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https://issues.apache.org/jira/browse/FLINK-5654?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15947506#comment-15947506
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ASF GitHub Bot commented on FLINK-5654:
---------------------------------------
Github user fhueske commented on a diff in the pull request:
https://github.com/apache/flink/pull/3641#discussion_r108722044
--- Diff:
flink-libraries/flink-table/src/main/scala/org/apache/flink/table/plan/nodes/datastream/DataStreamOverAggregate.scala
---
@@ -274,6 +286,57 @@ class DataStreamOverAggregate(
}
result
}
+
+ def createTimeBoundedProcessingTimeOverWindow(inputDS: DataStream[Row]):
DataStream[Row] = {
+
+ val overWindow: Group = logicWindow.groups.get(0)
+ val partitionKeys: Array[Int] = overWindow.keys.toArray
+ val namedAggregates: Seq[CalcitePair[AggregateCall, String]] =
generateNamedAggregates
+
+ val index =
overWindow.lowerBound.getOffset.asInstanceOf[RexInputRef].getIndex
+ val count = input.getRowType.getFieldCount
+ val lowerBoundIndex = index - count
+
+
+ val timeBoundary =
logicWindow.constants.get(lowerBoundIndex).getValue2 match {
+ case bd: java.math.BigDecimal => bd.longValue()
+ case _ => throw new TableException("OVER Window boundaries must be
numeric")
+ }
+
+ // get the output types
+ val rowTypeInfo =
FlinkTypeFactory.toInternalRowTypeInfo(getRowType).asInstanceOf[RowTypeInfo]
+
+ val result: DataStream[Row] =
+ // partitioned aggregation
+ if (partitionKeys.nonEmpty) {
+
+ val processFunction =
AggregateUtil.createTimeBoundedProcessingOverProcessFunction(
+ namedAggregates,
+ inputType,
+ timeBoundary)
+
+ inputDS
+ .keyBy(partitionKeys: _*)
+ .process(processFunction)
+ .returns(rowTypeInfo)
+ .name(aggOpName)
+ .asInstanceOf[DataStream[Row]]
+ } else { // non-partitioned aggregation
+ val processFunction =
AggregateUtil.createTimeBoundedProcessingOverProcessFunction(
--- End diff --
The `isPartitioned` parameter is not used in the
`createTimeBoundedProcessingOverProcessFunction()`. So both calls are identical
and we can move it outside of the condition and save a few LOCs.
> Add processing time OVER RANGE BETWEEN x PRECEDING aggregation to SQL
> ---------------------------------------------------------------------
>
> Key: FLINK-5654
> URL: https://issues.apache.org/jira/browse/FLINK-5654
> Project: Flink
> Issue Type: Sub-task
> Components: Table API & SQL
> Reporter: Fabian Hueske
> Assignee: radu
>
> The goal of this issue is to add support for OVER RANGE aggregations on
> processing 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 procTime() RANGE BETWEEN INTERVAL '1'
> HOUR PRECEDING AND CURRENT ROW) AS sumB,
> MIN(b) OVER (PARTITION BY c ORDER BY procTime() RANGE BETWEEN INTERVAL '1'
> HOUR 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 optional (no partitioning results in single
> threaded execution).
> - The ORDER BY clause may only have procTime() as parameter. procTime() is a
> parameterless scalar function that just indicates processing time mode.
> - UNBOUNDED PRECEDING is not supported (see FLINK-5657)
> - 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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