TaskSchedulerImpl — Default TaskScheduler

TaskSchedulerImpl is the default TaskScheduler.

TaskSchedulerImpl can schedule tasks for multiple types of cluster managers by means of SchedulerBackends.

When a Spark application starts (and so an instance of SparkContext is created) TaskSchedulerImpl with a SchedulerBackend and DAGScheduler are created and soon started.

taskschedulerimpl sparkcontext schedulerbackend dagscheduler.png

Figure 1. TaskSchedulerImpl and Other Services

TaskSchedulerImpl generates tasks for executor resource offers.

TaskSchedulerImpl can track racks per host and port (that however is only used with Hadoop YARN cluster manager).

Using spark.scheduler.mode setting you can select the scheduling policy.

TaskSchedulerImpl submits tasks using SchedulableBuilders.

Table 1. TaskSchedulerImpl’s Internal Registries and Counters
Name	Description
`hasLaunchedTask`	Flag…FIXME Used when…FIXME
`dagScheduler`	DAGScheduler Used when…FIXME
`executorIdToHost`	Lookup table of hosts per executor. Used when…FIXME
`executorIdToRunningTaskIds`	Lookup table of running tasks per executor. Used when…FIXME
`executorIdToTaskCount`	Lookup table of the number of running tasks by executor.
`executorsByHost`	Collection of executors per host
`hostToExecutors`	Lookup table of executors per hosts in a cluster. Used when…FIXME
`hostsByRack`	Lookup table of hosts per rack. Used when…FIXME
`nextTaskId`	The next task id counting from `0`. Used when `TaskSchedulerImpl`…
`taskSetsByStageIdAndAttempt`	Lookup table of TaskSet by stage and attempt ids.
`taskIdToExecutorId`	Lookup table of executor by task id.
`taskIdToTaskSetManager`	Registry of active TaskSetManager per task id.

Tip	Enable `INFO` or `DEBUG` logging levels for `org.apache.spark.scheduler.TaskSchedulerImpl` logger to see what happens inside. Add the following line to `conf/log4j.properties`: `log4j.logger.org.apache.spark.scheduler.TaskSchedulerImpl=DEBUG` Refer to Logging.

`getExecutorsAliveOnHost` Method

Caution

FIXME

`createTaskSetManager` Method

Caution

FIXME

Note	`createTaskSetManager` is used exclusively when `TaskSchedulerImpl` submits tasks (for a given `TaskSet`).

`isExecutorAlive` Method

Caution

FIXME

`hasExecutorsAliveOnHost` Method

Caution

FIXME

`hasHostAliveOnRack` Method

Caution

FIXME

Creating TaskDescriptions For Available Executor Resource Offers (with CPU Cores) — `resourceOffers` Method

resourceOffers(offers: Seq[WorkerOffer]): Seq[Seq[TaskDescription]]

resourceOffers takes the resources offers (as WorkerOffers) and generates a collection of tasks (as TaskDescription) to launch (given the resources available).

Note	WorkerOffer represents a resource offer with CPU cores free to use on an executor.

Figure 2. Processing Executor Resource Offers

Internally, resourceOffers first updates hostToExecutors and executorIdToHost lookup tables to record new hosts and executors (given the input offers).

For new executors (not in executorIdToRunningTaskIds) resourceOffers notifies DAGScheduler that an executor was added.

Note	`TaskSchedulerImpl` uses `resourceOffers` to track active executors.

Caution

FIXME a picture with executorAdded call from TaskSchedulerImpl to DAGScheduler.

resourceOffers requests BlacklistTracker to applyBlacklistTimeout and filters out offers on blacklisted nodes and executors.

Note	`resourceOffers` uses the optional BlacklistTracker that was given when `TaskSchedulerImpl` was created.

Caution

FIXME Expand on blacklisting

resourceOffers then randomly shuffles offers (to evenly distribute tasks across executors and avoid over-utilizing some executors) and initializes the local data structures tasks and availableCpus (as shown in the figure below).

TaskSchedulerImpl resourceOffers internal structures.png

Figure 3. Internal Structures of resourceOffers with 5 WorkerOffers (with 4, 2, 0, 3, 2 free cores)

resourceOffers takes TaskSets in scheduling order from top-level Schedulable Pool.

TaskSchedulerImpl resourceOffers rootPool getSortedTaskSetQueue.png

Figure 4. TaskSchedulerImpl Requesting TaskSets (as TaskSetManagers) from Root Pool

Note

rootPool is configured when TaskSchedulerImpl is initialized.

rootPool is a part of the TaskScheduler Contract and exclusively managed by SchedulableBuilders, i.e. FIFOSchedulableBuilder and FairSchedulableBuilder (that manage registering TaskSetManagers with the root pool).

