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Spark Configuration Properties

spark.app.id

Unique identifier of a Spark application that Spark uses to uniquely identify metric sources.

Default: TaskScheduler.applicationId()

Set when SparkContext is created

spark.broadcast.blockSize

The size of each piece of a block (in kB unless the unit is specified)

Default: 4m

Too large a value decreases parallelism during broadcast (makes it slower); however, if it is too small, BlockManager might take a performance hit

Used when:

  • TorrentBroadcast is requested to setConf

spark.broadcast.compress

Controls broadcast variable compression (before sending them over the wire)

Default: true

Generally a good idea. Compression will use spark.io.compression.codec

Used when:

  • TorrentBroadcast is requested to setConf
  • SerializerManager is created

spark.cleaner.referenceTracking

Controls whether to enable ContextCleaner

Default: true

spark.diskStore.subDirectories

Number of subdirectories inside each path listed in spark.local.dir for hashing block files into.

Default: 64

Used by BlockManager and DiskBlockManager

spark.driver.host

Address of the driver (endpoints)

Default: Utils.localCanonicalHostName

spark.driver.maxResultSize

Maximum size of task results (in bytes)

Default: 1g

Used when:

spark.driver.port

Port of the driver (endpoints)

Default: 0

spark.executor.cores

Number of CPU cores for Executor

Default: 1

spark.executor.id

Default: (undefined)

spark.executor.memory

Amount of memory to use for an Executor

Default: 1g

Equivalent to SPARK_EXECUTOR_MEMORY environment variable.

spark.executor.metrics.fileSystemSchemes

A comma-separated list of the file system schemes to report in executor metrics

Default: file,hdfs

spark.extraListeners

A comma-separated list of fully-qualified class names of SparkListeners (to be registered when SparkContext is created)

Default: (empty)

spark.file.transferTo

Controls whether to use Java FileChannels (Java NIO) for copying data between two Java FileInputStreams to improve copy performance

Default: true

Used when:

spark.files

The files to be added to a Spark application (that can be defined directly as a configuration property or indirectly using --files option of spark-submit script)

Default: (empty)

Used when:

spark.io.encryption.enabled

Controls local disk I/O encryption

Default: false

Used when:

spark.jars

Default: (empty)

spark.kryo.pool

Default: true

Used when:

spark.kryo.unsafe

Whether KryoSerializer should use Unsafe-based IO for serialization

Default: false

spark.local.dir

A comma-separated list of directory paths for "scratch" space (a temporary storage for map output files, RDDs that get stored on disk, etc.). It is recommended to use paths on fast local disks in your system (e.g. SSDs).

Default: java.io.tmpdir System property

spark.logConf

Default: false

spark.logLineage

Default: false

spark.master

Master URL of the cluster manager to connect the Spark application to

spark.memory.fraction

Fraction of JVM heap space used for execution and storage.

Default: 0.6

The lower the more frequent spills and cached data eviction. The purpose of this config is to set aside memory for internal metadata, user data structures, and imprecise size estimation in the case of sparse, unusually large records. Leaving this at the default value is recommended.

spark.memory.offHeap.enabled

Controls whether Tungsten memory will be allocated on the JVM heap (false) or off-heap (true / using sun.misc.Unsafe).

Default: false

When enabled, spark.memory.offHeap.size must be greater than 0.

Used when:

spark.memory.offHeap.size

Maximum memory (in bytes) for off-heap memory allocation

Default: 0

This setting has no impact on heap memory usage, so if your executors' total memory consumption must fit within some hard limit then be sure to shrink your JVM heap size accordingly.

Must not be negative and be set to a positive value when spark.memory.offHeap.enabled is enabled

spark.memory.storageFraction

Amount of storage memory immune to eviction, expressed as a fraction of the size of the region set aside by spark.memory.fraction.

Default: 0.5

The higher the less working memory may be available to execution and tasks may spill to disk more often. The default value is recommended.

Must be in [0,1)

Used when:

spark.network.maxRemoteBlockSizeFetchToMem

Remote block will be fetched to disk when size of the block is above this threshold in bytes

This is to avoid a giant request takes too much memory. Note this configuration will affect both shuffle fetch and block manager remote block fetch.

