| /* |
| * 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.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.streaming.api.datastream.{AllWindowedStream, DataStream, KeyedStream, WindowedStream} |
| import org.apache.flink.streaming.api.windowing.assigners._ |
| import org.apache.flink.streaming.api.windowing.triggers.PurgingTrigger |
| import org.apache.flink.streaming.api.windowing.windows.{Window => DataStreamWindow} |
| import org.apache.flink.table.api.{StreamQueryConfig, StreamTableEnvironment, TableException} |
| import org.apache.flink.table.calcite.FlinkRelBuilder.NamedWindowProperty |
| import org.apache.flink.table.codegen.AggregationCodeGenerator |
| import org.apache.flink.table.expressions.ExpressionUtils._ |
| import org.apache.flink.table.expressions.ResolvedFieldReference |
| import org.apache.flink.table.plan.logical._ |
| import org.apache.flink.table.plan.nodes.CommonAggregate |
| import org.apache.flink.table.plan.nodes.datastream.DataStreamGroupWindowAggregate._ |
| import org.apache.flink.table.plan.rules.datastream.DataStreamRetractionRules |
| import org.apache.flink.table.plan.schema.RowSchema |
| import org.apache.flink.table.runtime.aggregate.AggregateUtil._ |
| import org.apache.flink.table.runtime.aggregate._ |
| import org.apache.flink.table.runtime.triggers.StateCleaningCountTrigger |
| import org.apache.flink.table.runtime.types.{CRow, CRowTypeInfo} |
| import org.apache.flink.table.runtime.{CRowKeySelector, RowtimeProcessFunction} |
| import org.apache.flink.table.typeutils.TypeCheckUtils.isTimeInterval |
| import org.apache.flink.table.util.Logging |
| import org.apache.flink.types.Row |
| |
| class DataStreamGroupWindowAggregate( |
| window: LogicalWindow, |
| namedProperties: Seq[NamedWindowProperty], |
| cluster: RelOptCluster, |
| traitSet: RelTraitSet, |
| inputNode: RelNode, |
| namedAggregates: Seq[CalcitePair[AggregateCall, String]], |
| schema: RowSchema, |
| inputSchema: RowSchema, |
| grouping: Array[Int]) |
| extends SingleRel(cluster, traitSet, inputNode) |
| with CommonAggregate |
| with DataStreamRel |
| with Logging { |
| |
| override def deriveRowType(): RelDataType = schema.relDataType |
| |
| override def needsUpdatesAsRetraction = true |
| |
| override def consumesRetractions = true |
| |
| def getGroupings: Array[Int] = grouping |
| |
| def getWindowProperties: Seq[NamedWindowProperty] = namedProperties |
| |
| override def copy(traitSet: RelTraitSet, inputs: java.util.List[RelNode]): RelNode = { |
| new DataStreamGroupWindowAggregate( |
| window, |
| namedProperties, |
| cluster, |
| traitSet, |
| inputs.get(0), |
| namedAggregates, |
| schema, |
| inputSchema, |
| grouping) |
| } |
| |
| override def toString: String = { |
| s"Aggregate(${ |
| if (!grouping.isEmpty) { |
| s"groupBy: (${groupingToString(inputSchema.relDataType, grouping)}), " |
| } else { |
| "" |
| } |
| }window: ($window), " + |
| s"select: (${ |
| aggregationToString( |
| inputSchema.relDataType, |
| grouping, |
| getRowType, |
| namedAggregates, |
| namedProperties) |
| }))" |
| } |
| |
| override def explainTerms(pw: RelWriter): RelWriter = { |
| super.explainTerms(pw) |
| .itemIf("groupBy", groupingToString(inputSchema.relDataType, grouping), !grouping.isEmpty) |
| .item("window", window) |
| .item( |
| "select", aggregationToString( |
| inputSchema.relDataType, |
| grouping, |
| schema.relDataType, |
| namedAggregates, |
| namedProperties)) |
| } |
| |
| override def translateToPlan( |
| tableEnv: StreamTableEnvironment, |
| queryConfig: StreamQueryConfig): DataStream[CRow] = { |
| |
| val inputDS = input.asInstanceOf[DataStreamRel].translateToPlan(tableEnv, queryConfig) |
| |
| val inputIsAccRetract = DataStreamRetractionRules.isAccRetract(input) |
| |
| if (inputIsAccRetract) { |
| throw new TableException( |
| "Retraction on windowed GroupBy aggregation is not supported yet. " + |
| "Note: Windowed GroupBy aggregation should not follow a " + |
| "non-windowed GroupBy aggregation.") |
| } |
| |
| val isCountWindow = window match { |
| case TumblingGroupWindow(_, _, size) if isRowCountLiteral(size) => true |
| case SlidingGroupWindow(_, _, size, _) if isRowCountLiteral(size) => true |
| case _ => false |
| } |
| |
| if (isCountWindow && grouping.length > 0 && queryConfig.getMinIdleStateRetentionTime < 0) { |