TaskSetManager manages execution of the tasks in a single TaskSet that represents a single Stage.

For every TaskSetManager (in scheduling order), you should see the following DEBUG message in the logs:

DEBUG TaskSchedulerImpl: parentName: [name], name: [name], runningTasks: [count]

Only if a new executor was added, resourceOffers notifies every TaskSetManager about the change (to recompute locality preferences).

resourceOffers then takes every TaskSetManager (in scheduling order) and offers them each node in increasing order of locality levels (per TaskSetManager’s valid locality levels).

Note	A `TaskSetManager` computes locality levels of the tasks it manages.

For every TaskSetManager and the TaskSetManager's valid locality level, resourceOffers resourceOfferSingleTaskSet as long as the TaskSetManager manages to launch a task (given the locality level).

If resourceOffers did not manage to offer resources to a TaskSetManager so it could launch any task, resourceOffers requests the TaskSetManager to abort the TaskSet if completely blacklisted.

When resourceOffers managed to launch a task, the internal hasLaunchedTask flag gets enabled (that effectively means what the name says "there were executors and I managed to launch a task").

Note	`resourceOffers` is used when: `CoarseGrainedSchedulerBackend` (via RPC endpoint) makes executor resource offers `LocalEndpoint` revives resource offers Spark on Mesos' `MesosFineGrainedSchedulerBackend` does `resourceOffers`

TaskLocality — Task Locality Preference

TaskLocality represents a task locality preference and can be one of the following (from most localized to the widest):

PROCESS_LOCAL
NODE_LOCAL
NO_PREF
RACK_LOCAL
ANY

WorkerOffer — Free CPU Cores on Executor

WorkerOffer(executorId: String, host: String, cores: Int)

WorkerOffer represents a resource offer with free CPU cores available on an executor (by executorId) on a host.

`executorLost` Method

Caution

FIXME

`mapOutputTracker`

Caution

FIXME

`starvationTimer`

Caution

FIXME

`executorHeartbeatReceived` Method

executorHeartbeatReceived(
  execId: String,
  accumUpdates: Array[(Long, Seq[AccumulatorV2[_, _]])],
  blockManagerId: BlockManagerId): Boolean

executorHeartbeatReceived is…

Caution

FIXME

Note	`executorHeartbeatReceived` is a part of the TaskScheduler Contract.

Cancelling Tasks for Stage — `cancelTasks` Method

cancelTasks(stageId: Int, interruptThread: Boolean): Unit

cancelTasks cancels all tasks submitted for execution in a stage stageId.

Note	It is currently called by `DAGScheduler` when it cancels a stage.

`handleSuccessfulTask` Method

handleSuccessfulTask(
  taskSetManager: TaskSetManager,
  tid: Long,
  taskResult: DirectTaskResult[_]): Unit

handleSuccessfulTask simply forwards the call to the input taskSetManager (passing tid and taskResult).

Note	`handleSuccessfulTask` is called when `TaskSchedulerGetter` has managed to deserialize the task result of a task that finished successfully.

`handleTaskGettingResult` Method

handleTaskGettingResult(taskSetManager: TaskSetManager, tid: Long): Unit

handleTaskGettingResult simply forwards the call to the taskSetManager.

Note	`handleTaskGettingResult` is used to inform that `TaskResultGetter` enqueues a successful task with `IndirectTaskResult` task result (and so is about to fetch a remote block from a `BlockManager`).

`applicationAttemptId` Method

applicationAttemptId(): Option[String]

Caution

FIXME

`schedulableBuilder` Attribute

schedulableBuilder is a SchedulableBuilder for the TaskSchedulerImpl.

It is set up when a TaskSchedulerImpl is initialized and can be one of two available builders:

FIFOSchedulableBuilder when scheduling policy is FIFO (which is the default scheduling policy).
FairSchedulableBuilder for FAIR scheduling policy.

Note	Use spark.scheduler.mode setting to select the scheduling policy.

Tracking Racks per Hosts and Ports — `getRackForHost` Method

getRackForHost(value: String): Option[String]

getRackForHost is a method to know about the racks per hosts and ports. By default, it assumes that racks are unknown (i.e. the method returns None).