With an external shuffle service use at least 2.3.0

Default: 200m

Used when:

spark.network.timeout

Network timeout (in seconds) to use for RPC remote endpoint lookup

Default: 120s

spark.network.timeoutInterval

(in millis)

Default: spark.storage.blockManagerTimeoutIntervalMs

spark.rdd.compress

Controls whether to compress RDD partitions when stored serialized

Default: false

spark.reducer.maxBlocksInFlightPerAddress

Maximum number of remote blocks being fetched per reduce task from a given host port

When a large number of blocks are being requested from a given address in a single fetch or simultaneously, this could crash the serving executor or a Node Manager. This is especially useful to reduce the load on the Node Manager when external shuffle is enabled. You can mitigate the issue by setting it to a lower value.

Default: (unlimited)

Used when:

spark.reducer.maxReqsInFlight

Maximum number of remote requests to fetch blocks at any given point

When the number of hosts in the cluster increase, it might lead to very large number of inbound connections to one or more nodes, causing the workers to fail under load. By allowing it to limit the number of fetch requests, this scenario can be mitigated

Default: (unlimited)

Used when:

spark.reducer.maxSizeInFlight

Maximum size of all map outputs to fetch simultaneously from each reduce task (in MiB unless otherwise specified)

Since each output requires us to create a buffer to receive it, this represents a fixed memory overhead per reduce task, so keep it small unless you have a large amount of memory

Default: 48m

Used when:

spark.repl.class.uri

Controls whether to compress RDD partitions when stored serialized

Default: false

spark.rpc.lookupTimeout

Default Endpoint Lookup Timeout

Default: 120s

spark.rpc.message.maxSize

Maximum allowed message size for RPC communication (in MB unless specified)

Default: 128

Must be below 2047MB (Int.MaxValue / 1024 / 1024)

Used when:

spark.scheduler.minRegisteredResourcesRatio

Minimum ratio of (registered resources / total expected resources) before submitting tasks

Default: (undefined)

spark.scheduler.revive.interval

Revive Interval that is the time (in millis) between resource offers revives

Default: 1s

Used when:

  • DriverEndpoint is requested to onStart

spark.serializer

The fully-qualified class name of the Serializer (of the driver and executors)

Default: org.apache.spark.serializer.JavaSerializer

Used when:

spark.shuffle.compress

Controls whether to compress shuffle output when stored

Default: true

spark.shuffle.detectCorrupt

Controls corruption detection in fetched blocks

Default: true

Used when:

spark.shuffle.detectCorrupt.useExtraMemory

If enabled, part of a compressed/encrypted stream will be de-compressed/de-crypted by using extra memory to detect early corruption. Any IOException thrown will cause the task to be retried once and if it fails again with same exception, then FetchFailedException will be thrown to retry previous stage

Default: false

Used when:

spark.shuffle.file.buffer

Size of the in-memory buffer for each shuffle file output stream, in KiB unless otherwise specified. These buffers reduce the number of disk seeks and system calls made in creating intermediate shuffle files.

Default: 32k

Must be greater than 0 and less than or equal to 2097151 ((Integer.MAX_VALUE - 15) / 1024)

Used when the following are created:

spark.shuffle.manager

A fully-qualified class name or the alias of the ShuffleManager in a Spark application

Default: sort

Supported aliases:

  • sort
  • tungsten-sort

Used when SparkEnv object is requested to create a "base" SparkEnv for a driver or an executor

spark.shuffle.mapOutput.parallelAggregationThreshold

(internal) Multi-thread is used when the number of mappers * shuffle partitions is greater than or equal to this threshold. Note that the actual parallelism is calculated by number of mappers * shuffle partitions / this threshold + 1, so this threshold should be positive.

Default: 10000000

Used when:

spark.shuffle.minNumPartitionsToHighlyCompress

(internal) Minimum number of partitions (threshold) for MapStatus utility to prefer a HighlyCompressedMapStatus (over CompressedMapStatus) (for ShuffleWriters).

Default: 2000

Must be a positive integer (above 0)

spark.shuffle.readHostLocalDisk

If enabled (with spark.shuffle.useOldFetchProtocol disabled and spark.shuffle.service.enabled enabled), shuffle blocks requested from those block managers which are running on the same host are read from the disk directly instead of being fetched as remote blocks over the network.

Default: true

spark.shuffle.registration.maxAttempts

How many attempts to register a BlockManager with External Shuffle Service

Default: 3

Used when BlockManager is requested to register with External Shuffle Server

spark.shuffle.sort.bypassMergeThreshold

Maximum number of reduce partitions below which SortShuffleManager avoids merge-sorting data for no map-side aggregation

Default: 200

Used when:

  • SortShuffleWriter utility is used to shouldBypassMergeSort
  • ShuffleExchangeExec (Spark SQL) physical operator is requested to prepareShuffleDependency

spark.shuffle.sort.io.plugin.class

Name of the class to use for shuffle IO

Default: LocalDiskShuffleDataIO

spark.shuffle.spill.initialMemoryThreshold

Initial threshold for the size of an in-memory collection

Default: 5MB

Used by Spillable

spark.shuffle.spill.numElementsForceSpillThreshold

(internal) The maximum number of elements in memory before forcing the shuffle sorter to spill.

Default: Integer.MAX_VALUE

The default value is to never force the sorter to spill, until Spark reaches some limitations, like the max page size limitation for the pointer array in the sorter.

Used when:

  • ShuffleExternalSorter is created
  • Spillable is created
  • Spark SQL's SortBasedAggregator is requested for an UnsafeKVExternalSorter
  • Spark SQL's ObjectAggregationMap is requested to dumpToExternalSorter
  • Spark SQL's UnsafeExternalRowSorter is created
  • Spark SQL's UnsafeFixedWidthAggregationMap is requested for an UnsafeKVExternalSorter

spark.shuffle.sync

Controls whether DiskBlockObjectWriter should force outstanding writes to disk while committing a single atomic block (i.e. all operating system buffers should synchronize with the disk to ensure that all changes to a file are in fact recorded in the storage)

Default: false

Used when BlockManager is requested for a DiskBlockObjectWriter

spark.shuffle.useOldFetchProtocol

Whether to use the old protocol while doing the shuffle block fetching. It is only enabled while we need the compatibility in the scenario of new Spark version job fetching shuffle blocks from old version external shuffle service.

Default: false

spark.speculation

Controls Speculative Execution of Tasks

Default: false

spark.speculation.interval

The time interval to use before checking for speculative tasks in Speculative Execution of Tasks.

Default: 100ms

spark.speculation.multiplier

Default: 1.5

spark.speculation.quantile

The percentage of tasks that has not finished yet at which to start speculation in Speculative Execution of Tasks.

Default: 0.75

spark.storage.blockManagerSlaveTimeoutMs

(in millis)

Default: spark.network.timeout

spark.storage.blockManagerTimeoutIntervalMs

(in millis)

Default: 60s

spark.storage.localDiskByExecutors.cacheSize

The max number of executors for which the local dirs are stored. This size is both applied for the driver and both for the executors side to avoid having an unbounded store. This cache will be used to avoid the network in case of fetching disk persisted RDD blocks or shuffle blocks (when spark.shuffle.readHostLocalDisk is set) from the same host.

Default: 1000

spark.storage.replication.policy

Default: RandomBlockReplicationPolicy

spark.storage.unrollMemoryThreshold

Initial memory threshold (in bytes) to unroll (materialize) a block to store in memory

Default: 1024 * 1024

Must be at most the total amount of memory available for storage

Used when:

spark.task.cpus

The number of CPU cores to schedule (allocate) to a task

Default: 1

Used when:

spark.task.maxDirectResultSize

Maximum size of a task result (in bytes) to be sent to the driver as a DirectTaskResult

Default: 1048576B (1L << 20)

Used when:

spark.task.maxFailures

Number of failures of a single task (of a TaskSet) before giving up on the entire TaskSet and then the job

Default: 4

spark.plugins

A comma-separated list of class names implementing org.apache.spark.api.plugin.SparkPlugin to load into a Spark application.

Default: (empty)

Since: 3.0.0

Set when SparkContext is created

spark.plugins.defaultList

FIXME

spark.ui.showConsoleProgress

Controls whether to enable ConsoleProgressBar and show the progress bar in the console

Default: false

== [[properties]] Properties

[cols="1m,1",options="header",width="100%"] |=== | Name | Description

| spark.blockManager.port a| [[spark.blockManager.port]][[BLOCK_MANAGER_PORT]] Port to use for block managers to listen on when a more specific setting is not provided (i.e. <> for the driver).

