Github user fhueske commented on a diff in the pull request:
https://github.com/apache/flink/pull/3590#discussion_r107642048
--- Diff:
flink-libraries/flink-table/src/main/scala/org/apache/flink/table/plan/nodes/datastream/DataStreamOverAggregate.scala
---
@@ -119,6 +150,57 @@ class DataStreamOverAggregate(
}
+ 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 time_boundary =
logicWindow.constants.get(lowerboundIndex).getValue2 match {
+ case _: java.math.BigDecimal =>
logicWindow.constants.get(lowerboundIndex)
+ .getValue2.asInstanceOf[java.math.BigDecimal].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,
+ time_boundary)
+
+ inputDS
+ .keyBy(partitionKeys: _*)
+ .process(processFunction)
+ .returns(rowTypeInfo)
+ .name(aggOpName)
+ .asInstanceOf[DataStream[Row]]
+ } else { // non-partitioned aggregation
+ val processFunction =
AggregateUtil.CreateTimeBoundedProcessingOverProcessFunction(
--- End diff --
When using the RocksDB state backend, keys will be iterated in sorted
order. Hence, sorting will be of linear complexity.
I think you underestimate the effort of serializing and deserializing
(which includes the effort to create objects and gc them later). Typically a
large percentage of cycles is spent for this.
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