diff options
Diffstat (limited to 'resource-managers/yarn/src/main')
28 files changed, 6436 insertions, 0 deletions
diff --git a/resource-managers/yarn/src/main/resources/META-INF/services/org.apache.spark.deploy.yarn.security.ServiceCredentialProvider b/resource-managers/yarn/src/main/resources/META-INF/services/org.apache.spark.deploy.yarn.security.ServiceCredentialProvider new file mode 100644 index 0000000000..22ead56d23 --- /dev/null +++ b/resource-managers/yarn/src/main/resources/META-INF/services/org.apache.spark.deploy.yarn.security.ServiceCredentialProvider @@ -0,0 +1,3 @@ +org.apache.spark.deploy.yarn.security.HDFSCredentialProvider +org.apache.spark.deploy.yarn.security.HBaseCredentialProvider +org.apache.spark.deploy.yarn.security.HiveCredentialProvider diff --git a/resource-managers/yarn/src/main/resources/META-INF/services/org.apache.spark.scheduler.ExternalClusterManager b/resource-managers/yarn/src/main/resources/META-INF/services/org.apache.spark.scheduler.ExternalClusterManager new file mode 100644 index 0000000000..6e8a1ebfc6 --- /dev/null +++ b/resource-managers/yarn/src/main/resources/META-INF/services/org.apache.spark.scheduler.ExternalClusterManager @@ -0,0 +1 @@ +org.apache.spark.scheduler.cluster.YarnClusterManager diff --git a/resource-managers/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala b/resource-managers/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala new file mode 100644 index 0000000000..0378ef4fac --- /dev/null +++ b/resource-managers/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala @@ -0,0 +1,791 @@ +/* + * 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.deploy.yarn + +import java.io.{File, IOException} +import java.lang.reflect.InvocationTargetException +import java.net.{Socket, URI, URL} +import java.util.concurrent.{TimeoutException, TimeUnit} + +import scala.collection.mutable.HashMap +import scala.concurrent.Promise +import scala.concurrent.duration.Duration +import scala.util.control.NonFatal + +import org.apache.hadoop.fs.{FileSystem, Path} +import org.apache.hadoop.yarn.api._ +import org.apache.hadoop.yarn.api.records._ +import org.apache.hadoop.yarn.conf.YarnConfiguration +import org.apache.hadoop.yarn.util.{ConverterUtils, Records} + +import org.apache.spark._ +import org.apache.spark.deploy.SparkHadoopUtil +import org.apache.spark.deploy.history.HistoryServer +import org.apache.spark.deploy.yarn.config._ +import org.apache.spark.deploy.yarn.security.{AMCredentialRenewer, ConfigurableCredentialManager} +import org.apache.spark.internal.Logging +import org.apache.spark.internal.config._ +import org.apache.spark.rpc._ +import org.apache.spark.scheduler.cluster.{CoarseGrainedSchedulerBackend, YarnSchedulerBackend} +import org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages._ +import org.apache.spark.util._ + +/** + * Common application master functionality for Spark on Yarn. + */ +private[spark] class ApplicationMaster( + args: ApplicationMasterArguments, + client: YarnRMClient) + extends Logging { + + // TODO: Currently, task to container is computed once (TaskSetManager) - which need not be + // optimal as more containers are available. Might need to handle this better. + + private val sparkConf = new SparkConf() + private val yarnConf: YarnConfiguration = SparkHadoopUtil.get.newConfiguration(sparkConf) + .asInstanceOf[YarnConfiguration] + private val isClusterMode = args.userClass != null + + // Default to twice the number of executors (twice the maximum number of executors if dynamic + // allocation is enabled), with a minimum of 3. + + private val maxNumExecutorFailures = { + val effectiveNumExecutors = + if (Utils.isDynamicAllocationEnabled(sparkConf)) { + sparkConf.get(DYN_ALLOCATION_MAX_EXECUTORS) + } else { + sparkConf.get(EXECUTOR_INSTANCES).getOrElse(0) + } + // By default, effectiveNumExecutors is Int.MaxValue if dynamic allocation is enabled. We need + // avoid the integer overflow here. + val defaultMaxNumExecutorFailures = math.max(3, + if (effectiveNumExecutors > Int.MaxValue / 2) Int.MaxValue else (2 * effectiveNumExecutors)) + + sparkConf.get(MAX_EXECUTOR_FAILURES).getOrElse(defaultMaxNumExecutorFailures) + } + + @volatile private var exitCode = 0 + @volatile private var unregistered = false + @volatile private var finished = false + @volatile private var finalStatus = getDefaultFinalStatus + @volatile private var finalMsg: String = "" + @volatile private var userClassThread: Thread = _ + + @volatile private var reporterThread: Thread = _ + @volatile private var allocator: YarnAllocator = _ + + // Lock for controlling the allocator (heartbeat) thread. + private val allocatorLock = new Object() + + // Steady state heartbeat interval. We want to be reasonably responsive without causing too many + // requests to RM. + private val heartbeatInterval = { + // Ensure that progress is sent before YarnConfiguration.RM_AM_EXPIRY_INTERVAL_MS elapses. + val expiryInterval = yarnConf.getInt(YarnConfiguration.RM_AM_EXPIRY_INTERVAL_MS, 120000) + math.max(0, math.min(expiryInterval / 2, sparkConf.get(RM_HEARTBEAT_INTERVAL))) + } + + // Initial wait interval before allocator poll, to allow for quicker ramp up when executors are + // being requested. + private val initialAllocationInterval = math.min(heartbeatInterval, + sparkConf.get(INITIAL_HEARTBEAT_INTERVAL)) + + // Next wait interval before allocator poll. + private var nextAllocationInterval = initialAllocationInterval + + private var rpcEnv: RpcEnv = null + private var amEndpoint: RpcEndpointRef = _ + + // In cluster mode, used to tell the AM when the user's SparkContext has been initialized. + private val sparkContextPromise = Promise[SparkContext]() + + private var credentialRenewer: AMCredentialRenewer = _ + + // Load the list of localized files set by the client. This is used when launching executors, + // and is loaded here so that these configs don't pollute the Web UI's environment page in + // cluster mode. + private val localResources = { + logInfo("Preparing Local resources") + val resources = HashMap[String, LocalResource]() + + def setupDistributedCache( + file: String, + rtype: LocalResourceType, + timestamp: String, + size: String, + vis: String): Unit = { + val uri = new URI(file) + val amJarRsrc = Records.newRecord(classOf[LocalResource]) + amJarRsrc.setType(rtype) + amJarRsrc.setVisibility(LocalResourceVisibility.valueOf(vis)) + amJarRsrc.setResource(ConverterUtils.getYarnUrlFromURI(uri)) + amJarRsrc.setTimestamp(timestamp.toLong) + amJarRsrc.setSize(size.toLong) + + val fileName = Option(uri.getFragment()).getOrElse(new Path(uri).getName()) + resources(fileName) = amJarRsrc + } + + val distFiles = sparkConf.get(CACHED_FILES) + val fileSizes = sparkConf.get(CACHED_FILES_SIZES) + val timeStamps = sparkConf.get(CACHED_FILES_TIMESTAMPS) + val visibilities = sparkConf.get(CACHED_FILES_VISIBILITIES) + val resTypes = sparkConf.get(CACHED_FILES_TYPES) + + for (i <- 0 to distFiles.size - 1) { + val resType = LocalResourceType.valueOf(resTypes(i)) + setupDistributedCache(distFiles(i), resType, timeStamps(i).toString, fileSizes(i).toString, + visibilities(i)) + } + + // Distribute the conf archive to executors. + sparkConf.get(CACHED_CONF_ARCHIVE).foreach { path => + val uri = new URI(path) + val fs = FileSystem.get(uri, yarnConf) + val status = fs.getFileStatus(new Path(uri)) + // SPARK-16080: Make sure to use the correct name for the destination when distributing the + // conf archive to executors. + val destUri = new URI(uri.getScheme(), uri.getRawSchemeSpecificPart(), + Client.LOCALIZED_CONF_DIR) + setupDistributedCache(destUri.toString(), LocalResourceType.ARCHIVE, + status.getModificationTime().toString, status.getLen.toString, + LocalResourceVisibility.PRIVATE.name()) + } + + // Clean up the configuration so it doesn't show up in the Web UI (since it's really noisy). + CACHE_CONFIGS.foreach { e => + sparkConf.remove(e) + sys.props.remove(e.key) + } + + resources.toMap + } + + def getAttemptId(): ApplicationAttemptId = { + client.getAttemptId() + } + + final def run(): Int = { + try { + val appAttemptId = client.getAttemptId() + + var attemptID: Option[String] = None + + if (isClusterMode) { + // Set the web ui port to be ephemeral for yarn so we don't conflict with + // other spark processes running on the same box + System.setProperty("spark.ui.port", "0") + + // Set the master and deploy mode property to match the requested mode. + System.setProperty("spark.master", "yarn") + System.setProperty("spark.submit.deployMode", "cluster") + + // Set this internal configuration if it is running on cluster mode, this + // configuration will be checked in SparkContext to avoid misuse of yarn cluster mode. + System.setProperty("spark.yarn.app.id", appAttemptId.getApplicationId().toString()) + + attemptID = Option(appAttemptId.getAttemptId.toString) + } + + new CallerContext( + "APPMASTER", sparkConf.get(APP_CALLER_CONTEXT), + Option(appAttemptId.getApplicationId.toString), attemptID).setCurrentContext() + + logInfo("ApplicationAttemptId: " + appAttemptId) + + val fs = FileSystem.get(yarnConf) + + // This shutdown hook should run *after* the SparkContext is shut down. + val priority = ShutdownHookManager.SPARK_CONTEXT_SHUTDOWN_PRIORITY - 1 + ShutdownHookManager.addShutdownHook(priority) { () => + val maxAppAttempts = client.getMaxRegAttempts(sparkConf, yarnConf) + val isLastAttempt = client.getAttemptId().getAttemptId() >= maxAppAttempts + + if (!finished) { + // The default state of ApplicationMaster is failed if it is invoked by shut down hook. + // This behavior is different compared to 1.x version. + // If user application is exited ahead of time by calling System.exit(N), here mark + // this application as failed with EXIT_EARLY. For a good shutdown, user shouldn't call + // System.exit(0) to terminate the application. + finish(finalStatus, + ApplicationMaster.EXIT_EARLY, + "Shutdown hook called before final status was reported.") + } + + if (!unregistered) { + // we only want to unregister if we don't want the RM to retry + if (finalStatus == FinalApplicationStatus.SUCCEEDED || isLastAttempt) { + unregister(finalStatus, finalMsg) + cleanupStagingDir(fs) + } + } + } + + // Call this to force generation of secret so it gets populated into the + // Hadoop UGI. This has to happen before the startUserApplication which does a + // doAs in order for the credentials to be passed on to the executor containers. + val securityMgr = new SecurityManager(sparkConf) + + // If the credentials file config is present, we must periodically renew tokens. So create + // a new AMDelegationTokenRenewer + if (sparkConf.contains(CREDENTIALS_FILE_PATH.key)) { + // If a principal and keytab have been set, use that to create new credentials for executors + // periodically + credentialRenewer = + new ConfigurableCredentialManager(sparkConf, yarnConf).credentialRenewer() + credentialRenewer.scheduleLoginFromKeytab() + } + + if (isClusterMode) { + runDriver(securityMgr) + } else { + runExecutorLauncher(securityMgr) + } + } catch { + case e: Exception => + // catch everything else if not specifically handled + logError("Uncaught exception: ", e) + finish(FinalApplicationStatus.FAILED, + ApplicationMaster.EXIT_UNCAUGHT_EXCEPTION, + "Uncaught exception: " + e) + } + exitCode + } + + /** + * Set the default final application status for client mode to UNDEFINED to handle + * if YARN HA restarts the application so that it properly retries. Set the final + * status to SUCCEEDED in cluster mode to handle if the user calls System.exit + * from the application code. + */ + final def getDefaultFinalStatus(): FinalApplicationStatus = { + if (isClusterMode) { + FinalApplicationStatus.FAILED + } else { + FinalApplicationStatus.UNDEFINED + } + } + + /** + * unregister is used to completely unregister the application from the ResourceManager. + * This means the ResourceManager will not retry the application attempt on your behalf if + * a failure occurred. + */ + final def unregister(status: FinalApplicationStatus, diagnostics: String = null): Unit = { + synchronized { + if (!unregistered) { + logInfo(s"Unregistering ApplicationMaster with $status" + + Option(diagnostics).map(msg => s" (diag message: $msg)").getOrElse("")) + unregistered = true + client.unregister(status, Option(diagnostics).getOrElse("")) + } + } + } + + final def finish(status: FinalApplicationStatus, code: Int, msg: String = null): Unit = { + synchronized { + if (!finished) { + val inShutdown = ShutdownHookManager.inShutdown() + logInfo(s"Final app status: $status, exitCode: $code" + + Option(msg).map(msg => s", (reason: $msg)").getOrElse("")) + exitCode = code + finalStatus = status + finalMsg = msg + finished = true + if (!inShutdown && Thread.currentThread() != reporterThread && reporterThread != null) { + logDebug("shutting down reporter thread") + reporterThread.interrupt() + } + if (!inShutdown && Thread.currentThread() != userClassThread && userClassThread != null) { + logDebug("shutting down user thread") + userClassThread.interrupt() + } + if (!inShutdown && credentialRenewer != null) { + credentialRenewer.stop() + credentialRenewer = null + } + } + } + } + + private def sparkContextInitialized(sc: SparkContext) = { + sparkContextPromise.success(sc) + } + + private def registerAM( + _sparkConf: SparkConf, + _rpcEnv: RpcEnv, + driverRef: RpcEndpointRef, + uiAddress: String, + securityMgr: SecurityManager) = { + val appId = client.getAttemptId().getApplicationId().toString() + val attemptId = client.getAttemptId().getAttemptId().toString() + val historyAddress = + _sparkConf.get(HISTORY_SERVER_ADDRESS) + .map { text => SparkHadoopUtil.get.substituteHadoopVariables(text, yarnConf) } + .map { address => s"${address}${HistoryServer.UI_PATH_PREFIX}/${appId}/${attemptId}" } + .getOrElse("") + + val driverUrl = RpcEndpointAddress( + _sparkConf.get("spark.driver.host"), + _sparkConf.get("spark.driver.port").toInt, + CoarseGrainedSchedulerBackend.ENDPOINT_NAME).toString + + // Before we initialize the allocator, let's log the information about how executors will + // be run up front, to avoid printing this out for every single executor being launched. + // Use placeholders for information that changes such as executor IDs. + logInfo { + val executorMemory = sparkConf.get(EXECUTOR_MEMORY).toInt + val executorCores = sparkConf.get(EXECUTOR_CORES) + val dummyRunner = new ExecutorRunnable(None, yarnConf, sparkConf, driverUrl, "<executorId>", + "<hostname>", executorMemory, executorCores, appId, securityMgr, localResources) + dummyRunner.launchContextDebugInfo() + } + + allocator = client.register(driverUrl, + driverRef, + yarnConf, + _sparkConf, + uiAddress, + historyAddress, + securityMgr, + localResources) + + allocator.allocateResources() + reporterThread = launchReporterThread() + } + + /** + * Create an [[RpcEndpoint]] that communicates with the driver. + * + * In cluster mode, the AM and the driver belong to same process + * so the AMEndpoint need not monitor lifecycle of the driver. + * + * @return A reference to the driver's RPC endpoint. + */ + private def runAMEndpoint( + host: String, + port: String, + isClusterMode: Boolean): RpcEndpointRef = { + val driverEndpoint = rpcEnv.setupEndpointRef( + RpcAddress(host, port.toInt), + YarnSchedulerBackend.ENDPOINT_NAME) + amEndpoint = + rpcEnv.setupEndpoint("YarnAM", new AMEndpoint(rpcEnv, driverEndpoint, isClusterMode)) + driverEndpoint + } + + private def runDriver(securityMgr: SecurityManager): Unit = { + addAmIpFilter() + userClassThread = startUserApplication() + + // This a bit hacky, but we need to wait until the spark.driver.port property has + // been set by the Thread executing the user class. + logInfo("Waiting for spark context initialization...") + val totalWaitTime = sparkConf.get(AM_MAX_WAIT_TIME) + try { + val sc = ThreadUtils.awaitResult(sparkContextPromise.future, + Duration(totalWaitTime, TimeUnit.MILLISECONDS)) + if (sc != null) { + rpcEnv = sc.env.rpcEnv + val driverRef = runAMEndpoint( + sc.getConf.get("spark.driver.host"), + sc.getConf.get("spark.driver.port"), + isClusterMode = true) + registerAM(sc.getConf, rpcEnv, driverRef, sc.ui.map(_.webUrl).getOrElse(""), + securityMgr) + } else { + // Sanity check; should never happen in normal operation, since sc should only be null + // if the user app did not create a SparkContext. + if (!finished) { + throw new IllegalStateException("SparkContext is null but app is still running!") + } + } + userClassThread.join() + } catch { + case e: SparkException if e.getCause().isInstanceOf[TimeoutException] => + logError( + s"SparkContext did not initialize after waiting for $totalWaitTime ms. " + + "Please check earlier log output for errors. Failing the application.") + finish(FinalApplicationStatus.FAILED, + ApplicationMaster.EXIT_SC_NOT_INITED, + "Timed out waiting for SparkContext.") + } + } + + private def runExecutorLauncher(securityMgr: SecurityManager): Unit = { + val port = sparkConf.get(AM_PORT) + rpcEnv = RpcEnv.create("sparkYarnAM", Utils.localHostName, port, sparkConf, securityMgr, + clientMode = true) + val driverRef = waitForSparkDriver() + addAmIpFilter() + registerAM(sparkConf, rpcEnv, driverRef, sparkConf.get("spark.driver.appUIAddress", ""), + securityMgr) + + // In client mode the actor will stop the reporter thread. + reporterThread.join() + } + + private def launchReporterThread(): Thread = { + // The number of failures in a row until Reporter thread give up + val reporterMaxFailures = sparkConf.get(MAX_REPORTER_THREAD_FAILURES) + + val t = new Thread { + override def run() { + var failureCount = 0 + while (!finished) { + try { + if (allocator.getNumExecutorsFailed >= maxNumExecutorFailures) { + finish(FinalApplicationStatus.FAILED, + ApplicationMaster.EXIT_MAX_EXECUTOR_FAILURES, + s"Max number of executor failures ($maxNumExecutorFailures) reached") + } else { + logDebug("Sending progress") + allocator.allocateResources() + } + failureCount = 0 + } catch { + case i: InterruptedException => + case e: Throwable => + failureCount += 1 + // this exception was introduced in hadoop 2.4 and this code would not compile + // with earlier versions if we refer it directly. + if ("org.apache.hadoop.yarn.exceptions.ApplicationAttemptNotFoundException" == + e.getClass().getName()) { + logError("Exception from Reporter thread.", e) + finish(FinalApplicationStatus.FAILED, ApplicationMaster.EXIT_REPORTER_FAILURE, + e.getMessage) + } else if (!NonFatal(e) || failureCount >= reporterMaxFailures) { + finish(FinalApplicationStatus.FAILED, + ApplicationMaster.EXIT_REPORTER_FAILURE, "Exception was thrown " + + s"$failureCount time(s) from Reporter thread.") + } else { + logWarning(s"Reporter thread fails $failureCount time(s) in a row.", e) + } + } + try { + val numPendingAllocate = allocator.getPendingAllocate.size + var sleepStart = 0L + var sleepInterval = 200L // ms + allocatorLock.synchronized { + sleepInterval = + if (numPendingAllocate > 0 || allocator.getNumPendingLossReasonRequests > 0) { + val currentAllocationInterval = + math.min(heartbeatInterval, nextAllocationInterval) + nextAllocationInterval = currentAllocationInterval * 2 // avoid overflow + currentAllocationInterval + } else { + nextAllocationInterval = initialAllocationInterval + heartbeatInterval + } + sleepStart = System.currentTimeMillis() + allocatorLock.wait(sleepInterval) + } + val sleepDuration = System.currentTimeMillis() - sleepStart + if (sleepDuration < sleepInterval) { + // log when sleep is interrupted + logDebug(s"Number of pending allocations is $numPendingAllocate. " + + s"Slept for $sleepDuration/$sleepInterval ms.") + // if sleep was less than the minimum interval, sleep for the rest of it + val toSleep = math.max(0, initialAllocationInterval - sleepDuration) + if (toSleep > 0) { + logDebug(s"Going back to sleep for $toSleep ms") + // use Thread.sleep instead of allocatorLock.wait. there is no need to be woken up + // by the methods that signal allocatorLock because this is just finishing the min + // sleep interval, which should happen even if this is signalled again. + Thread.sleep(toSleep) + } + } else { + logDebug(s"Number of pending allocations is $numPendingAllocate. " + + s"Slept for $sleepDuration/$sleepInterval.") + } + } catch { + case e: InterruptedException => + } + } + } + } + // setting to daemon status, though this is usually not a good idea. + t.setDaemon(true) + t.setName("Reporter") + t.start() + logInfo(s"Started progress reporter thread with (heartbeat : $heartbeatInterval, " + + s"initial allocation : $initialAllocationInterval) intervals") + t + } + + /** + * Clean up the staging directory. + */ + private def cleanupStagingDir(fs: FileSystem) { + var stagingDirPath: Path = null + try { + val preserveFiles = sparkConf.get(PRESERVE_STAGING_FILES) + if (!preserveFiles) { + stagingDirPath = new Path(System.getenv("SPARK_YARN_STAGING_DIR")) + if (stagingDirPath == null) { + logError("Staging directory is null") + return + } + logInfo("Deleting staging directory " + stagingDirPath) + fs.delete(stagingDirPath, true) + } + } catch { + case ioe: IOException => + logError("Failed to cleanup staging dir " + stagingDirPath, ioe) + } + } + + private def waitForSparkDriver(): RpcEndpointRef = { + logInfo("Waiting for Spark driver to be reachable.") + var driverUp = false + val hostport = args.userArgs(0) + val (driverHost, driverPort) = Utils.parseHostPort(hostport) + + // Spark driver should already be up since it launched us, but we don't want to + // wait forever, so wait 100 seconds max to match the cluster mode setting. + val totalWaitTimeMs = sparkConf.get(AM_MAX_WAIT_TIME) + val deadline = System.currentTimeMillis + totalWaitTimeMs + + while (!driverUp && !finished && System.currentTimeMillis < deadline) { + try { + val socket = new Socket(driverHost, driverPort) + socket.close() + logInfo("Driver now available: %s:%s".format(driverHost, driverPort)) + driverUp = true + } catch { + case e: Exception => + logError("Failed to connect to driver at %s:%s, retrying ...". + format(driverHost, driverPort)) + Thread.sleep(100L) + } + } + + if (!driverUp) { + throw new SparkException("Failed to connect to driver!") + } + + sparkConf.set("spark.driver.host", driverHost) + sparkConf.set("spark.driver.port", driverPort.toString) + + runAMEndpoint(driverHost, driverPort.toString, isClusterMode = false) + } + + /** Add the Yarn IP filter that is required for properly securing the UI. */ + private def addAmIpFilter() = { + val proxyBase = System.getenv(ApplicationConstants.APPLICATION_WEB_PROXY_BASE_ENV) + val amFilter = "org.apache.hadoop.yarn.server.webproxy.amfilter.AmIpFilter" + val params = client.getAmIpFilterParams(yarnConf, proxyBase) + if (isClusterMode) { + System.setProperty("spark.ui.filters", amFilter) + params.foreach { case (k, v) => System.setProperty(s"spark.$amFilter.param.$k", v) } + } else { + amEndpoint.send(AddWebUIFilter(amFilter, params.toMap, proxyBase)) + } + } + + /** + * Start the user class, which contains the spark driver, in a separate Thread. + * If the main routine exits cleanly or exits with System.exit(N) for any N + * we assume it was successful, for all other cases we assume failure. + * + * Returns the user thread that was started. + */ + private def startUserApplication(): Thread = { + logInfo("Starting the user application in a separate Thread") + + val classpath = Client.getUserClasspath(sparkConf) + val urls = classpath.map { entry => + new URL("file:" + new File(entry.getPath()).getAbsolutePath()) + } + val userClassLoader = + if (Client.isUserClassPathFirst(sparkConf, isDriver = true)) { + new ChildFirstURLClassLoader(urls, Utils.getContextOrSparkClassLoader) + } else { + new MutableURLClassLoader(urls, Utils.getContextOrSparkClassLoader) + } + + var userArgs = args.userArgs + if (args.primaryPyFile != null && args.primaryPyFile.endsWith(".py")) { + // When running pyspark, the app is run using PythonRunner. The second argument is the list + // of files to add to PYTHONPATH, which Client.scala already handles, so it's empty. + userArgs = Seq(args.primaryPyFile, "") ++ userArgs + } + if (args.primaryRFile != null && args.primaryRFile.endsWith(".R")) { + // TODO(davies): add R dependencies here + } + val mainMethod = userClassLoader.loadClass(args.userClass) + .getMethod("main", classOf[Array[String]]) + + val userThread = new Thread { + override def run() { + try { + mainMethod.invoke(null, userArgs.toArray) + finish(FinalApplicationStatus.SUCCEEDED, ApplicationMaster.EXIT_SUCCESS) + logDebug("Done running users class") + } catch { + case e: InvocationTargetException => + e.getCause match { + case _: InterruptedException => + // Reporter thread can interrupt to stop user class + case SparkUserAppException(exitCode) => + val msg = s"User application exited with status $exitCode" + logError(msg) + finish(FinalApplicationStatus.FAILED, exitCode, msg) + case cause: Throwable => + logError("User class threw exception: " + cause, cause) + finish(FinalApplicationStatus.FAILED, + ApplicationMaster.EXIT_EXCEPTION_USER_CLASS, + "User class threw exception: " + cause) + } + sparkContextPromise.tryFailure(e.getCause()) + } finally { + // Notify the thread waiting for the SparkContext, in case the application did not + // instantiate one. This will do nothing when the user code instantiates a SparkContext + // (with the correct master), or when the user code throws an exception (due to the + // tryFailure above). + sparkContextPromise.trySuccess(null) + } + } + } + userThread.setContextClassLoader(userClassLoader) + userThread.setName("Driver") + userThread.start() + userThread + } + + private def resetAllocatorInterval(): Unit = allocatorLock.synchronized { + nextAllocationInterval = initialAllocationInterval + allocatorLock.notifyAll() + } + + /** + * An [[RpcEndpoint]] that communicates with the driver's scheduler backend. + */ + private class AMEndpoint( + override val rpcEnv: RpcEnv, driver: RpcEndpointRef, isClusterMode: Boolean) + extends RpcEndpoint with Logging { + + override def onStart(): Unit = { + driver.send(RegisterClusterManager(self)) + } + + override def receive: PartialFunction[Any, Unit] = { + case x: AddWebUIFilter => + logInfo(s"Add WebUI Filter. $x") + driver.send(x) + } + + override def receiveAndReply(context: RpcCallContext): PartialFunction[Any, Unit] = { + case RequestExecutors(requestedTotal, localityAwareTasks, hostToLocalTaskCount) => + Option(allocator) match { + case Some(a) => + if (a.requestTotalExecutorsWithPreferredLocalities(requestedTotal, + localityAwareTasks, hostToLocalTaskCount)) { + resetAllocatorInterval() + } + context.reply(true) + + case None => + logWarning("Container allocator is not ready to request executors yet.") + context.reply(false) + } + + case KillExecutors(executorIds) => + logInfo(s"Driver requested to kill executor(s) ${executorIds.mkString(", ")}.") + Option(allocator) match { + case Some(a) => executorIds.foreach(a.killExecutor) + case None => logWarning("Container allocator is not ready to kill executors yet.") + } + context.reply(true) + + case GetExecutorLossReason(eid) => + Option(allocator) match { + case Some(a) => + a.enqueueGetLossReasonRequest(eid, context) + resetAllocatorInterval() + case None => + logWarning("Container allocator is not ready to find executor loss reasons yet.") + } + } + + override def onDisconnected(remoteAddress: RpcAddress): Unit = { + // In cluster mode, do not rely on the disassociated event to exit + // This avoids potentially reporting incorrect exit codes if the driver fails + if (!isClusterMode) { + logInfo(s"Driver terminated or disconnected! Shutting down. $remoteAddress") + finish(FinalApplicationStatus.SUCCEEDED, ApplicationMaster.EXIT_SUCCESS) + } + } + } + +} + +object ApplicationMaster extends Logging { + + // exit codes for different causes, no reason behind the values + private val EXIT_SUCCESS = 0 + private val EXIT_UNCAUGHT_EXCEPTION = 10 + private val EXIT_MAX_EXECUTOR_FAILURES = 11 + private val EXIT_REPORTER_FAILURE = 12 + private val EXIT_SC_NOT_INITED = 13 + private val EXIT_SECURITY = 14 + private val EXIT_EXCEPTION_USER_CLASS = 15 + private val EXIT_EARLY = 16 + + private var master: ApplicationMaster = _ + + def main(args: Array[String]): Unit = { + SignalUtils.registerLogger(log) + val amArgs = new ApplicationMasterArguments(args) + + // Load the properties file with the Spark configuration and set entries as system properties, + // so that user code run inside the AM also has access to them. + // Note: we must do this before SparkHadoopUtil instantiated + if (amArgs.propertiesFile != null) { + Utils.getPropertiesFromFile(amArgs.propertiesFile).foreach { case (k, v) => + sys.props(k) = v + } + } + SparkHadoopUtil.get.runAsSparkUser { () => + master = new ApplicationMaster(amArgs, new YarnRMClient) + System.exit(master.run()) + } + } + + private[spark] def sparkContextInitialized(sc: SparkContext): Unit = { + master.sparkContextInitialized(sc) + } + + private[spark] def getAttemptId(): ApplicationAttemptId = { + master.getAttemptId + } + +} + +/** + * This object does not provide any special functionality. It exists so that it's easy to tell + * apart the client-mode AM from the cluster-mode AM when using tools such as ps or jps. + */ +object ExecutorLauncher { + + def main(args: Array[String]): Unit = { + ApplicationMaster.main(args) + } + +} diff --git a/resource-managers/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMasterArguments.scala b/resource-managers/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMasterArguments.scala new file mode 100644 index 0000000000..5cdec87667 --- /dev/null +++ b/resource-managers/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMasterArguments.scala @@ -0,0 +1,105 @@ +/* + * 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.deploy.yarn + +import scala.collection.mutable.ArrayBuffer + +import org.apache.spark.util.