Default: 0

In Spark on Kubernetes the default port is 7079

| spark.default.parallelism a| [[spark.default.parallelism]] Number of partitions to use for rdd:HashPartitioner.md[HashPartitioner]

spark.default.parallelism corresponds to scheduler:SchedulerBackend.md#defaultParallelism[default parallelism] of a scheduler backend and is as follows:

  • The number of threads for local/spark-LocalSchedulerBackend.md[LocalSchedulerBackend].
  • the number of CPU cores in spark-mesos.md#defaultParallelism[Spark on Mesos] and defaults to 8.
  • Maximum of totalCoreCount and 2 in scheduler:CoarseGrainedSchedulerBackend.md#defaultParallelism[CoarseGrainedSchedulerBackend].

| spark.driver.blockManager.port a| [[spark.driver.blockManager.port]][[DRIVER_BLOCK_MANAGER_PORT]] Port the storage:BlockManager.md[block manager] on the driver listens on

Default: <>

| spark.executor.extraClassPath a| [[spark.executor.extraClassPath]][[EXECUTOR_CLASS_PATH]] User-defined class path for executors, i.e. URLs representing user-defined class path entries that are added to an executor's class path. URLs are separated by system-dependent path separator, i.e. : on Unix-like systems and ; on Microsoft Windows.

Default: (empty)

Used when:

  • Spark Standalone's StandaloneSchedulerBackend is requested to spark-standalone:StandaloneSchedulerBackend.md#start[start] (and creates a command for executor:CoarseGrainedExecutorBackend.md[])

  • Spark local's LocalSchedulerBackend is requested for the spark-local:spark-LocalSchedulerBackend.md#getUserClasspath[user-defined class path for executors]

  • Spark on Mesos' MesosCoarseGrainedSchedulerBackend is requested to spark-on-mesos:spark-mesos-MesosCoarseGrainedSchedulerBackend.md#createCommand[create a command for CoarseGrainedExecutorBackend]

  • Spark on Mesos' MesosFineGrainedSchedulerBackend is requested to create a command for MesosExecutorBackend

  • Spark on Kubernetes' BasicExecutorFeatureStep is requested to configurePod

  • Spark on YARN's ExecutorRunnable is requested to spark-on-yarn:spark-yarn-ExecutorRunnable.md#prepareEnvironment[prepareEnvironment] (for CoarseGrainedExecutorBackend)

| spark.executor.extraJavaOptions a| [[spark.executor.extraJavaOptions]] Extra Java options of an executor:Executor.md[]

Used when Spark on YARN's ExecutorRunnable is requested to spark-on-yarn:spark-yarn-ExecutorRunnable.md#prepareCommand[prepare the command to launch CoarseGrainedExecutorBackend in a YARN container]

| spark.executor.extraLibraryPath a| [[spark.executor.extraLibraryPath]] Extra library paths separated by system-dependent path separator, i.e. : on Unix/MacOS systems and ; on Microsoft Windows

Used when Spark on YARN's ExecutorRunnable is requested to spark-on-yarn:spark-yarn-ExecutorRunnable.md#prepareCommand[prepare the command to launch CoarseGrainedExecutorBackend in a YARN container]

| spark.executor.uri a| [[spark.executor.uri]] Equivalent to SPARK_EXECUTOR_URI

| spark.executor.logs.rolling.time.interval a| [[spark.executor.logs.rolling.time.interval]]

| spark.executor.logs.rolling.strategy a| [[spark.executor.logs.rolling.strategy]]

| spark.executor.logs.rolling.maxRetainedFiles a| [[spark.executor.logs.rolling.maxRetainedFiles]]

| spark.executor.logs.rolling.maxSize a| [[spark.executor.logs.rolling.maxSize]]

| spark.executor.heartbeatInterval a| [[spark.executor.heartbeatInterval]] Interval after which an executor:Executor.md[] reports heartbeat and metrics for active tasks to the driver

Default: 10s

Refer to executor:Executor.md#heartbeats-and-active-task-metrics[Sending heartbeats and partial metrics for active tasks]

| spark.executor.heartbeat.maxFailures a| [[spark.executor.heartbeat.maxFailures]] Number of times an executor:Executor.md[] will try to send heartbeats to the driver before it gives up and exits (with exit code 56).

Default: 60

NOTE: Introduced in https://issues.apache.org/jira/browse/SPARK-13522[SPARK-13522 Executor should kill itself when it's unable to heartbeat to the driver more than N times].

| spark.executor.instances a| [[spark.executor.instances]] Number of executor:Executor.md[] in use

Default: 0

| spark.executor.userClassPathFirst a| [[spark.executor.userClassPathFirst]] Flag to control whether to load classes in user jars before those in Spark jars

Default: false

| spark.executor.port a| [[spark.executor.port]]

| spark.launcher.port a| [[spark.launcher.port]]

| spark.launcher.secret a| [[spark.launcher.secret]]

| spark.locality.wait a| [[spark.locality.wait]] For locality-aware delay scheduling for PROCESS_LOCAL, NODE_LOCAL, and RACK_LOCAL scheduler:TaskSchedulerImpl.md#TaskLocality[TaskLocalities] when locality-specific setting is not set.

