parquet: optimize CachedArrayReader byte-array coalescing#9743
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ClSlaid wants to merge 1 commit intoapache:mainfrom
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parquet: optimize CachedArrayReader byte-array coalescing#9743ClSlaid wants to merge 1 commit intoapache:mainfrom
ClSlaid wants to merge 1 commit intoapache:mainfrom
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When CachedArrayReader builds output from multiple cached batches, the old path materialized filtered byte arrays and then concatenated them. Replace that path for Utf8/Binary arrays with a direct coalescer that builds offsets, values, and validity in one output array, while keeping the existing generic MutableArrayData path for other types. Add a dedicated CachedArrayReader benchmark and a sparse string regression test so this path is measured directly and covered independently of broader parquet reader benchmarks. Benchmark vs main: - cached_array_reader/utf8_sparse_cross_batch_4m_rows/consume_batch: 11.949 ms -> 4.153 ms (-65.2%) - arrow_reader_clickbench/sync/Q24 (same filter/projection as ClickBench Q26): 28.377 ms -> 28.443 ms (+0.2%, no measurable change) Signed-off-by: cl <cailue@apache.org>
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@alamb I've tried to optimize with GPT 5.4, the improvement is not that obvious in the original test case you gave. So I let it wrote a new benchmark and optimized on it. However, I'm still not really confident about the current implementation, so please have a look. |
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When CachedArrayReader builds output from multiple cached batches, the old path materialized filtered byte arrays and then concatenated them. Replace that path for Utf8/Binary arrays with a direct coalescer that builds offsets, values, and validity in one output array, while keeping the existing generic MutableArrayData path for other types.
Add a dedicated CachedArrayReader benchmark and a sparse string regression test so this path is measured directly and covered independently of broader parquet reader benchmarks.
Benchmark vs main:
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