Spark Configuration

Spark provides three locations to configure the system:

Spark Properties

Spark properties control most application settings and are configured separately for each application. These properties can be set directly on a SparkConf passed to your SparkContext. SparkConf allows you to configure some of the common properties (e.g. master URL and application name), as well as arbitrary key-value pairs through the set() method. For example, we could initialize an application with two threads as follows:

Note that we run with local[2], meaning two threads - which represents “minimal” parallelism, which can help detect bugs that only exist when we run in a distributed context.

val conf = new SparkConf()
             .setMaster("local[2]")
             .setAppName("CountingSheep")
val sc = new SparkContext(conf)

Note that we can have more than 1 thread in local mode, and in cases like Spark Streaming, we may actually require more than 1 thread to prevent any sort of starvation issues.

Properties that specify some time duration should be configured with a unit of time. The following format is accepted:

25ms (milliseconds)
5s (seconds)
10m or 10min (minutes)
3h (hours)
5d (days)
1y (years)

Properties that specify a byte size should be configured with a unit of size. The following format is accepted:

1b (bytes)
1k or 1kb (kibibytes = 1024 bytes)
1m or 1mb (mebibytes = 1024 kibibytes)
1g or 1gb (gibibytes = 1024 mebibytes)
1t or 1tb (tebibytes = 1024 gibibytes)
1p or 1pb (pebibytes = 1024 tebibytes)

While numbers without units are generally interpreted as bytes, a few are interpreted as KiB or MiB. See documentation of individual configuration properties. Specifying units is desirable where possible.

Dynamically Loading Spark Properties

In some cases, you may want to avoid hard-coding certain configurations in a SparkConf. For instance, if you’d like to run the same application with different masters or different amounts of memory. Spark allows you to simply create an empty conf:

val sc = new SparkContext(new SparkConf())

Then, you can supply configuration values at runtime:

./bin/spark-submit --name "My app" --master local[4] --conf spark.eventLog.enabled=false
  --conf "spark.executor.extraJavaOptions=-XX:+PrintGCDetails -XX:+PrintGCTimeStamps" myApp.jar

The Spark shell and spark-submit tool support two ways to load configurations dynamically. The first is command line options, such as --master, as shown above. spark-submit can accept any Spark property using the --conf flag, but uses special flags for properties that play a part in launching the Spark application. Running ./bin/spark-submit --help will show the entire list of these options.

bin/spark-submit will also read configuration options from conf/spark-defaults.conf, in which each line consists of a key and a value separated by whitespace. For example:

spark.master            spark://5.6.7.8:7077
spark.executor.memory   4g
spark.eventLog.enabled  true
spark.serializer        org.apache.spark.serializer.KryoSerializer

Any values specified as flags or in the properties file will be passed on to the application and merged with those specified through SparkConf. Properties set directly on the SparkConf take highest precedence, then flags passed to spark-submit or spark-shell, then options in the spark-defaults.conf file. A few configuration keys have been renamed since earlier versions of Spark; in such cases, the older key names are still accepted, but take lower precedence than any instance of the newer key.

Spark properties mainly can be divided into two kinds: one is related to deploy, like “spark.driver.memory”, “spark.executor.instances”, this kind of properties may not be affected when setting programmatically through SparkConf in runtime, or the behavior is depending on which cluster manager and deploy mode you choose, so it would be suggested to set through configuration file or spark-submit command line options; another is mainly related to Spark runtime control, like “spark.task.maxFailures”, this kind of properties can be set in either way.

Viewing Spark Properties

The application web UI at http://<driver>:4040 lists Spark properties in the “Environment” tab. This is a useful place to check to make sure that your properties have been set correctly. Note that only values explicitly specified through spark-defaults.conf, SparkConf, or the command line will appear. For all other configuration properties, you can assume the default value is used.

Available Properties

Most of the properties that control internal settings have reasonable default values. Some of the most common options to set are:

Application Properties

Property NameDefaultMeaning
spark.app.name (none) The name of your application. This will appear in the UI and in log data.
spark.driver.cores 1 Number of cores to use for the driver process, only in cluster mode.
spark.driver.maxResultSize 1g Limit of total size of serialized results of all partitions for each Spark action (e.g. collect) in bytes. Should be at least 1M, or 0 for unlimited. Jobs will be aborted if the total size is above this limit. Having a high limit may cause out-of-memory errors in driver (depends on spark.driver.memory and memory overhead of objects in JVM). Setting a proper limit can protect the driver from out-of-memory errors.
spark.driver.memory 1g Amount of memory to use for the driver process, i.e. where SparkContext is initialized, in the same format as JVM memory strings with a size unit suffix ("k", "m", "g" or "t") (e.g. 512m, 2g).
Note: In client mode, this config must not be set through the SparkConf directly in your application, because the driver JVM has already started at that point. Instead, please set this through the --driver-memory command line option or in your default properties file.
spark.driver.memoryOverhead driverMemory * 0.10, with minimum of 384 The amount of off-heap memory to be allocated per driver in cluster mode, in MiB unless otherwise specified. This is memory that accounts for things like VM overheads, interned strings, other native overheads, etc. This tends to grow with the container size (typically 6-10%). This option is currently supported on YARN and Kubernetes.
spark.executor.memory 1g Amount of memory to use per executor process, in the same format as JVM memory strings with a size unit suffix ("k", "m", "g" or "t") (e.g. 512m, 2g).
spark.executor.pyspark.memory Not set The amount of memory to be allocated to PySpark in each executor, in MiB unless otherwise specified. If set, PySpark memory for an executor will be limited to this amount. If not set, Spark will not limit Python's memory use and it is up to the application to avoid exceeding the overhead memory space shared with other non-JVM processes. When PySpark is run in YARN or Kubernetes, this memory is added to executor resource requests. NOTE: Python memory usage may not be limited on platforms that do not support resource limiting, such as Windows.
spark.executor.memoryOverhead executorMemory * 0.10, with minimum of 384 The amount of off-heap memory to be allocated per executor, in MiB unless otherwise specified. This is memory that accounts for things like VM overheads, interned strings, other native overheads, etc. This tends to grow with the executor size (typically 6-10%). This option is currently supported on YARN and Kubernetes.
spark.extraListeners (none) A comma-separated list of classes that implement SparkListener; when initializing SparkContext, instances of these classes will be created and registered with Spark's listener bus. If a class has a single-argument constructor that accepts a SparkConf, that constructor will be called; otherwise, a zero-argument constructor will be called. If no valid constructor can be found, the SparkContext creation will fail with an exception.
spark.local.dir /tmp Directory to use for "scratch" space in Spark, including map output files and RDDs that get stored on disk. This should be on a fast, local disk in your system. It can also be a comma-separated list of multiple directories on different disks. NOTE: In Spark 1.0 and later this will be overridden by SPARK_LOCAL_DIRS (Standalone), MESOS_SANDBOX (Mesos) or LOCAL_DIRS (YARN) environment variables set by the cluster manager.
spark.logConf false Logs the effective SparkConf as INFO when a SparkContext is started.
spark.master (none) The cluster manager to connect to. See the list of allowed master URL's.
spark.submit.deployMode (none) The deploy mode of Spark driver program, either "client" or "cluster", Which means to launch driver program locally ("client") or remotely ("cluster") on one of the nodes inside the cluster.
spark.log.callerContext (none) Application information that will be written into Yarn RM log/HDFS audit log when running on Yarn/HDFS. Its length depends on the Hadoop configuration hadoop.caller.context.max.size. It should be concise, and typically can have up to 50 characters.
spark.driver.supervise false If true, restarts the driver automatically if it fails with a non-zero exit status. Only has effect in Spark standalone mode or Mesos cluster deploy mode.

