sririshindra commented on a change in pull request #28330:
URL: https://github.com/apache/spark/pull/28330#discussion_r415370594
##########
File path:
sql/core/src/test/scala/org/apache/spark/sql/execution/metric/SQLMetricsSuite.scala
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@@ -341,6 +341,62 @@ class SQLMetricsSuite extends SharedSparkSession with
SQLMetricsTestUtils
}
}
+ test("ShuffledHashJoin(outer) metrics") {
+ withSQLConf(SQLConf.AUTO_BROADCASTJOIN_THRESHOLD.key -> "40",
+ SQLConf.SHUFFLE_PARTITIONS.key -> "2",
+ SQLConf.PREFER_SORTMERGEJOIN.key -> "false") {
+ val df1 = Seq((1, "1"), (2, "2")).toDF("key", "value")
+ val df2 = (1 to 10).map(i => (i, i.toString)).toSeq.toDF("key2", "value")
+
+ Seq(("right_outer", 0L, df1, df2, false), ("left_outer", 0L, df2, df1,
false),
+ ("right_outer", 0L, df1, df2, true), ("left_outer", 0L, df2, df1,
true))
+ .foreach { case (joinType, nodeId, df1, df2, enableWholeStage) =>
+ val df = df1.join(df2, $"key" === $"key2", joinType)
+ testSparkPlanMetrics(df, 1, Map(
+ nodeId -> (("ShuffledHashJoin", Map(
+ "number of output rows" -> 10L)))),
+ enableWholeStage
+ )
+ }
+ }
+ }
+
+ test("ShuffledHashJoin(left-anti) metrics") {
+ withSQLConf(SQLConf.AUTO_BROADCASTJOIN_THRESHOLD.key -> "-1",
+ SQLConf.SHUFFLE_PARTITIONS.key -> "2",
+ SQLConf.PREFER_SORTMERGEJOIN.key -> "false") {
+ val df1 = Seq((1, "1"), (2, "2")).toDF("key", "value")
+ val df2 = (1 to 10).map(i => (i, i.toString)).toSeq.toDF("key2", "value")
+
+ Seq((2L, true), (1L, false)).foreach { case (nodeId, enableWholeStage) =>
+ val df = df2.join(df1.hint("shuffle_hash"), $"key" === $"key2",
"left_anti")
+ testSparkPlanMetrics(df, 1, Map(
+ nodeId -> (("ShuffledHashJoin", Map(
+ "number of output rows" -> 8L)))),
+ enableWholeStage
+ )
+ }
+ }
+ }
+
+ test("ShuffledHashJoin(left-semi) metrics") {
+ withSQLConf(SQLConf.AUTO_BROADCASTJOIN_THRESHOLD.key -> "-1",
+ SQLConf.SHUFFLE_PARTITIONS.key -> "2",
+ SQLConf.PREFER_SORTMERGEJOIN.key -> "false") {
+ val df1 = Seq((1, "1"), (2, "2")).toDF("key", "value")
+ val df2 = (1 to 10).map(i => (i, i.toString)).toSeq.toDF("key2", "value")
+
+ Seq((1L, false), (2L, true)).foreach { case (nodeId, enableWholeStage) =>
+ val df = df2.join(df1.hint("shuffle_hash"), $"key" === $"key2",
"left_semi")
+ testSparkPlanMetrics(df, 1, Map(
+ nodeId -> (("ShuffledHashJoin", Map(
+ "number of output rows" -> 2L)))),
Review comment:
fixed in the latest commit
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