SparkContext.scala

/*
 * 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

import java.io._
import java.net.URI
import java.util.{Arrays, Locale, Properties, ServiceLoader, UUID}
import java.util.concurrent.{ConcurrentHashMap, ConcurrentMap}
import java.util.concurrent.atomic.{AtomicBoolean, AtomicInteger, AtomicReference}

import scala.collection.JavaConverters._
import scala.collection.Map
import scala.collection.immutable
import scala.collection.mutable.HashMap
import scala.language.implicitConversions
import scala.reflect.{classTag, ClassTag}
import scala.util.control.NonFatal

import com.google.common.collect.MapMaker
import org.apache.hadoop.conf.Configuration
import org.apache.hadoop.fs.{FileSystem, Path}
import org.apache.hadoop.io.{ArrayWritable, BooleanWritable, BytesWritable, DoubleWritable, FloatWritable, IntWritable, LongWritable, NullWritable, Text, Writable}
import org.apache.hadoop.mapred.{FileInputFormat, InputFormat, JobConf, SequenceFileInputFormat, TextInputFormat}
import org.apache.hadoop.mapreduce.{InputFormat => NewInputFormat, Job => NewHadoopJob}
import org.apache.hadoop.mapreduce.lib.input.{FileInputFormat => NewFileInputFormat}

import org.apache.spark.annotation.DeveloperApi
import org.apache.spark.broadcast.Broadcast
import org.apache.spark.deploy.{LocalSparkCluster, SparkHadoopUtil}
import org.apache.spark.executor.{Executor, ExecutorMetrics, ExecutorMetricsSource}
import org.apache.spark.input.{FixedLengthBinaryInputFormat, PortableDataStream, StreamInputFormat, WholeTextFileInputFormat}
import org.apache.spark.internal.Logging
import org.apache.spark.internal.config._
import org.apache.spark.internal.config.Tests._
import org.apache.spark.internal.config.UI._
import org.apache.spark.internal.plugin.PluginContainer
import org.apache.spark.io.CompressionCodec
import org.apache.spark.metrics.source.JVMCPUSource
import org.apache.spark.partial.{ApproximateEvaluator, PartialResult}
import org.apache.spark.rdd._
import org.apache.spark.resource._
import org.apache.spark.resource.ResourceUtils._
import org.apache.spark.rpc.RpcEndpointRef
import org.apache.spark.scheduler._
import org.apache.spark.scheduler.cluster.StandaloneSchedulerBackend
import org.apache.spark.scheduler.local.LocalSchedulerBackend
import org.apache.spark.shuffle.ShuffleDataIOUtils
import org.apache.spark.shuffle.api.ShuffleDriverComponents
import org.apache.spark.status.{AppStatusSource, AppStatusStore}
import org.apache.spark.status.api.v1.ThreadStackTrace
import org.apache.spark.storage._
import org.apache.spark.storage.BlockManagerMessages.TriggerThreadDump
import org.apache.spark.ui.{ConsoleProgressBar, SparkUI}
import org.apache.spark.util._
import org.apache.spark.util.logging.DriverLogger

/**
 * Main entry point for Spark functionality. A SparkContext represents the connection to a Spark
 * cluster, and can be used to create RDDs, accumulators and broadcast variables on that cluster.
 *
 * @note Only one `SparkContext` should be active per JVM. You must `stop()` the
 *   active `SparkContext` before creating a new one.
 * @param config a Spark Config object describing the application configuration. Any settings in
 *   this config overrides the default configs as well as system properties.
 */
class SparkContext(config: SparkConf) extends Logging {

  // The call site where this SparkContext was constructed.
  private val creationSite: CallSite = Utils.getCallSite()

  if (!config.get(EXECUTOR_ALLOW_SPARK_CONTEXT)) {
    // In order to prevent SparkContext from being created in executors.
    SparkContext.assertOnDriver()
  }

  // In order to prevent multiple SparkContexts from being active at the same time, mark this
  // context as having started construction.
  // NOTE: this must be placed at the beginning of the SparkContext constructor.
  SparkContext.markPartiallyConstructed(this)

  val startTime = System.currentTimeMillis()

  private[spark] val stopped: AtomicBoolean = new AtomicBoolean(false)

  private[spark] def assertNotStopped(): Unit = {
    if (stopped.get()) {
      val activeContext = SparkContext.activeContext.get()
      val activeCreationSite =
        if (activeContext == null) {
          "(No active SparkContext.)"
        } else {
          activeContext.creationSite.longForm
        }
      throw new IllegalStateException(
        s"""Cannot call methods on a stopped SparkContext.
           |This stopped SparkContext was created at:
           |
           |${creationSite.longForm}
           |
           |The currently active SparkContext was created at:
           |
           |$activeCreationSite
         """.stripMargin)
    }
  }

  /**
   * Create a SparkContext that loads settings from system properties (for instance, when
   * launching with ./bin/spark-submit).
   */
  def this() = this(new SparkConf())

  /**
   * Alternative constructor that allows setting common Spark properties directly
   *
   * @param master Cluster URL to connect to (e.g. mesos://host:port, spark://host:port, local[4]).
   * @param appName A name for your application, to display on the cluster web UI
   * @param conf a [[org.apache.spark.SparkConf]] object specifying other Spark parameters
   */
  def this(master: String, appName: String, conf: SparkConf) =
    this(SparkContext.updatedConf(conf, master, appName))

  /**
   * Alternative constructor that allows setting common Spark properties directly
   *
   * @param master Cluster URL to connect to (e.g. mesos://host:port, spark://host:port, local[4]).
   * @param appName A name for your application, to display on the cluster web UI.
   * @param sparkHome Location where Spark is installed on cluster nodes.
   * @param jars Collection of JARs to send to the cluster. These can be paths on the local file
   *             system or HDFS, HTTP, HTTPS, or FTP URLs.
   * @param environment Environment variables to set on worker nodes.
   */
  def this(
      master: String,
      appName: String,
      sparkHome: String = null,
      jars: Seq[String] = Nil,
      environment: Map[String, String] = Map()) = {
    this(SparkContext.updatedConf(new SparkConf(), master, appName, sparkHome, jars, environment))
  }

  // The following constructors are required when Java code accesses SparkContext directly.
  // Please see SI-4278

  /**
   * Alternative constructor that allows setting common Spark properties directly
   *
   * @param master Cluster URL to connect to (e.g. mesos://host:port, spark://host:port, local[4]).
   * @param appName A name for your application, to display on the cluster web UI.
   */
  private[spark] def this(master: String, appName: String) =
    this(master, appName, null, Nil, Map())

  /**
   * Alternative constructor that allows setting common Spark properties directly
   *
   * @param master Cluster URL to connect to (e.g. mesos://host:port, spark://host:port, local[4]).
   * @param appName A name for your application, to display on the cluster web UI.
   * @param sparkHome Location where Spark is installed on cluster nodes.
   */
  private[spark] def this(master: String, appName: String, sparkHome: String) =
    this(master, appName, sparkHome, Nil, Map())

  /**
   * Alternative constructor that allows setting common Spark properties directly
   *
   * @param master Cluster URL to connect to (e.g. mesos://host:port, spark://host:port, local[4]).
   * @param appName A name for your application, to display on the cluster web UI.
   * @param sparkHome Location where Spark is installed on cluster nodes.
   * @param jars Collection of JARs to send to the cluster. These can be paths on the local file
   *             system or HDFS, HTTP, HTTPS, or FTP URLs.
   */
  private[spark] def this(master: String, appName: String, sparkHome: String, jars: Seq[String]) =
    this(master, appName, sparkHome, jars, Map())

  // log out Spark Version in Spark driver log
  logInfo(s"Running Spark version $SPARK_VERSION")

  /* ------------------------------------------------------------------------------------- *
   | Private variables. These variables keep the internal state of the context, and are    |
   | not accessible by the outside world. They're mutable since we want to initialize all  |
   | of them to some neutral value ahead of time, so that calling "stop()" while the       |
   | constructor is still running is safe.                                                 |
   * ------------------------------------------------------------------------------------- */

  private var _conf: SparkConf = _
  private var _eventLogDir: Option[URI] = None
  private var _eventLogCodec: Option[String] = None
  private var _listenerBus: LiveListenerBus = _
  private var _env: SparkEnv = _
  private var _statusTracker: SparkStatusTracker = _
  private var _progressBar: Option[ConsoleProgressBar] = None
  private var _ui: Option[SparkUI] = None
  private var _hadoopConfiguration: Configuration = _
  private var _executorMemory: Int = _
  private var _schedulerBackend: SchedulerBackend = _
  private var _taskScheduler: TaskScheduler = _
  private var _heartbeatReceiver: RpcEndpointRef = _
  @volatile private var _dagScheduler: DAGScheduler = _
  private var _applicationId: String = _
  private var _applicationAttemptId: Option[String] = None
  private var _eventLogger: Option[EventLoggingListener] = None
  private var _driverLogger: Option[DriverLogger] = None
  private var _executorAllocationManager: Option[ExecutorAllocationManager] = None
  private var _cleaner: Option[ContextCleaner] = None
  private var _listenerBusStarted: Boolean = false
  private var _jars: Seq[String] = _
  private var _files: Seq[String] = _
  private var _shutdownHookRef: AnyRef = _
  private var _statusStore: AppStatusStore = _
  private var _heartbeater: Heartbeater = _
  private var _resources: immutable.Map[String, ResourceInformation] = _
  private var _shuffleDriverComponents: ShuffleDriverComponents = _
  private var _plugins: Option[PluginContainer] = None
  private var _resourceProfileManager: ResourceProfileManager = _

  /* ------------------------------------------------------------------------------------- *
   | Accessors and public fields. These provide access to the internal state of the        |
   | context.                                                                              |
   * ------------------------------------------------------------------------------------- */

  private[spark] def conf: SparkConf = _conf

  /**
   * Return a copy of this SparkContext's configuration. The configuration ''cannot'' be
   * changed at runtime.
   */
  def getConf: SparkConf = conf.clone()

  def resources: Map[String, ResourceInformation] = _resources

  def jars: Seq[String] = _jars
  def files: Seq[String] = _files
  def master: String = _conf.get("spark.master")
  def deployMode: String = _conf.get(SUBMIT_DEPLOY_MODE)
  def appName: String = _conf.get("spark.app.name")

  private[spark] def isEventLogEnabled: Boolean = _conf.get(EVENT_LOG_ENABLED)
  private[spark] def eventLogDir: Option[URI] = _eventLogDir
  private[spark] def eventLogCodec: Option[String] = _eventLogCodec

  def isLocal: Boolean = Utils.isLocalMaster(_conf)

  /**
   * @return true if context is stopped or in the midst of stopping.
   */
  def isStopped: Boolean = stopped.get()

  private[spark] def statusStore: AppStatusStore = _statusStore

  // An asynchronous listener bus for Spark events
  private[spark] def listenerBus: LiveListenerBus = _listenerBus

  // This function allows components created by SparkEnv to be mocked in unit tests:
  private[spark] def createSparkEnv(
      conf: SparkConf,
      isLocal: Boolean,
      listenerBus: LiveListenerBus): SparkEnv = {
    SparkEnv.createDriverEnv(conf, isLocal, listenerBus, SparkContext.numDriverCores(master, conf))
  }

  private[spark] def env: SparkEnv = _env

  // Used to store a URL for each static file/jar together with the file's local timestamp
  private[spark] val addedFiles = new ConcurrentHashMap[String, Long]().asScala
  private[spark] val addedJars = new ConcurrentHashMap[String, Long]().asScala

  // Keeps track of all persisted RDDs
  private[spark] val persistentRdds = {
    val map: ConcurrentMap[Int, RDD[_]] = new MapMaker().weakValues().makeMap[Int, RDD[_]]()
    map.asScala
  }
  def statusTracker: SparkStatusTracker = _statusTracker

  private[spark] def progressBar: Option[ConsoleProgressBar] = _progressBar

  private[spark] def ui: Option[SparkUI] = _ui

  def uiWebUrl: Option[String] = _ui.map(_.webUrl)

  /**
   * A default Hadoop Configuration for the Hadoop code (e.g. file systems) that we reuse.
   *
   * @note As it will be reused in all Hadoop RDDs, it's better not to modify it unless you
   * plan to set some global configurations for all Hadoop RDDs.
   */
  def hadoopConfiguration: Configuration = _hadoopConfiguration

  private[spark] def executorMemory: Int = _executorMemory

  // Environment variables to pass to our executors.
  private[spark] val executorEnvs = HashMap[String, String]()

  // Set SPARK_USER for user who is running SparkContext.
  val sparkUser = Utils.getCurrentUserName()

  private[spark] def schedulerBackend: SchedulerBackend = _schedulerBackend

  private[spark] def taskScheduler: TaskScheduler = _taskScheduler
  private[spark] def taskScheduler_=(ts: TaskScheduler): Unit = {
    _taskScheduler = ts
  }

  private[spark] def dagScheduler: DAGScheduler = _dagScheduler
  private[spark] def dagScheduler_=(ds: DAGScheduler): Unit = {
    _dagScheduler = ds
  }

  private[spark] def shuffleDriverComponents: ShuffleDriverComponents = _shuffleDriverComponents

  /**
   * A unique identifier for the Spark application.
   * Its format depends on the scheduler implementation.
   * (i.e.
   *  in case of local spark app something like 'local-1433865536131'
   *  in case of YARN something like 'application_1433865536131_34483'
   *  in case of MESOS something like 'driver-20170926223339-0001'
   * )
   */
  def applicationId: String = _applicationId
  def applicationAttemptId: Option[String] = _applicationAttemptId

  private[spark] def eventLogger: Option[EventLoggingListener] = _eventLogger

  private[spark] def executorAllocationManager: Option[ExecutorAllocationManager] =
    _executorAllocationManager

  private[spark] def resourceProfileManager: ResourceProfileManager = _resourceProfileManager

  private[spark] def cleaner: Option[ContextCleaner] = _cleaner

  private[spark] var checkpointDir: Option[String] = None

  // Thread Local variable that can be used by users to pass information down the stack
  protected[spark] val localProperties = new InheritableThreadLocal[Properties] {
    override protected def childValue(parent: Properties): Properties = {
      // Note: make a clone such that changes in the parent properties aren't reflected in
      // the those of the children threads, which has confusing semantics (SPARK-10563).
      Utils.cloneProperties(parent)
    }
    override protected def initialValue(): Properties = new Properties()
  }

  /* ------------------------------------------------------------------------------------- *
   | Initialization. This code initializes the context in a manner that is exception-safe. |
   | All internal fields holding state are initialized here, and any error prompts the     |
   | stop() method to be called.                                                           |
   * ------------------------------------------------------------------------------------- */

  private def warnSparkMem(value: String): String = {
    logWarning("Using SPARK_MEM to set amount of memory to use per executor process is " +
      "deprecated, please use spark.executor.memory instead.")
    value
  }

