| Commit message (Collapse) | Author | Age | Files | Lines |
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Deduplicate Local and Cluster schedulers.
The code in LocalScheduler/LocalTaskSetManager was nearly identical
to the code in ClusterScheduler/ClusterTaskSetManager. The redundancy
made making updating the schedulers unnecessarily painful and error-
prone. This commit combines the two into a single TaskScheduler/
TaskSetManager.
Unfortunately the diff makes this change look much more invasive than it is -- TaskScheduler.scala is only superficially changed (names updated, overrides removed) from the old ClusterScheduler.scala, and the same with
TaskSetManager.scala.
Thanks @rxin for suggesting this change!
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Conflicts:
core/src/main/scala/org/apache/spark/scheduler/cluster/ClusterTaskSetManager.scala
core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala
core/src/test/scala/org/apache/spark/scheduler/TaskSetManagerSuite.scala
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LocalScheduler
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Conflicts:
core/src/main/scala/org/apache/spark/scheduler/cluster/ClusterTaskSetManager.scala
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When a task fails, we need to call reviveOffers() so that the
task can be rescheduled on a different machine. In the current code,
the state in ClusterTaskSetManager indicating which tasks are
pending may be updated after revive offers is called (there's a
race condition here), so when revive offers is called, the task set
manager does not yet realize that there are failed tasks that need
to be relaunched.
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Conflicts:
core/src/main/scala/org/apache/spark/scheduler/ClusterScheduler.scala
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Also changed the default maximum number of task failures to be
0 when running in local mode.
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The code in LocalScheduler/LocalTaskSetManager was nearly identical
to the code in ClusterScheduler/ClusterTaskSetManager. The redundancy
made making updating the schedulers unnecessarily painful and error-
prone. This commit combines the two into a single TaskScheduler/
TaskSetManager.
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Clean up shuffle files once their metadata is gone
Previously, we would only clean the in-memory metadata for consolidated shuffle files.
Additionally, fixes a bug where the Metadata Cleaner was ignoring type-specific TTLs.
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Previously, we would only clean the in-memory metadata for consolidated
shuffle files.
Additionally, fixes a bug where the Metadata Cleaner was ignoring type-
specific TTLs.
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Refactored the streaming scheduler and added StreamingListener interface
- Refactored the streaming scheduler for cleaner code. Specifically, the JobManager was renamed to JobScheduler, as it does the actual scheduling of Spark jobs to the SparkContext. The earlier Scheduler was renamed to JobGenerator, as it actually generates the jobs from the DStreams. The JobScheduler starts the JobGenerator. Also, moved all the scheduler related code from spark.streaming to spark.streaming.scheduler package.
- Implemented the StreamingListener interface, similar to SparkListener. The streaming version of StatusReportListener prints the batch processing time statistics (for now). Added StreamingListernerSuite to test it.
- Refactored streaming TestSuiteBase for deduping code in the other streaming testsuites.
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Conflicts:
streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala
streaming/src/main/scala/org/apache/spark/streaming/dstream/ForEachDStream.scala
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multiple batches.
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- Refactored Scheduler + JobManager to JobGenerator + JobScheduler and
added JobSet for cleaner code. Moved scheduler related code to
streaming.scheduler package.
- Added StreamingListener trait (similar to SparkListener) to enable
gathering to streaming stats like processing times and delays.
StreamingContext.addListener() to added listeners.
- Deduped some code in streaming tests by modifying TestSuiteBase, and
added StreamingListenerSuite.
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Added SPARK-968 implementation for review
Added SPARK-968 implementation for review
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table
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grouped by executor id
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Minor cleanup for standalone scheduler
See commit messages
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As a lonely child with no one to care for it... we had to put it down.
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Track and report task result serialisation time.
- DirectTaskResult now has a ByteBuffer valueBytes instead of a T value.
- DirectTaskResult now has a member function T value() that deserialises valueBytes.
- Executor serialises value into a ByteBuffer and passes it to DTR's ctor.
- Executor tracks the time taken to do so and puts it in a new field in TaskMetrics.
- StagePage now reports serialisation time from TaskMetrics along with the other things it reported.
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Add collectPartition to JavaRDD interface.
This interface is useful for implementing `take` from other language frontends where the data is serialized. Also remove `takePartition` from PythonRDD and use `collectPartition` in rdd.py.
Thanks @concretevitamin for the original change and tests.
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Change the implementation to use runJob instead of PartitionPruningRDD.
Also update the unit tests and the python take implementation
to use the new interface.
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Also remove takePartition from PythonRDD and use collectPartition in rdd.py.
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