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[FLINK-19364][python] Add Batch Physical Pandas Group Window Aggregat…
…e Rule and RelNode This closes #13460.
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167
...che/flink/table/planner/plan/rules/physical/batch/BatchExecPythonWindowAggregateRule.java
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| /* | ||
| * 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. | ||
| */ | ||
|
|
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| package org.apache.flink.table.planner.plan.rules.physical.batch; | ||
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| import org.apache.flink.table.api.TableException; | ||
| import org.apache.flink.table.functions.UserDefinedFunction; | ||
| import org.apache.flink.table.functions.python.PythonFunctionKind; | ||
| import org.apache.flink.table.planner.calcite.FlinkRelFactories; | ||
| import org.apache.flink.table.planner.plan.logical.LogicalWindow; | ||
| import org.apache.flink.table.planner.plan.logical.SessionGroupWindow; | ||
| import org.apache.flink.table.planner.plan.logical.SlidingGroupWindow; | ||
| import org.apache.flink.table.planner.plan.logical.TumblingGroupWindow; | ||
| import org.apache.flink.table.planner.plan.nodes.FlinkConventions; | ||
| import org.apache.flink.table.planner.plan.nodes.logical.FlinkLogicalWindowAggregate; | ||
| import org.apache.flink.table.planner.plan.nodes.physical.batch.BatchExecPythonGroupWindowAggregate; | ||
| import org.apache.flink.table.planner.plan.trait.FlinkRelDistribution; | ||
| import org.apache.flink.table.planner.plan.utils.AggregateUtil; | ||
| import org.apache.flink.table.planner.plan.utils.FlinkRelOptUtil; | ||
| import org.apache.flink.table.planner.plan.utils.PythonUtil; | ||
| import org.apache.flink.table.types.DataType; | ||
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| import org.apache.calcite.plan.RelOptRule; | ||
| import org.apache.calcite.plan.RelOptRuleCall; | ||
| import org.apache.calcite.plan.RelTraitSet; | ||
| import org.apache.calcite.rel.RelCollation; | ||
| import org.apache.calcite.rel.RelCollations; | ||
| import org.apache.calcite.rel.RelFieldCollation; | ||
| import org.apache.calcite.rel.RelNode; | ||
| import org.apache.calcite.rel.core.AggregateCall; | ||
| import org.apache.calcite.rel.type.RelDataType; | ||
| import org.apache.calcite.sql.type.SqlTypeName; | ||
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|
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| import java.util.LinkedList; | ||
| import java.util.List; | ||
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| import scala.Tuple2; | ||
| import scala.Tuple3; | ||
| import scala.collection.Seq; | ||
|
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| /** | ||
| * The physical rule is responsible for convert {@link FlinkLogicalWindowAggregate} to | ||
| * {@link BatchExecPythonGroupWindowAggregate}. | ||
| */ | ||
| public class BatchExecPythonWindowAggregateRule extends RelOptRule { | ||
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| public static final RelOptRule INSTANCE = new BatchExecPythonWindowAggregateRule(); | ||
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| private BatchExecPythonWindowAggregateRule() { | ||
| super(operand(FlinkLogicalWindowAggregate.class, | ||
| operand(RelNode.class, any())), | ||
| FlinkRelFactories.LOGICAL_BUILDER_WITHOUT_AGG_INPUT_PRUNE(), | ||
| "BatchExecPythonWindowAggregateRule"); | ||
| } | ||
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| @Override | ||
| public boolean matches(RelOptRuleCall call) { | ||
| FlinkLogicalWindowAggregate agg = call.rel(0); | ||
| List<AggregateCall> aggCalls = agg.getAggCallList(); | ||
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| boolean existGeneralPythonFunction = | ||
| aggCalls.stream().anyMatch(x -> PythonUtil.isPythonAggregate(x, PythonFunctionKind.GENERAL)); | ||
| boolean existPandasFunction = | ||
| aggCalls.stream().anyMatch(x -> PythonUtil.isPythonAggregate(x, PythonFunctionKind.PANDAS)); | ||
| boolean existJavaFunction = | ||
| aggCalls.stream().anyMatch(x -> !PythonUtil.isPythonAggregate(x, null)); | ||
| if (existPandasFunction || existGeneralPythonFunction) { | ||