Default: 3s

| spark.locality.wait.node a| [[spark.locality.wait.node]] Scheduling delay for NODE_LOCAL scheduler:TaskSchedulerImpl.md#TaskLocality[TaskLocality]

Default: The value of <>

| spark.locality.wait.process a| [[spark.locality.wait.process]] Scheduling delay for PROCESS_LOCAL scheduler:TaskSchedulerImpl.md#TaskLocality[TaskLocality]

Default: The value of <>

| spark.locality.wait.rack a| [[spark.locality.wait.rack]] Scheduling delay for RACK_LOCAL scheduler:TaskSchedulerImpl.md#TaskLocality[TaskLocality]

Default: The value of <>

| spark.logging.exceptionPrintInterval a| [[spark.logging.exceptionPrintInterval]] How frequently to reprint duplicate exceptions in full (in millis).

Default: 10000

| spark.scheduler.allocation.file a| [[spark.scheduler.allocation.file]] Path to the configuration file of <>

Default: fairscheduler.xml (on a Spark application's class path)

| spark.scheduler.executorTaskBlacklistTime a| [[spark.scheduler.executorTaskBlacklistTime]] How long to wait before a task can be re-launched on the executor where it once failed. It is to prevent repeated task failures due to executor failures.

Default: 0L

| spark.scheduler.mode a| [[spark.scheduler.mode]][[SCHEDULER_MODE_PROPERTY]] Scheduling Mode of the scheduler:TaskSchedulerImpl.md[TaskSchedulerImpl], i.e. case-insensitive name of the spark-scheduler-SchedulingMode.md[scheduling mode] that TaskSchedulerImpl uses to choose between the <> for task scheduling (of tasks of jobs submitted for execution to the same SparkContext)

Default: FIFO

Supported values:

  • FAIR for fair sharing (of cluster resources)
  • FIFO (default) for queueing jobs one after another

Task scheduling is an algorithm that is used to assign cluster resources (CPU cores and memory) to tasks (that are part of jobs with one or more stages). Fair sharing allows for executing tasks of different jobs at the same time (that were all submitted to the same SparkContext). In FIFO scheduling mode a single SparkContext can submit a single job for execution only (regardless of how many cluster resources the job really use which could lead to a inefficient utilization of cluster resources and a longer execution of the Spark application overall).

Scheduling mode is particularly useful in multi-tenant environments in which a single SparkContext could be shared across different users (to make a cluster resource utilization more efficient).

| spark.starvation.timeout a| [[spark.starvation.timeout]] Threshold above which Spark warns a user that an initial TaskSet may be starved

Default: 15s

| spark.storage.exceptionOnPinLeak a| [[spark.storage.exceptionOnPinLeak]]

| spark.unsafe.exceptionOnMemoryLeak a| [[spark.unsafe.exceptionOnMemoryLeak]]

|===

== [[spark.shuffle.spill.batchSize]] spark.shuffle.spill.batchSize

Size of object batches when reading or writing from serializers.

Default: 10000

Used by shuffle:ExternalAppendOnlyMap.md[ExternalAppendOnlyMap] and shuffle:ExternalSorter.md[ExternalSorter]

== [[spark.shuffle.mapOutput.dispatcher.numThreads]] spark.shuffle.mapOutput.dispatcher.numThreads

Default: 8

== [[spark.shuffle.mapOutput.minSizeForBroadcast]] spark.shuffle.mapOutput.minSizeForBroadcast

Size of serialized shuffle map output statuses when scheduler:MapOutputTrackerMaster.md#MessageLoop[MapOutputTrackerMaster] uses to determine whether to use a broadcast variable to send them to executors

Default: 512k

Must be below spark.rpc.message.maxSize (to prevent sending an RPC message that is too large)

== [[spark.shuffle.reduceLocality.enabled]] spark.shuffle.reduceLocality.enabled

Enables locality preferences for reduce tasks

Default: true

When enabled (true), MapOutputTrackerMaster will scheduler:MapOutputTrackerMaster.md#getPreferredLocationsForShuffle[compute the preferred hosts] on which to run a given map output partition in a given shuffle, i.e. the nodes that the most outputs for that partition are on.