{IntParam, MemoryParam} + +class ApplicationMasterArguments(val args: Array[String]) { + var userJar: String = null + var userClass: String = null + var primaryPyFile: String = null + var primaryRFile: String = null + var userArgs: Seq[String] = Nil + var propertiesFile: String = null + + parseArgs(args.toList) + + private def parseArgs(inputArgs: List[String]): Unit = { + val userArgsBuffer = new ArrayBuffer[String]() + + var args = inputArgs + + while (!args.isEmpty) { + // --num-workers, --worker-memory, and --worker-cores are deprecated since 1.0, + // the properties with executor in their names are preferred. + args match { + case ("--jar") :: value :: tail => + userJar = value + args = tail + + case ("--class") :: value :: tail => + userClass = value + args = tail + + case ("--primary-py-file") :: value :: tail => + primaryPyFile = value + args = tail + + case ("--primary-r-file") :: value :: tail => + primaryRFile = value + args = tail + + case ("--arg") :: value :: tail => + userArgsBuffer += value + args = tail + + case ("--properties-file") :: value :: tail => + propertiesFile = value + args = tail + + case _ => + printUsageAndExit(1, args) + } + } + + if (primaryPyFile != null && primaryRFile != null) { + // scalastyle:off println + System.err.println("Cannot have primary-py-file and primary-r-file at the same time") + // scalastyle:on println + System.exit(-1) + } + + userArgs = userArgsBuffer.toList + } + + def printUsageAndExit(exitCode: Int, unknownParam: Any = null) { + // scalastyle:off println + if (unknownParam != null) { + System.err.println("Unknown/unsupported param " + unknownParam) + } + System.err.println(""" + |Usage: org.apache.spark.deploy.yarn.ApplicationMaster [options] + |Options: + | --jar JAR_PATH Path to your application's JAR file + | --class CLASS_NAME Name of your application's main class + | --primary-py-file A main Python file + | --primary-r-file A main R file + | --arg ARG Argument to be passed to your application's main class. + | Multiple invocations are possible, each will be passed in order. + | --properties-file FILE Path to a custom Spark properties file. + """.stripMargin) + // scalastyle:on println + System.exit(exitCode) + } +} + +object ApplicationMasterArguments { + val DEFAULT_NUMBER_EXECUTORS = 2 +} diff --git a/resource-managers/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala b/resource-managers/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala new file mode 100644 index 0000000000..be419cee77 --- /dev/null +++ b/resource-managers/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala @@ -0,0 +1,1541 @@ +/* + * 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.deploy.yarn + +import java.io.{File, FileOutputStream, IOException, OutputStreamWriter} +import java.net.{InetAddress, UnknownHostException, URI} +import java.nio.ByteBuffer +import java.nio.charset.StandardCharsets +import java.util.{Properties, UUID} +import java.util.zip.{ZipEntry, ZipOutputStream} + +import scala.collection.JavaConverters._ +import scala.collection.mutable.{ArrayBuffer, HashMap, HashSet, ListBuffer, Map} +import scala.util.{Failure, Success, Try} +import scala.util.control.NonFatal + +import com.google.common.base.Objects +import com.google.common.io.Files +import org.apache.hadoop.conf.Configuration +import org.apache.hadoop.fs._ +import org.apache.hadoop.fs.permission.FsPermission +import org.apache.hadoop.io.DataOutputBuffer +import org.apache.hadoop.mapreduce.MRJobConfig +import org.apache.hadoop.security.{Credentials, UserGroupInformation} +import org.apache.hadoop.util.StringUtils +import org.apache.hadoop.yarn.api._ +import org.apache.hadoop.yarn.api.ApplicationConstants.Environment +import org.apache.hadoop.yarn.api.protocolrecords._ +import org.apache.hadoop.yarn.api.records._ +import org.apache.hadoop.yarn.client.api.{YarnClient, YarnClientApplication} +import org.apache.hadoop.yarn.conf.YarnConfiguration +import org.apache.hadoop.yarn.exceptions.ApplicationNotFoundException +import org.apache.hadoop.yarn.util.Records + +import org.apache.spark.{SecurityManager, SparkConf, SparkContext, SparkException} +import org.apache.spark.deploy.SparkHadoopUtil +import org.apache.spark.deploy.yarn.config._ +import org.apache.spark.deploy.yarn.security.ConfigurableCredentialManager +import org.apache.spark.internal.Logging +import org.apache.spark.internal.config._ +import org.apache.spark.launcher.{LauncherBackend, SparkAppHandle, YarnCommandBuilderUtils} +import org.apache.spark.util.{CallerContext, Utils} + +private[spark] class Client( + val args: ClientArguments, + val hadoopConf: Configuration, + val sparkConf: SparkConf) + extends Logging { + + import Client._ + import YarnSparkHadoopUtil._ + + def this(clientArgs: ClientArguments, spConf: SparkConf) = + this(clientArgs, SparkHadoopUtil.get.newConfiguration(spConf), spConf) + + private val yarnClient = YarnClient.createYarnClient + private val yarnConf = new YarnConfiguration(hadoopConf) + + private val isClusterMode = sparkConf.get("spark.submit.deployMode", "client") == "cluster" + + // AM related configurations + private val amMemory = if (isClusterMode) { + sparkConf.get(DRIVER_MEMORY).toInt + } else { + sparkConf.get(AM_MEMORY).toInt + } + private val amMemoryOverhead = { + val amMemoryOverheadEntry = if (isClusterMode) DRIVER_MEMORY_OVERHEAD else AM_MEMORY_OVERHEAD + sparkConf.get(amMemoryOverheadEntry).getOrElse( + math.max((MEMORY_OVERHEAD_FACTOR * amMemory).toLong, MEMORY_OVERHEAD_MIN)).toInt + } + private val amCores = if (isClusterMode) { + sparkConf.get(DRIVER_CORES) + } else { + sparkConf.get(AM_CORES) + } + + // Executor related configurations + private val executorMemory = sparkConf.get(EXECUTOR_MEMORY) + private val executorMemoryOverhead = sparkConf.get(EXECUTOR_MEMORY_OVERHEAD).getOrElse( + math.max((MEMORY_OVERHEAD_FACTOR * executorMemory).toLong, MEMORY_OVERHEAD_MIN)).toInt + + private val distCacheMgr = new ClientDistributedCacheManager() + + private var loginFromKeytab = false + private var principal: String = null + private var keytab: String = null + private var credentials: Credentials = null + + private val launcherBackend = new LauncherBackend() { + override def onStopRequest(): Unit = { + if (isClusterMode && appId != null) { + yarnClient.killApplication(appId) + } else { + setState(SparkAppHandle.State.KILLED) + stop() + } + } + } + private val fireAndForget = isClusterMode && !sparkConf.get(WAIT_FOR_APP_COMPLETION) + + private var appId: ApplicationId = null + + // The app staging dir based on the STAGING_DIR configuration if configured + // otherwise based on the users home directory. + private val appStagingBaseDir = sparkConf.get(STAGING_DIR).map { new Path(_) } + .getOrElse(FileSystem.get(hadoopConf).getHomeDirectory()) + + private val credentialManager = new ConfigurableCredentialManager(sparkConf, hadoopConf) + + def reportLauncherState(state: SparkAppHandle.State): Unit = { + launcherBackend.setState(state) + } + + def stop(): Unit = { + launcherBackend.close() + yarnClient.stop() + // Unset YARN mode system env variable, to allow switching between cluster types. + System.clearProperty("SPARK_YARN_MODE") + } + + /** + * Submit an application running our ApplicationMaster to the ResourceManager. + * + * The stable Yarn API provides a convenience method (YarnClient#createApplication) for + * creating applications and setting up the application submission context. This was not + * available in the alpha API. + */ + def submitApplication(): ApplicationId = { + var appId: ApplicationId = null + try { + launcherBackend.connect() + // Setup the credentials before doing anything else, + // so we have don't have issues at any point. + setupCredentials() + yarnClient.init(yarnConf) + yarnClient.start() + + logInfo("Requesting a new application from cluster with %d NodeManagers" + .format(yarnClient.getYarnClusterMetrics.getNumNodeManagers)) + + // Get a new application from our RM + val newApp = yarnClient.createApplication() + val newAppResponse = newApp.getNewApplicationResponse() + appId = newAppResponse.getApplicationId() + reportLauncherState(SparkAppHandle.State.SUBMITTED) + launcherBackend.setAppId(appId.toString) + + new CallerContext("CLIENT", sparkConf.get(APP_CALLER_CONTEXT), + Option(appId.toString)).setCurrentContext() + + // Verify whether the cluster has enough resources for our AM + verifyClusterResources(newAppResponse) + + // Set up the appropriate contexts to launch our AM + val containerContext = createContainerLaunchContext(newAppResponse) + val appContext = createApplicationSubmissionContext(newApp, containerContext) + + // Finally, submit and monitor the application + logInfo(s"Submitting application $appId to ResourceManager") + yarnClient.submitApplication(appContext) + appId + } catch { + case e: Throwable => + if (appId != null) { + cleanupStagingDir(appId) + } + throw e + } + } + + /** + * Cleanup application staging directory. + */ + private def cleanupStagingDir(appId: ApplicationId): Unit = { + val stagingDirPath = new Path(appStagingBaseDir, getAppStagingDir(appId)) + try { + val preserveFiles = sparkConf.get(PRESERVE_STAGING_FILES) + val fs = stagingDirPath.getFileSystem(hadoopConf) + if (!preserveFiles && fs.delete(stagingDirPath, true)) { + logInfo(s"Deleted staging directory $stagingDirPath") + } + } catch { + case ioe: IOException => + logWarning("Failed to cleanup staging dir " + stagingDirPath, ioe) + } + } + + /** + * Set up the context for submitting our ApplicationMaster. + * This uses the YarnClientApplication not available in the Yarn alpha API. + */ + def createApplicationSubmissionContext( + newApp: YarnClientApplication, + containerContext: ContainerLaunchContext): ApplicationSubmissionContext = { + val appContext = newApp.getApplicationSubmissionContext + appContext.setApplicationName(sparkConf.get("spark.app.name", "Spark")) + appContext.setQueue(sparkConf.get(QUEUE_NAME)) + appContext.setAMContainerSpec(containerContext) + appContext.setApplicationType("SPARK") + + sparkConf.get(APPLICATION_TAGS).foreach { tags => + try { + // The setApplicationTags method was only introduced in Hadoop 2.4+, so we need to use + // reflection to set it, printing a warning if a tag was specified but the YARN version + // doesn't support it. + val method = appContext.getClass().getMethod( + "setApplicationTags", classOf[java.util.Set[String]]) + method.invoke(appContext, new java.util.HashSet[String](tags.asJava)) + } catch { + case e: NoSuchMethodException => + logWarning(s"Ignoring ${APPLICATION_TAGS.key} because this version of " + + "YARN does not support it") + } + } + sparkConf.get(MAX_APP_ATTEMPTS) match { + case Some(v) => appContext.setMaxAppAttempts(v) + case None => logDebug(s"${MAX_APP_ATTEMPTS.key} is not set. " + + "Cluster's default value will be used.") + } + + sparkConf.get(AM_ATTEMPT_FAILURE_VALIDITY_INTERVAL_MS).foreach { interval => + try { + val method = appContext.getClass().getMethod( + "setAttemptFailuresValidityInterval", classOf[Long]) + method.invoke(appContext, interval: java.lang.Long) + } catch { + case e: NoSuchMethodException => + logWarning(s"Ignoring ${AM_ATTEMPT_FAILURE_VALIDITY_INTERVAL_MS.key} because " + + "the version of YARN does not support it") + } + } + + val capability = Records.newRecord(classOf[Resource]) + capability.setMemory(amMemory + amMemoryOverhead) + capability.setVirtualCores(amCores) + + sparkConf.get(AM_NODE_LABEL_EXPRESSION) match { + case Some(expr) => + try { + val amRequest = Records.newRecord(classOf[ResourceRequest]) + amRequest.setResourceName(ResourceRequest.ANY) + amRequest.setPriority(Priority.newInstance(0)) + amRequest.setCapability(capability) + amRequest.setNumContainers(1) + val method = amRequest.getClass.getMethod("setNodeLabelExpression", classOf[String]) + method.invoke(amRequest, expr) + + val setResourceRequestMethod = + appContext.getClass.getMethod("setAMContainerResourceRequest", classOf[ResourceRequest]) + setResourceRequestMethod.invoke(appContext, amRequest) + } catch { + case e: NoSuchMethodException => + logWarning(s"Ignoring ${AM_NODE_LABEL_EXPRESSION.key} because the version " + + "of YARN does not support it") + appContext.setResource(capability) + } + case None => + appContext.setResource(capability) + } + + sparkConf.get(ROLLED_LOG_INCLUDE_PATTERN).foreach { includePattern => + try { + val logAggregationContext = Records.newRecord( + Utils.classForName("org.apache.hadoop.yarn.api.records.LogAggregationContext")) + .asInstanceOf[Object] + + val setRolledLogsIncludePatternMethod = + logAggregationContext.getClass.getMethod("setRolledLogsIncludePattern", classOf[String]) + setRolledLogsIncludePatternMethod.invoke(logAggregationContext, includePattern) + + sparkConf.get(ROLLED_LOG_EXCLUDE_PATTERN).foreach { excludePattern => + val setRolledLogsExcludePatternMethod = + logAggregationContext.getClass.getMethod("setRolledLogsExcludePattern", classOf[String]) + setRolledLogsExcludePatternMethod.invoke(logAggregationContext, excludePattern) + } + + val setLogAggregationContextMethod = + appContext.getClass.getMethod("setLogAggregationContext", + Utils.classForName("org.apache.hadoop.yarn.api.records.LogAggregationContext")) + setLogAggregationContextMethod.invoke(appContext, logAggregationContext) + } catch { + case NonFatal(e) => + logWarning(s"Ignoring ${ROLLED_LOG_INCLUDE_PATTERN.key} because the version of YARN " + + s"does not support it", e) + } + } + + appContext + } + + /** Set up security tokens for launching our ApplicationMaster container. */ + private def setupSecurityToken(amContainer: ContainerLaunchContext): Unit = { + val dob = new DataOutputBuffer + credentials.writeTokenStorageToStream(dob) + amContainer.setTokens(ByteBuffer.wrap(dob.getData)) + } + + /** Get the application report from the ResourceManager for an application we have submitted. */ + def getApplicationReport(appId: ApplicationId): ApplicationReport = + yarnClient.getApplicationReport(appId) + + /** + * Return the security token used by this client to communicate with the ApplicationMaster. + * If no security is enabled, the token returned by the report is null. + */ + private def getClientToken(report: ApplicationReport): String = + Option(report.getClientToAMToken).map(_.toString).getOrElse("") + + /** + * Fail fast if we have requested more resources per container than is available in the cluster. + */ + private def verifyClusterResources(newAppResponse: GetNewApplicationResponse): Unit = { + val maxMem = newAppResponse.getMaximumResourceCapability().getMemory() + logInfo("Verifying our application has not requested more than the maximum " + + s"memory capability of the cluster ($maxMem MB per container)") + val executorMem = executorMemory + executorMemoryOverhead + if (executorMem > maxMem) { + throw new IllegalArgumentException(s"Required executor memory ($executorMemory" + + s"+$executorMemoryOverhead MB) is above the max threshold ($maxMem MB) of this cluster! " + + "Please check the values of 'yarn.scheduler.maximum-allocation-mb' and/or " + + "'yarn.nodemanager.resource.memory-mb'.") + } + val amMem = amMemory + amMemoryOverhead + if (amMem > maxMem) { + throw new IllegalArgumentException(s"Required AM memory ($amMemory" + + s"+$amMemoryOverhead MB) is above the max threshold ($maxMem MB) of this cluster! " + + "Please increase the value of 'yarn.scheduler.maximum-allocation-mb'.") + } + logInfo("Will allocate AM container, with %d MB memory including %d MB overhead".format( + amMem, + amMemoryOverhead)) + + // We could add checks to make sure the entire cluster has enough resources but that involves + // getting all the node reports and computing ourselves. + } + + /** + * Copy the given file to a remote file system (e.g. HDFS) if needed. + * The file is only copied if the source and destination file systems are different. This is used + * for preparing resources for launching the ApplicationMaster container. Exposed for testing. + */ + private[yarn] def copyFileToRemote( + destDir: Path, + srcPath: Path, + replication: Short, + force: Boolean = false, + destName: Option[String] = None): Path = { + val destFs = destDir.getFileSystem(hadoopConf) + val srcFs = srcPath.getFileSystem(hadoopConf) + var destPath = srcPath + if (force || !compareFs(srcFs, destFs)) { + destPath = new Path(destDir, destName.getOrElse(srcPath.getName())) + logInfo(s"Uploading resource $srcPath -> $destPath") + FileUtil.copy(srcFs, srcPath, destFs, destPath, false, hadoopConf) + destFs.setReplication(destPath, replication) + destFs.setPermission(destPath, new FsPermission(APP_FILE_PERMISSION)) + } else { + logInfo(s"Source and destination file systems are the same. Not copying $srcPath") + } + // Resolve any symlinks in the URI path so using a "current" symlink to point to a specific + // version shows the specific version in the distributed cache configuration + val qualifiedDestPath = destFs.makeQualified(destPath) + val fc = FileContext.getFileContext(qualifiedDestPath.toUri(), hadoopConf) + fc.resolvePath(qualifiedDestPath) + } + + /** + * Upload any resources to the distributed cache if needed. If a resource is intended to be + * consumed locally, set up the appropriate config for downstream code to handle it properly. + * This is used for setting up a container launch context for our ApplicationMaster. + * Exposed for testing. + */ + def prepareLocalResources( + destDir: Path, + pySparkArchives: Seq[String]): HashMap[String, LocalResource] = { + logInfo("Preparing resources for our AM container") + // Upload Spark and the application JAR to the remote file system if necessary, + // and add them as local resources to the application master. + val fs = destDir.getFileSystem(hadoopConf) + + // Merge credentials obtained from registered providers + val nearestTimeOfNextRenewal = credentialManager.obtainCredentials(hadoopConf, credentials) + + if (credentials != null) { + logDebug(YarnSparkHadoopUtil.get.dumpTokens(credentials).mkString("\n")) + } + + // If we use principal and keytab to login, also credentials can be renewed some time + // after current time, we should pass the next renewal and updating time to credential + // renewer and updater. + if (loginFromKeytab && nearestTimeOfNextRenewal > System.currentTimeMillis() && + nearestTimeOfNextRenewal != Long.MaxValue) { + + // Valid renewal time is 75% of next renewal time, and the valid update time will be + // slightly later then renewal time (80% of next renewal time). This is to make sure + // credentials are renewed and updated before expired. + val currTime = System.currentTimeMillis() + val renewalTime = (nearestTimeOfNextRenewal - currTime) * 0.75 + currTime + val updateTime = (nearestTimeOfNextRenewal - currTime) * 0.8 + currTime + + sparkConf.set(CREDENTIALS_RENEWAL_TIME, renewalTime.toLong) + sparkConf.set(CREDENTIALS_UPDATE_TIME, updateTime.toLong) + } + + // Used to keep track of URIs added to the distributed cache. If the same URI is added + // multiple times, YARN will fail to launch containers for the app with an internal + // error. + val distributedUris = new HashSet[String] + // Used to keep track of URIs(files) added to the distribute cache have the same name. If + // same name but different path files are added multiple time, YARN will fail to launch + // containers for the app with an internal error. + val distributedNames = new HashSet[String] + + val replication = sparkConf.get(STAGING_FILE_REPLICATION).map(_.toShort) + .getOrElse(fs.getDefaultReplication(destDir)) + val localResources = HashMap[String, LocalResource]() + FileSystem.mkdirs(fs, destDir, new FsPermission(STAGING_DIR_PERMISSION)) + + val statCache: Map[URI, FileStatus] = HashMap[URI, FileStatus]() + + def addDistributedUri(uri: URI): Boolean = { + val uriStr = uri.toString() + val fileName = new File(uri.getPath).getName + if (distributedUris.contains(uriStr)) { + logWarning(s"Same path resource $uri added multiple times to distributed cache.") + false + } else if (distributedNames.contains(fileName)) { + logWarning(s"Same name resource $uri added multiple times to distributed cache") + false + } else { + distributedUris += uriStr + distributedNames += fileName + true + } + } + + /** + * Distribute a file to the cluster. + * + * If the file's path is a "local:" URI, it's actually not distributed. Other files are copied + * to HDFS (if not already there) and added to the application's distributed cache. + * + * @param path URI of the file to distribute. + * @param resType Type of resource being distributed. + * @param destName Name of the file in the distributed cache. + * @param targetDir Subdirectory where to place the file. + * @param appMasterOnly Whether to distribute only to the AM. + * @return A 2-tuple. First item is whether the file is a "local:" URI. Second item is the + * localized path for non-local paths, or the input `path` for local paths. + * The localized path will be null if the URI has already been added to the cache. + */ + def distribute( + path: String, + resType: LocalResourceType = LocalResourceType.FILE, + destName: Option[String] = None, + targetDir: Option[String] = None, + appMasterOnly: Boolean = false): (Boolean, String) = { + val trimmedPath = path.trim() + val localURI = Utils.resolveURI(trimmedPath) + if (localURI.getScheme != LOCAL_SCHEME) { + if (addDistributedUri(localURI)) { + val localPath = getQualifiedLocalPath(localURI, hadoopConf) + val linkname = targetDir.map(_ + "/").getOrElse("") + + destName.orElse(Option(localURI.getFragment())).getOrElse(localPath.getName()) + val destPath = copyFileToRemote(destDir, localPath, replication) + val destFs = FileSystem.get(destPath.toUri(), hadoopConf) + distCacheMgr.addResource( + destFs, hadoopConf, destPath, localResources, resType, linkname, statCache, + appMasterOnly = appMasterOnly) + (false, linkname) + } else { + (false, null) + } + } else { + (true, trimmedPath) + } + } + + // If we passed in a keytab, make sure we copy the keytab to the staging directory on + // HDFS, and setup the relevant environment vars, so the AM can login again. + if (loginFromKeytab) { + logInfo("To enable the AM to login from keytab, credentials are being copied over to the AM" + + " via the YARN Secure Distributed Cache.") + val (_, localizedPath) = distribute(keytab, + destName = sparkConf.get(KEYTAB), + appMasterOnly = true) + require(localizedPath != null, "Keytab file already distributed.") + } + + /** + * Add Spark to the cache. There are two settings that control what files to add to the cache: + * - if a Spark archive is defined, use the archive. The archive is expected to contain + * jar files at its root directory. + * - if a list of jars is provided, filter the non-local ones, resolve globs, and + * add the found files to the cache. + * + * Note that the archive cannot be a "local" URI. If none of the above settings are found, + * then upload all files found in $SPARK_HOME/jars. + */ + val sparkArchive = sparkConf.get(SPARK_ARCHIVE) + if (sparkArchive.isDefined) { + val archive = sparkArchive.get + require(!isLocalUri(archive), s"${SPARK_ARCHIVE.key} cannot be a local URI.") + distribute(Utils.resolveURI(archive).toString, + resType = LocalResourceType.ARCHIVE, + destName = Some(LOCALIZED_LIB_DIR)) + } else { + sparkConf.get(SPARK_JARS) match { + case Some(jars) => + // Break the list of jars to upload, and resolve globs. + val localJars = new ArrayBuffer[String]() + jars.foreach { jar => + if (!isLocalUri(jar)) { + val path = getQualifiedLocalPath(Utils.resolveURI(jar), hadoopConf) + val pathFs = FileSystem.get(path.toUri(), hadoopConf) + pathFs.globStatus(path).filter(_.isFile()).foreach { entry => + distribute(entry.getPath().toUri().toString(), + targetDir = Some(LOCALIZED_LIB_DIR)) + } + } else { + localJars += jar + } + } + + // Propagate the local URIs to the containers using the configuration. + sparkConf.set(SPARK_JARS, localJars) + + case None => + // No configuration, so fall back to uploading local jar files. + logWarning(s"Neither ${SPARK_JARS.key} nor ${SPARK_ARCHIVE.key} is set, falling back " + + "to uploading libraries under SPARK_HOME.") + val jarsDir = new File(YarnCommandBuilderUtils.findJarsDir( + sparkConf.getenv("SPARK_HOME"))) + val jarsArchive = File.createTempFile(LOCALIZED_LIB_DIR, ".zip", + new File(Utils.getLocalDir(sparkConf))) + val jarsStream = new ZipOutputStream(new FileOutputStream(jarsArchive)) + + try { + jarsStream.setLevel(0) + jarsDir.listFiles().foreach { f => + if (f.isFile && f.getName.toLowerCase().endsWith(".jar") && f.canRead) { + jarsStream.putNextEntry(new ZipEntry(f.getName)) + Files.copy(f, jarsStream) + jarsStream.closeEntry() + } + } + } finally { + jarsStream.close() + } + + distribute(jarsArchive.toURI.getPath, + resType = LocalResourceType.ARCHIVE, + destName = Some(LOCALIZED_LIB_DIR)) + } + } + + /** + * Copy user jar to the distributed cache if their scheme is not "local". + * Otherwise, set the corresponding key in our SparkConf to handle it downstream. + */ + Option(args.userJar).filter(_.trim.nonEmpty).foreach { jar => + val (isLocal, localizedPath) = distribute(jar, destName = Some(APP_JAR_NAME)) + if (isLocal) { + require(localizedPath != null, s"Path $jar already distributed") + // If the resource is intended for local use only, handle this downstream + // by setting the appropriate property + sparkConf.set(APP_JAR, localizedPath) + } + } + + /** + * Do the same for any additional resources passed in through ClientArguments. + * Each resource category is represented by a 3-tuple of: + * (1) comma separated list of resources in this category, + * (2) resource type, and + * (3) whether to add these resources to the classpath + */ + val cachedSecondaryJarLinks = ListBuffer.empty[String] + List( + (sparkConf.get(JARS_TO_DISTRIBUTE), LocalResourceType.FILE, true), + (sparkConf.get(FILES_TO_DISTRIBUTE), LocalResourceType.FILE, false), + (sparkConf.get(ARCHIVES_TO_DISTRIBUTE), LocalResourceType.ARCHIVE, false) + ).foreach { case (flist, resType, addToClasspath) => + flist.foreach { file => + val (_, localizedPath) = distribute(file, resType = resType) + // If addToClassPath, we ignore adding jar multiple times to distitrbuted cache. + if (addToClasspath) { + if (localizedPath != null) { + cachedSecondaryJarLinks += localizedPath + } + } else { + if (localizedPath == null) { + throw new IllegalArgumentException(s"Attempt to add ($file) multiple times" + + " to the distributed cache.") + } + } + } + } + if (cachedSecondaryJarLinks.nonEmpty) { + sparkConf.set(SECONDARY_JARS, cachedSecondaryJarLinks) + } + + if (isClusterMode && args.primaryPyFile != null) { + distribute(args.primaryPyFile, appMasterOnly = true) + } + + pySparkArchives.foreach { f => distribute(f) } + + // The python files list needs to be treated especially. All files that are not an + // archive need to be placed in a subdirectory that will be added to PYTHONPATH. + sparkConf.get(PY_FILES).foreach { f => + val targetDir = if (f.endsWith(".py")) Some(LOCALIZED_PYTHON_DIR) else None + distribute(f, targetDir = targetDir) + } + + // Update the configuration with all the distributed files, minus the conf archive. The + // conf archive will be handled by the AM differently so that we avoid having to send + // this configuration by other means. See SPARK-14602 for one reason of why this is needed. + distCacheMgr.updateConfiguration(sparkConf) + + // Upload the conf archive to HDFS manually, and record its location in the configuration. + // This will allow the AM to know where the conf archive is in HDFS, so that it can be + // distributed to the containers. + // + // This code forces the archive to be copied, so that unit tests pass (since in that case both + // file systems are the same and the archive wouldn't normally be copied). In most (all?) + // deployments, the archive would be copied anyway, since it's a temp file in the local file + // system. + val remoteConfArchivePath = new Path(destDir, LOCALIZED_CONF_ARCHIVE) + val remoteFs = FileSystem.get(remoteConfArchivePath.toUri(), hadoopConf) + sparkConf.set(CACHED_CONF_ARCHIVE, remoteConfArchivePath.toString()) + + val localConfArchive = new Path(createConfArchive().toURI()) + copyFileToRemote(destDir, localConfArchive, replication, force = true, + destName = Some(LOCALIZED_CONF_ARCHIVE)) + + // Manually add the config archive to the cache manager so that the AM is