| if (existJavaFunction) { | ||
| throw new TableException("Python UDAF and Java/Scala UDAF cannot be used together."); | ||
| } | ||
| if (existPandasFunction && existGeneralPythonFunction) { | ||
| throw new TableException("Pandas UDAF and non-Pandas UDAF cannot be used together."); | ||
| } | ||
| return true; | ||
| } else { | ||
| return false; | ||
| } | ||
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|
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| } | ||
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| @Override | ||
| public void onMatch(RelOptRuleCall call) { | ||
| FlinkLogicalWindowAggregate agg = call.rel(0); | ||
| RelNode input = agg.getInput(); | ||
| LogicalWindow window = agg.getWindow(); | ||
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| if (!(window instanceof TumblingGroupWindow && | ||
| AggregateUtil.hasTimeIntervalType(((TumblingGroupWindow) window).size()) | ||
| || window instanceof SlidingGroupWindow && | ||
| AggregateUtil.hasTimeIntervalType(((SlidingGroupWindow) window).size()) | ||
| || window instanceof SessionGroupWindow)) { | ||
| // sliding & tumbling count window and session window not supported | ||
| throw new TableException("Window " + window + " is not supported right now."); | ||
| } | ||
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| int[] groupSet = agg.getGroupSet().toArray(); | ||
| RelTraitSet traitSet = agg.getTraitSet().replace(FlinkConventions.BATCH_PHYSICAL()); | ||
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| Tuple2<int[], Seq<AggregateCall>> auxGroupSetAndCallsTuple = AggregateUtil.checkAndSplitAggCalls(agg); | ||
| int[] auxGroupSet = auxGroupSetAndCallsTuple._1; | ||
| Seq<AggregateCall> aggCallsWithoutAuxGroupCalls = auxGroupSetAndCallsTuple._2; | ||
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| Tuple3<int[][], DataType[][], UserDefinedFunction[]> aggBufferTypesAndFunctions = | ||
| AggregateUtil.transformToBatchAggregateFunctions( | ||
| aggCallsWithoutAuxGroupCalls, input.getRowType(), null); | ||
| UserDefinedFunction[] aggFunctions = aggBufferTypesAndFunctions._3(); | ||
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| int inputTimeFieldIndex = AggregateUtil.timeFieldIndex( | ||
| input.getRowType(), call.builder(), window.timeAttribute()); | ||
| RelDataType inputTimeFieldType = input.getRowType().getFieldList().get(inputTimeFieldIndex).getType(); | ||
| boolean inputTimeIsDate = inputTimeFieldType.getSqlTypeName() == SqlTypeName.DATE; | ||
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| RelTraitSet requiredTraitSet = agg.getTraitSet().replace(FlinkConventions.BATCH_PHYSICAL()); | ||
| if (groupSet.length != 0) { | ||
| FlinkRelDistribution requiredDistribution = | ||
| FlinkRelDistribution.hash(groupSet, false); | ||
| requiredTraitSet = requiredTraitSet.replace(requiredDistribution); | ||
| } else { | ||
| requiredTraitSet = requiredTraitSet.replace(FlinkRelDistribution.SINGLETON()); | ||
| } | ||
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| RelCollation sortCollation = createRelCollation(groupSet, inputTimeFieldIndex); | ||
| requiredTraitSet = requiredTraitSet.replace(sortCollation); | ||
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| RelNode newInput = RelOptRule.convert(input, requiredTraitSet); | ||
| BatchExecPythonGroupWindowAggregate windowAgg = new BatchExecPythonGroupWindowAggregate( | ||
| agg.getCluster(), | ||
| call.builder(), | ||
| traitSet, | ||
| newInput, | ||
| agg.getRowType(), | ||
| newInput.getRowType(), | ||
| groupSet, | ||
| auxGroupSet, | ||
| aggCallsWithoutAuxGroupCalls, | ||
| aggFunctions, | ||
| window, | ||
| inputTimeFieldIndex, | ||
| inputTimeIsDate, | ||
| agg.getNamedProperties()); | ||
| call.transformTo(windowAgg); | ||
| } | ||
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| private RelCollation createRelCollation(int[] groupSet, int timeIndex) { | ||
| List<RelFieldCollation> fields = new LinkedList<>(); | ||
| for (int value : groupSet) { | ||
| fields.add(FlinkRelOptUtil.ofRelFieldCollation(value)); | ||
| } | ||
| fields.add(FlinkRelOptUtil.ofRelFieldCollation(timeIndex)); | ||
| return RelCollations.of(fields); | ||
| } | ||
| } |
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