  /** Control our logLevel. This overrides any user-defined log settings.
   * @param logLevel The desired log level as a string.
   * Valid log levels include: ALL, DEBUG, ERROR, FATAL, INFO, OFF, TRACE, WARN
   */
  def setLogLevel(logLevel: String): Unit = {
    // let's allow lowercase or mixed case too
    val upperCased = logLevel.toUpperCase(Locale.ROOT)
    require(SparkContext.VALID_LOG_LEVELS.contains(upperCased),
      s"Supplied level $logLevel did not match one of:" +
        s" ${SparkContext.VALID_LOG_LEVELS.mkString(",")}")
    Utils.setLogLevel(org.apache.log4j.Level.toLevel(upperCased))
  }

  try {
    _conf = config.clone()
    _conf.validateSettings()
    _conf.set("spark.app.startTime", startTime.toString)

    if (!_conf.contains("spark.master")) {
      throw new SparkException("A master URL must be set in your configuration")
    }
    if (!_conf.contains("spark.app.name")) {
      throw new SparkException("An application name must be set in your configuration")
    }

    _driverLogger = DriverLogger(_conf)

    val resourcesFileOpt = conf.get(DRIVER_RESOURCES_FILE)
    _resources = getOrDiscoverAllResources(_conf, SPARK_DRIVER_PREFIX, resourcesFileOpt)
    logResourceInfo(SPARK_DRIVER_PREFIX, _resources)

    // log out spark.app.name in the Spark driver logs
    logInfo(s"Submitted application: $appName")

    // System property spark.yarn.app.id must be set if user code ran by AM on a YARN cluster
    if (master == "yarn" && deployMode == "cluster" && !_conf.contains("spark.yarn.app.id")) {
      throw new SparkException("Detected yarn cluster mode, but isn't running on a cluster. " +
        "Deployment to YARN is not supported directly by SparkContext. Please use spark-submit.")
    }

    if (_conf.getBoolean("spark.logConf", false)) {
      logInfo("Spark configuration:\n" + _conf.toDebugString)
    }

    // Set Spark driver host and port system properties. This explicitly sets the configuration
    // instead of relying on the default value of the config constant.
    _conf.set(DRIVER_HOST_ADDRESS, _conf.get(DRIVER_HOST_ADDRESS))
    _conf.setIfMissing(DRIVER_PORT, 0)

    _conf.set(EXECUTOR_ID, SparkContext.DRIVER_IDENTIFIER)

    _jars = Utils.getUserJars(_conf)
    _files = _conf.getOption(FILES.key).map(_.split(",")).map(_.filter(_.nonEmpty))
      .toSeq.flatten

    _eventLogDir =
      if (isEventLogEnabled) {
        val unresolvedDir = conf.get(EVENT_LOG_DIR).stripSuffix("/")
        Some(Utils.resolveURI(unresolvedDir))
      } else {
        None
      }

    _eventLogCodec = {
      val compress = _conf.get(EVENT_LOG_COMPRESS)
      if (compress && isEventLogEnabled) {
        Some(_conf.get(EVENT_LOG_COMPRESSION_CODEC)).map(CompressionCodec.getShortName)
      } else {
        None
      }
    }

    _listenerBus = new LiveListenerBus(_conf)
    _resourceProfileManager = new ResourceProfileManager(_conf, _listenerBus)

    // Initialize the app status store and listener before SparkEnv is created so that it gets
    // all events.
    val appStatusSource = AppStatusSource.createSource(conf)
    _statusStore = AppStatusStore.createLiveStore(conf, appStatusSource)
    listenerBus.addToStatusQueue(_statusStore.listener.get)

    // Create the Spark execution environment (cache, map output tracker, etc)
    _env = createSparkEnv(_conf, isLocal, listenerBus)
    SparkEnv.set(_env)

    // If running the REPL, register the repl's output dir with the file server.
    _conf.getOption("spark.repl.class.outputDir").foreach { path =>
      val replUri = _env.rpcEnv.fileServer.addDirectory("/classes", new File(path))
      _conf.set("spark.repl.class.uri", replUri)
    }

    _statusTracker = new SparkStatusTracker(this, _statusStore)

    _progressBar =
      if (_conf.get(UI_SHOW_CONSOLE_PROGRESS)) {
        Some(new ConsoleProgressBar(this))
      } else {
        None
      }

    _ui =
      if (conf.get(UI_ENABLED)) {
        Some(SparkUI.create(Some(this), _statusStore, _conf, _env.securityManager, appName, "",
          startTime))
      } else {
        // For tests, do not enable the UI
        None
      }
    // Bind the UI before starting the task scheduler to communicate
    // the bound port to the cluster manager properly
    _ui.foreach(_.bind())

    _hadoopConfiguration = SparkHadoopUtil.get.newConfiguration(_conf)
    // Performance optimization: this dummy call to .size() triggers eager evaluation of
    // Configuration's internal  `properties` field, guaranteeing that it will be computed and
    // cached before SessionState.newHadoopConf() uses `sc.hadoopConfiguration` to create
    // a new per-session Configuration. If `properties` has not been computed by that time
    // then each newly-created Configuration will perform its own expensive IO and XML
    // parsing to load configuration defaults and populate its own properties. By ensuring
    // that we've pre-computed the parent's properties, the child Configuration will simply
    // clone the parent's properties.
    _hadoopConfiguration.size()

    // Add each JAR given through the constructor
    if (jars != null) {
      jars.foreach(jar => addJar(jar, true))
      if (addedJars.nonEmpty) {
        _conf.set("spark.app.initial.jar.urls", addedJars.keys.toSeq.mkString(","))
      }
    }

    if (files != null) {
      files.foreach(file => addFile(file, false, true))
      if (addedFiles.nonEmpty) {
        _conf.set("spark.app.initial.file.urls", addedFiles.keys.toSeq.mkString(","))
      }
    }

    _executorMemory = _conf.getOption(EXECUTOR_MEMORY.key)
      .orElse(Option(System.getenv("SPARK_EXECUTOR_MEMORY")))
      .orElse(Option(System.getenv("SPARK_MEM"))
      .map(warnSparkMem))
      .map(Utils.memoryStringToMb)
      .getOrElse(1024)

    // Convert java options to env vars as a work around
    // since we can't set env vars directly in sbt.
    for { (envKey, propKey) <- Seq(("SPARK_TESTING", IS_TESTING.key))
      value <- Option(System.getenv(envKey)).orElse(Option(System.getProperty(propKey)))} {
      executorEnvs(envKey) = value
    }
    Option(System.getenv("SPARK_PREPEND_CLASSES")).foreach { v =>
      executorEnvs("SPARK_PREPEND_CLASSES") = v
    }
    // The Mesos scheduler backend relies on this environment variable to set executor memory.
    // TODO: Set this only in the Mesos scheduler.
    executorEnvs("SPARK_EXECUTOR_MEMORY") = executorMemory + "m"
    executorEnvs ++= _conf.getExecutorEnv
    executorEnvs("SPARK_USER") = sparkUser

    _shuffleDriverComponents = ShuffleDataIOUtils.loadShuffleDataIO(config).driver()
    _shuffleDriverComponents.initializeApplication().asScala.foreach { case (k, v) =>
      _conf.set(ShuffleDataIOUtils.SHUFFLE_SPARK_CONF_PREFIX + k, v)
    }

    // We need to register "HeartbeatReceiver" before "createTaskScheduler" because Executor will
    // retrieve "HeartbeatReceiver" in the constructor. (SPARK-6640)
    _heartbeatReceiver = env.rpcEnv.setupEndpoint(
      HeartbeatReceiver.ENDPOINT_NAME, new HeartbeatReceiver(this))

    // Initialize any plugins before the task scheduler is initialized.
    _plugins = PluginContainer(this, _resources.asJava)

    // Create and start the scheduler
    val (sched, ts) = SparkContext.createTaskScheduler(this, master, deployMode)
    _schedulerBackend = sched
    _taskScheduler = ts
    _dagScheduler = new DAGScheduler(this)
    _heartbeatReceiver.ask[Boolean](TaskSchedulerIsSet)

    val _executorMetricsSource =
      if (_conf.get(METRICS_EXECUTORMETRICS_SOURCE_ENABLED)) {
        Some(new ExecutorMetricsSource)
      } else {
        None
      }

    // create and start the heartbeater for collecting memory metrics
    _heartbeater = new Heartbeater(
      () => SparkContext.this.reportHeartBeat(_executorMetricsSource),
      "driver-heartbeater",
      conf.get(EXECUTOR_HEARTBEAT_INTERVAL))
    _heartbeater.start()

    // start TaskScheduler after taskScheduler sets DAGScheduler reference in DAGScheduler's
    // constructor
    _taskScheduler.start()

    _applicationId = _taskScheduler.applicationId()
    _applicationAttemptId = _taskScheduler.applicationAttemptId()
    _conf.set("spark.app.id", _applicationId)
    if (_conf.get(UI_REVERSE_PROXY)) {
      val proxyUrl = _conf.get(UI_REVERSE_PROXY_URL.key, "").stripSuffix("/") +
        "/proxy/" + _applicationId
      System.setProperty("spark.ui.proxyBase", proxyUrl)
    }
    _ui.foreach(_.setAppId(_applicationId))
    _env.blockManager.initialize(_applicationId)

    // The metrics system for Driver need to be set spark.app.id to app ID.
    // So it should start after we get app ID from the task scheduler and set spark.app.id.
    _env.metricsSystem.start(_conf.get(METRICS_STATIC_SOURCES_ENABLED))
    // Attach the driver metrics servlet handler to the web ui after the metrics system is started.
    _env.metricsSystem.getServletHandlers.foreach(handler => ui.foreach(_.attachHandler(handler)))

    _eventLogger =
      if (isEventLogEnabled) {
        val logger =
          new EventLoggingListener(_applicationId, _applicationAttemptId, _eventLogDir.get,
            _conf, _hadoopConfiguration)
        logger.start()
        listenerBus.addToEventLogQueue(logger)
        Some(logger)
      } else {
        None
      }

    _cleaner =
      if (_conf.get(CLEANER_REFERENCE_TRACKING)) {
        Some(new ContextCleaner(this, _shuffleDriverComponents))
      } else {
        None
      }
    _cleaner.foreach(_.start())

    val dynamicAllocationEnabled = Utils.isDynamicAllocationEnabled(_conf)
    _executorAllocationManager =
      if (dynamicAllocationEnabled) {
        schedulerBackend match {
          case b: ExecutorAllocationClient =>
            Some(new ExecutorAllocationManager(
              schedulerBackend.asInstanceOf[ExecutorAllocationClient], listenerBus, _conf,
              cleaner = cleaner, resourceProfileManager = resourceProfileManager))
          case _ =>
            None
        }
      } else {
        None
      }
    _executorAllocationManager.foreach(_.start())

    setupAndStartListenerBus()
    postEnvironmentUpdate()
    postApplicationStart()

    // Post init
    _taskScheduler.postStartHook()
    if (isLocal) {
      _env.metricsSystem.registerSource(Executor.executorSourceLocalModeOnly)
    }
    _env.metricsSystem.registerSource(_dagScheduler.metricsSource)
    _env.metricsSystem.registerSource(new BlockManagerSource(_env.blockManager))
    _env.metricsSystem.registerSource(new JVMCPUSource())
    _executorMetricsSource.foreach(_.register(_env.metricsSystem))
    _executorAllocationManager.foreach { e =>
      _env.metricsSystem.registerSource(e.executorAllocationManagerSource)
    }
    appStatusSource.foreach(_env.metricsSystem.registerSource(_))
    _plugins.foreach(_.registerMetrics(applicationId))
    // Make sure the context is stopped if the user forgets about it. This avoids leaving
    // unfinished event logs around after the JVM exits cleanly. It doesn't help if the JVM
    // is killed, though.
    logDebug("Adding shutdown hook") // force eager creation of logger
    _shutdownHookRef = ShutdownHookManager.addShutdownHook(
      ShutdownHookManager.SPARK_CONTEXT_SHUTDOWN_PRIORITY) { () =>
      logInfo("Invoking stop() from shutdown hook")
      try {
        stop()
      } catch {
        case e: Throwable =>
          logWarning("Ignoring Exception while stopping SparkContext from shutdown hook", e)
      }
    }
  } catch {
    case NonFatal(e) =>
      logError("Error initializing SparkContext.", e)
      try {
        stop()
      } catch {
        case NonFatal(inner) =>
          logError("Error stopping SparkContext after init error.", inner)
      } finally {
        throw e
      }
  }

  /**
   * Called by the web UI to obtain executor thread dumps.  This method may be expensive.
   * Logs an error and returns None if we failed to obtain a thread dump, which could occur due
   * to an executor being dead or unresponsive or due to network issues while sending the thread
   * dump message back to the driver.
   */
  private[spark] def getExecutorThreadDump(executorId: String): Option[Array[ThreadStackTrace]] = {
    try {
      if (executorId == SparkContext.DRIVER_IDENTIFIER) {
        Some(Utils.getThreadDump())
      } else {
        val endpointRef = env.blockManager.master.getExecutorEndpointRef(executorId).get
        Some(endpointRef.askSync[Array[ThreadStackTrace]](TriggerThreadDump))
      }
    } catch {
      case e: Exception =>
        logError(s"Exception getting thread dump from executor $executorId", e)
        None
    }
  }

  private[spark] def getLocalProperties: Properties = localProperties.get()

  private[spark] def setLocalProperties(props: Properties): Unit = {
    localProperties.set(props)
  }

  /**
   * Set a local property that affects jobs submitted from this thread, such as the Spark fair
   * scheduler pool. User-defined properties may also be set here. These properties are propagated
   * through to worker tasks and can be accessed there via
   * [[org.apache.spark.TaskContext#getLocalProperty]].
   *
   * These properties are inherited by child threads spawned from this thread. This
   * may have unexpected consequences when working with thread pools. The standard java
   * implementation of thread pools have worker threads spawn other worker threads.
   * As a result, local properties may propagate unpredictably.
   */
  def setLocalProperty(key: String, value: String): Unit = {
    if (value == null) {
      localProperties.get.remove(key)
    } else {
      localProperties.get.setProperty(key, value)
    }
  }

  /**
   * Get a local property set in this thread, or null if it is missing. See
   * `org.apache.spark.SparkContext.setLocalProperty`.
   */
  def getLocalProperty(key: String): String =
    Option(localProperties.get).map(_.getProperty(key)).orNull

  /** Set a human readable description of the current job. */
  def setJobDescription(value: String): Unit = {
    setLocalProperty(SparkContext.SPARK_JOB_DESCRIPTION, value)
  }

  /**
   * Assigns a group ID to all the jobs started by this thread until the group ID is set to a
   * different value or cleared.
   *
   * Often, a unit of execution in an application consists of multiple Spark actions or jobs.
   * Application programmers can use this method to group all those jobs together and give a
   * group description. Once set, the Spark web UI will associate such jobs with this group.
   *
   * The application can also use `org.apache.spark.SparkContext.cancelJobGroup` to cancel all
   * running jobs in this group. For example,
   * {{{
   * // In the main thread:
   * sc.setJobGroup("some_job_to_cancel", "some job description")
   * sc.parallelize(1 to 10000, 2).map { i => Thread.sleep(10); i }.count()
   *
   * // In a separate thread:
   * sc.cancelJobGroup("some_job_to_cancel")
   * }}}
   *
   * @param interruptOnCancel If true, then job cancellation will result in `Thread.interrupt()`
   * being called on the job's executor threads. This is useful to help ensure that the tasks
   * are actually stopped in a timely manner, but is off by default due to HDFS-1208, where HDFS
   * may respond to Thread.interrupt() by marking nodes as dead.
   */
  def setJobGroup(groupId: String,
      description: String, interruptOnCancel: Boolean = false): Unit = {
    setLocalProperty(SparkContext.SPARK_JOB_DESCRIPTION, description)
    setLocalProperty(SparkContext.SPARK_JOB_GROUP_ID, groupId)
    // Note: Specifying interruptOnCancel in setJobGroup (rather than cancelJobGroup) avoids
    // changing several public APIs and allows Spark cancellations outside of the cancelJobGroup
    // APIs to also take advantage of this property (e.g., internal job failures or canceling from
    // JobProgressTab UI) on a per-job basis.
    setLocalProperty(SparkContext.SPARK_JOB_INTERRUPT_ON_CANCEL, interruptOnCancel.toString)
  }

