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 title: Job Scheduling
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 * This will become a table of contents (this text will be scraped).
 {:toc}
 
 # Overview
 
 Spark has several facilities for scheduling resources between computations. First, recall that, as described
 in the [cluster mode overview](cluster-overview.html), each Spark application (instance of SparkContext)
 runs an independent set of executor processes. The cluster managers that Spark runs on provide
 facilities for [scheduling across applications](#scheduling-across-applications). Second,
 _within_ each Spark application, multiple "jobs" (Spark actions) may be running concurrently
 if they were submitted by different threads. This is common if your application is serving requests
 over the network. Spark includes a [fair scheduler](#scheduling-within-an-application) to schedule resources within each SparkContext.
 
 # Scheduling Across Applications
 
 When running on a cluster, each Spark application gets an independent set of executor JVMs that only
 run tasks and store data for that application. If multiple users need to share your cluster, there are
 different options to manage allocation, depending on the cluster manager.
 
 The simplest option, available on all cluster managers, is _static partitioning_ of resources. With
 this approach, each application is given a maximum amount of resources it can use and holds onto them
 for its whole duration. This is the approach used in Spark's [standalone](spark-standalone.html)
 and [YARN](running-on-yarn.html) modes, as well as the
 [coarse-grained Mesos mode](running-on-mesos.html#mesos-run-modes).
 Resource allocation can be configured as follows, based on the cluster type:
 
 * **Standalone mode:** By default, applications submitted to the standalone mode cluster will run in
   FIFO (first-in-first-out) order, and each application will try to use all available nodes. You can limit
   the number of nodes an application uses by setting the `spark.cores.max` configuration property in it,
   or change the default for applications that don't set this setting through `spark.deploy.defaultCores`.
   Finally, in addition to controlling cores, each application's `spark.executor.memory` setting controls
   its memory use.
 * **Mesos:** To use static partitioning on Mesos, set the `spark.mesos.coarse` configuration property to `true`,
   and optionally set `spark.cores.max` to limit each application's resource share as in the standalone mode.
   You should also set `spark.executor.memory` to control the executor memory.
 * **YARN:** The `--num-executors` option to the Spark YARN client controls how many executors it will allocate
   on the cluster (`spark.executor.instances` as configuration property), while `--executor-memory`
   (`spark.executor.memory` configuration property) and `--executor-cores` (`spark.executor.cores` configuration
   property) control the resources per executor. For more information, see the
   [YARN Spark Properties](running-on-yarn.html).
 
 A second option available on Mesos is _dynamic sharing_ of CPU cores. In this mode, each Spark application
 still has a fixed and independent memory allocation (set by `spark.executor.memory`), but when the
 application is not running tasks on a machine, other applications may run tasks on those cores. This mode
 is useful when you expect large numbers of not overly active applications, such as shell sessions from
 separate users. However, it comes with a risk of less predictable latency, because it may take a while for
 an application to gain back cores on one node when it has work to do. To use this mode, simply use a
 `mesos://` URL and set `spark.mesos.coarse` to false.
 
 Note that none of the modes currently provide memory sharing across applications. If you would like to share
 data this way, we recommend running a single server application that can serve multiple requests by querying
 the same RDDs.
 
 ## Dynamic Resource Allocation
 
 Spark provides a mechanism to dynamically adjust the resources your application occupies based
 on the workload. This means that your application may give resources back to the cluster if they
 are no longer used and request them again later when there is demand. This feature is particularly
 useful if multiple applications share resources in your Spark cluster.
 
 This feature is disabled by default and available on all coarse-grained cluster managers, i.e.
 [standalone mode](spark-standalone.html), [YARN mode](running-on-yarn.html), and
 [Mesos coarse-grained mode](running-on-mesos.html#mesos-run-modes).
 
 ### Configuration and Setup
 
 There are two requirements for using this feature. First, your application must set
 `spark.dynamicAllocation.enabled` to `true`. Second, you must set up an *external shuffle service*
 on each worker node in the same cluster and set `spark.shuffle.service.enabled` to true in your
 application. The purpose of the external shuffle service is to allow executors to be removed
 without deleting shuffle files written by them (more detail described
 [below](job-scheduling.html#graceful-decommission-of-executors)). The way to set up this service
 varies across cluster managers:
 
 In standalone mode, simply start your workers with `spark.shuffle.service.enabled` set to `true`.
 
 In Mesos coarse-grained mode, run `$SPARK_HOME/sbin/start-mesos-shuffle-service.sh` on all
 slave nodes with `spark.shuffle.service.enabled` set to `true`. For instance, you may do so
 through Marathon.
 
 In YARN mode, follow the instructions [here](running-on-yarn.html#configuring-the-external-shuffle-service).
 
 All other relevant configurations are optional and under the `spark.dynamicAllocation.*` and
 `spark.shuffle.service.*` namespaces. For more detail, see the
 [configurations page](configuration.html#dynamic-allocation).
 
