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. The preferred way to set them is by passing a SparkConf class to your SparkContext constructor. Alternatively, Spark will also load them from Java system properties, for compatibility with old versions of Spark.

SparkConf lets you configure most of the common properties to initialize a cluster (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 as follows:

val conf = new SparkConf()
             .setAppName("My application")
             .set("spark.executor.memory", "1g")
val sc = new SparkContext(conf)

Most of the properties control internal settings that have reasonable default values. However, there are at least five properties that you will commonly want to control:

Property NameDefaultMeaning
spark.executor.memory 512m Amount of memory to use per executor process, in the same format as JVM memory strings (e.g. 512m, 2g).
spark.serializer org.apache.spark.serializer.
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.kryo.registrator (none) If you use Kryo serialization, set this class to register your custom classes with Kryo. It should be set to a class that extends KryoRegistrator. See the tuning guide for more details.
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.
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.

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

Property NameDefaultMeaning
spark.default.parallelism 8 Default number of tasks to use across the cluster for distributed shuffle operations (groupByKey, reduceByKey, etc) when not set by user. 0.6 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.shuffle.memoryFraction 0.3 Fraction of Java heap to use for aggregation and cogroups during shuffles, if spark.shuffle.spill is true. 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.mesos.coarse false If set to "true", runs over Mesos clusters in "coarse-grained" sharing mode, where Spark acquires one long-lived Mesos task on each machine instead of one Mesos task per Spark task. This gives lower-latency scheduling for short queries, but leaves resources in use for the whole duration of the Spark job.
spark.ui.port 4040 Port for your application's dashboard, which shows memory and workload data
spark.ui.retainedStages 1000 How many stages the Spark UI remembers before garbage collecting.
spark.shuffle.compress true Whether to compress map output files. Generally a good idea.
spark.shuffle.spill.compress true Whether to compress data spilled during shuffles.
spark.broadcast.compress true Whether to compress broadcast variables before sending them. Generally a good idea.
spark.rdd.compress false Whether to compress serialized RDD partitions (e.g. for StorageLevel.MEMORY_ONLY_SER). Can save substantial space at the cost of some extra CPU time.
The codec used to compress internal data such as RDD partitions and shuffle outputs. By default, Spark provides two codecs: and 32768 Block size (in bytes) used in Snappy compression, in the case when Snappy compression codec is used.
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.reducer.maxMbInFlight 48 Maximum size (in megabytes) of map outputs to fetch simultaneously from each reduce task. 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.closure.serializer org.apache.spark.serializer.
Serializer class to use for closures. Generally Java is fine unless your distributed functions (e.g. map functions) reference large objects in the driver program.
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.kryoserializer.buffer.mb 2 Maximum object size to allow within Kryo (the library needs to create a buffer at least as large as the largest single object you'll serialize). Increase this if you get a "buffer limit exceeded" exception inside Kryo. Note that there will be one buffer per core on each worker.
spark.broadcast.factory org.apache.spark.broadcast.
Which broadcast implementation to use.
spark.locality.wait 3000 Number of milliseconds 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.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.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.rack spark.locality.wait Customize the locality wait for rack locality.
spark.worker.timeout 60 Number of seconds after which the standalone deploy master considers a worker lost if it receives no heartbeats.
spark.akka.frameSize 10 Maximum message size to allow in "control plane" communication (for serialized tasks and task results), in MB. Increase this if your tasks need to send back large results to the driver (e.g. using collect() on a large dataset).
spark.akka.threads 4 Number of actor threads to use for communication. Can be useful to increase on large clusters when the driver has a lot of CPU cores.
spark.akka.timeout 100 Communication timeout between Spark nodes, in seconds.
spark.akka.heartbeat.pauses 600 This is set to a larger value to disable failure detector that comes inbuilt akka. It can be enabled again, if you plan to use this feature (Not recommended). Acceptable heart beat pause in seconds for akka. This can be used to control sensitivity to gc pauses. Tune this in combination of `spark.akka.heartbeat.interval` and `spark.akka.failure-detector.threshold` if you need to.
