feat: add fair_unified_task_shared memory pool to fix 2x memory allocation [experimental]#3924
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andygrove wants to merge 1 commit intoapache:mainfrom
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feat: add fair_unified_task_shared memory pool to fix 2x memory allocation [experimental]#3924andygrove wants to merge 1 commit intoapache:mainfrom
andygrove wants to merge 1 commit intoapache:mainfrom
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…ation When Comet executes a shuffle, it creates two separate native plans (the child plan and the shuffle writer plan) that run concurrently in a pipelined fashion. Previously, each plan got its own memory pool at the full per-task limit, effectively allowing 2x the intended memory to be consumed. The new `fair_unified_task_shared` pool type shares a single CometFairMemoryPool across all native plans within the same Spark task. This ensures the total memory stays within the per-task limit while dynamically distributing memory among operators based on how many register as memory consumers (e.g. if the child plan is a simple scan+filter, the shuffle writer gets 100% of the pool). This is now the default for off-heap mode. Closes apache#3921 Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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Which issue does this PR close?
Closes #3921
Rationale for this change
When Comet executes a shuffle, it creates two separate native plans (the child plan and the shuffle writer plan) that run concurrently in a pipelined fashion. Previously, each plan got its own memory pool at the full per-task limit, effectively allowing 2x the intended memory to be consumed.
The new
fair_unified_task_sharedpool type shares a single CometFairMemoryPool across all native plans within the same Spark task. This ensures the total memory stays within the per-task limit while dynamically distributing memory among operators based on how many register as memory consumers (e.g. if the child plan is a simple scan+filter, the shuffle writer gets 100% of the pool).This is now the default for off-heap mode.
What changes are included in this PR?
How are these changes tested?