Revert "[SPARK-48628][CORE] Add task peak on/off heap memory metrics" #47747
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This reverts commit 717a6da.
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cc @liuzqt , @JoshRosen, @cloud-fan , @jiangxb1987 , @Ngone51 , @mridulm from #47192 |
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cloud-fan
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Aug 14, 2024
LuciferYang
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Aug 14, 2024
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Thank you, @cloud-fan and @LuciferYang . |
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All relevant tests passed. |
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### What changes were proposed in this pull request? This PR is trying to revive #47192, which was [reverted](#47747) due to regression in `ExternalAppendOnlyUnsafeRowArrayBenchmark`. **Root cause** We eventually decided to aggregate peak memory usage from all consumers on each `acquireExecutionMemory` invocation. (see [this discussion](#47192 (comment))), which is O(n) complexity where `n` is the number of consumers. `ExternalAppendOnlyUnsafeRowArrayBenchmark` is implemented in a way that all iterations are run in a single task context, therefore the number of consumers is exploding. Notice that `TaskMemoryManager.consumers` is never cleaned up the whole lifecycle, and `TaskMemoryManager.acquireExecutionMemory` is a very frequent operation, doing a linear complexity(in terms of number of consumers) operation here might not be a good choice. This benchmark might be a corner case, but it's still possible to have a large number of consumers in a large query plan. I fallback to the previous implementation: maintain current execution memory with an extra lock. cc Ngone51 #### Benchmark result [ExternalAppendOnlyUnsafeRowArrayBenchmark-results](https://github.com/liuzqt/spark/actions/runs/10415213026) [ExternalAppendOnlyUnsafeRowArrayBenchmark-jdk21-results](https://github.com/liuzqt/spark/actions/runs/10414246805) ### Why are the changes needed? ### Does this PR introduce _any_ user-facing change? NO ### How was this patch tested? New unit tests. ### Was this patch authored or co-authored using generative AI tooling? NO Closes #47776 from liuzqt/SPARK-48628. Authored-by: Ziqi Liu <[email protected]> Signed-off-by: Josh Rosen <[email protected]>
a0x8o
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### What changes were proposed in this pull request? This PR is trying to revive apache/spark#47192, which was [reverted](apache/spark#47747) due to regression in `ExternalAppendOnlyUnsafeRowArrayBenchmark`. **Root cause** We eventually decided to aggregate peak memory usage from all consumers on each `acquireExecutionMemory` invocation. (see [this discussion](apache/spark#47192 (comment))), which is O(n) complexity where `n` is the number of consumers. `ExternalAppendOnlyUnsafeRowArrayBenchmark` is implemented in a way that all iterations are run in a single task context, therefore the number of consumers is exploding. Notice that `TaskMemoryManager.consumers` is never cleaned up the whole lifecycle, and `TaskMemoryManager.acquireExecutionMemory` is a very frequent operation, doing a linear complexity(in terms of number of consumers) operation here might not be a good choice. This benchmark might be a corner case, but it's still possible to have a large number of consumers in a large query plan. I fallback to the previous implementation: maintain current execution memory with an extra lock. cc Ngone51 #### Benchmark result [ExternalAppendOnlyUnsafeRowArrayBenchmark-results](https://github.com/liuzqt/spark/actions/runs/10415213026) [ExternalAppendOnlyUnsafeRowArrayBenchmark-jdk21-results](https://github.com/liuzqt/spark/actions/runs/10414246805) ### Why are the changes needed? ### Does this PR introduce _any_ user-facing change? NO ### How was this patch tested? New unit tests. ### Was this patch authored or co-authored using generative AI tooling? NO Closes #47776 from liuzqt/SPARK-48628. Authored-by: Ziqi Liu <[email protected]> Signed-off-by: Josh Rosen <[email protected]>
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What changes were proposed in this pull request?
This reverts commit 717a6da.
Why are the changes needed?
To fix a performance regression.
During the regular performance audit,
ExternalAppendOnlyUnsafeRowArrayBenchmarkdetected a performance regression caused by SPARK-48626.Does this PR introduce any user-facing change?
No. This is not released yet.
How was this patch tested?
Pass the CIs.
Was this patch authored or co-authored using generative AI tooling?
No.