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Posted to reviews@spark.apache.org by "brentondwalker (via GitHub)" <gi...@apache.org> on 2026/02/14 23:30:35 UTC
[PR] [WIP][SPARK-55531] [CORE] Start pending barrier stages immediately when enough cores are available [spark]
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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Re: [PR] [WIP][SPARK-55531] [CORE] Start pending barrier stages immediately when enough cores are available [spark]
Posted by "github-actions[bot] (via GitHub)" <gi...@apache.org>.
github-actions[bot] closed pull request #54326: [WIP][SPARK-55531] [CORE] Start pending barrier stages immediately when enough cores are available
URL: https://github.com/apache/spark/pull/54326
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Re: [PR] [WIP][SPARK-55531] [CORE] Start pending barrier stages immediately when enough cores are available [spark]
Posted by "github-actions[bot] (via GitHub)" <gi...@apache.org>.
github-actions[bot] commented on PR #54326:
URL: https://github.com/apache/spark/pull/54326#issuecomment-4588842515
We're closing this PR because it hasn't been updated in a while. This isn't a judgement on the merit of the PR in any way. It's just a way of keeping the PR queue manageable.
If you'd like to revive this PR, please reopen it and ask a committer to remove the Stale tag!
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