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[FLINK-19694][table-planner-blink] Support upsert ChangelogMode for S…
…canTableSource in planner This closes #13721
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139 changes: 139 additions & 0 deletions
139
.../apache/flink/table/planner/plan/nodes/physical/stream/StreamExecChangelogNormalize.scala
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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.nodes.physical.stream | ||
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| import org.apache.calcite.plan.{RelOptCluster, RelTraitSet} | ||
| import org.apache.calcite.rel.`type`.RelDataType | ||
| import org.apache.calcite.rel.{RelNode, RelWriter, SingleRel} | ||
| import org.apache.flink.api.dag.Transformation | ||
| import org.apache.flink.streaming.api.operators.KeyedProcessOperator | ||
| import org.apache.flink.streaming.api.transformations.OneInputTransformation | ||
| import org.apache.flink.table.api.config.ExecutionConfigOptions | ||
| import org.apache.flink.table.data.RowData | ||
| import org.apache.flink.table.planner.delegation.StreamPlanner | ||
| import org.apache.flink.table.planner.plan.nodes.exec.{ExecNode, StreamExecNode} | ||
| import org.apache.flink.table.planner.plan.utils.{AggregateUtil, ChangelogPlanUtils, KeySelectorUtil} | ||
| import org.apache.flink.table.runtime.operators.bundle.KeyedMapBundleOperator | ||
| import org.apache.flink.table.runtime.operators.deduplicate.{DeduplicateKeepLastRowFunction, MiniBatchDeduplicateKeepLastRowFunction} | ||
| import org.apache.flink.table.runtime.typeutils.InternalTypeInfo | ||
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| import java.util | ||
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| import scala.collection.JavaConversions._ | ||
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| /** | ||
| * Stream physical RelNode which normalizes a changelog stream which maybe an upsert stream or | ||
| * a changelog stream containing duplicate events. This node normalize such stream into a regular | ||
| * changelog stream that contains INSERT/UPDATE_BEFORE/UPDATE_AFTER/DELETE records without | ||
| * duplication. | ||
| */ | ||
| class StreamExecChangelogNormalize( | ||
| cluster: RelOptCluster, | ||
| traitSet: RelTraitSet, | ||
| input: RelNode, | ||
| val uniqueKeys: Array[Int]) | ||
| extends SingleRel(cluster, traitSet, input) | ||
| with StreamPhysicalRel | ||
| with StreamExecNode[RowData] { | ||
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| override def requireWatermark: Boolean = false | ||
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| override def deriveRowType(): RelDataType = getInput.getRowType | ||
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| override def copy(traitSet: RelTraitSet, inputs: util.List[RelNode]): RelNode = { | ||
| new StreamExecChangelogNormalize( | ||
| cluster, | ||
| traitSet, | ||
| inputs.get(0), | ||
| uniqueKeys) | ||
| } | ||
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| override def explainTerms(pw: RelWriter): RelWriter = { | ||
| val fieldNames = getRowType.getFieldNames | ||
| super.explainTerms(pw) | ||
| .item("key", uniqueKeys.map(fieldNames.get).mkString(", ")) | ||
| } | ||
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| //~ ExecNode methods ----------------------------------------------------------- | ||
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| override def getInputNodes: util.List[ExecNode[StreamPlanner, _]] = { | ||
| List(getInput.asInstanceOf[ExecNode[StreamPlanner, _]]) | ||
| } | ||
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| override def replaceInputNode( | ||
| ordinalInParent: Int, | ||
| newInputNode: ExecNode[StreamPlanner, _]): Unit = { | ||
| replaceInput(ordinalInParent, newInputNode.asInstanceOf[RelNode]) | ||
| } | ||
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| override protected def translateToPlanInternal( | ||
| planner: StreamPlanner): Transformation[RowData] = { | ||
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| val inputTransform = getInputNodes.get(0).translateToPlan(planner) | ||
| .asInstanceOf[Transformation[RowData]] | ||
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| val rowTypeInfo = inputTransform.getOutputType.asInstanceOf[InternalTypeInfo[RowData]] | ||
| val generateUpdateBefore = ChangelogPlanUtils.generateUpdateBefore(this) | ||
| val tableConfig = planner.getTableConfig | ||
| val isMiniBatchEnabled = tableConfig.getConfiguration.getBoolean( | ||
| ExecutionConfigOptions.TABLE_EXEC_MINIBATCH_ENABLED) | ||
| val operator = if (isMiniBatchEnabled) { | ||
| val exeConfig = planner.getExecEnv.getConfig | ||
| val rowSerializer = rowTypeInfo.createSerializer(exeConfig) | ||
| val processFunction = new MiniBatchDeduplicateKeepLastRowFunction( | ||
| rowTypeInfo, | ||
| generateUpdateBefore, | ||
| true, // generateInsert | ||
| false, // inputInsertOnly | ||
| rowSerializer, | ||
| // disable state ttl, the changelog normalize should keep all state to have data integrity | ||
| // we can enable state ttl if this is really needed in some cases | ||
| -1) | ||
| val trigger = AggregateUtil.createMiniBatchTrigger(tableConfig) | ||
| new KeyedMapBundleOperator( | ||
| processFunction, | ||
| trigger) | ||
| } else { | ||
| val processFunction = new DeduplicateKeepLastRowFunction( | ||
| -1, // disable state ttl | ||
| rowTypeInfo, | ||
| generateUpdateBefore, | ||
| true, // generateInsert | ||
| false) // inputInsertOnly | ||
| new KeyedProcessOperator[RowData, RowData, RowData](processFunction) | ||
| } | ||
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| val ret = new OneInputTransformation( | ||
| inputTransform, | ||
| getRelDetailedDescription, | ||
| operator, | ||
| rowTypeInfo, | ||
| inputTransform.getParallelism) | ||
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| if (inputsContainSingleton()) { | ||
| ret.setParallelism(1) | ||
| ret.setMaxParallelism(1) | ||
| } | ||
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| val selector = KeySelectorUtil.getRowDataSelector(uniqueKeys, rowTypeInfo) | ||
| ret.setStateKeySelector(selector) | ||
| ret.setStateKeyType(selector.getProducedType) | ||
| ret | ||
| } | ||
| } |
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