SparkEnv — Spark Runtime Environment

Spark Runtime Environment (SparkEnv) is the runtime environment with Spark’s public services that interact with each other to establish a distributed computing platform for a Spark application.

Spark Runtime Environment is represented by a SparkEnv object that holds all the required runtime services for a running Spark application with separate environments for the driver and executors.

The idiomatic way in Spark to access the current SparkEnv when on the driver or executors is to use get method.

import org.apache.spark._
scala> SparkEnv.get
res0: org.apache.spark.SparkEnv = org.apache.spark.SparkEnv@49322d04
Table 1. SparkEnv’s Services
Property Service Description

























Table 2. SparkEnv’s Internal Properties
Name Initial Value Description


Disabled, i.e. false

Used to mark SparkEnv stopped. FIXME


Enable INFO or DEBUG logging level for org.apache.spark.SparkEnv logger to see what happens inside.

Add the following line to conf/

Refer to Logging.

SparkEnv Factory Object

Creating "Base" SparkEnv — create Method

  conf: SparkConf,
  executorId: String,
  hostname: String,
  port: Int,
  isDriver: Boolean,
  isLocal: Boolean,
  numUsableCores: Int,
  listenerBus: LiveListenerBus = null,
  mockOutputCommitCoordinator: Option[OutputCommitCoordinator] = None): SparkEnv

create is a internal helper method to create a "base" SparkEnv (for a driver or an executor).

Table 3. create’s Input Arguments and Their Usage
Input Argument Usage


Used to create RpcEnv and NettyBlockTransferService.


Used to create RpcEnv and NettyBlockTransferService.


Used to create MemoryManager, NettyBlockTransferService and BlockManager.

When executed, create creates a Serializer (based on spark.serializer setting). You should see the following DEBUG message in the logs:

DEBUG SparkEnv: Using serializer: [serializer]

create creates a closure Serializer (based on spark.closure.serializer).

create creates a ShuffleManager given the value of spark.shuffle.manager Spark property.

create creates a MemoryManager based on spark.memory.useLegacyMode setting (with UnifiedMemoryManager being the default and numCores the input numUsableCores).

create creates a NettyBlockTransferService with the following ports:

create uses the NettyBlockTransferService to create a BlockManager.
FIXME A picture with SparkEnv, NettyBlockTransferService and the ports "armed".

create creates a BlockManagerMaster object with the BlockManagerMaster RPC endpoint reference (by registering or looking it up by name and BlockManagerMasterEndpoint), the input SparkConf, and the input isDriver flag.

sparkenv driver blockmanager
Figure 1. Creating BlockManager for the Driver
create registers the BlockManagerMaster RPC endpoint for the driver and looks it up for executors.
sparkenv executor blockmanager
Figure 2. Creating BlockManager for Executor

create creates a BlockManager (using the above BlockManagerMaster, NettyBlockTransferService and other services).

create creates a BroadcastManager.

create creates a MapOutputTrackerMaster or MapOutputTrackerWorker for the driver and executors, respectively.

The choice of the real implementation of MapOutputTracker is based on whether the input executorId is driver or not.

create registers or looks up RpcEndpoint as MapOutputTracker. It registers MapOutputTrackerMasterEndpoint on the driver and creates a RPC endpoint reference on executors. The RPC endpoint reference gets assigned as the MapOutputTracker RPC endpoint.


It creates a CacheManager.

It creates a MetricsSystem for a driver and a worker separately.

It initializes userFiles temporary directory used for downloading dependencies for a driver while this is the executor’s current working directory for an executor.

An OutputCommitCoordinator is created.

create is used when SparkEnv is requested for the SparkEnv for the driver and executors.

Registering or Looking up RPC Endpoint by Name — registerOrLookupEndpoint Method

registerOrLookupEndpoint(name: String, endpointCreator: => RpcEndpoint)

registerOrLookupEndpoint registers or looks up a RPC endpoint by name.

If called from the driver, you should see the following INFO message in the logs:

INFO SparkEnv: Registering [name]

And the RPC endpoint is registered in the RPC environment.

Otherwise, it obtains a RPC endpoint reference by name.

Creating SparkEnv for Driver — createDriverEnv Method

  conf: SparkConf,
  isLocal: Boolean,
  listenerBus: LiveListenerBus,
  numCores: Int,
  mockOutputCommitCoordinator: Option[OutputCommitCoordinator] = None): SparkEnv

createDriverEnv creates a SparkEnv execution environment for the driver.

sparkenv driver
Figure 3. Spark Environment for driver

createDriverEnv accepts an instance of SparkConf, whether it runs in local mode or not, LiveListenerBus, the number of cores to use for execution in local mode or 0 otherwise, and a OutputCommitCoordinator (default: none).

createDriverEnv ensures that and spark.driver.port settings are defined.

It then passes the call straight on to the create helper method (with driver executor id, isDriver enabled, and the input parameters).

createDriverEnv is exclusively used by SparkContext to create a SparkEnv (while a SparkContext is being created for the driver).

Creating SparkEnv for Executor — createExecutorEnv Method

  conf: SparkConf,
  executorId: String,
  hostname: String,
  port: Int,
  numCores: Int,
  ioEncryptionKey: Option[Array[Byte]],
  isLocal: Boolean): SparkEnv

createExecutorEnv creates an executor’s (execution) environment that is the Spark execution environment for an executor.

sparkenv executor
Figure 4. Spark Environment for executor
createExecutorEnv is a private[spark] method.

createExecutorEnv simply creates the base SparkEnv (passing in all the input parameters) and sets it as the current SparkEnv.

The number of cores numCores is configured using --cores command-line option of CoarseGrainedExecutorBackend and is specific to a cluster manager.

Getting Current SparkEnv — get Method

get: SparkEnv

get returns the current SparkEnv.

import org.apache.spark._
scala> SparkEnv.get
res0: org.apache.spark.SparkEnv = org.apache.spark.SparkEnv@49322d04

Stopping SparkEnv — stop Method

stop(): Unit

stop checks isStopped internal flag and does nothing when enabled.

stop is a private[spark] method.

Otherwise, stop turns isStopped flag on, stops all pythonWorkers and requests the following services to stop:

Only on the driver, stop deletes the temporary directory. You can see the following WARN message in the logs if the deletion fails.

WARN Exception while deleting Spark temp dir: [path]
stop is used when SparkContext stops (on the driver) and Executor stops.

set Method

set(e: SparkEnv): Unit

set saves the input SparkEnv to env internal registry (as the default SparkEnv).

set is used when…​FIXME


Table 4. Spark Properties
Spark Property Default Value Description




TIP: Enable DEBUG logging level for org.apache.spark.SparkEnv logger to see the current value.

` DEBUG SparkEnv: Using serializer: [serializer] `






Controls what type of the MemoryManager to use. When enabled (i.e. true) it is the legacy StaticMemoryManager while UnifiedMemoryManager otherwise (i.e. false).

environmentDetails Object Method

  conf: SparkConf,
  schedulingMode: String,
  addedJars: Seq[String],
  addedFiles: Seq[String]): Map[String, Seq[(String, String)]]


environmentDetails is used exclusively when SparkContext is requested to postEnvironmentUpdate.