org.apache.spark.deploy.yarn.Client.scala Maven / Gradle / Ivy
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* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.apache.spark.deploy.yarn
import java.io.{ByteArrayInputStream, DataInputStream, 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.hdfs.security.token.delegation.DelegationTokenIdentifier
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.internal.Logging
import org.apache.spark.internal.config._
import org.apache.spark.launcher.{LauncherBackend, SparkAppHandle, YarnCommandBuilderUtils}
import org.apache.spark.util.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())
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)
// 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.exists(stagingDirPath)) {
logInfo("Deleting staging directory " + stagingDirPath)
fs.delete(stagingDirPath, true)
}
} 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 ${AM_NODE_LABEL_EXPRESSION.key} because the version " +
"of YARN does not support it")
appContext.setResource(capability)
}
case None =>
appContext.setResource(capability)
}
appContext
}
/** Set up security tokens for launching our ApplicationMaster container. */
private def setupSecurityToken(amContainer: ContainerLaunchContext): Unit = {
val dob = new DataOutputBuffer
credentials.writeTokenStorageToStream(dob)
amContainer.setTokens(ByteBuffer.wrap(dob.getData))
}
/** Get the application report from the ResourceManager for an application we have submitted. */
def getApplicationReport(appId: ApplicationId): ApplicationReport =
yarnClient.getApplicationReport(appId)
/**
* Return the security token used by this client to communicate with the ApplicationMaster.
* If no security is enabled, the token returned by the report is null.
*/
private def getClientToken(report: ApplicationReport): String =
Option(report.getClientToAMToken).map(_.toString).getOrElse("")
/**
* Fail fast if we have requested more resources per container than is available in the cluster.
*/
private def verifyClusterResources(newAppResponse: GetNewApplicationResponse): Unit = {
val maxMem = newAppResponse.getMaximumResourceCapability().getMemory()
logInfo("Verifying our application has not requested more than the maximum " +
s"memory capability of the cluster ($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)
val nns = YarnSparkHadoopUtil.get.getNameNodesToAccess(sparkConf) + destDir
YarnSparkHadoopUtil.get.obtainTokensForNamenodes(nns, hadoopConf, credentials)
// 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]
YarnSparkHadoopUtil.get.obtainTokenForHiveMetastore(sparkConf, hadoopConf, credentials)
YarnSparkHadoopUtil.get.obtainTokenForHBase(sparkConf, hadoopConf, credentials)
if (credentials != null) {
logDebug(YarnSparkHadoopUtil.get.dumpTokens(credentials).mkString("\n"))
}
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 && localizedPath != null) {
cachedSecondaryJarLinks += localizedPath
}
}
}
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
}
/**
* Get the renewal interval for tokens.
*/
private def getTokenRenewalInterval(stagingDirPath: Path): Long = {
// We cannot use the tokens generated above since those have renewer yarn. Trying to renew
// those will fail with an access control issue. So create new tokens with the logged in
// user as renewer.
val creds = new Credentials()
val nns = YarnSparkHadoopUtil.get.getNameNodesToAccess(sparkConf) + stagingDirPath
YarnSparkHadoopUtil.get.obtainTokensForNamenodes(
nns, hadoopConf, creds, sparkConf.get(PRINCIPAL))
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 set to $interval")
interval
}
/**
* 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")
val renewalInterval = getTokenRenewalInterval(stagingDirPath)
sparkConf.set(TOKEN_RENEWAL_INTERVAL, renewalInterval)
}
// 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
sys.env.get("PYSPARK_DRIVER_PYTHON").foreach(env("PYSPARK_DRIVER_PYTHON") = _)
sys.env.get("PYSPARK_PYTHON").foreach(env("PYSPARK_PYTHON") = _)
}
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.")
return (YarnApplicationState.KILLED, FinalApplicationStatus.KILLED)
case NonFatal(e) =>
logError(s"Failed to contact YARN for application $appId.", e)
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-0.10.3-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
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:")
}
}
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