org.apache.flink.table.planner.plan.nodes.physical.batch.BatchPhysicalHashWindowAggregate.scala Maven / Gradle / Ivy
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This module bridges Table/SQL API and runtime. It contains
all resources that are required during pre-flight and runtime
phase. The content of this module is work-in-progress. It will
replace flink-table-planner once it is stable. See FLINK-11439
and FLIP-32 for more details.
/*
* 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.physical.batch
import org.apache.flink.table.functions.UserDefinedFunction
import org.apache.flink.table.planner.calcite.FlinkTypeFactory
import org.apache.flink.table.planner.expressions.PlannerNamedWindowProperty
import org.apache.flink.table.planner.plan.logical.LogicalWindow
import org.apache.flink.table.planner.plan.nodes.exec.batch.BatchExecHashWindowAggregate
import org.apache.flink.table.planner.plan.nodes.exec.{ExecNode, InputProperty}
import org.apache.calcite.plan.{RelOptCluster, RelTraitSet}
import org.apache.calcite.rel.RelNode
import org.apache.calcite.rel.`type`.RelDataType
import org.apache.calcite.rel.core.AggregateCall
import java.util
/**
* Batch physical RelNode for (global) hash-based window aggregate.
*/
class BatchPhysicalHashWindowAggregate(
cluster: RelOptCluster,
traitSet: RelTraitSet,
inputRel: RelNode,
outputRowType: RelDataType,
aggInputRowType: RelDataType,
grouping: Array[Int],
auxGrouping: Array[Int],
aggCallToAggFunction: Seq[(AggregateCall, UserDefinedFunction)],
window: LogicalWindow,
inputTimeFieldIndex: Int,
inputTimeIsDate: Boolean,
namedWindowProperties: Seq[PlannerNamedWindowProperty],
enableAssignPane: Boolean = false,
isMerge: Boolean)
extends BatchPhysicalHashWindowAggregateBase(
cluster,
traitSet,
inputRel,
outputRowType,
grouping,
auxGrouping,
aggCallToAggFunction,
window,
namedWindowProperties,
enableAssignPane,
isMerge,
isFinal = true) {
override def copy(traitSet: RelTraitSet, inputs: util.List[RelNode]): RelNode = {
new BatchPhysicalHashWindowAggregate(
cluster,
traitSet,
inputs.get(0),
getRowType,
aggInputRowType,
grouping,
auxGrouping,
aggCallToAggFunction,
window,
inputTimeFieldIndex,
inputTimeIsDate,
namedWindowProperties,
enableAssignPane,
isMerge)
}
override def translateToExecNode(): ExecNode[_] = {
new BatchExecHashWindowAggregate(
grouping,
auxGrouping,
getAggCallList.toArray,
window,
inputTimeFieldIndex,
inputTimeIsDate,
namedWindowProperties.toArray,
FlinkTypeFactory.toLogicalRowType(aggInputRowType),
enableAssignPane,
isMerge,
true, // isFinal is always true
InputProperty.builder().damBehavior(InputProperty.DamBehavior.END_INPUT).build(),
FlinkTypeFactory.toLogicalRowType(getRowType),
getRelDetailedDescription
)
}
}