Apart from these, the following properties are also available, and may be useful in some situations:

Runtime Environment

Property NameDefaultMeaning
spark.driver.extraClassPath (none) Extra classpath entries to prepend to the classpath of the driver.
Note: In client mode, this config must not be set through the SparkConf directly in your application, because the driver JVM has already started at that point. Instead, please set this through the --driver-class-path command line option or in your default properties file.
spark.driver.extraJavaOptions (none) A string of extra JVM options to pass to the driver. For instance, GC settings or other logging. Note that it is illegal to set maximum heap size (-Xmx) settings with this option. Maximum heap size settings can be set with spark.driver.memory in the cluster mode and through the --driver-memory command line option in the client mode.
Note: In client mode, this config must not be set through the SparkConf directly in your application, because the driver JVM has already started at that point. Instead, please set this through the --driver-java-options command line option or in your default properties file.
spark.driver.extraLibraryPath (none) Set a special library path to use when launching the driver JVM.
Note: In client mode, this config must not be set through the SparkConf directly in your application, because the driver JVM has already started at that point. Instead, please set this through the --driver-library-path command line option or in your default properties file.
spark.driver.userClassPathFirst false (Experimental) Whether to give user-added jars precedence over Spark's own jars when loading classes in the driver. This feature can be used to mitigate conflicts between Spark's dependencies and user dependencies. It is currently an experimental feature. This is used in cluster mode only.
spark.executor.extraClassPath (none) Extra classpath entries to prepend to the classpath of executors. This exists primarily for backwards-compatibility with older versions of Spark. Users typically should not need to set this option.
spark.executor.extraJavaOptions (none) A string of extra JVM options to pass to executors. For instance, GC settings or other logging. Note that it is illegal to set Spark properties or maximum heap size (-Xmx) settings with this option. Spark properties should be set using a SparkConf object or the spark-defaults.conf file used with the spark-submit script. Maximum heap size settings can be set with spark.executor.memory. The following symbols, if present will be interpolated: will be replaced by application ID and will be replaced by executor ID. For example, to enable verbose gc logging to a file named for the executor ID of the app in /tmp, pass a 'value' of: -verbose:gc -Xloggc:/tmp/-.gc
spark.executor.extraLibraryPath (none) Set a special library path to use when launching executor JVM's.
spark.executor.logs.rolling.maxRetainedFiles (none) Sets the number of latest rolling log files that are going to be retained by the system. Older log files will be deleted. Disabled by default.
spark.executor.logs.rolling.enableCompression false Enable executor log compression. If it is enabled, the rolled executor logs will be compressed. Disabled by default.
spark.executor.logs.rolling.maxSize (none) Set the max size of the file in bytes by which the executor logs will be rolled over. Rolling is disabled by default. See spark.executor.logs.rolling.maxRetainedFiles for automatic cleaning of old logs.
spark.executor.logs.rolling.strategy (none) Set the strategy of rolling of executor logs. By default it is disabled. It can be set to "time" (time-based rolling) or "size" (size-based rolling). For "time", use spark.executor.logs.rolling.time.interval to set the rolling interval. For "size", use spark.executor.logs.rolling.maxSize to set the maximum file size for rolling.
spark.executor.logs.rolling.time.interval daily Set the time interval by which the executor logs will be rolled over. Rolling is disabled by default. Valid values are daily, hourly, minutely or any interval in seconds. See spark.executor.logs.rolling.maxRetainedFiles for automatic cleaning of old logs.
spark.executor.userClassPathFirst false (Experimental) Same functionality as spark.driver.userClassPathFirst, but applied to executor instances.
spark.executorEnv.[EnvironmentVariableName] (none) Add the environment variable specified by EnvironmentVariableName to the Executor process. The user can specify multiple of these to set multiple environment variables.
spark.redaction.regex (?i)secret|password Regex to decide which Spark configuration properties and environment variables in driver and executor environments contain sensitive information. When this regex matches a property key or value, the value is redacted from the environment UI and various logs like YARN and event logs.
spark.python.profile false Enable profiling in Python worker, the profile result will show up by sc.show_profiles(), or it will be displayed before the driver exits. It also can be dumped into disk by sc.dump_profiles(path). If some of the profile results had been displayed manually, they will not be displayed automatically before driver exiting. By default the pyspark.profiler.BasicProfiler will be used, but this can be overridden by passing a profiler class in as a parameter to the SparkContext constructor.
spark.python.profile.dump (none) The directory which is used to dump the profile result before driver exiting. The results will be dumped as separated file for each RDD. They can be loaded by pstats.Stats(). If this is specified, the profile result will not be displayed automatically.
spark.python.worker.memory 512m Amount of memory to use per python worker process during aggregation, in the same format as JVM memory strings with a size unit suffix ("k", "m", "g" or "t") (e.g. 512m, 2g). If the memory used during aggregation goes above this amount, it will spill the data into disks.
spark.python.worker.reuse true Reuse Python worker or not. If yes, it will use a fixed number of Python workers, does not need to fork() a Python process for every task. It will be very useful if there is large broadcast, then the broadcast will not be needed to transferred from JVM to Python worker for every task.
spark.files Comma-separated list of files to be placed in the working directory of each executor. Globs are allowed.
spark.submit.pyFiles Comma-separated list of .zip, .egg, or .py files to place on the PYTHONPATH for Python apps. Globs are allowed.
spark.jars Comma-separated list of jars to include on the driver and executor classpaths. Globs are allowed.
spark.jars.packages Comma-separated list of Maven coordinates of jars to include on the driver and executor classpaths. The coordinates should be groupId:artifactId:version. If spark.jars.ivySettings is given artifacts will be resolved according to the configuration in the file, otherwise artifacts will be searched for in the local maven repo, then maven central and finally any additional remote repositories given by the command-line option --repositories. For more details, see Advanced Dependency Management.
spark.jars.excludes Comma-separated list of groupId:artifactId, to exclude while resolving the dependencies provided in spark.jars.packages to avoid dependency conflicts.
spark.jars.ivy Path to specify the Ivy user directory, used for the local Ivy cache and package files from spark.jars.packages. This will override the Ivy property ivy.default.ivy.user.dir which defaults to ~/.ivy2.
spark.jars.ivySettings Path to an Ivy settings file to customize resolution of jars specified using spark.jars.packages instead of the built-in defaults, such as maven central. Additional repositories given by the command-line option --repositories or spark.jars.repositories will also be included. Useful for allowing Spark to resolve artifacts from behind a firewall e.g. via an in-house artifact server like Artifactory. Details on the settings file format can be found at http://ant.apache.org/ivy/history/latest-milestone/settings.html
spark.jars.repositories Comma-separated list of additional remote repositories to search for the maven coordinates given with --packages or spark.jars.packages.
spark.pyspark.driver.python Python binary executable to use for PySpark in driver. (default is spark.pyspark.python)
spark.pyspark.python Python binary executable to use for PySpark in both driver and executors.

