

    • In cluster deploy mode , all the slave or worker-nodes act as an Executor.
    • But one of them will act as Spark Driver too. Means which is where the SparkContext will live for the lifetime of the app.
    • This basically means One specific node will submit the JAR(or .py file )and we can track the execution using web UI. However note this Particular node will also act as an executor at the same time.
  • -- deploy-mode client -

    • In client mode, the node where the spark-submit is invoked , will act as the Spark driver. Means which is where the SparkContext will live for the lifetime of the app.
    • But this node WILL NOT execute the DAG as this it is designated JUST as a driver for the spark cluster.
    • However all the other nodes will act as executors for running the job.
    • We can track the execution of the jobs through the Web UI .
  Also watch our video where we explain this in a more lucid way.   Below are the spark-submit command examples for both the deploy modes  -


spark-submit --class org.apache.spark.examples.SparkApp \\
    --master yarn-client \\
    --**deploy-mode cluster** \\
    --num-executors 10 \\
    --driver-memory 512m \\
    --executor-memory 512m \\
    --executor-cores 1 \\
    /sample/jars/spark-app\*.jar 10



spark-submit --class org.apache.spark.examples.SparkApp \\ 
--master yarn-client \\ 
--**deploy-mode client** \\ 
--num-executors 10 \\ 
--driver-memory 512m \\ 
--executor-memory 512m \\ 
--executor-cores 1 \\ 
/sample/jars/spark-app\*.jar 10

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