launched with + // the proper files set up. + distCacheMgr.addResource( + remoteFs, hadoopConf, remoteConfArchivePath, localResources, LocalResourceType.ARCHIVE, + LOCALIZED_CONF_DIR, statCache, appMasterOnly = false) + + // Clear the cache-related entries from the configuration to avoid them polluting the + // UI's environment page. This works for client mode; for cluster mode, this is handled + // by the AM. + CACHE_CONFIGS.foreach(sparkConf.remove) + + localResources + } + + /** + * Create an archive with the config files for distribution. + * + * These will be used by AM and executors. The files are zipped and added to the job as an + * archive, so that YARN will explode it when distributing to AM and executors. This directory + * is then added to the classpath of AM and executor process, just to make sure that everybody + * is using the same default config. + * + * This follows the order of precedence set by the startup scripts, in which HADOOP_CONF_DIR + * shows up in the classpath before YARN_CONF_DIR. + * + * Currently this makes a shallow copy of the conf directory. If there are cases where a + * Hadoop config directory contains subdirectories, this code will have to be fixed. + * + * The archive also contains some Spark configuration. Namely, it saves the contents of + * SparkConf in a file to be loaded by the AM process. + */ + private def createConfArchive(): File = { + val hadoopConfFiles = new HashMap[String, File]() + + // Uploading $SPARK_CONF_DIR/log4j.properties file to the distributed cache to make sure that + // the executors will use the latest configurations instead of the default values. This is + // required when user changes log4j.properties directly to set the log configurations. If + // configuration file is provided through --files then executors will be taking configurations + // from --files instead of $SPARK_CONF_DIR/log4j.properties. + + // Also uploading metrics.properties to distributed cache if exists in classpath. + // If user specify this file using --files then executors will use the one + // from --files instead. + for { prop <- Seq("log4j.properties", "metrics.properties") + url <- Option(Utils.getContextOrSparkClassLoader.getResource(prop)) + if url.getProtocol == "file" } { + hadoopConfFiles(prop) = new File(url.getPath) + } + + Seq("HADOOP_CONF_DIR", "YARN_CONF_DIR").foreach { envKey => + sys.env.get(envKey).foreach { path => + val dir = new File(path) + if (dir.isDirectory()) { + val files = dir.listFiles() + if (files == null) { + logWarning("Failed to list files under directory " + dir) + } else { + files.foreach { file => + if (file.isFile && !hadoopConfFiles.contains(file.getName())) { + hadoopConfFiles(file.getName()) = file + } + } + } + } + } + } + + val confArchive = File.createTempFile(LOCALIZED_CONF_DIR, ".zip", + new File(Utils.getLocalDir(sparkConf))) + val confStream = new ZipOutputStream(new FileOutputStream(confArchive)) + + try { + confStream.setLevel(0) + hadoopConfFiles.foreach { case (name, file) => + if (file.canRead()) { + confStream.putNextEntry(new ZipEntry(name)) + Files.copy(file, confStream) + confStream.closeEntry() + } + } + + // Save Spark configuration to a file in the archive. + val props = new Properties() + sparkConf.getAll.foreach { case (k, v) => props.setProperty(k, v) } + confStream.putNextEntry(new ZipEntry(SPARK_CONF_FILE)) + val writer = new OutputStreamWriter(confStream, StandardCharsets.UTF_8) + props.store(writer, "Spark configuration.") + writer.flush() + confStream.closeEntry() + } finally { + confStream.close() + } + confArchive + } + + /** + * Set up the environment for launching our ApplicationMaster container. + */ + private def setupLaunchEnv( + stagingDirPath: Path, + pySparkArchives: Seq[String]): HashMap[String, String] = { + logInfo("Setting up the launch environment for our AM container") + val env = new HashMap[String, String]() + populateClasspath(args, yarnConf, sparkConf, env, sparkConf.get(DRIVER_CLASS_PATH)) + env("SPARK_YARN_MODE") = "true" + env("SPARK_YARN_STAGING_DIR") = stagingDirPath.toString + env("SPARK_USER") = UserGroupInformation.getCurrentUser().getShortUserName() + if (loginFromKeytab) { + val credentialsFile = "credentials-" + UUID.randomUUID().toString + sparkConf.set(CREDENTIALS_FILE_PATH, new Path(stagingDirPath, credentialsFile).toString) + logInfo(s"Credentials file set to: $credentialsFile") + } + + // Pick up any environment variables for the AM provided through spark.yarn.appMasterEnv.* + val amEnvPrefix = "spark.yarn.appMasterEnv." + sparkConf.getAll + .filter { case (k, v) => k.startsWith(amEnvPrefix) } + .map { case (k, v) => (k.substring(amEnvPrefix.length), v) } + .foreach { case (k, v) => YarnSparkHadoopUtil.addPathToEnvironment(env, k, v) } + + // Keep this for backwards compatibility but users should move to the config + sys.env.get("SPARK_YARN_USER_ENV").foreach { userEnvs => + // Allow users to specify some environment variables. + YarnSparkHadoopUtil.setEnvFromInputString(env, userEnvs) + // Pass SPARK_YARN_USER_ENV itself to the AM so it can use it to set up executor environments. + env("SPARK_YARN_USER_ENV") = userEnvs + } + + // If pyFiles contains any .py files, we need to add LOCALIZED_PYTHON_DIR to the PYTHONPATH + // of the container processes too. Add all non-.py files directly to PYTHONPATH. + // + // NOTE: the code currently does not handle .py files defined with a "local:" scheme. + val pythonPath = new ListBuffer[String]() + val (pyFiles, pyArchives) = sparkConf.get(PY_FILES).partition(_.endsWith(".py")) + if (pyFiles.nonEmpty) { + pythonPath += buildPath(YarnSparkHadoopUtil.expandEnvironment(Environment.PWD), + LOCALIZED_PYTHON_DIR) + } + (pySparkArchives ++ pyArchives).foreach { path => + val uri = Utils.resolveURI(path) + if (uri.getScheme != LOCAL_SCHEME) { + pythonPath += buildPath(YarnSparkHadoopUtil.expandEnvironment(Environment.PWD), + new Path(uri).getName()) + } else { + pythonPath += uri.getPath() + } + } + + // Finally, update the Spark config to propagate PYTHONPATH to the AM and executors. + if (pythonPath.nonEmpty) { + val pythonPathStr = (sys.env.get("PYTHONPATH") ++ pythonPath) + .mkString(YarnSparkHadoopUtil.getClassPathSeparator) + env("PYTHONPATH") = pythonPathStr + sparkConf.setExecutorEnv("PYTHONPATH", pythonPathStr) + } + + // In cluster mode, if the deprecated SPARK_JAVA_OPTS is set, we need to propagate it to + // executors. But we can't just set spark.executor.extraJavaOptions, because the driver's + // SparkContext will not let that set spark* system properties, which is expected behavior for + // Yarn clients. So propagate it through the environment. + // + // Note that to warn the user about the deprecation in cluster mode, some code from + // SparkConf#validateSettings() is duplicated here (to avoid triggering the condition + // described above). + if (isClusterMode) { + sys.env.get("SPARK_JAVA_OPTS").foreach { value => + val warning = + s""" + |SPARK_JAVA_OPTS was detected (set to '$value'). + |This is deprecated in Spark 1.0+. + | + |Please instead use: + | - ./spark-submit with conf/spark-defaults.conf to set defaults for an application + | - ./spark-submit with --driver-java-options to set -X options for a driver + | - spark.executor.extraJavaOptions to set -X options for executors + """.stripMargin + logWarning(warning) + for (proc <- Seq("driver", "executor")) { + val key = s"spark.$proc.extraJavaOptions" + if (sparkConf.contains(key)) { + throw new SparkException(s"Found both $key and SPARK_JAVA_OPTS. Use only the former.") + } + } + env("SPARK_JAVA_OPTS") = value + } + // propagate PYSPARK_DRIVER_PYTHON and PYSPARK_PYTHON to driver in cluster mode + Seq("PYSPARK_DRIVER_PYTHON", "PYSPARK_PYTHON").foreach { envname => + if (!env.contains(envname)) { + sys.env.get(envname).foreach(env(envname) = _) + } + } + } + + sys.env.get(ENV_DIST_CLASSPATH).foreach { dcp => + env(ENV_DIST_CLASSPATH) = dcp + } + + env + } + + /** + * Set up a ContainerLaunchContext to launch our ApplicationMaster container. + * This sets up the launch environment, java options, and the command for launching the AM. + */ + private def createContainerLaunchContext(newAppResponse: GetNewApplicationResponse) + : ContainerLaunchContext = { + logInfo("Setting up container launch context for our AM") + val appId = newAppResponse.getApplicationId + val appStagingDirPath = new Path(appStagingBaseDir, getAppStagingDir(appId)) + val pySparkArchives = + if (sparkConf.get(IS_PYTHON_APP)) { + findPySparkArchives() + } else { + Nil + } + val launchEnv = setupLaunchEnv(appStagingDirPath, pySparkArchives) + val localResources = prepareLocalResources(appStagingDirPath, pySparkArchives) + + val amContainer = Records.newRecord(classOf[ContainerLaunchContext]) + amContainer.setLocalResources(localResources.asJava) + amContainer.setEnvironment(launchEnv.asJava) + + val javaOpts = ListBuffer[String]() + + // Set the environment variable through a command prefix + // to append to the existing value of the variable + var prefixEnv: Option[String] = None + + // Add Xmx for AM memory + javaOpts += "-Xmx" + amMemory + "m" + + val tmpDir = new Path( + YarnSparkHadoopUtil.expandEnvironment(Environment.PWD), + YarnConfiguration.DEFAULT_CONTAINER_TEMP_DIR + ) + javaOpts += "-Djava.io.tmpdir=" + tmpDir + + // TODO: Remove once cpuset version is pushed out. + // The context is, default gc for server class machines ends up using all cores to do gc - + // hence if there are multiple containers in same node, Spark GC affects all other containers' + // performance (which can be that of other Spark containers) + // Instead of using this, rely on cpusets by YARN to enforce "proper" Spark behavior in + // multi-tenant environments. Not sure how default Java GC behaves if it is limited to subset + // of cores on a node. + val useConcurrentAndIncrementalGC = launchEnv.get("SPARK_USE_CONC_INCR_GC").exists(_.toBoolean) + if (useConcurrentAndIncrementalGC) { + // In our expts, using (default) throughput collector has severe perf ramifications in + // multi-tenant machines + javaOpts += "-XX:+UseConcMarkSweepGC" + javaOpts += "-XX:MaxTenuringThreshold=31" + javaOpts += "-XX:SurvivorRatio=8" + javaOpts += "-XX:+CMSIncrementalMode" + javaOpts += "-XX:+CMSIncrementalPacing" + javaOpts += "-XX:CMSIncrementalDutyCycleMin=0" + javaOpts += "-XX:CMSIncrementalDutyCycle=10" + } + + // Include driver-specific java options if we are launching a driver + if (isClusterMode) { + val driverOpts = sparkConf.get(DRIVER_JAVA_OPTIONS).orElse(sys.env.get("SPARK_JAVA_OPTS")) + driverOpts.foreach { opts => + javaOpts ++= Utils.splitCommandString(opts).map(YarnSparkHadoopUtil.escapeForShell) + } + val libraryPaths = Seq(sparkConf.get(DRIVER_LIBRARY_PATH), + sys.props.get("spark.driver.libraryPath")).flatten + if (libraryPaths.nonEmpty) { + prefixEnv = Some(getClusterPath(sparkConf, Utils.libraryPathEnvPrefix(libraryPaths))) + } + if (sparkConf.get(AM_JAVA_OPTIONS).isDefined) { + logWarning(s"${AM_JAVA_OPTIONS.key} will not take effect in cluster mode") + } + } else { + // Validate and include yarn am specific java options in yarn-client mode. + sparkConf.get(AM_JAVA_OPTIONS).foreach { opts => + if (opts.contains("-Dspark")) { + val msg = s"${AM_JAVA_OPTIONS.key} is not allowed to set Spark options (was '$opts')." + throw new SparkException(msg) + } + if (opts.contains("-Xmx")) { + val msg = s"${AM_JAVA_OPTIONS.key} is not allowed to specify max heap memory settings " + + s"(was '$opts'). Use spark.yarn.am.memory instead." + throw new SparkException(msg) + } + javaOpts ++= Utils.splitCommandString(opts).map(YarnSparkHadoopUtil.escapeForShell) + } + sparkConf.get(AM_LIBRARY_PATH).foreach { paths => + prefixEnv = Some(getClusterPath(sparkConf, Utils.libraryPathEnvPrefix(Seq(paths)))) + } + } + + // For log4j configuration to reference + javaOpts += ("-Dspark.yarn.app.container.log.dir=" + ApplicationConstants.LOG_DIR_EXPANSION_VAR) + YarnCommandBuilderUtils.addPermGenSizeOpt(javaOpts) + + val userClass = + if (isClusterMode) { + Seq("--class", YarnSparkHadoopUtil.escapeForShell(args.userClass)) + } else { + Nil + } + val userJar = + if (args.userJar != null) { + Seq("--jar", args.userJar) + } else { + Nil + } + val primaryPyFile = + if (isClusterMode && args.primaryPyFile != null) { + Seq("--primary-py-file", new Path(args.primaryPyFile).getName()) + } else { + Nil + } + val primaryRFile = + if (args.primaryRFile != null) { + Seq("--primary-r-file", args.primaryRFile) + } else { + Nil + } + val amClass = + if (isClusterMode) { + Utils.classForName("org.apache.spark.deploy.yarn.ApplicationMaster").getName + } else { + Utils.classForName("org.apache.spark.deploy.yarn.ExecutorLauncher").getName + } + if (args.primaryRFile != null && args.primaryRFile.endsWith(".R")) { + args.userArgs = ArrayBuffer(args.primaryRFile) ++ args.userArgs + } + val userArgs = args.userArgs.flatMap { arg => + Seq("--arg", YarnSparkHadoopUtil.escapeForShell(arg)) + } + val amArgs = + Seq(amClass) ++ userClass ++ userJar ++ primaryPyFile ++ primaryRFile ++ + userArgs ++ Seq( + "--properties-file", buildPath(YarnSparkHadoopUtil.expandEnvironment(Environment.PWD), + LOCALIZED_CONF_DIR, SPARK_CONF_FILE)) + + // Command for the ApplicationMaster + val commands = prefixEnv ++ Seq( + YarnSparkHadoopUtil.expandEnvironment(Environment.JAVA_HOME) + "/bin/java", "-server" + ) ++ + javaOpts ++ amArgs ++ + Seq( + "1>", ApplicationConstants.LOG_DIR_EXPANSION_VAR + "/stdout", + "2>", ApplicationConstants.LOG_DIR_EXPANSION_VAR + "/stderr") + + // TODO: it would be nicer to just make sure there are no null commands here + val printableCommands = commands.map(s => if (s == null) "null" else s).toList + amContainer.setCommands(printableCommands.asJava) + + logDebug("===============================================================================") + logDebug("YARN AM launch context:") + logDebug(s" user class: ${Option(args.userClass).getOrElse("N/A")}") + logDebug(" env:") + launchEnv.foreach { case (k, v) => logDebug(s" $k -> $v") } + logDebug(" resources:") + localResources.foreach { case (k, v) => logDebug(s" $k -> $v")} + logDebug(" command:") + logDebug(s" ${printableCommands.mkString(" ")}") + logDebug("===============================================================================") + + // send the acl settings into YARN to control who has access via YARN interfaces + val securityManager = new SecurityManager(sparkConf) + amContainer.setApplicationACLs( + YarnSparkHadoopUtil.getApplicationAclsForYarn(securityManager).asJava) + setupSecurityToken(amContainer) + amContainer + } + + def setupCredentials(): Unit = { + loginFromKeytab = sparkConf.contains(PRINCIPAL.key) + if (loginFromKeytab) { + principal = sparkConf.get(PRINCIPAL).get + keytab = sparkConf.get(KEYTAB).orNull + + require(keytab != null, "Keytab must be specified when principal is specified.") + logInfo("Attempting to login to the Kerberos" + + s" using principal: $principal and keytab: $keytab") + val f = new File(keytab) + // Generate a file name that can be used for the keytab file, that does not conflict + // with any user file. + val keytabFileName = f.getName + "-" + UUID.randomUUID().toString + sparkConf.set(KEYTAB.key, keytabFileName) + sparkConf.set(PRINCIPAL.key, principal) + } + // Defensive copy of the credentials + credentials = new Credentials(UserGroupInformation.getCurrentUser.getCredentials) + } + + /** + * Report the state of an application until it has exited, either successfully or + * due to some failure, then return a pair of the yarn application state (FINISHED, FAILED, + * KILLED, or RUNNING) and the final application state (UNDEFINED, SUCCEEDED, FAILED, + * or KILLED). + * + * @param appId ID of the application to monitor. + * @param returnOnRunning Whether to also return the application state when it is RUNNING. + * @param logApplicationReport Whether to log details of the application report every iteration. + * @return A pair of the yarn application state and the final application state. + */ + def monitorApplication( + appId: ApplicationId, + returnOnRunning: Boolean = false, + logApplicationReport: Boolean = true): (YarnApplicationState, FinalApplicationStatus) = { + val interval = sparkConf.get(REPORT_INTERVAL) + var lastState: YarnApplicationState = null + while (true) { + Thread.sleep(interval) + val report: ApplicationReport = + try { + getApplicationReport(appId) + } catch { + case e: ApplicationNotFoundException => + logError(s"Application $appId not found.") + cleanupStagingDir(appId) + return (YarnApplicationState.KILLED, FinalApplicationStatus.KILLED) + case NonFatal(e) => + logError(s"Failed to contact YARN for application $appId.", e) + // Don't necessarily clean up staging dir because status is unknown + return (YarnApplicationState.FAILED, FinalApplicationStatus.FAILED) + } + val state = report.getYarnApplicationState + + if (logApplicationReport) { + logInfo(s"Application report for $appId (state: $state)") + + // If DEBUG is enabled, log report details every iteration + // Otherwise, log them every time the application changes state + if (log.isDebugEnabled) { + logDebug(formatReportDetails(report)) + } else if (lastState != state) { + logInfo(formatReportDetails(report)) + } + } + + if (lastState != state) { + state match { + case YarnApplicationState.RUNNING => + reportLauncherState(SparkAppHandle.State.RUNNING) + case YarnApplicationState.FINISHED => + report.getFinalApplicationStatus match { + case FinalApplicationStatus.FAILED => + reportLauncherState(SparkAppHandle.State.FAILED) + case FinalApplicationStatus.KILLED => + reportLauncherState(SparkAppHandle.State.KILLED) + case _ => + reportLauncherState(SparkAppHandle.State.FINISHED) + } + case YarnApplicationState.FAILED => + reportLauncherState(SparkAppHandle.State.FAILED) + case YarnApplicationState.KILLED => + reportLauncherState(SparkAppHandle.State.KILLED) + case _ => + } + } + + if (state == YarnApplicationState.FINISHED || + state == YarnApplicationState.FAILED || + state == YarnApplicationState.KILLED) { + cleanupStagingDir(appId) + return (state, report.getFinalApplicationStatus) + } + + if (returnOnRunning && state == YarnApplicationState.RUNNING) { + return (state, report.getFinalApplicationStatus) + } + + lastState = state + } + + // Never reached, but keeps compiler happy + throw new SparkException("While loop is depleted! This should never happen...") + } + + private def formatReportDetails(report: ApplicationReport): String = { + val details = Seq[(String, String)]( + ("client token", getClientToken(report)), + ("diagnostics", report.getDiagnostics), + ("ApplicationMaster host", report.getHost), + ("ApplicationMaster RPC port", report.getRpcPort.toString), + ("queue", report.getQueue), + ("start time", report.getStartTime.toString), + ("final status", report.getFinalApplicationStatus.toString), + ("tracking URL", report.getTrackingUrl), + ("user", report.getUser) + ) + + // Use more loggable format if value is null or empty + details.map { case (k, v) => + val newValue = Option(v).filter(_.nonEmpty).getOrElse("N/A") + s"\n\t $k: $newValue" + }.mkString("") + } + + /** + * Submit an application to the ResourceManager. + * If set spark.yarn.submit.waitAppCompletion to true, it will stay alive + * reporting the application's status until the application has exited for any reason. + * Otherwise, the client process will exit after submission. + * If the application finishes with a failed, killed, or undefined status, + * throw an appropriate SparkException. + */ + def run(): Unit = { + this.appId = submitApplication() + if (!launcherBackend.isConnected() && fireAndForget) { + val report = getApplicationReport(appId) + val state = report.getYarnApplicationState + logInfo(s"Application report for $appId (state: $state)") + logInfo(formatReportDetails(report)) + if (state == YarnApplicationState.FAILED || state == YarnApplicationState.KILLED) { + throw new SparkException(s"Application $appId finished with status: $state") + } + } else { + val (yarnApplicationState, finalApplicationStatus) = monitorApplication(appId) + if (yarnApplicationState == YarnApplicationState.FAILED || + finalApplicationStatus == FinalApplicationStatus.FAILED) { + throw new SparkException(s"Application $appId finished with failed status") + } + if (yarnApplicationState == YarnApplicationState.KILLED || + finalApplicationStatus == FinalApplicationStatus.KILLED) { + throw new SparkException(s"Application $appId is killed") + } + if (finalApplicationStatus == FinalApplicationStatus.UNDEFINED) { + throw new SparkException(s"The final status of application $appId is undefined") + } + } + } + + private def findPySparkArchives(): Seq[String] = { + sys.env.get("PYSPARK_ARCHIVES_PATH") + .map(_.split(",").toSeq) + .getOrElse { + val pyLibPath = Seq(sys.env("SPARK_HOME"), "python", "lib").mkString(File.separator) + val pyArchivesFile = new File(pyLibPath, "pyspark.zip") + require(pyArchivesFile.exists(), + s"$pyArchivesFile not found; cannot run pyspark application in YARN mode.") + val py4jFile = new File(pyLibPath, "py4j-0.10.4-src.zip") + require(py4jFile.exists(), + s"$py4jFile not found; cannot run pyspark application in YARN mode.") + Seq(pyArchivesFile.getAbsolutePath(), py4jFile.getAbsolutePath()) + } + } + +} + +private object Client extends Logging { + + def main(argStrings: Array[String]) { + if (!sys.props.contains("SPARK_SUBMIT")) { + logWarning("WARNING: This client is deprecated and will be removed in a " + + "future version of Spark. Use ./bin/spark-submit with \"--master yarn\"") + } + + // Set an env variable indicating we are running in YARN mode. + // Note that any env variable with the SPARK_ prefix gets propagated to all (remote) processes + System.setProperty("SPARK_YARN_MODE", "true") + val sparkConf = new SparkConf + // SparkSubmit would use yarn cache to distribute files & jars in yarn mode, + // so remove them from sparkConf here for yarn mode. + sparkConf.remove("spark.jars") + sparkConf.remove("spark.files") + val args = new ClientArguments(argStrings) + new Client(args, sparkConf).run() + } + + // Alias for the user jar + val APP_JAR_NAME: String = "__app__.jar" + + // URI scheme that identifies local resources + val LOCAL_SCHEME = "local" + + // Staging directory for any temporary jars or files + val SPARK_STAGING: String = ".sparkStaging" + + + // Staging directory is private! -> rwx-------- + val STAGING_DIR_PERMISSION: FsPermission = + FsPermission.createImmutable(Integer.parseInt("700", 8).toShort) + + // App files are world-wide readable and owner writable -> rw-r--r-- + val APP_FILE_PERMISSION: FsPermission = + FsPermission.createImmutable(Integer.parseInt("644", 8).toShort) + + // Distribution-defined classpath to add to processes + val ENV_DIST_CLASSPATH = "SPARK_DIST_CLASSPATH" + + // Subdirectory where the user's Spark and Hadoop config files will be placed. + val LOCALIZED_CONF_DIR = "__spark_conf__" + + // File containing the conf archive in the AM. See prepareLocalResources(). + val LOCALIZED_CONF_ARCHIVE = LOCALIZED_CONF_DIR + ".zip" + + // Name of the file in the conf archive containing Spark configuration. + val SPARK_CONF_FILE = "__spark_conf__.properties" + + // Subdirectory where the user's python files (not archives) will be placed. + val LOCALIZED_PYTHON_DIR = "__pyfiles__" + + // Subdirectory where Spark libraries will be placed. + val LOCALIZED_LIB_DIR = "__spark_libs__" + + /** + * Return the path to the given application's staging directory. + */ + private def getAppStagingDir(appId: ApplicationId): String = { + buildPath(SPARK_STAGING, appId.toString()) + } + + /** + * Populate the classpath entry in the given environment map with any application + * classpath specified through the Hadoop and Yarn configurations. + */ + private[yarn] def populateHadoopClasspath(conf: Configuration, env: HashMap[String, String]) + : Unit = { + val classPathElementsToAdd = getYarnAppClasspath(conf) ++ getMRAppClasspath(conf) + for (c <- classPathElementsToAdd.flatten) { + YarnSparkHadoopUtil.addPathToEnvironment(env, Environment.CLASSPATH.name, c.trim) + } + } + + private def getYarnAppClasspath(conf: Configuration): Option[Seq[String]] = + Option(conf.getStrings(YarnConfiguration.YARN_APPLICATION_CLASSPATH)) match { + case Some(s) => Some(s.toSeq) + case None => getDefaultYarnApplicationClasspath + } + + private def getMRAppClasspath(conf: Configuration): Option[Seq[String]] = + Option(conf.getStrings("mapreduce.application.classpath")) match { + case Some(s) => Some(s.toSeq) + case None => getDefaultMRApplicationClasspath + } + + private[yarn] def getDefaultYarnApplicationClasspath: Option[Seq[String]] = { + val triedDefault = Try[Seq[String]] { + val field = classOf[YarnConfiguration].getField("DEFAULT_YARN_APPLICATION_CLASSPATH") + val value = field.get(null).asInstanceOf[Array[String]] + value.toSeq + } recoverWith { + case e: NoSuchFieldException => Success(Seq.empty[String]) + } + + triedDefault match { + case f: Failure[_] => + logError("Unable to obtain the default YARN Application classpath.", f.exception) + case s: Success[Seq[String]] => + logDebug(s"Using the default YARN application classpath: ${s.get.mkString(",")}") + } + + triedDefault.toOption + } + + private[yarn] def getDefaultMRApplicationClasspath: Option[Seq[String]] = { + val triedDefault = Try[Seq[String]] { + val field = classOf[MRJobConfig].getField("DEFAULT_MAPREDUCE_APPLICATION_CLASSPATH") + StringUtils.getStrings(field.get(null).asInstanceOf[String]).toSeq + } recoverWith { + case e: NoSuchFieldException => Success(Seq.empty[String]) + } + + triedDefault match { + case f: Failure[_] => + logError("Unable to obtain the default MR Application classpath.", f.exception) + case s: Success[Seq[String]] => + logDebug(s"Using the default MR application classpath: ${s.get.mkString(",")}") + } + + triedDefault.toOption + } + + /** + * Populate the classpath entry in the given environment map. + * + * User jars are generally not added to the JVM's system classpath; those are handled by the AM + * and executor backend. When the deprecated `spark.yarn.user.classpath.first` is used, user jars + * are included in the system classpath, though. The extra class path and other uploaded files are + * always made available through the system class path. + * + * @param args Client arguments (when starting the AM) or null (when starting executors). + */ + private[yarn] def populateClasspath( + args: ClientArguments, + conf: Configuration, + sparkConf: SparkConf, + env: HashMap[String, String], + extraClassPath: Option[String] = None): Unit = { + extraClassPath.foreach { cp => + addClasspathEntry(getClusterPath(sparkConf, cp), env) + } + + addClasspathEntry(YarnSparkHadoopUtil.expandEnvironment(Environment.PWD), env) + + addClasspathEntry( + YarnSparkHadoopUtil.expandEnvironment(Environment.PWD) + Path.SEPARATOR + + LOCALIZED_CONF_DIR, env) + + if (sparkConf.get(USER_CLASS_PATH_FIRST)) { + // in order to properly add the app jar when user classpath is first + // we have to do the mainJar separate in order to send the right thing + // into addFileToClasspath + val mainJar = + if (args != null) { + getMainJarUri(Option(args.userJar)) + } else { + getMainJarUri(sparkConf.get(APP_JAR)) + } + mainJar.foreach(addFileToClasspath(sparkConf, conf, _, APP_JAR_NAME, env)) + + val secondaryJars = + if (args != null) { + getSecondaryJarUris(Option(sparkConf.get(JARS_TO_DISTRIBUTE))) + } else { + getSecondaryJarUris(sparkConf.get(SECONDARY_JARS)) + } + secondaryJars.foreach { x => + addFileToClasspath(sparkConf, conf, x, null, env) + } + } + + // Add the Spark jars to the classpath, depending on how they were distributed. + addClasspathEntry(buildPath(YarnSparkHadoopUtil.expandEnvironment(Environment.PWD), + LOCALIZED_LIB_DIR, "*"), env) + if (!sparkConf.get(SPARK_ARCHIVE).isDefined) { + sparkConf.get(SPARK_JARS).foreach { jars => + jars.filter(isLocalUri).foreach { jar => + addClasspathEntry(getClusterPath(sparkConf, jar), env) + } + } + } + + populateHadoopClasspath(conf, env) + sys.env.get(ENV_DIST_CLASSPATH).foreach { cp => + addClasspathEntry(getClusterPath(sparkConf, cp), env) + } + } + + /** + * Returns a list of URIs representing the user classpath. + * + * @param conf Spark configuration. + */ + def getUserClasspath(conf: SparkConf): Array[URI] = { + val mainUri = getMainJarUri(conf.get(APP_JAR)) + val secondaryUris = getSecondaryJarUris(conf.get(SECONDARY_JARS)) + (mainUri ++ secondaryUris).toArray + } + + private def getMainJarUri(mainJar: Option[String]): Option[URI] = { + mainJar.flatMap { path => + val uri = Utils.resolveURI(path) + if (uri.getScheme == LOCAL_SCHEME) Some(uri) else None + }.orElse(Some(new URI(APP_JAR_NAME))) + } + + private def getSecondaryJarUris(secondaryJars: Option[Seq[String]]): Seq[URI] = { + secondaryJars.getOrElse(Nil).map(new URI(_)) + } + + /** + * Adds the given path to the classpath, handling "local:" URIs correctly. + * + * If an alternate name for the file is given, and it's not a "local:" file, the alternate + * name will be added to the classpath (relative to the job's work directory). + * + * If not a "local:" file and no