brentondwalker opened a new pull request, #54326:
URL: https://github.com/apache/spark/pull/54326
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### What changes were proposed in this pull request?
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- core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala:
add checkBarrierTasks() function which returns true if any pending taskSets
have isBarrier set.
-
core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala:
in receive(), if a task has finished, check if barrier taskSets are pending.
If so, make a global resource offer (all executors).
-
core/src/test/scala/org/apache/spark/scheduler/BarrierTaskContextSuite.scala:
add unit test to check that the patch is working. The test is
performance-based. It checks the time it takes to run the test code, which is
drastically reduced by the patch.
### Why are the changes needed?
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If a barrier mode job (requiring _k_ cores) is pending, it is not serviced
immediately when _k_ cores become available, unless all _k_ available cores are
on the same executor.
The events that can cause a pending barrier job to be serviced are:
* revive timer (1 sec interval)
* job arrival
* rare state changes like executor add/remove
This leads to an average 500 ms delay in starting pending barrier stages.
Barrier jobs already have scaling issues because cores have to sit idle until
enough are available to meet the requirements. The extra 500 ms could have a
large effect on performance.
*More detail:*
When a task completes and the core becomes available,
CoarseGrainedSchedulerBackend only makes a resource offer of that one executor.
That is OK for normal tasks, but if a barrier taskSet is pending, the cores
needed to service it may be on other executors.
A global offer of all executors is only triggered by the events listed above.
*Why?*
Not clear if this is intentional or not.
If there are N cores, then the rate of task completions is proportional to
N. The number of executors to be iterated over for a global resource offer is
also proportional to N. So the load of doing global resource offers for every
task completion is O(N^2), which is not so nice. The current situation is an
understandable optimization.
*Suggested fix:*
Lots of ways this could be addressed. I implemented a minor patch that,
when a task finishes, checks if any barrier tasks are pending. if barrier
tasks are pending it makes a global offer.
This would keep the behavior the same for non-barrier jobs, but allow
barrier jobs to be serviced immediately when enough cores are available.
### Does this PR introduce _any_ user-facing change?
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No
### How was this patch tested?
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Ran all unit tests.
Wrote some small benchmarking code to verify before/after performance
difference. That code is in the JIRA ticket.
Added unit test based on that benchmarking code that verifies the change.
### Was this patch authored or co-authored using generative AI tooling?
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No
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