wenlong88 commented on a change in pull request #14605:
URL: https://github.com/apache/flink/pull/14605#discussion_r556283456
##########
File path:
flink-table/flink-table-planner-blink/src/main/scala/org/apache/flink/table/planner/codegen/over/MultiFieldRangeBoundComparatorCodeGenerator.scala
##########
@@ -48,9 +46,14 @@ class MultiFieldRangeBoundComparatorCodeGenerator(
}
val ctx = CodeGeneratorContext(conf)
-
val compareCode = GenerateUtils.generateRowCompare(
Review comment:
use the new method accepts sortSpec
##########
File path:
flink-table/flink-table-planner-blink/src/main/java/org/apache/flink/table/planner/plan/nodes/exec/stream/StreamExecOverAggregate.java
##########
@@ -0,0 +1,380 @@
+/*
+ * 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.planner.plan.nodes.exec.stream;
+
+import org.apache.flink.api.dag.Transformation;
+import org.apache.flink.streaming.api.functions.KeyedProcessFunction;
+import org.apache.flink.streaming.api.operators.KeyedProcessOperator;
+import org.apache.flink.streaming.api.transformations.OneInputTransformation;
+import org.apache.flink.table.api.TableConfig;
+import org.apache.flink.table.api.TableException;
+import org.apache.flink.table.data.RowData;
+import org.apache.flink.table.planner.calcite.FlinkTypeFactory;
+import org.apache.flink.table.planner.codegen.CodeGeneratorContext;
+import org.apache.flink.table.planner.codegen.agg.AggsHandlerCodeGenerator;
+import org.apache.flink.table.planner.delegation.PlannerBase;
+import org.apache.flink.table.planner.plan.nodes.exec.ExecEdge;
+import org.apache.flink.table.planner.plan.nodes.exec.ExecNode;
+import org.apache.flink.table.planner.plan.nodes.exec.ExecNodeBase;
+import org.apache.flink.table.planner.plan.nodes.exec.utils.OverSpec;
+import org.apache.flink.table.planner.plan.utils.AggregateInfoList;
+import org.apache.flink.table.planner.plan.utils.AggregateUtil;
+import org.apache.flink.table.planner.plan.utils.KeySelectorUtil;
+import org.apache.flink.table.planner.plan.utils.OverAggregateUtil;
+import org.apache.flink.table.planner.utils.JavaScalaConversionUtil;
+import org.apache.flink.table.runtime.generated.GeneratedAggsHandleFunction;
+import org.apache.flink.table.runtime.keyselector.RowDataKeySelector;
+import
org.apache.flink.table.runtime.operators.over.ProcTimeRangeBoundedPrecedingFunction;
+import
org.apache.flink.table.runtime.operators.over.ProcTimeRowsBoundedPrecedingFunction;
+import
org.apache.flink.table.runtime.operators.over.ProcTimeUnboundedPrecedingFunction;
+import
org.apache.flink.table.runtime.operators.over.RowTimeRangeBoundedPrecedingFunction;
+import
org.apache.flink.table.runtime.operators.over.RowTimeRangeUnboundedPrecedingFunction;
+import
org.apache.flink.table.runtime.operators.over.RowTimeRowsBoundedPrecedingFunction;
+import
org.apache.flink.table.runtime.operators.over.RowTimeRowsUnboundedPrecedingFunction;
+import org.apache.flink.table.runtime.types.LogicalTypeDataTypeConverter;
+import org.apache.flink.table.runtime.typeutils.InternalTypeInfo;
+import org.apache.flink.table.types.logical.LogicalType;
+import org.apache.flink.table.types.logical.RowType;
+import org.apache.flink.table.types.logical.TimestampKind;
+import org.apache.flink.table.types.logical.TimestampType;
+
+import org.apache.calcite.rel.core.AggregateCall;
+import org.apache.calcite.rex.RexLiteral;
+import org.apache.calcite.tools.RelBuilder;
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+
+import java.math.BigDecimal;
+import java.util.ArrayList;
+import java.util.Arrays;
+import java.util.Collections;
+import java.util.List;
+import java.util.stream.IntStream;
+
+/** Stream {@link ExecNode} for time-based over operator. */
+public class StreamExecOverAggregate extends ExecNodeBase<RowData>
+ implements StreamExecNode<RowData> {
+ private static final Logger LOG =
LoggerFactory.getLogger(StreamExecOverAggregate.class);
+
+ private final OverSpec overSpec;
+
+ public StreamExecOverAggregate(
+ OverSpec overSpec, ExecEdge inputEdge, RowType outputType, String
description) {
+ super(Collections.singletonList(inputEdge), outputType, description);
+ this.overSpec = overSpec;
+ }
+
+ @SuppressWarnings("unchecked")
+ @Override
+ protected Transformation<RowData> translateToPlanInternal(PlannerBase
