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Developing Custom SparkListener to monitor DAGScheduler in Scala

== Exercise: Developing Custom SparkListener to monitor DAGScheduler in Scala

The example shows how to develop a custom Spark Listener. You should read[] first to understand the motivation for the example.

=== Requirements

  1.[IntelliJ IDEA] (or eventually[sbt] alone if you're adventurous).
  2. Access to Internet to download Apache Spark's dependencies.

=== Setting up Scala project using IntelliJ IDEA

Create a new project custom-spark-listener.

Add the following line to build.sbt (the main configuration file for the sbt project) that adds the dependency on Apache Spark.

libraryDependencies += "org.apache.spark" %% "spark-core" % "2.0.1"

build.sbt should look as follows:

[source, scala]

name := "custom-spark-listener" organization := "pl.jaceklaskowski.spark" version := "1.0"

scalaVersion := "2.11.8"

libraryDependencies += "org.apache.spark" %% "spark-core" % "2.0.1"

=== Custom Listener - pl.jaceklaskowski.spark.CustomSparkListener

Create a Scala class -- CustomSparkListener -- for your custom SparkListener. It should be under src/main/scala directory (create one if it does not exist).

The aim of the class is to intercept scheduler events about jobs being started and tasks completed.


package pl.jaceklaskowski.spark

import org.apache.spark.scheduler.{SparkListenerStageCompleted, SparkListener, SparkListenerJobStart}

class CustomSparkListener extends SparkListener { override def onJobStart(jobStart: SparkListenerJobStart) { println(s"Job started with ${jobStart.stageInfos.size} stages: $jobStart") }

override def onStageCompleted(stageCompleted: SparkListenerStageCompleted): Unit = { println(s"Stage ${stageCompleted.stageInfo.stageId} completed with ${stageCompleted.stageInfo.numTasks} tasks.") } }

=== Creating deployable package

Package the custom Spark listener. Execute sbt package command in the custom-spark-listener project's main directory.

$ sbt package
[info] Loading global plugins from /Users/jacek/.sbt/0.13/plugins
[info] Loading project definition from /Users/jacek/dev/workshops/spark-workshop/solutions/custom-spark-listener/project
[info] Updating {file:/Users/jacek/dev/workshops/spark-workshop/solutions/custom-spark-listener/project/}custom-spark-listener-build...
[info] Resolving org.fusesource.jansi#jansi;1.4 ...
[info] Done updating.
[info] Set current project to custom-spark-listener (in build file:/Users/jacek/dev/workshops/spark-workshop/solutions/custom-spark-listener/)
[info] Updating {file:/Users/jacek/dev/workshops/spark-workshop/solutions/custom-spark-listener/}custom-spark-listener...
[info] Resolving jline#jline;2.12.1 ...
[info] Done updating.
[info] Compiling 1 Scala source to /Users/jacek/dev/workshops/spark-workshop/solutions/custom-spark-listener/target/scala-2.11/classes...
[info] Packaging /Users/jacek/dev/workshops/spark-workshop/solutions/custom-spark-listener/target/scala-2.11/custom-spark-listener_2.11-1.0.jar ...
[info] Done packaging.
[success] Total time: 8 s, completed Oct 27, 2016 11:23:50 AM

You should find the result jar file with the custom scheduler listener ready under target/scala-2.11 directory, e.g. target/scala-2.11/custom-spark-listener_2.11-1.0.jar.

=== Activating Custom Listener in Spark shell

Start ../[spark-shell] with additional configurations for the extra custom listener and the jar that includes the class.

$ spark-shell \
  --conf spark.logConf=true \
  --conf spark.extraListeners=pl.jaceklaskowski.spark.CustomSparkListener \
  --driver-class-path target/scala-2.11/custom-spark-listener_2.11-1.0.jar

Create a ../[Dataset] and execute an action like show to start a job as follows:

[CustomSparkListener] Job started with 2 stages: SparkListenerJobStart(1,1473946006715,WrappedArray(org.apache.spark.scheduler.StageInfo@71515592, org.apache.spark.scheduler.StageInfo@6852819d),{spark.rdd.scope.noOverride=true, spark.rdd.scope={"id":"14","name":"collect"},})
[CustomSparkListener] Stage 1 completed with 1 tasks.
[CustomSparkListener] Stage 2 completed with 1 tasks.
res0: Long = 7

The lines with [CustomSparkListener] came from your custom Spark listener. Congratulations! The exercise's over.

=== BONUS Activating Custom Listener in Spark Application

TIP: Read[Registering SparkListener].

=== Questions

  1. What are the pros and cons of using the command line version vs inside a Spark application?

Last update: 2020-10-06