ANY_VALUE memory regression in Comet 1.2.0

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#7,541 1 comentario 0 reacciones 0 asignados Ver en GitHub

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@andygrove ya está trabajando en esto.

Desde el 26/10/2026.

  • #7249 de @andygrove — abierto

Evaluación

Dificultad
3/5
Tiempo estimado
1-2 días
Aptitud para principiantes
68/100
Tipo de issue
Error
Claridad
Bien especificado
Estado de actividad
Activo
Stack tecnológico
rust, scala, spark
Área
backend, performance

Línea de trabajo

Start with the aggregate dependency configuration and native tests for TrivialFirstValueAccumulator::merge_batch; the issue identifies the DataFusion 55.2.0 behavior and upstream fix #26743. Check which released DataFusion version contains that fix and how Comet pins it, then run the focused native regression tests. Done means the dependency includes the fix and the tests pass; validate the rebuilt JAR with the supplied Spark reproducer if possible.

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

Descripción

area:aggregation bug priority:medium

Comet 1.2.0 fails ANY_VALUE(array<string>) with an oversized memory reservation.
Spark lowers this to first, which uses DataFusion 55.2.0's
TrivialFirstValueAccumulator::merge_batch. That method retains the input's
backing buffers and counts them repeatedly across groups.

This is fixed by apache/datafusion#26743.
Could we update to a dependency containing it when released?

Reproducer

With Spark 4.1.3, Java 21, and the Comet JAR installed in $SPARK_HOME/jars,
save this as inline_repro.py:

from pyspark.sql import SparkSession

spark = (
    SparkSession.builder.config("spark.ui.enabled", "false")
    .config("spark.sql.adaptive.enabled", "false")
    .config("spark.sql.shuffle.partitions", "2")
    .config("spark.plugins", "org.apache.spark.CometPlugin")
    .config(
        "spark.shuffle.manager",
        "org.apache.spark.sql.comet.execution.shuffle.CometShuffleManager",
    )
    .config("spark.comet.enabled", "true")
    .config("spark.comet.exec.enabled", "true")
    .config("spark.comet.shuffle.enabled", "true")
    .config("spark.comet.nativeLoadRequired", "true")
    .config("spark.comet.exec.aggregate.enabled", "true")
    .config("spark.memory.offHeap.enabled", "true")
    .config("spark.memory.offHeap.size", "128m")
    .getOrCreate()
)

try:
    path = "/tmp/comet-any-value-input"
    spark.range(256, numPartitions=4).selectExpr(
        "CAST(id AS STRING) AS user_id",
        "transform(sequence(0, 199), x -> concat(lpad(CAST(id AS STRING), 8, '0'), ':', lpad(CAST(x AS STRING), 4, '0'))) AS values",
    ).write.mode("overwrite").parquet(path)
    spark.read.parquet(path).createOrReplaceTempView("input")
    result = spark.sql(
        "SELECT user_id, ANY_VALUE(values) AS values FROM input GROUP BY user_id"
    )
    result.explain()
    rows = result.collect()
    expected = {str(i): [f"{i:08d}:{j:04d}" for j in range(200)] for i in range(256)}
    assert len(rows) == 256
    assert {row.user_id: row["values"] for row in rows} == expected
finally:
    spark.stop()

"$SPARK_HOME/bin/spark-submit" --master 'local[1]' --driver-memory 1g \
  inline_repro.py

The script sets a 128 MiB native pool and two shuffle partitions. It fails in
OrderedFinalAggregateStream, requesting 80,775,440 bytes against a
67,108,864-byte share. The strings contain just 650 KiB of data.

The same script succeeds with Comet 1.1.0, or with
spark.comet.exec.aggregate.enabled set to false on 1.2.0. It checks every
output array.

Fix verification

Focused native tests use DataFusion 55.2.0 and Arrow 59.3.0. For the same
selected array, update_batch reports 3,832 bytes while merge_batch reports
1,311,296 bytes. Adding the upstream fix makes both report 3,832 bytes:

 self.first = ScalarValue::try_from_array(first, 0)?;
+self.first.compact();
 self.is_set = true;

Both native regression tests fail before this change and pass afterward,
with unchanged output values. A rebuilt Comet JAR has not yet been tested.

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.

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