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https://issues.apache.org/jira/browse/FLINK-5990?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15938638#comment-15938638
]
ASF GitHub Bot commented on FLINK-5990:
---------------------------------------
Github user fhueske commented on a diff in the pull request:
https://github.com/apache/flink/pull/3585#discussion_r107670323
--- Diff:
flink-libraries/flink-table/src/main/scala/org/apache/flink/table/plan/nodes/datastream/DataStreamOverAggregate.scala
---
@@ -130,32 +167,72 @@ class DataStreamOverAggregate(
val rowTypeInfo =
FlinkTypeFactory.toInternalRowTypeInfo(getRowType).asInstanceOf[RowTypeInfo]
val result: DataStream[Row] =
- // partitioned aggregation
- if (partitionKeys.nonEmpty) {
- val processFunction =
AggregateUtil.CreateUnboundedProcessingOverProcessFunction(
- namedAggregates,
- inputType)
+ // partitioned aggregation
+ if (partitionKeys.nonEmpty) {
+ val processFunction =
AggregateUtil.createUnboundedProcessingOverProcessFunction(
+ namedAggregates,
+ inputType)
- inputDS
+ inputDS
.keyBy(partitionKeys: _*)
.process(processFunction)
.returns(rowTypeInfo)
.name(aggOpName)
.asInstanceOf[DataStream[Row]]
- }
- // non-partitioned aggregation
- else {
- val processFunction =
AggregateUtil.CreateUnboundedProcessingOverProcessFunction(
- namedAggregates,
- inputType,
- false)
-
- inputDS
-
.process(processFunction).setParallelism(1).setMaxParallelism(1)
- .returns(rowTypeInfo)
- .name(aggOpName)
- .asInstanceOf[DataStream[Row]]
- }
+ }
+ // non-partitioned aggregation
+ else {
+ val processFunction =
AggregateUtil.createUnboundedProcessingOverProcessFunction(
+ namedAggregates,
+ inputType,
+ false)
+
+ inputDS
+ .process(processFunction).setParallelism(1).setMaxParallelism(1)
+ .returns(rowTypeInfo)
+ .name(aggOpName)
+ .asInstanceOf[DataStream[Row]]
+ }
+ result
+ }
+
+ def createRowsClauseBoundedAndCurrentRowOverWindow(
+ inputDS: DataStream[Row],
+ isRowTimeType: Boolean = false): DataStream[Row] = {
+
+ val overWindow: Group = logicWindow.groups.get(0)
+ val partitionKeys: Array[Int] = overWindow.keys.toArray
+ val namedAggregates: Seq[CalcitePair[AggregateCall, String]] =
generateNamedAggregates
+ val inputFields = (0 until inputType.getFieldCount).toArray
+
+ val precedingOffset =
+ getLowerBoundary(logicWindow, overWindow, getInput()) + 1
+
+ // get the output types
+ val rowTypeInfo =
FlinkTypeFactory.toInternalRowTypeInfo(getRowType).asInstanceOf[RowTypeInfo]
+
+ val result: DataStream[Row] =
+ // partitioned aggregation
+ if (partitionKeys.nonEmpty) {
+ val processFunction =
AggregateUtil.createRowsClauseBoundedOverProcessFunction(
+ namedAggregates,
+ inputType,
+ inputFields,
+ precedingOffset,
+ isRowTimeType
+ )
+ inputDS
+ .keyBy(partitionKeys: _*)
+ .process(processFunction)
+ .returns(rowTypeInfo)
+ .name(aggOpName)
+ .asInstanceOf[DataStream[Row]]
+ }
+ // non-partitioned aggregation
+ else {
+ throw TableException(
--- End diff --
Isn't the non-partitioned case analogous if we use `NullByteKeyExtractor`?
> Add [partitioned] event time OVER ROWS BETWEEN x PRECEDING aggregation to SQL
> -----------------------------------------------------------------------------
>
> Key: FLINK-5990
> URL: https://issues.apache.org/jira/browse/FLINK-5990
> Project: Flink
> Issue Type: Sub-task
> Components: Table API & SQL
> Reporter: sunjincheng
> Assignee: sunjincheng
>
> The goal of this issue is to add support for OVER ROWS aggregations on event
> time streams to the SQL interface.
> Queries similar to the following should be supported:
> {code}
> SELECT
> a,
> SUM(b) OVER (PARTITION BY c ORDER BY rowTime() ROWS BETWEEN 2 PRECEDING AND
> CURRENT ROW) AS sumB,
> MIN(b) OVER (PARTITION BY c ORDER BY rowTime() ROWS BETWEEN 2 PRECEDING AND
> CURRENT ROW) AS minB
> FROM myStream
> {code}
> The following restrictions should initially apply:
> - All OVER clauses in the same SELECT clause must be exactly the same.
> - The PARTITION BY clause is required
> - The ORDER BY clause may only have rowTime() as parameter. rowTime() is a
> parameterless scalar function that just indicates event time mode.
> - UNBOUNDED PRECEDING is not supported (see FLINK-5803)
> - FOLLOWING is not supported.
> The restrictions will be resolved in follow up issues. If we find that some
> of the restrictions are trivial to address, we can add the functionality in
> this issue as well.
> This issue includes:
> - Design of the DataStream operator to compute OVER ROW aggregates
> - Translation from Calcite's RelNode representation (LogicalProject with
> RexOver expression).
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