  /** Clear the current thread's job group ID and its description. */
  def clearJobGroup(): Unit = {
    setLocalProperty(SparkContext.SPARK_JOB_DESCRIPTION, null)
    setLocalProperty(SparkContext.SPARK_JOB_GROUP_ID, null)
    setLocalProperty(SparkContext.SPARK_JOB_INTERRUPT_ON_CANCEL, null)
  }

  /**
   * Execute a block of code in a scope such that all new RDDs created in this body will
   * be part of the same scope. For more detail, see {{org.apache.spark.rdd.RDDOperationScope}}.
   *
   * @note Return statements are NOT allowed in the given body.
   */
  private[spark] def withScope[U](body: => U): U = RDDOperationScope.withScope[U](this)(body)

  // Methods for creating RDDs

  /** Distribute a local Scala collection to form an RDD.
   *
   * @note Parallelize acts lazily. If `seq` is a mutable collection and is altered after the call
   * to parallelize and before the first action on the RDD, the resultant RDD will reflect the
   * modified collection. Pass a copy of the argument to avoid this.
   * @note avoid using `parallelize(Seq())` to create an empty `RDD`. Consider `emptyRDD` for an
   * RDD with no partitions, or `parallelize(Seq[T]())` for an RDD of `T` with empty partitions.
   * @param seq Scala collection to distribute
   * @param numSlices number of partitions to divide the collection into
   * @return RDD representing distributed collection
   */
  def parallelize[T: ClassTag](
      seq: Seq[T],
      numSlices: Int = defaultParallelism): RDD[T] = withScope {
    assertNotStopped()
    new ParallelCollectionRDD[T](this, seq, numSlices, Map[Int, Seq[String]]())
  }

  /**
   * Creates a new RDD[Long] containing elements from `start` to `end`(exclusive), increased by
   * `step` every element.
   *
   * @note if we need to cache this RDD, we should make sure each partition does not exceed limit.
   *
   * @param start the start value.
   * @param end the end value.
   * @param step the incremental step
   * @param numSlices number of partitions to divide the collection into
   * @return RDD representing distributed range
   */
  def range(
      start: Long,
      end: Long,
      step: Long = 1,
      numSlices: Int = defaultParallelism): RDD[Long] = withScope {
    assertNotStopped()
    // when step is 0, range will run infinitely
    require(step != 0, "step cannot be 0")
    val numElements: BigInt = {
      val safeStart = BigInt(start)
      val safeEnd = BigInt(end)
      if ((safeEnd - safeStart) % step == 0 || (safeEnd > safeStart) != (step > 0)) {
        (safeEnd - safeStart) / step
      } else {
        // the remainder has the same sign with range, could add 1 more
        (safeEnd - safeStart) / step + 1
      }
    }
    parallelize(0 until numSlices, numSlices).mapPartitionsWithIndex { (i, _) =>
      val partitionStart = (i * numElements) / numSlices * step + start
      val partitionEnd = (((i + 1) * numElements) / numSlices) * step + start
      def getSafeMargin(bi: BigInt): Long =
        if (bi.isValidLong) {
          bi.toLong
        } else if (bi > 0) {
          Long.MaxValue
        } else {
          Long.MinValue
        }
      val safePartitionStart = getSafeMargin(partitionStart)
      val safePartitionEnd = getSafeMargin(partitionEnd)

      new Iterator[Long] {
        private[this] var number: Long = safePartitionStart
        private[this] var overflow: Boolean = false

        override def hasNext =
          if (!overflow) {
            if (step > 0) {
              number < safePartitionEnd
            } else {
              number > safePartitionEnd
            }
          } else false

        override def next() = {
          val ret = number
          number += step
          if (number < ret ^ step < 0) {
            // we have Long.MaxValue + Long.MaxValue < Long.MaxValue
            // and Long.MinValue + Long.MinValue > Long.MinValue, so iff the step causes a step
            // back, we are pretty sure that we have an overflow.
            overflow = true
          }
          ret
        }
      }
    }
  }

  /** Distribute a local Scala collection to form an RDD.
   *
   * This method is identical to `parallelize`.
   * @param seq Scala collection to distribute
   * @param numSlices number of partitions to divide the collection into
   * @return RDD representing distributed collection
   */
  def makeRDD[T: ClassTag](
      seq: Seq[T],
      numSlices: Int = defaultParallelism): RDD[T] = withScope {
    parallelize(seq, numSlices)
  }

  /**
   * Distribute a local Scala collection to form an RDD, with one or more
   * location preferences (hostnames of Spark nodes) for each object.
   * Create a new partition for each collection item.
   * @param seq list of tuples of data and location preferences (hostnames of Spark nodes)
   * @return RDD representing data partitioned according to location preferences
   */
  def makeRDD[T: ClassTag](seq: Seq[(T, Seq[String])]): RDD[T] = withScope {
    assertNotStopped()
    val indexToPrefs = seq.zipWithIndex.map(t => (t._2, t._1._2)).toMap
    new ParallelCollectionRDD[T](this, seq.map(_._1), math.max(seq.size, 1), indexToPrefs)
  }

  /**
   * Read a text file from HDFS, a local file system (available on all nodes), or any
   * Hadoop-supported file system URI, and return it as an RDD of Strings.
   * The text files must be encoded as UTF-8.
   *
   * @param path path to the text file on a supported file system
   * @param minPartitions suggested minimum number of partitions for the resulting RDD
   * @return RDD of lines of the text file
   */
  def textFile(
      path: String,
      minPartitions: Int = defaultMinPartitions): RDD[String] = withScope {
    assertNotStopped()
    hadoopFile(path, classOf[TextInputFormat], classOf[LongWritable], classOf[Text],
      minPartitions).map(pair => pair._2.toString).setName(path)
  }

  /**
   * Read a directory of text files from HDFS, a local file system (available on all nodes), or any
   * Hadoop-supported file system URI. Each file is read as a single record and returned in a
   * key-value pair, where the key is the path of each file, the value is the content of each file.
   * The text files must be encoded as UTF-8.
   *
   * <p> For example, if you have the following files:
   * {{{
   *   hdfs://a-hdfs-path/part-00000
   *   hdfs://a-hdfs-path/part-00001
   *   ...
   *   hdfs://a-hdfs-path/part-nnnnn
   * }}}
   *
   * Do `val rdd = sparkContext.wholeTextFile("hdfs://a-hdfs-path")`,
   *
   * <p> then `rdd` contains
   * {{{
   *   (a-hdfs-path/part-00000, its content)
   *   (a-hdfs-path/part-00001, its content)
   *   ...
   *   (a-hdfs-path/part-nnnnn, its content)
   * }}}
   *
   * @note Small files are preferred, large file is also allowable, but may cause bad performance.
   * @note On some filesystems, `.../path/&#42;` can be a more efficient way to read all files
   *       in a directory rather than `.../path/` or `.../path`
   * @note Partitioning is determined by data locality. This may result in too few partitions
   *       by default.
   *
   * @param path Directory to the input data files, the path can be comma separated paths as the
   *             list of inputs.
   * @param minPartitions A suggestion value of the minimal splitting number for input data.
   * @return RDD representing tuples of file path and the corresponding file content
   */
  def wholeTextFiles(
      path: String,
      minPartitions: Int = defaultMinPartitions): RDD[(String, String)] = withScope {
    assertNotStopped()
    val job = NewHadoopJob.getInstance(hadoopConfiguration)
    // Use setInputPaths so that wholeTextFiles aligns with hadoopFile/textFile in taking
    // comma separated files as input. (see SPARK-7155)
    NewFileInputFormat.setInputPaths(job, path)
    val updateConf = job.getConfiguration
    new WholeTextFileRDD(
      this,
      classOf[WholeTextFileInputFormat],
      classOf[Text],
      classOf[Text],
      updateConf,
      minPartitions).map(record => (record._1.toString, record._2.toString)).setName(path)
  }

  /**
   * Get an RDD for a Hadoop-readable dataset as PortableDataStream for each file
   * (useful for binary data)
   *
   * For example, if you have the following files:
   * {{{
   *   hdfs://a-hdfs-path/part-00000
   *   hdfs://a-hdfs-path/part-00001
   *   ...
   *   hdfs://a-hdfs-path/part-nnnnn
   * }}}
   *
   * Do
   * `val rdd = sparkContext.binaryFiles("hdfs://a-hdfs-path")`,
   *
   * then `rdd` contains
   * {{{
   *   (a-hdfs-path/part-00000, its content)
   *   (a-hdfs-path/part-00001, its content)
   *   ...
   *   (a-hdfs-path/part-nnnnn, its content)
   * }}}
   *
   * @note Small files are preferred; very large files may cause bad performance.
   * @note On some filesystems, `.../path/&#42;` can be a more efficient way to read all files
   *       in a directory rather than `.../path/` or `.../path`
   * @note Partitioning is determined by data locality. This may result in too few partitions
   *       by default.
   *
   * @param path Directory to the input data files, the path can be comma separated paths as the
   *             list of inputs.
   * @param minPartitions A suggestion value of the minimal splitting number for input data.
   * @return RDD representing tuples of file path and corresponding file content
   */
  def binaryFiles(
      path: String,
      minPartitions: Int = defaultMinPartitions): RDD[(String, PortableDataStream)] = withScope {
    assertNotStopped()
    val job = NewHadoopJob.getInstance(hadoopConfiguration)
    // Use setInputPaths so that binaryFiles aligns with hadoopFile/textFile in taking
    // comma separated files as input. (see SPARK-7155)
    NewFileInputFormat.setInputPaths(job, path)
    val updateConf = job.getConfiguration
    new BinaryFileRDD(
      this,
      classOf[StreamInputFormat],
      classOf[String],
      classOf[PortableDataStream],
      updateConf,
      minPartitions).setName(path)
  }

  /**
   * Load data from a flat binary file, assuming the length of each record is constant.
   *
   * @note We ensure that the byte array for each record in the resulting RDD
   * has the provided record length.
   *
   * @param path Directory to the input data files, the path can be comma separated paths as the
   *             list of inputs.
   * @param recordLength The length at which to split the records
   * @param conf Configuration for setting up the dataset.
   *
   * @return An RDD of data with values, represented as byte arrays
   */
  def binaryRecords(
      path: String,
      recordLength: Int,
      conf: Configuration = hadoopConfiguration): RDD[Array[Byte]] = withScope {
    assertNotStopped()
    conf.setInt(FixedLengthBinaryInputFormat.RECORD_LENGTH_PROPERTY, recordLength)
    val br = newAPIHadoopFile[LongWritable, BytesWritable, FixedLengthBinaryInputFormat](path,
      classOf[FixedLengthBinaryInputFormat],
      classOf[LongWritable],
      classOf[BytesWritable],
      conf = conf)
    br.map { case (k, v) =>
      val bytes = v.copyBytes()
      assert(bytes.length == recordLength, "Byte array does not have correct length")
      bytes
    }
  }

  /**
   * Get an RDD for a Hadoop-readable dataset from a Hadoop JobConf given its InputFormat and other
   * necessary info (e.g. file name for a filesystem-based dataset, table name for HyperTable),
   * using the older MapReduce API (`org.apache.hadoop.mapred`).
   *
   * @param conf JobConf for setting up the dataset. Note: This will be put into a Broadcast.
   *             Therefore if you plan to reuse this conf to create multiple RDDs, you need to make
   *             sure you won't modify the conf. A safe approach is always creating a new conf for
   *             a new RDD.
   * @param inputFormatClass storage format of the data to be read
   * @param keyClass `Class` of the key associated with the `inputFormatClass` parameter
   * @param valueClass `Class` of the value associated with the `inputFormatClass` parameter
   * @param minPartitions Minimum number of Hadoop Splits to generate.
   * @return RDD of tuples of key and corresponding value
   *
   * @note Because Hadoop's RecordReader class re-uses the same Writable object for each
   * record, directly caching the returned RDD or directly passing it to an aggregation or shuffle
   * operation will create many references to the same object.
   * If you plan to directly cache, sort, or aggregate Hadoop writable objects, you should first
   * copy them using a `map` function.
   */
  def hadoopRDD[K, V](
      conf: JobConf,
      inputFormatClass: Class[_ <: InputFormat[K, V]],
      keyClass: Class[K],
      valueClass: Class[V],
      minPartitions: Int = defaultMinPartitions): RDD[(K, V)] = withScope {
    assertNotStopped()

    // This is a hack to enforce loading hdfs-site.xml.
    // See SPARK-11227 for details.
    FileSystem.getLocal(conf)

    // Add necessary security credentials to the JobConf before broadcasting it.
    SparkHadoopUtil.get.addCredentials(conf)
    new HadoopRDD(this, conf, inputFormatClass, keyClass, valueClass, minPartitions)
  }

  /** Get an RDD for a Hadoop file with an arbitrary InputFormat
   *
   * @note Because Hadoop's RecordReader class re-uses the same Writable object for each
   * record, directly caching the returned RDD or directly passing it to an aggregation or shuffle
   * operation will create many references to the same object.
   * If you plan to directly cache, sort, or aggregate Hadoop writable objects, you should first
   * copy them using a `map` function.
   * @param path directory to the input data files, the path can be comma separated paths
   * as a list of inputs
   * @param inputFormatClass storage format of the data to be read
   * @param keyClass `Class` of the key associated with the `inputFormatClass` parameter
   * @param valueClass `Class` of the value associated with the `inputFormatClass` parameter
   * @param minPartitions suggested minimum number of partitions for the resulting RDD
   * @return RDD of tuples of key and corresponding value
   */
  def hadoopFile[K, V](
      path: String,
      inputFormatClass: Class[_ <: InputFormat[K, V]],
      keyClass: Class[K],
      valueClass: Class[V],
      minPartitions: Int = defaultMinPartitions): RDD[(K, V)] = withScope {
    assertNotStopped()

    // This is a hack to enforce loading hdfs-site.xml.
    // See SPARK-11227 for details.
    FileSystem.getLocal(hadoopConfiguration)

    // A Hadoop configuration can be about 10 KiB, which is pretty big, so broadcast it.
    val confBroadcast = broadcast(new SerializableConfiguration(hadoopConfiguration))
    val setInputPathsFunc = (jobConf: JobConf) => FileInputFormat.setInputPaths(jobConf, path)
    new HadoopRDD(
      this,
      confBroadcast,
      Some(setInputPathsFunc),
      inputFormatClass,
      keyClass,
      valueClass,
      minPartitions).setName(path)
  }

