Exercise: Developing Custom SparkListener to monitor DAGScheduler in Scala

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


  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:

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:

scala> spark.read.text("README.md").count
[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"}, spark.sql.execution.id=2})
[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.


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