[Doc] Add map_batches shuffle section to shuffling data guide#62576
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richardliaw merged 7 commits intoApr 15, 2026
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Add documentation for using map_batches as a distributed shuffle pipeline stage, with benchmark results comparing it to local buffer shuffle (80-90% vs 9-13% of baseline throughput). Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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This pull request introduces a benchmark script and documentation for a distributed shuffling method using map_batches in Ray Data, highlighting its performance advantages over local buffer shuffling. The review feedback suggests allocating CPU resources to the shuffle task to prevent performance issues and improving error handling in the benchmark script by logging exceptions during metric retrieval.
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Wrap map_batches in backticks to fix Vale.Spelling errors and use contraction "doesn't" per Google.Contractions style. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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changes to slim down the PR
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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Co-authored-by: Richard Liaw <rliaw@berkeley.edu> Signed-off-by: Richard Liaw <rliaw@berkeley.edu>
Signed-off-by: Richard Liaw <rliaw@berkeley.edu>
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…oject#62576) Add documentation for using map_batches as a distributed shuffle pipeline stage, with benchmark results comparing it to local buffer shuffle (80-90% vs 9-13% of baseline throughput). --------- Signed-off-by: Richard Liaw <rliaw@berkeley.edu> Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com> Co-authored-by: Richard Liaw <rliaw@berkeley.edu>
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…oject#62576) Add documentation for using map_batches as a distributed shuffle pipeline stage, with benchmark results comparing it to local buffer shuffle (80-90% vs 9-13% of baseline throughput). --------- Signed-off-by: Richard Liaw <rliaw@berkeley.edu> Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com> Co-authored-by: Richard Liaw <rliaw@berkeley.edu>
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Add documentation for using map_batches as a distributed shuffle pipeline stage, with benchmark results comparing it to local buffer shuffle (80-90% vs 9-13% of baseline throughput).