  /**
   * Smarter version of hadoopFile() that uses class tags to figure out the classes of keys,
   * values and the InputFormat so that users don't need to pass them directly. Instead, callers
   * can just write, for example,
   * {{{
   * val file = sparkContext.hadoopFile[LongWritable, Text, TextInputFormat](path, minPartitions)
   * }}}
   *
   * @note Because Hadoop's RecordReader class re-uses the same Writable object for each
   * record, directly caching the returned RDD or directly passing it to an aggregation or shuffle
   * operation will create many references to the same object.
   * If you plan to directly cache, sort, or aggregate Hadoop writable objects, you should first
   * copy them using a `map` function.
   * @param path directory to the input data files, the path can be comma separated paths
   * as a list of inputs
   * @param minPartitions suggested minimum number of partitions for the resulting RDD
   * @return RDD of tuples of key and corresponding value
   */
  def hadoopFile[K, V, F <: InputFormat[K, V]]
      (path: String, minPartitions: Int)
      (implicit km: ClassTag[K], vm: ClassTag[V], fm: ClassTag[F]): RDD[(K, V)] = withScope {
    hadoopFile(path,
      fm.runtimeClass.asInstanceOf[Class[F]],
      km.runtimeClass.asInstanceOf[Class[K]],
      vm.runtimeClass.asInstanceOf[Class[V]],
      minPartitions)
  }

  /**
   * Smarter version of hadoopFile() that uses class tags to figure out the classes of keys,
   * values and the InputFormat so that users don't need to pass them directly. Instead, callers
   * can just write, for example,
   * {{{
   * val file = sparkContext.hadoopFile[LongWritable, Text, TextInputFormat](path)
   * }}}
   *
   * @note Because Hadoop's RecordReader class re-uses the same Writable object for each
   * record, directly caching the returned RDD or directly passing it to an aggregation or shuffle
   * operation will create many references to the same object.
   * If you plan to directly cache, sort, or aggregate Hadoop writable objects, you should first
   * copy them using a `map` function.
   * @param path directory to the input data files, the path can be comma separated paths as
   * a list of inputs
   * @return RDD of tuples of key and corresponding value
   */
  def hadoopFile[K, V, F <: InputFormat[K, V]](path: String)
      (implicit km: ClassTag[K], vm: ClassTag[V], fm: ClassTag[F]): RDD[(K, V)] = withScope {
    hadoopFile[K, V, F](path, defaultMinPartitions)
  }

  /**
   * Smarter version of `newApiHadoopFile` that uses class tags to figure out the classes of keys,
   * values and the `org.apache.hadoop.mapreduce.InputFormat` (new MapReduce API) so that user
   * don't need to pass them directly. Instead, callers can just write, for example:
   * ```
   * val file = sparkContext.hadoopFile[LongWritable, Text, TextInputFormat](path)
   * ```
   *
   * @note Because Hadoop's RecordReader class re-uses the same Writable object for each
   * record, directly caching the returned RDD or directly passing it to an aggregation or shuffle
   * operation will create many references to the same object.
   * If you plan to directly cache, sort, or aggregate Hadoop writable objects, you should first
   * copy them using a `map` function.
   * @param path directory to the input data files, the path can be comma separated paths
   * as a list of inputs
   * @return RDD of tuples of key and corresponding value
   */
  def newAPIHadoopFile[K, V, F <: NewInputFormat[K, V]]
      (path: String)
      (implicit km: ClassTag[K], vm: ClassTag[V], fm: ClassTag[F]): RDD[(K, V)] = withScope {
    newAPIHadoopFile(
      path,
      fm.runtimeClass.asInstanceOf[Class[F]],
      km.runtimeClass.asInstanceOf[Class[K]],
      vm.runtimeClass.asInstanceOf[Class[V]])
  }

  /**
   * Get an RDD for a given Hadoop file with an arbitrary new API InputFormat
   * and extra configuration options to pass to the input format.
   *
   * @note Because Hadoop's RecordReader class re-uses the same Writable object for each
   * record, directly caching the returned RDD or directly passing it to an aggregation or shuffle
   * operation will create many references to the same object.
   * If you plan to directly cache, sort, or aggregate Hadoop writable objects, you should first
   * copy them using a `map` function.
   * @param path directory to the input data files, the path can be comma separated paths
   * as a list of inputs
   * @param fClass storage format of the data to be read
   * @param kClass `Class` of the key associated with the `fClass` parameter
   * @param vClass `Class` of the value associated with the `fClass` parameter
   * @param conf Hadoop configuration
   * @return RDD of tuples of key and corresponding value
   */
  def newAPIHadoopFile[K, V, F <: NewInputFormat[K, V]](
      path: String,
      fClass: Class[F],
      kClass: Class[K],
      vClass: Class[V],
      conf: Configuration = hadoopConfiguration): RDD[(K, V)] = withScope {
    assertNotStopped()

    // This is a hack to enforce loading hdfs-site.xml.
    // See SPARK-11227 for details.
    FileSystem.getLocal(hadoopConfiguration)

    // The call to NewHadoopJob automatically adds security credentials to conf,
    // so we don't need to explicitly add them ourselves
    val job = NewHadoopJob.getInstance(conf)
    // Use setInputPaths so that newAPIHadoopFile aligns with hadoopFile/textFile in taking
    // comma separated files as input. (see SPARK-7155)
    NewFileInputFormat.setInputPaths(job, path)
    val updatedConf = job.getConfiguration
    new NewHadoopRDD(this, fClass, kClass, vClass, updatedConf).setName(path)
  }

  /**
   * Get an RDD for a given Hadoop file with an arbitrary new API InputFormat
   * and extra configuration options to pass to the input format.
   *
   * @param conf Configuration for setting up the dataset. Note: This will be put into a Broadcast.
   *             Therefore if you plan to reuse this conf to create multiple RDDs, you need to make
   *             sure you won't modify the conf. A safe approach is always creating a new conf for
   *             a new RDD.
   * @param fClass storage format of the data to be read
   * @param kClass `Class` of the key associated with the `fClass` parameter
   * @param vClass `Class` of the value associated with the `fClass` parameter
   *
   * @note Because Hadoop's RecordReader class re-uses the same Writable object for each
   * record, directly caching the returned RDD or directly passing it to an aggregation or shuffle
   * operation will create many references to the same object.
   * If you plan to directly cache, sort, or aggregate Hadoop writable objects, you should first
   * copy them using a `map` function.
   */
  def newAPIHadoopRDD[K, V, F <: NewInputFormat[K, V]](
      conf: Configuration = hadoopConfiguration,
      fClass: Class[F],
      kClass: Class[K],
      vClass: Class[V]): RDD[(K, V)] = withScope {
    assertNotStopped()

    // This is a hack to enforce loading hdfs-site.xml.
    // See SPARK-11227 for details.
    FileSystem.getLocal(conf)

    // Add necessary security credentials to the JobConf. Required to access secure HDFS.
    val jconf = new JobConf(conf)
    SparkHadoopUtil.get.addCredentials(jconf)
    new NewHadoopRDD(this, fClass, kClass, vClass, jconf)
  }

  /**
   * Get an RDD for a Hadoop SequenceFile with given key and value types.
   *
   * @note Because Hadoop's RecordReader class re-uses the same Writable object for each
   * record, directly caching the returned RDD or directly passing it to an aggregation or shuffle
   * operation will create many references to the same object.
   * If you plan to directly cache, sort, or aggregate Hadoop writable objects, you should first
   * copy them using a `map` function.
   * @param path directory to the input data files, the path can be comma separated paths
   * as a list of inputs
   * @param keyClass `Class` of the key associated with `SequenceFileInputFormat`
   * @param valueClass `Class` of the value associated with `SequenceFileInputFormat`
   * @param minPartitions suggested minimum number of partitions for the resulting RDD
   * @return RDD of tuples of key and corresponding value
   */
  def sequenceFile[K, V](path: String,
      keyClass: Class[K],
      valueClass: Class[V],
      minPartitions: Int
      ): RDD[(K, V)] = withScope {
    assertNotStopped()
    val inputFormatClass = classOf[SequenceFileInputFormat[K, V]]
    hadoopFile(path, inputFormatClass, keyClass, valueClass, minPartitions)
  }

  /**
   * Get an RDD for a Hadoop SequenceFile with given key and value types.
   *
   * @note Because Hadoop's RecordReader class re-uses the same Writable object for each
   * record, directly caching the returned RDD or directly passing it to an aggregation or shuffle
   * operation will create many references to the same object.
   * If you plan to directly cache, sort, or aggregate Hadoop writable objects, you should first
   * copy them using a `map` function.
   * @param path directory to the input data files, the path can be comma separated paths
   * as a list of inputs
   * @param keyClass `Class` of the key associated with `SequenceFileInputFormat`
   * @param valueClass `Class` of the value associated with `SequenceFileInputFormat`
   * @return RDD of tuples of key and corresponding value
   */
  def sequenceFile[K, V](
      path: String,
      keyClass: Class[K],
      valueClass: Class[V]): RDD[(K, V)] = withScope {
    assertNotStopped()
    sequenceFile(path, keyClass, valueClass, defaultMinPartitions)
  }

  /**
   * Version of sequenceFile() for types implicitly convertible to Writables through a
   * WritableConverter. For example, to access a SequenceFile where the keys are Text and the
   * values are IntWritable, you could simply write
   * {{{
   * sparkContext.sequenceFile[String, Int](path, ...)
   * }}}
   *
   * WritableConverters are provided in a somewhat strange way (by an implicit function) to support
   * both subclasses of Writable and types for which we define a converter (e.g. Int to
   * IntWritable). The most natural thing would've been to have implicit objects for the
   * converters, but then we couldn't have an object for every subclass of Writable (you can't
   * have a parameterized singleton object). We use functions instead to create a new converter
   * for the appropriate type. In addition, we pass the converter a ClassTag of its type to
   * allow it to figure out the Writable class to use in the subclass case.
   *
   * @note Because Hadoop's RecordReader class re-uses the same Writable object for each
   * record, directly caching the returned RDD or directly passing it to an aggregation or shuffle
   * operation will create many references to the same object.
   * If you plan to directly cache, sort, or aggregate Hadoop writable objects, you should first
   * copy them using a `map` function.
   * @param path directory to the input data files, the path can be comma separated paths
   * as a list of inputs
   * @param minPartitions suggested minimum number of partitions for the resulting RDD
   * @return RDD of tuples of key and corresponding value
   */
   def sequenceFile[K, V]
       (path: String, minPartitions: Int = defaultMinPartitions)
       (implicit km: ClassTag[K], vm: ClassTag[V],
        kcf: () => WritableConverter[K], vcf: () => WritableConverter[V]): RDD[(K, V)] = {
    withScope {
      assertNotStopped()
      val kc = clean(kcf)()
      val vc = clean(vcf)()
      val format = classOf[SequenceFileInputFormat[Writable, Writable]]
      val writables = hadoopFile(path, format,
        kc.writableClass(km).asInstanceOf[Class[Writable]],
        vc.writableClass(vm).asInstanceOf[Class[Writable]], minPartitions)
      writables.map { case (k, v) => (kc.convert(k), vc.convert(v)) }
    }
  }

  /**
   * Load an RDD saved as a SequenceFile containing serialized objects, with NullWritable keys and
   * BytesWritable values that contain a serialized partition. This is still an experimental
   * storage format and may not be supported exactly as is in future Spark releases. It will also
   * be pretty slow if you use the default serializer (Java serialization),
   * though the nice thing about it is that there's very little effort required to save arbitrary
   * objects.
   *
   * @param path directory to the input data files, the path can be comma separated paths
   * as a list of inputs
   * @param minPartitions suggested minimum number of partitions for the resulting RDD
   * @return RDD representing deserialized data from the file(s)
   */
  def objectFile[T: ClassTag](
      path: String,
      minPartitions: Int = defaultMinPartitions): RDD[T] = withScope {
    assertNotStopped()
    sequenceFile(path, classOf[NullWritable], classOf[BytesWritable], minPartitions)
      .flatMap(x => Utils.deserialize[Array[T]](x._2.getBytes, Utils.getContextOrSparkClassLoader))
  }

  protected[spark] def checkpointFile[T: ClassTag](path: String): RDD[T] = withScope {
    new ReliableCheckpointRDD[T](this, path)
  }

  /** Build the union of a list of RDDs. */
  def union[T: ClassTag](rdds: Seq[RDD[T]]): RDD[T] = withScope {
    val nonEmptyRdds = rdds.filter(!_.partitions.isEmpty)
    val partitioners = nonEmptyRdds.flatMap(_.partitioner).toSet
    if (nonEmptyRdds.forall(_.partitioner.isDefined) && partitioners.size == 1) {
      new PartitionerAwareUnionRDD(this, nonEmptyRdds)
    } else {
      new UnionRDD(this, nonEmptyRdds)
    }
  }

  /** Build the union of a list of RDDs passed as variable-length arguments. */
  def union[T: ClassTag](first: RDD[T], rest: RDD[T]*): RDD[T] = withScope {
    union(Seq(first) ++ rest)
  }

  /** Get an RDD that has no partitions or elements. */
  def emptyRDD[T: ClassTag]: RDD[T] = new EmptyRDD[T](this)

  // Methods for creating shared variables

  /**
   * Register the given accumulator.
   *
   * @note Accumulators must be registered before use, or it will throw exception.
   */
  def register(acc: AccumulatorV2[_, _]): Unit = {
    acc.register(this)
  }

  /**
   * Register the given accumulator with given name.
   *
   * @note Accumulators must be registered before use, or it will throw exception.
   */
  def register(acc: AccumulatorV2[_, _], name: String): Unit = {
    acc.register(this, name = Option(name))
  }

  /**
   * Create and register a long accumulator, which starts with 0 and accumulates inputs by `add`.
   */
  def longAccumulator: LongAccumulator = {
    val acc = new LongAccumulator
    register(acc)
    acc
  }

  /**
   * Create and register a long accumulator, which starts with 0 and accumulates inputs by `add`.
   */
  def longAccumulator(name: String): LongAccumulator = {
    val acc = new LongAccumulator
    register(acc, name)
    acc
  }

  /**
   * Create and register a double accumulator, which starts with 0 and accumulates inputs by `add`.
   */
  def doubleAccumulator: DoubleAccumulator = {
    val acc = new DoubleAccumulator
    register(acc)
    acc
  }

  /**
   * Create and register a double accumulator, which starts with 0 and accumulates inputs by `add`.
   */
  def doubleAccumulator(name: String): DoubleAccumulator = {
    val acc = new DoubleAccumulator
    register(acc, name)
    acc
  }

  /**
   * Create and register a `CollectionAccumulator`, which starts with empty list and accumulates
   * inputs by adding them into the list.
   */
  def collectionAccumulator[T]: CollectionAccumulator[T] = {
    val acc = new CollectionAccumulator[T]
    register(acc)
    acc
  }

  /**
   * Create and register a `CollectionAccumulator`, which starts with empty list and accumulates
   * inputs by adding them into the list.
   */
  def collectionAccumulator[T](name: String): CollectionAccumulator[T] = {
    val acc = new CollectionAccumulator[T]
    register(acc, name)
    acc
  }

