[GitHub] [flink] wenlong88 commented on a change in pull request #14605: [FLINK-20883][table-planner-blink] Separate the implementation of BatchExecOverAggregate and StreamExecOverAggregate

[GitBox <gi...@apache.org>]

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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