Note	It is overriden by the YARN-specific TaskScheduler YarnScheduler.

getRackForHost is currently used in two places:

TaskSchedulerImpl.resourceOffers to track hosts per rack (using the internal hostsByRack registry) while processing resource offers.
TaskSchedulerImpl.removeExecutor to…FIXME
TaskSetManager.addPendingTask, TaskSetManager.dequeueTask, and TaskSetManager.dequeueSpeculativeTask

Creating TaskSchedulerImpl Instance

TaskSchedulerImpl takes the following when created:

SparkContext
Acceptable number of task failures
optional BlacklistTracker
optional isLocal flag to differentiate between local and cluster run modes (defaults to false)

TaskSchedulerImpl initializes the internal registries and counters.

Note	There is another `TaskSchedulerImpl` constructor that requires a SparkContext object only and sets maxTaskFailures to spark.task.maxFailures or, if not set, defaults to `4`.

TaskSchedulerImpl sets schedulingMode to the value of spark.scheduler.mode setting (defaults to FIFO).

Note	`schedulingMode` is part of TaskScheduler Contract.

Failure to set schedulingMode results in a SparkException:

Unrecognized spark.scheduler.mode: [schedulingModeConf]

Ultimately, TaskSchedulerImpl creates a TaskResultGetter.

Initializing TaskSchedulerImpl — `initialize` Method

initialize(backend: SchedulerBackend): Unit

initialize initializes a TaskSchedulerImpl object.

Figure 5. TaskSchedulerImpl initialization

Note	`initialize` is called while SparkContext is being created and creates `SchedulerBackend` and `TaskScheduler`.

initialize saves the reference to the current SchedulerBackend (as backend) and sets rootPool to be an empty-named Pool with already-initialized schedulingMode (while creating a TaskSchedulerImpl object), initMinShare and initWeight as 0.

Note	`schedulingMode` and `rootPool` are a part of TaskScheduler Contract.

It then creates the internal SchedulableBuilder object (as schedulableBuilder) based on schedulingMode:

FIFOSchedulableBuilder for FIFO scheduling mode
FairSchedulableBuilder for FAIR scheduling mode

With the schedulableBuilder object created, initialize requests it to build pools.

Caution

FIXME Why are rootPool and schedulableBuilder created only now? What do they need that it is not available when TaskSchedulerImpl is created?

Starting TaskSchedulerImpl — `start` Method

As part of initialization of a SparkContext, TaskSchedulerImpl is started (using start from the TaskScheduler Contract).

start(): Unit

start starts the scheduler backend.

Figure 6. Starting TaskSchedulerImpl in Spark Standalone

start also starts task-scheduler-speculation executor service.

task-scheduler-speculation Scheduled Executor Service — `speculationScheduler` Internal Attribute

speculationScheduler is a java.util.concurrent.ScheduledExecutorService with the name task-scheduler-speculation for speculative execution of tasks.

When TaskSchedulerImpl starts (in non-local run mode) with spark.speculation enabled, speculationScheduler is used to schedule checkSpeculatableTasks to execute periodically every spark.speculation.interval after the initial spark.speculation.interval passes.

speculationScheduler is shut down when TaskSchedulerImpl stops.

Checking for Speculatable Tasks — `checkSpeculatableTasks` Method

checkSpeculatableTasks(): Unit

checkSpeculatableTasks requests rootPool to check for speculatable tasks (if they ran for more than 100 ms) and, if there any, requests SchedulerBackend to revive offers.

Note	`checkSpeculatableTasks` is executed periodically as part of speculative execution of tasks.

Acceptable Number of Task Failures — `maxTaskFailures` Attribute

The acceptable number of task failures (maxTaskFailures) can be explicitly defined when creating TaskSchedulerImpl instance or based on spark.task.maxFailures setting that defaults to 4 failures.

Note	It is exclusively used when submitting tasks through TaskSetManager.

Cleaning up After Removing Executor — `removeExecutor` Internal Method

removeExecutor(executorId: String, reason: ExecutorLossReason): Unit

removeExecutor removes the executorId executor from the following internal registries: executorIdToTaskCount, executorIdToHost, executorsByHost, and hostsByRack. If the affected hosts and racks are the last entries in executorsByHost and hostsByRack, appropriately, they are removed from the registries.

Unless reason is LossReasonPending, the executor is removed from executorIdToHost registry and TaskSetManagers get notified.

Note	The internal `removeExecutor` is called as part of statusUpdate and executorLost.

Intercepting Nearly-Completed SparkContext Initialization — `postStartHook` Callback

postStartHook is a custom implementation of postStartHook from the TaskScheduler Contract that waits until a scheduler backend is ready (using the internal blocking waitBackendReady).

Note	`postStartHook` is used when SparkContext is created (before it is fully created) and YarnClusterScheduler.postStartHook.