  /**
   * Broadcast a read-only variable to the cluster, returning a
   * [[org.apache.spark.broadcast.Broadcast]] object for reading it in distributed functions.
   * The variable will be sent to each cluster only once.
   *
   * @param value value to broadcast to the Spark nodes
   * @return `Broadcast` object, a read-only variable cached on each machine
   */
  def broadcast[T: ClassTag](value: T): Broadcast[T] = {
    assertNotStopped()
    require(!classOf[RDD[_]].isAssignableFrom(classTag[T].runtimeClass),
      "Can not directly broadcast RDDs; instead, call collect() and broadcast the result.")
    val bc = env.broadcastManager.newBroadcast[T](value, isLocal)
    val callSite = getCallSite
    logInfo("Created broadcast " + bc.id + " from " + callSite.shortForm)
    cleaner.foreach(_.registerBroadcastForCleanup(bc))
    bc
  }

  /**
   * Add a file to be downloaded with this Spark job on every node.
   *
   * If a file is added during execution, it will not be available until the next TaskSet starts.
   *
   * @param path can be either a local file, a file in HDFS (or other Hadoop-supported
   * filesystems), or an HTTP, HTTPS or FTP URI. To access the file in Spark jobs,
   * use `SparkFiles.get(fileName)` to find its download location.
   *
   * @note A path can be added only once. Subsequent additions of the same path are ignored.
   */
  def addFile(path: String): Unit = {
    addFile(path, false, false)
  }

  /**
   * Returns a list of file paths that are added to resources.
   */
  def listFiles(): Seq[String] = addedFiles.keySet.toSeq

  /**
   * Add a file to be downloaded with this Spark job on every node.
   *
   * If a file is added during execution, it will not be available until the next TaskSet starts.
   *
   * @param path can be either a local file, a file in HDFS (or other Hadoop-supported
   * filesystems), or an HTTP, HTTPS or FTP URI. To access the file in Spark jobs,
   * use `SparkFiles.get(fileName)` to find its download location.
   * @param recursive if true, a directory can be given in `path`. Currently directories are
   * only supported for Hadoop-supported filesystems.
   *
   * @note A path can be added only once. Subsequent additions of the same path are ignored.
   */
  def addFile(path: String, recursive: Boolean): Unit = {
    addFile(path, recursive, false)
  }

  private def addFile(path: String, recursive: Boolean, addedOnSubmit: Boolean): Unit = {
    val uri = new Path(path).toUri
    val schemeCorrectedURI = uri.getScheme match {
      case null => new File(path).getCanonicalFile.toURI
      case "local" =>
        logWarning(s"File with 'local' scheme $path is not supported to add to file server, " +
          s"since it is already available on every node.")
        return
      case _ => uri
    }

    val hadoopPath = new Path(schemeCorrectedURI)
    val scheme = schemeCorrectedURI.getScheme
    if (!Array("http", "https", "ftp").contains(scheme)) {
      val fs = hadoopPath.getFileSystem(hadoopConfiguration)
      val isDir = fs.getFileStatus(hadoopPath).isDirectory
      if (!isLocal && scheme == "file" && isDir) {
        throw new SparkException(s"addFile does not support local directories when not running " +
          "local mode.")
      }
      if (!recursive && isDir) {
        throw new SparkException(s"Added file $hadoopPath is a directory and recursive is not " +
          "turned on.")
      }
    } else {
      // SPARK-17650: Make sure this is a valid URL before adding it to the list of dependencies
      Utils.validateURL(uri)
    }

    val key = if (!isLocal && scheme == "file") {
      env.rpcEnv.fileServer.addFile(new File(uri.getPath))
    } else {
        if (uri.getScheme == null) {
          schemeCorrectedURI.toString
        } else {
          path
        }
    }
    val timestamp = if (addedOnSubmit) startTime else System.currentTimeMillis
    if (addedFiles.putIfAbsent(key, timestamp).isEmpty) {
      logInfo(s"Added file $path at $key with timestamp $timestamp")
      // Fetch the file locally so that closures which are run on the driver can still use the
      // SparkFiles API to access files.
      Utils.fetchFile(uri.toString, new File(SparkFiles.getRootDirectory()), conf,
        env.securityManager, hadoopConfiguration, timestamp, useCache = false)
      postEnvironmentUpdate()
    } else {
      logWarning(s"The path $path has been added already. Overwriting of added paths " +
        "is not supported in the current version.")
    }
  }

  /**
   * :: DeveloperApi ::
   * Register a listener to receive up-calls from events that happen during execution.
   */
  @DeveloperApi
  def addSparkListener(listener: SparkListenerInterface): Unit = {
    listenerBus.addToSharedQueue(listener)
  }

  /**
   * :: DeveloperApi ::
   * Deregister the listener from Spark's listener bus.
   */
  @DeveloperApi
  def removeSparkListener(listener: SparkListenerInterface): Unit = {
    listenerBus.removeListener(listener)
  }

  private[spark] def getExecutorIds(): Seq[String] = {
    schedulerBackend match {
      case b: ExecutorAllocationClient =>
        b.getExecutorIds()
      case _ =>
        logWarning("Requesting executors is not supported by current scheduler.")
        Nil
    }
  }

  /**
   * Get the max number of tasks that can be concurrent launched based on the ResourceProfile
   * could be used, even if some of them are being used at the moment.
   * Note that please don't cache the value returned by this method, because the number can change
   * due to add/remove executors.
   *
   * @param rp ResourceProfile which to use to calculate max concurrent tasks.
   * @return The max number of tasks that can be concurrent launched currently.
   */
  private[spark] def maxNumConcurrentTasks(rp: ResourceProfile): Int = {
    schedulerBackend.maxNumConcurrentTasks(rp)
  }

  /**
   * Update the cluster manager on our scheduling needs. Three bits of information are included
   * to help it make decisions. This applies to the default ResourceProfile.
   * @param numExecutors The total number of executors we'd like to have. The cluster manager
   *                     shouldn't kill any running executor to reach this number, but,
   *                     if all existing executors were to die, this is the number of executors
   *                     we'd want to be allocated.
   * @param localityAwareTasks The number of tasks in all active stages that have a locality
   *                           preferences. This includes running, pending, and completed tasks.
   * @param hostToLocalTaskCount A map of hosts to the number of tasks from all active stages
   *                             that would like to like to run on that host.
   *                             This includes running, pending, and completed tasks.
   * @return whether the request is acknowledged by the cluster manager.
   */
  @DeveloperApi
  def requestTotalExecutors(
      numExecutors: Int,
      localityAwareTasks: Int,
      hostToLocalTaskCount: immutable.Map[String, Int]
    ): Boolean = {
    schedulerBackend match {
      case b: ExecutorAllocationClient =>
        // this is being applied to the default resource profile, would need to add api to support
        // others
        val defaultProfId = resourceProfileManager.defaultResourceProfile.id
        b.requestTotalExecutors(immutable.Map(defaultProfId-> numExecutors),
          immutable.Map(localityAwareTasks -> defaultProfId),
          immutable.Map(defaultProfId -> hostToLocalTaskCount))
      case _ =>
        logWarning("Requesting executors is not supported by current scheduler.")
        false
    }
  }

  /**
   * :: DeveloperApi ::
   * Request an additional number of executors from the cluster manager.
   * @return whether the request is received.
   */
  @DeveloperApi
  def requestExecutors(numAdditionalExecutors: Int): Boolean = {
    schedulerBackend match {
      case b: ExecutorAllocationClient =>
        b.requestExecutors(numAdditionalExecutors)
      case _ =>
        logWarning("Requesting executors is not supported by current scheduler.")
        false
    }
  }

  /**
   * :: DeveloperApi ::
   * Request that the cluster manager kill the specified executors.
   *
   * This is not supported when dynamic allocation is turned on.
   *
   * @note This is an indication to the cluster manager that the application wishes to adjust
   * its resource usage downwards. If the application wishes to replace the executors it kills
   * through this method with new ones, it should follow up explicitly with a call to
   * {{SparkContext#requestExecutors}}.
   *
   * @return whether the request is received.
   */
  @DeveloperApi
  def killExecutors(executorIds: Seq[String]): Boolean = {
    schedulerBackend match {
      case b: ExecutorAllocationClient =>
        require(executorAllocationManager.isEmpty,
          "killExecutors() unsupported with Dynamic Allocation turned on")
        b.killExecutors(executorIds, adjustTargetNumExecutors = true, countFailures = false,
          force = true).nonEmpty
      case _ =>
        logWarning("Killing executors is not supported by current scheduler.")
        false
    }
  }

  /**
   * :: DeveloperApi ::
   * Request that the cluster manager kill the specified executor.
   *
   * @note This is an indication to the cluster manager that the application wishes to adjust
   * its resource usage downwards. If the application wishes to replace the executor it kills
   * through this method with a new one, it should follow up explicitly with a call to
   * {{SparkContext#requestExecutors}}.
   *
   * @return whether the request is received.
   */
  @DeveloperApi
  def killExecutor(executorId: String): Boolean = killExecutors(Seq(executorId))

  /**
   * Request that the cluster manager kill the specified executor without adjusting the
   * application resource requirements.
   *
   * The effect is that a new executor will be launched in place of the one killed by
   * this request. This assumes the cluster manager will automatically and eventually
   * fulfill all missing application resource requests.
   *
   * @note The replace is by no means guaranteed; another application on the same cluster
   * can steal the window of opportunity and acquire this application's resources in the
   * mean time.
   *
   * @return whether the request is received.
   */
  private[spark] def killAndReplaceExecutor(executorId: String): Boolean = {
    schedulerBackend match {
      case b: ExecutorAllocationClient =>
        b.killExecutors(Seq(executorId), adjustTargetNumExecutors = false, countFailures = true,
          force = true).nonEmpty
      case _ =>
        logWarning("Killing executors is not supported by current scheduler.")
        false
    }
  }

  /** The version of Spark on which this application is running. */
  def version: String = SPARK_VERSION

  /**
   * Return a map from the block manager to the max memory available for caching and the remaining
   * memory available for caching.
   */
  def getExecutorMemoryStatus: Map[String, (Long, Long)] = {
    assertNotStopped()
    env.blockManager.master.getMemoryStatus.map { case(blockManagerId, mem) =>
      (blockManagerId.host + ":" + blockManagerId.port, mem)
    }
  }

  /**
   * :: DeveloperApi ::
   * Return information about what RDDs are cached, if they are in mem or on disk, how much space
   * they take, etc.
   */
  @DeveloperApi
  def getRDDStorageInfo: Array[RDDInfo] = {
    getRDDStorageInfo(_ => true)
  }

  private[spark] def getRDDStorageInfo(filter: RDD[_] => Boolean): Array[RDDInfo] = {
    assertNotStopped()
    val rddInfos = persistentRdds.values.filter(filter).map(RDDInfo.fromRdd).toArray
    rddInfos.foreach { rddInfo =>
      val rddId = rddInfo.id
      val rddStorageInfo = statusStore.asOption(statusStore.rdd(rddId))
      rddInfo.numCachedPartitions = rddStorageInfo.map(_.numCachedPartitions).getOrElse(0)
      rddInfo.memSize = rddStorageInfo.map(_.memoryUsed).getOrElse(0L)
      rddInfo.diskSize = rddStorageInfo.map(_.diskUsed).getOrElse(0L)
    }
    rddInfos.filter(_.isCached)
  }

  /**
   * Returns an immutable map of RDDs that have marked themselves as persistent via cache() call.
   *
   * @note This does not necessarily mean the caching or computation was successful.
   */
  def getPersistentRDDs: Map[Int, RDD[_]] = persistentRdds.toMap

  /**
   * :: DeveloperApi ::
   * Return pools for fair scheduler
   */
  @DeveloperApi
  def getAllPools: Seq[Schedulable] = {
    assertNotStopped()
    // TODO(xiajunluan): We should take nested pools into account
    taskScheduler.rootPool.schedulableQueue.asScala.toSeq
  }

  /**
   * :: DeveloperApi ::
   * Return the pool associated with the given name, if one exists
   */
  @DeveloperApi
  def getPoolForName(pool: String): Option[Schedulable] = {
    assertNotStopped()
    Option(taskScheduler.rootPool.schedulableNameToSchedulable.get(pool))
  }

  /**
   * Return current scheduling mode
   */
  def getSchedulingMode: SchedulingMode.SchedulingMode = {
    assertNotStopped()
    taskScheduler.schedulingMode
  }

  /**
   * Gets the locality information associated with the partition in a particular rdd
   * @param rdd of interest
   * @param partition to be looked up for locality
   * @return list of preferred locations for the partition
   */
  private [spark] def getPreferredLocs(rdd: RDD[_], partition: Int): Seq[TaskLocation] = {
    dagScheduler.getPreferredLocs(rdd, partition)
  }

  /**
   * Register an RDD to be persisted in memory and/or disk storage
   */
  private[spark] def persistRDD(rdd: RDD[_]): Unit = {
    persistentRdds(rdd.id) = rdd
  }

  /**
   * Unpersist an RDD from memory and/or disk storage
   */
  private[spark] def unpersistRDD(rddId: Int, blocking: Boolean): Unit = {
    env.blockManager.master.removeRdd(rddId, blocking)
    persistentRdds.remove(rddId)
    listenerBus.post(SparkListenerUnpersistRDD(rddId))
  }

  /**
   * Adds a JAR dependency for all tasks to be executed on this `SparkContext` in the future.
   *
   * If a jar is added during execution, it will not be available until the next TaskSet starts.
   *
   * @param path can be either a local file, a file in HDFS (or other Hadoop-supported filesystems),
   * an HTTP, HTTPS or FTP URI, or local:/path for a file on every worker node.
   *
   * @note A path can be added only once. Subsequent additions of the same path are ignored.
   */
  def addJar(path: String): Unit = {
    addJar(path, false)
  }

  private def addJar(path: String, addedOnSubmit: Boolean): Unit = {
    def addLocalJarFile(file: File): Seq[String] = {
      try {
        if (!file.exists()) {
          throw new FileNotFoundException(s"Jar ${file.getAbsolutePath} not found")
        }
        if (file.isDirectory) {
          throw new IllegalArgumentException(
            s"Directory ${file.getAbsoluteFile} is not allowed for addJar")
        }
        Seq(env.rpcEnv.fileServer.addJar(file))
      } catch {
        case NonFatal(e) =>
          logError(s"Failed to add $path to Spark environment", e)
          Nil
      }
    }

    def checkRemoteJarFile(path: String): Seq[String] = {
      val hadoopPath = new Path(path)
      val scheme = hadoopPath.toUri.getScheme
      if (!Array("http", "https", "ftp").contains(scheme)) {
        try {
          val fs = hadoopPath.getFileSystem(hadoopConfiguration)
          if (!fs.exists(hadoopPath)) {
            throw new FileNotFoundException(s"Jar ${path} not found")
          }
          if (fs.getFileStatus(hadoopPath).isDirectory) {
            throw new IllegalArgumentException(
              s"Directory ${path} is not allowed for addJar")
          }
          Seq(path)
        } catch {
          case NonFatal(e) =>
            logError(s"Failed to add $path to Spark environment", e)
            Nil
        }
      } else {
        Seq(path)
      }
    }

    if (path == null || path.isEmpty) {
      logWarning("null or empty path specified as parameter to addJar")
    } else {
      val (keys, schema) = if (path.contains("\\") && Utils.isWindows) {
        // For local paths with backslashes on Windows, URI throws an exception
        (addLocalJarFile(new File(path)), "local")
      } else {
        val uri = new Path(path).toUri
        // SPARK-17650: Make sure this is a valid URL before adding it to the list of dependencies
        Utils.validateURL(uri)
        val uriSchema = uri.getScheme
        val jarPaths = uriSchema match {
          // A JAR file which exists only on the driver node
          case null =>
            // SPARK-22585 path without schema is not url encoded
            addLocalJarFile(new File(uri.getPath))
          // A JAR file which exists only on the driver node
          case "file" => addLocalJarFile(new File(uri.getPath))
          // A JAR file which exists locally on every worker node
          case "local" => Seq("file:" + uri.getPath)
          case "ivy" =>
            // Since `new Path(path).toUri` will lose query information,
            // so here we use `URI.create(path)`
            DependencyUtils.resolveMavenDependencies(URI.create(path))
          case _ => checkRemoteJarFile(path)
        }
        (jarPaths, uriSchema)
      }
      if (keys.nonEmpty) {
        val timestamp = if (addedOnSubmit) startTime else System.currentTimeMillis
        val (added, existed) = keys.partition(addedJars.putIfAbsent(_, timestamp).isEmpty)
        if (added.nonEmpty) {
          val jarMessage = if (schema != "ivy") "JAR" else "dependency jars of ivy uri"
          logInfo(s"Added $jarMessage $path at ${added.mkString(",")} with timestamp $timestamp")
          postEnvironmentUpdate()
        }
        if (existed.nonEmpty) {
          val jarMessage = if (schema != "ivy") "JAR" else "dependency jars of ivy uri"
          logInfo(s"The $jarMessage $path at ${existed.mkString(",")} has been added already." +
            s" Overwriting of added jar is not supported in the current version.")
        }
      }
    }
  }

