org.apache.spark.SparkEnv.scala Maven / Gradle / Ivy
Go to download
Show more of this group Show more artifacts with this name
Show all versions of snappy-spark-core_2.10 Show documentation
Show all versions of snappy-spark-core_2.10 Show documentation
SnappyData distributed data store and execution engine
The newest version!
/*
* 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.
*/
/*
* Changes for SnappyData data platform.
*
* Portions Copyright (c) 2016 SnappyData, Inc. All rights reserved.
*
* Licensed 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. See accompanying
* LICENSE file.
*/
package org.apache.spark
import java.io.File
import java.net.Socket
import scala.collection.mutable
import scala.util.Properties
import akka.actor.ActorSystem
import com.google.common.collect.MapMaker
import org.apache.spark.annotation.DeveloperApi
import org.apache.spark.api.python.PythonWorkerFactory
import org.apache.spark.broadcast.BroadcastManager
import org.apache.spark.metrics.MetricsSystem
import org.apache.spark.memory.{MemoryManager, StaticMemoryManager, UnifiedMemoryManager}
import org.apache.spark.network.BlockTransferService
import org.apache.spark.network.netty.NettyBlockTransferService
import org.apache.spark.rpc.{RpcEndpointRef, RpcEndpoint, RpcEnv}
import org.apache.spark.rpc.akka.AkkaRpcEnv
import org.apache.spark.scheduler.{OutputCommitCoordinator, LiveListenerBus}
import org.apache.spark.scheduler.OutputCommitCoordinator.OutputCommitCoordinatorEndpoint
import org.apache.spark.serializer.Serializer
import org.apache.spark.shuffle.ShuffleManager
import org.apache.spark.storage._
import org.apache.spark.util.{AkkaUtils, RpcUtils, Utils}
/**
* :: DeveloperApi ::
* Holds all the runtime environment objects for a running Spark instance (either master or worker),
* including the serializer, Akka actor system, block manager, map output tracker, etc. Currently
* Spark code finds the SparkEnv through a global variable, so all the threads can access the same
* SparkEnv. It can be accessed by SparkEnv.get (e.g. after creating a SparkContext).
*
* NOTE: This is not intended for external use. This is exposed for Shark and may be made private
* in a future release.
*/
@DeveloperApi
class SparkEnv (
val executorId: String,
private[spark] val rpcEnv: RpcEnv,
_actorSystem: ActorSystem, // TODO Remove actorSystem
val serializer: Serializer,
val closureSerializer: Serializer,
val cacheManager: CacheManager,
val mapOutputTracker: MapOutputTracker,
val shuffleManager: ShuffleManager,
val broadcastManager: BroadcastManager,
val blockTransferService: BlockTransferService,
val blockManager: BlockManager,
val securityManager: SecurityManager,
val sparkFilesDir: String,
val metricsSystem: MetricsSystem,
val memoryManager: MemoryManager,
val outputCommitCoordinator: OutputCommitCoordinator,
val conf: SparkConf) extends Logging {
// TODO Remove actorSystem
@deprecated("Actor system is no longer supported as of 1.4.0", "1.4.0")
val actorSystem: ActorSystem = _actorSystem
private[spark] var isStopped = false
private val pythonWorkers = mutable.HashMap[(String, Map[String, String]), PythonWorkerFactory]()
// A general, soft-reference map for metadata needed during HadoopRDD split computation
// (e.g., HadoopFileRDD uses this to cache JobConfs and InputFormats).
private[spark] val hadoopJobMetadata = new MapMaker().softValues().makeMap[String, Any]()
private var driverTmpDirToDelete: Option[String] = None
private[spark] def stop() {
if (!isStopped) {
isStopped = true
pythonWorkers.values.foreach(_.stop())
mapOutputTracker.stop()
shuffleManager.stop()
broadcastManager.stop()
blockManager.stop()
blockManager.master.stop()
metricsSystem.stop()
outputCommitCoordinator.stop()
if (!rpcEnv.isInstanceOf[AkkaRpcEnv]) {
actorSystem.shutdown()
}
rpcEnv.shutdown()
// Unfortunately Akka's awaitTermination doesn't actually wait for the Netty server to shut
// down, but let's call it anyway in case it gets fixed in a later release
// UPDATE: In Akka 2.1.x, this hangs if there are remote actors, so we can't call it.
// actorSystem.awaitTermination()
// Note that blockTransferService is stopped by BlockManager since it is started by it.