Stopping TaskSchedulerImpl — `stop` Method

stop(): Unit

stop() stops all the internal services, i.e. task-scheduler-speculation executor service, SchedulerBackend, TaskResultGetter, and starvationTimer timer.

Calculating Default Level of Parallelism — `defaultParallelism` Method

Default level of parallelism is a hint for sizing jobs. It is a part of the TaskScheduler contract and used by SparkContext to create RDDs with the right number of partitions when not specified explicitly.

TaskSchedulerImpl uses SchedulerBackend.defaultParallelism() to calculate the value, i.e. it just passes it along to a scheduler backend.

Submitting Tasks (from TaskSet) — `submitTasks` Method

Note	`submitTasks` is a part of TaskScheduler Contract.

submitTasks(taskSet: TaskSet): Unit

submitTasks creates a TaskSetManager for the input TaskSet and adds it to the Schedulable root pool.

Note	The root pool can be a single flat linked queue (in FIFO scheduling mode) or a hierarchy of pools of `Schedulables` (in FAIR scheduling mode).

It makes sure that the requested resources, i.e. CPU and memory, are assigned to the Spark application for a non-local environment before requesting the current SchedulerBackend to revive offers.

Figure 7. TaskSchedulerImpl.submitTasks

Note	If there are tasks to launch for missing partitions in a stage, DAGScheduler executes `submitTasks` (see submitMissingTasks for Stage and Job).

When submitTasks is called, you should see the following INFO message in the logs:

INFO TaskSchedulerImpl: Adding task set [id] with [length] tasks

It creates a new TaskSetManager for the input taskSet and the acceptable number of task failures.

Note	The acceptable number of task failures is specified when a TaskSchedulerImpl is created.

Note	A `TaskSet` knows the tasks to execute (as `tasks`) and stage id (as `stageId`) the tasks belong to. Read TaskSets.

The TaskSet is registered in the internal taskSetsByStageIdAndAttempt registry with the TaskSetManager.

If there is more than one active TaskSetManager for the stage, a IllegalStateException is thrown with the message:

more than one active taskSet for stage [stage]: [TaskSet ids]

Note	`TaskSetManager` is considered active when it is not a zombie.

The TaskSetManager is added to the Schedulable pool (via SchedulableBuilder).

When the method is called the very first time (hasReceivedTask is false) in cluster mode only (i.e. isLocal of the TaskSchedulerImpl is false), starvationTimer is scheduled to execute after spark.starvation.timeout to ensure that the requested resources, i.e. CPUs and memory, were assigned by a cluster manager.

Note	After the first `spark.starvation.timeout` passes, the internal `hasReceivedTask` flag becomes `true`.

Every time the starvation timer thread is executed and hasLaunchedTask flag is false, the following WARN message is printed out to the logs:

WARN Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources

Otherwise, when the hasLaunchedTask flag is true the timer thread cancels itself.

Ultimately, submitTasks requests the SchedulerBackend to revive offers.

Tip	Use `dag-scheduler-event-loop` thread to step through the code in a debugger.

`resourceOfferSingleTaskSet` Method

resourceOfferSingleTaskSet(
  taskSet: TaskSetManager,
  maxLocality: TaskLocality,
  shuffledOffers: Seq[WorkerOffer],
  availableCpus: Array[Int],
  tasks: Seq[ArrayBuffer[TaskDescription]]): Boolean

resourceOfferSingleTaskSet is a private helper method that is executed when…

`statusUpdate` Method

statusUpdate(
  tid: Long,
  state: TaskState.TaskState,
  serializedData: ByteBuffer): Unit

statusUpdate removes a lost executor when a tid task has failed. For all task states, statusUpdate removes the tid task from the internal registries, i.e. taskIdToTaskSetManager and taskIdToExecutorId, and decrements the number of running tasks in executorIdToTaskCount registry. For tid in FINISHED, FAILED, KILLED or LOST states, statusUpdate informs the TaskSetManager that the task can be removed from the running tasks. For tid in FINISHED state statusUpdate schedules an asynchrounous task to deserialize the task result (and notify TaskSchedulerImpl) while for FAILED, KILLED or LOST states it calls TaskResultGetter.enqueueFailedTask. Ultimately, given an executor that has been lost, statusUpdate informs informs DAGScheduler that the executor was lost and SchedulerBackend is requested to revive offers.

For tid task in LOST state and an executor still assigned for the task and tracked in executorIdToTaskCount registry, the executor is removed (with reason Task [tid] was lost, so marking the executor as lost as well.).

Caution

FIXME Why is SchedulerBackend.reviveOffers() called only for lost executors?

statusUpdate looks up the TaskSetManager for tid (in taskIdToTaskSetManager registry).

