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.
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.
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.
Change the implementation to use runJob instead of PartitionPruningRDD.
Also update the unit tests and the python take implementation
to use the new interface.
despite having a low number of nodes and relatively small workload (16 nodes, <1.5 TB data).
This would cause an entire job to fail at the beginning of the reduce phase.
There is no particular reason for this value to be small as a timeout should only occur
in an exceptional situation.
Also centralized the reading of spark.akka.askTimeout to AkkaUtils (surely this can later
be cleaned up to use Typesafe).
Finally, deleted some lurking implicits. If anyone can think of a reason they should still
be there, please let me know.
Fix for spark.task.maxFailures not enforced correctly.
Docs at http://spark.incubator.apache.org/docs/latest/configuration.html say:
```
spark.task.maxFailures
Number of individual task failures before giving up on the job. Should be greater than or equal to 1. Number of allowed retries = this value - 1.
```
Previous implementation worked incorrectly. When for example `spark.task.maxFailures` was set to 1, the job was aborted only after the second task failure, not after the first one.
- 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.