alternate name, the linkName will be added to the classpath. + * + * @param conf Spark configuration. + * @param hadoopConf Hadoop configuration. + * @param uri URI to add to classpath (optional). + * @param fileName Alternate name for the file (optional). + * @param env Map holding the environment variables. + */ + private def addFileToClasspath( + conf: SparkConf, + hadoopConf: Configuration, + uri: URI, + fileName: String, + env: HashMap[String, String]): Unit = { + if (uri != null && uri.getScheme == LOCAL_SCHEME) { + addClasspathEntry(getClusterPath(conf, uri.getPath), env) + } else if (fileName != null) { + addClasspathEntry(buildPath( + YarnSparkHadoopUtil.expandEnvironment(Environment.PWD), fileName), env) + } else if (uri != null) { + val localPath = getQualifiedLocalPath(uri, hadoopConf) + val linkName = Option(uri.getFragment()).getOrElse(localPath.getName()) + addClasspathEntry(buildPath( + YarnSparkHadoopUtil.expandEnvironment(Environment.PWD), linkName), env) + } + } + + /** + * Add the given path to the classpath entry of the given environment map. + * If the classpath is already set, this appends the new path to the existing classpath. + */ + private def addClasspathEntry(path: String, env: HashMap[String, String]): Unit = + YarnSparkHadoopUtil.addPathToEnvironment(env, Environment.CLASSPATH.name, path) + + /** + * Returns the path to be sent to the NM for a path that is valid on the gateway. + * + * This method uses two configuration values: + * + * - spark.yarn.config.gatewayPath: a string that identifies a portion of the input path that may + * only be valid in the gateway node. + * - spark.yarn.config.replacementPath: a string with which to replace the gateway path. This may + * contain, for example, env variable references, which will be expanded by the NMs when + * starting containers. + * + * If either config is not available, the input path is returned. + */ + def getClusterPath(conf: SparkConf, path: String): String = { + val localPath = conf.get(GATEWAY_ROOT_PATH) + val clusterPath = conf.get(REPLACEMENT_ROOT_PATH) + if (localPath != null && clusterPath != null) { + path.replace(localPath, clusterPath) + } else { + path + } + } + + /** + * Return whether the two file systems are the same. + */ + private def compareFs(srcFs: FileSystem, destFs: FileSystem): Boolean = { + val srcUri = srcFs.getUri() + val dstUri = destFs.getUri() + if (srcUri.getScheme() == null || srcUri.getScheme() != dstUri.getScheme()) { + return false + } + + var srcHost = srcUri.getHost() + var dstHost = dstUri.getHost() + + // In HA or when using viewfs, the host part of the URI may not actually be a host, but the + // name of the HDFS namespace. Those names won't resolve, so avoid even trying if they + // match. + if (srcHost != null && dstHost != null && srcHost != dstHost) { + try { + srcHost = InetAddress.getByName(srcHost).getCanonicalHostName() + dstHost = InetAddress.getByName(dstHost).getCanonicalHostName() + } catch { + case e: UnknownHostException => + return false + } + } + + Objects.equal(srcHost, dstHost) && srcUri.getPort() == dstUri.getPort() + } + + /** + * Given a local URI, resolve it and return a qualified local path that corresponds to the URI. + * This is used for preparing local resources to be included in the container launch context. + */ + private def getQualifiedLocalPath(localURI: URI, hadoopConf: Configuration): Path = { + val qualifiedURI = + if (localURI.getScheme == null) { + // If not specified, assume this is in the local filesystem to keep the behavior + // consistent with that of Hadoop + new URI(FileSystem.getLocal(hadoopConf).makeQualified(new Path(localURI)).toString) + } else { + localURI + } + new Path(qualifiedURI) + } + + /** + * Whether to consider jars provided by the user to have precedence over the Spark jars when + * loading user classes. + */ + def isUserClassPathFirst(conf: SparkConf, isDriver: Boolean): Boolean = { + if (isDriver) { + conf.get(DRIVER_USER_CLASS_PATH_FIRST) + } else { + conf.get(EXECUTOR_USER_CLASS_PATH_FIRST) + } + } + + /** + * Joins all the path components using Path.SEPARATOR. + */ + def buildPath(components: String*): String = { + components.mkString(Path.SEPARATOR) + } + + /** Returns whether the URI is a "local:" URI. */ + def isLocalUri(uri: String): Boolean = { + uri.startsWith(s"$LOCAL_SCHEME:") + } + +} diff --git a/resource-managers/yarn/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala b/resource-managers/yarn/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala new file mode 100644 index 0000000000..61c027ec44 --- /dev/null +++ b/resource-managers/yarn/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala @@ -0,0 +1,86 @@ +/* + * 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.deploy.yarn + +import scala.collection.mutable.ArrayBuffer + +// TODO: Add code and support for ensuring that yarn resource 'tasks' are location aware ! +private[spark] class ClientArguments(args: Array[String]) { + + var userJar: String = null + var userClass: String = null + var primaryPyFile: String = null + var primaryRFile: String = null + var userArgs: ArrayBuffer[String] = new ArrayBuffer[String]() + + parseArgs(args.toList) + + private def parseArgs(inputArgs: List[String]): Unit = { + var args = inputArgs + + while (!args.isEmpty) { + args match { + case ("--jar") :: value :: tail => + userJar = value + args = tail + + case ("--class") :: value :: tail => + userClass = value + args = tail + + case ("--primary-py-file") :: value :: tail => + primaryPyFile = value + args = tail + + case ("--primary-r-file") :: value :: tail => + primaryRFile = value + args = tail + + case ("--arg") :: value :: tail => + userArgs += value + args = tail + + case Nil => + + case _ => + throw new IllegalArgumentException(getUsageMessage(args)) + } + } + + if (primaryPyFile != null && primaryRFile != null) { + throw new IllegalArgumentException("Cannot have primary-py-file and primary-r-file" + + " at the same time") + } + } + + private def getUsageMessage(unknownParam: List[String] = null): String = { + val message = if (unknownParam != null) s"Unknown/unsupported param $unknownParam\n" else "" + message + + s""" + |Usage: org.apache.spark.deploy.yarn.Client [options] + |Options: + | --jar JAR_PATH Path to your application's JAR file (required in yarn-cluster + | mode) + | --class CLASS_NAME Name of your application's main class (required) + | --primary-py-file A main Python file + | --primary-r-file A main R file + | --arg ARG Argument to be passed to your application's main class. + | Multiple invocations are possible, each will be passed in order. + """.stripMargin + } +} diff --git a/resource-managers/yarn/src/main/scala/org/apache/spark/deploy/yarn/ClientDistributedCacheManager.scala b/resource-managers/yarn/src/main/scala/org/apache/spark/deploy/yarn/ClientDistributedCacheManager.scala new file mode 100644 index 0000000000..dcc2288dd1 --- /dev/null +++ b/resource-managers/yarn/src/main/scala/org/apache/spark/deploy/yarn/ClientDistributedCacheManager.scala @@ -0,0 +1,186 @@ +/* + * 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.deploy.yarn + +import java.net.URI + +import scala.collection.mutable.{HashMap, ListBuffer, Map} + +import org.apache.hadoop.conf.Configuration +import org.apache.hadoop.fs.{FileStatus, FileSystem, Path} +import org.apache.hadoop.fs.permission.FsAction +import org.apache.hadoop.yarn.api.records._ +import org.apache.hadoop.yarn.util.{ConverterUtils, Records} + +import org.apache.spark.SparkConf +import org.apache.spark.deploy.yarn.config._ +import org.apache.spark.internal.Logging + +private case class CacheEntry( + uri: URI, + size: Long, + modTime: Long, + visibility: LocalResourceVisibility, + resType: LocalResourceType) + +/** Client side methods to setup the Hadoop distributed cache */ +private[spark] class ClientDistributedCacheManager() extends Logging { + + private val distCacheEntries = new ListBuffer[CacheEntry]() + + /** + * Add a resource to the list of distributed cache resources. This list can + * be sent to the ApplicationMaster and possibly the executors so that it can + * be downloaded into the Hadoop distributed cache for use by this application. + * Adds the LocalResource to the localResources HashMap passed in and saves + * the stats of the resources to they can be sent to the executors and verified. + * + * @param fs FileSystem + * @param conf Configuration + * @param destPath path to the resource + * @param localResources localResource hashMap to insert the resource into + * @param resourceType LocalResourceType + * @param link link presented in the distributed cache to the destination + * @param statCache cache to store the file/directory stats + * @param appMasterOnly Whether to only add the resource to the app master + */ + def addResource( + fs: FileSystem, + conf: Configuration, + destPath: Path, + localResources: HashMap[String, LocalResource], + resourceType: LocalResourceType, + link: String, + statCache: Map[URI, FileStatus], + appMasterOnly: Boolean = false): Unit = { + val destStatus = fs.getFileStatus(destPath) + val amJarRsrc = Records.newRecord(classOf[LocalResource]) + amJarRsrc.setType(resourceType) + val visibility = getVisibility(conf, destPath.toUri(), statCache) + amJarRsrc.setVisibility(visibility) + amJarRsrc.setResource(ConverterUtils.getYarnUrlFromPath(destPath)) + amJarRsrc.setTimestamp(destStatus.getModificationTime()) + amJarRsrc.setSize(destStatus.getLen()) + require(link != null && link.nonEmpty, "You must specify a valid link name.") + localResources(link) = amJarRsrc + + if (!appMasterOnly) { + val uri = destPath.toUri() + val pathURI = new URI(uri.getScheme(), uri.getAuthority(), uri.getPath(), null, link) + distCacheEntries += CacheEntry(pathURI, destStatus.getLen(), destStatus.getModificationTime(), + visibility, resourceType) + } + } + + /** + * Writes down information about cached files needed in executors to the given configuration. + */ + def updateConfiguration(conf: SparkConf): Unit = { + conf.set(CACHED_FILES, distCacheEntries.map(_.uri.toString)) + conf.set(CACHED_FILES_SIZES, distCacheEntries.map(_.size)) + conf.set(CACHED_FILES_TIMESTAMPS, distCacheEntries.map(_.modTime)) + conf.set(CACHED_FILES_VISIBILITIES, distCacheEntries.map(_.visibility.name())) + conf.set(CACHED_FILES_TYPES, distCacheEntries.map(_.resType.name())) + } + + /** + * Returns the local resource visibility depending on the cache file permissions + * @return LocalResourceVisibility + */ + private[yarn] def getVisibility( + conf: Configuration, + uri: URI, + statCache: Map[URI, FileStatus]): LocalResourceVisibility = { + if (isPublic(conf, uri, statCache)) { + LocalResourceVisibility.PUBLIC + } else { + LocalResourceVisibility.PRIVATE + } + } + + /** + * Returns a boolean to denote whether a cache file is visible to all (public) + * @return true if the path in the uri is visible to all, false otherwise + */ + private def isPublic(conf: Configuration, uri: URI, statCache: Map[URI, FileStatus]): Boolean = { + val fs = FileSystem.get(uri, conf) + val current = new Path(uri.getPath()) + // the leaf level file should be readable by others + if (!checkPermissionOfOther(fs, current, FsAction.READ, statCache)) { + return false + } + ancestorsHaveExecutePermissions(fs, current.getParent(), statCache) + } + + /** + * Returns true if all ancestors of the specified path have the 'execute' + * permission set for all users (i.e. that other users can traverse + * the directory hierarchy to the given path) + * @return true if all ancestors have the 'execute' permission set for all users + */ + private def ancestorsHaveExecutePermissions( + fs: FileSystem, + path: Path, + statCache: Map[URI, FileStatus]): Boolean = { + var current = path + while (current != null) { + // the subdirs in the path should have execute permissions for others + if (!checkPermissionOfOther(fs, current, FsAction.EXECUTE, statCache)) { + return false + } + current = current.getParent() + } + true + } + + /** + * Checks for a given path whether the Other permissions on it + * imply the permission in the passed FsAction + * @return true if the path in the uri is visible to all, false otherwise + */ + private def checkPermissionOfOther( + fs: FileSystem, + path: Path, + action: FsAction, + statCache: Map[URI, FileStatus]): Boolean = { + val status = getFileStatus(fs, path.toUri(), statCache) + val perms = status.getPermission() + val otherAction = perms.getOtherAction() + otherAction.implies(action) + } + + /** + * Checks to see if the given uri exists in the cache, if it does it + * returns the existing FileStatus, otherwise it stats the uri, stores + * it in the cache, and returns the FileStatus. + * @return FileStatus + */ + private[yarn] def getFileStatus( + fs: FileSystem, + uri: URI, + statCache: Map[URI, FileStatus]): FileStatus = { + val stat = statCache.get(uri) match { + case Some(existstat) => existstat + case None => + val newStat = fs.getFileStatus(new Path(uri)) + statCache.put(uri, newStat) + newStat + } + stat + } +} diff --git a/resource-managers/yarn/src/main/scala/org/apache/spark/deploy/yarn/ExecutorRunnable.scala b/resource-managers/yarn/src/main/scala/org/apache/spark/deploy/yarn/ExecutorRunnable.scala new file mode 100644 index 0000000000..868c2edc5a --- /dev/null +++ b/resource-managers/yarn/src/main/scala/org/apache/spark/deploy/yarn/ExecutorRunnable.scala @@ -0,0 +1,266 @@ +/* + * 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.deploy.yarn + +import java.io.File +import java.nio.ByteBuffer +import java.util.Collections + +import scala.collection.JavaConverters._ +import scala.collection.mutable.{HashMap, ListBuffer} + +import org.apache.hadoop.fs.Path +import org.apache.hadoop.io.DataOutputBuffer +import org.apache.hadoop.security.UserGroupInformation +import org.apache.hadoop.yarn.api._ +import org.apache.hadoop.yarn.api.ApplicationConstants.Environment +import org.apache.hadoop.yarn.api.records._ +import org.apache.hadoop.yarn.client.api.NMClient +import org.apache.hadoop.yarn.conf.YarnConfiguration +import org.apache.hadoop.yarn.ipc.YarnRPC +import org.apache.hadoop.yarn.util.{ConverterUtils, Records} + +import org.apache.spark.{SecurityManager, SparkConf, SparkException} +import org.apache.spark.internal.Logging +import org.apache.spark.internal.config._ +import org.apache.spark.launcher.YarnCommandBuilderUtils +import org.apache.spark.network.util.JavaUtils +import org.apache.spark.util.Utils + +private[yarn] class ExecutorRunnable( + container: Option[Container], + conf: YarnConfiguration, + sparkConf: SparkConf, + masterAddress: String, + executorId: String, + hostname: String, + executorMemory: Int, + executorCores: Int, + appId: String, + securityMgr: SecurityManager, + localResources: Map[String, LocalResource]) extends Logging { + + var rpc: YarnRPC = YarnRPC.create(conf) + var nmClient: NMClient = _ + + def run(): Unit = { + logDebug("Starting Executor Container") + nmClient = NMClient.createNMClient() + nmClient.init(conf) + nmClient.start() + startContainer() + } + + def launchContextDebugInfo(): String = { + val commands = prepareCommand() + val env = prepareEnvironment() + + s""" + |=============================================================================== + |YARN executor launch context: + | env: + |${Utils.redact(sparkConf, env.toSeq).map { case (k, v) => s" $k -> $v\n" }.mkString} + | command: + | ${commands.mkString(" \\ \n ")} + | + | resources: + |${localResources.map { case (k, v) => s" $k -> $v\n" }.mkString} + |===============================================================================""".stripMargin + } + + def startContainer(): java.util.Map[String, ByteBuffer] = { + val ctx = Records.newRecord(classOf[ContainerLaunchContext]) + .asInstanceOf[ContainerLaunchContext] + val env = prepareEnvironment().asJava + + ctx.setLocalResources(localResources.asJava) + ctx.setEnvironment(env) + + val credentials = UserGroupInformation.getCurrentUser().getCredentials() + val dob = new DataOutputBuffer() + credentials.writeTokenStorageToStream(dob) + ctx.setTokens(ByteBuffer.wrap(dob.getData())) + + val commands = prepareCommand() + + ctx.setCommands(commands.asJava) + ctx.setApplicationACLs( + YarnSparkHadoopUtil.getApplicationAclsForYarn(securityMgr).asJava) + + // If external shuffle service is enabled, register with the Yarn shuffle service already + // started on the NodeManager and, if authentication is enabled, provide it with our secret + // key for fetching shuffle files later + if (sparkConf.get(SHUFFLE_SERVICE_ENABLED)) { + val secretString = securityMgr.getSecretKey() + val secretBytes = + if (secretString != null) { + // This conversion must match how the YarnShuffleService decodes our secret + JavaUtils.stringToBytes(secretString) + } else { + // Authentication is not enabled, so just provide dummy metadata + ByteBuffer.allocate(0) + } + ctx.setServiceData(Collections.singletonMap("spark_shuffle", secretBytes)) + } + + // Send the start request to the ContainerManager + try { + nmClient.startContainer(container.get, ctx) + } catch { + case ex: Exception => + throw new SparkException(s"Exception while starting container ${container.get.getId}" + + s" on host $hostname", ex) + } + } + + private def prepareCommand(): List[String] = { + // Extra options for the JVM + val javaOpts = ListBuffer[String]() + + // Set the environment variable through a command prefix + // to append to the existing value of the variable + var prefixEnv: Option[String] = None + + // Set the JVM memory + val executorMemoryString = executorMemory + "m" + javaOpts += "-Xmx" + executorMemoryString + + // Set extra Java options for the executor, if defined + sparkConf.get(EXECUTOR_JAVA_OPTIONS).foreach { opts => + javaOpts ++= Utils.splitCommandString(opts).map(YarnSparkHadoopUtil.escapeForShell) + } + sys.env.get("SPARK_JAVA_OPTS").foreach { opts => + javaOpts ++= Utils.splitCommandString(opts).map(YarnSparkHadoopUtil.escapeForShell) + } + sparkConf.get(EXECUTOR_LIBRARY_PATH).foreach { p => + prefixEnv = Some(Client.getClusterPath(sparkConf, Utils.libraryPathEnvPrefix(Seq(p)))) + } + + javaOpts += "-Djava.io.tmpdir=" + + new Path( + YarnSparkHadoopUtil.expandEnvironment(Environment.PWD), + YarnConfiguration.DEFAULT_CONTAINER_TEMP_DIR + ) + + // Certain configs need to be passed here because they are needed before the Executor + // registers with the Scheduler and transfers the spark configs. Since the Executor backend + // uses RPC to connect to the scheduler, the RPC settings are needed as well as the + // authentication settings. + sparkConf.getAll + .filter { case (k, v) => SparkConf.isExecutorStartupConf(k) } + .foreach { case (k, v) => javaOpts += YarnSparkHadoopUtil.escapeForShell(s"-D$k=$v") } + + // Commenting it out for now - so that people can refer to the properties if required. Remove + // it once cpuset version is pushed out. + // The context is, default gc for server class machines end up using all cores to do gc - hence + // if there are multiple containers in same node, spark gc effects all other containers + // performance (which can also be other spark containers) + // Instead of using this, rely on cpusets by YARN to enforce spark behaves 'properly' in + // multi-tenant environments. Not sure how default java gc behaves if it is limited to subset + // of cores on a node. + /* + else { + // If no java_opts specified, default to using -XX:+CMSIncrementalMode + // It might be possible that other modes/config is being done in + // spark.executor.extraJavaOptions, so we don't want to mess with it. + // In our expts, using (default) throughput collector has severe perf ramifications in + // multi-tenant machines + // The options are based on + // http://www.oracle.com/technetwork/java/gc-tuning-5-138395.html#0.0.0.%20When%20to%20Use + // %20the%20Concurrent%20Low%20Pause%20Collector|outline + javaOpts += "-XX:+UseConcMarkSweepGC" + javaOpts += "-XX:+CMSIncrementalMode" + javaOpts += "-XX:+CMSIncrementalPacing" + javaOpts += "-XX:CMSIncrementalDutyCycleMin=0" + javaOpts += "-XX:CMSIncrementalDutyCycle=10" + } + */ + + // For log4j configuration to reference + javaOpts += ("-Dspark.yarn.app.container.log.dir=" + ApplicationConstants.LOG_DIR_EXPANSION_VAR) + YarnCommandBuilderUtils.addPermGenSizeOpt(javaOpts) + + val userClassPath = Client.getUserClasspath(sparkConf).flatMap { uri => + val absPath = + if (new File(uri.getPath()).isAbsolute()) { + Client.getClusterPath(sparkConf, uri.getPath()) + } else { + Client.buildPath(Environment.PWD.$(), uri.getPath()) + } + Seq("--user-class-path", "file:" + absPath) + }.toSeq + + YarnSparkHadoopUtil.addOutOfMemoryErrorArgument(javaOpts) + val commands = prefixEnv ++ Seq( + YarnSparkHadoopUtil.expandEnvironment(Environment.JAVA_HOME) + "/bin/java", + "-server") ++ + javaOpts ++ + Seq("org.apache.spark.executor.CoarseGrainedExecutorBackend", + "--driver-url", masterAddress, + "--executor-id", executorId, + "--hostname", hostname, + "--cores", executorCores.toString, + "--app-id", appId) ++ + userClassPath ++ + Seq( + s"1>${ApplicationConstants.LOG_DIR_EXPANSION_VAR}/stdout", + s"2>${ApplicationConstants.LOG_DIR_EXPANSION_VAR}/stderr") + + // TODO: it would be nicer to just make sure there are no null commands here + commands.map(s => if (s == null) "null" else s).toList + } + + private def prepareEnvironment(): HashMap[String, String] = { + val env = new HashMap[String, String]() + Client.populateClasspath(null, conf, sparkConf, env, sparkConf.get(EXECUTOR_CLASS_PATH)) + + sparkConf.getExecutorEnv.foreach { case (key, value) => + // This assumes each executor environment variable set here is a path + // This is kept for backward compatibility and consistency with hadoop + YarnSparkHadoopUtil.addPathToEnvironment(env, key, value) + } + + // Keep this for backwards compatibility but users should move to the config + sys.env.get("SPARK_YARN_USER_ENV").foreach { userEnvs => + YarnSparkHadoopUtil.setEnvFromInputString(env, userEnvs) + } + + // lookup appropriate http scheme for container log urls + val yarnHttpPolicy = conf.get( + YarnConfiguration.YARN_HTTP_POLICY_KEY, + YarnConfiguration.YARN_HTTP_POLICY_DEFAULT + ) + val httpScheme = if (yarnHttpPolicy == "HTTPS_ONLY") "https://" else "http://" + + // Add log urls + container.foreach { c => + sys.env.get("SPARK_USER").foreach { user => + val containerId = ConverterUtils.toString(c.getId) + val address = c.getNodeHttpAddress + val baseUrl = s"$httpScheme$address/node/containerlogs/$containerId/$user" + + env("SPARK_LOG_URL_STDERR") = s"$baseUrl/stderr?start=-4096" + env("SPARK_LOG_URL_STDOUT") = s"$baseUrl/stdout?start=-4096" + } + } + + System.getenv().asScala.filterKeys(_.startsWith("SPARK")) + .foreach { case (k, v) => env(k) = v } + env + } +} diff --git a/resource-managers/yarn/src/main/scala/org/apache/spark/deploy/yarn/LocalityPreferredContainerPlacementStrategy.scala b/resource-managers/yarn/src/main/scala/org/apache/spark/deploy/yarn/LocalityPreferredContainerPlacementStrategy.scala new file mode 100644 index 0000000000..8772e26f43 --- /dev/null +++ b/resource-managers/yarn/src/main/scala/org/apache/spark/deploy/yarn/LocalityPreferredContainerPlacementStrategy.scala @@ -0,0 +1,224 @@ +/* + * 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.deploy.yarn + +import scala.collection.mutable.{ArrayBuffer, HashMap, Set} +import scala.collection.JavaConverters._ + +import org.apache.hadoop.conf.Configuration +import org.apache.hadoop.yarn.api.records.{ContainerId, Resource} +import org.apache.hadoop.yarn.client.api.AMRMClient.ContainerRequest +import org.apache.hadoop.yarn.util.RackResolver + +import org.apache.spark.SparkConf +import org.apache.spark.internal.config._ + +private[yarn] case class ContainerLocalityPreferences(nodes: Array[String], racks: Array[String]) + +/** + * This strategy is calculating the optimal locality preferences of YARN containers by considering + * the node ratio of pending tasks, number of required cores/containers and and locality of current + * existing and pending allocated containers. The target of this algorithm is to maximize the number + * of tasks that would run locally. + * + * Consider a situation in which we have 20 tasks that require (host1, host2, host3) + * and 10 tasks that require (host1, host2, host4), besides each container has 2 cores + * and cpus per task is 1, so the required container number is 15, + * and host ratio is (host1: 30, host2: 30, host3: 20, host4: 10). + * + * 1. If requested container number (18) is more than the required container number (15): + * + * requests for 5 containers with nodes: (host1, host2, host3, host4) + * requests for 5 containers with nodes: (host1, host2, host3) + * requests for 5 containers with nodes: (host1, host2) + * requests for 3 containers with no locality preferences. + * + * The placement ratio is 3 : 3 : 2 : 1, and set the additional containers with no locality + * preferences. + * + * 2. If requested container number (10) is less than or equal to the required container number + * (15): + * + * requests for 4 containers with nodes: (host1, host2, host3, host4) + * requests for 3 containers with nodes: (host1, host2, host3) + * requests for 3 containers with nodes: (host1, host2) + * + * The placement ratio is 10 : 10 : 7 : 4, close to expected ratio (3 : 3 : 2 : 1) + * + * 3. If containers exist but none of them can match the requested localities, + * follow the method of 1 and 2. + * + * 4. If containers exist and some of them can match the requested localities. + * For example if we have 1 containers on each node (host1: 1, host2: 1: host3: 1, host4: 1), + * and the expected containers on each node would be (host1: 5, host2: 5, host3: 4, host4: 2), + * so the newly requested containers on each node would be updated to (host1: 4, host2: 4, + * host3: 3, host4: 1), 12 containers by total. + * + * 4.1 If requested container number (18) is more than newly required containers (12). Follow + * method 1 with updated ratio 4 : 4 : 3 : 1. + * + * 4.2 If request container number (10) is more than newly required containers (12). Follow + * method 2 with updated ratio 4 : 4 : 3 : 1. + * + * 5. If containers exist and existing localities can fully cover the requested localities. + * For example if we have 5 containers on each node (host1: 5, host2: 5, host3: 5, host4: 5), + * which could cover the current requested localities. This algorithm will allocate all the + * requested containers with no localities. + */ +private[yarn] class LocalityPreferredContainerPlacementStrategy( + val sparkConf: SparkConf, + val yarnConf: Configuration, + val resource: Resource) { + + /** + * Calculate each container's node locality and rack locality + * @param numContainer number of containers to calculate + * @param numLocalityAwareTasks number of locality required tasks + * @param hostToLocalTaskCount a map to store the preferred hostname and possible task + * numbers running on it, used as hints for container allocation + * @param allocatedHostToContainersMap host to allocated containers map, used to calculate the + * expected locality preference by considering the existing + * containers + * @param localityMatchedPendingAllocations A sequence of pending container request which + * matches the localities of current required tasks. + * @return node localities and rack localities, each locality is an array of string, + * the length of localities is the same as number of containers + */ + def localityOfRequestedContainers( + numContainer: Int, + numLocalityAwareTasks: Int, + hostToLocalTaskCount: Map[String, Int], + allocatedHostToContainersMap: HashMap[String, Set[ContainerId]], + localityMatchedPendingAllocations: Seq[ContainerRequest] + ): Array[ContainerLocalityPreferences] = { + val updatedHostToContainerCount = expectedHostToContainerCount( + numLocalityAwareTasks, hostToLocalTaskCount, allocatedHostToContainersMap, + localityMatchedPendingAllocations) + val updatedLocalityAwareContainerNum = updatedHostToContainerCount.values.sum + + // The number of containers to allocate, divided into two groups, one with preferred locality, + // and the other without locality preference. + val requiredLocalityFreeContainerNum = + math.max(0, numContainer - updatedLocalityAwareContainerNum) + val requiredLocalityAwareContainerNum = numContainer - requiredLocalityFreeContainerNum + + val containerLocalityPreferences = ArrayBuffer[ContainerLocalityPreferences]() + if (requiredLocalityFreeContainerNum > 0) { + for (i <- 0 until requiredLocalityFreeContainerNum) { + containerLocalityPreferences += ContainerLocalityPreferences( + null.asInstanceOf[Array[String]], null.asInstanceOf[Array[String]]) + } + } + + if (requiredLocalityAwareContainerNum > 0) { + val largestRatio = updatedHostToContainerCount.values.max + // Round the ratio of preferred locality to the number of locality required container + // number, which is used for locality preferred host calculating. + var preferredLocalityRatio = updatedHostToContainerCount.mapValues { ratio => + val adjustedRatio = ratio.toDouble * requiredLocalityAwareContainerNum / largestRatio + adjustedRatio.ceil.toInt + } + + for (i <- 0 until requiredLocalityAwareContainerNum) { + // Only filter out the ratio which is larger than 0, which means the current host can + // still be allocated with new container request. + val hosts = preferredLocalityRatio.filter(_._2 > 0).keys.toArray + val racks = hosts.map { h => + RackResolver.resolve(yarnConf, h).getNetworkLocation + }.toSet + containerLocalityPreferences += ContainerLocalityPreferences(hosts, racks.toArray) + + // Minus 1 each time when the host is used. When the current ratio is 0, + // which means all the required ratio is satisfied, this host will not be allocated again. + preferredLocalityRatio = preferredLocalityRatio.mapValues(_ - 1) + } + } + + containerLocalityPreferences.toArray + } + + /** + * Calculate the number of executors need to satisfy the given number of pending tasks. + */ + private def numExecutorsPending(numTasksPending: Int): Int = { + val coresPerExecutor = resource.getVirtualCores + (numTasksPending * sparkConf.get(CPUS_PER_TASK) + coresPerExecutor - 1) / coresPerExecutor + } + + /** + * Calculate the expected host to number of containers by considering with allocated containers. + * @param localityAwareTasks number of locality aware tasks + * @param hostToLocalTaskCount a map to store the preferred hostname and possible task + * numbers running on it, used as hints for container allocation + * @param allocatedHostToContainersMap host to allocated containers map, used to calculate the + * expected locality preference by considering the existing + * containers + * @param localityMatchedPendingAllocations A sequence of pending container request which + * matches the localities of current required tasks. + * @return a map with hostname as key and required number of containers on this host as value + */ + private def expectedHostToContainerCount( + localityAwareTasks: Int, + hostToLocalTaskCount: Map[String, Int], + allocatedHostToContainersMap: HashMap[String, Set[ContainerId]], + localityMatchedPendingAllocations: Seq[ContainerRequest] + ): Map[String, Int] = { + val totalLocalTaskNum = hostToLocalTaskCount.values.sum + val pendingHostToContainersMap = pendingHostToContainerCount(localityMatchedPendingAllocations) + + hostToLocalTaskCount.map { case (host, count) => + val expectedCount = + count.toDouble * numExecutorsPending(localityAwareTasks) / totalLocalTaskNum + // Take the locality of pending containers into consideration + val existedCount = allocatedHostToContainersMap.get(host).map(_.size).getOrElse(0) + + pendingHostToContainersMap.getOrElse(host, 0.0) + + // If existing container can not fully satisfy the expected number of container, + // the required container number is expected count minus existed count. Otherwise the + // required container number is 0. + (host, math.max(0, (expectedCount - existedCount).ceil.toInt)) + } + } + + /** + * According to the locality ratio and number of container requests, calculate the host to + * possible number of containers for pending allocated containers. + * + * If current locality ratio of hosts is: Host1 : Host2 : Host3 = 20 : 20 : 10, + * and pending container requests is 3, so the possible number of containers on + * Host1 : Host2 : Host3 will be 1.2 : 1.2 : 0.6. + * @param localityMatchedPendingAllocations A sequence of pending container request which + * matches the localities of current required tasks. + * @return a Map with hostname as key and possible number of containers on this host as value + */ + private def pendingHostToContainerCount( + localityMatchedPendingAllocations: Seq[ContainerRequest]): Map[String, Double] = { + val pendingHostToContainerCount = new HashMap[String, Int]() + localityMatchedPendingAllocations.foreach { cr => + cr.getNodes.asScala.foreach { n => + val count = pendingHostToContainerCount.getOrElse(n, 0) + 1 + pendingHostToContainerCount(n) = count + } + } + + val possibleTotalContainerNum = pendingHostToContainerCount.values.sum + val localityMatchedPendingNum = localityMatchedPendingAllocations.size.toDouble + pendingHostToContainerCount.mapValues(_ * localityMatchedPendingNum / possibleTotalContainerNum) + .toMap + } +} diff --git a/resource-managers/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocator.scala b/resource-managers/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocator.scala new file mode 100644 index 0000000000..0b66d1cf08 --- /dev/null +++ b/resource-managers/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocator.scala @@ -0,0 +1,727 @@ +/* + * 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.deploy.yarn + +import java.util.Collections +import java.util.concurrent._ +import java.util.regex.Pattern + +import scala.collection.mutable +import scala.collection.mutable.{ArrayBuffer, HashMap, HashSet, Queue} +import scala.collection.JavaConverters._ +import scala.util.control.NonFatal + +import org.apache.hadoop.yarn.api.records._ +import org.apache.hadoop.yarn.client.api.AMRMClient +import org.apache.hadoop.yarn.client.api.AMRMClient.ContainerRequest +import org.apache.hadoop.yarn.conf.YarnConfiguration +import org.apache.hadoop.yarn.util.RackResolver +import org.apache.log4j.{Level, Logger} + +import org.apache.spark.{SecurityManager, SparkConf, SparkException} +import org.apache.spark.deploy.yarn.YarnSparkHadoopUtil._ +import org.apache.spark.deploy.yarn.config._ +import org.apache.spark.internal.Logging +import org.apache.spark.internal.config._ +import org.apache.spark.rpc.{RpcCallContext, RpcEndpointRef} +import org.apache.spark.scheduler.{ExecutorExited, ExecutorLossReason} +import org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages.RemoveExecutor +import org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages.RetrieveLastAllocatedExecutorId +import org.apache.spark.util.{Clock, SystemClock, ThreadUtils} + +/** + * YarnAllocator is charged with requesting containers from the YARN ResourceManager and deciding + * what to do with containers when YARN fulfills these requests. + * + * This class makes use of YARN's AMRMClient APIs. We interact with the AMRMClient in three ways: + * * Making our resource needs known, which updates local bookkeeping about containers requested. + * * Calling "allocate", which syncs our local container requests with the RM, and returns any + * containers that YARN has granted to us. This also functions as a heartbeat. + * * Processing the containers granted to us to possibly launch executors inside of them. + * + * The public methods of this class are thread-safe. All methods that mutate state are + * synchronized. + */ +private[yarn] class YarnAllocator( + driverUrl: String, + driverRef: RpcEndpointRef, + conf: YarnConfiguration, + sparkConf: SparkConf, + amClient: AMRMClient[ContainerRequest], + appAttemptId: ApplicationAttemptId, + securityMgr: SecurityManager, + localResources: Map[String, LocalResource]) + extends Logging { + + import YarnAllocator._ + + // RackResolver logs an INFO message whenever it resolves a rack, which is way too often. + if (Logger.getLogger(classOf[RackResolver]).getLevel == null) { + Logger.getLogger(classOf[RackResolver]).setLevel(Level.WARN) + } + + // Visible for testing. + val allocatedHostToContainersMap = new HashMap[String, collection.mutable.Set[ContainerId]] + val allocatedContainerToHostMap = new HashMap[ContainerId, String] + + // Containers that we no longer care about. We've either already told the RM to release them or + // will on the next heartbeat. Containers get removed from this map after the RM tells us they've + // completed. + private val releasedContainers = Collections.newSetFromMap[ContainerId]( + new ConcurrentHashMap[ContainerId, java.lang.Boolean]) + + @volatile private var numExecutorsRunning = 0 + + /** + * Used to generate a unique ID per executor + * + * Init `executorIdCounter`. when AM restart, `executorIdCounter` will reset to 0. Then + * the id of new executor will start from 1, this will conflict with the executor has + * already created before. So, we should initialize the `executorIdCounter` by getting + * the max executorId from driver. + * + * And this situation of executorId conflict is just in yarn client mode, so this is an issue + * in yarn client mode. For more details, can check in jira. + * + * @see SPARK-12864 + */ + private var executorIdCounter: Int = + driverRef.askWithRetry[Int](RetrieveLastAllocatedExecutorId) + + // Queue to store the timestamp of failed executors + private val failedExecutorsTimeStamps = new Queue[Long]() + + private var clock: Clock = new SystemClock + + private val executorFailuresValidityInterval = + sparkConf.get(EXECUTOR_ATTEMPT_FAILURE_VALIDITY_INTERVAL_MS).getOrElse(-1L) + + @volatile private var targetNumExecutors = + YarnSparkHadoopUtil.getInitialTargetExecutorNumber(sparkConf) + + // Executor loss reason requests that are pending - maps from executor ID for inquiry to a + // list of requesters that should be responded to once we find out why the given executor + // was lost. + private val pendingLossReasonRequests = new HashMap[String, mutable.Buffer[RpcCallContext]] + + // Maintain loss reasons for already released executors, it will be added when executor loss + // reason is got from AM-RM call, and be removed after querying this loss reason. + private val releasedExecutorLossReasons = new HashMap[String, ExecutorLossReason] + + // Keep track of which container is running which executor to remove the executors later + // Visible for testing. + private[yarn] val executorIdToContainer = new HashMap[String, Container] + + private var numUnexpectedContainerRelease = 0L + private val containerIdToExecutorId = new HashMap[ContainerId, String] + + // Executor memory in MB. + protected val executorMemory = sparkConf.get(EXECUTOR_MEMORY).toInt + // Additional memory overhead. + protected val memoryOverhead: Int = sparkConf.get(EXECUTOR_MEMORY_OVERHEAD).getOrElse( + math.max((MEMORY_OVERHEAD_FACTOR * executorMemory).toInt, MEMORY_OVERHEAD_MIN)).toInt + // Number of cores per executor. + protected val executorCores = sparkConf.get(EXECUTOR_CORES) + // Resource capability requested for each executors + private[yarn] val resource = Resource.newInstance(executorMemory + memoryOverhead, executorCores) + + private val launcherPool = ThreadUtils.newDaemonCachedThreadPool( + "ContainerLauncher", sparkConf.get(CONTAINER_LAUNCH_MAX_THREADS)) + + // For testing + private val launchContainers = sparkConf.getBoolean("spark.yarn.launchContainers", true) + + private val labelExpression = sparkConf.get(EXECUTOR_NODE_LABEL_EXPRESSION) + + // ContainerRequest constructor that can take a node label expression. We grab it through + // reflection because it's only available in later versions of YARN. + private val nodeLabelConstructor = labelExpression.flatMap { expr => + try { + Some(classOf[ContainerRequest].getConstructor(classOf[Resource], + classOf[Array[String]], classOf[Array[String]], classOf[Priority], classOf[Boolean], + classOf[String])) + } catch { + case e: NoSuchMethodException => + logWarning(s"Node label expression $expr will be ignored because YARN version on" + + " classpath does not support it.") + None + } + } + + // A map to store preferred hostname and possible task numbers running on it. + private var hostToLocalTaskCounts: Map[String, Int] = Map.empty + + // Number of tasks that have locality preferences in active stages + private var numLocalityAwareTasks: Int = 0 + + // A container placement strategy based on pending tasks' locality preference + private[yarn] val containerPlacementStrategy = + new LocalityPreferredContainerPlacementStrategy(sparkConf, conf, resource) + + /** + * Use a different clock for YarnAllocator. This is mainly used for testing. + */ + def setClock(newClock: Clock): Unit = { + clock = newClock + } + + def getNumExecutorsRunning: Int = numExecutorsRunning + + def getNumExecutorsFailed: Int = synchronized { + val endTime = clock.getTimeMillis() + + while (executorFailuresValidityInterval > 0 + && failedExecutorsTimeStamps.nonEmpty + && failedExecutorsTimeStamps.head < endTime - executorFailuresValidityInterval) { + failedExecutorsTimeStamps.dequeue() + } + + failedExecutorsTimeStamps.size + } + + /** + * A sequence of pending container requests that have not yet been fulfilled. + */ + def getPendingAllocate: Seq[ContainerRequest] = getPendingAtLocation(ANY_HOST) + + /** + * A sequence of pending container requests at the given location that have not yet been + * fulfilled. + */ + private def getPendingAtLocation(location: String): Seq[ContainerRequest] = { + amClient.getMatchingRequests(RM_REQUEST_PRIORITY, location, resource).asScala + .flatMap(_.asScala) + .toSeq + } + + /** + * Request as many executors from the ResourceManager as needed to reach the desired total. If + * the requested total is smaller than the current number of running executors, no executors will + * be killed. + * @param requestedTotal total number of containers requested + * @param localityAwareTasks number of locality aware tasks to be used as container placement hint + * @param hostToLocalTaskCount a map of preferred hostname to possible task counts to be used as + * container placement hint. + * @return Whether the new requested total is different than the old value. + */ + def requestTotalExecutorsWithPreferredLocalities( + requestedTotal: Int, + localityAwareTasks: Int, + hostToLocalTaskCount: Map[String, Int]): Boolean = synchronized { + this.numLocalityAwareTasks = localityAwareTasks + this.hostToLocalTaskCounts = hostToLocalTaskCount + + if (requestedTotal != targetNumExecutors) { + logInfo(s"Driver requested a total number of $requestedTotal executor(s).") + targetNumExecutors = requestedTotal + true + } else { + false + } + } + + /** + * Request that the ResourceManager release the container running the specified executor. + */ + def killExecutor(executorId: String): Unit = synchronized { + if (executorIdToContainer.contains(executorId)) { + val container = executorIdToContainer.get(executorId).get + internalReleaseContainer(container) + numExecutorsRunning -= 1 + } else { + logWarning(s"Attempted to kill unknown executor $executorId!") + } + } + + /** + * Request resources such that, if YARN gives us all we ask for, we'll have a number of containers + * equal to maxExecutors. + * + * Deal with any containers YARN has granted to us by possibly launching executors in them. + * + * This must be synchronized because variables read in this method are mutated by other methods. + */ + def allocateResources(): Unit = synchronized { + updateResourceRequests() + + val progressIndicator = 0.1f + // Poll the ResourceManager. This doubles as a heartbeat if there are no pending container + // requests. + val allocateResponse = amClient.allocate(progressIndicator) + + val allocatedContainers = allocateResponse.getAllocatedContainers() + + if (allocatedContainers.size > 0) { + logDebug("Allocated containers: %d. Current executor count: %d. Cluster resources: %s." + .format( + allocatedContainers.size, + numExecutorsRunning, + allocateResponse.getAvailableResources)) + + handleAllocatedContainers(allocatedContainers.asScala) + } + + val completedContainers = allocateResponse.getCompletedContainersStatuses() + if (completedContainers.size > 0) { + logDebug("Completed %d containers".format(completedContainers.size)) + processCompletedContainers(completedContainers.asScala) + logDebug("Finished processing %d completed containers. Current running executor count: %d." + .format(completedContainers.size, numExecutorsRunning)) + } + } + + /** + * Update the set of container requests that we will sync with the RM based on the number of + * executors we have currently running and our target number of executors. + * + * Visible for testing. + */ + def updateResourceRequests(): Unit = { + val pendingAllocate = getPendingAllocate + val numPendingAllocate = pendingAllocate.size + val missing = targetNumExecutors - numPendingAllocate - numExecutorsRunning + + if (missing > 0) { + logInfo(s"Will request $missing executor container(s), each with " + + s"${resource.getVirtualCores} core(s) and " + + s"${resource.getMemory} MB memory (including $memoryOverhead MB of overhead)") + + // Split the pending container request into three groups: locality matched list, locality + // unmatched list and non-locality list. Take the locality matched container request into + // consideration of container placement, treat as allocated containers. + // For locality unmatched and locality free container requests, cancel these container + // requests, since required locality preference has been changed, recalculating using + // container placement strategy. + val (localRequests, staleRequests, anyHostRequests) = splitPendingAllocationsByLocality( + hostToLocalTaskCounts, pendingAllocate) + + // cancel "stale" requests for locations that are no longer needed + staleRequests.foreach { stale => + amClient.removeContainerRequest(stale) + } + val cancelledContainers = staleRequests.size + if (cancelledContainers > 0) { + logInfo(s"Canceled $cancelledContainers container request(s) (locality no longer needed)") + } + + // consider the number of new containers and cancelled stale containers available + val availableContainers = missing + cancelledContainers + + // to maximize locality, include requests with no locality preference that can be cancelled + val potentialContainers = availableContainers + anyHostRequests.size + + val containerLocalityPreferences = containerPlacementStrategy.localityOfRequestedContainers( + potentialContainers, numLocalityAwareTasks, hostToLocalTaskCounts, + allocatedHostToContainersMap, localRequests) + + val newLocalityRequests = new mutable.ArrayBuffer[ContainerRequest] + containerLocalityPreferences.foreach { + case ContainerLocalityPreferences(nodes, racks) if nodes != null => + newLocalityRequests += createContainerRequest(resource, nodes, racks) + case _ => + } + + if (availableContainers >= newLocalityRequests.size) { + // more containers are available than needed for locality, fill in requests for any host + for (i <- 0 until (availableContainers - newLocalityRequests.size)) { + newLocalityRequests += createContainerRequest(resource, null, null) + } + } else { + val numToCancel = newLocalityRequests.size - availableContainers + // cancel some requests without locality preferences to schedule more local containers + anyHostRequests.slice(0, numToCancel).foreach { nonLocal => + amClient.removeContainerRequest(nonLocal) + } + if (numToCancel > 0) { + logInfo(s"Canceled $numToCancel unlocalized container requests to resubmit with locality") + } + } + + newLocalityRequests.foreach { request => + amClient.addContainerRequest(request) + } + + if (log.isInfoEnabled()) { + val (localized, anyHost) = newLocalityRequests.partition(_.getNodes() != null) + if (anyHost.nonEmpty) { + logInfo(s"Submitted ${anyHost.size} unlocalized container requests.") + } + localized.foreach { request => + logInfo(s"Submitted container request for host ${hostStr(request)}.") + } + } + } else if (numPendingAllocate > 0 && missing < 0) { + val numToCancel = math.min(numPendingAllocate, -missing) + logInfo(s"Canceling requests for $numToCancel executor container(s) to have a new desired " + + s"total $targetNumExecutors executors.") + + val matchingRequests = amClient.getMatchingRequests(RM_REQUEST_PRIORITY, ANY_HOST, resource) + if (!matchingRequests.isEmpty) { + matchingRequests.iterator().next().asScala + .take(numToCancel).foreach(amClient.removeContainerRequest) + } else { + logWarning("Expected to find pending requests, but found none.") + } + } + } + + private def hostStr(request: ContainerRequest): String = { + Option(request.getNodes) match { + case Some(nodes) => nodes.asScala.mkString(",") + case None => "Any" + } + } + + /** + * Creates a container request, handling the reflection required to use YARN features that were + * added in recent versions. + */ + private def createContainerRequest( + resource: Resource, + nodes: Array[String], + racks: Array[String]): ContainerRequest = { + nodeLabelConstructor.map { constructor => + constructor.newInstance(resource, nodes, racks, RM_REQUEST_PRIORITY, true: java.lang.Boolean, + labelExpression.orNull) + }.getOrElse(new ContainerRequest(resource, nodes, racks, RM_REQUEST_PRIORITY)) + } + + /** + * Handle containers granted by the RM by launching executors on them. + * + * Due to the way the YARN allocation protocol works, certain healthy race conditions can result + * in YARN granting containers that we no longer need. In this case, we release them. + * + * Visible for testing. + */ + def handleAllocatedContainers(allocatedContainers: Seq[Container]): Unit = { + val containersToUse = new ArrayBuffer[Container](allocatedContainers.size) + + // Match incoming requests by host + val remainingAfterHostMatches = new ArrayBuffer[Container] + for (allocatedContainer <- allocatedContainers) { + matchContainerToRequest(allocatedContainer, allocatedContainer.getNodeId.getHost, + containersToUse, remainingAfterHostMatches) + } + + // Match remaining by rack + val remainingAfterRackMatches = new ArrayBuffer[Container] + for (allocatedContainer <- remainingAfterHostMatches) { + val rack = RackResolver.resolve(conf, allocatedContainer.getNodeId.getHost).getNetworkLocation + matchContainerToRequest(allocatedContainer, rack, containersToUse, + remainingAfterRackMatches) + } + + // Assign remaining that are neither node-local nor rack-local + val remainingAfterOffRackMatches = new ArrayBuffer[Container] + for (allocatedContainer <- remainingAfterRackMatches) { + matchContainerToRequest(allocatedContainer, ANY_HOST, containersToUse, + remainingAfterOffRackMatches) + } + + if (!remainingAfterOffRackMatches.isEmpty) { + logDebug(s"Releasing ${remainingAfterOffRackMatches.size} unneeded containers that were " + + s"allocated to us") + for (container <- remainingAfterOffRackMatches) { + internalReleaseContainer(container) + } + } + + runAllocatedContainers(containersToUse) + + logInfo("Received %d containers from YARN, launching executors on %d of them." + .format(allocatedContainers.size, containersToUse.size)) + } + + /** + * Looks for requests for the given location that match the given container allocation. If it + * finds one, removes the request so that it won't be submitted again. Places the container into + * containersToUse or remaining. + * + * @param allocatedContainer container that was given to us by YARN + * @param location resource name, either a node, rack, or * + * @param containersToUse list of containers that will be used + * @param remaining list of containers that will not be used + */ + private def matchContainerToRequest( + allocatedContainer: Container, + location: String, + containersToUse: ArrayBuffer[Container], + remaining: ArrayBuffer[Container]): Unit = { + // SPARK-6050: certain Yarn configurations return a virtual core count that doesn't match the + // request; for example, capacity scheduler + DefaultResourceCalculator. So match on requested + // memory, but use the asked vcore count for matching, effectively disabling matching on vcore + // count. + val matchingResource = Resource.newInstance(allocatedContainer.getResource.getMemory, + resource.getVirtualCores) + val matchingRequests = amClient.getMatchingRequests(allocatedContainer.getPriority, location, + matchingResource) + + // Match the allocation to a request + if (!matchingRequests.isEmpty) { + val containerRequest = matchingRequests.get(0).iterator.next + amClient.removeContainerRequest(containerRequest) + containersToUse += allocatedContainer + } else { + remaining += allocatedContainer + } + } + + /** + * Launches executors in the allocated containers. + */ + private def runAllocatedContainers(containersToUse: ArrayBuffer[Container]): Unit = { + for (container <- containersToUse) { + executorIdCounter += 1 + val executorHostname = container.getNodeId.getHost + val containerId = container.getId + val executorId = executorIdCounter.toString + assert(container.getResource.getMemory >= resource.getMemory) + logInfo(s"Launching container $containerId on host $executorHostname") + + def updateInternalState(): Unit = synchronized { + numExecutorsRunning += 1 + executorIdToContainer(executorId) = container + containerIdToExecutorId(container.getId) = executorId + + val containerSet = allocatedHostToContainersMap.getOrElseUpdate(executorHostname, + new HashSet[ContainerId]) + containerSet += containerId + allocatedContainerToHostMap.put(containerId, executorHostname) + } + + if (numExecutorsRunning < targetNumExecutors) { + if (launchContainers) { + launcherPool.execute(new Runnable { + override def run(): Unit = { + try { + new ExecutorRunnable( + Some(container), + conf, + sparkConf, + driverUrl, + executorId, + executorHostname, + executorMemory, + executorCores, + appAttemptId.getApplicationId.toString, + securityMgr, + localResources + ).run() + updateInternalState() + } catch { + case NonFatal(e) => + logError(s"Failed to launch executor $executorId on container $containerId", e) + // Assigned container should be released immediately to avoid unnecessary resource + // occupation. + amClient.releaseAssignedContainer(containerId) + } + } + }) + } else { + // For test only + updateInternalState() + } + } else { + logInfo(("Skip launching executorRunnable as runnning Excecutors count: %d " + + "reached target Executors count: %d.").format(numExecutorsRunning, targetNumExecutors)) + } + } + } + + // Visible for testing. + private[yarn] def processCompletedContainers(completedContainers: Seq[ContainerStatus]): Unit = { + for (completedContainer <- completedContainers) { + val containerId = completedContainer.getContainerId + val alreadyReleased = releasedContainers.remove(containerId) + val hostOpt = allocatedContainerToHostMap.get(containerId) + val onHostStr = hostOpt.map(host => s" on host: $host").getOrElse("") + val exitReason = if (!alreadyReleased) { + // Decrement the number of executors running. The next iteration of + // the ApplicationMaster's reporting thread will take care of allocating. + numExecutorsRunning -= 1 + logInfo("Completed container %s%s (state: %s, exit status: %s)".format( + containerId, + onHostStr, + completedContainer.getState, + completedContainer.getExitStatus)) + // Hadoop 2.2.X added a ContainerExitStatus we should switch to use + // there are some exit status' we shouldn't necessarily count against us, but for + // now I think its ok as none of the containers are expected to exit. + val exitStatus = completedContainer.getExitStatus + val (exitCausedByApp, containerExitReason) = exitStatus match { + case ContainerExitStatus.SUCCESS => + (false, s"Executor for container $containerId exited because of a YARN event (e.g., " + + "pre-emption) and not because of an error in the running job.") + case ContainerExitStatus.PREEMPTED => + // Preemption is not the fault of the running tasks, since YARN preempts containers + // merely to do resource sharing, and tasks that fail due to preempted executors could + // just as easily finish on any other executor. See SPARK-8167. + (false, s"Container ${containerId}${onHostStr} was preempted.") + // Should probably still count memory exceeded exit codes towards task failures + case VMEM_EXCEEDED_EXIT_CODE => + (true, memLimitExceededLogMessage( + completedContainer.getDiagnostics, + VMEM_EXCEEDED_PATTERN)) + case PMEM_EXCEEDED_EXIT_CODE => + (true, memLimitExceededLogMessage( + completedContainer.getDiagnostics, + PMEM_EXCEEDED_PATTERN)) + case _ => + // Enqueue the timestamp of failed executor + failedExecutorsTimeStamps.enqueue(clock.getTimeMillis()) + (true, "Container marked as failed: " + containerId + onHostStr + + ". Exit status: " + completedContainer.getExitStatus + + ". Diagnostics: " + completedContainer.getDiagnostics) + + } + if (exitCausedByApp) { + logWarning(containerExitReason) + } else { + logInfo(containerExitReason) + } + ExecutorExited(exitStatus, exitCausedByApp, containerExitReason) + } else { + // If we have already released this container, then it must mean + // that the driver has explicitly requested it to be killed + ExecutorExited(completedContainer.getExitStatus, exitCausedByApp = false, + s"Container $containerId exited from explicit termination request.") + } + + for { + host <- hostOpt + containerSet <- allocatedHostToContainersMap.get(host) + } { + containerSet.remove(containerId) + if (containerSet.isEmpty) { + allocatedHostToContainersMap.remove(host) + } else { + allocatedHostToContainersMap.update(host, containerSet) + } + + allocatedContainerToHostMap.remove(containerId) + } + + containerIdToExecutorId.remove(containerId).foreach { eid => + executorIdToContainer.remove(eid) + pendingLossReasonRequests.remove(eid) match { + case Some(pendingRequests) => + // Notify application of executor loss reasons so it can decide whether it should abort + pendingRequests.foreach(_.reply(exitReason)) + + case None => + // We cannot find executor for pending reasons. This is because completed container + // is processed before querying pending result. We should store it for later query. + // This is usually happened when explicitly killing a container, the result will be + // returned in one AM-RM communication. So query RPC will be later than this completed + // container process. + releasedExecutorLossReasons.put(eid, exitReason) + } + if (!alreadyReleased) { + // The executor could have gone away (like no route to host, node failure, etc) + // Notify backend about the failure of the executor + numUnexpectedContainerRelease += 1 + driverRef.send(RemoveExecutor(eid, exitReason)) + } + } + } + } + + /** + * Register that some RpcCallContext has asked the AM why the executor was lost. Note that + * we can only find the loss reason to send back in the next call to allocateResources(). + */ + private[yarn] def enqueueGetLossReasonRequest( + eid: String, + context: RpcCallContext): Unit = synchronized { + if (executorIdToContainer.contains(eid)) { + pendingLossReasonRequests + .getOrElseUpdate(eid, new ArrayBuffer[RpcCallContext]) += context + } else if (releasedExecutorLossReasons.contains(eid)) { + // Executor is already released explicitly before getting the loss reason, so directly send + // the pre-stored lost reason + context.reply(releasedExecutorLossReasons.remove(eid).get) + } else { + logWarning(s"Tried to get the loss reason for non-existent executor $eid") + context.sendFailure( + new SparkException(s"Fail to find loss reason for non-existent executor $eid")) + } + } + + private def internalReleaseContainer(container: Container): Unit = { + releasedContainers.add(container.getId()) + amClient.releaseAssignedContainer(container.getId()) + } + + private[yarn] def getNumUnexpectedContainerRelease = numUnexpectedContainerRelease + + private[yarn] def getNumPendingLossReasonRequests: Int = synchronized { + pendingLossReasonRequests.size + } + + /** + * Split the pending container requests into 3 groups based on current localities of pending + * tasks. + * @param hostToLocalTaskCount a map of preferred hostname to possible task counts to be used as + * container placement hint. + * @param pendingAllocations A sequence of pending allocation container request. + * @return A tuple of 3 sequences, first is a sequence of locality matched container + * requests, second is a sequence of locality unmatched container requests, and third is a + * sequence of locality free container requests. + */ + private def splitPendingAllocationsByLocality( + hostToLocalTaskCount: Map[String, Int], + pendingAllocations: Seq[ContainerRequest] + ): (Seq[ContainerRequest], Seq[ContainerRequest], Seq[ContainerRequest]) = { + val localityMatched = ArrayBuffer[ContainerRequest]() + val localityUnMatched = ArrayBuffer[ContainerRequest]() + val localityFree = ArrayBuffer[ContainerRequest]() + + val preferredHosts = hostToLocalTaskCount.keySet + pendingAllocations.foreach { cr => + val nodes = cr.getNodes + if (nodes == null) { + localityFree += cr + } else if (nodes.asScala.toSet.intersect(preferredHosts).nonEmpty) { + localityMatched += cr + } else { + localityUnMatched += cr + } + } + + (localityMatched.toSeq, localityUnMatched.toSeq, localityFree.toSeq) + } + +} + +private object YarnAllocator { + val MEM_REGEX = "[0-9.]