When the TaskSetManager is found and the task is in a finished state, the task is removed from the internal registries, i.e. taskIdToTaskSetManager and taskIdToExecutorId, and the number of currently running tasks for the executor is decremented (in executorIdToTaskCount registry).

For a task in FINISHED state, the task is removed from the running tasks and an asynchrounous task is scheduled to deserialize the task result (and notify TaskSchedulerImpl).

For a task in FAILED, KILLED, or LOST state, the task is removed from the running tasks (as for the FINISHED state) and then TaskResultGetter.enqueueFailedTask is called.

If the TaskSetManager for tid could not be found (in taskIdToTaskSetManager registry), you should see the following ERROR message in the logs:

ERROR Ignoring update with state [state] for TID [tid] because its task set is gone (this is likely the result of receiving duplicate task finished status updates)

Any exception is caught and reported as ERROR message in the logs:

ERROR Exception in statusUpdate

Ultimately, for tid task with an executor marked as lost, statusUpdate informs DAGScheduler that the executor was lost (with SlaveLost and the reason Task [tid] was lost, so marking the executor as lost as well.) and SchedulerBackend is requested to revive offers.

Caution

FIXME image with scheduler backends calling TaskSchedulerImpl.statusUpdate.

Note	`statusUpdate` is used when CoarseGrainedSchedulerBackend, `LocalSchedulerBackend` and `MesosFineGrainedSchedulerBackend` inform about changes in task states.

Notifying TaskSetManager that Task Failed — `handleFailedTask` Method

handleFailedTask(
  taskSetManager: TaskSetManager,
  tid: Long,
  taskState: TaskState,
  reason: TaskFailedReason): Unit

handleFailedTask notifies taskSetManager that tid task has failed and, only when taskSetManager is not in zombie state and tid is not in KILLED state, requests SchedulerBackend to revive offers.

Note	`handleFailedTask` is called when `TaskResultGetter` deserializes a `TaskFailedReason` for a failed task.

`taskSetFinished` Method

taskSetFinished(manager: TaskSetManager): Unit

taskSetFinished looks all TaskSets up by the stage id (in taskSetsByStageIdAndAttempt registry) and removes the stage attempt from them, possibly with removing the entire stage record from taskSetsByStageIdAndAttempt registry completely (if there are no other attempts registered).

taskschedulerimpl tasksetmanager tasksetfinished.png

Figure 8. TaskSchedulerImpl.taskSetFinished is called when all tasks are finished

Note	A `TaskSetManager` manages a `TaskSet` for a stage.

taskSetFinished then removes manager from the parent’s schedulable pool.

You should see the following INFO message in the logs:

INFO Removed TaskSet [id], whose tasks have all completed, from pool [name]

Note	`taskSetFinished` method is called when `TaskSetManager` has received the results of all the tasks in a `TaskSet`.

Notifying DAGScheduler About New Executor — `executorAdded` Method

executorAdded(execId: String, host: String)

executorAdded just notifies DAGScheduler that an executor was added.

Caution

FIXME Image with a call from TaskSchedulerImpl to DAGScheduler, please.

Note	`executorAdded` uses DAGScheduler that was given when setDAGScheduler.

Waiting Until SchedulerBackend is Ready — `waitBackendReady` Internal Method

waitBackendReady(): Unit

waitBackendReady waits until a SchedulerBackend is ready.

Note	`SchedulerBackend` is ready by default.

waitBackendReady keeps checking the status every 100 milliseconds until SchedulerBackend is ready or the SparkContext is stopped.

If the SparkContext happens to be stopped while waiting, waitBackendReady reports a IllegalStateException:

Spark context stopped while waiting for backend

Note	`waitBackendReady` is used when `TaskSchedulerImpl` is notified that `SparkContext` is near to get fully initialized.

Settings

Table 2. Spark Properties
Spark Property	Default Value	Description
`spark.task.maxFailures`	`4` in cluster mode `1` in local except local-with-retries	The number of individual task failures before giving up on the entire TaskSet and the job afterwards.
`spark.task.cpus`	`1`	The number of CPU cores per task.
`spark.starvation.timeout`	`15s`	Threshold above which Spark warns a user that an initial TaskSet may be starved.
`spark.scheduler.mode`	`FIFO`	A case-insensitive name of the scheduling mode — `FAIR`, `FIFO`, or `NONE`. NOTE: Only `FAIR` and `FIFO` are supported by `TaskSchedulerImpl`. See schedulableBuilder.

TaskSchedulerImpl — Default TaskScheduler