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feat: Support prefetch in index lookup join #12611
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This pull request was exported from Phabricator. Differential Revision: D70909786 |
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Summary: This PR adds prefetch for index join with query config. The index join operator can prefetch up to the configured prefetch limit to enable: (1) parallel prefetches at backend for parallel execution (1-1) in case of multiple backend shards or (1-2) enable backend to batch multiple requests to improve throughput; (2) pipeline the table scan and index lookup execution in the same driver pipeline. The table scan is sync executed while index lookup is async. This achieve pipelining without relying exchange which might cause non-deterministic execution and Meta internal use case needs deterministic execution for checkpointing. With Meta internal testing, this can achieve 2x throughput improvement (measured in rows per second) with 33% memory overhead with up to 4 batches prefetch. The follow is to add memory based prefetch throttling to integrate with Meta internal ML use case and memory pool wiring to ease performance (memory overhead) analysis Reviewed By: wenqiwooo Differential Revision: D70909786
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This pull request was exported from Phabricator. Differential Revision: D70909786 |
xiaoxmeng
added a commit
to xiaoxmeng/velox
that referenced
this pull request
Mar 12, 2025
Summary: This PR adds prefetch for index join with query config. The index join operator can prefetch up to the configured prefetch limit to enable: (1) parallel prefetches at backend for parallel execution (1-1) in case of multiple backend shards or (1-2) enable backend to batch multiple requests to improve throughput; (2) pipeline the table scan and index lookup execution in the same driver pipeline. The table scan is sync executed while index lookup is async. This achieve pipelining without relying exchange which might cause non-deterministic execution and Meta internal use case needs deterministic execution for checkpointing. With Meta internal testing, this can achieve 2x throughput improvement (measured in rows per second) with 33% memory overhead with up to 4 batches prefetch. The follow is to add memory based prefetch throttling to integrate with Meta internal ML use case and memory pool wiring to ease performance (memory overhead) analysis Reviewed By: wenqiwooo Differential Revision: D70909786
Summary: This PR adds prefetch for index join with query config. The index join operator can prefetch up to the configured prefetch limit to enable: (1) parallel prefetches at backend for parallel execution (1-1) in case of multiple backend shards or (1-2) enable backend to batch multiple requests to improve throughput; (2) pipeline the table scan and index lookup execution in the same driver pipeline. The table scan is sync executed while index lookup is async. This achieve pipelining without relying exchange which might cause non-deterministic execution and Meta internal use case needs deterministic execution for checkpointing. With Meta internal testing, this can achieve 2x throughput improvement (measured in rows per second) with 33% memory overhead with up to 4 batches prefetch. The follow is to add memory based prefetch throttling to integrate with Meta internal ML use case and memory pool wiring to ease performance (memory overhead) analysis Reviewed By: wenqiwooo Differential Revision: D70909786
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This pull request was exported from Phabricator. Differential Revision: D70909786 |
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Summary:
This PR adds prefetch for index join with query config. The index join operator can prefetch up to the
configured prefetch limit to enable: (1) parallel prefetches at backend for parallel execution (1-1) in case
of multiple backend shards or (1-2) enable backend to batch multiple requests to improve throughput;
(2) pipeline the table scan and index lookup execution in the same driver pipeline. The table scan is sync
executed while index lookup is async. This achieve pipelining without relying exchange which might cause
non-deterministic execution and Meta internal use case needs deterministic execution for checkpointing.
With Meta internal testing, this can achieve 2x throughput improvement (measured in rows per second)
with 33% memory overhead with up to 4 batches prefetch.
The follow is to add memory based prefetch throttling to integrate with Meta internal
ML use case and memory pool wiring to ease performance (memory overhead) analysis
Differential Revision: D70909786