+ [KMG]B" + val PMEM_EXCEEDED_PATTERN = + Pattern.compile(s"$MEM_REGEX of $MEM_REGEX physical memory used") + val VMEM_EXCEEDED_PATTERN = + Pattern.compile(s"$MEM_REGEX of $MEM_REGEX virtual memory used") + val VMEM_EXCEEDED_EXIT_CODE = -103 + val PMEM_EXCEEDED_EXIT_CODE = -104 + + def memLimitExceededLogMessage(diagnostics: String, pattern: Pattern): String = { + val matcher = pattern.matcher(diagnostics) + val diag = if (matcher.find()) " " + matcher.group() + "." else "" + ("Container killed by YARN for exceeding memory limits." + diag + + " Consider boosting spark.yarn.executor.memoryOverhead.") + } +} diff --git a/resource-managers/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnRMClient.scala b/resource-managers/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnRMClient.scala new file mode 100644 index 0000000000..53df11eb66 --- /dev/null +++ b/resource-managers/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnRMClient.scala @@ -0,0 +1,135 @@ +/* + * 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.deploy.yarn + +import java.util.{List => JList} + +import scala.collection.JavaConverters._ +import scala.util.Try + +import org.apache.hadoop.conf.Configuration +import org.apache.hadoop.yarn.api.records._ +import org.apache.hadoop.yarn.client.api.AMRMClient +import org.apache.hadoop.yarn.client.api.AMRMClient.ContainerRequest +import org.apache.hadoop.yarn.conf.YarnConfiguration +import org.apache.hadoop.yarn.webapp.util.WebAppUtils + +import org.apache.spark.{SecurityManager, SparkConf} +import org.apache.spark.deploy.yarn.config._ +import org.apache.spark.internal.Logging +import org.apache.spark.rpc.RpcEndpointRef +import org.apache.spark.util.Utils + +/** + * Handles registering and unregistering the application with the YARN ResourceManager. + */ +private[spark] class YarnRMClient extends Logging { + + private var amClient: AMRMClient[ContainerRequest] = _ + private var uiHistoryAddress: String = _ + private var registered: Boolean = false + + /** + * Registers the application master with the RM. + * + * @param conf The Yarn configuration. + * @param sparkConf The Spark configuration. + * @param uiAddress Address of the SparkUI. + * @param uiHistoryAddress Address of the application on the History Server. + * @param securityMgr The security manager. + * @param localResources Map with information about files distributed via YARN's cache. + */ + def register( + driverUrl: String, + driverRef: RpcEndpointRef, + conf: YarnConfiguration, + sparkConf: SparkConf, + uiAddress: String, + uiHistoryAddress: String, + securityMgr: SecurityManager, + localResources: Map[String, LocalResource] + ): YarnAllocator = { + amClient = AMRMClient.createAMRMClient() + amClient.init(conf) + amClient.start() + this.uiHistoryAddress = uiHistoryAddress + + logInfo("Registering the ApplicationMaster") + synchronized { + amClient.registerApplicationMaster(Utils.localHostName(), 0, uiAddress) + registered = true + } + new YarnAllocator(driverUrl, driverRef, conf, sparkConf, amClient, getAttemptId(), securityMgr, + localResources) + } + + /** + * Unregister the AM. Guaranteed to only be called once. + * + * @param status The final status of the AM. + * @param diagnostics Diagnostics message to include in the final status. + */ + def unregister(status: FinalApplicationStatus, diagnostics: String = ""): Unit = synchronized { + if (registered) { + amClient.unregisterApplicationMaster(status, diagnostics, uiHistoryAddress) + } + } + + /** Returns the attempt ID. */ + def getAttemptId(): ApplicationAttemptId = { + YarnSparkHadoopUtil.get.getContainerId.getApplicationAttemptId() + } + + /** Returns the configuration for the AmIpFilter to add to the Spark UI. */ + def getAmIpFilterParams(conf: YarnConfiguration, proxyBase: String): Map[String, String] = { + // Figure out which scheme Yarn is using. Note the method seems to have been added after 2.2, + // so not all stable releases have it. + val prefix = Try(classOf[WebAppUtils].getMethod("getHttpSchemePrefix", classOf[Configuration]) + .invoke(null, conf).asInstanceOf[String]).getOrElse("http://") + + // If running a new enough Yarn, use the HA-aware API for retrieving the RM addresses. + try { + val method = classOf[WebAppUtils].getMethod("getProxyHostsAndPortsForAmFilter", + classOf[Configuration]) + val proxies = method.invoke(null, conf).asInstanceOf[JList[String]] + val hosts = proxies.asScala.map { proxy => proxy.split(":")(0) } + val uriBases = proxies.asScala.map { proxy => prefix + proxy + proxyBase } + Map("PROXY_HOSTS" -> hosts.mkString(","), "PROXY_URI_BASES" -> uriBases.mkString(",")) + } catch { + case e: NoSuchMethodException => + val proxy = WebAppUtils.getProxyHostAndPort(conf) + val parts = proxy.split(":") + val uriBase = prefix + proxy + proxyBase + Map("PROXY_HOST" -> parts(0), "PROXY_URI_BASE" -> uriBase) + } + } + + /** Returns the maximum number of attempts to register the AM. */ + def getMaxRegAttempts(sparkConf: SparkConf, yarnConf: YarnConfiguration): Int = { + val sparkMaxAttempts = sparkConf.get(MAX_APP_ATTEMPTS).map(_.toInt) + val yarnMaxAttempts = yarnConf.getInt( + YarnConfiguration.RM_AM_MAX_ATTEMPTS, YarnConfiguration.DEFAULT_RM_AM_MAX_ATTEMPTS) + val retval: Int = sparkMaxAttempts match { + case Some(x) => if (x <= yarnMaxAttempts) x else yarnMaxAttempts + case None => yarnMaxAttempts + } + + retval + } + +} diff --git a/resource-managers/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtil.scala b/resource-managers/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtil.scala new file mode 100644 index 0000000000..cc53b1b06e --- /dev/null +++ b/resource-managers/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtil.scala @@ -0,0 +1,317 @@ +/* + * 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.deploy.yarn + +import java.io.File +import java.nio.charset.StandardCharsets.UTF_8 +import java.util.regex.Matcher +import java.util.regex.Pattern + +import scala.collection.mutable.{HashMap, ListBuffer} +import scala.util.Try + +import org.apache.hadoop.conf.Configuration +import org.apache.hadoop.io.Text +import org.apache.hadoop.mapred.JobConf +import org.apache.hadoop.security.Credentials +import org.apache.hadoop.security.UserGroupInformation +import org.apache.hadoop.yarn.api.ApplicationConstants +import org.apache.hadoop.yarn.api.ApplicationConstants.Environment +import org.apache.hadoop.yarn.api.records.{ApplicationAccessType, ContainerId, Priority} +import org.apache.hadoop.yarn.conf.YarnConfiguration +import org.apache.hadoop.yarn.util.ConverterUtils + +import org.apache.spark.{SecurityManager, SparkConf, SparkException} +import org.apache.spark.deploy.SparkHadoopUtil +import org.apache.spark.deploy.yarn.security.{ConfigurableCredentialManager, CredentialUpdater} +import org.apache.spark.internal.config._ +import org.apache.spark.launcher.YarnCommandBuilderUtils +import org.apache.spark.util.Utils + +/** + * Contains util methods to interact with Hadoop from spark. + */ +class YarnSparkHadoopUtil extends SparkHadoopUtil { + + private var credentialUpdater: CredentialUpdater = _ + + override def transferCredentials(source: UserGroupInformation, dest: UserGroupInformation) { + dest.addCredentials(source.getCredentials()) + } + + // Note that all params which start with SPARK are propagated all the way through, so if in yarn + // mode, this MUST be set to true. + override def isYarnMode(): Boolean = { true } + + // Return an appropriate (subclass) of Configuration. Creating a config initializes some Hadoop + // subsystems. Always create a new config, don't reuse yarnConf. + override def newConfiguration(conf: SparkConf): Configuration = + new YarnConfiguration(super.newConfiguration(conf)) + + // Add any user credentials to the job conf which are necessary for running on a secure Hadoop + // cluster + override def addCredentials(conf: JobConf) { + val jobCreds = conf.getCredentials() + jobCreds.mergeAll(UserGroupInformation.getCurrentUser().getCredentials()) + } + + override def getCurrentUserCredentials(): Credentials = { + UserGroupInformation.getCurrentUser().getCredentials() + } + + override def addCurrentUserCredentials(creds: Credentials) { + UserGroupInformation.getCurrentUser().addCredentials(creds) + } + + override def addSecretKeyToUserCredentials(key: String, secret: String) { + val creds = new Credentials() + creds.addSecretKey(new Text(key), secret.getBytes(UTF_8)) + addCurrentUserCredentials(creds) + } + + override def getSecretKeyFromUserCredentials(key: String): Array[Byte] = { + val credentials = getCurrentUserCredentials() + if (credentials != null) credentials.getSecretKey(new Text(key)) else null + } + + private[spark] override def startCredentialUpdater(sparkConf: SparkConf): Unit = { + credentialUpdater = + new ConfigurableCredentialManager(sparkConf, newConfiguration(sparkConf)).credentialUpdater() + credentialUpdater.start() + } + + private[spark] override def stopCredentialUpdater(): Unit = { + if (credentialUpdater != null) { + credentialUpdater.stop() + credentialUpdater = null + } + } + + private[spark] def getContainerId: ContainerId = { + val containerIdString = System.getenv(ApplicationConstants.Environment.CONTAINER_ID.name()) + ConverterUtils.toContainerId(containerIdString) + } +} + +object YarnSparkHadoopUtil { + // Additional memory overhead + // 10% was arrived at experimentally. In the interest of minimizing memory waste while covering + // the common cases. Memory overhead tends to grow with container size. + + val MEMORY_OVERHEAD_FACTOR = 0.10 + val MEMORY_OVERHEAD_MIN = 384L + + val ANY_HOST = "*" + + val DEFAULT_NUMBER_EXECUTORS = 2 + + // All RM requests are issued with same priority : we do not (yet) have any distinction between + // request types (like map/reduce in hadoop for example) + val RM_REQUEST_PRIORITY = Priority.newInstance(1) + + def get: YarnSparkHadoopUtil = { + val yarnMode = java.lang.Boolean.parseBoolean( + System.getProperty("SPARK_YARN_MODE", System.getenv("SPARK_YARN_MODE"))) + if (!yarnMode) { + throw new SparkException("YarnSparkHadoopUtil is not available in non-YARN mode!") + } + SparkHadoopUtil.get.asInstanceOf[YarnSparkHadoopUtil] + } + /** + * Add a path variable to the given environment map. + * If the map already contains this key, append the value to the existing value instead. + */ + def addPathToEnvironment(env: HashMap[String, String], key: String, value: String): Unit = { + val newValue = if (env.contains(key)) { env(key) + getClassPathSeparator + value } else value + env.put(key, newValue) + } + + /** + * Set zero or more environment variables specified by the given input string. + * The input string is expected to take the form "KEY1=VAL1,KEY2=VAL2,KEY3=VAL3". + */ + def setEnvFromInputString(env: HashMap[String, String], inputString: String): Unit = { + if (inputString != null && inputString.length() > 0) { + val childEnvs = inputString.split(",") + val p = Pattern.compile(environmentVariableRegex) + for (cEnv <- childEnvs) { + val parts = cEnv.split("=") // split on '=' + val m = p.matcher(parts(1)) + val sb = new StringBuffer + while (m.find()) { + val variable = m.group(1) + var replace = "" + if (env.get(variable) != None) { + replace = env.get(variable).get + } else { + // if this key is not configured for the child .. get it from the env + replace = System.getenv(variable) + if (replace == null) { + // the env key is note present anywhere .. simply set it + replace = "" + } + } + m.appendReplacement(sb, Matcher.quoteReplacement(replace)) + } + m.appendTail(sb) + // This treats the environment variable as path variable delimited by `File.pathSeparator` + // This is kept for backward compatibility and consistency with Hadoop's behavior + addPathToEnvironment(env, parts(0), sb.toString) + } + } + } + + private val environmentVariableRegex: String = { + if (Utils.isWindows) { + "%([A-Za-z_][A-Za-z0-9_]*?)%" + } else { + "\\$([A-Za-z_][A-Za-z0-9_]*)" + } + } + + /** + * Kill if OOM is raised - leverage yarn's failure handling to cause rescheduling. + * Not killing the task leaves various aspects of the executor and (to some extent) the jvm in + * an inconsistent state. + * TODO: If the OOM is not recoverable by rescheduling it on different node, then do + * 'something' to fail job ... akin to blacklisting trackers in mapred ? + * + * The handler if an OOM Exception is thrown by the JVM must be configured on Windows + * differently: the 'taskkill' command should be used, whereas Unix-based systems use 'kill'. + * + * As the JVM interprets both %p and %%p as the same, we can use either of them. However, + * some tests on Windows computers suggest, that the JVM only accepts '%%p'. + * + * Furthermore, the behavior of the character '%' on the Windows command line differs from + * the behavior of '%' in a .cmd file: it gets interpreted as an incomplete environment + * variable. Windows .cmd files escape a '%' by '%%'. Thus, the correct way of writing + * '%%p' in an escaped way is '%%%%p'. + */ + private[yarn] def addOutOfMemoryErrorArgument(javaOpts: ListBuffer[String]): Unit = { + if (!javaOpts.exists(_.contains("-XX:OnOutOfMemoryError"))) { + if (Utils.isWindows) { + javaOpts += escapeForShell("-XX:OnOutOfMemoryError=taskkill /F /PID %%%%p") + } else { + javaOpts += "-XX:OnOutOfMemoryError='kill %p'" + } + } + } + + /** + * Escapes a string for inclusion in a command line executed by Yarn. Yarn executes commands + * using either + * + * (Unix-based) `bash -c "command arg1 arg2"` and that means plain quoting doesn't really work. + * The argument is enclosed in single quotes and some key characters are escaped. + * + * (Windows-based) part of a .cmd file in which case windows escaping for each argument must be + * applied. Windows is quite lenient, however it is usually Java that causes trouble, needing to + * distinguish between arguments starting with '-' and class names. If arguments are surrounded + * by ' java takes the following string as is, hence an argument is mistakenly taken as a class + * name which happens to start with a '-'. The way to avoid this, is to surround nothing with + * a ', but instead with a ". + * + * @param arg A single argument. + * @return Argument quoted for execution via Yarn's generated shell script. + */ + def escapeForShell(arg: String): String = { + if (arg != null) { + if (Utils.isWindows) { + YarnCommandBuilderUtils.quoteForBatchScript(arg) + } else { + val escaped = new StringBuilder("'") + for (i <- 0 to arg.length() - 1) { + arg.charAt(i) match { + case '$' => escaped.append("\\$") + case '"' => escaped.append("\\\"") + case '\'' => escaped.append("'\\''") + case c => escaped.append(c) + } + } + escaped.append("'").toString() + } + } else { + arg + } + } + + // YARN/Hadoop acls are specified as user1,user2 group1,group2 + // Users and groups are separated by a space and hence we need to pass the acls in same format + def getApplicationAclsForYarn(securityMgr: SecurityManager) + : Map[ApplicationAccessType, String] = { + Map[ApplicationAccessType, String] ( + ApplicationAccessType.VIEW_APP -> (securityMgr.getViewAcls + " " + + securityMgr.getViewAclsGroups), + ApplicationAccessType.MODIFY_APP -> (securityMgr.getModifyAcls + " " + + securityMgr.getModifyAclsGroups) + ) + } + + /** + * Expand environment variable using Yarn API. + * If environment.$$() is implemented, return the result of it. + * Otherwise, return the result of environment.$() + * Note: $$() is added in Hadoop 2.4. + */ + private lazy val expandMethod = + Try(classOf[Environment].getMethod("$$")) + .getOrElse(classOf[Environment].getMethod("$")) + + def expandEnvironment(environment: Environment): String = + expandMethod.invoke(environment).asInstanceOf[String] + + /** + * Get class path separator using Yarn API. + * If ApplicationConstants.CLASS_PATH_SEPARATOR is implemented, return it. + * Otherwise, return File.pathSeparator + * Note: CLASS_PATH_SEPARATOR is added in Hadoop 2.4. + */ + private lazy val classPathSeparatorField = + Try(classOf[ApplicationConstants].getField("CLASS_PATH_SEPARATOR")) + .getOrElse(classOf[File].getField("pathSeparator")) + + def getClassPathSeparator(): String = { + classPathSeparatorField.get(null).asInstanceOf[String] + } + + /** + * Getting the initial target number of executors depends on whether dynamic allocation is + * enabled. + * If not using dynamic allocation it gets the number of executors requested by the user. + */ + def getInitialTargetExecutorNumber( + conf: SparkConf, + numExecutors: Int = DEFAULT_NUMBER_EXECUTORS): Int = { + if (Utils.isDynamicAllocationEnabled(conf)) { + val minNumExecutors = conf.get(DYN_ALLOCATION_MIN_EXECUTORS) + val initialNumExecutors = Utils.getDynamicAllocationInitialExecutors(conf) + val maxNumExecutors = conf.get(DYN_ALLOCATION_MAX_EXECUTORS) + require(initialNumExecutors >= minNumExecutors && initialNumExecutors <= maxNumExecutors, + s"initial executor number $initialNumExecutors must between min executor number " + + s"$minNumExecutors and max executor number $maxNumExecutors") + + initialNumExecutors + } else { + val targetNumExecutors = + sys.env.get("SPARK_EXECUTOR_INSTANCES").map(_.toInt).getOrElse(numExecutors) + // System property can override environment variable. + conf.get(EXECUTOR_INSTANCES).getOrElse(targetNumExecutors) + } + } +} + diff --git a/resource-managers/yarn/src/main/scala/org/apache/spark/deploy/yarn/config.scala b/resource-managers/yarn/src/main/scala/org/apache/spark/deploy/yarn/config.scala new file mode 100644 index 0000000000..666cb456a9 --- /dev/null +++ b/resource-managers/yarn/src/main/scala/org/apache/spark/deploy/yarn/config.scala @@ -0,0 +1,347 @@ +/* + * 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.deploy.yarn + +import java.util.concurrent.TimeUnit + +import org.apache.spark.internal.config.ConfigBuilder +import org.apache.spark.network.util.ByteUnit + +package object config { + + /* Common app configuration. */ + + private[spark] val APPLICATION_TAGS = ConfigBuilder("spark.yarn.tags") + .doc("Comma-separated list of strings to pass through as YARN application tags appearing " + + "in YARN Application Reports, which can be used for filtering when querying YARN.") + .stringConf + .toSequence + .createOptional + + private[spark] val AM_ATTEMPT_FAILURE_VALIDITY_INTERVAL_MS = + ConfigBuilder("spark.yarn.am.attemptFailuresValidityInterval") + .doc("Interval after which AM failures will be considered independent and " + + "not accumulate towards the attempt count.") + .timeConf(TimeUnit.MILLISECONDS) + .createOptional + + private[spark] val AM_PORT = + ConfigBuilder("spark.yarn.am.port") + .intConf + .createWithDefault(0) + + private[spark] val EXECUTOR_ATTEMPT_FAILURE_VALIDITY_INTERVAL_MS = + ConfigBuilder("spark.yarn.executor.failuresValidityInterval") + .doc("Interval after which Executor failures will be considered independent and not " + + "accumulate towards the attempt count.") + .timeConf(TimeUnit.MILLISECONDS) + .createOptional + + private[spark] val MAX_APP_ATTEMPTS = ConfigBuilder("spark.yarn.maxAppAttempts") + .doc("Maximum number of AM attempts before failing the app.") + .intConf + .createOptional + + private[spark] val USER_CLASS_PATH_FIRST = ConfigBuilder("spark.yarn.user.classpath.first") + .doc("Whether to place user jars in front of Spark's classpath.") + .booleanConf + .createWithDefault(false) + + private[spark] val GATEWAY_ROOT_PATH = ConfigBuilder("spark.yarn.config.gatewayPath") + .doc("Root of configuration paths that is present on gateway nodes, and will be replaced " + + "with the corresponding path in cluster machines.") + .stringConf + .createWithDefault(null) + + private[spark] val REPLACEMENT_ROOT_PATH = ConfigBuilder("spark.yarn.config.replacementPath") + .doc(s"Path to use as a replacement for ${GATEWAY_ROOT_PATH.key} when launching processes " + + "in the YARN cluster.") + .stringConf + .createWithDefault(null) + + private[spark] val QUEUE_NAME = ConfigBuilder("spark.yarn.queue") + .stringConf + .createWithDefault("default") + + private[spark] val HISTORY_SERVER_ADDRESS = ConfigBuilder("spark.yarn.historyServer.address") + .stringConf + .createOptional + + /* File distribution. */ + + private[spark] val SPARK_ARCHIVE = ConfigBuilder("spark.yarn.archive") + .doc("Location of archive containing jars files with Spark classes.") + .stringConf + .createOptional + + private[spark] val SPARK_JARS = ConfigBuilder("spark.yarn.jars") + .doc("Location of jars containing Spark classes.") + .stringConf + .toSequence + .createOptional + + private[spark] val ARCHIVES_TO_DISTRIBUTE = ConfigBuilder("spark.yarn.dist.archives") + .stringConf + .toSequence + .createWithDefault(Nil) + + private[spark] val FILES_TO_DISTRIBUTE = ConfigBuilder("spark.yarn.dist.files") + .stringConf + .toSequence + .createWithDefault(Nil) + + private[spark] val JARS_TO_DISTRIBUTE = ConfigBuilder("spark.yarn.dist.jars") + .stringConf + .toSequence + .createWithDefault(Nil) + + private[spark] val PRESERVE_STAGING_FILES = ConfigBuilder("spark.yarn.preserve.staging.files") + .doc("Whether to preserve temporary files created by the job in HDFS.") + .booleanConf + .createWithDefault(false) + + private[spark] val STAGING_FILE_REPLICATION = ConfigBuilder("spark.yarn.submit.file.replication") + .doc("Replication factor for files uploaded by Spark to HDFS.") + .intConf + .createOptional + + private[spark] val STAGING_DIR = ConfigBuilder("spark.yarn.stagingDir") + .doc("Staging directory used while submitting applications.") + .stringConf + .createOptional + + /* Cluster-mode launcher configuration. */ + + private[spark] val WAIT_FOR_APP_COMPLETION = ConfigBuilder("spark.yarn.submit.waitAppCompletion") + .doc("In cluster mode, whether to wait for the application to finish before exiting the " + + "launcher process.") + .booleanConf + .createWithDefault(true) + + private[spark] val REPORT_INTERVAL = ConfigBuilder("spark.yarn.report.interval") + .doc("Interval between reports of the current app status in cluster mode.") + .timeConf(TimeUnit.MILLISECONDS) + .createWithDefaultString("1s") + + /* Shared Client-mode AM / Driver configuration. */ + + private[spark] val AM_MAX_WAIT_TIME = ConfigBuilder("spark.yarn.am.waitTime") + .timeConf(TimeUnit.MILLISECONDS) + .createWithDefaultString("100s") + + private[spark] val AM_NODE_LABEL_EXPRESSION = ConfigBuilder("spark.yarn.am.nodeLabelExpression") + .doc("Node label expression for the AM.") + .stringConf + .createOptional + + private[spark] val CONTAINER_LAUNCH_MAX_THREADS = + ConfigBuilder("spark.yarn.containerLauncherMaxThreads") + .intConf + .createWithDefault(25) + + private[spark] val MAX_EXECUTOR_FAILURES = ConfigBuilder("spark.yarn.max.executor.failures") + .intConf + .createOptional + + private[spark] val MAX_REPORTER_THREAD_FAILURES = + ConfigBuilder("spark.yarn.scheduler.reporterThread.maxFailures") + .intConf + .createWithDefault(5) + + private[spark] val RM_HEARTBEAT_INTERVAL = + ConfigBuilder("spark.yarn.scheduler.heartbeat.interval-ms") + .timeConf(TimeUnit.MILLISECONDS) + .createWithDefaultString("3s") + + private[spark] val INITIAL_HEARTBEAT_INTERVAL = + ConfigBuilder("spark.yarn.scheduler.initial-allocation.interval") + .timeConf(TimeUnit.MILLISECONDS) + .createWithDefaultString("200ms") + + private[spark] val SCHEDULER_SERVICES = ConfigBuilder("spark.yarn.services") + .doc("A comma-separated list of class names of services to add to the scheduler.") + .stringConf + .toSequence + .createWithDefault(Nil) + + /* Client-mode AM configuration. */ + + private[spark] val AM_CORES = ConfigBuilder("spark.yarn.am.cores") + .intConf + .createWithDefault(1) + + private[spark] val AM_JAVA_OPTIONS = ConfigBuilder("spark.yarn.am.extraJavaOptions") + .doc("Extra Java options for the client-mode AM.") + .stringConf + .createOptional + + private[spark] val AM_LIBRARY_PATH = ConfigBuilder("spark.yarn.am.extraLibraryPath") + .doc("Extra native library path for the client-mode AM.") + .stringConf + .createOptional + + private[spark] val AM_MEMORY_OVERHEAD = ConfigBuilder("spark.yarn.am.memoryOverhead") + .bytesConf(ByteUnit.MiB) + .createOptional + + private[spark] val AM_MEMORY = ConfigBuilder("spark.yarn.am.memory") + .bytesConf(ByteUnit.MiB) + .createWithDefaultString("512m") + + /* Driver configuration. */ + + private[spark] val DRIVER_CORES = ConfigBuilder("spark.driver.cores") + .intConf + .createWithDefault(1) + + private[spark] val DRIVER_MEMORY_OVERHEAD = ConfigBuilder("spark.yarn.driver.memoryOverhead") + .bytesConf(ByteUnit.MiB) + .createOptional + + /* Executor configuration. */ + + private[spark] val EXECUTOR_CORES = ConfigBuilder("spark.executor.cores") + .intConf + .createWithDefault(1) + + private[spark] val EXECUTOR_MEMORY_OVERHEAD = ConfigBuilder("spark.yarn.executor.memoryOverhead") + .bytesConf(ByteUnit.MiB) + .createOptional + + private[spark] val EXECUTOR_NODE_LABEL_EXPRESSION = + ConfigBuilder("spark.yarn.executor.nodeLabelExpression") + .doc("Node label expression for executors.") + .stringConf + .createOptional + + /* Security configuration. */ + + private[spark] val CREDENTIAL_FILE_MAX_COUNT = + ConfigBuilder("spark.yarn.credentials.file.retention.count") + .intConf + .createWithDefault(5) + + private[spark] val CREDENTIALS_FILE_MAX_RETENTION = + ConfigBuilder("spark.yarn.credentials.file.retention.days") + .intConf + .createWithDefault(5) + + private[spark] val NAMENODES_TO_ACCESS = ConfigBuilder("spark.yarn.access.namenodes") + .doc("Extra NameNode URLs for which to request delegation tokens. The NameNode that hosts " + + "fs.defaultFS does not need to be listed here.") + .stringConf + .toSequence + .createWithDefault(Nil) + + /* Rolled log aggregation configuration. */ + + private[spark] val ROLLED_LOG_INCLUDE_PATTERN = + ConfigBuilder("spark.yarn.rolledLog.includePattern") + .doc("Java Regex to filter the log files which match the defined include pattern and those " + + "log files will be aggregated in a rolling fashion.") + .stringConf + .createOptional + + private[spark] val ROLLED_LOG_EXCLUDE_PATTERN = + ConfigBuilder("spark.yarn.rolledLog.excludePattern") + .doc("Java Regex to filter the log files which match the defined exclude pattern and those " + + "log files will not be aggregated in a rolling fashion.") + .stringConf + .createOptional + + /* Private configs. */ + + private[spark] val CREDENTIALS_FILE_PATH = ConfigBuilder("spark.yarn.credentials.file") + .internal() + .stringConf + .createWithDefault(null) + + // Internal config to propagate the location of the user's jar to the driver/executors + private[spark] val APP_JAR = ConfigBuilder("spark.yarn.user.jar") + .internal() + .stringConf + .createOptional + + // Internal config to propagate the locations of any extra jars to add to the classpath + // of the executors + private[spark] val SECONDARY_JARS = ConfigBuilder("spark.yarn.secondary.jars") + .internal() + .stringConf + .toSequence + .createOptional + + /* Configuration and cached file propagation. */ + + private[spark] val CACHED_FILES = ConfigBuilder("spark.yarn.cache.filenames") + .internal() + .stringConf + .toSequence + .createWithDefault(Nil) + + private[spark] val CACHED_FILES_SIZES = ConfigBuilder("spark.yarn.cache.sizes") + .internal() + .longConf + .toSequence + .createWithDefault(Nil) + + private[spark] val CACHED_FILES_TIMESTAMPS = ConfigBuilder("spark.yarn.cache.timestamps") + .internal() + .longConf + .toSequence + .createWithDefault(Nil) + + private[spark] val CACHED_FILES_VISIBILITIES = ConfigBuilder("spark.yarn.cache.visibilities") + .internal() + .stringConf + .toSequence + .createWithDefault(Nil) + + // Either "file" or "archive", for each file. + private[spark] val CACHED_FILES_TYPES = ConfigBuilder("spark.yarn.cache.types") + .internal() + .stringConf + .toSequence + .createWithDefault(Nil) + + // The location of the conf archive in HDFS. + private[spark] val CACHED_CONF_ARCHIVE = ConfigBuilder("spark.yarn.cache.confArchive") + .internal() + .stringConf + .createOptional + + private[spark] val CREDENTIALS_RENEWAL_TIME = ConfigBuilder("spark.yarn.credentials.renewalTime") + .internal() + .timeConf(TimeUnit.MILLISECONDS) + .createWithDefault(Long.MaxValue) + + private[spark] val CREDENTIALS_UPDATE_TIME = ConfigBuilder("spark.yarn.credentials.updateTime") + .internal() + .timeConf(TimeUnit.MILLISECONDS) + .createWithDefault(Long.MaxValue) + + // The list of cache-related config entries. This is used by Client and the AM to clean + // up the environment so that these settings do not appear on the web UI. + private[yarn] val CACHE_CONFIGS = Seq( + CACHED_FILES, + CACHED_FILES_SIZES, + CACHED_FILES_TIMESTAMPS, + CACHED_FILES_VISIBILITIES, + CACHED_FILES_TYPES, + CACHED_CONF_ARCHIVE) + +} diff --git a/resource-managers/yarn/src/main/scala/org/apache/spark/deploy/yarn/security/AMCredentialRenewer.scala b/resource-managers/yarn/src/main/scala/org/apache/spark/deploy/yarn/security/AMCredentialRenewer.scala new file mode 100644 index 0000000000..7e76f402db --- /dev/null +++ b/resource-managers/yarn/src/main/scala/org/apache/spark/deploy/yarn/security/AMCredentialRenewer.scala @@ -0,0 +1,235 @@ +/* + * 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.deploy.yarn.security + +import java.security.PrivilegedExceptionAction +import java.util.concurrent.