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/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.apache.spark.streaming.scheduler
import java.util.concurrent.{ConcurrentHashMap, TimeUnit}
import scala.collection.JavaConverters._
import scala.util.Failure
import org.apache.spark.ExecutorAllocationClient
import org.apache.spark.internal.Logging
import org.apache.spark.internal.io.SparkHadoopWriterUtils
import org.apache.spark.rdd.RDD
import org.apache.spark.streaming._
import org.apache.spark.streaming.api.python.PythonDStream
import org.apache.spark.ui.{UIUtils => SparkUIUtils}
import org.apache.spark.util.{EventLoop, ThreadUtils, Utils}
private[scheduler] sealed trait JobSchedulerEvent
private[scheduler] case class JobStarted(job: Job, startTime: Long) extends JobSchedulerEvent
private[scheduler] case class JobCompleted(job: Job, completedTime: Long) extends JobSchedulerEvent
private[scheduler] case class ErrorReported(msg: String, e: Throwable) extends JobSchedulerEvent
/**
* This class schedules jobs to be run on Spark. It uses the JobGenerator to generate
* the jobs and runs them using a thread pool.
*/
private[streaming]
class JobScheduler(val ssc: StreamingContext) extends Logging {
// Use of ConcurrentHashMap.keySet later causes an odd runtime problem due to Java 7/8 diff
// https://gist.github.com/AlainODea/1375759b8720a3f9f094
private val jobSets: java.util.Map[Time, JobSet] = new ConcurrentHashMap[Time, JobSet]
private val numConcurrentJobs = ssc.conf.getInt("spark.streaming.concurrentJobs", 1)
private val jobExecutor =
ThreadUtils.newDaemonFixedThreadPool(numConcurrentJobs, "streaming-job-executor")
private[streaming] val jobGenerator = new JobGenerator(this)
val clock = jobGenerator.clock
val listenerBus = new StreamingListenerBus(ssc.sparkContext.listenerBus)
// These two are created only when scheduler starts.
// eventLoop not being null means the scheduler has been started and not stopped
var receiverTracker: ReceiverTracker = null
// A tracker to track all the input stream information as well as processed record number
var inputInfoTracker: InputInfoTracker = null
private var executorAllocationManager: Option[ExecutorAllocationManager] = None
private var eventLoop: EventLoop[JobSchedulerEvent] = null
def start(): Unit = synchronized {
if (eventLoop != null) return // scheduler has already been started
logDebug("Starting JobScheduler")
eventLoop = new EventLoop[JobSchedulerEvent]("JobScheduler") {
override protected def onReceive(event: JobSchedulerEvent): Unit = processEvent(event)
override protected def onError(e: Throwable): Unit = reportError("Error in job scheduler", e)
}
eventLoop.start()
// attach rate controllers of input streams to receive batch completion updates
for {
inputDStream <- ssc.graph.getInputStreams
rateController <- inputDStream.rateController
} ssc.addStreamingListener(rateController)
listenerBus.start()
receiverTracker = new ReceiverTracker(ssc)
inputInfoTracker = new InputInfoTracker(ssc)
val executorAllocClient: ExecutorAllocationClient = ssc.sparkContext.schedulerBackend match {
case b: ExecutorAllocationClient => b.asInstanceOf[ExecutorAllocationClient]
case _ => null
}
executorAllocationManager = ExecutorAllocationManager.createIfEnabled(
executorAllocClient,
receiverTracker,
ssc.conf,
ssc.graph.batchDuration.milliseconds,
clock)
executorAllocationManager.foreach(ssc.addStreamingListener)
receiverTracker.start()
jobGenerator.start()
executorAllocationManager.foreach(_.start())
logInfo("Started JobScheduler")
}
def stop(processAllReceivedData: Boolean): Unit = synchronized {
if (eventLoop == null) return // scheduler has already been stopped
logDebug("Stopping JobScheduler")
if (receiverTracker != null) {
// First, stop receiving
receiverTracker.stop(processAllReceivedData)
}
if (executorAllocationManager != null) {
executorAllocationManager.foreach(_.stop())
}
// Second, stop generating jobs. If it has to process all received data,
// then this will wait for all the processing through JobScheduler to be over.