// If we only stop sc, but the driver process still run as a services then we need to delete
// the tmp dir, if not, it will create too many tmp dirs.
// We only need to delete the tmp dir create by driver, because sparkFilesDir is point to the
// current working dir in executor which we do not need to delete.
driverTmpDirToDelete match {
case Some(path) => {
try {
Utils.deleteRecursively(new File(path))
} catch {
case e: Exception =>
logWarning(s"Exception while deleting Spark temp dir: $path", e)
}
}
case None => // We just need to delete tmp dir created by driver, so do nothing on executor
}
}
}
private[spark]
def createPythonWorker(pythonExec: String, envVars: Map[String, String]): java.net.Socket = {
synchronized {
val key = (pythonExec, envVars)
pythonWorkers.getOrElseUpdate(key, new PythonWorkerFactory(pythonExec, envVars)).create()
}
}
private[spark]
def destroyPythonWorker(pythonExec: String, envVars: Map[String, String], worker: Socket) {
synchronized {
val key = (pythonExec, envVars)
pythonWorkers.get(key).foreach(_.stopWorker(worker))
}
}
private[spark]
def releasePythonWorker(pythonExec: String, envVars: Map[String, String], worker: Socket) {
synchronized {
val key = (pythonExec, envVars)
pythonWorkers.get(key).foreach(_.releaseWorker(worker))
}
}
}
object SparkEnv extends Logging {
@volatile private var env: SparkEnv = _
private[spark] val driverActorSystemName = "sparkDriver"
private[spark] val executorActorSystemName = "sparkExecutor"
def set(e: SparkEnv) {
env = e
}
/**
* Returns the SparkEnv.
*/
def get: SparkEnv = {
env
}
/**
* Returns the ThreadLocal SparkEnv.
*/
@deprecated("Use SparkEnv.get instead", "1.2.0")
def getThreadLocal: SparkEnv = {
env
}
/**
* Create a SparkEnv for the driver.
*/
private[spark] def createDriverEnv(
conf: SparkConf,
isLocal: Boolean,
listenerBus: LiveListenerBus,
numCores: Int,
mockOutputCommitCoordinator: Option[OutputCommitCoordinator] = None): SparkEnv = {
assert(conf.contains("spark.driver.host"), "spark.driver.host is not set on the driver!")
assert(conf.contains("spark.driver.port"), "spark.driver.port is not set on the driver!")
val hostname = conf.get("spark.driver.host")
val port = conf.get("spark.driver.port").toInt
create(
conf,
SparkContext.DRIVER_IDENTIFIER,
hostname,
port,
isDriver = true,
isLocal = isLocal,
numUsableCores = numCores,
listenerBus = listenerBus,
mockOutputCommitCoordinator = mockOutputCommitCoordinator
)
}
/**
* Create a SparkEnv for an executor.
* In coarse-grained mode, the executor provides an actor system that is already instantiated.
*/
private[spark] def createExecutorEnv(
conf: SparkConf,
executorId: String,
hostname: String,
port: Int,
numCores: Int,
isLocal: Boolean): SparkEnv = {
val env = create(
conf,
executorId,
hostname,
port,
isDriver = false,
isLocal = isLocal,
numUsableCores = numCores
)
SparkEnv.set(env)
env
}
/**
* Helper method to create a SparkEnv for a driver or an executor.
*/
private def create(
conf: SparkConf,
executorId: String,
hostname: String,
port: Int,
isDriver: Boolean,
isLocal: Boolean,
numUsableCores: Int,
listenerBus: LiveListenerBus = null,
mockOutputCommitCoordinator: Option[OutputCommitCoordinator] = None): SparkEnv = {
// Listener bus is only used on the driver
if (isDriver) {
assert(listenerBus != null, "Attempted to create driver SparkEnv with null listener bus!")
}
val securityManager = new SecurityManager(conf)
// Create the ActorSystem for Akka and get the port it binds to.
val actorSystemName = if (isDriver) driverActorSystemName else executorActorSystemName
val rpcEnv = RpcEnv.create(actorSystemName, hostname, port, conf, securityManager,
clientMode = !isDriver)
val actorSystem: ActorSystem =
if (rpcEnv.isInstanceOf[AkkaRpcEnv]) {
rpcEnv.asInstanceOf[AkkaRpcEnv].actorSystem
} else {
val actorSystemPort =
if (port == 0 || rpcEnv.address == null) {
port
} else {
rpcEnv.address.port + 1
}
// Create a ActorSystem for legacy codes
AkkaUtils.createActorSystem(
actorSystemName + "ActorSystem",
hostname,
actorSystemPort,
conf,
securityManager
)._1
}
// Figure out which port Akka actually bound to in case the original port is 0 or occupied.