 ### Resource Allocation Policy
 
 At a high level, Spark should relinquish executors when they are no longer used and acquire
 executors when they are needed. Since there is no definitive way to predict whether an executor
 that is about to be removed will run a task in the near future, or whether a new executor that is
 about to be added will actually be idle, we need a set of heuristics to determine when to remove
 and request executors.
 
 #### Request Policy
 
 A Spark application with dynamic allocation enabled requests additional executors when it has
 pending tasks waiting to be scheduled. This condition necessarily implies that the existing set
 of executors is insufficient to simultaneously saturate all tasks that have been submitted but
 not yet finished.
 
 Spark requests executors in rounds. The actual request is triggered when there have been pending
 tasks for `spark.dynamicAllocation.schedulerBacklogTimeout` seconds, and then triggered again
 every `spark.dynamicAllocation.sustainedSchedulerBacklogTimeout` seconds thereafter if the queue
 of pending tasks persists. Additionally, the number of executors requested in each round increases
 exponentially from the previous round. For instance, an application will add 1 executor in the
 first round, and then 2, 4, 8 and so on executors in the subsequent rounds.
 
 The motivation for an exponential increase policy is twofold. First, an application should request
 executors cautiously in the beginning in case it turns out that only a few additional executors is
 sufficient. This echoes the justification for TCP slow start. Second, the application should be
 able to ramp up its resource usage in a timely manner in case it turns out that many executors are
 actually needed.
 
 #### Remove Policy
 
 The policy for removing executors is much simpler. A Spark application removes an executor when
 it has been idle for more than `spark.dynamicAllocation.executorIdleTimeout` seconds. Note that,
 under most circumstances, this condition is mutually exclusive with the request condition, in that
 an executor should not be idle if there are still pending tasks to be scheduled.
 
 ### Graceful Decommission of Executors
 
 Before dynamic allocation, a Spark executor exits either on failure or when the associated
 application has also exited. In both scenarios, all state associated with the executor is no
 longer needed and can be safely discarded. With dynamic allocation, however, the application
 is still running when an executor is explicitly removed. If the application attempts to access
 state stored in or written by the executor, it will have to perform a recompute the state. Thus,
 Spark needs a mechanism to decommission an executor gracefully by preserving its state before
 removing it.
 
 This requirement is especially important for shuffles. During a shuffle, the Spark executor first
 writes its own map outputs locally to disk, and then acts as the server for those files when other
 executors attempt to fetch them. In the event of stragglers, which are tasks that run for much
 longer than their peers, dynamic allocation may remove an executor before the shuffle completes,
 in which case the shuffle files written by that executor must be recomputed unnecessarily.
 
 The solution for preserving shuffle files is to use an external shuffle service, also introduced
 in Spark 1.2. This service refers to a long-running process that runs on each node of your cluster
 independently of your Spark applications and their executors. If the service is enabled, Spark
 executors will fetch shuffle files from the service instead of from each other. This means any
 shuffle state written by an executor may continue to be served beyond the executor's lifetime.
 
 In addition to writing shuffle files, executors also cache data either on disk or in memory.
 When an executor is removed, however, all cached data will no longer be accessible.  To mitigate this,
 by default executors containing cached data are never removed.  You can configure this behavior with
 `spark.dynamicAllocation.cachedExecutorIdleTimeout`.  In future releases, the cached data may be
 preserved through an off-heap storage similar in spirit to how shuffle files are preserved through
 the external shuffle service.
 
 # Scheduling Within an Application
 
 Inside a given Spark application (SparkContext instance), multiple parallel jobs can run simultaneously if
 they were submitted from separate threads. By "job", in this section, we mean a Spark action (e.g. `save`,
 `collect`) and any tasks that need to run to evaluate that action. Spark's scheduler is fully thread-safe
 and supports this use case to enable applications that serve multiple requests (e.g. queries for
 multiple users).
 
 By default, Spark's scheduler runs jobs in FIFO fashion. Each job is divided into "stages" (e.g. map and
 reduce phases), and the first job gets priority on all available resources while its stages have tasks to
 launch, then the second job gets priority, etc. If the jobs at the head of the queue don't need to use
 the whole cluster, later jobs can start to run right away, but if the jobs at the head of the queue are
 large, then later jobs may be delayed significantly.
 
 Starting in Spark 0.8, it is also possible to configure fair sharing between jobs. Under fair sharing,
 Spark assigns tasks between jobs in a "round robin" fashion, so that all jobs get a roughly equal share
 of cluster resources. This means that short jobs submitted while a long job is running can start receiving
 resources right away and still get good response times, without waiting for the long job to finish. This
 mode is best for multi-user settings.
 
 To enable the fair scheduler, simply set the `spark.scheduler.mode` property to `FAIR` when configuring
 a SparkContext:
 
 {% highlight scala %}
 val conf = new SparkConf().setMaster(...).setAppName(...)
 conf.set("spark.scheduler.mode", "FAIR")
 val sc = new SparkContext(conf)
 {% endhighlight %}
 
 ## Fair Scheduler Pools
 
 The fair scheduler also supports grouping jobs into _pools_, and setting different scheduling options
 (e.g. weight) for each pool. This can be useful to create a "high-priority" pool for more important jobs,
 for example, or to group the jobs of each user together and give _users_ equal shares regardless of how
 many concurrent jobs they have instead of giving _jobs_ equal shares. This approach is modeled after the
 [Hadoop Fair Scheduler](http://hadoop.apache.org/docs/current/hadoop-yarn/hadoop-yarn-site/FairScheduler.html).
 
