Github user fhueske commented on a diff in the pull request: https://github.com/apache/flink/pull/3364#discussion_r104925507 --- Diff: flink-libraries/flink-table/src/main/scala/org/apache/flink/table/runtime/aggregate/AggregateUtil.scala --- @@ -186,6 +200,130 @@ object AggregateUtil { } /** + * Create a [[org.apache.flink.api.common.functions.GroupReduceFunction]] that prepares for + * partial aggregates of sliding windows (time and count-windows). + * It requires a prepared input (with intermediate aggregate fields and aligned rowtime for + * pre-tumbling in case of time-windows), pre-aggregates (pre-tumbles) rows, aligns the + * window-start, and replicates or omits records for different panes of a sliding window. + * + * The output of the function contains the grouping keys, the intermediate aggregate values of + * all aggregate function and the aligned window start. Window start must not be a timestamp, + * but can also be a count value for count-windows. + * + * The output is stored in Row by the following format: + * + * {{{ + * avg(x) aggOffsetInRow = 2 count(z) aggOffsetInRow = 5 + * | | + * v v + * +---------+---------+--------+--------+--------+--------+-------------+ + * |groupKey1|groupKey2| sum1 | count1 | sum2 | count2 | windowStart | + * +---------+---------+--------+--------+--------+--------+-------------+ + * ^ ^ + * | | + * sum(y) aggOffsetInRow = 4 window start for pane mapping + * }}} + * + * NOTE: this function is only used for sliding windows with partial aggregates on batch tables. + */ + def createDataSetSlideWindowPrepareGroupReduceFunction( + window: LogicalWindow, + namedAggregates: Seq[CalcitePair[AggregateCall, String]], + groupings: Array[Int], + inputType: RelDataType, + isParserCaseSensitive: Boolean) + : RichGroupReduceFunction[Row, Row] = { + + val aggregates = transformToAggregateFunctions( + namedAggregates.map(_.getKey), + inputType, + needRetraction = false)._2 + + val returnType: RowTypeInfo = createDataSetAggregateBufferDataType( + groupings, + aggregates, + inputType, + Some(Array(BasicTypeInfo.LONG_TYPE_INFO))) + + window match { + case EventTimeSlidingGroupWindow(_, _, size, slide) if isTimeInterval(size.resultType) => + // sliding time-window + // for partial aggregations + new DataSetSlideTimeWindowAggReduceCombineFunction( + aggregates, + groupings.length, + returnType.getArity - 1, + asLong(size), + asLong(slide), + returnType) + + case _ => + throw new UnsupportedOperationException(s"$window is currently not supported on batch.") + } + } + + /** + * Create a [[org.apache.flink.api.common.functions.FlatMapFunction]] that prepares for + * non-incremental aggregates of sliding windows (time-windows). + * + * It requires a prepared input (with intermediate aggregate fields), aligns the + * window-start, and replicates or omits records for different panes of a sliding window. + * + * The output of the function contains the grouping keys, the intermediate aggregate values of + * all aggregate function and the aligned window start. + * + * The output is stored in Row by the following format: + * + * {{{ + * avg(x) aggOffsetInRow = 2 count(z) aggOffsetInRow = 5 + * | | + * v v + * +---------+---------+--------+--------+--------+--------+-------------+ + * |groupKey1|groupKey2| sum1 | count1 | sum2 | count2 | windowStart | + * +---------+---------+--------+--------+--------+--------+-------------+ + * ^ ^ + * | | + * sum(y) aggOffsetInRow = 4 window start for pane mapping + * }}} + * + * NOTE: this function is only used for time-based sliding windows on batch tables. + */ + def createDataSetSlideWindowPrepareFlatMapFunction( + window: LogicalWindow, + namedAggregates: Seq[CalcitePair[AggregateCall, String]], + groupings: Array[Int], + inputType: RelDataType, + isParserCaseSensitive: Boolean) + : FlatMapFunction[Row, Row] = { + + val aggregates = transformToAggregateFunctions( + namedAggregates.map(_.getKey), + inputType, + needRetraction = false)._2 + + val mapReturnType: RowTypeInfo = createDataSetAggregateBufferDataType( --- End diff -- output type should be equal to input type.
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