spark.akka.failure-detector.threshold 300.0 This is set to a larger value to disable failure detector that comes inbuilt akka. It can be enabled again, if you plan to use this feature (Not recommended). This maps to akka's `akka.remote.transport-failure-detector.threshold`. Tune this in combination of `spark.akka.heartbeat.pauses` and `spark.akka.heartbeat.interval` if you need to.
spark.akka.heartbeat.interval 1000 This is set to a larger value to disable failure detector that comes inbuilt akka. It can be enabled again, if you plan to use this feature (Not recommended). A larger interval value in seconds reduces network overhead and a smaller value ( ~ 1 s) might be more informative for akka's failure detector. Tune this in combination of `spark.akka.heartbeat.pauses` and `spark.akka.failure-detector.threshold` if you need to. Only positive use case for using failure detector can be, a sensistive failure detector can help evict rogue executors really quick. However this is usually not the case as gc pauses and network lags are expected in a real spark cluster. Apart from that enabling this leads to a lot of exchanges of heart beats between nodes leading to flooding the network with those. (local hostname) Hostname or IP address for the driver to listen on.
spark.driver.port (random) Port for the driver to listen on.
spark.cleaner.ttl (infinite) Duration (seconds) of how long Spark will remember any metadata (stages generated, tasks generated, etc.). Periodic cleanups will ensure that metadata older than this duration will be forgetten. This is useful for running Spark for many hours / days (for example, running 24/7 in case of Spark Streaming applications). Note that any RDD that persists in memory for more than this duration will be cleared as well.
spark.streaming.blockInterval 200 Duration (milliseconds) of how long to batch new objects coming from network receivers used in Spark Streaming.
spark.streaming.unpersist false Force RDDs generated and persisted by Spark Streaming to be automatically unpersisted from Spark's memory. Setting this to true is likely to reduce Spark's RDD memory usage.
spark.task.maxFailures 4 Number of individual task failures before giving up on the job. Should be greater than or equal to 1. Number of allowed retries = this value - 1.
spark.broadcast.blockSize 4096 Size of each piece of a block in kilobytes for TorrentBroadcastFactory. Too large a value decreases parallelism during broadcast (makes it slower); however, if it is too small, BlockManager might take a performance hit.
spark.shuffle.consolidateFiles false If set to "true", consolidates intermediate files created during a shuffle. Creating fewer files can improve filesystem performance for shuffles with large numbers of reduce tasks. It is recommended to set this to "true" when using ext4 or xfs filesystems. On ext3, this option might degrade performance on machines with many (>8) cores due to filesystem limitations.
spark.shuffle.file.buffer.kb 100 Size of the in-memory buffer for each shuffle file output stream, in kilobytes. These buffers reduce the number of disk seeks and system calls made in creating intermediate shuffle files.
spark.shuffle.spill true If set to "true", limits the amount of memory used during reduces by spilling data out to disk. This spilling threshold is specified by spark.shuffle.memoryFraction.
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 100 How often Spark will check for tasks to speculate, in milliseconds.
spark.speculation.quantile 0.75 Percentage of tasks which must be complete before speculation is enabled for a particular stage.
spark.speculation.multiplier 1.5 How many times slower a task is than the median to be considered for speculation.
spark.logConf false Log the supplied SparkConf as INFO at start of spark context.
spark.deploy.spreadOut true Whether the standalone cluster manager should spread applications out across nodes or try to consolidate them onto as few nodes as possible. Spreading out is usually better for data locality in HDFS, but consolidating is more efficient for compute-intensive workloads.
Note: this setting needs to be configured in the standalone cluster master, not in individual applications; you can set it through SPARK_JAVA_OPTS in
spark.deploy.defaultCores (infinite) Default number of cores to give to applications in Spark's standalone mode if they don't set spark.cores.max. If not set, applications always get all available cores unless they configure spark.cores.max themselves. Set this lower on a shared cluster to prevent users from grabbing the whole cluster by default.
Note: this setting needs to be configured in the standalone cluster master, not in individual applications; you can set it through SPARK_JAVA_OPTS in

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.

Environment Variables

Certain Spark settings can be configured through environment variables, which are read from the conf/ script in the directory where Spark is installed (or conf/spark-env.cmd on Windows). These variables are meant to be for machine-specific settings, such as library search paths. While Spark properties can also be set there through SPARK_JAVA_OPTS, for per-application settings, we recommend setting these properties within the application instead of in so that different applications can use different settings.

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

The following variables can be set in

Since 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.

Configuring Logging

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