It is possible although it is a bit involved. We can use Py4j callback mechanism to pass message from a SparkListener. First lets create a Scala package with all required classes. Directory structure:
.
├── build.sbt
└── src
└── main
└── scala
└── net
└── zero323
└── spark
└── examples
└── listener
├── Listener.scala
├── Manager.scala
└── TaskListener.scala
build.sbt:
name := "listener"
organization := "net.zero323"
scalaVersion := "2.11.7"
val sparkVersion = "2.1.0"
libraryDependencies ++= List(
"org.apache.spark" %% "spark-core" % sparkVersion,
"net.sf.py4j" % "py4j" % "0.10.4" // Just for the record
)
Listener.scala defines a Python interface we are going to implement later
package net.zero323.spark.examples.listener
/* You can add arbitrary methods here,
* as long as these match corresponding Python interface
*/
trait Listener {
/* This will be implemented by a Python class.
* You can of course use more specific types,
* for example here String => Unit */
def notify(x: Any): Any
}
Manager.scala will be used to forward messages to Python listener:
package net.zero323.spark.examples.listener
object Manager {
var listeners: Map[String, Listener] = Map()
def register(listener: Listener): String = {
this.synchronized {
val uuid = java.util.UUID.randomUUID().toString
listeners = listeners + (uuid -> listener)
uuid
}
}
def unregister(uuid: String) = {
this.synchronized {
listeners = listeners - uuid
}
}
def notifyAll(message: String): Unit = {
for { (_, listener) <- listeners } listener.notify(message)
}
}
Finally a simple SparkListener:
package net.zero323.spark.examples.listener
import org.apache.spark.scheduler.{SparkListener, SparkListenerTaskEnd}
import org.json4s._
import org.json4s.JsonDSL._
import org.json4s.jackson.JsonMethods._
/* A simple listener which captures SparkListenerTaskEnd,
* extracts numbers of records written by the task
* and converts to JSON. You can of course add handlers
* for other events as well.
*/
class PythonNotifyListener extends SparkListener {
override def onTaskEnd(taskEnd: SparkListenerTaskEnd) {
val recordsWritten = taskEnd.taskMetrics.outputMetrics.recordsWritten
val message = compact(render(
("recordsWritten" -> recordsWritten)
))
Manager.notifyAll(message)
}
}
Lets' package our extension:
sbt package
and start PySpark session adding a generated jar to the class path and registering listener:
$SPARK_HOME/bin/pyspark \
--driver-class-path target/scala-2.11/listener_2.11-0.1-SNAPSHOT.jar \
--conf spark.extraListeners=net.zero323.spark.examples.listener.PythonNotifyListener
Next we have to define a Python object which implements Listener interface:
class PythonListener(object):
package = "net.zero323.spark.examples.listener"
@staticmethod
def get_manager():
jvm = SparkContext.getOrCreate()._jvm
manager = getattr(jvm, "{}.{}".format(PythonListener.package, "Manager"))
return manager
def __init__(self):
self.uuid = None
def notify(self, obj):
"""This method is required by Scala Listener interface
we defined above.
"""
print(obj)
def register(self):
manager = PythonListener.get_manager()
self.uuid = manager.register(self)
return self.uuid
def unregister(self):
manager = PythonListener.get_manager()
manager.unregister(self.uuid)
self.uuid = None
class Java:
implements = ["net.zero323.spark.examples.listener.Listener"]
start callback server:
sc._gateway.start_callback_server()
create and register listener:
listener = PythonListener()
register it:
listener.register()
and test:
>>> sc.parallelize(range(100), 3).saveAsTextFile("/tmp/listener_test")
{"recordsWritten":33}
{"recordsWritten":34}
{"recordsWritten":33}
On exit you should shutdown the callback server:
sc._gateway.shutdown_callback_server()
Note:
This should be used with caution when working with Spark streaming, which internally uses callback server.
Edit:
If this is to much hassle you could just define org.apache.spark.scheduler.SparkListenerInterface:
class SparkListener(object):
def onApplicationEnd(self, applicationEnd):
pass
def onApplicationStart(self, applicationStart):
pass
def onBlockManagerRemoved(self, blockManagerRemoved):
pass
def onBlockUpdated(self, blockUpdated):
pass
def onEnvironmentUpdate(self, environmentUpdate):
pass
def onExecutorAdded(self, executorAdded):
pass
def onExecutorMetricsUpdate(self, executorMetricsUpdate):
pass
def onExecutorRemoved(self, executorRemoved):
pass
def onJobEnd(self, jobEnd):
pass
def onJobStart(self, jobStart):
pass
def onOtherEvent(self, event):
pass
def onStageCompleted(self, stageCompleted):
pass
def onStageSubmitted(self, stageSubmitted):
pass
def onTaskEnd(self, taskEnd):
pass
def onTaskGettingResult(self, taskGettingResult):
pass
def onTaskStart(self, taskStart):
pass
def onUnpersistRDD(self, unpersistRDD):
pass
class Java:
implements = ["org.apache.spark.scheduler.SparkListenerInterface"]
extend it:
class TaskEndListener(SparkListener):
def onTaskEnd(self, taskEnd):
print(taskEnd.toString())
and use it directly:
>>> sc._gateway.start_callback_server()
True
>>> listener = TaskEndListener()
>>> sc._jsc.sc().addSparkListener(listener)
>>> sc.parallelize(range(100), 3).saveAsTextFile("/tmp/listener_test_simple")
SparkListenerTaskEnd(0,0,ResultTask,Success,org.apache.spark.scheduler.TaskInfo@9e7514a,org.apache.spark.executor.TaskMetrics@51b8ba92)
SparkListenerTaskEnd(0,0,ResultTask,Success,org.apache.spark.scheduler.TaskInfo@71278a44,org.apache.spark.executor.TaskMetrics@bdc06d)
SparkListenerTaskEnd(0,0,ResultTask,Success,org.apache.spark.scheduler.TaskInfo@336)
While simpler, this method is not selective (more traffic between JVM and Python) requires handling Java objects inside Python session.