planner) {
+ if (overSpec.getGroups().size() > 1) {
+ throw new TableException("All aggregates must be computed on the
same window.");
+ }
+
+ final OverSpec.GroupSpec group = overSpec.getGroups().get(0);
+ final int[] orderKeys = group.getSort().getFieldIndices();
+ final boolean[] isAscendingOrders =
group.getSort().getAscendingOrders();
+ if (orderKeys.length != 1 || isAscendingOrders.length != 1) {
+ throw new TableException("The window can only be ordered by a
single time column.");
+ }
+
+ if (!isAscendingOrders[0]) {
+ throw new TableException("The window can only be ordered in
ASCENDING mode.");
+ }
+
+ final int[] partitionKeys = overSpec.getPartition().getFieldIndices();
+ final TableConfig tableConfig = planner.getTableConfig();
+ if (partitionKeys.length > 0 &&
tableConfig.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.");
+ }
+
+ final ExecNode<RowData> inputNode = (ExecNode<RowData>)
getInputNodes().get(0);
+ final Transformation<RowData> inputTransform =
inputNode.translateToPlan(planner);
+ final RowType inputRowType = (RowType) inputNode.getOutputType();
+
+ final int orderKey = orderKeys[0];
+ final LogicalType orderKeyType =
inputRowType.getFields().get(orderKey).getType();
+ // check time field && identify window rowtime attribute
+ final int rowTimeIdx;
+ if (orderKeyType instanceof TimestampType
+ && ((TimestampType) orderKeyType).getKind() ==
TimestampKind.ROWTIME) {
+ rowTimeIdx = orderKey;
+ } else if (orderKeyType instanceof TimestampType
+ && ((TimestampType) orderKeyType).getKind() ==
TimestampKind.PROCTIME) {
+ rowTimeIdx = -1;
+ } else {
+ throw new TableException(
+ "OVER windows' ordering in stream mode must be defined on
a time attribute.");
+ }
+
+ final List<RexLiteral> constants = overSpec.getConstants();
+ final List<String> fieldNames = new
ArrayList<>(inputRowType.getFieldNames());
+ final List<LogicalType> fieldTypes = new
ArrayList<>(inputRowType.getChildren());
+ IntStream.range(0, constants.size()).forEach(i -> fieldNames.add("TMP"
+ i));
+ for (int i = 0; i < constants.size(); ++i) {
+ fieldNames.add("TMP" + i);
+
fieldTypes.add(FlinkTypeFactory.toLogicalType(constants.get(i).getType()));
+ }
+
+ final RowType aggInputRowType =
+ RowType.of(
+ fieldTypes.toArray(new LogicalType[0]),
fieldNames.toArray(new String[0]));
Review comment:
not need to create the fieldName?
##########
File path:
flink-table/flink-table-planner-blink/src/main/scala/org/apache/flink/table/planner/plan/nodes/physical/batch/BatchPhysicalOverAggregateBase.scala
##########
@@ -18,88 +18,50 @@
package org.apache.flink.table.planner.plan.nodes.physical.batch
-import org.apache.flink.table.data.RowData
-import org.apache.flink.table.functions.UserDefinedFunction
+import org.apache.flink.table.api.TableException
import org.apache.flink.table.planner.CalcitePair
-import org.apache.flink.table.planner.calcite.FlinkTypeFactory
import org.apache.flink.table.planner.plan.`trait`.{FlinkRelDistribution,
FlinkRelDistributionTraitDef}
import org.apache.flink.table.planner.plan.cost.{FlinkCost, FlinkCostFactory}
-import org.apache.flink.table.planner.plan.nodes.exec.{LegacyBatchExecNode,
ExecEdge}
-import
org.apache.flink.table.planner.plan.nodes.physical.batch.OverWindowMode.OverWindowMode
import
org.apache.flink.table.planner.plan.rules.physical.batch.BatchExecJoinRuleBase
+import
org.apache.flink.table.planner.plan.utils.OverAggregateUtil.splitOutOffsetOrInsensitiveGroup
import org.apache.flink.table.planner.plan.utils.{FlinkRelOptUtil,
OverAggregateUtil, RelExplainUtil}
-import com.google.common.collect.ImmutableList
import org.apache.calcite.plan._
import org.apache.calcite.rel.RelDistribution.Type._
import org.apache.calcite.rel._
import org.apache.calcite.rel.`type`.RelDataType
import org.apache.calcite.rel.core.Window.Group
import org.apache.calcite.rel.core.{AggregateCall, Window}
import org.apache.calcite.rel.metadata.RelMetadataQuery
-import org.apache.calcite.rex.RexLiteral
-import org.apache.calcite.sql.fun.SqlLeadLagAggFunction
-import org.apache.calcite.tools.RelBuilder
import org.apache.calcite.util.ImmutableIntList
import java.util
import scala.collection.JavaConversions._
-import scala.collection.mutable.ArrayBuffer
/**
- * Batch physical RelNode for sort-based over [[Window]] aggregate.
