--- layout: global title: Running Spark on YARN --- Support for running on [YARN (Hadoop NextGen)](http://hadoop.apache.org/docs/stable/hadoop-yarn/hadoop-yarn-site/YARN.html) was added to Spark in version 0.6.0, and improved in subsequent releases. # Preparations Running Spark-on-YARN requires a binary distribution of Spark which is built with YARN support. Binary distributions can be downloaded from the Spark project website. To build Spark yourself, refer to [Building Spark](building-spark.html). # Configuration Most of the configs are the same for Spark on YARN as for other deployment modes. See the [configuration page](configuration.html) for more information on those. These are configs that are specific to Spark on YARN. #### Spark Properties
Property Name | Default | Meaning |
---|---|---|
spark.yarn.am.memory |
512m |
Amount of memory to use for the YARN Application Master in client mode, in the same format as JVM memory strings (e.g. 512m , 2g ).
In cluster mode, use spark.driver.memory instead.
|
spark.driver.cores |
1 |
Number of cores used by the driver in YARN cluster mode.
Since the driver is run in the same JVM as the YARN Application Master in cluster mode, this also controls the cores used by the YARN AM.
In client mode, use spark.yarn.am.cores to control the number of cores used by the YARN AM instead.
|
spark.yarn.am.cores |
1 |
Number of cores to use for the YARN Application Master in client mode.
In cluster mode, use spark.driver.cores instead.
|
spark.yarn.am.waitTime |
100s | In yarn-cluster mode, time for the application master to wait for the SparkContext to be initialized. In yarn-client mode, time for the application master to wait for the driver to connect to it. |
spark.yarn.submit.file.replication |
The default HDFS replication (usually 3) | HDFS replication level for the files uploaded into HDFS for the application. These include things like the Spark jar, the app jar, and any distributed cache files/archives. |
spark.yarn.preserve.staging.files |
false | Set to true to preserve the staged files (Spark jar, app jar, distributed cache files) at the end of the job rather than delete them. |
spark.yarn.scheduler.heartbeat.interval-ms |
3000 |
The interval in ms in which the Spark application master heartbeats into the YARN ResourceManager.
The value is capped at half the value of YARN's configuration for the expiry interval
(yarn.am.liveness-monitor.expiry-interval-ms ).
|
spark.yarn.scheduler.initial-allocation.interval |
200ms |
The initial interval in which the Spark application master eagerly heartbeats to the YARN ResourceManager
when there are pending container allocation requests. It should be no larger than
spark.yarn.scheduler.heartbeat.interval-ms . The allocation interval will doubled on
successive eager heartbeats if pending containers still exist, until
spark.yarn.scheduler.heartbeat.interval-ms is reached.
|
spark.yarn.max.executor.failures |
numExecutors * 2, with minimum of 3 | The maximum number of executor failures before failing the application. |
spark.yarn.historyServer.address |
(none) | The address of the Spark history server (i.e. host.com:18080). The address should not contain a scheme (http://). Defaults to not being set since the history server is an optional service. This address is given to the YARN ResourceManager when the Spark application finishes to link the application from the ResourceManager UI to the Spark history server UI. For this property, YARN properties can be used as variables, and these are substituted by Spark at runtime. For eg, if the Spark history server runs on the same node as the YARN ResourceManager, it can be set to `${hadoopconf-yarn.resourcemanager.hostname}:18080`. |
spark.yarn.dist.archives |
(none) | Comma separated list of archives to be extracted into the working directory of each executor. |
spark.yarn.dist.files |
(none) | Comma-separated list of files to be placed in the working directory of each executor. |
spark.executor.instances |
2 |
The number of executors. Note that this property is incompatible with spark.dynamicAllocation.enabled .
|
spark.yarn.executor.memoryOverhead |
executorMemory * 0.10, with minimum of 384 | The amount of off heap memory (in megabytes) to be allocated per executor. 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%). |
spark.yarn.driver.memoryOverhead |
driverMemory * 0.07, with minimum of 384 | The amount of off heap memory (in megabytes) to be allocated per driver in cluster mode. 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%). |
spark.yarn.am.memoryOverhead |
AM memory * 0.07, with minimum of 384 |
Same as spark.yarn.driver.memoryOverhead , but for the Application Master in client mode.
