Github user andrewor14 commented on a diff in the pull request:
https://github.com/apache/spark/pull/10934#discussion_r51330867
--- Diff:
streaming/src/test/scala/org/apache/spark/streaming/CheckpointSuite.scala ---
@@ -821,6 +821,70 @@ class CheckpointSuite extends TestSuiteBase with
DStreamCheckpointTester
checkpointWriter.stop()
}
+ test("SPARK-6847: stack overflow when updateStateByKey is followed by a
checkpointed dstream") {
+ // In this test, there are two updateStateByKey operators. The RDD DAG
is as follows:
+ //
+ // batch 1 batch 2 batch 3 ...
+ //
+ // 1) input rdd input rdd input rdd
+ // | | |
+ // 2) cogroup rdd ---> cogroup rdd ---> cogroup rdd ...
+ // | / | / |
+ // 3) map rdd --- map rdd --- map rdd ...
+ // |
+ // 4) cogroup rdd ---> cogroup rdd ---> cogroup rdd ...
+ // | / | / |
+ // 5) map rdd --- map rdd --- map rdd ...
+ //
+ // Every batch depends on its previous batch, so "updateStateByKey"
needs to do checkpoint to
+ // break the RDD chain. However, before SPARK-6847, when the state RDD
(layer 5) of the second
+ // "updateStateByKey" does checkpoint, it won't checkpoint the state
RDD (layer 3) of the first
+ // "updateStateByKey" (Note: "updateStateByKey" has already marked
that its state RDD (layer 3)
+ // should be checkpointed). Hence, the connections between layer 2 and
layer 3 won't be broken
+ // and the RDD chain will grow infinitely and cause StackOverflow.
+ //
+ // Therefore SPARK-6847 introduces
"spark.checkpoint.checkpointAllMarked" to force checkpointing
+ // all marked RDDs in the DAG to resolve this issue. (For the previous
example, it will break
+ // connections between layer 2 and layer 3)
+ ssc = new StreamingContext(master, framework, batchDuration)
+ val batchCounter = new BatchCounter(ssc)
+ ssc.checkpoint(checkpointDir)
+ val inputDStream = new CheckpointInputDStream(ssc)
+ val updateFunc = (values: Seq[Int], state: Option[Int]) => {
+ Some(values.sum + state.getOrElse(0))
+ }
+ @volatile var checkpointAllMarkedRDDsEnable = false
--- End diff --
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