Shuffle Behavior

Property NameDefaultMeaning
spark.reducer.maxSizeInFlight 48m Maximum size of 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.
spark.reducer.maxReqsInFlight Int.MaxValue This configuration limits the 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.
spark.reducer.maxBlocksInFlightPerAddress Int.MaxValue This configuration limits the 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 Node Manager. This is especially useful to reduce the load on the Node Manager when external shuffle is enabled. You can mitigate this issue by setting it to a lower value.
spark.maxRemoteBlockSizeFetchToMem Int.MaxValue - 512 The 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 that takes too much memory. By default, this is only enabled for blocks > 2GB, as those cannot be fetched directly into memory, no matter what resources are available. But it can be turned down to a much lower value (eg. 200m) to avoid using too much memory on smaller blocks as well. Note this configuration will affect both shuffle fetch and block manager remote block fetch. For users who enabled external shuffle service, this feature can only be used when external shuffle service is newer than Spark 2.2.
spark.shuffle.compress true Whether to compress map output files. Generally a good idea. Compression will use spark.io.compression.codec.
spark.shuffle.file.buffer 32k 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.
spark.shuffle.io.maxRetries 3 (Netty only) Fetches that fail due to IO-related exceptions are automatically retried if this is set to a non-zero value. This retry logic helps stabilize large shuffles in the face of long GC pauses or transient network connectivity issues.
spark.shuffle.io.numConnectionsPerPeer 1 (Netty only) Connections between hosts are reused in order to reduce connection buildup for large clusters. For clusters with many hard disks and few hosts, this may result in insufficient concurrency to saturate all disks, and so users may consider increasing this value.
spark.shuffle.io.preferDirectBufs true (Netty only) Off-heap buffers are used to reduce garbage collection during shuffle and cache block transfer. For environments where off-heap memory is tightly limited, users may wish to turn this off to force all allocations from Netty to be on-heap.
spark.shuffle.io.retryWait 5s (Netty only) How long to wait between retries of fetches. The maximum delay caused by retrying is 15 seconds by default, calculated as maxRetries * retryWait.
spark.shuffle.service.enabled false Enables the external shuffle service. This service preserves the shuffle files written by executors so the executors can be safely removed. This must be enabled if spark.dynamicAllocation.enabled is "true". The external shuffle service must be set up in order to enable it. See dynamic allocation configuration and setup documentation for more information.
spark.shuffle.service.port 7337 Port on which the external shuffle service will run.
spark.shuffle.service.index.cache.size 100m Cache entries limited to the specified memory footprint in bytes.
spark.shuffle.maxChunksBeingTransferred Long.MAX_VALUE The max number of chunks allowed to be transferred at the same time on shuffle service. Note that new incoming connections will be closed when the max number is hit. The client will retry according to the shuffle retry configs (see spark.shuffle.io.maxRetries and spark.shuffle.io.retryWait), if those limits are reached the task will fail with fetch failure.
spark.shuffle.sort.bypassMergeThreshold 200 (Advanced) In the sort-based shuffle manager, avoid merge-sorting data if there is no map-side aggregation and there are at most this many reduce partitions.
spark.shuffle.spill.compress true Whether to compress data spilled during shuffles. Compression will use spark.io.compression.codec.
spark.shuffle.accurateBlockThreshold 100 * 1024 * 1024 Threshold in bytes above which the size of shuffle blocks in HighlyCompressedMapStatus is accurately recorded. This helps to prevent OOM by avoiding underestimating shuffle block size when fetch shuffle blocks.
spark.shuffle.registration.timeout 5000 Timeout in milliseconds for registration to the external shuffle service.
spark.shuffle.registration.maxAttempts 3 When we fail to register to the external shuffle service, we will retry for maxAttempts times.

Spark UI

Property NameDefaultMeaning
spark.eventLog.logBlockUpdates.enabled false Whether to log events for every block update, if spark.eventLog.enabled is true. *Warning*: This will increase the size of the event log considerably.
spark.eventLog.longForm.enabled false If true, use the long form of call sites in the event log. Otherwise use the short form.
spark.eventLog.compress false Whether to compress logged events, if spark.eventLog.enabled is true. Compression will use spark.io.compression.codec.
spark.eventLog.dir file:///tmp/spark-events Base directory in which Spark events are logged, if spark.eventLog.enabled is true. Within this base directory, Spark creates a sub-directory for each application, and logs the events specific to the application in this directory. Users may want to set this to a unified location like an HDFS directory so history files can be read by the history server.
spark.eventLog.enabled false Whether to log Spark events, useful for reconstructing the Web UI after the application has finished.
spark.eventLog.overwrite false Whether to overwrite any existing files.
spark.eventLog.buffer.kb 100k Buffer size to use when writing to output streams, in KiB unless otherwise specified.
spark.ui.dagGraph.retainedRootRDDs Int.MaxValue How many DAG graph nodes the Spark UI and status APIs remember before garbage collecting.
spark.ui.enabled true Whether to run the web UI for the Spark application.
spark.ui.killEnabled true Allows jobs and stages to be killed from the web UI.
spark.ui.liveUpdate.period 100ms How often to update live entities. -1 means "never update" when replaying applications, meaning only the last write will happen. For live applications, this avoids a few operations that we can live without when rapidly processing incoming task events.
spark.ui.liveUpdate.minFlushPeriod 1s Minimum time elapsed before stale UI data is flushed. This avoids UI staleness when incoming task events are not fired frequently.
spark.ui.port 4040 Port for your application's dashboard, which shows memory and workload data.
spark.ui.retainedJobs 1000 How many jobs the Spark UI and status APIs remember before garbage collecting. This is a target maximum, and fewer elements may be retained in some circumstances.
spark.ui.retainedStages 1000 How many stages the Spark UI and status APIs remember before garbage collecting. This is a target maximum, and fewer elements may be retained in some circumstances.
spark.ui.retainedTasks 100000 How many tasks the Spark UI and status APIs remember before garbage collecting. This is a target maximum, and fewer elements may be retained in some circumstances.
spark.ui.reverseProxy false Enable running Spark Master as reverse proxy for worker and application UIs. In this mode, Spark master will reverse proxy the worker and application UIs to enable access without requiring direct access to their hosts. Use it with caution, as worker and application UI will not be accessible directly, you will only be able to access them through spark master/proxy public URL. This setting affects all the workers and application UIs running in the cluster and must be set on all the workers, drivers and masters.
spark.ui.reverseProxyUrl This is the URL where your proxy is running. This URL is for proxy which is running in front of Spark Master. This is useful when running proxy for authentication e.g. OAuth proxy. Make sure this is a complete URL including scheme (http/https) and port to reach your proxy.
spark.ui.showConsoleProgress true Show the progress bar in the console. The progress bar shows the progress of stages that run for longer than 500ms. If multiple stages run at the same time, multiple progress bars will be displayed on the same line.
spark.worker.ui.retainedExecutors 1000 How many finished executors the Spark UI and status APIs remember before garbage collecting.
spark.worker.ui.retainedDrivers 1000 How many finished drivers the Spark UI and status APIs remember before garbage collecting.
spark.sql.ui.retainedExecutions 1000 How many finished executions the Spark UI and status APIs remember before garbage collecting.
spark.streaming.ui.retainedBatches 1000 How many finished batches the Spark UI and status APIs remember before garbage collecting.
spark.ui.retainedDeadExecutors 100 How many dead executors the Spark UI and status APIs remember before garbage collecting.
spark.ui.filters None Comma separated list of filter class names to apply to the Spark Web UI. The filter should be a standard javax servlet Filter.
Filter parameters can also be specified in the configuration, by setting config entries of the form spark.<class name of filter>.param.<param name>=<value>
For example:
spark.ui.filters=com.test.filter1
spark.com.test.filter1.param.name1=foo
spark.com.test.filter1.param.name2=bar
spark.ui.requestHeaderSize 8k The maximum allowed size for a HTTP request header, in bytes unless otherwise specified. This setting applies for the Spark History Server too.

