Github user tejasapatil commented on a diff in the pull request:
https://github.com/apache/spark/pull/14864#discussion_r77118552
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/DataSourceScanExec.scala
---
@@ -156,24 +156,56 @@ case class FileSourceScanExec(
false
}
- override val outputPartitioning: Partitioning = {
+ override val (outputPartitioning, outputOrdering): (Partitioning,
Seq[SortOrder]) = {
val bucketSpec = if
(relation.sparkSession.sessionState.conf.bucketingEnabled) {
relation.bucketSpec
} else {
None
}
- bucketSpec.map { spec =>
- val numBuckets = spec.numBuckets
- val bucketColumns = spec.bucketColumnNames.flatMap { n =>
- output.find(_.name == n)
- }
- if (bucketColumns.size == spec.bucketColumnNames.size) {
- HashPartitioning(bucketColumns, numBuckets)
- } else {
- UnknownPartitioning(0)
- }
- }.getOrElse {
- UnknownPartitioning(0)
+ bucketSpec match {
+ case Some(spec) =>
+ val numBuckets = spec.numBuckets
+ val bucketColumns = spec.bucketColumnNames.flatMap { n =>
+ output.find(_.name == n)
+ }
+ if (bucketColumns.size == spec.bucketColumnNames.size) {
+ val partitioning = HashPartitioning(bucketColumns, numBuckets)
+
+ val sortOrder = if (spec.sortColumnNames.nonEmpty) {
+ // In case of bucketing, its possible to have multiple files
belonging to the
+ // same bucket in a given relation. Each of these files are
locally sorted
+ // but those files combined together are not globally sorted.
Given that,
+ // the RDD partition will not be sorted even if the relation
has sort columns set
+ // Current solution is to check if all the buckets have a
single file in it
+
+ val files =
+
relation.location.listFiles(partitionFilters).flatMap(partition =>
partition.files)
+ val bucketToFilesGrouping =
+ files.map(_.getPath.getName).groupBy(file =>
BucketingUtils.getBucketId(file))
+ val singleFilePartitions = bucketToFilesGrouping.forall(p =>
p._2.length <= 1)
--- End diff --
For the sorting problem, one way to fix would be to do what Hive does :
create single file per bucket. For any other approach, since there would be
multiple files per bucket, one would have to globally sort them while reading
it. This would in a way be sub-optimal because tables tend to be "write-once,
read many" and spending more CPU once for write path to generate single file
would be better.
When I came across this, I wondered why it was designed this way. I even
posted about this to dev group earlier today :
http://apache-spark-developers-list.1001551.n3.nabble.com/Questions-about-bucketing-in-Spark-td18814.html
To give you some context, I am trying to drive adoption for Spark within
Facebook. We have lot of tables which would benefit from having full bucketing
support. So my high level goal is to get Spark's bucketing in par with Hive's
in terms of features and compatibility.
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