flink-issues mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From "ASF GitHub Bot (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (FLINK-5654) Add processing time OVER RANGE BETWEEN x PRECEDING aggregation to SQL
Date Thu, 23 Mar 2017 07:45:41 GMT

    [ https://issues.apache.org/jira/browse/FLINK-5654?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15937874#comment-15937874
] 

ASF GitHub Bot commented on FLINK-5654:
---------------------------------------

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

    https://github.com/apache/flink/pull/3590#discussion_r107604045
  
    --- 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 --
    
    @sunjincheng121 Thanks for the suggestion. As i mentioned bellow - i do not think using
the MapState is a good option because we need to go through all the elements to remove and
retract from the accumulator. That is O(n) complexity. This really makes it equivalent with
the window based implementation. 
    However, what i meant is that we should use
    Queue[JTuple2[Long,Row]]...we can put this either in a ValueState or any other form of
a state....it does not matter. Than we do the operations over the contents directly on this.
    I would suggest also for your implementation to use this approach as it can be more efficient
to keep the events timely sorted. Perhaps a Queue would not work for you but than you should
use a (Double)LinkedList to keep the events sorted by the event time
    @fhueske 


> 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).



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

Mime
View raw message