  /**
   * Returns a list of jar files that are added to resources.
   */
  def listJars(): Seq[String] = addedJars.keySet.toSeq

  /**
   * When stopping SparkContext inside Spark components, it's easy to cause dead-lock since Spark
   * may wait for some internal threads to finish. It's better to use this method to stop
   * SparkContext instead.
   */
  private[spark] def stopInNewThread(): Unit = {
    new Thread("stop-spark-context") {
      setDaemon(true)

      override def run(): Unit = {
        try {
          SparkContext.this.stop()
        } catch {
          case e: Throwable =>
            logError(e.getMessage, e)
            throw e
        }
      }
    }.start()
  }

  /**
   * Shut down the SparkContext.
   */
  def stop(): Unit = {
    if (LiveListenerBus.withinListenerThread.value) {
      throw new SparkException(s"Cannot stop SparkContext within listener bus thread.")
    }
    // Use the stopping variable to ensure no contention for the stop scenario.
    // Still track the stopped variable for use elsewhere in the code.
    if (!stopped.compareAndSet(false, true)) {
      logInfo("SparkContext already stopped.")
      return
    }
    if (_shutdownHookRef != null) {
      ShutdownHookManager.removeShutdownHook(_shutdownHookRef)
    }

    if (listenerBus != null) {
      Utils.tryLogNonFatalError {
        postApplicationEnd()
      }
    }
    Utils.tryLogNonFatalError {
      _driverLogger.foreach(_.stop())
    }
    Utils.tryLogNonFatalError {
      _ui.foreach(_.stop())
    }
    if (env != null) {
      Utils.tryLogNonFatalError {
        env.metricsSystem.report()
      }
    }
    Utils.tryLogNonFatalError {
      _cleaner.foreach(_.stop())
    }
    Utils.tryLogNonFatalError {
      _executorAllocationManager.foreach(_.stop())
    }
    if (_dagScheduler != null) {
      Utils.tryLogNonFatalError {
        _dagScheduler.stop()
      }
      _dagScheduler = null
    }
    if (_listenerBusStarted) {
      Utils.tryLogNonFatalError {
        listenerBus.stop()
        _listenerBusStarted = false
      }
    }
    Utils.tryLogNonFatalError {
      _plugins.foreach(_.shutdown())
    }
    Utils.tryLogNonFatalError {
      _eventLogger.foreach(_.stop())
    }
    if (_heartbeater != null) {
      Utils.tryLogNonFatalError {
        _heartbeater.stop()
      }
      _heartbeater = null
    }
    if (_shuffleDriverComponents != null) {
      Utils.tryLogNonFatalError {
        _shuffleDriverComponents.cleanupApplication()
      }
    }
    if (env != null && _heartbeatReceiver != null) {
      Utils.tryLogNonFatalError {
        env.rpcEnv.stop(_heartbeatReceiver)
      }
    }
    Utils.tryLogNonFatalError {
      _progressBar.foreach(_.stop())
    }
    _taskScheduler = null
    // TODO: Cache.stop()?
    if (_env != null) {
      Utils.tryLogNonFatalError {
        _env.stop()
      }
      SparkEnv.set(null)
    }
    if (_statusStore != null) {
      _statusStore.close()
    }
    // Clear this `InheritableThreadLocal`, or it will still be inherited in child threads even this
    // `SparkContext` is stopped.
    localProperties.remove()
    ResourceProfile.clearDefaultProfile()
    // Unset YARN mode system env variable, to allow switching between cluster types.
    SparkContext.clearActiveContext()
    logInfo("Successfully stopped SparkContext")
  }


  /**
   * Get Spark's home location from either a value set through the constructor,
   * or the spark.home Java property, or the SPARK_HOME environment variable
   * (in that order of preference). If neither of these is set, return None.
   */
  private[spark] def getSparkHome(): Option[String] = {
    conf.getOption("spark.home").orElse(Option(System.getenv("SPARK_HOME")))
  }

  /**
   * Set the thread-local property for overriding the call sites
   * of actions and RDDs.
   */
  def setCallSite(shortCallSite: String): Unit = {
    setLocalProperty(CallSite.SHORT_FORM, shortCallSite)
  }

  /**
   * Set the thread-local property for overriding the call sites
   * of actions and RDDs.
   */
  private[spark] def setCallSite(callSite: CallSite): Unit = {
    setLocalProperty(CallSite.SHORT_FORM, callSite.shortForm)
    setLocalProperty(CallSite.LONG_FORM, callSite.longForm)
  }

  /**
   * Clear the thread-local property for overriding the call sites
   * of actions and RDDs.
   */
  def clearCallSite(): Unit = {
    setLocalProperty(CallSite.SHORT_FORM, null)
    setLocalProperty(CallSite.LONG_FORM, null)
  }

  /**
   * Capture the current user callsite and return a formatted version for printing. If the user
   * has overridden the call site using `setCallSite()`, this will return the user's version.
   */
  private[spark] def getCallSite(): CallSite = {
    lazy val callSite = Utils.getCallSite()
    CallSite(
      Option(getLocalProperty(CallSite.SHORT_FORM)).getOrElse(callSite.shortForm),
      Option(getLocalProperty(CallSite.LONG_FORM)).getOrElse(callSite.longForm)
    )
  }

  /**
   * Run a function on a given set of partitions in an RDD and pass the results to the given
   * handler function. This is the main entry point for all actions in Spark.
   *
   * @param rdd target RDD to run tasks on
   * @param func a function to run on each partition of the RDD
   * @param partitions set of partitions to run on; some jobs may not want to compute on all
   * partitions of the target RDD, e.g. for operations like `first()`
   * @param resultHandler callback to pass each result to
   */
  def runJob[T, U: ClassTag](
      rdd: RDD[T],
      func: (TaskContext, Iterator[T]) => U,
      partitions: Seq[Int],
      resultHandler: (Int, U) => Unit): Unit = {
    if (stopped.get()) {
      throw new IllegalStateException("SparkContext has been shutdown")
    }
    val callSite = getCallSite
    val cleanedFunc = clean(func)
    logInfo("Starting job: " + callSite.shortForm)
    if (conf.getBoolean("spark.logLineage", false)) {
      logInfo("RDD's recursive dependencies:\n" + rdd.toDebugString)
    }
    dagScheduler.runJob(rdd, cleanedFunc, partitions, callSite, resultHandler, localProperties.get)
    progressBar.foreach(_.finishAll())
    rdd.doCheckpoint()
  }

  /**
   * Run a function on a given set of partitions in an RDD and return the results as an array.
   * The function that is run against each partition additionally takes `TaskContext` argument.
   *
   * @param rdd target RDD to run tasks on
   * @param func a function to run on each partition of the RDD
   * @param partitions set of partitions to run on; some jobs may not want to compute on all
   * partitions of the target RDD, e.g. for operations like `first()`
   * @return in-memory collection with a result of the job (each collection element will contain
   * a result from one partition)
   */
  def runJob[T, U: ClassTag](
      rdd: RDD[T],
      func: (TaskContext, Iterator[T]) => U,
      partitions: Seq[Int]): Array[U] = {
    val results = new Array[U](partitions.size)
    runJob[T, U](rdd, func, partitions, (index, res) => results(index) = res)
    results
  }

  /**
   * Run a function on a given set of partitions in an RDD and return the results as an array.
   *
   * @param rdd target RDD to run tasks on
   * @param func a function to run on each partition of the RDD
   * @param partitions set of partitions to run on; some jobs may not want to compute on all
   * partitions of the target RDD, e.g. for operations like `first()`
   * @return in-memory collection with a result of the job (each collection element will contain
   * a result from one partition)
   */
  def runJob[T, U: ClassTag](
      rdd: RDD[T],
      func: Iterator[T] => U,
      partitions: Seq[Int]): Array[U] = {
    val cleanedFunc = clean(func)
    runJob(rdd, (ctx: TaskContext, it: Iterator[T]) => cleanedFunc(it), partitions)
  }

  /**
   * Run a job on all partitions in an RDD and return the results in an array. The function
   * that is run against each partition additionally takes `TaskContext` argument.
   *
   * @param rdd target RDD to run tasks on
   * @param func a function to run on each partition of the RDD
   * @return in-memory collection with a result of the job (each collection element will contain
   * a result from one partition)
   */
  def runJob[T, U: ClassTag](rdd: RDD[T], func: (TaskContext, Iterator[T]) => U): Array[U] = {
    runJob(rdd, func, 0 until rdd.partitions.length)
  }

  /**
   * Run a job on all partitions in an RDD and return the results in an array.
   *
   * @param rdd target RDD to run tasks on
   * @param func a function to run on each partition of the RDD
   * @return in-memory collection with a result of the job (each collection element will contain
   * a result from one partition)
   */
  def runJob[T, U: ClassTag](rdd: RDD[T], func: Iterator[T] => U): Array[U] = {
    runJob(rdd, func, 0 until rdd.partitions.length)
  }

  /**
   * Run a job on all partitions in an RDD and pass the results to a handler function. The function
   * that is run against each partition additionally takes `TaskContext` argument.
   *
   * @param rdd target RDD to run tasks on
   * @param processPartition a function to run on each partition of the RDD
   * @param resultHandler callback to pass each result to
   */
  def runJob[T, U: ClassTag](
    rdd: RDD[T],
    processPartition: (TaskContext, Iterator[T]) => U,
    resultHandler: (Int, U) => Unit): Unit = {
    runJob[T, U](rdd, processPartition, 0 until rdd.partitions.length, resultHandler)
  }

  /**
   * Run a job on all partitions in an RDD and pass the results to a handler function.
   *
   * @param rdd target RDD to run tasks on
   * @param processPartition a function to run on each partition of the RDD
   * @param resultHandler callback to pass each result to
   */
  def runJob[T, U: ClassTag](
      rdd: RDD[T],
      processPartition: Iterator[T] => U,
      resultHandler: (Int, U) => Unit): Unit = {
    val processFunc = (context: TaskContext, iter: Iterator[T]) => processPartition(iter)
    runJob[T, U](rdd, processFunc, 0 until rdd.partitions.length, resultHandler)
  }

  /**
   * :: DeveloperApi ::
   * Run a job that can return approximate results.
   *
   * @param rdd target RDD to run tasks on
   * @param func a function to run on each partition of the RDD
   * @param evaluator `ApproximateEvaluator` to receive the partial results
   * @param timeout maximum time to wait for the job, in milliseconds
   * @return partial result (how partial depends on whether the job was finished before or
   * after timeout)
   */
  @DeveloperApi
  def runApproximateJob[T, U, R](
      rdd: RDD[T],
      func: (TaskContext, Iterator[T]) => U,
      evaluator: ApproximateEvaluator[U, R],
      timeout: Long): PartialResult[R] = {
    assertNotStopped()
    val callSite = getCallSite
    logInfo("Starting job: " + callSite.shortForm)
    val start = System.nanoTime
    val cleanedFunc = clean(func)
    val result = dagScheduler.runApproximateJob(rdd, cleanedFunc, evaluator, callSite, timeout,
      localProperties.get)
    logInfo(
      "Job finished: " + callSite.shortForm + ", took " + (System.nanoTime - start) / 1e9 + " s")
    result
  }

  /**
   * Submit a job for execution and return a FutureJob holding the result.
   *
   * @param rdd target RDD to run tasks on
   * @param processPartition a function to run on each partition of the RDD
   * @param partitions set of partitions to run on; some jobs may not want to compute on all
   * partitions of the target RDD, e.g. for operations like `first()`
   * @param resultHandler callback to pass each result to
   * @param resultFunc function to be executed when the result is ready
   */
  def submitJob[T, U, R](
      rdd: RDD[T],
      processPartition: Iterator[T] => U,
      partitions: Seq[Int],
      resultHandler: (Int, U) => Unit,
      resultFunc: => R): SimpleFutureAction[R] =
  {
    assertNotStopped()
    val cleanF = clean(processPartition)
    val callSite = getCallSite
    val waiter = dagScheduler.submitJob(
      rdd,
      (context: TaskContext, iter: Iterator[T]) => cleanF(iter),
      partitions,
      callSite,
      resultHandler,
      localProperties.get)
    new SimpleFutureAction(waiter, resultFunc)
  }

  /**
   * Submit a map stage for execution. This is currently an internal API only, but might be
   * promoted to DeveloperApi in the future.
   */
  private[spark] def submitMapStage[K, V, C](dependency: ShuffleDependency[K, V, C])
      : SimpleFutureAction[MapOutputStatistics] = {
    assertNotStopped()
    val callSite = getCallSite()
    var result: MapOutputStatistics = null
    val waiter = dagScheduler.submitMapStage(
      dependency,
      (r: MapOutputStatistics) => { result = r },
      callSite,
      localProperties.get)
    new SimpleFutureAction[MapOutputStatistics](waiter, result)
  }

  /**
   * Cancel active jobs for the specified group. See `org.apache.spark.SparkContext.setJobGroup`
   * for more information.
   */
  def cancelJobGroup(groupId: String): Unit = {
    assertNotStopped()
    dagScheduler.cancelJobGroup(groupId)
  }

  /** Cancel all jobs that have been scheduled or are running.  */
  def cancelAllJobs(): Unit = {
    assertNotStopped()
    dagScheduler.cancelAllJobs()
  }

  /**
   * Cancel a given job if it's scheduled or running.
   *
   * @param jobId the job ID to cancel
   * @param reason optional reason for cancellation
   * @note Throws `InterruptedException` if the cancel message cannot be sent
   */
  def cancelJob(jobId: Int, reason: String): Unit = {
    dagScheduler.cancelJob(jobId, Option(reason))
  }