Compression and Serialization

Property NameDefaultMeaning
spark.broadcast.compress true Whether to compress broadcast variables before sending them. Generally a good idea. Compression will use spark.io.compression.codec.
spark.io.compression.codec lz4 The codec used to compress internal data such as RDD partitions, event log, broadcast variables and shuffle outputs. By default, Spark provides four codecs: lz4, lzf, snappy, and zstd. You can also use fully qualified class names to specify the codec, e.g. org.apache.spark.io.LZ4CompressionCodec, org.apache.spark.io.LZFCompressionCodec, org.apache.spark.io.SnappyCompressionCodec, and org.apache.spark.io.ZStdCompressionCodec.
spark.io.compression.lz4.blockSize 32k Block size in bytes used in LZ4 compression, in the case when LZ4 compression codec is used. Lowering this block size will also lower shuffle memory usage when LZ4 is used.
spark.io.compression.snappy.blockSize 32k Block size in bytes used in Snappy compression, in the case when Snappy compression codec is used. Lowering this block size will also lower shuffle memory usage when Snappy is used.
spark.io.compression.zstd.level 1 Compression level for Zstd compression codec. Increasing the compression level will result in better compression at the expense of more CPU and memory.
spark.io.compression.zstd.bufferSize 32k Buffer size in bytes used in Zstd compression, in the case when Zstd compression codec is used. Lowering this size will lower the shuffle memory usage when Zstd is used, but it might increase the compression cost because of excessive JNI call overhead.
spark.kryo.classesToRegister (none) If you use Kryo serialization, give a comma-separated list of custom class names to register with Kryo. See the tuning guide for more details.
spark.kryo.referenceTracking true Whether to track references to the same object when serializing data with Kryo, which is necessary if your object graphs have loops and useful for efficiency if they contain multiple copies of the same object. Can be disabled to improve performance if you know this is not the case.
spark.kryo.registrationRequired false Whether to require registration with Kryo. If set to 'true', Kryo will throw an exception if an unregistered class is serialized. If set to false (the default), Kryo will write unregistered class names along with each object. Writing class names can cause significant performance overhead, so enabling this option can enforce strictly that a user has not omitted classes from registration.
spark.kryo.registrator (none) If you use Kryo serialization, give a comma-separated list of classes that register your custom classes with Kryo. This property is useful if you need to register your classes in a custom way, e.g. to specify a custom field serializer. Otherwise spark.kryo.classesToRegister is simpler. It should be set to classes that extend KryoRegistrator. See the tuning guide for more details.
spark.kryo.unsafe false Whether to use unsafe based Kryo serializer. Can be substantially faster by using Unsafe Based IO.
spark.kryoserializer.buffer.max 64m Maximum allowable size of Kryo serialization buffer, in MiB unless otherwise specified. This must be larger than any object you attempt to serialize and must be less than 2048m. Increase this if you get a "buffer limit exceeded" exception inside Kryo.
spark.kryoserializer.buffer 64k Initial size of Kryo's serialization buffer, in KiB unless otherwise specified. Note that there will be one buffer per core on each worker. This buffer will grow up to spark.kryoserializer.buffer.max if needed.
spark.rdd.compress false Whether to compress serialized RDD partitions (e.g. for StorageLevel.MEMORY_ONLY_SER in Java and Scala or StorageLevel.MEMORY_ONLY in Python). Can save substantial space at the cost of some extra CPU time. Compression will use spark.io.compression.codec.
spark.serializer org.apache.spark.serializer.
JavaSerializer 		Class to use for serializing objects that will be sent over the network or need to be cached in serialized form. The default of Java serialization works with any Serializable Java object but is quite slow, so we recommend using org.apache.spark.serializer.KryoSerializer and configuring Kryo serialization when speed is necessary. Can be any subclass of org.apache.spark.Serializer.
spark.serializer.objectStreamReset 100 When serializing using org.apache.spark.serializer.JavaSerializer, the serializer caches objects to prevent writing redundant data, however that stops garbage collection of those objects. By calling 'reset' you flush that info from the serializer, and allow old objects to be collected. To turn off this periodic reset set it to -1. By default it will reset the serializer every 100 objects.

