Github user fhueske commented on a diff in the pull request:
https://github.com/apache/flink/pull/3364#discussion_r104507420
--- Diff:
flink-libraries/flink-table/src/main/scala/org/apache/flink/table/runtime/aggregate/AggregateUtil.scala
---
@@ -153,6 +170,156 @@ object AggregateUtil {
}
/**
+ * Create a
[[org.apache.flink.api.common.functions.GroupReduceFunction]] that prepares for
+ * incremental 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 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,
+ groupings.length)._2
+
+ val returnType: RowTypeInfo = createAggregateBufferDataType(
+ groupings,
+ aggregates,
+ inputType,
+ Some(Array(BasicTypeInfo.LONG_TYPE_INFO)))
+
+ window match {
+ case EventTimeSlidingGroupWindow(_, _, size, slide) if
isTimeInterval(size.resultType) =>
+ // sliding time-window
+ if (aggregates.forall(_.supportPartial)) {
+ // for incremental aggregations
+ new DataSetSlideTimeWindowAggReduceCombineFunction(
+ aggregates,
+ groupings.length,
+ returnType.getArity - 1,
+ asLong(size),
+ asLong(slide),
+ returnType)
+ } else {
+ // for non-incremental aggregations
--- End diff --
Do we need this case? Without partial aggregation, we cannot use tumbling
windows to compute sliding windows. So this method is only called if all
aggregates support partial aggregation, right?
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