Github user fhueske commented on a diff in the pull request:
https://github.com/apache/flink/pull/2562#discussion_r82147739
--- Diff:
flink-libraries/flink-table/src/main/scala/org/apache/flink/api/table/plan/nodes/datastream/DataStreamAggregate.scala
---
@@ -0,0 +1,261 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.flink.api.table.plan.nodes.datastream
+
+import org.apache.calcite.plan.{RelOptCluster, RelTraitSet}
+import org.apache.calcite.rel.`type`.RelDataType
+import org.apache.calcite.rel.core.AggregateCall
+import org.apache.calcite.rel.{RelNode, RelWriter, SingleRel}
+import org.apache.flink.api.common.typeinfo.{BasicTypeInfo,
TypeInformation}
+import org.apache.flink.api.java.tuple.Tuple
+import org.apache.flink.api.table.expressions.{Expression, Literal}
+import org.apache.flink.api.table.plan.logical._
+import org.apache.flink.api.table.plan.nodes.FlinkAggregate
+import
org.apache.flink.api.table.plan.nodes.datastream.DataStreamAggregate.{createKeyedWindowedStream,
createNonKeyedWindowedStream}
+import org.apache.flink.api.table.runtime.aggregate.AggregateUtil._
+import
org.apache.flink.api.table.runtime.aggregate.{AggregateAllWindowFunction,
AggregateUtil, AggregateWindowFunction}
+import org.apache.flink.api.table.typeutils.TypeCheckUtils.isTimeInterval
+import org.apache.flink.api.table.typeutils.{IntervalTypeInfo,
RowTypeInfo, TypeCheckUtils, TypeConverter}
+import org.apache.flink.api.table.{FlinkTypeFactory, Row,
StreamTableEnvironment}
+import org.apache.flink.streaming.api.datastream.{AllWindowedStream,
DataStream, KeyedStream, WindowedStream}
+import
org.apache.flink.streaming.api.functions.windowing.{AllWindowFunction,
WindowFunction}
+import org.apache.flink.streaming.api.windowing.assigners._
+import org.apache.flink.streaming.api.windowing.time.Time
+import org.apache.flink.streaming.api.windowing.windows.{Window =>
DataStreamWindow}
+
+import scala.collection.JavaConverters._
+
+class DataStreamAggregate(
+ window: LogicalWindow,
+ cluster: RelOptCluster,
+ traitSet: RelTraitSet,
+ inputNode: RelNode,
+ namedAggregates: Seq[CalcitePair[AggregateCall, String]],
+ rowRelDataType: RelDataType,
+ inputType: RelDataType,
+ grouping: Array[Int])
+ extends SingleRel(cluster, traitSet, inputNode)
+ with FlinkAggregate
+ with DataStreamRel {
+
+ override def deriveRowType() = rowRelDataType
+
+ override def copy(traitSet: RelTraitSet, inputs:
java.util.List[RelNode]): RelNode = {
+ new DataStreamAggregate(
+ window,
+ cluster,
+ traitSet,
+ inputs.get(0),
+ namedAggregates,
+ getRowType,
+ inputType,
+ grouping)
+ }
+
+ override def toString: String = {
+ s"Aggregate(${ if (!grouping.isEmpty) {
+ s"groupBy: (${groupingToString(inputType, grouping)}), "
+ } else {
+ ""
+ }}window: ($window), " +
+ s"select: (${aggregationToString(inputType, grouping, getRowType,
namedAggregates)}))"
+ }
+
+ override def explainTerms(pw: RelWriter): RelWriter = {
+ super.explainTerms(pw)
+ .itemIf("groupBy", groupingToString(inputType, grouping),
!grouping.isEmpty)
+ .item("window", window)
+ .item("select", aggregationToString(inputType, grouping, getRowType,
namedAggregates))
+ }
+
+ override def translateToPlan(
+ tableEnv: StreamTableEnvironment,
+ expectedType: Option[TypeInformation[Any]]): DataStream[Any] = {
+ val config = tableEnv.getConfig
+
+ val groupingKeys = grouping.indices.toArray
+ // add grouping fields, position keys in the input, and input type
+ val aggregateResult =
AggregateUtil.createOperatorFunctionsForAggregates(namedAggregates,
+ inputType, getRowType, grouping, config)
+
+ val inputDS = input.asInstanceOf[DataStreamRel].translateToPlan(
+ tableEnv,
+ // tell the input operator that this operator currently only
supports Rows as input
+ Some(TypeConverter.DEFAULT_ROW_TYPE))
+
+ // get the output types
+ val fieldTypes: Array[TypeInformation[_]] =
getRowType.getFieldList.asScala
+ .map(field => FlinkTypeFactory.toTypeInfo(field.getType))
+ .toArray
+
+ val aggString = aggregationToString(inputType, grouping, getRowType,
namedAggregates)
+ val prepareOpName = s"prepare select: ($aggString)"
+ val mappedInput = inputDS
+ .map(aggregateResult._1)
+ .name(prepareOpName)
+
+ val groupReduceFunction = aggregateResult._2
+ val rowTypeInfo = new RowTypeInfo(fieldTypes)
+
+ val result = {
+ // grouped / keyed aggregation
+ if (groupingKeys.length > 0) {
+ val aggOpName = s"groupBy: (${groupingToString(inputType,
grouping)}), " +
+ s"window: ($window), " +
+ s"select: ($aggString)"
+ val aggregateFunction = new
