adapters: fix OOM in ad-hoc query hashing for large data#6016
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adapters: fix OOM in ad-hoc query hashing for large data#6016
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Replace arrow-digest's RecordDigestV0 (order-dependent, required sorting the entire DataFrame) with a streaming, order-independent BatchHasher. The old approach sorted rows by every column before hashing. For large views it lead to OOM. The new approach uses DataFusion's create_hashes with two independent ahash seeds, accumulating row hashes via wrapping addition (commutative — no sort needed). Memory usage is O(batch_size). Signed-off-by: Swanand Mulay <73115739+swanandx@users.noreply.github.com>
Signed-off-by: feldera-bot <feldera-bot@feldera.com>
mythical-fred
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Apr 10, 2026
mythical-fred
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One blocker: please squash the [ci] apply automatic fixes commit. Dirty history is still a hard no for ready PRs.
gz
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Apr 10, 2026
| fn new() -> Self { | ||
| Self { | ||
| rs1: ahash::RandomState::with_seeds('M' as u64, 'U' as u64, 'A' as u64, 'Y' as u64), | ||
| rs2: ahash::RandomState::with_seeds('T' as u64, 'H' as u64, 'A' as u64, 'I' as u64), |
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Replace arrow-digest's RecordDigestV0 (order-dependent, required sorting the entire DataFrame) with a streaming, order-independent BatchHasher.
The old approach sorted rows by every column before hashing. For large views it lead to OOM. The new approach uses DataFusion's create_hashes with two independent ahash seeds, accumulating row hashes via wrapping addition (commutative — no sort needed).
Memory usage is O(batch_size).
Describe Manual Test Plan
tested by adding tests 💡
Checklist
Breaking Changes?
Describe Incompatible Changes
Yes, it changes the HASH generation, invalidating older hashes for same data. But we only use it internally so should be fine.