Kay Ousterhout created SPARK-18890:
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Summary: Do all task serialization in CoarseGrainedExecutorBackend
thread (rather than TaskSchedulerImpl)
Key: SPARK-18890
URL: https://issues.apache.org/jira/browse/SPARK-18890
Project: Spark
Issue Type: Bug
Components: Scheduler
Affects Versions: 2.1.0
Reporter: Kay Ousterhout
Priority: Minor
As part of benchmarking this change:
https://github.com/apache/spark/pull/15505 and alternatives, [~shivaram] and I
found that moving task serialization from TaskSetManager (which happens as part
of the TaskSchedulerImpl's thread) to CoarseGranedSchedulerBackend leads to
approximately a 10% reduction in job runtime for a job that counted 10,000
partitions (that each had 1 int) using 20 machines. Similar performance
improvements were reported in the pull request linked above. This would appear
to be because the TaskSchedulerImpl thread is the bottleneck, so moving
serialization to CGSB reduces runtime. This change may *not* improve runtime
(and could potentially worsen runtime) in scenarios where the CGSB thread is
the bottleneck (e.g., if tasks are very large, so calling launch to send the
tasks to the executor blocks on the network).
One benefit of implementing this change is that it makes it easier to
parallelize the serialization of tasks (different tasks could be serialized by
different threads). Another benefit is that all of the serialization occurs in
the same place (currently, the Task is serialized in TaskSetManager, and the
TaskDescription is serialized in CGSB).
I'm not totally convinced we should fix this because it seems like there are
better ways of reducing the serialization time (e.g., by re-using the Task
object within a stage) but I wanted to open this JIRA to document the
discussion.
cc [~witgo]
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