  /**
   * Cancel a given job if it's scheduled or running.
   *
   * @param jobId the job ID to cancel
   * @note Throws `InterruptedException` if the cancel message cannot be sent
   */
  def cancelJob(jobId: Int): Unit = {
    dagScheduler.cancelJob(jobId, None)
  }

  /**
   * Cancel a given stage and all jobs associated with it.
   *
   * @param stageId the stage ID to cancel
   * @param reason reason for cancellation
   * @note Throws `InterruptedException` if the cancel message cannot be sent
   */
  def cancelStage(stageId: Int, reason: String): Unit = {
    dagScheduler.cancelStage(stageId, Option(reason))
  }

  /**
   * Cancel a given stage and all jobs associated with it.
   *
   * @param stageId the stage ID to cancel
   * @note Throws `InterruptedException` if the cancel message cannot be sent
   */
  def cancelStage(stageId: Int): Unit = {
    dagScheduler.cancelStage(stageId, None)
  }

  /**
   * Kill and reschedule the given task attempt. Task ids can be obtained from the Spark UI
   * or through SparkListener.onTaskStart.
   *
   * @param taskId the task ID to kill. This id uniquely identifies the task attempt.
   * @param interruptThread whether to interrupt the thread running the task.
   * @param reason the reason for killing the task, which should be a short string. If a task
   *   is killed multiple times with different reasons, only one reason will be reported.
   *
   * @return Whether the task was successfully killed.
   */
  def killTaskAttempt(
      taskId: Long,
      interruptThread: Boolean = true,
      reason: String = "killed via SparkContext.killTaskAttempt"): Boolean = {
    dagScheduler.killTaskAttempt(taskId, interruptThread, reason)
  }

  /**
   * Clean a closure to make it ready to be serialized and sent to tasks
   * (removes unreferenced variables in $outer's, updates REPL variables)
   * If <tt>checkSerializable</tt> is set, <tt>clean</tt> will also proactively
   * check to see if <tt>f</tt> is serializable and throw a <tt>SparkException</tt>
   * if not.
   *
   * @param f the closure to clean
   * @param checkSerializable whether or not to immediately check <tt>f</tt> for serializability
   * @throws SparkException if <tt>checkSerializable</tt> is set but <tt>f</tt> is not
   *   serializable
   * @return the cleaned closure
   */
  private[spark] def clean[F <: AnyRef](f: F, checkSerializable: Boolean = true): F = {
    ClosureCleaner.clean(f, checkSerializable)
    f
  }

  /**
   * Set the directory under which RDDs are going to be checkpointed.
   * @param directory path to the directory where checkpoint files will be stored
   * (must be HDFS path if running in cluster)
   */
  def setCheckpointDir(directory: String): Unit = {

    // If we are running on a cluster, log a warning if the directory is local.
    // Otherwise, the driver may attempt to reconstruct the checkpointed RDD from
    // its own local file system, which is incorrect because the checkpoint files
    // are actually on the executor machines.
    if (!isLocal && Utils.nonLocalPaths(directory).isEmpty) {
      logWarning("Spark is not running in local mode, therefore the checkpoint directory " +
        s"must not be on the local filesystem. Directory '$directory' " +
        "appears to be on the local filesystem.")
    }

    checkpointDir = Option(directory).map { dir =>
      val path = new Path(dir, UUID.randomUUID().toString)
      val fs = path.getFileSystem(hadoopConfiguration)
      fs.mkdirs(path)
      fs.getFileStatus(path).getPath.toString
    }
  }

  def getCheckpointDir: Option[String] = checkpointDir

  /** Default level of parallelism to use when not given by user (e.g. parallelize and makeRDD). */
  def defaultParallelism: Int = {
    assertNotStopped()
    taskScheduler.defaultParallelism
  }

  /**
   * Default min number of partitions for Hadoop RDDs when not given by user
   * Notice that we use math.min so the "defaultMinPartitions" cannot be higher than 2.
   * The reasons for this are discussed in https://github.com/mesos/spark/pull/718
   */
  def defaultMinPartitions: Int = math.min(defaultParallelism, 2)

  private val nextShuffleId = new AtomicInteger(0)

  private[spark] def newShuffleId(): Int = nextShuffleId.getAndIncrement()

  private val nextRddId = new AtomicInteger(0)

  /** Register a new RDD, returning its RDD ID */
  private[spark] def newRddId(): Int = nextRddId.getAndIncrement()

  /**
   * Registers listeners specified in spark.extraListeners, then starts the listener bus.
   * This should be called after all internal listeners have been registered with the listener bus
   * (e.g. after the web UI and event logging listeners have been registered).
   */
  private def setupAndStartListenerBus(): Unit = {
    try {
      conf.get(EXTRA_LISTENERS).foreach { classNames =>
        val listeners = Utils.loadExtensions(classOf[SparkListenerInterface], classNames, conf)
        listeners.foreach { listener =>
          listenerBus.addToSharedQueue(listener)
          logInfo(s"Registered listener ${listener.getClass().getName()}")
        }
      }
    } catch {
      case e: Exception =>
        try {
          stop()
        } finally {
          throw new SparkException(s"Exception when registering SparkListener", e)
        }
    }

    listenerBus.start(this, _env.metricsSystem)
    _listenerBusStarted = true
  }

  /** Post the application start event */
  private def postApplicationStart(): Unit = {
    // Note: this code assumes that the task scheduler has been initialized and has contacted
    // the cluster manager to get an application ID (in case the cluster manager provides one).
    listenerBus.post(SparkListenerApplicationStart(appName, Some(applicationId),
      startTime, sparkUser, applicationAttemptId, schedulerBackend.getDriverLogUrls,
      schedulerBackend.getDriverAttributes))
    _driverLogger.foreach(_.startSync(_hadoopConfiguration))
  }

  /** Post the application end event */
  private def postApplicationEnd(): Unit = {
    listenerBus.post(SparkListenerApplicationEnd(System.currentTimeMillis))
  }

  /** Post the environment update event once the task scheduler is ready */
  private def postEnvironmentUpdate(): Unit = {
    if (taskScheduler != null) {
      val schedulingMode = getSchedulingMode.toString
      val addedJarPaths = addedJars.keys.toSeq
      val addedFilePaths = addedFiles.keys.toSeq
      val environmentDetails = SparkEnv.environmentDetails(conf, hadoopConfiguration,
        schedulingMode, addedJarPaths, addedFilePaths)
      val environmentUpdate = SparkListenerEnvironmentUpdate(environmentDetails)
      listenerBus.post(environmentUpdate)
    }
  }

  /** Reports heartbeat metrics for the driver. */
  private def reportHeartBeat(executorMetricsSource: Option[ExecutorMetricsSource]): Unit = {
    val currentMetrics = ExecutorMetrics.getCurrentMetrics(env.memoryManager)
    executorMetricsSource.foreach(_.updateMetricsSnapshot(currentMetrics))

    val driverUpdates = new HashMap[(Int, Int), ExecutorMetrics]
    // In the driver, we do not track per-stage metrics, so use a dummy stage for the key
    driverUpdates.put(EventLoggingListener.DRIVER_STAGE_KEY, new ExecutorMetrics(currentMetrics))
    val accumUpdates = new Array[(Long, Int, Int, Seq[AccumulableInfo])](0)
    listenerBus.post(SparkListenerExecutorMetricsUpdate("driver", accumUpdates,
      driverUpdates))
  }

  // In order to prevent multiple SparkContexts from being active at the same time, mark this
  // context as having finished construction.
  // NOTE: this must be placed at the end of the SparkContext constructor.
  SparkContext.setActiveContext(this)
}

/**
 * The SparkContext object contains a number of implicit conversions and parameters for use with
 * various Spark features.
 */
object SparkContext extends Logging {
  private val VALID_LOG_LEVELS =
    Set("ALL", "DEBUG", "ERROR", "FATAL", "INFO", "OFF", "TRACE", "WARN")

  /**
   * Lock that guards access to global variables that track SparkContext construction.
   */
  private val SPARK_CONTEXT_CONSTRUCTOR_LOCK = new Object()

  /**
   * The active, fully-constructed SparkContext. If no SparkContext is active, then this is `null`.
   *
   * Access to this field is guarded by `SPARK_CONTEXT_CONSTRUCTOR_LOCK`.
   */
  private val activeContext: AtomicReference[SparkContext] =
    new AtomicReference[SparkContext](null)

  /**
   * Points to a partially-constructed SparkContext if another thread is in the SparkContext
   * constructor, or `None` if no SparkContext is being constructed.
   *
   * Access to this field is guarded by `SPARK_CONTEXT_CONSTRUCTOR_LOCK`.
   */
  private var contextBeingConstructed: Option[SparkContext] = None

  /**
   * Called to ensure that no other SparkContext is running in this JVM.
   *
   * Throws an exception if a running context is detected and logs a warning if another thread is
   * constructing a SparkContext. This warning is necessary because the current locking scheme
   * prevents us from reliably distinguishing between cases where another context is being
   * constructed and cases where another constructor threw an exception.
   */
  private def assertNoOtherContextIsRunning(sc: SparkContext): Unit = {
    SPARK_CONTEXT_CONSTRUCTOR_LOCK.synchronized {
      Option(activeContext.get()).filter(_ ne sc).foreach { ctx =>
          val errMsg = "Only one SparkContext should be running in this JVM (see SPARK-2243)." +
            s"The currently running SparkContext was created at:\n${ctx.creationSite.longForm}"
          throw new SparkException(errMsg)
        }

      contextBeingConstructed.filter(_ ne sc).foreach { otherContext =>
        // Since otherContext might point to a partially-constructed context, guard against
        // its creationSite field being null:
        val otherContextCreationSite =
          Option(otherContext.creationSite).map(_.longForm).getOrElse("unknown location")
        val warnMsg = "Another SparkContext is being constructed (or threw an exception in its" +
          " constructor). This may indicate an error, since only one SparkContext should be" +
          " running in this JVM (see SPARK-2243)." +
          s" The other SparkContext was created at:\n$otherContextCreationSite"
        logWarning(warnMsg)
      }
    }
  }

  /**
   * Called to ensure that SparkContext is created or accessed only on the Driver.
   *
   * Throws an exception if a SparkContext is about to be created in executors.
   */
  private def assertOnDriver(): Unit = {
    if (TaskContext.get != null) {
      // we're accessing it during task execution, fail.
      throw new IllegalStateException(
        "SparkContext should only be created and accessed on the driver.")
    }
  }

  /**
   * This function may be used to get or instantiate a SparkContext and register it as a
   * singleton object. Because we can only have one active SparkContext per JVM,
   * this is useful when applications may wish to share a SparkContext.
   *
   * @param config `SparkConfig` that will be used for initialisation of the `SparkContext`
   * @return current `SparkContext` (or a new one if it wasn't created before the function call)
   */
  def getOrCreate(config: SparkConf): SparkContext = {
    // Synchronize to ensure that multiple create requests don't trigger an exception
    // from assertNoOtherContextIsRunning within setActiveContext
    SPARK_CONTEXT_CONSTRUCTOR_LOCK.synchronized {
      if (activeContext.get() == null) {
        setActiveContext(new SparkContext(config))
      } else {
        if (config.getAll.nonEmpty) {
          logWarning("Using an existing SparkContext; some configuration may not take effect.")
        }
      }
      activeContext.get()
    }
  }

  /**
   * This function may be used to get or instantiate a SparkContext and register it as a
   * singleton object. Because we can only have one active SparkContext per JVM,
   * this is useful when applications may wish to share a SparkContext.
   *
   * This method allows not passing a SparkConf (useful if just retrieving).
   *
   * @return current `SparkContext` (or a new one if wasn't created before the function call)
   */
  def getOrCreate(): SparkContext = {
    SPARK_CONTEXT_CONSTRUCTOR_LOCK.synchronized {
      if (activeContext.get() == null) {
        setActiveContext(new SparkContext())
      }
      activeContext.get()
    }
  }

  /** Return the current active [[SparkContext]] if any. */
  private[spark] def getActive: Option[SparkContext] = {
    SPARK_CONTEXT_CONSTRUCTOR_LOCK.synchronized {
      Option(activeContext.get())
    }
  }

  /**
   * Called at the beginning of the SparkContext constructor to ensure that no SparkContext is
   * running. Throws an exception if a running context is detected and logs a warning if another
   * thread is constructing a SparkContext. This warning is necessary because the current locking
   * scheme prevents us from reliably distinguishing between cases where another context is being
   * constructed and cases where another constructor threw an exception.
   */
  private[spark] def markPartiallyConstructed(sc: SparkContext): Unit = {
    SPARK_CONTEXT_CONSTRUCTOR_LOCK.synchronized {
      assertNoOtherContextIsRunning(sc)
      contextBeingConstructed = Some(sc)
    }
  }

  /**
   * Called at the end of the SparkContext constructor to ensure that no other SparkContext has
   * raced with this constructor and started.
   */
  private[spark] def setActiveContext(sc: SparkContext): Unit = {
    SPARK_CONTEXT_CONSTRUCTOR_LOCK.synchronized {
      assertNoOtherContextIsRunning(sc)
      contextBeingConstructed = None
      activeContext.set(sc)
    }
  }

  /**
   * Clears the active SparkContext metadata. This is called by `SparkContext#stop()`. It's
   * also called in unit tests to prevent a flood of warnings from test suites that don't / can't
   * properly clean up their SparkContexts.
   */
  private[spark] def clearActiveContext(): Unit = {
    SPARK_CONTEXT_CONSTRUCTOR_LOCK.synchronized {
      activeContext.set(null)
    }
  }

  private[spark] val SPARK_JOB_DESCRIPTION = "spark.job.description"
  private[spark] val SPARK_JOB_GROUP_ID = "spark.jobGroup.id"
  private[spark] val SPARK_JOB_INTERRUPT_ON_CANCEL = "spark.job.interruptOnCancel"
  private[spark] val SPARK_SCHEDULER_POOL = "spark.scheduler.pool"
  private[spark] val RDD_SCOPE_KEY = "spark.rdd.scope"
  private[spark] val RDD_SCOPE_NO_OVERRIDE_KEY = "spark.rdd.scope.noOverride"

  /**
   * Executor id for the driver.  In earlier versions of Spark, this was `<driver>`, but this was
   * changed to `driver` because the angle brackets caused escaping issues in URLs and XML (see
   * SPARK-6716 for more details).
   */
  private[spark] val DRIVER_IDENTIFIER = "driver"


  private implicit def arrayToArrayWritable[T <: Writable : ClassTag](arr: Iterable[T])
    : ArrayWritable = {
    def anyToWritable[U <: Writable](u: U): Writable = u

    new ArrayWritable(classTag[T].runtimeClass.asInstanceOf[Class[Writable]],
        arr.map(x => anyToWritable(x)).toArray)
  }

  /**
   * Find the JAR from which a given class was loaded, to make it easy for users to pass
   * their JARs to SparkContext.
   *
   * @param cls class that should be inside of the jar
   * @return jar that contains the Class, `None` if not found
   */
  def jarOfClass(cls: Class[_]): Option[String] = {
    val uri = cls.getResource("/" + cls.getName.replace('.', '/') + ".class")
    if (uri != null) {
      val uriStr = uri.toString
      if (uriStr.startsWith("jar:file:")) {
        // URI will be of the form "jar:file:/path/foo.jar!/package/cls.class",
        // so pull out the /path/foo.jar
        Some(uriStr.substring("jar:file:".length, uriStr.indexOf('!')))
      } else {
        None
      }
    } else {
      None
    }
  }