jobGenerator.stop(processAllReceivedData)
// Stop the executor for receiving new jobs
logDebug("Stopping job executor")
jobExecutor.shutdown()
// Wait for the queued jobs to complete if indicated
val terminated = if (processAllReceivedData) {
jobExecutor.awaitTermination(1, TimeUnit.HOURS) // just a very large period of time
} else {
jobExecutor.awaitTermination(2, TimeUnit.SECONDS)
}
if (!terminated) {
jobExecutor.shutdownNow()
}
logDebug("Stopped job executor")
// Stop everything else
listenerBus.stop()
eventLoop.stop()
eventLoop = null
logInfo("Stopped JobScheduler")
}
def submitJobSet(jobSet: JobSet): Unit = {
if (jobSet.jobs.isEmpty) {
logInfo("No jobs added for time " + jobSet.time)
} else {
listenerBus.post(StreamingListenerBatchSubmitted(jobSet.toBatchInfo))
jobSets.put(jobSet.time, jobSet)
jobSet.jobs.foreach(job => jobExecutor.execute(new JobHandler(job)))
logInfo("Added jobs for time " + jobSet.time)
}
}
def getPendingTimes(): Seq[Time] = {
jobSets.asScala.keys.toSeq
}
def reportError(msg: String, e: Throwable): Unit = {
eventLoop.post(ErrorReported(msg, e))
}
def isStarted(): Boolean = synchronized {
eventLoop != null
}
private def processEvent(event: JobSchedulerEvent): Unit = {
try {
event match {
case JobStarted(job, startTime) => handleJobStart(job, startTime)
case JobCompleted(job, completedTime) => handleJobCompletion(job, completedTime)
case ErrorReported(m, e) => handleError(m, e)
}
} catch {
case e: Throwable =>
reportError("Error in job scheduler", e)
}
}
private def handleJobStart(job: Job, startTime: Long): Unit = {
val jobSet = jobSets.get(job.time)
val isFirstJobOfJobSet = !jobSet.hasStarted
jobSet.handleJobStart(job)
if (isFirstJobOfJobSet) {
// "StreamingListenerBatchStarted" should be posted after calling "handleJobStart" to get the
// correct "jobSet.processingStartTime".
listenerBus.post(StreamingListenerBatchStarted(jobSet.toBatchInfo))
}
job.setStartTime(startTime)
listenerBus.post(StreamingListenerOutputOperationStarted(job.toOutputOperationInfo))
logInfo("Starting job " + job.id + " from job set of time " + jobSet.time)
}
private def handleJobCompletion(job: Job, completedTime: Long): Unit = {
val jobSet = jobSets.get(job.time)
jobSet.handleJobCompletion(job)
job.setEndTime(completedTime)
listenerBus.post(StreamingListenerOutputOperationCompleted(job.toOutputOperationInfo))
logInfo("Finished job " + job.id + " from job set of time " + jobSet.time)
if (jobSet.hasCompleted) {
listenerBus.post(StreamingListenerBatchCompleted(jobSet.toBatchInfo))
}
job.result match {
case Failure(e) =>
reportError("Error running job " + job, e)
case _ =>
if (jobSet.hasCompleted) {
jobSets.remove(jobSet.time)
jobGenerator.onBatchCompletion(jobSet.time)
logInfo("Total delay: %.3f s for time %s (execution: %.3f s)".format(
jobSet.totalDelay / 1000.0, jobSet.time.toString,
jobSet.processingDelay / 1000.0
))
}
}
}
private def handleError(msg: String, e: Throwable): Unit = {
logError(msg, e)
ssc.waiter.notifyError(e)
PythonDStream.stopStreamingContextIfPythonProcessIsDead(e)
}
private class JobHandler(job: Job) extends Runnable with Logging {
import JobScheduler._
def run(): Unit = {
val oldProps = ssc.sparkContext.getLocalProperties
try {
ssc.sparkContext.setLocalProperties(Utils.cloneProperties(ssc.savedProperties.get()))
val formattedTime = SparkUIUtils.formatBatchTime(
job.time.milliseconds, ssc.graph.batchDuration.milliseconds, showYYYYMMSS = false)
val batchUrl = s"/streaming/batch/?id=${job.time.milliseconds}"
val batchLinkText = s"[output operation ${job.outputOpId}, batch time ${formattedTime}]"
ssc.sc.setJobDescription(
s"""Streaming job from <a href="$batchUrl">$batchLinkText</a>""")
ssc.sc.setLocalProperty(BATCH_TIME_PROPERTY_KEY, job.time.milliseconds.toString)
ssc.sc.setLocalProperty(OUTPUT_OP_ID_PROPERTY_KEY, job.outputOpId.toString)
// Checkpoint all RDDs marked for checkpointing to ensure their lineages are
// truncated periodically. Otherwise, we may run into stack overflows (SPARK-6847).
ssc.sparkContext.setLocalProperty(RDD.CHECKPOINT_ALL_MARKED_ANCESTORS, "true")
// We need to assign `eventLoop` to a temp variable. Otherwise, because
// `JobScheduler.stop(false)` may set `eventLoop` to null when this method is running, then
// it's possible that when `post` is called, `eventLoop` happens to null.
var _eventLoop = eventLoop
if (_eventLoop != null) {
_eventLoop.post(JobStarted(job, clock.getTimeMillis()))
// Disable checks for existing output directories in jobs launched by the streaming
// scheduler, since we may need to write output to an existing directory during checkpoint
// recovery; see SPARK-4835 for more details.
SparkHadoopWriterUtils.disableOutputSpecValidation.withValue(true) {
job.run()
}
_eventLoop = eventLoop
if (_eventLoop != null) {
_eventLoop.post(JobCompleted(job, clock.getTimeMillis()))
}
} else {
// JobScheduler has been stopped.
}
} finally {
ssc.sparkContext.setLocalProperties(oldProps)
}
}
}
}
private[streaming] object JobScheduler {
val BATCH_TIME_PROPERTY_KEY = "spark.streaming.internal.batchTime"
val OUTPUT_OP_ID_PROPERTY_KEY = "spark.streaming.internal.outputOpId"
}
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