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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.scheduler.cluster
import scala.collection.mutable.ArrayBuffer
import org.apache.hadoop.yarn.api.records.YarnApplicationState
import org.apache.spark.{SparkContext, SparkException}
import org.apache.spark.deploy.yarn.{Client, ClientArguments, YarnSparkHadoopUtil}
import org.apache.spark.internal.Logging
import org.apache.spark.launcher.SparkAppHandle
import org.apache.spark.scheduler.TaskSchedulerImpl
private[spark] class YarnClientSchedulerBackend(
scheduler: TaskSchedulerImpl,
sc: SparkContext)
extends YarnSchedulerBackend(scheduler, sc)
with Logging {
private var client: Client = null
private var monitorThread: MonitorThread = null
/**
* Create a Yarn client to submit an application to the ResourceManager.
* This waits until the application is running.
*/
override def start() {
val driverHost = conf.get("spark.driver.host")
val driverPort = conf.get("spark.driver.port")
val hostport = driverHost + ":" + driverPort
sc.ui.foreach { ui => conf.set("spark.driver.appUIAddress", ui.appUIAddress) }
val argsArrayBuf = new ArrayBuffer[String]()
argsArrayBuf += ("--arg", hostport)
logDebug("ClientArguments called with: " + argsArrayBuf.mkString(" "))
val args = new ClientArguments(argsArrayBuf.toArray)
totalExpectedExecutors = YarnSparkHadoopUtil.getInitialTargetExecutorNumber(conf)
client = new Client(args, conf)
bindToYarn(client.submitApplication(), None)
// SPARK-8687: Ensure all necessary properties have already been set before
// we initialize our driver scheduler backend, which serves these properties
// to the executors
super.start()
waitForApplication()
// SPARK-8851: In yarn-client mode, the AM still does the credentials refresh. The driver
// reads the credentials from HDFS, just like the executors and updates its own credentials
// cache.
if (conf.contains("spark.yarn.credentials.file")) {
YarnSparkHadoopUtil.get.startExecutorDelegationTokenRenewer(conf)
}
monitorThread = asyncMonitorApplication()
monitorThread.start()
}
/**
* Report the state of the application until it is running.
* If the application has finished, failed or been killed in the process, throw an exception.
* This assumes both `client` and `appId` have already been set.
*/
private def waitForApplication(): Unit = {
assert(client != null && appId.isDefined, "Application has not been submitted yet!")
val (state, _) = client.monitorApplication(appId.get, returnOnRunning = true) // blocking
if (state == YarnApplicationState.FINISHED ||
state == YarnApplicationState.FAILED ||
state == YarnApplicationState.KILLED) {
throw new SparkException("Yarn application has already ended! " +
"It might have been killed or unable to launch application master.")
}
if (state == YarnApplicationState.RUNNING) {
logInfo(s"Application ${appId.get} has started running.")
}
}
/**
* We create this class for SPARK-9519. Basically when we interrupt the monitor thread it's
* because the SparkContext is being shut down(sc.stop() called by user code), but if
* monitorApplication return, it means the Yarn application finished before sc.stop() was called,
* which means we should call sc.stop() here, and we don't allow the monitor to be interrupted
* before SparkContext stops successfully.
*/
private class MonitorThread extends Thread {
private var allowInterrupt = true
override def run() {
try {
val (state, _) = client.monitorApplication(appId.get, logApplicationReport = false)
logError(s"Yarn application has already exited with state $state!")
allowInterrupt = false
sc.stop()
} catch {
case e: InterruptedException => logInfo("Interrupting monitor thread")
}
}
def stopMonitor(): Unit = {
if (allowInterrupt) {
this.interrupt()
}
}
}
/**
* Monitor the application state in a separate thread.
* If the application has exited for any reason, stop the SparkContext.
* This assumes both `client` and `appId` have already been set.
*/
private def asyncMonitorApplication(): MonitorThread = {
assert(client != null && appId.isDefined, "Application has not been submitted yet!")
val t = new MonitorThread
t.setName("Yarn application state monitor")
t.setDaemon(true)
t
}
/**
* Stop the scheduler. This assumes `start()` has already been called.
*/
override def stop() {
assert(client != null, "Attempted to stop this scheduler before starting it!")
if (monitorThread != null) {
monitorThread.stopMonitor()
}
// Report a final state to the launcher if one is connected. This is needed since in client
// mode this backend doesn't let the app monitor loop run to completion, so it does not report
// the final state itself.
//
// Note: there's not enough information at this point to provide a better final state,
// so assume the application was successful.
client.reportLauncherState(SparkAppHandle.State.FINISHED)
super.stop()
YarnSparkHadoopUtil.get.stopExecutorDelegationTokenRenewer()
client.stop()
logInfo("Stopped")
}
}
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