ulysses-you commented on code in PR #37612:
URL: https://github.com/apache/spark/pull/37612#discussion_r960162375
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sql/core/src/test/scala/org/apache/spark/sql/execution/adaptive/AdaptiveQueryExecSuite.scala:
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@@ -2643,6 +2643,49 @@ class AdaptiveQueryExecSuite
assert(findTopLevelBroadcastNestedLoopJoin(adaptivePlan).size == 1)
}
}
+
+ test("SPARK-39915: Dataset.repartition(N) may not create N partitions") {
+ withSQLConf(SQLConf.SHUFFLE_PARTITIONS.key -> "6") {
+ // partitioning: HashPartitioning
+ // shuffleOrigin: REPARTITION_BY_NUM
+ assert(spark.range(0).repartition(5, $"id").rdd.getNumPartitions == 5)
+ // shuffleOrigin: REPARTITION_BY_COL
+ // The minimum partition number after AQE coalesce is 1
+ assert(spark.range(0).repartition($"id").rdd.getNumPartitions == 1)
+ // through project
+ assert(spark.range(0).selectExpr("id % 3 as c1", "id % 7 as c2")
+ .repartition(5, $"c1").select($"c2").rdd.getNumPartitions == 5)
+
+ // partitioning: RangePartitioning
+ // shuffleOrigin: REPARTITION_BY_NUM
+ // The minimum partition number of RangePartitioner is 1
+ assert(spark.range(0).repartitionByRange(5, $"id").rdd.getNumPartitions
== 1)
Review Comment:
it's a magic from `RangePartitioner`, not sure it is a bug.
`RangePartitioner` do sample to decide the range partition bounds, and the
bounds will be empty if the data is empty. Then the output partition number is
1.
https://github.com/apache/spark/blob/a027db1dc0379d823cbd638181bb25095f4e6577/core/src/main/scala/org/apache/spark/Partitioner.scala#L220
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