HeartSaVioR commented on code in PR #39931:
URL: https://github.com/apache/spark/pull/39931#discussion_r1116477889
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sql/core/src/test/scala/org/apache/spark/sql/streaming/MultiStatefulOperatorsSuite.scala:
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@@ -463,6 +437,442 @@ class MultiStatefulOperatorsSuite
)
}
+ test("stream-stream time interval left outer join -> aggregation, append
mode") {
+ val input1 = MemoryStream[(String, Timestamp)]
+ val input2 = MemoryStream[(String, Timestamp)]
+
+ val s1 = input1.toDF()
+ .selectExpr("_1 AS id1", "_2 AS timestamp1")
+ .withWatermark("timestamp1", "0 seconds")
+ .as("s1")
+
+ val s2 = input2.toDF()
+ .selectExpr("_1 AS id2", "_2 AS timestamp2")
+ .withWatermark("timestamp2", "0 seconds")
+ .as("s2")
+
+ val s3 = s1.join(s2, expr("s1.id1 = s2.id2 AND (s1.timestamp1 BETWEEN " +
+ "s2.timestamp2 - INTERVAL 1 hour AND s2.timestamp2 + INTERVAL 1 hour)"),
"leftOuter")
+
+ val agg = s3.groupBy(window($"timestamp1", "10 minutes"))
Review Comment:
I see more and more places which simply pick up the first occurrence of
event time column. (Logic in stream-stream join also assumes there is only one
event time column which I missed. Will add assertion.)
That said, considering multiple event time columns for all stateful
operators needs non-trivial reasoning. Let's leave this as a follow-up.
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