c21 commented on a change in pull request #31413:
URL: https://github.com/apache/spark/pull/31413#discussion_r567589223
##########
File path:
sql/core/src/test/scala/org/apache/spark/sql/sources/DisableUnnecessaryBucketedScanSuite.scala
##########
@@ -95,7 +95,7 @@ abstract class DisableUnnecessaryBucketedScanSuite
("SELECT i FROM t1", 0, 1),
("SELECT j FROM t1", 0, 0),
// Filter on bucketed column
- ("SELECT * FROM t1 WHERE i = 1", 1, 1),
+ ("SELECT * FROM t1 WHERE i = 1", 0, 1),
Review comment:
This unit test change is expected, as we no longer need to do bucket
scan for this kind of query. See related change in
`DisableUnnecessaryBucketedScan.scala`
##########
File path:
sql/core/src/test/scala/org/apache/spark/sql/sources/BucketedReadSuite.scala
##########
@@ -148,19 +151,53 @@ abstract class BucketedReadSuite extends QueryTest with
SQLTestUtils with Adapti
if (invalidBuckets.nonEmpty) {
fail(s"Buckets ${invalidBuckets.mkString(",")} should have been
pruned from:\n$plan")
}
+
+ withSQLConf(SQLConf.BUCKETING_ENABLED.key -> "false") {
+ // Bucket pruning should still work when bucketing is disabled
+ val planWithBucketDisabled =
spark.table("bucketed_table").select("i", "j", "k").filter(filterCondition)
+ .queryExecution.executedPlan
+ val fileScanWithBucketDisabled = getFileScan(planWithBucketDisabled)
+ assert(!fileScanWithBucketDisabled.bucketedScan,
+ "except no bucketed scan when disabling bucketing but found\n" +
+ s"$fileScanWithBucketDisabled")
+
+ val tableSchema = fileScanWithBucketDisabled.schema
+ val bucketColumnIndex =
tableSchema.fieldIndex(bucketColumnNames.head)
+ val bucketColumn = tableSchema.toAttributes(bucketColumnIndex)
+ val bucketColumnType = tableSchema.apply(bucketColumnIndex).dataType
+ val rowsWithInvalidBuckets =
fileScanWithBucketDisabled.execute().filter(row => {
+ // Return rows should have been pruned
+ val bucketColumnValue = row.get(bucketColumnIndex,
bucketColumnType)
+ val bucketId = BucketingUtils.getBucketIdFromValue(
+ bucketColumn, numBuckets, bucketColumnValue)
+ !matchedBuckets.get(bucketId)
+ }).collect()
+
+ if (rowsWithInvalidBuckets.nonEmpty) {
+ fail(s"Rows ${rowsWithInvalidBuckets.mkString(",")} should have
been pruned from:\n" +
+ s"$planWithBucketDisabled")
+ }
+ }
}
+ val expectedDataFrame =
originalDataFrame.filter(filterCondition).orderBy("i", "j", "k")
checkAnswer(
bucketedDataFrame.filter(filterCondition).orderBy("i", "j", "k"),
- originalDataFrame.filter(filterCondition).orderBy("i", "j", "k"))
+ expectedDataFrame)
+
+ withSQLConf(SQLConf.BUCKETING_ENABLED.key -> "false") {
+ checkAnswer(
+ spark.table("bucketed_table").select("i", "j",
"k").filter(filterCondition)
+ .orderBy("i", "j", "k"),
+ expectedDataFrame)
+ }
}
}
test("read partitioning bucketed tables with bucket pruning filters") {
withTable("bucketed_table") {
val numBuckets = NumBucketsForPruningDF
val bucketSpec = BucketSpec(numBuckets, Seq("j"), Nil)
- // json does not support predicate push-down, and thus json is used here
Review comment:
This is not true anymore as json filter push down was added in
https://issues.apache.org/jira/browse/SPARK-30648 .
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