|
spark.yarn.am.port |
(random) | Port for the YARN Application Master to listen on. In YARN client mode, this is used to communicate between the Spark driver running on a gateway and the Application Master running on YARN. In YARN cluster mode, this is used for the dynamic executor feature, where it handles the kill from the scheduler backend. |
spark.yarn.queue |
default | The name of the YARN queue to which the application is submitted. |
spark.yarn.jar |
(none) | The location of the Spark jar file, in case overriding the default location is desired. By default, Spark on YARN will use a Spark jar installed locally, but the Spark jar can also be in a world-readable location on HDFS. This allows YARN to cache it on nodes so that it doesn't need to be distributed each time an application runs. To point to a jar on HDFS, for example, set this configuration to "hdfs:///some/path". |
spark.yarn.access.namenodes |
(none) | A list of secure HDFS namenodes your Spark application is going to access. For example, `spark.yarn.access.namenodes=hdfs://nn1.com:8032,hdfs://nn2.com:8032`. The Spark application must have acess to the namenodes listed and Kerberos must be properly configured to be able to access them (either in the same realm or in a trusted realm). Spark acquires security tokens for each of the namenodes so that the Spark application can access those remote HDFS clusters. |
spark.yarn.appMasterEnv.[EnvironmentVariableName] |
(none) |
Add the environment variable specified by EnvironmentVariableName to the
Application Master process launched on YARN. The user can specify multiple of
these and to set multiple environment variables. In yarn-cluster mode this controls
the environment of the SPARK driver and in yarn-client mode it only controls
the environment of the executor launcher.
|
spark.yarn.containerLauncherMaxThreads |
25 | The maximum number of threads to use in the application master for launching executor containers. |
spark.yarn.am.extraJavaOptions |
(none) | A string of extra JVM options to pass to the YARN Application Master in client mode. In cluster mode, use spark.driver.extraJavaOptions instead. |
spark.yarn.am.extraLibraryPath |
(none) | Set a special library path to use when launching the application master in client mode. |
spark.yarn.maxAppAttempts |
yarn.resourcemanager.am.max-attempts in YARN | The maximum number of attempts that will be made to submit the application. It should be no larger than the global number of max attempts in the YARN configuration. |
spark.yarn.submit.waitAppCompletion |
true | In YARN cluster mode, controls whether the client waits to exit until the application completes. If set to true, the client process will stay alive reporting the application's status. Otherwise, the client process will exit after submission. |
spark.yarn.executor.nodeLabelExpression |
(none) | A YARN node label expression that restricts the set of nodes executors will be scheduled on. Only versions of YARN greater than or equal to 2.6 support node label expressions, so when running against earlier versions, this property will be ignored. |
spark.yarn.keytab |
(none) | The full path to the file that contains the keytab for the principal specified above. This keytab will be copied to the node running the Application Master via the Secure Distributed Cache, for renewing the login tickets and the delegation tokens periodically. |
spark.yarn.principal |
(none) | Principal to be used to login to KDC, while running on secure HDFS. |
spark.yarn.config.gatewayPath |
(none) |
A path that is valid on the gateway host (the host where a Spark application is started) but may
differ for paths for the same resource in other nodes in the cluster. Coupled with
spark.yarn.config.replacementPath , this is used to support clusters with
heterogeneous configurations, so that Spark can correctly launch remote processes.
The replacement path normally will contain a reference to some environment variable exported by
YARN (and, thus, visible to Spark containers).
For example, if the gateway node has Hadoop libraries installed on /disk1/hadoop , and
the location of the Hadoop install is exported by YARN as the HADOOP_HOME
environment variable, setting this value to /disk1/hadoop and the replacement path to
$HADOOP_HOME will make sure that paths used to launch remote processes properly
reference the local YARN configuration.
|
spark.yarn.config.replacementPath |
(none) |
See spark.yarn.config.gatewayPath .
|