A new ActiveJob can be registered and deregistered. The list of ActiveJobs registered are available using mapStageJobs. When ShuffleMapStage is created , outputLocs is empty, i. The size of outputLocs is exactly the number of partitions of the RDD the stage runs on. The number of available outputs for the partitions of the ShuffleMapStage. ShuffleMapStage initializes the internal registries and counters.
Internally, findMissingPartitions uses outputLocs internal registry to find indices with empty lists of MapStatus. Intentionally repeat the last action that submits a new job with two stages with one being shared as already-being-computed. If there is no entry for a partition, that position is filled with null.
Output locations can be missing, i. Seq [ Int ]. If the job was the only job for the stage, the stage and the stage id gets cleaned up from the registries, i. After all cleaning using stageIdToStage as the source registry , if the stage belonged to the one and only job , you should see the following DEBUG message in the logs:. The final stage of the job is removed, i. Internally, markMapStageJobAsFinished marks the zeroth partition finished and increases the number of tasks finished in job. The job listener is notified about the 0th task succeeded.
The state of the job and independent stages are cleaned up.
Ben Hur view profile. In the end, with no tasks to submit for execution, submitMissingTasks submits waiting child stages for execution and exits. The lookup table for stages per their ids. The Prime Minister says that Ali has saved the world. If however there are missing parent stages for the stage , submitStage submits all the parent stages , and the stage is recorded in the internal waitingStages registry. Hi Marcello and Arjun,.
Checks whether partitions reference available partitions of the input rdd. Returns a 0-task JobWaiter when the number of partitions is zero. You may see a IllegalArgumentException thrown when the input partitions references partitions not in the input rdd:. Internally, submitMapStage increments nextJobId internal counter to get the job id. If the number of partition to compute is 0 , submitMapStage throws a SparkException:. Internally, runJob executes submitJob and then waits until a result comes using JobWaiter.
When the job fails, you should see the following INFO message in the logs and the exception that led to the failure is thrown. It repeats the process for the RDDs of the parent shuffle dependencies. Internally, getShuffleDependencies takes the direct shuffle dependencies of the input RDD and direct shuffle dependencies of all the parent non- ShuffleDependencies in the dependency chain aka RDD lineage. The internal failJobAndIndependentStages method fails the input job and all the stages that are only used by the job. Otherwise, for every stage, failJobAndIndependentStages finds the job ids the stage belongs to.
If no stages could be found or the job is not referenced by the stages, you should see the following ERROR message in the logs:. Internally, abortStage looks the failedStage stage up in the internal stageIdToStage registry and exits if there the stage was not registered earlier.
If it was, abortStage finds all the active jobs in the internal activeJobs registry with the final stage depending on the failedStage stage. All the active jobs that depend on the failed stage as calculated above and the stages that do not belong to other jobs aka independent stages are failed with the failure reason being "Job aborted due to stage failure: If there are no jobs depending on the failed stage, you should see the following INFO message in the logs:.
It allows Spark to release the current thread when posting happens and let the event loop handle events on a separate thread - asynchronously. Internally, submitStage first finds the earliest-created job id that needs the stage.
If there are no jobs that require the stage , submitStage aborts it with the reason:. It simply exits otherwise. With the stage ready for submission, submitStage calculates the list of missing parent stages of the stage sorted by their job ids. When the stage has no parent stages missing, you should see the following INFO message in the logs:.
If however there are missing parent stages for the stage , submitStage submits all the parent stages , and the stage is recorded in the internal waitingStages registry. A single stage can be re-executed in multiple attempts due to fault recovery. If TaskScheduler reports that a task failed because a map output file from a previous stage was lost, the DAGScheduler resubmits the lost stage. DAGScheduler will wait a small amount of time to see whether other nodes or tasks fail, then resubmit TaskSets for any lost stage s that compute the missing tasks.
The latest StageInfo for the most recent attempt for a stage is accessible through latestInfo. DAGScheduler computes where to run each task in a stage based on the preferred locations of its underlying RDDs , or the location of cached or shuffle data. The work is currently in progress.
DAGScheduler uses the following ScheduledThreadPoolExecutors with the policy of removing cancelled tasks from a work queue at time of cancellation:. ScheduledThreadPoolExecutor with core pool size 1. They are created using ThreadUtils. If the ShuffleMapStage is not available , it is added to the set of missing map stages. The input stage 's pendingPartitions internal field is cleared it is later filled out with the partitions to run tasks for. For the missing partitions, submitMissingTasks computes their task locality preferences , i. The locality information of a RDD is called preferred locations.
In case of non-fatal exceptions at this time while getting the locality information , submitMissingTasks creates a new stage attempt. Despite the failure to submit any tasks, submitMissingTasks does announce that at least there was an attempt on LiveListenerBus by posting a SparkListenerStageSubmitted message. The stage is removed from the internal runningStages collection of stages and submitMissingTasks exits. When no exception was thrown while computing the locality information for tasks , submitMissingTasks creates a new stage attempt and announces it on LiveListenerBus by posting a SparkListenerStageSubmitted message.
The serialized so-called task binary bytes are "wrapped" as a broadcast variable to make it available for executors to execute later on. Any NotSerializableException exceptions lead to aborting the stage with the reason being "Task not serializable: Any non-fatal exceptions lead to aborting the stage with the reason being "Task serialization failed" followed by the exception and removing the stage from the internal runningStages collection of stages.
With no exceptions along the way, submitMissingTasks computes a collection of tasks to execute for the missing partitions of the stage. Any non-fatal exceptions lead to aborting the stage with the reason being "Task creation failed" followed by the exception and removing the stage from the internal runningStages collection of stages. If there are tasks to submit for execution i.
If however there are no tasks to submit for execution, submitMissingTasks marks the stage as finished with no errorMessage. In the end, with no tasks to submit for execution, submitMissingTasks submits waiting child stages for execution and exits. If rdd is not in cacheLocs internal registry, getCacheLocs branches per its storage level. For NONE storage level i.
Otherwise, if not found, getPreferredLocsInternal requests rdd for the preferred locations of partition and returns them. If all the attempts fail to yield any non-empty result, getPreferredLocsInternal returns an empty collection of TaskLocations. The private updateAccumulators method merges the partial values of accumulators from a completed task into their "source" accumulators on the driver.
For named accumulators with the update value being a non-zero value, i. Yes, I am kidding!
Inside Da G: inside da g, queenzflip, trevor robinson, funhouse Paperback – Large Print, August 8, by Trevor Flip Robinson JR (Author), Marie Delus (Author), Mike Valentino (Editor) & 1 more. Browse our editors' picks for the best books of the year so far in fiction. ShuffleMapStage (aka shuffle map stage or simply map stage) is an intermediate stage in the physical execution DAG that corresponds to a ShuffleDependency.
DAGScheduler does three things in Spark thorough explanations follow: Computes an execution DAG , i. DAG of stages, for a job. Determines the preferred locations to run each task on. Handles failures due to shuffle output files being lost.
Array [ Int ], jobId: After all cleaning using stageIdToStage as the source registry , if the stage belonged to the one and only job , you should see the following DEBUG message in the logs: RDD [ T ], func: Seq [ Int ], callSite: JobWaiter [ U ]. Increments nextJobId internal job counter.
Posts a JobSubmitted event and returns a JobWaiter. You may see a IllegalArgumentException thrown when the input partitions references partitions not in the input rdd: