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

    https://github.com/apache/flink/pull/3266#discussion_r101016955
  
    --- Diff: 
flink-libraries/flink-table/src/main/scala/org/apache/flink/table/runtime/aggregate/AggregateUtil.scala
 ---
    @@ -306,6 +307,85 @@ object AggregateUtil {
       }
     
       /**
    +    * Create a 
[[org.apache.flink.api.common.functions.MapPartitionFunction]] that aggregation
    +    * for aggregates.
    +    * The function returns aggregate values of all aggregate function 
which are
    +    * organized by the following format:
    +    *
    +    * {{{
    +    *       avg(x) aggOffsetInRow = 2  count(z) aggOffsetInRow = 5
    +    *           |                          |          
windowEnd(max(rowtime)
    +    *           |                          |                   |
    +    *           v                          v                   v
    +    *        +--------+--------+--------+--------+-----------+---------+
    +    *        |  sum1  | count1 |  sum2  | count2 |windowStart|windowEnd|
    +    *        +--------+--------+--------+--------+-----------+---------+
    +    *                               ^                 ^
    +    *                               |                 |
    +    *             sum(y) aggOffsetInRow = 4    windowStart(min(rowtime))
    +    *
    +    * }}}
    +    *
    +    */
    +  def createDataSetWindowAggregationMapPartitionFunction(
    +    window: LogicalWindow,
    +    namedAggregates: Seq[CalcitePair[AggregateCall, String]],
    +    inputType: RelDataType,
    +    outputType: RelDataType = null,
    +    properties: Seq[NamedWindowProperty] = null,
    +    isPreMapPartition: Boolean = true,
    +    isInputCombined: Boolean = false): MapPartitionFunction[Row, Row] = {
    +
    +    val aggregates = transformToAggregateFunctions(
    +      namedAggregates.map(_.getKey),
    +      inputType,
    +      0)._2
    +
    +    val intermediateRowArity = 
aggregates.map(_.intermediateDataType.length).sum
    +
    +    window match {
    +      case EventTimeSessionGroupWindow(_, _, gap) =>
    +        if (isPreMapPartition) {
    +          val preMapReturnType: RowTypeInfo =
    +            createAggregateBufferDataType(
    +              Array(),
    +              aggregates,
    +              inputType,
    +              Option(Array(BasicTypeInfo.LONG_TYPE_INFO, 
BasicTypeInfo.LONG_TYPE_INFO)))
    +
    +          new DataSetSessionWindowAggregatePreProcessor(
    +            aggregates,
    +            Array(),
    +            // the addition two fields are used to store window-start and 
window-end attributes
    +            intermediateRowArity + 2,
    +            asLong(gap),
    +            preMapReturnType)
    +
    +        } else {
    +          val (startPos, endPos) = 
computeWindowStartEndPropertyPos(properties)
    --- End diff --
    
    We do not need this case if we compute the final aggregates with a 
`GroupReduceFunction`.


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