HuangXingBo commented on a change in pull request #13369: URL: https://github.com/apache/flink/pull/13369#discussion_r489266726
########## File path: flink-python/src/main/java/org/apache/flink/table/runtime/operators/python/aggregate/arrow/AbstractArrowPythonAggregateFunctionOperator.java ########## @@ -0,0 +1,197 @@ +/* + * 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.runtime.operators.python.aggregate.arrow; + +import org.apache.flink.annotation.Internal; +import org.apache.flink.configuration.Configuration; +import org.apache.flink.fnexecution.v1.FlinkFnApi; +import org.apache.flink.streaming.api.watermark.Watermark; +import org.apache.flink.streaming.runtime.streamrecord.StreamRecord; +import org.apache.flink.table.api.TableConfig; +import org.apache.flink.table.data.JoinedRowData; +import org.apache.flink.table.data.RowData; +import org.apache.flink.table.data.binary.BinaryRowData; +import org.apache.flink.table.functions.AggregateFunction; +import org.apache.flink.table.functions.python.PythonEnv; +import org.apache.flink.table.functions.python.PythonFunctionInfo; +import org.apache.flink.table.planner.codegen.CodeGeneratorContext; +import org.apache.flink.table.planner.codegen.ProjectionCodeGenerator; +import org.apache.flink.table.runtime.arrow.serializers.ArrowSerializer; +import org.apache.flink.table.runtime.arrow.serializers.RowDataArrowSerializer; +import org.apache.flink.table.runtime.generated.GeneratedProjection; +import org.apache.flink.table.runtime.generated.Projection; +import org.apache.flink.table.runtime.operators.python.AbstractStatelessFunctionOperator; +import org.apache.flink.table.types.logical.RowType; +import org.apache.flink.util.Preconditions; + +/** + * The Abstract class of Arrow Aggregate Operator for Pandas {@link AggregateFunction}. + */ +@Internal +public abstract class AbstractArrowPythonAggregateFunctionOperator + extends AbstractStatelessFunctionOperator<RowData, RowData, RowData> { + + private static final long serialVersionUID = 1L; + + private static final String SCHEMA_ARROW_CODER_URN = "flink:coder:schema:arrow:v1"; + + private static final String PANDAS_AGGREGATE_FUNCTION_URN = "flink:transform:pandas_aggregate_function:v1"; + + /** + * The Pandas {@link AggregateFunction}s to be executed. + */ + private final PythonFunctionInfo[] pandasAggFunctions; + + protected final int[] groupingSet; + + protected transient ArrowSerializer<RowData> arrowSerializer; + + /** + * The collector used to collect records. + */ + protected transient StreamRecordRowDataWrappingCollector rowDataWrapper; + + /** + * The JoinedRowData reused holding the execution result. + */ + protected transient JoinedRowData reuseJoinedRow; + + /** + * The current number of elements to be included in an arrow batch. + */ + protected transient int currentBatchCount; + + /** + * The Projection which projects the udaf input fields from the input row. + */ + private transient Projection<RowData, BinaryRowData> udafInputProjection; + + public AbstractArrowPythonAggregateFunctionOperator( + Configuration config, + PythonFunctionInfo[] pandasAggFunctions, + RowType inputType, + RowType outputType, + int[] groupingSet, + int[] udafInputOffsets) { + super(config, inputType, outputType, udafInputOffsets); + this.pandasAggFunctions = Preconditions.checkNotNull(pandasAggFunctions); + this.groupingSet = Preconditions.checkNotNull(groupingSet); + } + + @Override + public void open() throws Exception { + super.open(); + rowDataWrapper = new StreamRecordRowDataWrappingCollector(output); + reuseJoinedRow = new JoinedRowData(); + + udafInputProjection = createUdafInputProjection(); + arrowSerializer = new RowDataArrowSerializer(userDefinedFunctionInputType, userDefinedFunctionOutputType); + arrowSerializer.open(bais, baos); + currentBatchCount = 0; + } + + @Override + public void dispose() throws Exception { + super.dispose(); + arrowSerializer.close(); + } + + @Override + public void processElement(StreamRecord<RowData> element) throws Exception { + RowData value = element.getValue(); + bufferInput(value); + processElementInternal(value); + emitResults(); + } + + @Override + protected void checkInvokeFinishBundleByCount() throws Exception { + elementCount += currentBatchCount; + currentBatchCount = 0; + if (elementCount >= maxBundleSize) { + invokeFinishBundle(); + } + } + + @Override + public void processWatermark(Watermark mark) throws Exception { Review comment: Yes. Make sense. I will extract the logic about deciding whether there is buffered unprocessed data into a method so that we can avoid overriding `processWatermark` method ---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: [email protected]
