[ 
https://issues.apache.org/jira/browse/FLINK-5653?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15926374#comment-15926374
 ] 

ASF GitHub Bot commented on FLINK-5653:
---------------------------------------

Github user sunjincheng121 commented on a diff in the pull request:

    https://github.com/apache/flink/pull/3547#discussion_r106195116
  
    --- Diff: 
flink-libraries/flink-table/src/main/scala/org/apache/flink/table/plan/nodes/datastream/DataStreamOverAggregate.scala
 ---
    @@ -159,6 +168,42 @@ class DataStreamOverAggregate(
         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 result: DataStream[Row] =
    +      // partitioned aggregation
    +      if (partitionKeys.nonEmpty) {
    +        val windowFunction = 
AggregateUtil.CreateBoundedProcessingOverWindowFunction(
    +          namedAggregates,
    +          inputType)
    +
    +        val lowerbound: Int = AggregateUtil.getLowerBoundary(
    +          logicWindow.constants,
    +          overWindow.lowerBound,
    +          getInput())
    +
    +        inputDS
    +          .keyBy(partitionKeys: _*)
    +          .countWindow(lowerbound, 1).apply(windowFunction)
    +          .returns(rowTypeInfo)
    +          .name(aggOpName)
    +          .asInstanceOf[DataStream[Row]]
    +      } // global non-partitioned aggregation
    +      else {
    +        throw TableException(
    --- End diff --
    
    Can you supported not-partitioned case in this JIRA.


> Add processing time OVER ROWS BETWEEN x PRECEDING aggregation to SQL
> --------------------------------------------------------------------
>
>                 Key: FLINK-5653
>                 URL: https://issues.apache.org/jira/browse/FLINK-5653
>             Project: Flink
>          Issue Type: Sub-task
>          Components: Table API & SQL
>            Reporter: Fabian Hueske
>            Assignee: Stefano Bortoli
>
> The goal of this issue is to add support for OVER ROWS 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() ROWS BETWEEN 2 PRECEDING 
> AND CURRENT ROW) AS sumB,
>   MIN(b) OVER (PARTITION BY c ORDER BY procTime() 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 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-5656)
> - 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).



--
This message was sent by Atlassian JIRA
(v6.3.15#6346)

Reply via email to