{Executors, TimeUnit} + +import org.apache.hadoop.conf.Configuration +import org.apache.hadoop.fs.{FileSystem, Path} +import org.apache.hadoop.security.UserGroupInformation + +import org.apache.spark.SparkConf +import org.apache.spark.deploy.SparkHadoopUtil +import org.apache.spark.deploy.yarn.YarnSparkHadoopUtil +import org.apache.spark.deploy.yarn.config._ +import org.apache.spark.internal.Logging +import org.apache.spark.internal.config._ +import org.apache.spark.util.ThreadUtils + +/** + * The following methods are primarily meant to make sure long-running apps like Spark + * Streaming apps can run without interruption while accessing secured services. The + * scheduleLoginFromKeytab method is called on the AM to get the new credentials. + * This method wakes up a thread that logs into the KDC + * once 75% of the renewal interval of the original credentials used for the container + * has elapsed. It then obtains new credentials and writes them to HDFS in a + * pre-specified location - the prefix of which is specified in the sparkConf by + * spark.yarn.credentials.file (so the file(s) would be named c-timestamp1-1, c-timestamp2-2 etc. + * - each update goes to a new file, with a monotonically increasing suffix), also the + * timestamp1, timestamp2 here indicates the time of next update for CredentialUpdater. + * After this, the credentials are renewed once 75% of the new tokens renewal interval has elapsed. + * + * On the executor and driver (yarn client mode) side, the updateCredentialsIfRequired method is + * called once 80% of the validity of the original credentials has elapsed. At that time the + * executor finds the credentials file with the latest timestamp and checks if it has read those + * credentials before (by keeping track of the suffix of the last file it read). If a new file has + * appeared, it will read the credentials and update the currently running UGI with it. This + * process happens again once 80% of the validity of this has expired. + */ +private[yarn] class AMCredentialRenewer( + sparkConf: SparkConf, + hadoopConf: Configuration, + credentialManager: ConfigurableCredentialManager) extends Logging { + + private var lastCredentialsFileSuffix = 0 + + private val credentialRenewer = + Executors.newSingleThreadScheduledExecutor( + ThreadUtils.namedThreadFactory("Credential Refresh Thread")) + + private val hadoopUtil = YarnSparkHadoopUtil.get + + private val credentialsFile = sparkConf.get(CREDENTIALS_FILE_PATH) + private val daysToKeepFiles = sparkConf.get(CREDENTIALS_FILE_MAX_RETENTION) + private val numFilesToKeep = sparkConf.get(CREDENTIAL_FILE_MAX_COUNT) + private val freshHadoopConf = + hadoopUtil.getConfBypassingFSCache(hadoopConf, new Path(credentialsFile).toUri.getScheme) + + @volatile private var timeOfNextRenewal = sparkConf.get(CREDENTIALS_RENEWAL_TIME) + + /** + * Schedule a login from the keytab and principal set using the --principal and --keytab + * arguments to spark-submit. This login happens only when the credentials of the current user + * are about to expire. This method reads spark.yarn.principal and spark.yarn.keytab from + * SparkConf to do the login. This method is a no-op in non-YARN mode. + * + */ + private[spark] def scheduleLoginFromKeytab(): Unit = { + val principal = sparkConf.get(PRINCIPAL).get + val keytab = sparkConf.get(KEYTAB).get + + /** + * Schedule re-login and creation of new credentials. If credentials have already expired, this + * method will synchronously create new ones. + */ + def scheduleRenewal(runnable: Runnable): Unit = { + // Run now! + val remainingTime = timeOfNextRenewal - System.currentTimeMillis() + if (remainingTime <= 0) { + logInfo("Credentials have expired, creating new ones now.") + runnable.run() + } else { + logInfo(s"Scheduling login from keytab in $remainingTime millis.") + credentialRenewer.schedule(runnable, remainingTime, TimeUnit.MILLISECONDS) + } + } + + // This thread periodically runs on the AM to update the credentials on HDFS. + val credentialRenewerRunnable = + new Runnable { + override def run(): Unit = { + try { + writeNewCredentialsToHDFS(principal, keytab) + cleanupOldFiles() + } catch { + case e: Exception => + // Log the error and try to write new tokens back in an hour + logWarning("Failed to write out new credentials to HDFS, will try again in an " + + "hour! If this happens too often tasks will fail.", e) + credentialRenewer.schedule(this, 1, TimeUnit.HOURS) + return + } + scheduleRenewal(this) + } + } + // Schedule update of credentials. This handles the case of updating the credentials right now + // as well, since the renewal interval will be 0, and the thread will get scheduled + // immediately. + scheduleRenewal(credentialRenewerRunnable) + } + + // Keeps only files that are newer than daysToKeepFiles days, and deletes everything else. At + // least numFilesToKeep files are kept for safety + private def cleanupOldFiles(): Unit = { + import scala.concurrent.duration._ + try { + val remoteFs = FileSystem.get(freshHadoopConf) + val credentialsPath = new Path(credentialsFile) + val thresholdTime = System.currentTimeMillis() - (daysToKeepFiles.days).toMillis + hadoopUtil.listFilesSorted( + remoteFs, credentialsPath.getParent, + credentialsPath.getName, SparkHadoopUtil.SPARK_YARN_CREDS_TEMP_EXTENSION) + .dropRight(numFilesToKeep) + .takeWhile(_.getModificationTime < thresholdTime) + .foreach(x => remoteFs.delete(x.getPath, true)) + } catch { + // Such errors are not fatal, so don't throw. Make sure they are logged though + case e: Exception => + logWarning("Error while attempting to cleanup old credentials. If you are seeing many " + + "such warnings there may be an issue with your HDFS cluster.", e) + } + } + + private def writeNewCredentialsToHDFS(principal: String, keytab: String): Unit = { + // Keytab is copied by YARN to the working directory of the AM, so full path is + // not needed. + + // HACK: + // HDFS will not issue new delegation tokens, if the Credentials object + // passed in already has tokens for that FS even if the tokens are expired (it really only + // checks if there are tokens for the service, and not if they are valid). So the only real + // way to get new tokens is to make sure a different Credentials object is used each time to + // get new tokens and then the new tokens are copied over the current user's Credentials. + // So: + // - we login as a different user and get the UGI + // - use that UGI to get the tokens (see doAs block below) + // - copy the tokens over to the current user's credentials (this will overwrite the tokens + // in the current user's Credentials object for this FS). + // The login to KDC happens each time new tokens are required, but this is rare enough to not + // have to worry about (like once every day or so). This makes this code clearer than having + // to login and then relogin every time (the HDFS API may not relogin since we don't use this + // UGI directly for HDFS communication. + logInfo(s"Attempting to login to KDC using principal: $principal") + val keytabLoggedInUGI = UserGroupInformation.loginUserFromKeytabAndReturnUGI(principal, keytab) + logInfo("Successfully logged into KDC.") + val tempCreds = keytabLoggedInUGI.getCredentials + val credentialsPath = new Path(credentialsFile) + val dst = credentialsPath.getParent + var nearestNextRenewalTime = Long.MaxValue + keytabLoggedInUGI.doAs(new PrivilegedExceptionAction[Void] { + // Get a copy of the credentials + override def run(): Void = { + nearestNextRenewalTime = credentialManager.obtainCredentials(freshHadoopConf, tempCreds) + null + } + }) + + val currTime = System.currentTimeMillis() + val timeOfNextUpdate = if (nearestNextRenewalTime <= currTime) { + // If next renewal time is earlier than current time, we set next renewal time to current + // time, this will trigger next renewal immediately. Also set next update time to current + // time. There still has a gap between token renewal and update will potentially introduce + // issue. + logWarning(s"Next credential renewal time ($nearestNextRenewalTime) is earlier than " + + s"current time ($currTime), which is unexpected, please check your credential renewal " + + "related configurations in the target services.") + timeOfNextRenewal = currTime + currTime + } else { + // Next valid renewal time is about 75% of credential renewal time, and update time is + // slightly later than valid renewal time (80% of renewal time). + timeOfNextRenewal = ((nearestNextRenewalTime - currTime) * 0.75 + currTime).toLong + ((nearestNextRenewalTime - currTime) * 0.8 + currTime).toLong + } + + // Add the temp credentials back to the original ones. + UserGroupInformation.getCurrentUser.addCredentials(tempCreds) + val remoteFs = FileSystem.get(freshHadoopConf) + // If lastCredentialsFileSuffix is 0, then the AM is either started or restarted. If the AM + // was restarted, then the lastCredentialsFileSuffix might be > 0, so find the newest file + // and update the lastCredentialsFileSuffix. + if (lastCredentialsFileSuffix == 0) { + hadoopUtil.listFilesSorted( + remoteFs, credentialsPath.getParent, + credentialsPath.getName, SparkHadoopUtil.SPARK_YARN_CREDS_TEMP_EXTENSION) + .lastOption.foreach { status => + lastCredentialsFileSuffix = hadoopUtil.getSuffixForCredentialsPath(status.getPath) + } + } + val nextSuffix = lastCredentialsFileSuffix + 1 + + val tokenPathStr = + credentialsFile + SparkHadoopUtil.SPARK_YARN_CREDS_COUNTER_DELIM + + timeOfNextUpdate.toLong.toString + SparkHadoopUtil.SPARK_YARN_CREDS_COUNTER_DELIM + + nextSuffix + val tokenPath = new Path(tokenPathStr) + val tempTokenPath = new Path(tokenPathStr + SparkHadoopUtil.SPARK_YARN_CREDS_TEMP_EXTENSION) + + logInfo("Writing out delegation tokens to " + tempTokenPath.toString) + val credentials = UserGroupInformation.getCurrentUser.getCredentials + credentials.writeTokenStorageFile(tempTokenPath, freshHadoopConf) + logInfo(s"Delegation Tokens written out successfully. Renaming file to $tokenPathStr") + remoteFs.rename(tempTokenPath, tokenPath) + logInfo("Delegation token file rename complete.") + lastCredentialsFileSuffix = nextSuffix + } + + def stop(): Unit = { + credentialRenewer.shutdown() + } +} diff --git a/resource-managers/yarn/src/main/scala/org/apache/spark/deploy/yarn/security/ConfigurableCredentialManager.scala b/resource-managers/yarn/src/main/scala/org/apache/spark/deploy/yarn/security/ConfigurableCredentialManager.scala new file mode 100644 index 0000000000..c4c07b4930 --- /dev/null +++ b/resource-managers/yarn/src/main/scala/org/apache/spark/deploy/yarn/security/ConfigurableCredentialManager.scala @@ -0,0 +1,105 @@ +/* + * 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.deploy.yarn.security + +import java.util.ServiceLoader + +import scala.collection.JavaConverters._ + +import org.apache.hadoop.conf.Configuration +import org.apache.hadoop.security.Credentials + +import org.apache.spark.SparkConf +import org.apache.spark.internal.Logging +import org.apache.spark.util.Utils + +/** + * A ConfigurableCredentialManager to manage all the registered credential providers and offer + * APIs for other modules to obtain credentials as well as renewal time. By default + * [[HDFSCredentialProvider]], [[HiveCredentialProvider]] and [[HBaseCredentialProvider]] will + * be loaded in if not explicitly disabled, any plugged-in credential provider wants to be + * managed by ConfigurableCredentialManager needs to implement [[ServiceCredentialProvider]] + * interface and put into resources/META-INF/services to be loaded by ServiceLoader. + * + * Also each credential provider is controlled by + * spark.yarn.security.credentials.{service}.enabled, it will not be loaded in if set to false. + */ +private[yarn] final class ConfigurableCredentialManager( + sparkConf: SparkConf, hadoopConf: Configuration) extends Logging { + private val deprecatedProviderEnabledConfig = "spark.yarn.security.tokens.%s.enabled" + private val providerEnabledConfig = "spark.yarn.security.credentials.%s.enabled" + + // Maintain all the registered credential providers + private val credentialProviders = { + val providers = ServiceLoader.load(classOf[ServiceCredentialProvider], + Utils.getContextOrSparkClassLoader).asScala + + // Filter out credentials in which spark.yarn.security.credentials.{service}.enabled is false. + providers.filter { p => + sparkConf.getOption(providerEnabledConfig.format(p.serviceName)) + .orElse { + sparkConf.getOption(deprecatedProviderEnabledConfig.format(p.serviceName)).map { c => + logWarning(s"${deprecatedProviderEnabledConfig.format(p.serviceName)} is deprecated, " + + s"using ${providerEnabledConfig.format(p.serviceName)} instead") + c + } + }.map(_.toBoolean).getOrElse(true) + }.map { p => (p.serviceName, p) }.toMap + } + + /** + * Get credential provider for the specified service. + */ + def getServiceCredentialProvider(service: String): Option[ServiceCredentialProvider] = { + credentialProviders.get(service) + } + + /** + * Obtain credentials from all the registered providers. + * @return nearest time of next renewal, Long.MaxValue if all the credentials aren't renewable, + * otherwise the nearest renewal time of any credentials will be returned. + */ + def obtainCredentials(hadoopConf: Configuration, creds: Credentials): Long = { + credentialProviders.values.flatMap { provider => + if (provider.credentialsRequired(hadoopConf)) { + provider.obtainCredentials(hadoopConf, sparkConf, creds) + } else { + logDebug(s"Service ${provider.serviceName} does not require a token." + + s" Check your configuration to see if security is disabled or not.") + None + } + }.foldLeft(Long.MaxValue)(math.min) + } + + /** + * Create an [[AMCredentialRenewer]] instance, caller should be responsible to stop this + * instance when it is not used. AM will use it to renew credentials periodically. + */ + def credentialRenewer(): AMCredentialRenewer = { + new AMCredentialRenewer(sparkConf, hadoopConf, this) + } + + /** + * Create an [[CredentialUpdater]] instance, caller should be resposible to stop this intance + * when it is not used. Executors and driver (client mode) will use it to update credentials. + * periodically. + */ + def credentialUpdater(): CredentialUpdater = { + new CredentialUpdater(sparkConf, hadoopConf, this) + } +} diff --git a/resource-managers/yarn/src/main/scala/org/apache/spark/deploy/yarn/security/CredentialUpdater.scala b/resource-managers/yarn/src/main/scala/org/apache/spark/deploy/yarn/security/CredentialUpdater.scala new file mode 100644 index 0000000000..5df4fbd9c1 --- /dev/null +++ b/resource-managers/yarn/src/main/scala/org/apache/spark/deploy/yarn/security/CredentialUpdater.scala @@ -0,0 +1,130 @@ +/* + * 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.deploy.yarn.security + +import java.util.concurrent.{Executors, TimeUnit} + +import scala.util.control.NonFatal + +import org.apache.hadoop.conf.Configuration +import org.apache.hadoop.fs.{FileSystem, Path} +import org.apache.hadoop.security.{Credentials, UserGroupInformation} + +import org.apache.spark.SparkConf +import org.apache.spark.deploy.SparkHadoopUtil +import org.apache.spark.deploy.yarn.config._ +import org.apache.spark.internal.Logging +import org.apache.spark.util.{ThreadUtils, Utils} + +private[spark] class CredentialUpdater( + sparkConf: SparkConf, + hadoopConf: Configuration, + credentialManager: ConfigurableCredentialManager) extends Logging { + + @volatile private var lastCredentialsFileSuffix = 0 + + private val credentialsFile = sparkConf.get(CREDENTIALS_FILE_PATH) + private val freshHadoopConf = + SparkHadoopUtil.get.getConfBypassingFSCache( + hadoopConf, new Path(credentialsFile).toUri.getScheme) + + private val credentialUpdater = + Executors.newSingleThreadScheduledExecutor( + ThreadUtils.namedThreadFactory("Credential Refresh Thread")) + + // This thread wakes up and picks up new credentials from HDFS, if any. + private val credentialUpdaterRunnable = + new Runnable { + override def run(): Unit = Utils.logUncaughtExceptions(updateCredentialsIfRequired()) + } + + /** Start the credential updater task */ + def start(): Unit = { + val startTime = sparkConf.get(CREDENTIALS_RENEWAL_TIME) + val remainingTime = startTime - System.currentTimeMillis() + if (remainingTime <= 0) { + credentialUpdater.schedule(credentialUpdaterRunnable, 1, TimeUnit.MINUTES) + } else { + logInfo(s"Scheduling credentials refresh from HDFS in $remainingTime millis.") + credentialUpdater.schedule(credentialUpdaterRunnable, remainingTime, TimeUnit.MILLISECONDS) + } + } + + private def updateCredentialsIfRequired(): Unit = { + val timeToNextUpdate = try { + val credentialsFilePath = new Path(credentialsFile) + val remoteFs = FileSystem.get(freshHadoopConf) + SparkHadoopUtil.get.listFilesSorted( + remoteFs, credentialsFilePath.getParent, + credentialsFilePath.getName, SparkHadoopUtil.SPARK_YARN_CREDS_TEMP_EXTENSION) + .lastOption.map { credentialsStatus => + val suffix = SparkHadoopUtil.get.getSuffixForCredentialsPath(credentialsStatus.getPath) + if (suffix > lastCredentialsFileSuffix) { + logInfo("Reading new credentials from " + credentialsStatus.getPath) + val newCredentials = getCredentialsFromHDFSFile(remoteFs, credentialsStatus.getPath) + lastCredentialsFileSuffix = suffix + UserGroupInformation.getCurrentUser.addCredentials(newCredentials) + logInfo("Credentials updated from credentials file.") + + val remainingTime = getTimeOfNextUpdateFromFileName(credentialsStatus.getPath) + - System.currentTimeMillis() + if (remainingTime <= 0) TimeUnit.MINUTES.toMillis(1) else remainingTime + } else { + // If current credential file is older than expected, sleep 1 hour and check again. + TimeUnit.HOURS.toMillis(1) + } + }.getOrElse { + // Wait for 1 minute to check again if there's no credential file currently + TimeUnit.MINUTES.toMillis(1) + } + } catch { + // Since the file may get deleted while we are reading it, catch the Exception and come + // back in an hour to try again + case NonFatal(e) => + logWarning("Error while trying to update credentials, will try again in 1 hour", e) + TimeUnit.HOURS.toMillis(1) + } + + credentialUpdater.schedule( + credentialUpdaterRunnable, timeToNextUpdate, TimeUnit.MILLISECONDS) + } + + private def getCredentialsFromHDFSFile(remoteFs: FileSystem, tokenPath: Path): Credentials = { + val stream = remoteFs.open(tokenPath) + try { + val newCredentials = new Credentials() + newCredentials.readTokenStorageStream(stream) + newCredentials + } finally { + stream.close() + } + } + + private def getTimeOfNextUpdateFromFileName(credentialsPath: Path): Long = { + val name = credentialsPath.getName + val index = name.lastIndexOf(SparkHadoopUtil.SPARK_YARN_CREDS_COUNTER_DELIM) + val slice = name.substring(0, index) + val last2index = slice.lastIndexOf(SparkHadoopUtil.SPARK_YARN_CREDS_COUNTER_DELIM) + name.substring(last2index + 1, index).toLong + } + + def stop(): Unit = { + credentialUpdater.shutdown() + } + +} diff --git a/resource-managers/yarn/src/main/scala/org/apache/spark/deploy/yarn/security/HBaseCredentialProvider.scala b/resource-managers/yarn/src/main/scala/org/apache/spark/deploy/yarn/security/HBaseCredentialProvider.scala new file mode 100644 index 0000000000..5571df09a2 --- /dev/null +++ b/resource-managers/yarn/src/main/scala/org/apache/spark/deploy/yarn/security/HBaseCredentialProvider.scala @@ -0,0 +1,74 @@ +/* + * 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.deploy.yarn.security + +import scala.reflect.runtime.universe +import scala.util.control.NonFatal + +import org.apache.hadoop.conf.Configuration +import org.apache.hadoop.security.Credentials +import org.apache.hadoop.security.token.{Token, TokenIdentifier} + +import org.apache.spark.SparkConf +import org.apache.spark.internal.Logging + +private[security] class HBaseCredentialProvider extends ServiceCredentialProvider with Logging { + + override def serviceName: String = "hbase" + + override def obtainCredentials( + hadoopConf: Configuration, + sparkConf: SparkConf, + creds: Credentials): Option[Long] = { + try { + val mirror = universe.runtimeMirror(getClass.getClassLoader) + val obtainToken = mirror.classLoader. + loadClass("org.apache.hadoop.hbase.security.token.TokenUtil"). + getMethod("obtainToken", classOf[Configuration]) + + logDebug("Attempting to fetch HBase security token.") + val token = obtainToken.invoke(null, hbaseConf(hadoopConf)) + .asInstanceOf[Token[_ <: TokenIdentifier]] + logInfo(s"Get token from HBase: ${token.toString}") + creds.addToken(token.getService, token) + } catch { + case NonFatal(e) => + logDebug(s"Failed to get token from service $serviceName", e) + } + + None + } + + override def credentialsRequired(hadoopConf: Configuration): Boolean = { + hbaseConf(hadoopConf).get("hbase.security.authentication") == "kerberos" + } + + private def hbaseConf(conf: Configuration): Configuration = { + try { + val mirror = universe.runtimeMirror(getClass.getClassLoader) + val confCreate = mirror.classLoader. + loadClass("org.apache.hadoop.hbase.HBaseConfiguration"). + getMethod("create", classOf[Configuration]) + confCreate.invoke(null, conf).asInstanceOf[Configuration] + } catch { + case NonFatal(e) => + logDebug("Fail to invoke HBaseConfiguration", e) + conf + } + } +} diff --git a/resource-managers/yarn/src/main/scala/org/apache/spark/deploy/yarn/security/HDFSCredentialProvider.scala b/resource-managers/yarn/src/main/scala/org/apache/spark/deploy/yarn/security/HDFSCredentialProvider.scala new file mode 100644 index 0000000000..8d06d735ba --- /dev/null +++ b/resource-managers/yarn/src/main/scala/org/apache/spark/deploy/yarn/security/HDFSCredentialProvider.scala @@ -0,0 +1,110 @@ +/* + * 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.deploy.yarn.security + +import java.io.{ByteArrayInputStream, DataInputStream} + +import scala.collection.JavaConverters._ + +import org.apache.hadoop.conf.Configuration +import org.apache.hadoop.fs.{FileSystem, Path} +import org.apache.hadoop.hdfs.security.token.delegation.DelegationTokenIdentifier +import org.apache.hadoop.mapred.Master +import org.apache.hadoop.security.Credentials + +import org.apache.spark.{SparkConf, SparkException} +import org.apache.spark.deploy.yarn.config._ +import org.apache.spark.internal.Logging +import org.apache.spark.internal.config._ + +private[security] class HDFSCredentialProvider extends ServiceCredentialProvider with Logging { + // Token renewal interval, this value will be set in the first call, + // if None means no token renewer specified, so cannot get token renewal interval. + private var tokenRenewalInterval: Option[Long] = null + + override val serviceName: String = "hdfs" + + override def obtainCredentials( + hadoopConf: Configuration, + sparkConf: SparkConf, + creds: Credentials): Option[Long] = { + // NameNode to access, used to get tokens from different FileSystems + nnsToAccess(hadoopConf, sparkConf).foreach { dst => + val dstFs = dst.getFileSystem(hadoopConf) + logInfo("getting token for namenode: " + dst) + dstFs.addDelegationTokens(getTokenRenewer(hadoopConf), creds) + } + + // Get the token renewal interval if it is not set. It will only be called once. + if (tokenRenewalInterval == null) { + tokenRenewalInterval = getTokenRenewalInterval(hadoopConf, sparkConf) + } + + // Get the time of next renewal. + tokenRenewalInterval.map { interval => + creds.getAllTokens.asScala + .filter(_.getKind == DelegationTokenIdentifier.HDFS_DELEGATION_KIND) + .map { t => + val identifier = new DelegationTokenIdentifier() + identifier.readFields(new DataInputStream(new ByteArrayInputStream(t.getIdentifier))) + identifier.getIssueDate + interval + }.foldLeft(0L)(math.max) + } + } + + private def getTokenRenewalInterval( + hadoopConf: Configuration, sparkConf: SparkConf): Option[Long] = { + // We cannot use the tokens generated with renewer yarn. Trying to renew + // those will fail with an access control issue. So create new tokens with the logged in + // user as renewer. + sparkConf.get(PRINCIPAL).map { renewer => + val creds = new Credentials() + nnsToAccess(hadoopConf, sparkConf).foreach { dst => + val dstFs = dst.getFileSystem(hadoopConf) + dstFs.addDelegationTokens(renewer, creds) + } + val t = creds.getAllTokens.asScala + .filter(_.getKind == DelegationTokenIdentifier.HDFS_DELEGATION_KIND) + .head + val newExpiration = t.renew(hadoopConf) + val identifier = new DelegationTokenIdentifier() + identifier.readFields(new DataInputStream(new ByteArrayInputStream(t.getIdentifier))) + val interval = newExpiration - identifier.getIssueDate + logInfo(s"Renewal Interval is $interval") + interval + } + } + + private def getTokenRenewer(conf: Configuration): String = { + val delegTokenRenewer = Master.getMasterPrincipal(conf) + logDebug("delegation token renewer is: " + delegTokenRenewer) + if (delegTokenRenewer == null || delegTokenRenewer.length() == 0) { + val errorMessage = "Can't get Master Kerberos principal for use as renewer" + logError(errorMessage) + throw new SparkException(errorMessage) + } + + delegTokenRenewer + } + + private def nnsToAccess(hadoopConf: Configuration, sparkConf: SparkConf): Set[Path] = { + sparkConf.get(NAMENODES_TO_ACCESS).map(new Path(_)).toSet + + sparkConf.get(STAGING_DIR).map(new Path(_)) + .getOrElse(FileSystem.get(hadoopConf).getHomeDirectory) + } +} diff --git a/resource-managers/yarn/src/main/scala/org/apache/spark/deploy/yarn/security/HiveCredentialProvider.scala b/resource-managers/yarn/src/main/scala/org/apache/spark/deploy/yarn/security/HiveCredentialProvider.scala new file mode 100644 index 0000000000..16d8fc32bb --- /dev/null +++ b/resource-managers/yarn/src/main/scala/org/apache/spark/deploy/yarn/security/HiveCredentialProvider.scala @@ -0,0 +1,129 @@ +/* + * 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.deploy.yarn.security + +import java.lang.reflect.UndeclaredThrowableException +import java.security.PrivilegedExceptionAction + +import scala.reflect.runtime.universe +import scala.util.control.NonFatal + +import org.apache.hadoop.conf.Configuration +import org.apache.hadoop.hdfs.security.token.delegation.DelegationTokenIdentifier +import org.apache.hadoop.io.Text +import org.apache.hadoop.security.