| LOG.warn( |
| "No state retention interval configured for a query which accumulates state. " + |
| "Please provide a query configuration with valid retention interval to prevent excessive " + |
| "state size. You may specify a retention time of 0 to not clean up the state.") |
| } |
| |
| val timestampedInput = if (isRowtimeAttribute(window.timeAttribute)) { |
| // copy the window rowtime attribute into the StreamRecord timestamp field |
| val timeAttribute = window.timeAttribute.asInstanceOf[ResolvedFieldReference].name |
| val timeIdx = inputSchema.fieldNames.indexOf(timeAttribute) |
| if (timeIdx < 0) { |
| throw new TableException("Time attribute could not be found. This is a bug.") |
| } |
| |
| inputDS |
| .process( |
| new RowtimeProcessFunction(timeIdx, CRowTypeInfo(inputSchema.typeInfo))) |
| .setParallelism(inputDS.getParallelism) |
| .name(s"time attribute: ($timeAttribute)") |
| } else { |
| inputDS |
| } |
| |
| val outRowType = CRowTypeInfo(schema.typeInfo) |
| |
| val aggString = aggregationToString( |
| inputSchema.relDataType, |
| grouping, |
| schema.relDataType, |
| namedAggregates, |
| namedProperties) |
| |
| val keyedAggOpName = s"groupBy: (${groupingToString(inputSchema.relDataType, grouping)}), " + |
| s"window: ($window), " + |
| s"select: ($aggString)" |
| val nonKeyedAggOpName = s"window: ($window), select: ($aggString)" |
| |
| val generator = new AggregationCodeGenerator( |
| tableEnv.getConfig, |
| false, |
| inputSchema.typeInfo, |
| None) |
| |
| val needMerge = window match { |
| case SessionGroupWindow(_, _, _) => true |
| case _ => false |
| } |
| // grouped / keyed aggregation |
| if (grouping.length > 0) { |
| val windowFunction = AggregateUtil.createAggregationGroupWindowFunction( |
| window, |
| grouping.length, |
| namedAggregates.size, |
| schema.arity, |
| namedProperties) |
| |
| val keySelector = new CRowKeySelector(grouping, inputSchema.projectedTypeInfo(grouping)) |
| |
| val keyedStream = timestampedInput.keyBy(keySelector) |
| val windowedStream = |
| createKeyedWindowedStream(queryConfig, window, keyedStream) |
| .asInstanceOf[WindowedStream[CRow, Row, DataStreamWindow]] |
| |
| val (aggFunction, accumulatorRowType) = |
| AggregateUtil.createDataStreamAggregateFunction( |
| generator, |
| namedAggregates, |
| inputSchema.relDataType, |
| inputSchema.fieldTypeInfos, |
| schema.relDataType, |
| grouping, |
| needMerge, |
| tableEnv.getConfig) |
| |
| windowedStream |
| .aggregate(aggFunction, windowFunction, accumulatorRowType, outRowType) |
| .name(keyedAggOpName) |
| } |
| // global / non-keyed aggregation |
| else { |
| val windowFunction = AggregateUtil.createAggregationAllWindowFunction( |
| window, |
| schema.arity, |
| namedProperties) |
| |
| val windowedStream = |
| createNonKeyedWindowedStream(queryConfig, window, timestampedInput) |
| .asInstanceOf[AllWindowedStream[CRow, DataStreamWindow]] |
| |
| val (aggFunction, accumulatorRowType) = |
| AggregateUtil.createDataStreamAggregateFunction( |
| generator, |
| namedAggregates, |
| inputSchema.relDataType, |
| inputSchema.fieldTypeInfos, |
| schema.relDataType, |
| Array[Int](), |
| needMerge, |
| tableEnv.getConfig) |
| |
| windowedStream |
| .aggregate(aggFunction, windowFunction, accumulatorRowType, outRowType) |
| .name(nonKeyedAggOpName) |
| } |
| } |
| } |
| |
| object DataStreamGroupWindowAggregate { |
| |
| private def createKeyedWindowedStream( |
| queryConfig: StreamQueryConfig, |
| groupWindow: LogicalWindow, |
| stream: KeyedStream[CRow, Row]): |
| WindowedStream[CRow, Row, _ <: DataStreamWindow] = groupWindow match { |
| |
| case TumblingGroupWindow(_, timeField, size) |
| if isProctimeAttribute(timeField) && isTimeIntervalLiteral(size)=> |
| stream.window(TumblingProcessingTimeWindows.of(toTime(size))) |
| |
| case TumblingGroupWindow(_, timeField, size) |
| if isProctimeAttribute(timeField) && isRowCountLiteral(size)=> |
| stream.countWindow(toLong(size)) |
| .trigger(PurgingTrigger.of(StateCleaningCountTrigger.of(queryConfig, toLong(size)))); |
| |
| case TumblingGroupWindow(_, timeField, size) |
| if isRowtimeAttribute(timeField) && isTimeIntervalLiteral(size) => |
| stream.window(TumblingEventTimeWindows.of(toTime(size))) |
| |
| case TumblingGroupWindow(_, _, size) => |
