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https://issues.apache.org/jira/browse/FLINK-5654?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15937941#comment-15937941
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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/3590#discussion_r107613580
--- 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 --
Hi @rtudoran, IMO `MapState` is the better option. Have a look at this
[comment](https://github.com/apache/flink/pull/3574#issuecomment-288646109)
where I explain the benefits of the `MapState` approach.
It is true, that we need to read all keys if we use `MapState`, but 1) this
is only read/deserialization 2) it is cheap `Long` values. The huge advantage
of `MapState` is that we only have to reading and writing relevant `Row` values.
With `ListState` and `ValueState` we always have to read and write all
`Row` values.
The complexity of operating on the deserialized structures should be very
similar for all approaches and be negligible compared to the cost of
de/serializing (which includes object instantiations)
> 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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