- */
-abstract class BatchExecOverAggregateBase(
+ * Base batch physical RelNode for sort-based over [[Window]] aggregate.
+ */
+abstract class BatchPhysicalOverAggregateBase(
cluster: RelOptCluster,
- relBuilder: RelBuilder,
traitSet: RelTraitSet,
inputRel: RelNode,
outputRowType: RelDataType,
inputRowType: RelDataType,
- grouping: Array[Int],
- orderKeyIndices: Array[Int],
- orders: Array[Boolean],
- nullIsLasts: Array[Boolean],
- windowGroupToAggCallToAggFunction: Seq[
- (Window.Group, Seq[(AggregateCall, UserDefinedFunction)])],
+ windowGroups: Seq[Window.Group],
logicWindow: Window)
extends SingleRel(cluster, traitSet, inputRel)
- with BatchPhysicalRel
- with LegacyBatchExecNode[RowData] {
-
- protected lazy val modeToGroupToAggCallToAggFunction:
- Seq[(OverWindowMode, Window.Group, Seq[(AggregateCall,
UserDefinedFunction)])] =
- splitOutOffsetOrInsensitiveGroup()
+ with BatchPhysicalRel {
- protected val constants: ImmutableList[RexLiteral] = logicWindow.constants
- protected val inputTypeWithConstants: RelDataType = {
- val constantTypes = constants.map(c =>
FlinkTypeFactory.toLogicalType(c.getType))
- val inputTypeNamesWithConstants =
- inputType.getFieldNames ++ constants.indices.map(i => "TMP" + i)
- val inputTypesWithConstants = inputType.getChildren ++ constantTypes
- cluster.getTypeFactory.asInstanceOf[FlinkTypeFactory]
- .buildRelNodeRowType(inputTypeNamesWithConstants,
inputTypesWithConstants)
+ val partitionKeyIndices: Array[Int] = windowGroups.head.keys.toArray
+ windowGroups.tail.foreach { g =>
+ if (!util.Arrays.equals(partitionKeyIndices, g.keys.toArray)) {
+ throw new TableException("" +
+ "BatchPhysicalOverAggregateBase requires all groups should have same
partition key.")
+ }
}
- lazy val aggregateCalls: Seq[AggregateCall] =
- windowGroupToAggCallToAggFunction.flatMap(_._2).map(_._1)
-
- protected lazy val inputType =
FlinkTypeFactory.toLogicalRowType(inputRowType)
-
- protected def isUnboundedWindow(group: Window.Group) =
- group.lowerBound.isUnbounded && group.upperBound.isUnbounded
-
- protected def isUnboundedPrecedingWindow(group: Window.Group) =
- group.lowerBound.isUnbounded && !group.upperBound.isUnbounded
-
- protected def isUnboundedFollowingWindow(group: Window.Group) =
- !group.lowerBound.isUnbounded && group.upperBound.isUnbounded
-
- protected def isSlidingWindow(group: Window.Group) =
- !group.lowerBound.isUnbounded && !group.upperBound.isUnbounded
-
- def getGrouping: Array[Int] = grouping
+ protected lazy val newWindowGroups: Seq[Group] =
splitOutOffsetOrInsensitiveGroup(windowGroups)
Review comment:
how about a more specified name？maybe reOrganizedGroups？
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