== [[spark.shuffle.sort.initialBufferSize]] spark.shuffle.sort.initialBufferSize

Initial buffer size for sorting

Default: shuffle:UnsafeShuffleWriter.md#DEFAULT_INITIAL_SORT_BUFFER_SIZE[4096]

Used exclusively when UnsafeShuffleWriter is requested to shuffle:UnsafeShuffleWriter.md#open[open] (and creates a shuffle:ShuffleExternalSorter.md[ShuffleExternalSorter])

== [[spark.shuffle.unsafe.file.output.buffer]] spark.shuffle.unsafe.file.output.buffer

The file system for this buffer size after each partition is written in unsafe shuffle writer. In KiB unless otherwise specified.

Default: 32k

Must be greater than 0 and less than or equal to 2097151 ((Integer.MAX_VALUE - 15) / 1024)

== [[spark.scheduler.maxRegisteredResourcesWaitingTime]] spark.scheduler.maxRegisteredResourcesWaitingTime

Time to wait for sufficient resources available

Default: 30s

== [[spark.shuffle.unsafe.fastMergeEnabled]] spark.shuffle.unsafe.fastMergeEnabled

Enables fast merge strategy for UnsafeShuffleWriter to shuffle:UnsafeShuffleWriter.md#mergeSpills[merge spill files].

Default: true

== [[spark.shuffle.spill.compress]] spark.shuffle.spill.compress

Controls whether to compress shuffle output temporarily spilled to disk.

Default: true

== [[spark.block.failures.beforeLocationRefresh]] spark.block.failures.beforeLocationRefresh

Default: 5

== [[spark.closure.serializer]] spark.closure.serializer

serializer:Serializer.md[Serializer]

Default: org.apache.spark.serializer.JavaSerializer

== [[spark.io.compression.codec]] spark.io.compression.codec

The default io:CompressionCodec.md[CompressionCodec]

Default: lz4

== [[spark.io.compression.lz4.blockSize]] spark.io.compression.lz4.blockSize

The block size of the io:CompressionCodec.md#LZ4CompressionCodec[LZ4CompressionCodec]

Default: 32k

== [[spark.io.compression.snappy.blockSize]] spark.io.compression.snappy.blockSize

The block size of the io:CompressionCodec.md#SnappyCompressionCodec[SnappyCompressionCodec]

Default: 32k

== [[spark.io.compression.zstd.bufferSize]] spark.io.compression.zstd.bufferSize

The buffer size of the BufferedOutputStream of the io:CompressionCodec.md#ZStdCompressionCodec[ZStdCompressionCodec]

Default: 32k

The buffer is used to avoid the overhead of excessive JNI calls while compressing or uncompressing small amount of data

== [[spark.io.compression.zstd.level]] spark.io.compression.zstd.level

The compression level of the io:CompressionCodec.md#ZStdCompressionCodec[ZStdCompressionCodec]

Default: 1

The default level is the fastest of all with reasonably high compression ratio

== [[spark.buffer.size]] spark.buffer.size

Default: 65536

== [[spark.cleaner.referenceTracking.cleanCheckpoints]] spark.cleaner.referenceTracking.cleanCheckpoints

Enables cleaning checkpoint files when a checkpointed reference is out of scope

Default: false

== [[spark.cleaner.periodicGC.interval]] spark.cleaner.periodicGC.interval

Controls how often to trigger a garbage collection

Default: 30min

== [[spark.cleaner.referenceTracking.blocking]] spark.cleaner.referenceTracking.blocking

Controls whether the cleaning thread should block on cleanup tasks (other than shuffle, which is controlled by <>)

Default: true

== [[spark.cleaner.referenceTracking.blocking.shuffle]] spark.cleaner.referenceTracking.blocking.shuffle

Controls whether the cleaning thread should block on shuffle cleanup tasks.

Default: false

== [[spark.app.name]] spark.app.name

Application Name

Default: (undefined)

== [[spark.rpc.numRetries]] spark.rpc.numRetries

Number of attempts to send a message to and receive a response from a remote endpoint.

Default: 3

== [[spark.rpc.retry.wait]] spark.rpc.retry.wait

Time to wait between retries.

Default: 3s

== [[spark.rpc.askTimeout]] spark.rpc.askTimeout

Timeout for RPC ask calls

Default: 120s

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