cloud-fan commented on a change in pull request #32084:
URL: https://github.com/apache/spark/pull/32084#discussion_r610652043
##########
File path:
sql/core/src/test/scala/org/apache/spark/sql/execution/adaptive/AdaptiveQueryExecSuite.scala
##########
@@ -1575,4 +1575,51 @@ class AdaptiveQueryExecSuite
checkNoCoalescePartitions(df.sort($"key"), ENSURE_REQUIREMENTS)
}
}
+
+ test("SPARK-34980: Support coalesce partition through union") {
+ def checkResultPartition(
+ df: Dataset[Row], shuffleReaderNumber: Int, partitionNumber: Int): Unit
= {
+ df.collect()
+ assert(
+ collect(df.queryExecution.executedPlan) {
+ case s: CustomShuffleReaderExec => s
+ }.size === shuffleReaderNumber
+ )
+ assert(df.rdd.partitions.length === partitionNumber)
+ }
+
+ withSQLConf(SQLConf.ADAPTIVE_EXECUTION_ENABLED.key -> "true",
+ SQLConf.COALESCE_PARTITIONS_ENABLED.key -> "true",
+ SQLConf.ADVISORY_PARTITION_SIZE_IN_BYTES.key -> "1048576",
+ SQLConf.COALESCE_PARTITIONS_MIN_PARTITION_NUM.key -> "1",
+ SQLConf.SHUFFLE_PARTITIONS.key -> "10") {
+ val df1 = spark.sparkContext.parallelize(
+ (1 to 10).map(i => TestData(i, i.toString)), 2).toDF()
+ val df2 = spark.sparkContext.parallelize(
+ (1 to 10).map(i => TestData(i, i.toString)), 4).toDF()
+
+ // positive test
+ checkResultPartition(
+ df1.groupBy("key").count().unionAll(df2),
+ 1,
+ 1 + 4)
+
+ checkResultPartition(
+ df1.groupBy("key").count().unionAll(df2).unionAll(df1),
+ 1,
+ 1 + 4 + 2)
+
+ checkResultPartition(
+
df1.groupBy("key").count().unionAll(df2).unionAll(df1.groupBy("key").count()),
+ 2,
+ 1 + 4 + 1)
Review comment:
+1
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