{Credentials, UserGroupInformation} +import org.apache.hadoop.security.token.Token + +import org.apache.spark.SparkConf +import org.apache.spark.internal.Logging +import org.apache.spark.util.Utils + +private[security] class HiveCredentialProvider extends ServiceCredentialProvider with Logging { + + override def serviceName: String = "hive" + + private def hiveConf(hadoopConf: Configuration): Configuration = { + try { + val mirror = universe.runtimeMirror(Utils.getContextOrSparkClassLoader) + // the hive configuration class is a subclass of Hadoop Configuration, so can be cast down + // to a Configuration and used without reflection + val hiveConfClass = mirror.classLoader.loadClass("org.apache.hadoop.hive.conf.HiveConf") + // using the (Configuration, Class) constructor allows the current configuration to be + // included in the hive config. + val ctor = hiveConfClass.getDeclaredConstructor(classOf[Configuration], + classOf[Object].getClass) + ctor.newInstance(hadoopConf, hiveConfClass).asInstanceOf[Configuration] + } catch { + case NonFatal(e) => + logDebug("Fail to create Hive Configuration", e) + hadoopConf + } + } + + override def credentialsRequired(hadoopConf: Configuration): Boolean = { + UserGroupInformation.isSecurityEnabled && + hiveConf(hadoopConf).getTrimmed("hive.metastore.uris", "").nonEmpty + } + + override def obtainCredentials( + hadoopConf: Configuration, + sparkConf: SparkConf, + creds: Credentials): Option[Long] = { + val conf = hiveConf(hadoopConf) + + val principalKey = "hive.metastore.kerberos.principal" + val principal = conf.getTrimmed(principalKey, "") + require(principal.nonEmpty, s"Hive principal $principalKey undefined") + val metastoreUri = conf.getTrimmed("hive.metastore.uris", "") + require(metastoreUri.nonEmpty, "Hive metastore uri undefined") + + val currentUser = UserGroupInformation.getCurrentUser() + logDebug(s"Getting Hive delegation token for ${currentUser.getUserName()} against " + + s"$principal at $metastoreUri") + + val mirror = universe.runtimeMirror(Utils.getContextOrSparkClassLoader) + val hiveClass = mirror.classLoader.loadClass("org.apache.hadoop.hive.ql.metadata.Hive") + val hiveConfClass = mirror.classLoader.loadClass("org.apache.hadoop.hive.conf.HiveConf") + val closeCurrent = hiveClass.getMethod("closeCurrent") + + try { + // get all the instance methods before invoking any + val getDelegationToken = hiveClass.getMethod("getDelegationToken", + classOf[String], classOf[String]) + val getHive = hiveClass.getMethod("get", hiveConfClass) + + doAsRealUser { + val hive = getHive.invoke(null, conf) + val tokenStr = getDelegationToken.invoke(hive, currentUser.getUserName(), principal) + .asInstanceOf[String] + val hive2Token = new Token[DelegationTokenIdentifier]() + hive2Token.decodeFromUrlString(tokenStr) + logInfo(s"Get Token from hive metastore: ${hive2Token.toString}") + creds.addToken(new Text("hive.server2.delegation.token"), hive2Token) + } + } catch { + case NonFatal(e) => + logDebug(s"Fail to get token from service $serviceName", e) + } finally { + Utils.tryLogNonFatalError { + closeCurrent.invoke(null) + } + } + + None + } + + /** + * Run some code as the real logged in user (which may differ from the current user, for + * example, when using proxying). + */ + private def doAsRealUser[T](fn: => T): T = { + val currentUser = UserGroupInformation.getCurrentUser() + val realUser = Option(currentUser.getRealUser()).getOrElse(currentUser) + + // For some reason the Scala-generated anonymous class ends up causing an + // UndeclaredThrowableException, even if you annotate the method with @throws. + try { + realUser.doAs(new PrivilegedExceptionAction[T]() { + override def run(): T = fn + }) + } catch { + case e: UndeclaredThrowableException => throw Option(e.getCause()).getOrElse(e) + } + } +} diff --git a/resource-managers/yarn/src/main/scala/org/apache/spark/deploy/yarn/security/ServiceCredentialProvider.scala b/resource-managers/yarn/src/main/scala/org/apache/spark/deploy/yarn/security/ServiceCredentialProvider.scala new file mode 100644 index 0000000000..4e3fcce8db --- /dev/null +++ b/resource-managers/yarn/src/main/scala/org/apache/spark/deploy/yarn/security/ServiceCredentialProvider.scala @@ -0,0 +1,57 @@ +/* + * 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.deploy.yarn.security + +import org.apache.hadoop.conf.Configuration +import org.apache.hadoop.security.{Credentials, UserGroupInformation} + +import org.apache.spark.SparkConf + +/** + * A credential provider for a service. User must implement this if they need to access a + * secure service from Spark. + */ +trait ServiceCredentialProvider { + + /** + * Name of the service to provide credentials. This name should unique, Spark internally will + * use this name to differentiate credential provider. + */ + def serviceName: String + + /** + * To decide whether credential is required for this service. By default it based on whether + * Hadoop security is enabled. + */ + def credentialsRequired(hadoopConf: Configuration): Boolean = { + UserGroupInformation.isSecurityEnabled + } + + /** + * Obtain credentials for this service and get the time of the next renewal. + * @param hadoopConf Configuration of current Hadoop Compatible system. + * @param sparkConf Spark configuration. + * @param creds Credentials to add tokens and security keys to. + * @return If this Credential is renewable and can be renewed, return the time of the next + * renewal, otherwise None should be returned. + */ + def obtainCredentials( + hadoopConf: Configuration, + sparkConf: SparkConf, + creds: Credentials): Option[Long] +} diff --git a/resource-managers/yarn/src/main/scala/org/apache/spark/launcher/YarnCommandBuilderUtils.scala b/resource-managers/yarn/src/main/scala/org/apache/spark/launcher/YarnCommandBuilderUtils.scala new file mode 100644 index 0000000000..6c3556a2ee --- /dev/null +++ b/resource-managers/yarn/src/main/scala/org/apache/spark/launcher/YarnCommandBuilderUtils.scala @@ -0,0 +1,53 @@ +/* + * 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.launcher + +import scala.collection.JavaConverters._ +import scala.collection.mutable.ListBuffer +import scala.util.Properties + +/** + * Exposes methods from the launcher library that are used by the YARN backend. + */ +private[spark] object YarnCommandBuilderUtils { + + def quoteForBatchScript(arg: String): String = { + CommandBuilderUtils.quoteForBatchScript(arg) + } + + def findJarsDir(sparkHome: String): String = { + val scalaVer = Properties.versionNumberString + .split("\\.") + .take(2) + .mkString(".") + CommandBuilderUtils.findJarsDir(sparkHome, scalaVer, true) + } + + /** + * Adds the perm gen configuration to the list of java options if needed and not yet added. + * + * Note that this method adds the option based on the local JVM version; if the node where + * the container is running has a different Java version, there's a risk that the option will + * not be added (e.g. if the AM is running Java 8 but the container's node is set up to use + * Java 7). + */ + def addPermGenSizeOpt(args: ListBuffer[String]): Unit = { + CommandBuilderUtils.addPermGenSizeOpt(args.asJava) + } + +} diff --git a/resource-managers/yarn/src/main/scala/org/apache/spark/scheduler/cluster/SchedulerExtensionService.scala b/resource-managers/yarn/src/main/scala/org/apache/spark/scheduler/cluster/SchedulerExtensionService.scala new file mode 100644 index 0000000000..4ed285230f --- /dev/null +++ b/resource-managers/yarn/src/main/scala/org/apache/spark/scheduler/cluster/SchedulerExtensionService.scala @@ -0,0 +1,143 @@ +/* + * 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 java.util.concurrent.atomic.AtomicBoolean + +import org.apache.hadoop.yarn.api.records.{ApplicationAttemptId, ApplicationId} + +import org.apache.spark.SparkContext +import org.apache.spark.deploy.yarn.config._ +import org.apache.spark.internal.Logging +import org.apache.spark.util.Utils + +/** + * An extension service that can be loaded into a Spark YARN scheduler. + * A Service that can be started and stopped. + * + * 1. For implementations to be loadable by `SchedulerExtensionServices`, + * they must provide an empty constructor. + * 2. The `stop()` operation MUST be idempotent, and succeed even if `start()` was + * never invoked. + */ +trait SchedulerExtensionService { + + /** + * Start the extension service. This should be a no-op if + * called more than once. + * @param binding binding to the spark application and YARN + */ + def start(binding: SchedulerExtensionServiceBinding): Unit + + /** + * Stop the service + * The `stop()` operation MUST be idempotent, and succeed even if `start()` was + * never invoked. + */ + def stop(): Unit +} + +/** + * Binding information for a [[SchedulerExtensionService]]. + * + * The attempt ID will be set if the service is started within a YARN application master; + * there is then a different attempt ID for every time that AM is restarted. + * When the service binding is instantiated in client mode, there's no attempt ID, as it lacks + * this information. + * @param sparkContext current spark context + * @param applicationId YARN application ID + * @param attemptId YARN attemptID. This will always be unset in client mode, and always set in + * cluster mode. + */ +case class SchedulerExtensionServiceBinding( + sparkContext: SparkContext, + applicationId: ApplicationId, + attemptId: Option[ApplicationAttemptId] = None) + +/** + * Container for [[SchedulerExtensionService]] instances. + * + * Loads Extension Services from the configuration property + * `"spark.yarn.services"`, instantiates and starts them. + * When stopped, it stops all child entries. + * + * The order in which child extension services are started and stopped + * is undefined. + */ +private[spark] class SchedulerExtensionServices extends SchedulerExtensionService + with Logging { + private var serviceOption: Option[String] = None + private var services: List[SchedulerExtensionService] = Nil + private val started = new AtomicBoolean(false) + private var binding: SchedulerExtensionServiceBinding = _ + + /** + * Binding operation will load the named services and call bind on them too; the + * entire set of services are then ready for `init()` and `start()` calls. + * + * @param binding binding to the spark application and YARN + */ + def start(binding: SchedulerExtensionServiceBinding): Unit = { + if (started.getAndSet(true)) { + logWarning("Ignoring re-entrant start operation") + return + } + require(binding.sparkContext != null, "Null context parameter") + require(binding.applicationId != null, "Null appId parameter") + this.binding = binding + val sparkContext = binding.sparkContext + val appId = binding.applicationId + val attemptId = binding.attemptId + logInfo(s"Starting Yarn extension services with app $appId and attemptId $attemptId") + + services = sparkContext.conf.get(SCHEDULER_SERVICES).map { sClass => + val instance = Utils.classForName(sClass) + .newInstance() + .asInstanceOf[SchedulerExtensionService] + // bind this service + instance.start(binding) + logInfo(s"Service $sClass started") + instance + }.toList + } + + /** + * Get the list of services. + * + * @return a list of services; Nil until the service is started + */ + def getServices: List[SchedulerExtensionService] = services + + /** + * Stop the services; idempotent. + * + */ + override def stop(): Unit = { + if (started.getAndSet(false)) { + logInfo(s"Stopping $this") + services.foreach { s => + Utils.tryLogNonFatalError(s.stop()) + } + } + } + + override def toString(): String = s"""SchedulerExtensionServices + |(serviceOption=$serviceOption, + | services=$services, + | started=$started)""".stripMargin +} diff --git a/resource-managers/yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala b/resource-managers/yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala new file mode 100644 index 0000000000..60da356ad1 --- /dev/null +++ b/resource-managers/yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala @@ -0,0 +1,157 @@ +/* + * 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.webUrl) } + + 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.startCredentialUpdater(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.stopCredentialUpdater() + client.stop() + logInfo("Stopped") + } + +} diff --git a/resource-managers/yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClusterManager.scala b/resource-managers/yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClusterManager.scala new file mode 100644 index 0000000000..64cd1bd088 --- /dev/null +++ b/resource-managers/yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClusterManager.scala @@ -0,0 +1,56 @@ +/* + * 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 org.apache.spark.{SparkContext, SparkException} +import org.apache.spark.scheduler.{ExternalClusterManager, SchedulerBackend, TaskScheduler, TaskSchedulerImpl} + +/** + * Cluster Manager for creation of Yarn scheduler and backend + */ +private[spark] class YarnClusterManager extends ExternalClusterManager { + + override def canCreate(masterURL: String): Boolean = { + masterURL == "yarn" + } + + override def createTaskScheduler(sc: SparkContext, masterURL: String): TaskScheduler = { + sc.deployMode match { + case "cluster" => new YarnClusterScheduler(sc) + case "client" => new YarnScheduler(sc) + case _ => throw new SparkException(s"Unknown deploy mode '${sc.deployMode}' for Yarn") + } + } + + override def createSchedulerBackend(sc: SparkContext, + masterURL: String, + scheduler: TaskScheduler): SchedulerBackend = { + sc.deployMode match { + case "cluster" => + new YarnClusterSchedulerBackend(scheduler.asInstanceOf[TaskSchedulerImpl], sc) + case "client" => + new YarnClientSchedulerBackend(scheduler.asInstanceOf[TaskSchedulerImpl], sc) + case _ => + throw new SparkException(s"Unknown deploy mode '${sc.deployMode}' for Yarn") + } + } + + override def initialize(scheduler: TaskScheduler, backend: SchedulerBackend): Unit = { + scheduler.asInstanceOf[TaskSchedulerImpl].initialize(backend) + } +} diff --git a/resource-managers/yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClusterScheduler.scala b/resource-managers/yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClusterScheduler.scala new file mode 100644 index 0000000000..96c9151fc3 --- /dev/null +++ b/resource-managers/yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClusterScheduler.scala @@ -0,0 +1,37 @@ +/* + * 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 org.apache.spark._ +import org.apache.spark.deploy.yarn.ApplicationMaster + +/** + * This is a simple extension to ClusterScheduler - to ensure that appropriate initialization of + * ApplicationMaster, etc is done + */ +private[spark] class YarnClusterScheduler(sc: SparkContext) extends YarnScheduler(sc) { + + logInfo("Created YarnClusterScheduler") + + override def postStartHook() { + ApplicationMaster.sparkContextInitialized(sc) + super.postStartHook() + logInfo("YarnClusterScheduler.postStartHook done") + } + +} diff --git a/resource-managers/yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClusterSchedulerBackend.scala b/resource-managers/yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClusterSchedulerBackend.scala new file mode 100644 index 0000000000..4f3d5ebf40 --- /dev/null +++ b/resource-managers/yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClusterSchedulerBackend.scala @@ -0,0 +1,67 @@ +/* + * 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 org.apache.hadoop.yarn.api.ApplicationConstants.Environment +import org.apache.hadoop.yarn.conf.YarnConfiguration + +import org.apache.spark.SparkContext +import org.apache.spark.deploy.yarn.{ApplicationMaster, YarnSparkHadoopUtil} +import org.apache.spark.scheduler.TaskSchedulerImpl +import org.apache.spark.util.Utils + +private[spark] class YarnClusterSchedulerBackend( + scheduler: TaskSchedulerImpl, + sc: SparkContext) + extends YarnSchedulerBackend(scheduler, sc) { + + override def start() { + val attemptId = ApplicationMaster.getAttemptId + bindToYarn(attemptId.getApplicationId(), Some(attemptId)) + super.start() + totalExpectedExecutors = YarnSparkHadoopUtil.getInitialTargetExecutorNumber(sc.conf) + } + + override def getDriverLogUrls: Option[Map[String, String]] = { + var driverLogs: Option[Map[String, String]] = None + try { + val yarnConf = new YarnConfiguration(sc.hadoopConfiguration) + val containerId = YarnSparkHadoopUtil.get.getContainerId + + val httpAddress = System.getenv(Environment.NM_HOST.name()) + + ":" + System.getenv(Environment.NM_HTTP_PORT.name()) + // lookup appropriate http scheme for container log urls + val yarnHttpPolicy = yarnConf.get( + YarnConfiguration.YARN_HTTP_POLICY_KEY, + YarnConfiguration.YARN_HTTP_POLICY_DEFAULT + ) + val user = Utils.getCurrentUserName() + val httpScheme = if (yarnHttpPolicy == "HTTPS_ONLY") "https://" else "http://" + val baseUrl = s"$httpScheme$httpAddress/node/containerlogs/$containerId/$user" + logDebug(s"Base URL for logs: $baseUrl") + driverLogs = Some(Map( + "stdout" -> s"$baseUrl/stdout?start=-4096", + "stderr" -> s"$baseUrl/stderr?start=-4096")) + } catch { + case e: Exception => + logInfo("Error while building AM log links, so AM" + + " logs link will not appear in application UI", e) + } + driverLogs + } +} diff --git a/resource-managers/yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnScheduler.scala b/resource-managers/yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnScheduler.scala new file mode 100644 index 0000000000..029382133d --- /dev/null +++ b/resource-managers/yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnScheduler.scala @@ -0,0 +1,39 @@ +/* + * 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 org.apache.hadoop.yarn.util.RackResolver +import org.apache.log4j.{Level, Logger} + +import org.apache.spark._ +import org.apache.spark.scheduler.TaskSchedulerImpl +import org.apache.spark.util.Utils + +private[spark] class YarnScheduler(sc: SparkContext) extends TaskSchedulerImpl(sc) { + + // RackResolver logs an INFO message whenever it resolves a rack, which is way too often. + if (Logger.getLogger(classOf[RackResolver]).getLevel == null) { + Logger.getLogger(classOf[RackResolver]).setLevel(Level.WARN) + } + + // By default, rack is unknown + override def getRackForHost(hostPort: String): Option[String] = { + val host = Utils.parseHostPort(hostPort)._1 + Option(RackResolver.resolve(sc.hadoopConfiguration, host).getNetworkLocation) + } +} diff --git a/resource-managers/yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnSchedulerBackend.scala b/resource-managers/yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnSchedulerBackend.scala new file mode 100644 index 0000000000..2f9ea1911f --- /dev/null +++ b/resource-managers/yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnSchedulerBackend.scala @@ -0,0 +1,315 @@ +/* + * 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.concurrent.{ExecutionContext, Future} +import scala.util.{Failure, Success} +import scala.util.control.NonFatal + +import org.apache.hadoop.yarn.api.records.{ApplicationAttemptId, ApplicationId} + +import org.apache.spark.SparkContext +import org.apache.spark.internal.Logging +import org.apache.spark.rpc._ +import org.apache.spark.scheduler._ +import org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages._ +import org.apache.spark.ui.JettyUtils +import org.apache.spark.util.{RpcUtils, ThreadUtils} + +/** + * Abstract Yarn scheduler backend that contains common logic + * between the client and cluster Yarn scheduler backends. + */ +private[spark] abstract class YarnSchedulerBackend( + scheduler: TaskSchedulerImpl, + sc: SparkContext) + extends CoarseGrainedSchedulerBackend(scheduler, sc.env.rpcEnv) { + + override val minRegisteredRatio = + if (conf.getOption("spark.scheduler.minRegisteredResourcesRatio").isEmpty) { + 0.8 + } else { + super.minRegisteredRatio + } + + protected var totalExpectedExecutors = 0 + + private val yarnSchedulerEndpoint = new YarnSchedulerEndpoint(rpcEnv) + + private val yarnSchedulerEndpointRef = rpcEnv.setupEndpoint( + YarnSchedulerBackend.ENDPOINT_NAME, yarnSchedulerEndpoint) + + private implicit val askTimeout = RpcUtils.askRpcTimeout(sc.conf) + + /** Application ID. */ + protected var appId: Option[ApplicationId] = None + + /** Attempt ID. This is unset for client-mode schedulers */ + private var attemptId: Option[ApplicationAttemptId] = None + + /** Scheduler extension services. */ + private val services: SchedulerExtensionServices = new SchedulerExtensionServices() + + // Flag to specify whether this schedulerBackend should be reset. + private var shouldResetOnAmRegister = false + + /** + * Bind to YARN. This *must* be done before calling [[start()]]. + * + * @param appId YARN application ID + * @param attemptId Optional YARN attempt ID + */ + protected def bindToYarn(appId: ApplicationId, attemptId: Option[ApplicationAttemptId]): Unit = { + this.appId = Some(appId) + this.attemptId = attemptId + } + + override def start() { + require(appId.isDefined, "application ID unset") + val binding = SchedulerExtensionServiceBinding(sc, appId.get, attemptId) + services.start(binding) + super.start() + } + + override def stop(): Unit = { + try { + // SPARK-12009: To prevent Yarn allocator from requesting backup for the executors which + // was Stopped by SchedulerBackend. + requestTotalExecutors(0, 0, Map.empty) + super.stop() + } finally { + services.stop() + } + } + + /** + * Get the attempt ID for this run, if the cluster manager supports multiple + * attempts. Applications run in client mode will not have attempt IDs. + * This attempt ID only includes attempt counter, like "1", "2". + * + * @return The application attempt id, if available. + */ + override def applicationAttemptId(): Option[String] = { + attemptId.map(_.getAttemptId.toString) + } + + /** + * Get an application ID associated with the job. + * This returns the string value of [[appId]] if set, otherwise + * the locally-generated ID from the superclass. + * @return The application ID + */ + override def applicationId(): String = { + appId.map(_.toString).getOrElse { + logWarning("Application ID is not initialized yet.") + super.applicationId + } + } + + /** + * Request executors from the ApplicationMaster by specifying the total number desired. + * This includes executors already pending or running. + */ + override def doRequestTotalExecutors(requestedTotal: Int): Future[Boolean] = { + yarnSchedulerEndpointRef.ask[Boolean]( + RequestExecutors(requestedTotal, localityAwareTasks, hostToLocalTaskCount)) + } + + /** + * Request that the ApplicationMaster kill the specified executors. + */ + override def doKillExecutors(executorIds: Seq[String]): Future[Boolean] = { + yarnSchedulerEndpointRef.ask[Boolean](KillExecutors(executorIds)) + } + + override def sufficientResourcesRegistered(): Boolean = { + totalRegisteredExecutors.get() >= totalExpectedExecutors * minRegisteredRatio + } + + /** + * Add filters to the SparkUI. + */ + private def addWebUIFilter( + filterName: String, + filterParams: Map[String, String], + proxyBase: String): Unit = { + if (proxyBase != null && proxyBase.nonEmpty) { + System.setProperty("spark.ui.proxyBase", proxyBase) + } + + val hasFilter = + filterName != null && filterName.nonEmpty && + filterParams != null && filterParams.nonEmpty + if (hasFilter) { + logInfo(s"Add WebUI Filter. $filterName, $filterParams, $proxyBase") + conf.set("spark.ui.filters", filterName) + filterParams.foreach { case (k, v) => conf.set(s"spark.$filterName.param.$k", v) } + scheduler.sc.ui.foreach { ui => JettyUtils.addFilters(ui.getHandlers, conf) } + } + } + + override def createDriverEndpoint(properties: Seq[(String, String)]): DriverEndpoint = { + new YarnDriverEndpoint(rpcEnv, properties) + } + + /** + * Reset the state of SchedulerBackend to the initial state. This is happened when AM is failed + * and re-registered itself to driver after a failure. The stale state in driver should be + * cleaned. + */ + override protected def reset(): Unit = { + super.reset() + sc.executorAllocationManager.foreach(_.reset()) + } + + /** + * Override the DriverEndpoint to add extra logic for the case when an executor is disconnected. + * This endpoint communicates with the executors and queries the AM for an executor's exit + * status when the executor is disconnected. + */ + private class YarnDriverEndpoint(rpcEnv: RpcEnv, sparkProperties: Seq[(String, String)]) + extends DriverEndpoint(rpcEnv, sparkProperties) { + + /** + * When onDisconnected is received at the driver endpoint, the superclass DriverEndpoint + * handles it by assuming the Executor was lost for a bad reason and removes the executor + * immediately. + * + * In YARN's case however it is crucial to talk to the application master and ask why the + * executor had exited. If the executor exited for some reason unrelated to the running tasks + * (e.g., preemption), according to the application master, then we pass that information down + * to the TaskSetManager to inform the TaskSetManager that tasks on that lost executor should + * not count towards a job failure. + */ + override def onDisconnected(rpcAddress: RpcAddress): Unit = { + addressToExecutorId.get(rpcAddress).foreach { executorId => + if (disableExecutor(executorId)) { + yarnSchedulerEndpoint.handleExecutorDisconnectedFromDriver(executorId, rpcAddress) + } + } + } + } + + /** + * An [[RpcEndpoint]] that communicates with the ApplicationMaster. + */ + private class YarnSchedulerEndpoint(override val rpcEnv: RpcEnv) + extends ThreadSafeRpcEndpoint with Logging { + private var amEndpoint: Option[RpcEndpointRef] = None + + private[YarnSchedulerBackend] def handleExecutorDisconnectedFromDriver( + executorId: String, + executorRpcAddress: RpcAddress): Unit = { + val removeExecutorMessage = amEndpoint match { + case Some(am) => + val lossReasonRequest = GetExecutorLossReason(executorId) + am.ask[ExecutorLossReason](lossReasonRequest, askTimeout) + .map { reason => RemoveExecutor(executorId, reason) }(ThreadUtils.sameThread) + .recover { + case NonFatal(e) => + logWarning(s"Attempted to get executor loss reason" + + s" for executor id ${executorId} at RPC address ${executorRpcAddress}," + + s" but got no response. Marking as slave lost.", e) + RemoveExecutor(executorId, SlaveLost()) + }(ThreadUtils.sameThread) + case None => + logWarning("Attempted to check for an executor loss reason" + + " before the AM has registered!") + Future.successful(RemoveExecutor(executorId, SlaveLost("AM is not yet registered."))) + } + + removeExecutorMessage + .flatMap { message => + driverEndpoint.ask[Boolean](message) + }(ThreadUtils.sameThread) + .onFailure { + case NonFatal(e) => logError( + s"Error requesting driver to remove executor $executorId after disconnection.", e) + }(ThreadUtils.sameThread) + } + + override def receive: PartialFunction[Any, Unit] = { + case RegisterClusterManager(am) => + logInfo(s"ApplicationMaster registered as $am") + amEndpoint = Option(am) + if (!shouldResetOnAmRegister) { + shouldResetOnAmRegister = true + } else { + // AM is already registered before, this potentially means that AM failed and + // a new one registered after the failure. This will only happen in yarn-client mode. + reset() + } + + case AddWebUIFilter(filterName, filterParams, proxyBase) => + addWebUIFilter(filterName, filterParams, proxyBase) + + case r @ RemoveExecutor(executorId, reason) => + logWarning(reason.toString) + driverEndpoint.ask[Boolean](r).onFailure { + case e => + logError("Error requesting driver to remove executor" + + s" $executorId for reason $reason", e) + }(ThreadUtils.sameThread) + } + + + override def receiveAndReply(context: RpcCallContext): PartialFunction[Any, Unit] = { + case r: RequestExecutors => + amEndpoint match { + case Some(am) => + am.ask[Boolean](r).andThen { + case Success(b) => context.reply(b) + case Failure(NonFatal(e)) => + logError(s"Sending $r to AM was unsuccessful", e) + context.sendFailure(e) + }(ThreadUtils.sameThread) + case None => + logWarning("Attempted to request executors before the AM has registered!") + context.reply(false) + } + + case k: KillExecutors => + amEndpoint match { + case Some(am) => + am.ask[Boolean](k).andThen { + case Success(b) => context.reply(b) + case Failure(NonFatal(e)) => + logError(s"Sending $k to AM was unsuccessful", e) + context.sendFailure(e) + }(ThreadUtils.sameThread) + case None => + logWarning("Attempted to kill executors before the AM has registered!") + context.reply(false) + } + + case RetrieveLastAllocatedExecutorId => + context.reply(currentExecutorIdCounter) + } + + override def onDisconnected(remoteAddress: RpcAddress): Unit = { + if (amEndpoint.exists(_.address == remoteAddress)) { + logWarning(s"ApplicationMaster has disassociated: $remoteAddress") + amEndpoint = None + } + } + } +} + +private[spark] object YarnSchedulerBackend { + val ENDPOINT_NAME = "YarnScheduler" +} |