[Bug] hll_sketch_agg, hll_union and hll_union_agg write compact sketch bytes where Spark writes updatable ones

Aperta
#7,548 0 commenti 0 reazioni 0 assegnatari Vedi su GitHub

I maintainer di solito rispondono entro 2 giorni

Nessuno ha ancora preso questa issue.

Valutazione

Difficoltà
4/5
Tempo stimato
3-5 giorni
Idoneità per principianti
57/100
Tipo di issue
Bug
Chiarezza
Specificata chiaramente
Stato di attività
Attiva
Stack tecnologico
rust, scala
Ambito
backend, databases

Direzione di ricerca

Read the affected files listed in the issue: native/spark-expr/src/agg_funcs/hll_sketch.rs, hll_sketch_agg.rs, hll_union_agg.rs, native/spark-expr/src/hll_scalar.rs, and the CometHll serdes under spark/src/main/spark-4.x/org/apache/comet/serde/. Start by tracing how these expressions serialize LIST and SET sketches and compare the bytes with Spark's output. Done means the returned bytes use Spark's updatable layout and match Spark for the reproduced query.

Scritto dal modello di indicizzazione a partire dal testo della issue.

Descrizione

area:aggregation area:expressions bug priority:medium
Describe the bug

On Spark 4.x, Comet's native hll_sketch_agg, hll_union and hll_union_agg return sketch bytes that differ from Spark's when the sketch is still in LIST or SET mode (a small number of distinct values).

Spark returns HllSketch.toUpdatableByteArray, which is documented as the "updatable binary representation". The datasketches crate (0.6.0) has only a compact serialize(). For LIST and SET it sets COMPACT_FLAG and writes the compact layout (list.rs:131, hash_set.rs:163).

Both layouts decode to the same sketch, so hll_sketch_estimate and hll_union read either one correctly and the estimates match. The bytes themselves still differ, so:

  • any query that compares, groups by, hashes, or stores the raw sketch bytes gets a different result under Comet;
  • DataSketches-Java HllSketch.writableWrap rejects Comet's output because it only accepts updatable images (HllSketch.java:251-254 in 6.2.0).

This is why these three expressions still need spark.comet.expression.<name>.allowIncompatible=true after the audit in https://github.com/apache/datafusion-comet/issues/7396 .

Steps to reproduce
-- Spark 4.1, few distinct values -> LIST mode
SELECT hex(hll_sketch_agg(col)) FROM VALUES (1), (2), (3) AS t(col);

Run it once with Comet disabled and once with spark.comet.expression.HllSketchAgg.allowIncompatible=true. The hex strings differ: Comet sets COMPACT_FLAG and writes only the coupons, while Spark writes the updatable layout. hll_sketch_estimate returns 3 in both cases.

Comparison experiment (DataSketches-Java 6.1.1 and 6.2.0 vs Comet with datasketches 0.6.0, 4030 cases): https://github.com/tlfua/datafusion-comet/actions/runs/42348769378 . Summary: https://github.com/apache/datafusion-comet/issues/7396#issuecomment-6703600973 .

Expected behavior

Comet writes the same updatable layout as Spark, so the bytes match and these expressions can run without the opt-in.

Possible fixes:

  1. Add an updatable serialization to datasketches-rust (https://github.com/apache/datasketches-rust ) and use it in Comet.
  2. Re-encode LIST/SET sketches into the updatable layout in Comet before returning them.
Additional context

Surfaced by the HLL audit for https://github.com/apache/datafusion-comet/issues/7396 . Affected Comet files: native/spark-expr/src/agg_funcs/hll_sketch.rs, hll_sketch_agg.rs, hll_union_agg.rs, native/spark-expr/src/hll_scalar.rs and the CometHll* serdes under spark/src/main/spark-4.x/org/apache/comet/serde/.

Separately, the estimate after merging partial sketches can differ by up to 0.81% (hll_sketch_agg with more than one partition) because SET-to-HLL promotion during a union visits coupons in a different order. Spark's own estimate for the same input changes with partitioning by a similar or larger amount.

Willingness to contribute

I would be willing to contribute a fix for this bug with guidance from the DataFusion community

Lingua principale
Scala
Stelle
1.3k
Fork
414
Merge medio
3g 5h
PR unite (30g)
210

Preparare l'ambiente

Come iniziare

  1. Leggi tutta la issue e poi la guida ai contributi del progetto.
  2. Commenta sulla issue per dire che te ne occupi tu — evita che due persone facciano lo stesso lavoro.
  3. Fai un fork del repository e lavora su un branch.
  4. Apri una pull request che faccia riferimento al numero della issue.

Altre issue di apache/datafusion-comet

Tutte le issue di apache/datafusion-comet

Issue simili

Altre issue su Scala

Ricevi le nuove issue nella tua casella

Un breve riepilogo di issue GitHub adatte ai principianti.