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Use sparkMeasure to instrument Scala code

SparkMeasure can be used to instrument parts of your Scala code to measure Apache Spark workload. Use this for example for performance troubleshooting, application instrumentation, workload studies, etc.

Run sparkMeasure using the packaged version from Maven Central

  • The alternative, see paragraph above, is to build a jar from master.
    bin/spark-submit --packages
    // or just download and use the jar (it is only needed in the driver) as in:
    bin/spark-submit --conf spark.driver.extraClassPath=<path>/spark-measure_2.11-0.13.jar ...

Download and build sparkMeasure (optional)

  • If you want to build from the latest development version:
    git clone
    cd sparkmeasure
    sbt package
    ls -l target/scala-2.11/spark-measure*.jar  # location of the compiled jar
    # Run as in one of these examples:
    bin/spark-submit --jars path>/spark-measure_2.11-0.14-SNAPSHOT.jar
    # alternative, set classpath for the driver (it is only needed in the driver)
    bin/spark-submit --conf spark.driver.extraClassPath=<path>/spark-measure_2.11-0.14-SNAPSHOT.jar ...

Example code

You can find an example of how to instrument a Scala application running Apache Spark jobs at this link:
link to example application

How to run the example:

# build the jar
sbt package

bin/spark-submit --master local[*] --packages --class <path>/testsparkmeasurescala_2.11-0.1.jar

Some relevant snippet of code are:

    val stageMetrics =
    stageMetrics.runAndMeasure {
      spark.sql("select count(*) from range(1000) cross join range(1000) cross join range(1000)").show()

    // print report to standard output

    //save session metrics data
    val df = stageMetrics.createStageMetricsDF("PerfStageMetrics")
    stageMetrics.saveData(df.orderBy("jobId", "stageId"), "/tmp/stagemetrics_test1")

    val aggregatedDF = stageMetrics.aggregateStageMetrics("PerfStageMetrics")
    stageMetrics.saveData(aggregatedDF, "/tmp/stagemetrics_report_test2")

Task metrics

Collecting Spark task metrics at the granularity of each task completion has additional overhead compare to collecting at the stage completion level, therefore this option should only be used if you need data with this finer granularity, for example because you want to study skew effects, otherwise consider using stagemetrics aggregation as preferred choice.

  • The API for collecting data at task level is similar to the stage metrics case. An example:
    val taskMetrics =
    taskMetrics.runAndMeasure {
      spark.sql("select count(*) from range(1000) cross join range(1000) cross join range(1000)").show()