AggregateWindowFunction(groupReduceFunction)
+
+ val keyedStream = mappedInput.keyBy(groupingKeys: _*)
+
+ val windowedStream = createKeyedWindowedStream(window, keyedStream)
+ .asInstanceOf[WindowedStream[Row, Tuple, DataStreamWindow]]
+
+ windowedStream
+ .apply(aggregateFunction)
+ .returns(rowTypeInfo)
+ .name(aggOpName)
+ .asInstanceOf[DataStream[Any]]
+ }
+ // global / non-keyed aggregation
+ else {
+ val aggOpName = s"window: ($window), select: ($aggString)"
+ val aggregateFunction = new
AggregateAllWindowFunction(groupReduceFunction)
+
+ val windowedStream = createNonKeyedWindowedStream(window,
mappedInput)
+ .asInstanceOf[AllWindowedStream[Row, DataStreamWindow]]
+
+ windowedStream
+ .apply(aggregateFunction)
+ .returns(rowTypeInfo)
+ .name(aggOpName)
+ .asInstanceOf[DataStream[Any]]
+ }
+ }
+
+ // if the expected type is not a Row, inject a mapper to convert to
the expected type
+ expectedType match {
+ case Some(typeInfo) if typeInfo.getTypeClass != classOf[Row] =>
+ val mapName = s"convert:
(${getRowType.getFieldNames.asScala.toList.mkString(", ")})"
+ result.map(getConversionMapper(
+ config = config,
+ nullableInput = false,
+ inputType = rowTypeInfo.asInstanceOf[TypeInformation[Any]],
+ expectedType = expectedType.get,
+ conversionOperatorName = "DataStreamAggregateConversion",
+ fieldNames = getRowType.getFieldNames.asScala
+ ))
+ .name(mapName)
+ case _ => result
+ }
+ }
+
+}
+
+object DataStreamAggregate {
+
+ private def createKeyedWindowedStream(groupWindow: LogicalWindow,
stream: KeyedStream[Row, Tuple])
+ : WindowedStream[Row, Tuple, _ <: DataStreamWindow] = groupWindow
match {
+
+ case ProcessingTimeTumblingGroupWindow(_, size) if
isTimeInterval(size.resultType) =>
+ stream.window(TumblingProcessingTimeWindows.of(asTime(size)))
+
+ case ProcessingTimeTumblingGroupWindow(_, size) =>
+ stream.countWindow(asCount(size))
+
+ case EventTimeTumblingGroupWindow(_, _, size) if
isTimeInterval(size.resultType) =>
+ stream
+ .window(TumblingEventTimeWindows.of(asTime(size)))
+
+ case EventTimeTumblingGroupWindow(_, _, size) =>
+ stream.countWindow(asCount(size))
+
+ case ProcessingTimeSlidingGroupWindow(_, size, slide) if
isTimeInterval(size.resultType) =>
+ stream.window(SlidingProcessingTimeWindows.of(asTime(size),
asTime(slide)))
+
+ case ProcessingTimeSlidingGroupWindow(_, size, slide) =>
+ stream.countWindow(asCount(size), asCount(slide))
+
+ case EventTimeSlidingGroupWindow(_, _, size, slide)
+ if isTimeInterval(size.resultType) =>
+ stream
+ .window(SlidingEventTimeWindows.of(asTime(size), asTime(slide)))
+
+ case EventTimeSlidingGroupWindow(_, _, size, slide) =>
+ stream.countWindow(asCount(size), asCount(slide))
+
+ case ProcessingTimeSessionGroupWindow(_, gap: Expression) =>
+ stream.window(ProcessingTimeSessionWindows.withGap(asTime(gap)))
+
+ case EventTimeSessionGroupWindow(_, _, gap) =>
+ stream
+ .window(EventTimeSessionWindows.withGap(asTime(gap)))
+ }
+
+ private def createNonKeyedWindowedStream(groupWindow: LogicalWindow,
stream: DataStream[Row])
+ : AllWindowedStream[Row, _ <: DataStreamWindow] = groupWindow match {
+
+ case ProcessingTimeTumblingGroupWindow(_, size) if
isTimeInterval(size.resultType) =>
+ stream.windowAll(TumblingProcessingTimeWindows.of(asTime(size)))
+
+ case ProcessingTimeTumblingGroupWindow(_, size) =>
+ stream.countWindowAll(asCount(size))
+
+ case EventTimeTumblingGroupWindow(_, _, size) if
isTimeInterval(size.resultType) =>
+ stream
+ .windowAll(TumblingEventTimeWindows.of(asTime(size)))
+
+ case EventTimeTumblingGroupWindow(_, _, size) =>
+ stream.countWindowAll(asCount(size))
+
+ case ProcessingTimeSlidingGroupWindow(_, size, slide) if
isTimeInterval(size.resultType) =>
+ stream.windowAll(SlidingProcessingTimeWindows.of(asTime(size),
asTime(slide)))
+
+ case ProcessingTimeSlidingGroupWindow(_, size, slide) =>
+ stream.countWindowAll(asCount(size), asCount(slide))
+
+ case EventTimeSlidingGroupWindow(_, _, size, slide)
+ if isTimeInterval(size.resultType) =>
+ stream
+ .windowAll(SlidingEventTimeWindows.of(asTime(size), asTime(slide)))
+
+ case EventTimeSlidingGroupWindow(_, _, size, slide) =>
+ stream.countWindowAll(asCount(size), asCount(slide))
--- End diff --
sort on event-time.
---
If your project is set up for it, you can reply to this email and have your
reply appear on GitHub as well. If your project does not have this feature
enabled and wishes so, or if the feature is enabled but not working, please
contact infrastructure at infrastruct...@apache.org or file a JIRA ticket
with INFRA.
---
