Github user viirya commented on a diff in the pull request:
https://github.com/apache/spark/pull/18542#discussion_r125802474
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/FileFormatWriter.scala
---
@@ -116,13 +116,26 @@ object FileFormatWriter extends Logging {
val partitionSet = AttributeSet(partitionColumns)
val dataColumns = allColumns.filterNot(partitionSet.contains)
- val bucketIdExpression = bucketSpec.map { spec =>
+ val bucketPartitioning = bucketSpec.map { spec =>
val bucketColumns = spec.bucketColumnNames.map(c =>
dataColumns.find(_.name == c).get)
+ HashPartitioning(bucketColumns, spec.numBuckets)
+ }
+
+ val bucketIdExpression = bucketPartitioning.map { partitioning =>
// Use `HashPartitioning.partitionIdExpression` as our bucket id
expression, so that we can
// guarantee the data distribution is same between shuffle and
bucketed data source, which
// enables us to only shuffle one side when join a bucketed table
and a normal one.
- HashPartitioning(bucketColumns,
spec.numBuckets).partitionIdExpression
+ partitioning.partitionIdExpression
}
+
+ // If the plan's outputPartitioning is the same as the the bucket
spec, then each row will have
+ // a constant bucket id. We possibly can avoid the sort altogether.
+ val bucketSortExpression = plan.outputPartitioning match {
+ case output: HashPartitioning =>
+
bucketPartitioning.filterNot(_.semanticEquals(output)).map(_.partitionIdExpression)
--- End diff --
The output partitioning doesn't guarantee that there's only one value for
the partitioning columns in the partition.
For example, assume the output partitioning is [a], it only guarantees that
the rows with the same values of [a] will be in the same partition. So a
partition can contains all the rows with `a = 1`, `a = 2`..., rather than all
rows in the partition have `a = 1`.
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