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From rtudoran <...@git.apache.org>
Subject [GitHub] flink pull request #3550: [FLINK-5654] - Add processing time OVER RANGE BETW...
Date Fri, 17 Mar 2017 14:50:10 GMT
Github user rtudoran commented on a diff in the pull request:

    --- Diff: flink-libraries/flink-table/src/main/scala/org/apache/flink/table/plan/nodes/datastream/DataStreamOverAggregate.scala
    @@ -119,6 +154,66 @@ 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
    +    var time_boundary = 0L
    +    logicWindow.constants.get(lowerboundIndex).getValue2 match {
    +      case _: java.math.BigDecimal => time_boundary = logicWindow.constants.get(lowerboundIndex)
    +         .getValue2.asInstanceOf[java.math.BigDecimal].longValue()
    +      case _ => throw new TableException("OVER Window boundaries must be numeric")
    +    }
    +    val (aggFields, aggregates) =  AggregateUtil.transformToAggregateFunctions(
    +         namedAggregates.map(_.getKey),inputType, needRetraction = false)
    +    // As we it is not possible to operate neither on sliding count neither
    +    // on sliding time we need to manage the eviction of the events that
    +    // expire ourselves based on the proctime (system time). Therefore the
    +    // current system time is assign as the timestamp of the event to be
    +    // recognize by the evictor
    +    val inputDataStreamTimed = inputDS
    +      .assignTimestampsAndWatermarks(new ProcTimeTimestampExtractor())
    +    // get the output types
    +    val rowTypeInfo = FlinkTypeFactory.toInternalRowTypeInfo(getRowType).asInstanceOf[RowTypeInfo]
    +    val result: DataStream[Row] =
    +      if (partitionKeys.nonEmpty) {
    +        inputDataStreamTimed.keyBy(partitionKeys:_*)
    +          .window(GlobalWindows.create())
    +          .trigger(CountTrigger.of(1))
    +          .evictor(TimeEvictor.of(Time.milliseconds(time_boundary))) 
    +          .apply(new DataStreamProcTimeAggregateWindowFunction[GlobalWindow]
    +               (aggregates,aggFields,inputType.getFieldCount))
    +          .returns(rowTypeInfo)
    +          .name(aggOpName)
    +          .asInstanceOf[DataStream[Row]]
    +      } else {
    +          inputDataStreamTimed.windowAll(GlobalWindows.create()).trigger(CountTrigger.of(1))
    +            .evictor(TimeEvictor.of(Time.milliseconds(time_boundary)))        
    +            .apply(new DataStreamProcTimeAggregateGlobalWindowFunction[GlobalWindow](
    --- End diff --
    If you are not strict with that I would like to keep the window based implementation.
Particularly that once this is merged the next thing is that I want to create additional JIRA
issues to support Distinct aggregates for this as well. Those for sure require having all
data at once.
    Regarding the support for FOLLOWING - I do not see any problems there as I can of course
work with the triggers to offer the same functionality (register the trigger to trigger x
elements/time in the future). 
    I do not know what capability support does processfunction provides for evicting events.
    I looked over the example
    and I do not see any eviction mechanism. This means there will be a lot of logic and overhead
moved in identifying what needs to be evicted.
    All in all...I am not sure how this would look in ProcessFunction and I have some doubts
that the end result is better. However, let me know what you think and whether you want it
explicitly as a processfunction (case in which I would like to ask if you can please point
me to some examples of how to manage eviction) or it can stay as it is.

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