Memory Management

Property NameDefaultMeaning
spark.memory.fraction 0.6 Fraction of (heap space - 300MB) used for execution and storage. The lower this is, the more frequently spills and cached data eviction occur. 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. For more detail, including important information about correctly tuning JVM garbage collection when increasing this value, see this description.
spark.memory.storageFraction 0.5 Amount of storage memory immune to eviction, expressed as a fraction of the size of the region set aside by spark.memory.fraction. The higher this is, the less working memory may be available to execution and tasks may spill to disk more often. Leaving this at the default value is recommended. For more detail, see this description.
spark.memory.offHeap.enabled false If true, Spark will attempt to use off-heap memory for certain operations. If off-heap memory use is enabled, then spark.memory.offHeap.size must be positive.
spark.memory.offHeap.size 0 The absolute amount of memory in bytes which can be used for off-heap allocation. 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. This must be set to a positive value when spark.memory.offHeap.enabled=true.
spark.memory.useLegacyMode false Whether to enable the legacy memory management mode used in Spark 1.5 and before. The legacy mode rigidly partitions the heap space into fixed-size regions, potentially leading to excessive spilling if the application was not tuned. The following deprecated memory fraction configurations are not read unless this is enabled: spark.shuffle.memoryFraction
spark.storage.memoryFraction
spark.storage.unrollFraction
spark.shuffle.memoryFraction 0.2 (deprecated) This is read only if spark.memory.useLegacyMode is enabled. Fraction of Java heap to use for aggregation and cogroups during shuffles. At any given time, the collective size of all in-memory maps used for shuffles is bounded by this limit, beyond which the contents will begin to spill to disk. If spills are often, consider increasing this value at the expense of spark.storage.memoryFraction.
spark.storage.memoryFraction 0.6 (deprecated) This is read only if spark.memory.useLegacyMode is enabled. Fraction of Java heap to use for Spark's memory cache. This should not be larger than the "old" generation of objects in the JVM, which by default is given 0.6 of the heap, but you can increase it if you configure your own old generation size.
spark.storage.unrollFraction 0.2 (deprecated) This is read only if spark.memory.useLegacyMode is enabled. Fraction of spark.storage.memoryFraction to use for unrolling blocks in memory. This is dynamically allocated by dropping existing blocks when there is not enough free storage space to unroll the new block in its entirety.
spark.storage.replication.proactive false Enables proactive block replication for RDD blocks. Cached RDD block replicas lost due to executor failures are replenished if there are any existing available replicas. This tries to get the replication level of the block to the initial number.
spark.cleaner.periodicGC.interval 30min Controls how often to trigger a garbage collection.

This context cleaner triggers cleanups only when weak references are garbage collected. In long-running applications with large driver JVMs, where there is little memory pressure on the driver, this may happen very occasionally or not at all. Not cleaning at all may lead to executors running out of disk space after a while.
spark.cleaner.referenceTracking true Enables or disables context cleaning.
spark.cleaner.referenceTracking.blocking true Controls whether the cleaning thread should block on cleanup tasks (other than shuffle, which is controlled by spark.cleaner.referenceTracking.blocking.shuffle Spark property).
spark.cleaner.referenceTracking.blocking.shuffle false Controls whether the cleaning thread should block on shuffle cleanup tasks.
spark.cleaner.referenceTracking.cleanCheckpoints false Controls whether to clean checkpoint files if the reference is out of scope.

Execution Behavior

Property NameDefaultMeaning
spark.broadcast.blockSize 4m Size of each piece of a block for TorrentBroadcastFactory, in KiB unless otherwise specified. Too large a value decreases parallelism during broadcast (makes it slower); however, if it is too small, BlockManager might take a performance hit.
spark.broadcast.checksum true Whether to enable checksum for broadcast. If enabled, broadcasts will include a checksum, which can help detect corrupted blocks, at the cost of computing and sending a little more data. It's possible to disable it if the network has other mechanisms to guarantee data won't be corrupted during broadcast.
spark.executor.cores 1 in YARN mode, all the available cores on the worker in standalone and Mesos coarse-grained modes. The number of cores to use on each executor. In standalone and Mesos coarse-grained modes, for more detail, see this description.
spark.default.parallelism For distributed shuffle operations like reduceByKey and join, the largest number of partitions in a parent RDD. For operations like parallelize with no parent RDDs, it depends on the cluster manager:
  • Local mode: number of cores on the local machine
  • Mesos fine grained mode: 8
  • Others: total number of cores on all executor nodes or 2, whichever is larger
 Default number of partitions in RDDs returned by transformations like join, reduceByKey, and parallelize when not set by user.
spark.executor.heartbeatInterval 10s Interval between each executor's heartbeats to the driver. Heartbeats let the driver know that the executor is still alive and update it with metrics for in-progress tasks. spark.executor.heartbeatInterval should be significantly less than spark.network.timeout
spark.files.fetchTimeout 60s Communication timeout to use when fetching files added through SparkContext.addFile() from the driver.
spark.files.useFetchCache true If set to true (default), file fetching will use a local cache that is shared by executors that belong to the same application, which can improve task launching performance when running many executors on the same host. If set to false, these caching optimizations will be disabled and all executors will fetch their own copies of files. This optimization may be disabled in order to use Spark local directories that reside on NFS filesystems (see SPARK-6313 for more details).
spark.files.overwrite false Whether to overwrite files added through SparkContext.addFile() when the target file exists and its contents do not match those of the source.
spark.files.maxPartitionBytes 134217728 (128 MB) The maximum number of bytes to pack into a single partition when reading files.
spark.files.openCostInBytes 4194304 (4 MB) The estimated cost to open a file, measured by the number of bytes could be scanned at the same time. This is used when putting multiple files into a partition. It is better to overestimate, then the partitions with small files will be faster than partitions with bigger files.
spark.hadoop.cloneConf false If set to true, clones a new Hadoop Configuration object for each task. This option should be enabled to work around Configuration thread-safety issues (see SPARK-2546 for more details). This is disabled by default in order to avoid unexpected performance regressions for jobs that are not affected by these issues.
spark.hadoop.validateOutputSpecs true If set to true, validates the output specification (e.g. checking if the output directory already exists) used in saveAsHadoopFile and other variants. This can be disabled to silence exceptions due to pre-existing output directories. We recommend that users do not disable this except if trying to achieve compatibility with previous versions of Spark. Simply use Hadoop's FileSystem API to delete output directories by hand. This setting is ignored for jobs generated through Spark Streaming's StreamingContext, since data may need to be rewritten to pre-existing output directories during checkpoint recovery.
spark.storage.memoryMapThreshold 2m Size in bytes of a block above which Spark memory maps when reading a block from disk. This prevents Spark from memory mapping very small blocks. In general, memory mapping has high overhead for blocks close to or below the page size of the operating system.
spark.hadoop.mapreduce.fileoutputcommitter.algorithm.version 1 The file output committer algorithm version, valid algorithm version number: 1 or 2. Version 2 may have better performance, but version 1 may handle failures better in certain situations, as per MAPREDUCE-4815.

