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

Abierto
  • ¿Algo no está bien?
#7,548 0 comentarios 0 reacciones 0 asignados Ver en GitHub

Los mantenedores suelen responder en 2 días

Nadie ha tomado este issue todavía.

Evaluación

Dificultad
4/5
Tiempo estimado
3-5 días
Aptitud para principiantes
57/100
Tipo de issue
Error
Claridad
Bien especificado
Estado de actividad
Activo
Stack tecnológico
rust, scala
Área
backend, databases

Línea de trabajo

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.

Escrito por el modelo de indexación a partir del texto del issue.

Descripción

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

Lenguaje dominante
Scala
Estrellas
1.3k
Forks
414
Merge medio
3 d 5 h
PR fusionados (30 d)
210

Preparar el entorno

Primeros pasos

  1. Lee el issue completo y luego la guía de contribución del proyecto.
  2. Comenta en el issue que vas a ocuparte — evita que dos personas hagan lo mismo.
  3. Haz un fork del repositorio y trabaja en una rama.
  4. Abre un pull request que haga referencia al número del issue.

Más de apache/datafusion-comet

Todos los issues de apache/datafusion-comet

Issues similares

Más issues de Scala

Recibe los nuevos issues en tu correo

Un resumen breve de issues de GitHub para principiantes.