| // TODO: EventTimeTumblingGroupWindow should sort the stream on event time |
| // before applying the windowing logic. Otherwise, this would be the same as a |
| // ProcessingTimeTumblingGroupWindow |
| throw new UnsupportedOperationException( |
| "Event-time grouping windows on row intervals are currently not supported.") |
| |
| case SlidingGroupWindow(_, timeField, size, slide) |
| if isProctimeAttribute(timeField) && isTimeIntervalLiteral(slide) => |
| stream.window(SlidingProcessingTimeWindows.of(toTime(size), toTime(slide))) |
| |
| case SlidingGroupWindow(_, timeField, size, slide) |
| if isProctimeAttribute(timeField) && isRowCountLiteral(size) => |
| stream.countWindow(toLong(size), toLong(slide)) |
| .trigger(StateCleaningCountTrigger.of(queryConfig, toLong(slide))); |
| |
| case SlidingGroupWindow(_, timeField, size, slide) |
| if isRowtimeAttribute(timeField) && isTimeIntervalLiteral(size)=> |
| stream.window(SlidingEventTimeWindows.of(toTime(size), toTime(slide))) |
| |
| case SlidingGroupWindow(_, _, size, slide) => |
| // TODO: EventTimeTumblingGroupWindow should sort the stream on event time |
| // before applying the windowing logic. Otherwise, this would be the same as a |
| // ProcessingTimeTumblingGroupWindow |
| throw new UnsupportedOperationException( |
| "Event-time grouping windows on row intervals are currently not supported.") |
| |
| case SessionGroupWindow(_, timeField, gap) |
| if isProctimeAttribute(timeField) => |
| stream.window(ProcessingTimeSessionWindows.withGap(toTime(gap))) |
| |
| case SessionGroupWindow(_, timeField, gap) |
| if isRowtimeAttribute(timeField) => |
| stream.window(EventTimeSessionWindows.withGap(toTime(gap))) |
| } |
| |
| private def createNonKeyedWindowedStream( |
| queryConfig: StreamQueryConfig, |
| groupWindow: LogicalWindow, |
| stream: DataStream[CRow]): |
| AllWindowedStream[CRow, _ <: DataStreamWindow] = groupWindow match { |
| |
| case TumblingGroupWindow(_, timeField, size) |
| if isProctimeAttribute(timeField) && isTimeIntervalLiteral(size) => |
| stream.windowAll(TumblingProcessingTimeWindows.of(toTime(size))) |
| |
| case TumblingGroupWindow(_, timeField, size) |
| if isProctimeAttribute(timeField) && isRowCountLiteral(size)=> |
| stream.countWindowAll(toLong(size)) |
| .trigger(PurgingTrigger.of(StateCleaningCountTrigger.of(queryConfig, toLong(size)))); |
| |
| case TumblingGroupWindow(_, _, size) if isTimeInterval(size.resultType) => |
| stream.windowAll(TumblingEventTimeWindows.of(toTime(size))) |
| |
| case TumblingGroupWindow(_, _, size) => |
| // TODO: EventTimeTumblingGroupWindow should sort the stream on event time |
| // before applying the windowing logic. Otherwise, this would be the same as a |
| // ProcessingTimeTumblingGroupWindow |
| throw new UnsupportedOperationException( |
| "Event-time grouping windows on row intervals are currently not supported.") |
| |
| case SlidingGroupWindow(_, timeField, size, slide) |
| if isProctimeAttribute(timeField) && isTimeIntervalLiteral(size) => |
| stream.windowAll(SlidingProcessingTimeWindows.of(toTime(size), toTime(slide))) |
| |
| case SlidingGroupWindow(_, timeField, size, slide) |
| if isProctimeAttribute(timeField) && isRowCountLiteral(size)=> |
| stream.countWindowAll(toLong(size), toLong(slide)) |
| .trigger(StateCleaningCountTrigger.of(queryConfig, toLong(slide))); |
| |
| case SlidingGroupWindow(_, timeField, size, slide) |
| if isRowtimeAttribute(timeField) && isTimeIntervalLiteral(size)=> |
| stream.windowAll(SlidingEventTimeWindows.of(toTime(size), toTime(slide))) |
| |
| case SlidingGroupWindow(_, _, size, slide) => |
| // TODO: EventTimeTumblingGroupWindow should sort the stream on event time |
| // before applying the windowing logic. Otherwise, this would be the same as a |
| // ProcessingTimeTumblingGroupWindow |
| throw new UnsupportedOperationException( |
| "Event-time grouping windows on row intervals are currently not supported.") |
| |
| case SessionGroupWindow(_, timeField, gap) |
| if isProctimeAttribute(timeField) && isTimeIntervalLiteral(gap) => |
| stream.windowAll(ProcessingTimeSessionWindows.withGap(toTime(gap))) |
| |
| case SessionGroupWindow(_, timeField, gap) |
| if isRowtimeAttribute(timeField) && isTimeIntervalLiteral(gap) => |
| stream.windowAll(EventTimeSessionWindows.withGap(toTime(gap))) |
| } |
| |
| } |
| |