 Without any intervention, newly submitted jobs go into a _default pool_, but jobs' pools can be set by
 adding the `spark.scheduler.pool` "local property" to the SparkContext in the thread that's submitting them.
 This is done as follows:
 
 {% highlight scala %}
 // Assuming sc is your SparkContext variable
 sc.setLocalProperty("spark.scheduler.pool", "pool1")
 {% endhighlight %}
 
 After setting this local property, _all_ jobs submitted within this thread (by calls in this thread
 to `RDD.save`, `count`, `collect`, etc) will use this pool name. The setting is per-thread to make
 it easy to have a thread run multiple jobs on behalf of the same user. If you'd like to clear the
 pool that a thread is associated with, simply call:
 
 {% highlight scala %}
 sc.setLocalProperty("spark.scheduler.pool", null)
 {% endhighlight %}
 
 ## Default Behavior of Pools
 
 By default, each pool gets an equal share of the cluster (also equal in share to each job in the default
 pool), but inside each pool, jobs run in FIFO order. For example, if you create one pool per user, this
 means that each user will get an equal share of the cluster, and that each user's queries will run in
 order instead of later queries taking resources from that user's earlier ones.
 
 ## Configuring Pool Properties
 
 Specific pools' properties can also be modified through a configuration file. Each pool supports three
 properties:
 
 * `schedulingMode`: This can be FIFO or FAIR, to control whether jobs within the pool queue up behind
   each other (the default) or share the pool's resources fairly.
 * `weight`: This controls the pool's share of the cluster relative to other pools. By default, all pools
   have a weight of 1. If you give a specific pool a weight of 2, for example, it will get 2x more
   resources as other active pools. Setting a high weight such as 1000 also makes it possible to implement
   _priority_ between pools---in essence, the weight-1000 pool will always get to launch tasks first
   whenever it has jobs active.
 * `minShare`: Apart from an overall weight, each pool can be given a _minimum shares_ (as a number of
   CPU cores) that the administrator would like it to have. The fair scheduler always attempts to meet
   all active pools' minimum shares before redistributing extra resources according to the weights.
   The `minShare` property can, therefore, be another way to ensure that a pool can always get up to a
   certain number of resources (e.g. 10 cores) quickly without giving it a high priority for the rest
   of the cluster. By default, each pool's `minShare` is 0.
 
 The pool properties can be set by creating an XML file, similar to `conf/fairscheduler.xml.template`,
 and either putting a file named `fairscheduler.xml` on the classpath, or setting `spark.scheduler.allocation.file` property in your
 [SparkConf](configuration.html#spark-properties).
 
 {% highlight scala %}
 conf.set("spark.scheduler.allocation.file", "/path/to/file")
 {% endhighlight %}
 
 The format of the XML file is simply a `<pool>` element for each pool, with different elements
 within it for the various settings. For example:
 
 {% highlight xml %}
 <?xml version="1.0"?>
 <allocations>
   <pool name="production">
     <schedulingMode>FAIR</schedulingMode>
     <weight>1</weight>
     <minShare>2</minShare>
   </pool>
   <pool name="test">
     <schedulingMode>FIFO</schedulingMode>
     <weight>2</weight>
     <minShare>3</minShare>
   </pool>
 </allocations>
 {% endhighlight %}
 
 A full example is also available in `conf/fairscheduler.xml.template`. Note that any pools not
 configured in the XML file will simply get default values for all settings (scheduling mode FIFO,
 weight 1, and minShare 0).
 
 ## Scheduling using JDBC Connections
 To set a [Fair Scheduler](job-scheduling.html#fair-scheduler-pools) pool for a JDBC client session,
 users can set the `spark.sql.thriftserver.scheduler.pool` variable:
 
 {% highlight SQL %}
 SET spark.sql.thriftserver.scheduler.pool=accounting;
 {% endhighlight %}
 
 ## Concurrent Jobs in PySpark
 
 PySpark, by default, does not support to synchronize PVM threads with JVM threads and 
 launching multiple jobs in multiple PVM threads does not guarantee to launch each job
 in each corresponding JVM thread. Due to this limitation, it is unable to set a different job group
 via `sc.setJobGroup` in a separate PVM thread, which also disallows to cancel the job via `sc.cancelJobGroup`
 later.
 
 In order to synchronize PVM threads with JVM threads, you should set `PYSPARK_PIN_THREAD` environment variable
 to `true`. This pinned thread mode allows one PVM thread has one corresponding JVM thread.
 
 However, currently it cannot inherit the local properties from the parent thread although it isolates
 each thread with its own local properties. To work around this, you should manually copy and set the
 local properties from the parent thread to the child thread when you create another thread in PVM.
 
 Note that `PYSPARK_PIN_THREAD` is currently experimental and not recommended for use in production.
 

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