Github user tdas commented on a diff in the pull request:
https://github.com/apache/spark/pull/19196#discussion_r138765597
--- Diff:
sql/core/src/test/scala/org/apache/spark/sql/streaming/StreamingAggregationSuite.scala
---
@@ -381,4 +388,233 @@ class StreamingAggregationSuite extends
StateStoreMetricsTest
AddData(streamInput, 0, 1, 2, 3),
CheckLastBatch((0, 0, 2), (1, 1, 3)))
}
+
+ private def checkAggregationChain(
+ sq: StreamingQuery,
+ requiresShuffling: Boolean,
+ expectedPartition: Int): Unit = {
+ val executedPlan =
sq.asInstanceOf[StreamingQueryWrapper].streamingQuery
+ .lastExecution.executedPlan
+ val restore = executedPlan
+ .collect { case ss: StateStoreRestoreExec => ss }
+ .head
+ restore.child match {
+ case node: UnaryExecNode =>
+ assert(node.outputPartitioning.numPartitions === expectedPartition)
+ if (requiresShuffling) {
+ assert(node.isInstanceOf[Exchange], s"Expected a shuffle, got:
${node.child}")
+ } else {
+ assert(!node.isInstanceOf[Exchange], "Didn't expect a shuffle")
+ }
+
+ case _ => fail("Expected no shuffling")
+ }
+ var reachedRestore = false
+ // Check that there should be no exchanges after
`StateStoreRestoreExec`
+ executedPlan.foreachUp { p =>
+ if (reachedRestore) {
+ assert(!p.isInstanceOf[Exchange], "There should be no further
exchanges")
+ } else {
+ reachedRestore = p.isInstanceOf[StateStoreRestoreExec]
+ }
+ }
+ }
+
+ test("SPARK-21977: coalesce(1) with 0 partition RDD should be
repartitioned accordingly") {
+ val inputSource = new NonLocalRelationSource(spark)
+ MockSourceProvider.withMockSources(inputSource) {
+ withTempDir { tempDir =>
+ val aggregated: Dataset[Long] =
+ spark.readStream
+ .format((new MockSourceProvider).getClass.getCanonicalName)
+ .load()
+ .coalesce(1)
+ .groupBy()
+ .count()
+ .as[Long]
+
+ val sq = aggregated.writeStream
+ .format("memory")
+ .outputMode("complete")
+ .queryName("agg_test")
+ .option("checkpointLocation", tempDir.getAbsolutePath)
+ .start()
+
+ try {
+
+ inputSource.addData(1)
+ inputSource.releaseLock()
+ sq.processAllAvailable()
+
+ checkDataset(
+ spark.table("agg_test").as[Long],
+ 1L)
+
+ checkAggregationChain(sq, requiresShuffling = false, 1)
+
+ inputSource.addData()
+ inputSource.releaseLock()
+ sq.processAllAvailable()
+
+ checkAggregationChain(sq, requiresShuffling = true, 1)
+
+ checkDataset(
+ spark.table("agg_test").as[Long],
+ 1L)
+
+ inputSource.addData(2, 3)
--- End diff --
This is a lot of duplicate code. I am sure you can create shortcuts like
AddData, and AddFileData for this source, and then you can use `testStream()`.
All the checkAggregation can be put inside an AssertQuery.
---
---------------------------------------------------------------------
To unsubscribe, e-mail: [email protected]
For additional commands, e-mail: [email protected]
