pyspark-spy 1.0.2

Collect and aggregate on spark events for profitz. In 🐍 way!

pyspark-spy

Collect and aggregate on spark events for profitz. In 🐍 way!

Installation

pip install pyspark-spy

How to

You register a listener

import pyspark_spy
listener = pyspark_spy.PersistingSparkListener()
pyspark_spy.register_listener(spark_context, listener)

Execute your spark job as usual

spark_context.range(1, 100).count()

And you have all metrics collected!

print(listener.stage_output_metrics_aggregate())
OutputMetrics(bytesWritten=12861, recordsWritten=2426)

Look Ma, no actions!

Tested on python 3.5 - 3.7 and pyspark 2.3 - 3.0

Available listeners

• pyspark_spy.interface.SparkListener - Base listener class. It defines on_spark_event(event_name, java_event) method that you can implement yourself for custom logic when any event is received.

• LoggingSparkListener - just logs event names received into supplied or automatically created logger.

• StdoutSparkListener - writes event names into stdout

• PersistingSparkListener - saves spark events into internal buffer

• ContextSparkListener - same as PersistingSparkListener but also allows you to record only events occured within python context manager scope. More on that later

PersistingSparkListener

Spark events collected (as java objects):

• applicationEnd
• applicationStart
• blockManagerRemoved
• blockUpdated
• environmentUpdate
• executorAdded
• executorMetricsUpdate
• executorRemoved
• jobEnd
• jobStart
• otherEvent
• stageCompleted
• stageSubmitted
• taskEnd
• taskGettingResult
• taskStart
• unpersistRDD

listener.java_events['executorMetricsUpdate'] # -> List of py4j java objects

View all possible spark events and their fields https://spark.apache.org/docs/2.3.1/api/java/org/apache/spark/scheduler/SparkListener.html

Events converted to python objects:

• jobEnd
• stageCompleted

listener.python_events['jobEnd']  # -> List of java events converted to typed namedtuples.
listener.jobEnd  # same

Available aggregations

Only in PersistingSparkListener and ContextSparkListener

• stage_input_metrics_aggregate - sums up all stageCompleted event inputMetrics into one

print(listener.stage_input_metrics_aggregate())
InputMetrics(bytesRead=21574, recordsRead=584)

• stage_output_metrics_aggregate - sums up all stageCompleted event outputMetrics into one

print(listener.stage_output_metrics_aggregate())
OutputMetrics(bytesWritten=12861, recordsWritten=2426)

ContextSparkListener

To collect events from different actions and to build separate aggregations, use ContextSparkListener.

listener = ContextSparkListener()
register_listener(sc, listener)

with listener as events: # events is basically another listener
    run_spark_job()
events.stage_output_metrics_aggregate()  # events collected only within context manager

with listener as events_2:
    run_other_spark_job()
events_2.stage_output_metrics_aggregate()  # metrics collected during second job

listener.stage_output_metrics_aggregate() # metrics collected for all jobs