Networking

Property NameDefaultMeaning
spark.rpc.message.maxSize 128 Maximum message size (in MB) to allow in "control plane" communication; generally only applies to map output size information sent between executors and the driver. Increase this if you are running jobs with many thousands of map and reduce tasks and see messages about the RPC message size.
spark.blockManager.port (random) Port for all block managers to listen on. These exist on both the driver and the executors.
spark.driver.blockManager.port (value of spark.blockManager.port) Driver-specific port for the block manager to listen on, for cases where it cannot use the same configuration as executors.
spark.driver.bindAddress (value of spark.driver.host) Hostname or IP address where to bind listening sockets. This config overrides the SPARK_LOCAL_IP environment variable (see below).
It also allows a different address from the local one to be advertised to executors or external systems. This is useful, for example, when running containers with bridged networking. For this to properly work, the different ports used by the driver (RPC, block manager and UI) need to be forwarded from the container's host.
spark.driver.host (local hostname) Hostname or IP address for the driver. This is used for communicating with the executors and the standalone Master.
spark.driver.port (random) Port for the driver to listen on. This is used for communicating with the executors and the standalone Master.
spark.network.timeout 120s Default timeout for all network interactions. This config will be used in place of spark.core.connection.ack.wait.timeout, spark.storage.blockManagerSlaveTimeoutMs, spark.shuffle.io.connectionTimeout, spark.rpc.askTimeout or spark.rpc.lookupTimeout if they are not configured.
spark.port.maxRetries 16 Maximum number of retries when binding to a port before giving up. When a port is given a specific value (non 0), each subsequent retry will increment the port used in the previous attempt by 1 before retrying. This essentially allows it to try a range of ports from the start port specified to port + maxRetries.
spark.rpc.numRetries 3 Number of times to retry before an RPC task gives up. An RPC task will run at most times of this number.
spark.rpc.retry.wait 3s Duration for an RPC ask operation to wait before retrying.
spark.rpc.askTimeout spark.network.timeout Duration for an RPC ask operation to wait before timing out.
spark.rpc.lookupTimeout 120s Duration for an RPC remote endpoint lookup operation to wait before timing out.
spark.core.connection.ack.wait.timeout spark.network.timeout How long for the connection to wait for ack to occur before timing out and giving up. To avoid unwilling timeout caused by long pause like GC, you can set larger value.

Scheduling

Property NameDefaultMeaning
spark.cores.max (not set) When running on a standalone deploy cluster or a Mesos cluster in "coarse-grained" sharing mode, the maximum amount of CPU cores to request for the application from across the cluster (not from each machine). If not set, the default will be spark.deploy.defaultCores on Spark's standalone cluster manager, or infinite (all available cores) on Mesos.
spark.locality.wait 3s How long to wait to launch a data-local task before giving up and launching it on a less-local node. The same wait will be used to step through multiple locality levels (process-local, node-local, rack-local and then any). It is also possible to customize the waiting time for each level by setting spark.locality.wait.node, etc. You should increase this setting if your tasks are long and see poor locality, but the default usually works well.
spark.locality.wait.node spark.locality.wait Customize the locality wait for node locality. For example, you can set this to 0 to skip node locality and search immediately for rack locality (if your cluster has rack information).
spark.locality.wait.process spark.locality.wait Customize the locality wait for process locality. This affects tasks that attempt to access cached data in a particular executor process.
spark.locality.wait.rack spark.locality.wait Customize the locality wait for rack locality.
spark.scheduler.maxRegisteredResourcesWaitingTime 30s Maximum amount of time to wait for resources to register before scheduling begins.
spark.scheduler.minRegisteredResourcesRatio 0.8 for KUBERNETES mode; 0.8 for YARN mode; 0.0 for standalone mode and Mesos coarse-grained mode The minimum ratio of registered resources (registered resources / total expected resources) (resources are executors in yarn mode and Kubernetes mode, CPU cores in standalone mode and Mesos coarse-grained mode ['spark.cores.max' value is total expected resources for Mesos coarse-grained mode] ) to wait for before scheduling begins. Specified as a double between 0.0 and 1.0. Regardless of whether the minimum ratio of resources has been reached, the maximum amount of time it will wait before scheduling begins is controlled by config spark.scheduler.maxRegisteredResourcesWaitingTime.
spark.scheduler.mode FIFO The scheduling mode between jobs submitted to the same SparkContext. Can be set to FAIR to use fair sharing instead of queueing jobs one after another. Useful for multi-user services.
spark.scheduler.revive.interval 1s The interval length for the scheduler to revive the worker resource offers to run tasks.
spark.scheduler.listenerbus.eventqueue.capacity 10000 Capacity for event queue in Spark listener bus, must be greater than 0. Consider increasing value (e.g. 20000) if listener events are dropped. Increasing this value may result in the driver using more memory.
spark.scheduler.blacklist.unschedulableTaskSetTimeout 120s The timeout in seconds to wait to acquire a new executor and schedule a task before aborting a TaskSet which is unschedulable because of being completely blacklisted.
spark.blacklist.enabled false If set to "true", prevent Spark from scheduling tasks on executors that have been blacklisted due to too many task failures. The blacklisting algorithm can be further controlled by the other "spark.blacklist" configuration options.
spark.blacklist.timeout 1h (Experimental) How long a node or executor is blacklisted for the entire application, before it is unconditionally removed from the blacklist to attempt running new tasks.
spark.blacklist.task.maxTaskAttemptsPerExecutor 1 (Experimental) For a given task, how many times it can be retried on one executor before the executor is blacklisted for that task.
spark.blacklist.task.maxTaskAttemptsPerNode 2 (Experimental) For a given task, how many times it can be retried on one node, before the entire node is blacklisted for that task.
spark.blacklist.stage.maxFailedTasksPerExecutor 2 (Experimental) How many different tasks must fail on one executor, within one stage, before the executor is blacklisted for that stage.
spark.blacklist.stage.maxFailedExecutorsPerNode 2 (Experimental) How many different executors are marked as blacklisted for a given stage, before the entire node is marked as failed for the stage.
spark.blacklist.application.maxFailedTasksPerExecutor 2 (Experimental) How many different tasks must fail on one executor, in successful task sets, before the executor is blacklisted for the entire application. Blacklisted executors will be automatically added back to the pool of available resources after the timeout specified by spark.blacklist.timeout. Note that with dynamic allocation, though, the executors may get marked as idle and be reclaimed by the cluster manager.
spark.blacklist.application.maxFailedExecutorsPerNode 2 (Experimental) How many different executors must be blacklisted for the entire application, before the node is blacklisted for the entire application. Blacklisted nodes will be automatically added back to the pool of available resources after the timeout specified by spark.blacklist.timeout. Note that with dynamic allocation, though, the executors on the node may get marked as idle and be reclaimed by the cluster manager.
spark.blacklist.killBlacklistedExecutors false (Experimental) If set to "true", allow Spark to automatically kill the executors when they are blacklisted on fetch failure or blacklisted for the entire application, as controlled by spark.blacklist.application.*. Note that, when an entire node is added to the blacklist, all of the executors on that node will be killed.
spark.blacklist.application.fetchFailure.enabled false (Experimental) If set to "true", Spark will blacklist the executor immediately when a fetch failure happens. If external shuffle service is enabled, then the whole node will be blacklisted.
spark.speculation false If set to "true", performs speculative execution of tasks. This means if one or more tasks are running slowly in a stage, they will be re-launched.
spark.speculation.interval 100ms How often Spark will check for tasks to speculate.
spark.speculation.multiplier 1.5 How many times slower a task is than the median to be considered for speculation.
spark.speculation.quantile 0.75 Fraction of tasks which must be complete before speculation is enabled for a particular stage.
spark.task.cpus 1 Number of cores to allocate for each task.
spark.task.maxFailures 4 Number of failures of any particular task before giving up on the job. The total number of failures spread across different tasks will not cause the job to fail; a particular task has to fail this number of attempts. Should be greater than or equal to 1. Number of allowed retries = this value - 1.
spark.task.reaper.enabled false Enables monitoring of killed / interrupted tasks. When set to true, any task which is killed will be monitored by the executor until that task actually finishes executing. See the other spark.task.reaper.* configurations for details on how to control the exact behavior of this monitoring. When set to false (the default), task killing will use an older code path which lacks such monitoring.
spark.task.reaper.pollingInterval 10s When spark.task.reaper.enabled = true, this setting controls the frequency at which executors will poll the status of killed tasks. If a killed task is still running when polled then a warning will be logged and, by default, a thread-dump of the task will be logged (this thread dump can be disabled via the spark.task.reaper.threadDump setting, which is documented below).
spark.task.reaper.threadDump true When spark.task.reaper.enabled = true, this setting controls whether task thread dumps are logged during periodic polling of killed tasks. Set this to false to disable collection of thread dumps.
spark.task.reaper.killTimeout -1 When spark.task.reaper.enabled = true, this setting specifies a timeout after which the executor JVM will kill itself if a killed task has not stopped running. The default value, -1, disables this mechanism and prevents the executor from self-destructing. The purpose of this setting is to act as a safety-net to prevent runaway noncancellable tasks from rendering an executor unusable.
spark.stage.maxConsecutiveAttempts 4 Number of consecutive stage attempts allowed before a stage is aborted.

