OnPair trainer should draw Rust's sample (StdRng + partial_shuffle) for byte-identical dictionaries

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难度
4/5
预计耗时
3-5 天
新手友好度
22/100
Issue 类型
缺陷
描述清晰度
描述清楚
活跃度
活跃
技术栈
java, rust
领域
data

调研方向

Start in OnPairEncodingEncoder.Encoder.train, which uses SplittableRandom(42) and a full Fisher-Yates shuffle, and compare it with the Rust training_order in encoding/trainer.rs. Port the partial_shuffle tail selection and doubling retry loop on top of SampleRng, and check that SampleRng.nextIntInclusive matches random_range from rand 0.9.5. Done when the 100k-row URL column gives the same dict_bytes_len and encoded size per chunk as the Rust 0.88.0 oracle.

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描述

enhancement PARKED

Problem

OnPairEncodingEncoder does not draw the same training sample as the Rust onpair crate, so it trains different dictionaries on identical data. Prime directive: behavioral parity with Rust.

Found by comparing a 100k-row URL column (https://example.com/products/<n>/reviews?page=<m>, 7 chunks of 16,384 rows) written by vortex-java and by Rust 0.88.0 (the release vortex-jni uses), both at cascade 3 with global dict off. Both pick vortex.onpair per chunk.

dict_bytes_len per chunk:

chunk Rust Java
0 8191 8429
1 8525 8368
2 8447 8387
3 8363 8452
4 8379 8473
5 8757 5670
6 1872 1877

Chunks 0-4 end up within 0.04% in total, but Java's chunk 5 is 18% smaller (131,904 B vs 161,507 B): its trainer built a 761-token dictionary and the cascade then dict-encoded the 467 distinct codes. That one chunk is 29.6 KB of the 31.6 KB (3.2%) by which Java's file is smaller. This is training luck, not a systematic advantage, and it could go the other way on other data.

Cause

  • Rust (onpair 0.2.1, training_order in encoding/trainer.rs): StdRng::seed_from_u64(42) (ChaCha12, rand 0.9.5) and order.partial_shuffle(&mut rng, k) with k = min(n, 2 * sample_fraction * n + 1024). Training consumes only the selected tail order[n-k..]; if that tail is shorter than the byte budget, k doubles and the shuffle restarts.
  • Java (OnPairEncodingEncoder.Encoder.train): SplittableRandom(42), a full Fisher-Yates shuffle, and training consumes order from the start.

Proposed fix

  1. Port rand 0.9.5 partial_shuffle on top of SampleRng (already a bit-exact ChaCha12 StdRng and random_range for u32). It uses IncreasingUniform / calculate_bound_u32, which batches several swaps into one random chunk. Check that SampleRng.nextIntInclusive (written against rand 0.10) gives the same values as 0.9.5's random_range.
  2. Reproduce the k computation, the tail consumption and the doubling retry loop in train.
  3. Parity test: the URL column above, Rust as the oracle, comparing dict_bytes_len and encoded size per chunk (expect identical).

Repro

Scratch crate with vortex = "=0.88.0"; data dumped from the Java CascadeDepthGainIntegrationTest patterns (utf8-urls), seed 42. Not committed.

主要语言
Java
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3
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