  /**
   * Find the JAR that contains the class of a particular object, to make it easy for users
   * to pass their JARs to SparkContext. In most cases you can call jarOfObject(this) in
   * your driver program.
   *
   * @param obj reference to an instance which class should be inside of the jar
   * @return jar that contains the class of the instance, `None` if not found
   */
  def jarOfObject(obj: AnyRef): Option[String] = jarOfClass(obj.getClass)

  /**
   * Creates a modified version of a SparkConf with the parameters that can be passed separately
   * to SparkContext, to make it easier to write SparkContext's constructors. This ignores
   * parameters that are passed as the default value of null, instead of throwing an exception
   * like SparkConf would.
   */
  private[spark] def updatedConf(
      conf: SparkConf,
      master: String,
      appName: String,
      sparkHome: String = null,
      jars: Seq[String] = Nil,
      environment: Map[String, String] = Map()): SparkConf =
  {
    val res = conf.clone()
    res.setMaster(master)
    res.setAppName(appName)
    if (sparkHome != null) {
      res.setSparkHome(sparkHome)
    }
    if (jars != null && !jars.isEmpty) {
      res.setJars(jars)
    }
    res.setExecutorEnv(environment.toSeq)
    res
  }

  /**
   * The number of cores available to the driver to use for tasks such as I/O with Netty
   */
  private[spark] def numDriverCores(master: String): Int = {
    numDriverCores(master, null)
  }

  /**
   * The number of cores available to the driver to use for tasks such as I/O with Netty
   */
  private[spark] def numDriverCores(master: String, conf: SparkConf): Int = {
    def convertToInt(threads: String): Int = {
      if (threads == "*") Runtime.getRuntime.availableProcessors() else threads.toInt
    }
    master match {
      case "local" => 1
      case SparkMasterRegex.LOCAL_N_REGEX(threads) => convertToInt(threads)
      case SparkMasterRegex.LOCAL_N_FAILURES_REGEX(threads, _) => convertToInt(threads)
      case "yarn" | SparkMasterRegex.KUBERNETES_REGEX(_) =>
        if (conf != null && conf.get(SUBMIT_DEPLOY_MODE) == "cluster") {
          conf.getInt(DRIVER_CORES.key, 0)
        } else {
          0
        }
      case _ => 0 // Either driver is not being used, or its core count will be interpolated later
    }
  }

  /**
   * Create a task scheduler based on a given master URL.
   * Return a 2-tuple of the scheduler backend and the task scheduler.
   */
  private def createTaskScheduler(
      sc: SparkContext,
      master: String,
      deployMode: String): (SchedulerBackend, TaskScheduler) = {
    import SparkMasterRegex._

    // When running locally, don't try to re-execute tasks on failure.
    val MAX_LOCAL_TASK_FAILURES = 1

    // Ensure that default executor's resources satisfies one or more tasks requirement.
    // This function is for cluster managers that don't set the executor cores config, for
    // others its checked in ResourceProfile.
    def checkResourcesPerTask(executorCores: Int): Unit = {
      val taskCores = sc.conf.get(CPUS_PER_TASK)
      if (!sc.conf.get(SKIP_VALIDATE_CORES_TESTING)) {
        validateTaskCpusLargeEnough(sc.conf, executorCores, taskCores)
      }
      val defaultProf = sc.resourceProfileManager.defaultResourceProfile
      ResourceUtils.warnOnWastedResources(defaultProf, sc.conf, Some(executorCores))
    }

    master match {
      case "local" =>
        checkResourcesPerTask(1)
        val scheduler = new TaskSchedulerImpl(sc, MAX_LOCAL_TASK_FAILURES, isLocal = true)
        val backend = new LocalSchedulerBackend(sc.getConf, scheduler, 1)
        scheduler.initialize(backend)
        (backend, scheduler)

      case LOCAL_N_REGEX(threads) =>
        def localCpuCount: Int = Runtime.getRuntime.availableProcessors()
        // local[*] estimates the number of cores on the machine; local[N] uses exactly N threads.
        val threadCount = if (threads == "*") localCpuCount else threads.toInt
        if (threadCount <= 0) {
          throw new SparkException(s"Asked to run locally with $threadCount threads")
        }
        checkResourcesPerTask(threadCount)
        val scheduler = new TaskSchedulerImpl(sc, MAX_LOCAL_TASK_FAILURES, isLocal = true)
        val backend = new LocalSchedulerBackend(sc.getConf, scheduler, threadCount)
        scheduler.initialize(backend)
        (backend, scheduler)

      case LOCAL_N_FAILURES_REGEX(threads, maxFailures) =>
        def localCpuCount: Int = Runtime.getRuntime.availableProcessors()
        // local[*, M] means the number of cores on the computer with M failures
        // local[N, M] means exactly N threads with M failures
        val threadCount = if (threads == "*") localCpuCount else threads.toInt
        checkResourcesPerTask(threadCount)
        val scheduler = new TaskSchedulerImpl(sc, maxFailures.toInt, isLocal = true)
        val backend = new LocalSchedulerBackend(sc.getConf, scheduler, threadCount)
        scheduler.initialize(backend)
        (backend, scheduler)

      case SPARK_REGEX(sparkUrl) =>
        val scheduler = new TaskSchedulerImpl(sc)
        val masterUrls = sparkUrl.split(",").map("spark://" + _)
        val backend = new StandaloneSchedulerBackend(scheduler, sc, masterUrls)
        scheduler.initialize(backend)
        (backend, scheduler)

      case LOCAL_CLUSTER_REGEX(numWorkers, coresPerWorker, memoryPerWorker) =>
        checkResourcesPerTask(coresPerWorker.toInt)
        // Check to make sure memory requested <= memoryPerWorker. Otherwise Spark will just hang.
        val memoryPerWorkerInt = memoryPerWorker.toInt
        if (sc.executorMemory > memoryPerWorkerInt) {
          throw new SparkException(
            "Asked to launch cluster with %d MiB RAM / worker but requested %d MiB/worker".format(
              memoryPerWorkerInt, sc.executorMemory))
        }

        // For host local mode setting the default of SHUFFLE_HOST_LOCAL_DISK_READING_ENABLED
        // to false because this mode is intended to be used for testing and in this case all the
        // executors are running on the same host. So if host local reading was enabled here then
        // testing of the remote fetching would be secondary as setting this config explicitly to
        // false would be required in most of the unit test (despite the fact that remote fetching
        // is much more frequent in production).
        sc.conf.setIfMissing(SHUFFLE_HOST_LOCAL_DISK_READING_ENABLED, false)

        val scheduler = new TaskSchedulerImpl(sc)
        val localCluster = new LocalSparkCluster(
          numWorkers.toInt, coresPerWorker.toInt, memoryPerWorkerInt, sc.conf)
        val masterUrls = localCluster.start()
        val backend = new StandaloneSchedulerBackend(scheduler, sc, masterUrls)
        scheduler.initialize(backend)
        backend.shutdownCallback = (backend: StandaloneSchedulerBackend) => {
          localCluster.stop()
        }
        (backend, scheduler)

      case masterUrl =>
        val cm = getClusterManager(masterUrl) match {
          case Some(clusterMgr) => clusterMgr
          case None => throw new SparkException("Could not parse Master URL: '" + master + "'")
        }
        try {
          val scheduler = cm.createTaskScheduler(sc, masterUrl)
          val backend = cm.createSchedulerBackend(sc, masterUrl, scheduler)
          cm.initialize(scheduler, backend)
          (backend, scheduler)
        } catch {
          case se: SparkException => throw se
          case NonFatal(e) =>
            throw new SparkException("External scheduler cannot be instantiated", e)
        }
    }
  }

  private def getClusterManager(url: String): Option[ExternalClusterManager] = {
    val loader = Utils.getContextOrSparkClassLoader
    val serviceLoaders =
      ServiceLoader.load(classOf[ExternalClusterManager], loader).asScala.filter(_.canCreate(url))
    if (serviceLoaders.size > 1) {
      throw new SparkException(
        s"Multiple external cluster managers registered for the url $url: $serviceLoaders")
    }
    serviceLoaders.headOption
  }
}

/**
 * A collection of regexes for extracting information from the master string.
 */
private object SparkMasterRegex {
  // Regular expression used for local[N] and local[*] master formats
  val LOCAL_N_REGEX = """local\[([0-9]+|\*)\]""".r
  // Regular expression for local[N, maxRetries], used in tests with failing tasks
  val LOCAL_N_FAILURES_REGEX = """local\[([0-9]+|\*)\s*,\s*([0-9]+)\]""".r
  // Regular expression for simulating a Spark cluster of [N, cores, memory] locally
  val LOCAL_CLUSTER_REGEX = """local-cluster\[\s*([0-9]+)\s*,\s*([0-9]+)\s*,\s*([0-9]+)\s*]""".r
  // Regular expression for connecting to Spark deploy clusters
  val SPARK_REGEX = """spark://(.*)""".r
  // Regular expression for connecting to kubernetes clusters
  val KUBERNETES_REGEX = """k8s://(.*)""".r
}

/**
 * A class encapsulating how to convert some type `T` from `Writable`. It stores both the `Writable`
 * class corresponding to `T` (e.g. `IntWritable` for `Int`) and a function for doing the
 * conversion.
 * The getter for the writable class takes a `ClassTag[T]` in case this is a generic object
 * that doesn't know the type of `T` when it is created. This sounds strange but is necessary to
 * support converting subclasses of `Writable` to themselves (`writableWritableConverter()`).
 */
private[spark] class WritableConverter[T](
    val writableClass: ClassTag[T] => Class[_ <: Writable],
    val convert: Writable => T)
  extends Serializable

object WritableConverter {

  // Helper objects for converting common types to Writable
  private[spark] def simpleWritableConverter[T, W <: Writable: ClassTag](convert: W => T)
  : WritableConverter[T] = {
    val wClass = classTag[W].runtimeClass.asInstanceOf[Class[W]]
    new WritableConverter[T](_ => wClass, x => convert(x.asInstanceOf[W]))
  }

  // The following implicit functions were in SparkContext before 1.3 and users had to
  // `import SparkContext._` to enable them. Now we move them here to make the compiler find
  // them automatically. However, we still keep the old functions in SparkContext for backward
  // compatibility and forward to the following functions directly.

  // The following implicit declarations have been added on top of the very similar ones
  // below in order to enable compatibility with Scala 2.12. Scala 2.12 deprecates eta
  // expansion of zero-arg methods and thus won't match a no-arg method where it expects
  // an implicit that is a function of no args.

  implicit val intWritableConverterFn: () => WritableConverter[Int] =
    () => simpleWritableConverter[Int, IntWritable](_.get)

  implicit val longWritableConverterFn: () => WritableConverter[Long] =
    () => simpleWritableConverter[Long, LongWritable](_.get)

  implicit val doubleWritableConverterFn: () => WritableConverter[Double] =
    () => simpleWritableConverter[Double, DoubleWritable](_.get)

  implicit val floatWritableConverterFn: () => WritableConverter[Float] =
    () => simpleWritableConverter[Float, FloatWritable](_.get)

  implicit val booleanWritableConverterFn: () => WritableConverter[Boolean] =
    () => simpleWritableConverter[Boolean, BooleanWritable](_.get)

  implicit val bytesWritableConverterFn: () => WritableConverter[Array[Byte]] = {
    () => simpleWritableConverter[Array[Byte], BytesWritable] { bw =>
      // getBytes method returns array which is longer then data to be returned
      Arrays.copyOfRange(bw.getBytes, 0, bw.getLength)
    }
  }

  implicit val stringWritableConverterFn: () => WritableConverter[String] =
    () => simpleWritableConverter[String, Text](_.toString)

  implicit def writableWritableConverterFn[T <: Writable : ClassTag]: () => WritableConverter[T] =
    () => new WritableConverter[T](_.runtimeClass.asInstanceOf[Class[T]], _.asInstanceOf[T])

  // These implicits remain included for backwards-compatibility. They fulfill the
  // same role as those above.

  implicit def intWritableConverter(): WritableConverter[Int] =
    simpleWritableConverter[Int, IntWritable](_.get)

  implicit def longWritableConverter(): WritableConverter[Long] =
    simpleWritableConverter[Long, LongWritable](_.get)

  implicit def doubleWritableConverter(): WritableConverter[Double] =
    simpleWritableConverter[Double, DoubleWritable](_.get)

  implicit def floatWritableConverter(): WritableConverter[Float] =
    simpleWritableConverter[Float, FloatWritable](_.get)

  implicit def booleanWritableConverter(): WritableConverter[Boolean] =
    simpleWritableConverter[Boolean, BooleanWritable](_.get)

  implicit def bytesWritableConverter(): WritableConverter[Array[Byte]] = {
    simpleWritableConverter[Array[Byte], BytesWritable] { bw =>
      // getBytes method returns array which is longer then data to be returned
      Arrays.copyOfRange(bw.getBytes, 0, bw.getLength)
    }
  }

  implicit def stringWritableConverter(): WritableConverter[String] =
    simpleWritableConverter[String, Text](_.toString)

  implicit def writableWritableConverter[T <: Writable](): WritableConverter[T] =
    new WritableConverter[T](_.runtimeClass.asInstanceOf[Class[T]], _.asInstanceOf[T])
}

/**
 * A class encapsulating how to convert some type `T` to `Writable`. It stores both the `Writable`
 * class corresponding to `T` (e.g. `IntWritable` for `Int`) and a function for doing the
 * conversion.
 * The `Writable` class will be used in `SequenceFileRDDFunctions`.
 */
private[spark] class WritableFactory[T](
    val writableClass: ClassTag[T] => Class[_ <: Writable],
    val convert: T => Writable) extends Serializable

object WritableFactory {

  private[spark] def simpleWritableFactory[T: ClassTag, W <: Writable : ClassTag](convert: T => W)
    : WritableFactory[T] = {
    val writableClass = implicitly[ClassTag[W]].runtimeClass.asInstanceOf[Class[W]]
    new WritableFactory[T](_ => writableClass, convert)
  }

  implicit def intWritableFactory: WritableFactory[Int] =
    simpleWritableFactory(new IntWritable(_))

  implicit def longWritableFactory: WritableFactory[Long] =
    simpleWritableFactory(new LongWritable(_))

  implicit def floatWritableFactory: WritableFactory[Float] =
    simpleWritableFactory(new FloatWritable(_))

  implicit def doubleWritableFactory: WritableFactory[Double] =
    simpleWritableFactory(new DoubleWritable(_))

  implicit def booleanWritableFactory: WritableFactory[Boolean] =
    simpleWritableFactory(new BooleanWritable(_))

  implicit def bytesWritableFactory: WritableFactory[Array[Byte]] =
    simpleWritableFactory(new BytesWritable(_))

  implicit def stringWritableFactory: WritableFactory[String] =
    simpleWritableFactory(new Text(_))

  implicit def writableWritableFactory[T <: Writable: ClassTag]: WritableFactory[T] =
    simpleWritableFactory(w => w)

}