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Use sparkMeasure with Scala shell or notebooks

Notes on how to use sparkMeasure to collect Spark workload metrics when Scala shell or a Scala notebook. See also README for an introduction to sparkMeasure and its architecture.

Run sparkMeasure using the packaged version from Maven Central

  • The alternative, see paragraph above, is to build a jar from master. 
    bin/spark-shell --packages ch.cern.sparkmeasure:spark-measure_2.12:0.22

// or just download and use the jar (it is only needed in the driver) as in:
bin/spark-shell --conf spark.driver.extraClassPath=<path>/spark-measure_2.12-0.22.jar

Download and build sparkMeasure (optional)

  • If you prefer to build from the latest development version: 
    git clone https://github.com/lucacanali/sparkmeasure
cd sparkmeasure
sbt +package
ls -l target/scala-2.12/spark-measure*.jar  # location of the compiled jar

# Run as in one of these examples:
bin/spark-shell --jars <path>/spark-measure_2.12-0.23-SNAPSHOT.jar

# alternative, set classpath for the driver (the jar is only needed in the driver)
bin/spark-shell --conf spark.driver.extraClassPath=<path>/spark-measure_2.11-0.23-SNAPSHOT.jar

Example: collect and print stage metrics with sparkMeasure

  1. Measure metrics at the Stage level, a basic example:

    bin/spark-shell --packages ch.cern.sparkmeasure:spark-measure_2.12:0.22

val stageMetrics = ch.cern.sparkmeasure.StageMetrics(spark) 
stageMetrics.runAndMeasure(spark.sql("select count(*) from range(1000) cross join range(1000) cross join range(1000)").show)

    Example output:

Scheduling mode = FIFO
Spark Context default degree of parallelism = 8

Aggregated Spark stage metrics:
numStages => 3
numTasks => 17
elapsedTime => 1218 (1 s)
stageDuration => 994 (1.0 s)
executorRunTime => 2625 (3 s)
executorCpuTime => 2224 (2 s)
executorDeserializeTime => 2945 (3 s)
executorDeserializeCpuTime => 1153 (1 s)
resultSerializationTime => 8 (8 ms)
jvmGCTime => 80 (80 ms)
shuffleFetchWaitTime => 0 (0 ms)
shuffleWriteTime => 30 (30 ms)
resultSize => 16220 (15.0 KB)
diskBytesSpilled => 0 (0 Bytes)
memoryBytesSpilled => 0 (0 Bytes)
peakExecutionMemory => 0
recordsRead => 2000
bytesRead => 0 (0 Bytes)
recordsWritten => 0
bytesWritten => 0 (0 Bytes)
shuffleRecordsRead => 8
shuffleTotalBlocksFetched => 8
shuffleLocalBlocksFetched => 8
shuffleRemoteBlocksFetched => 0
shuffleTotalBytesRead => 472 (472 Bytes)
shuffleLocalBytesRead => 472 (472 Bytes)
shuffleRemoteBytesRead => 0 (0 Bytes)
shuffleRemoteBytesReadToDisk => 0 (0 Bytes)
shuffleBytesWritten => 472 (472 Bytes)
shuffleRecordsWritten => 8

Stages and their duration:
Stage 0 duration => 573 (0.6 s)
Stage 1 duration => 354 (0.4 s)
Stage 3 duration => 67 (67 ms)
  • New in sparkMeasure v01: memory metrics report:
> stageMetrics.printMemoryReport

Additional stage-level executor metrics (memory usasge info):

Stage 0 JVMHeapMemory maxVal bytes => 322888344 (307.9 MB)
Stage 0 OnHeapExecutionMemory maxVal bytes => 0 (0 Bytes)
Stage 1 JVMHeapMemory maxVal bytes => 322888344 (307.9 MB)
Stage 1 OnHeapExecutionMemory maxVal bytes => 0 (0 Bytes)
Stage 3 JVMHeapMemory maxVal bytes => 322888344 (307.9 MB)
Stage 3 OnHeapExecutionMemory maxVal bytes => 0 (0 Bytes)
  1. An alternative way to collect and print metrics: 
    val stageMetrics = ch.cern.sparkmeasure.StageMetrics(spark) 
stageMetrics.begin()

...execute one or more Spark jobs...

stageMetrics.end()
stageMetrics.printReport()

Collecting metrics at finer granularity: use 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 = ch.cern.sparkmeasure.TaskMetrics(spark)
taskMetrics.runAndMeasure(spark.sql("select count(*) from range(1000) cross join range(1000) cross join range(1000)").show)

Exporting metrics data for archiving and/or further analysis

One simple use case is to make use of the data collected and reported by stagemetrics and taskmetrics printReport methods for immediate troubleshooting and workload analysis.
You also have options to save metrics aggregated as in the printReport output.
Another option is to export the metrics to an external system, such as Prometheus Pushgateway

  • Example on how to export raw "StageMetrics" into a DataFrame and save data in json format

    val stageMetrics = ch.cern.sparkmeasure.StageMetrics(spark) 
stageMetrics.runAndMeasure( ...your workload here ... )

val df = stageMetrics.createStageMetricsDF("PerfStageMetrics")
df.show()
stageMetrics.saveData(df.orderBy("jobId", "stageId"), "/tmp/stagemetrics_test1")

  • Example, save aggregated metrics (as found in the printReport output) in json format

    val stageMetrics = ch.cern.sparkmeasure.StageMetrics(spark) 
stageMetrics.runAndMeasure( ...your workload here ... )

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

Aggregated data in name,value format:

   val stageMetrics = ch.cern.sparkmeasure.StageMetrics(spark) 
   stageMetrics.runAndMeasure( ...your workload here ... )
   
   val df = stageMetrics.aggregateStageMetrics.toList.toDF("name","value")
   stageMetrics.saveData(df, "/tmp/stagemetrics_report_test3")
  • Example on how to export raw Task Metrics data in json format 
    val df = taskMetrics.createTaskMetricsDF("PerfTaskMetrics")
spark.sql("select * from PerfTaskMetrics").show()
df.show()
taskMetrics.saveData(df.orderBy("jobId", "stageId", "index"), "<path>/taskmetrics_test3")