Dynamic Allocation

Property NameDefaultMeaning
spark.dynamicAllocation.enabled false Whether to use dynamic resource allocation, which scales the number of executors registered with this application up and down based on the workload. For more detail, see the description here.

This requires spark.shuffle.service.enabled to be set. The following configurations are also relevant: spark.dynamicAllocation.minExecutors, spark.dynamicAllocation.maxExecutors, and spark.dynamicAllocation.initialExecutors spark.dynamicAllocation.executorAllocationRatio
spark.dynamicAllocation.executorIdleTimeout 60s If dynamic allocation is enabled and an executor has been idle for more than this duration, the executor will be removed. For more detail, see this description.
spark.dynamicAllocation.cachedExecutorIdleTimeout infinity If dynamic allocation is enabled and an executor which has cached data blocks has been idle for more than this duration, the executor will be removed. For more details, see this description.
spark.dynamicAllocation.initialExecutors spark.dynamicAllocation.minExecutors Initial number of executors to run if dynamic allocation is enabled.

If `--num-executors` (or `spark.executor.instances`) is set and larger than this value, it will be used as the initial number of executors.
spark.dynamicAllocation.maxExecutors infinity Upper bound for the number of executors if dynamic allocation is enabled.
spark.dynamicAllocation.minExecutors 0 Lower bound for the number of executors if dynamic allocation is enabled.
spark.dynamicAllocation.executorAllocationRatio 1 By default, the dynamic allocation will request enough executors to maximize the parallelism according to the number of tasks to process. While this minimizes the latency of the job, with small tasks this setting can waste a lot of resources due to executor allocation overhead, as some executor might not even do any work. This setting allows to set a ratio that will be used to reduce the number of executors w.r.t. full parallelism. Defaults to 1.0 to give maximum parallelism. 0.5 will divide the target number of executors by 2 The target number of executors computed by the dynamicAllocation can still be overridden by the spark.dynamicAllocation.minExecutors and spark.dynamicAllocation.maxExecutors settings
spark.dynamicAllocation.schedulerBacklogTimeout 1s If dynamic allocation is enabled and there have been pending tasks backlogged for more than this duration, new executors will be requested. For more detail, see this description.
spark.dynamicAllocation.sustainedSchedulerBacklogTimeout schedulerBacklogTimeout Same as spark.dynamicAllocation.schedulerBacklogTimeout, but used only for subsequent executor requests. For more detail, see this description.

Security

Please refer to the Security page for available options on how to secure different Spark subsystems.

Spark SQL

Running the SET -v command will show the entire list of the SQL configuration.

// spark is an existing SparkSession
spark.sql("SET -v").show(numRows = 200, truncate = false)
// spark is an existing SparkSession
spark.sql("SET -v").show(200, false);
# spark is an existing SparkSession
spark.sql("SET -v").show(n=200, truncate=False)
sparkR.session()
properties <- sql("SET -v")
showDF(properties, numRows = 200, truncate = FALSE)

Spark Streaming

Property NameDefaultMeaning
spark.streaming.backpressure.enabled false Enables or disables Spark Streaming's internal backpressure mechanism (since 1.5). This enables the Spark Streaming to control the receiving rate based on the current batch scheduling delays and processing times so that the system receives only as fast as the system can process. Internally, this dynamically sets the maximum receiving rate of receivers. This rate is upper bounded by the values spark.streaming.receiver.maxRate and spark.streaming.kafka.maxRatePerPartition if they are set (see below).
spark.streaming.backpressure.initialRate not set This is the initial maximum receiving rate at which each receiver will receive data for the first batch when the backpressure mechanism is enabled.
spark.streaming.blockInterval 200ms Interval at which data received by Spark Streaming receivers is chunked into blocks of data before storing them in Spark. Minimum recommended - 50 ms. See the performance tuning section in the Spark Streaming programing guide for more details.
spark.streaming.receiver.maxRate not set Maximum rate (number of records per second) at which each receiver will receive data. Effectively, each stream will consume at most this number of records per second. Setting this configuration to 0 or a negative number will put no limit on the rate. See the deployment guide in the Spark Streaming programing guide for mode details.
spark.streaming.receiver.writeAheadLog.enable false Enable write-ahead logs for receivers. All the input data received through receivers will be saved to write-ahead logs that will allow it to be recovered after driver failures. See the deployment guide in the Spark Streaming programing guide for more details.
spark.streaming.unpersist true Force RDDs generated and persisted by Spark Streaming to be automatically unpersisted from Spark's memory. The raw input data received by Spark Streaming is also automatically cleared. Setting this to false will allow the raw data and persisted RDDs to be accessible outside the streaming application as they will not be cleared automatically. But it comes at the cost of higher memory usage in Spark.
spark.streaming.stopGracefullyOnShutdown false If true, Spark shuts down the StreamingContext gracefully on JVM shutdown rather than immediately.
spark.streaming.kafka.maxRatePerPartition not set Maximum rate (number of records per second) at which data will be read from each Kafka partition when using the new Kafka direct stream API. See the Kafka Integration guide for more details.
spark.streaming.kafka.minRatePerPartition 1 Minimum rate (number of records per second) at which data will be read from each Kafka partition when using the new Kafka direct stream API.
spark.streaming.kafka.maxRetries 1 Maximum number of consecutive retries the driver will make in order to find the latest offsets on the leader of each partition (a default value of 1 means that the driver will make a maximum of 2 attempts). Only applies to the new Kafka direct stream API.
spark.streaming.ui.retainedBatches 1000 How many batches the Spark Streaming UI and status APIs remember before garbage collecting.
spark.streaming.driver.writeAheadLog.closeFileAfterWrite false Whether to close the file after writing a write-ahead log record on the driver. Set this to 'true' when you want to use S3 (or any file system that does not support flushing) for the metadata WAL on the driver.
spark.streaming.receiver.writeAheadLog.closeFileAfterWrite false Whether to close the file after writing a write-ahead log record on the receivers. Set this to 'true' when you want to use S3 (or any file system that does not support flushing) for the data WAL on the receivers.