// In the non-driver case, the RPC env's address may be null since it may not be listening
// for incoming connections.
if (isDriver) {
conf.set("spark.driver.port", rpcEnv.address.port.toString)
} else if (rpcEnv.address != null) {
conf.set("spark.executor.port", rpcEnv.address.port.toString)
}
// Create an instance of the class with the given name, possibly initializing it with our conf
def instantiateClass[T](className: String): T = {
val cls = Utils.classForName(className)
// Look for a constructor taking a SparkConf and a boolean isDriver, then one taking just
// SparkConf, then one taking no arguments
try {
cls.getConstructor(classOf[SparkConf], java.lang.Boolean.TYPE)
.newInstance(conf, new java.lang.Boolean(isDriver))
.asInstanceOf[T]
} catch {
case _: NoSuchMethodException =>
try {
cls.getConstructor(classOf[SparkConf]).newInstance(conf).asInstanceOf[T]
} catch {
case _: NoSuchMethodException =>
cls.getConstructor().newInstance().asInstanceOf[T]
}
}
}
// Create an instance of the class named by the given SparkConf property, or defaultClassName
// if the property is not set, possibly initializing it with our conf
def instantiateClassFromConf[T](propertyName: String, defaultClassName: String): T = {
instantiateClass[T](conf.get(propertyName, defaultClassName))
}
val serializer = instantiateClassFromConf[Serializer](
"spark.serializer", "org.apache.spark.serializer.JavaSerializer")
logDebug(s"Using serializer: ${serializer.getClass}")
val closureSerializer = instantiateClassFromConf[Serializer](
"spark.closure.serializer", "org.apache.spark.serializer.JavaSerializer")
def registerOrLookupEndpoint(
name: String, endpointCreator: => RpcEndpoint):
RpcEndpointRef = {
if (isDriver) {
logInfo("Registering " + name)
rpcEnv.setupEndpoint(name, endpointCreator)
} else {
RpcUtils.makeDriverRef(name, conf, rpcEnv)
}
}
val mapOutputTracker = if (isDriver) {
new MapOutputTrackerMaster(conf)
} else {
new MapOutputTrackerWorker(conf)
}
// Have to assign trackerActor after initialization as MapOutputTrackerActor
// requires the MapOutputTracker itself
mapOutputTracker.trackerEndpoint = registerOrLookupEndpoint(MapOutputTracker.ENDPOINT_NAME,
new MapOutputTrackerMasterEndpoint(
rpcEnv, mapOutputTracker.asInstanceOf[MapOutputTrackerMaster], conf))
// Let the user specify short names for shuffle managers
val shortShuffleMgrNames = Map(
"hash" -> "org.apache.spark.shuffle.hash.HashShuffleManager",
"sort" -> "org.apache.spark.shuffle.sort.SortShuffleManager",
"tungsten-sort" -> "org.apache.spark.shuffle.sort.SortShuffleManager")
val shuffleMgrName = conf.get("spark.shuffle.manager", "sort")
val shuffleMgrClass = shortShuffleMgrNames.getOrElse(shuffleMgrName.toLowerCase, shuffleMgrName)
val shuffleManager = instantiateClass[ShuffleManager](shuffleMgrClass)
val useLegacyMemoryManager = conf.getBoolean("spark.memory.useLegacyMode", false)
val memoryManager: MemoryManager =
conf.getOption("spark.memory.manager").map(Utils.classForName(_)
.getConstructor(classOf[SparkConf], classOf[Int])
.newInstance(conf, Int.box(numUsableCores))
.asInstanceOf[MemoryManager]).getOrElse {
if (useLegacyMemoryManager) {
new StaticMemoryManager(conf, numUsableCores)
} else {
UnifiedMemoryManager(conf, numUsableCores)
}
}
val blockTransferService = new NettyBlockTransferService(conf, securityManager, numUsableCores)
val blockManagerMaster = new BlockManagerMaster(registerOrLookupEndpoint(
BlockManagerMaster.DRIVER_ENDPOINT_NAME,
new BlockManagerMasterEndpoint(rpcEnv, isLocal, conf, listenerBus)),
conf, isDriver)
// NB: blockManager is not valid until initialize() is called later.
val blockManager = new BlockManager(executorId, rpcEnv, blockManagerMaster,
serializer, conf, memoryManager, mapOutputTracker, shuffleManager,
blockTransferService, securityManager, numUsableCores)
val broadcastManager = new BroadcastManager(isDriver, conf, securityManager)
val cacheManager = new CacheManager(blockManager)
val metricsSystem = if (isDriver) {
// Don't start metrics system right now for Driver.
