Github user fhueske commented on a diff in the pull request:
https://github.com/apache/flink/pull/3547#discussion_r106519205
--- Diff:
flink-libraries/flink-table/src/main/scala/org/apache/flink/table/plan/nodes/datastream/DataStreamOverAggregate.scala
---
@@ -130,32 +142,76 @@ 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 createBoundedAndCurrentRowProcessingTimeOverWindow(
+ 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
+
+ // get the output types
+ val rowTypeInfo =
FlinkTypeFactory.toInternalRowTypeInfo(getRowType).asInstanceOf[RowTypeInfo]
+
+ val lowerbound: Int = AggregateUtil.getLowerBoundary(
+ logicWindow.constants,
+ overWindow.lowerBound,
+ getInput())
+
+ val result: DataStream[Row] =
+ // partitioned aggregation
+ if (partitionKeys.nonEmpty) {
+ val windowFunction =
AggregateUtil.CreateBoundedProcessingOverWindowFunction(
+ namedAggregates,
+ inputType)
+ inputDS
+ .keyBy(partitionKeys: _*)
+ .countWindow(lowerbound,1)
+ .apply(windowFunction)
+ .returns(rowTypeInfo)
+ .name(aggOpName)
+ .asInstanceOf[DataStream[Row]]
+ } // global non-partitioned aggregation
+ else {
+ val windowFunction =
AggregateUtil.CreateBoundedProcessingOverGlobalWindowFunction(
+ namedAggregates,
+ inputType)
+
+ inputDS
+ .countWindowAll(lowerbound,1)
--- End diff --
From the [MS SQL Server
documentation](https://msdn.microsoft.com/en-us/library/ms189461.aspx):
> For example, ROWS BETWEEN 2 PRECEDING AND CURRENT ROW means that the
window of rows that the function operates on is three rows in size, starting
with 2 rows preceding until and including the current row.
So, since `lowerBound` is already `AggregateUtil.getLowerBoundary(...) +
1`, we should be good.
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