SparkR

Property NameDefaultMeaning
spark.r.numRBackendThreads 2 Number of threads used by RBackend to handle RPC calls from SparkR package.
spark.r.command Rscript Executable for executing R scripts in cluster modes for both driver and workers.
spark.r.driver.command spark.r.command Executable for executing R scripts in client modes for driver. Ignored in cluster modes.
spark.r.shell.command R Executable for executing sparkR shell in client modes for driver. Ignored in cluster modes. It is the same as environment variable SPARKR_DRIVER_R, but take precedence over it. spark.r.shell.command is used for sparkR shell while spark.r.driver.command is used for running R script.
spark.r.backendConnectionTimeout 6000 Connection timeout set by R process on its connection to RBackend in seconds.
spark.r.heartBeatInterval 100 Interval for heartbeats sent from SparkR backend to R process to prevent connection timeout.

GraphX

Property NameDefaultMeaning
spark.graphx.pregel.checkpointInterval -1 Checkpoint interval for graph and message in Pregel. It used to avoid stackOverflowError due to long lineage chains after lots of iterations. The checkpoint is disabled by default.

Deploy

Property NameDefaultMeaning
spark.deploy.recoveryMode NONE The recovery mode setting to recover submitted Spark jobs with cluster mode when it failed and relaunches. This is only applicable for cluster mode when running with Standalone or Mesos.
spark.deploy.zookeeper.url None When `spark.deploy.recoveryMode` is set to ZOOKEEPER, this configuration is used to set the zookeeper URL to connect to.
spark.deploy.zookeeper.dir None When `spark.deploy.recoveryMode` is set to ZOOKEEPER, this configuration is used to set the zookeeper directory to store recovery state.

Cluster Managers

Each cluster manager in Spark has additional configuration options. Configurations can be found on the pages for each mode:

YARN

Mesos

Kubernetes

Standalone Mode

Environment Variables

Certain Spark settings can be configured through environment variables, which are read from the conf/spark-env.sh script in the directory where Spark is installed (or conf/spark-env.cmd on Windows). In Standalone and Mesos modes, this file can give machine specific information such as hostnames. It is also sourced when running local Spark applications or submission scripts.

Note that conf/spark-env.sh does not exist by default when Spark is installed. However, you can copy conf/spark-env.sh.template to create it. Make sure you make the copy executable.

The following variables can be set in spark-env.sh:

Environment VariableMeaning
JAVA_HOME Location where Java is installed (if it's not on your default PATH).
PYSPARK_PYTHON Python binary executable to use for PySpark in both driver and workers (default is python2.7 if available, otherwise python). Property spark.pyspark.python take precedence if it is set
PYSPARK_DRIVER_PYTHON Python binary executable to use for PySpark in driver only (default is PYSPARK_PYTHON). Property spark.pyspark.driver.python take precedence if it is set
SPARKR_DRIVER_R R binary executable to use for SparkR shell (default is R). Property spark.r.shell.command take precedence if it is set
SPARK_LOCAL_IP IP address of the machine to bind to.
SPARK_PUBLIC_DNS Hostname your Spark program will advertise to other machines.

In addition to the above, there are also options for setting up the Spark standalone cluster scripts, such as number of cores to use on each machine and maximum memory.

Since spark-env.sh is a shell script, some of these can be set programmatically – for example, you might compute SPARK_LOCAL_IP by looking up the IP of a specific network interface.

Note: When running Spark on YARN in cluster mode, environment variables need to be set using the spark.yarn.appMasterEnv.[EnvironmentVariableName] property in your conf/spark-defaults.conf file. Environment variables that are set in spark-env.sh will not be reflected in the YARN Application Master process in cluster mode. See the YARN-related Spark Properties for more information.

Configuring Logging

Spark uses log4j for logging. You can configure it by adding a log4j.properties file in the conf directory. One way to start is to copy the existing log4j.properties.template located there.

Overriding configuration directory

To specify a different configuration directory other than the default “SPARK_HOME/conf”, you can set SPARK_CONF_DIR. Spark will use the configuration files (spark-defaults.conf, spark-env.sh, log4j.properties, etc) from this directory.

Inheriting Hadoop Cluster Configuration

If you plan to read and write from HDFS using Spark, there are two Hadoop configuration files that should be included on Spark’s classpath:

The location of these configuration files varies across Hadoop versions, but a common location is inside of /etc/hadoop/conf. Some tools create configurations on-the-fly, but offer a mechanism to download copies of them.

To make these files visible to Spark, set HADOOP_CONF_DIR in $SPARK_HOME/conf/spark-env.sh to a location containing the configuration files.

Custom Hadoop/Hive Configuration

If your Spark application is interacting with Hadoop, Hive, or both, there are probably Hadoop/Hive configuration files in Spark’s classpath.

Multiple running applications might require different Hadoop/Hive client side configurations. You can copy and modify hdfs-site.xml, core-site.xml, yarn-site.xml, hive-site.xml in Spark’s classpath for each application. In a Spark cluster running on YARN, these configuration files are set cluster-wide, and cannot safely be changed by the application.

The better choice is to use spark hadoop properties in the form of spark.hadoop.*. They can be considered as same as normal spark properties which can be set in $SPARK_HOME/conf/spark-defaults.conf

In some cases, you may want to avoid hard-coding certain configurations in a SparkConf. For instance, Spark allows you to simply create an empty conf and set spark/spark hadoop properties.

val conf = new SparkConf().set("spark.hadoop.abc.def","xyz")
val sc = new SparkContext(conf)

Also, you can modify or add configurations at runtime:

./bin/spark-submit \ 
  --name "My app" \ 
  --master local[4] \  
  --conf spark.eventLog.enabled=false \ 
  --conf "spark.executor.extraJavaOptions=-XX:+PrintGCDetails -XX:+PrintGCTimeStamps" \ 
  --conf spark.hadoop.abc.def=xyz \ 
  myApp.jar