// We need to wait for the task scheduler to give us an app ID.
// Then we can start the metrics system.
MetricsSystem.createMetricsSystem("driver", conf, securityManager)
} else {
// We need to set the executor ID before the MetricsSystem is created because sources and
// sinks specified in the metrics configuration file will want to incorporate this executor's
// ID into the metrics they report.
conf.set("spark.executor.id", executorId)
val ms = MetricsSystem.createMetricsSystem("executor", conf, securityManager)
ms.start()
ms
}
// Set the sparkFiles directory, used when downloading dependencies. In local mode,
// this is a temporary directory; in distributed mode, this is the executor's current working
// directory.
val sparkFilesDir: String = if (isDriver) {
Utils.createTempDir(Utils.getLocalDir(conf), "userFiles").getAbsolutePath
} else {
"."
}
val outputCommitCoordinator = mockOutputCommitCoordinator.getOrElse {
new OutputCommitCoordinator(conf, isDriver)
}
val outputCommitCoordinatorRef = registerOrLookupEndpoint("OutputCommitCoordinator",
new OutputCommitCoordinatorEndpoint(rpcEnv, outputCommitCoordinator))
outputCommitCoordinator.coordinatorRef = Some(outputCommitCoordinatorRef)
val envInstance = new SparkEnv(
executorId,
rpcEnv,
actorSystem,
serializer,
closureSerializer,
cacheManager,
mapOutputTracker,
shuffleManager,
broadcastManager,
blockTransferService,
blockManager,
securityManager,
sparkFilesDir,
metricsSystem,
memoryManager,
outputCommitCoordinator,
conf)
// Add a reference to tmp dir created by driver, we will delete this tmp dir when stop() is
// called, and we only need to do it for driver. Because driver may run as a service, and if we
// don't delete this tmp dir when sc is stopped, then will create too many tmp dirs.
if (isDriver) {
envInstance.driverTmpDirToDelete = Some(sparkFilesDir)
}
envInstance
}
/**
* Return a map representation of jvm information, Spark properties, system properties, and
* class paths. Map keys define the category, and map values represent the corresponding
* attributes as a sequence of KV pairs. This is used mainly for SparkListenerEnvironmentUpdate.
*/
private[spark]
def environmentDetails(
conf: SparkConf,
schedulingMode: String,
addedJars: Seq[String],
addedFiles: Seq[String]): Map[String, Seq[(String, String)]] = {
import Properties._
val jvmInformation = Seq(
("Java Version", s"$javaVersion ($javaVendor)"),
("Java Home", javaHome),
("Scala Version", versionString)
).sorted
// Spark properties
// This includes the scheduling mode whether or not it is configured (used by SparkUI)
val schedulerMode =
if (!conf.contains("spark.scheduler.mode")) {
Seq(("spark.scheduler.mode", schedulingMode))
} else {
Seq[(String, String)]()
}
val sparkProperties = (conf.getAll ++ schedulerMode).sorted
// System properties that are not java classpaths
val systemProperties = Utils.getSystemProperties.toSeq
val otherProperties = systemProperties.filter { case (k, _) =>
k != "java.class.path" && !k.startsWith("spark.")
}.sorted
// Class paths including all added jars and files
val classPathEntries = javaClassPath
.split(File.pathSeparator)
.filterNot(_.isEmpty)
.map((_, "System Classpath"))
val addedJarsAndFiles = (addedJars ++ addedFiles).map((_, "Added By User"))
val classPaths = (addedJarsAndFiles ++ classPathEntries).sorted
Map[String, Seq[(String, String)]](
"JVM Information" -> jvmInformation,
"Spark Properties" -> sparkProperties,
"System Properties" -> otherProperties,
"Classpath Entries" -> classPaths)
}
}
© 2015 - 2025 Weber Informatics LLC | Privacy Policy