LuciferYang opened a new pull request, #58511:
URL: https://github.com/apache/spark/pull/58511
### What changes were proposed in this pull request?
This backports SPARK-59141 (#58445, `d5fbdc173bd` on master) to branch-4.2.
`UnionExec.doExecuteColumnar` gets the same partitioning split that
`doExecute` already has: a union whose `outputPartitioning` is
index-co-locatable builds a `SQLPartitioningAwareUnionRDD` that interleaves
same-index partitions, and only an `UnknownPartitioning` union concatenates.
Both paths go through one helper rather than two copies of the dispatch, which
is what let them drift:
```scala
private def unionRDDs[T: ClassTag](executeChild: SparkPlan => RDD[T]):
RDD[T] = {
val partitioning = outputPartitioning
val rdds = children.map(executeChild)
if (partitioning.isInstanceOf[UnknownPartitioning]) {
sparkContext.union(rdds)
} else {
new SQLPartitioningAwareUnionRDD(
sparkContext, rdds.filter(!_.partitions.isEmpty),
partitioning.numPartitions)
}
}
```
Two differences from the master commit, both forced by this branch. The arm
is an `if` on `UnknownPartitioning` rather than a `match`, because
`KeyedPartitioning` does not participate in `UnionExec.outputPartitioning`
here: 4.2's `KeyedPartitioning` is not a `HashPartitioningLike`, so
`comparePartitioning` rejects it and such a union reports
`UnknownPartitioning`, taking the same concatenating arm master routes it to
explicitly. And the helper takes the execution action rather than the executed
RDDs, so `outputPartitioning` is read once, before the children run:
`doExecute` used to evaluate the predicate before executing them and then read
`outputPartitioning.numPartitions` after, which is the torn read master's
version removes. `isPlainUnion` is untouched and still gates whole-stage
codegen.
### Why are the changes needed?
Wrong results on default configuration. `doExecuteColumnar` concatenated its
children unconditionally, while `outputPartitioning` went on reporting whatever
the children agreed on, so a parent that dropped its exchange on the strength
of that report read a concatenation instead of an interleaving.
```scala
spark.conf.set("spark.sql.sources.bucketing.autoBucketedScanEnabled",
"false")
spark.conf.set("spark.sql.adaptive.enabled", "false")
spark.range(0, 20, 1, 1).selectExpr("id % 5 AS k").write.bucketBy(4,
"k").saveAsTable("t1")
spark.range(20, 40, 1, 1).selectExpr("id % 5 AS k").write.bucketBy(4,
"k").saveAsTable("t2")
sql("SELECT k, count(*) c FROM (SELECT k FROM t1 UNION ALL SELECT k FROM t2)
GROUP BY k").show()
```
Five groups of eight come back as ten rows of four, each group reported
twice. There is no exchange under the aggregate, because the union reported
`hashpartitioning(k, 4)` from its two bucketed children. `FileSourceScanExec`
overrides `supportsColumnar` but not `supportsRowBased`, whose default is its
negation, so a batch-readable bucketed scan is columnar-only and so is a union
of two of them. `ColumnarToRowExec` therefore goes above the union rather than
below it, the union runs `doExecuteColumnar`, and the aggregate reads
partitions in which a key can appear twice.
### Does this PR introduce _any_ user-facing change?
Yes. The query above returns five rows instead of ten. Any union of
columnar-only children that reports an index-co-locatable partitioning was
affected the same way.
A co-located columnar union now has as many partitions as it reports rather
than the sum of its children's, so the UNION ALL of two 4-bucket tables above
runs 4 tasks instead of 8, and each task reads one bucket from each table. The
row path has behaved this way since SPARK-52921, but a batch-readable bucketed
scan is columnar-only, so bucketed file scans never reached it. Setting
`spark.sql.unionOutputPartitioning` to false restores the old layout.
### How was this patch tested?
The new case from the master commit, ported unchanged into
`DataFrameSetOperationsSuite` next to the existing SPARK-52921 union
partitioning cases. It builds the shape above, asserts that the union is
columnar-only, that it reports a `HashPartitioning`, and that the aggregate has
no exchange, then compares against pinned expected rows. Reverting
`doExecuteColumnar` to this branch's unconditional `sparkContext.union` makes
it fail.
`DataFrameSetOperationsSuite`, `UnionCodegenSuite`,
`KeyGroupedPartitioningSuite` and `BucketedReadWithoutHiveSupportSuite` run 231
cases, all passing, and `sql/scalastyle` and `sql/Test/scalastyle` are clean.
### Was this patch authored or co-authored using generative AI tooling?
Generated-by: Claude Opus 5
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