Details
-
Improvement
-
Status: Closed
-
Minor
-
Resolution: Fixed
-
None
-
None
-
None
Description
Now, the optimizer will throw an exception if window aggregate has an input node which does not only generate insert records.
E.g 1 : a deduplicate on row-time is followed by window aggregate:
@Test def testWindowAggWithDeduplicateAsInput(): Unit = { val sql = """ |SELECT |b, |TUMBLE_START(rowtime, INTERVAL '0.005' SECOND) as w_start, |TUMBLE_END(rowtime, INTERVAL '0.005' SECOND) as w_end, |COUNT(1) AS cnt |FROM | ( | SELECT b, rowtime | FROM ( | SELECT *, | ROW_NUMBER() OVER (PARTITION BY b ORDER BY `rowtime` DESC) as rowNum | FROM MyTable | ) | WHERE rowNum = 1 |) |GROUP BY b, TUMBLE(rowtime, INTERVAL '0.005' SECOND) |""".stripMargin util.verifyRelPlan(sql, ExplainDetail.CHANGELOG_MODE) }
E.g 2: a window aggregate which allows early fire/late fire is followed by window aggregate:
@Test def testWindowAggWithLateFireWindowAggAsInput(): Unit = { util.conf.getConfiguration.setBoolean(TABLE_EXEC_EMIT_LATE_FIRE_ENABLED, true) util.conf.getConfiguration.set(TABLE_EXEC_EMIT_LATE_FIRE_DELAY, Duration.ofSeconds(5)) util.conf.setIdleStateRetentionTime(Time.hours(1), Time.hours(2)) val sql = """ |SELECT SUM(cnt) |FROM ( | SELECT COUNT(1) AS cnt, TUMBLE_ROWTIME(`rowtime`, INTERVAL '10' SECOND) AS ts | FROM MyTable | GROUP BY a, b, TUMBLE(`rowtime`, INTERVAL '10' SECOND) |) |GROUP BY TUMBLE(ts, INTERVAL '10' SECOND) |""".stripMargin util.verifyRelPlan(sql, ExplainDetail.CHANGELOG_MODE) }
The following exception will be thrown out for above cases:
org.apache.flink.table.api.TableException: StreamPhysicalGroupWindowAggregate doesn't support consuming update and delete changes which is produced by node Deduplicate(keep=[LastRow], key=[b], order=[ROWTIME])org.apache.flink.table.api.TableException: StreamPhysicalGroupWindowAggregate doesn't support consuming update and delete changes which is produced by node Deduplicate(keep=[LastRow], key=[b], order=[ROWTIME]) at org.apache.flink.table.planner.plan.optimize.program.FlinkChangelogModeInferenceProgram$SatisfyModifyKindSetTraitVisitor.createNewNode(FlinkChangelogModeInferenceProgram.scala:384) at org.apache.flink.table.planner.plan.optimize.program.FlinkChangelogModeInferenceProgram$SatisfyModifyKindSetTraitVisitor.visit(FlinkChangelogModeInferenceProgram.scala:165) at org.apache.flink.table.planner.plan.optimize.program.FlinkChangelogModeInferenceProgram$SatisfyModifyKindSetTraitVisitor.org$apache$flink$table$planner$plan$optimize$program$FlinkChangelogModeInferenceProgram$SatisfyModifyKindSetTraitVisitor$$visitChild(FlinkChangelogModeInferenceProgram.scala:343) at org.apache.flink.table.planner.plan.optimize.program.FlinkChangelogModeInferenceProgram$SatisfyModifyKindSetTraitVisitor$$anonfun$3.apply(FlinkChangelogModeInferenceProgram.scala:332) at org.apache.flink.table.planner.plan.optimize.program.FlinkChangelogModeInferenceProgram$SatisfyModifyKindSetTraitVisitor$$anonfun$3.apply(FlinkChangelogModeInferenceProgram.scala:331) at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234) at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234) at scala.collection.immutable.Range.foreach(Range.scala:160) at scala.collection.TraversableLike$class.map(TraversableLike.scala:234) at scala.collection.AbstractTraversable.map(Traversable.scala:104) at org.apache.flink.table.planner.plan.optimize.program.FlinkChangelogModeInferenceProgram$SatisfyModifyKindSetTraitVisitor.visitChildren(FlinkChangelogModeInferenceProgram.scala:331) at org.apache.flink.table.planner.plan.optimize.program.FlinkChangelogModeInferenceProgram$SatisfyModifyKindSetTraitVisitor.visit(FlinkChangelogModeInferenceProgram.scala:281) at org.apache.flink.table.planner.plan.optimize.program.FlinkChangelogModeInferenceProgram$SatisfyModifyKindSetTraitVisitor.org$apache$flink$table$planner$plan$optimize$program$FlinkChangelogModeInferenceProgram$SatisfyModifyKindSetTraitVisitor$$visitChild(FlinkChangelogModeInferenceProgram.scala:343) at org.apache.flink.table.planner.plan.optimize.program.FlinkChangelogModeInferenceProgram$SatisfyModifyKindSetTraitVisitor$$anonfun$3.apply(FlinkChangelogModeInferenceProgram.scala:332) at org.apache.flink.table.planner.plan.optimize.program.FlinkChangelogModeInferenceProgram$SatisfyModifyKindSetTraitVisitor$$anonfun$3.apply(FlinkChangelogModeInferenceProgram.scala:331) ...
`FlinkChangelogModeInferenceProgram` limits that
WindowAggregate could support insert-only in input. However, `WindowOperator` could handle insert, update-before, update-after, delete message. We could remove this restrict on the planner.
Attachments
Issue Links
- is duplicated by
-
FLINK-20487 Support to consume retractions for window aggregate operator
- Closed