Since there is no definitive way to predict whether an executor Resource Allocation PolicyĪt a high level, Spark should relinquish executors when they are no longer used and acquireĮxecutors when they are needed. For more detail, see theĬonfigurations page. In YARN mode, follow the instructions here.Īll other relevant configurations are optional and under the spark.dynamicAllocation.* and In Mesos coarse-grained mode, run $SPARK_HOME/sbin/start-mesos-shuffle-service.sh on all In standalone mode, simply start your workers with set to true. While it is simple to enable shuffle tracking, the way to set up the external shuffle service varies across cluster managers: Without deleting shuffle files written by them (more detail describedīelow). The purpose of the shuffle tracking or the external shuffle service is to allow executors to be removed Second, your application must set both and to trueĪfter you set up an external shuffle service on each worker node in the same cluster. There are two ways for using this feature.įirst, your application must set both and to true. When you want to use dynamic allocation in standalone mode, you are recommended to explicitly set cores for each executor before the issue SPARK-30299 got fixed. In this case, when dynamic allocation enabled, spark will possibly acquire much more executors than expected. In standalone mode, without explicitly setting, each executor will get all the available cores of a worker.This feature is disabled by default and available on all coarse-grained cluster managers, i.e. Useful if multiple applications share resources in your Spark cluster. This means that your application may give resources back to the cluster if theyĪre no longer used and request them again later when there is demand. Spark provides a mechanism to dynamically adjust the resources your application occupies based If you would like to shareĭata this way, we recommend running a single server application that can serve multiple requests by querying Note that none of the modes currently provide memory sharing across applications. However, it comes with a risk of less predictable latency, because it may take a while forĪn application to gain back cores on one node when it has work to do. Is useful when you expect large numbers of not overly active applications, such as shell sessions from Still has a fixed and independent memory allocation (set by ), but when theĪpplication is not running tasks on a machine, other applications may run tasks on those cores. For more information, see theĪ second option available on Mesos is dynamic sharing of CPU cores. Property) control the resources per executor. ( configuration property) and -executor-cores ( configuration On the cluster ( as configuration property), while -executor-memory YARN: The -num-executors option to the Spark YARN client controls how many executors it will allocate.You should also set to control the executor memory. Mesos: To use static partitioning on Mesos, set the configuration property to true,Īnd optionally set to limit each application’s resource share as in the standalone mode.Or change the default for applications that don’t set this setting through .įinally, in addition to controlling cores, each application’s setting controls The number of nodes an application uses by setting the configuration property in it, Standalone mode: By default, applications submitted to the standalone mode cluster will run inįIFO (first-in-first-out) order, and each application will try to use all available nodes.Resource allocation can be configured as follows, based on the cluster type: This is the approach used in Spark’s standalone This approach, each application is given a maximum amount of resources it can use and holds onto themįor its whole duration. The simplest option, available on all cluster managers, is static partitioning of resources. If multiple users need to share your cluster, there areĭifferent options to manage allocation, depending on the cluster manager. Run tasks and store data for that application. When running on a cluster, each Spark application gets an independent set of executor JVMs that only Spark includes a fair scheduler to schedule resources within each SparkContext. This is common if your application is serving requests If they were submitted by different threads. Within each Spark application, multiple “jobs” (Spark actions) may be running concurrently The cluster managers that Spark runs on provideįacilities for scheduling across applications. Runs an independent set of executor processes. In the cluster mode overview, each Spark application (instance of SparkContext) Spark has several facilities for scheduling resources between computations.
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