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From fhueske <...@git.apache.org>
Subject [GitHub] flink pull request #3364: [FLINK-5047] [table] Add sliding group-windows for...
Date Mon, 06 Mar 2017 21:40:42 GMT
Github user fhueske commented on a diff in the pull request:

    https://github.com/apache/flink/pull/3364#discussion_r104515406
  
    --- Diff: flink-libraries/flink-table/src/main/scala/org/apache/flink/table/plan/nodes/dataset/DataSetWindowAggregate.scala
---
    @@ -280,6 +285,138 @@ class DataSetWindowAggregate(
         }
       }
     
    +  private def createEventTimeSlidingWindowDataSet(
    +      inputDS: DataSet[Row],
    +      isTimeWindow: Boolean,
    +      isParserCaseSensitive: Boolean)
    +    : DataSet[Row] = {
    +
    +    // create MapFunction for initializing the aggregations
    +    // it aligns the rowtime for pre-tumbling in case of a time-window for incremental
aggregates
    +    val mapFunction = createDataSetWindowPrepareMapFunction(
    +      window,
    +      namedAggregates,
    +      grouping,
    +      inputType,
    +      isParserCaseSensitive)
    +
    +    val mappedDataSet = inputDS
    +      .map(mapFunction)
    +      .name(prepareOperatorName)
    +
    +    val mapReturnType = mappedDataSet.getType
    +
    +    val rowTypeInfo = FlinkTypeFactory.toInternalRowTypeInfo(getRowType)
    +    val groupingKeys = grouping.indices.toArray
    +
    +    // do incremental aggregation if possible
    +    val isIncremental = doAllSupportPartialAggregation(
    +      namedAggregates.map(_.getKey),
    +      inputType,
    +      grouping.length)
    +
    +    val preparedDataSet = if (isTimeWindow) {
    +      // time window
    +
    +      if (isIncremental) {
    +        // incremental aggregates
    +
    +        val groupingKeysAndAlignedRowtime = groupingKeys :+ mapReturnType.getArity -
1
    +
    +        // create GroupReduceFunction
    +        // for pre-tumbling and replicating/omitting the content for each pane
    +        val prepareReduceFunction = createDataSetSlideWindowPrepareGroupReduceFunction(
    +          window,
    +          namedAggregates,
    +          grouping,
    +          inputType,
    +          isParserCaseSensitive)
    +
    +        mappedDataSet.asInstanceOf[DataSet[Row]]
    +          .groupBy(groupingKeysAndAlignedRowtime: _*)
    +          .reduceGroup(prepareReduceFunction) // pre-tumbles and replicates/omits
    +          .name(prepareOperatorName)
    +      } else {
    +        // non-incremental aggregates
    +
    +        // create FlatMapFunction
    +        // for replicating/omitting the content for each pane
    +        val prepareFlatMapFunction = createDataSetSlideWindowPrepareFlatMapFunction(
    +          window,
    +          namedAggregates,
    +          grouping,
    +          inputType,
    +          isParserCaseSensitive)
    +
    +        mappedDataSet
    +          .flatMap(prepareFlatMapFunction) // replicates/omits
    +      }
    +    } else {
    +      // count window
    --- End diff --
    
    Can we break this PR into time and count windows? There are so many cases to consider...


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