HuangXingBo commented on a change in pull request #14775: URL: https://github.com/apache/flink/pull/14775#discussion_r569216546
########## File path: flink-python/src/test/java/org/apache/flink/table/runtime/operators/python/aggregate/PythonStreamGroupWindowAggregateOperatorTest.java ########## @@ -0,0 +1,1129 @@ +/* + * 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; + +import org.apache.flink.configuration.Configuration; +import org.apache.flink.core.memory.DataInputDeserializer; +import org.apache.flink.core.memory.DataOutputSerializer; +import org.apache.flink.python.PythonFunctionRunner; +import org.apache.flink.python.PythonOptions; +import org.apache.flink.streaming.api.operators.InternalTimer; +import org.apache.flink.streaming.api.operators.InternalTimerServiceImpl; +import org.apache.flink.streaming.api.operators.OneInputStreamOperator; +import org.apache.flink.streaming.api.operators.Triggerable; +import org.apache.flink.streaming.api.watermark.Watermark; +import org.apache.flink.streaming.runtime.streamrecord.StreamRecord; +import org.apache.flink.streaming.util.OneInputStreamOperatorTestHarness; +import org.apache.flink.table.api.DataTypes; +import org.apache.flink.table.api.TableConfig; +import org.apache.flink.table.data.GenericRowData; +import org.apache.flink.table.data.RowData; +import org.apache.flink.table.data.StringData; +import org.apache.flink.table.data.TimestampData; +import org.apache.flink.table.data.UpdatableRowData; +import org.apache.flink.table.data.binary.BinaryRowData; +import org.apache.flink.table.data.util.RowDataUtil; +import org.apache.flink.table.data.utils.JoinedRowData; +import org.apache.flink.table.expressions.FieldReferenceExpression; +import org.apache.flink.table.functions.python.PythonAggregateFunctionInfo; +import org.apache.flink.table.planner.codegen.CodeGeneratorContext; +import org.apache.flink.table.planner.codegen.ProjectionCodeGenerator; +import org.apache.flink.table.planner.expressions.PlannerWindowReference; +import org.apache.flink.table.planner.plan.logical.LogicalWindow; +import org.apache.flink.table.planner.plan.logical.SlidingGroupWindow; +import org.apache.flink.table.planner.typeutils.DataViewUtils; +import org.apache.flink.table.runtime.generated.GeneratedProjection; +import org.apache.flink.table.runtime.generated.Projection; +import org.apache.flink.table.runtime.operators.python.scalar.PythonScalarFunctionOperatorTestBase; +import org.apache.flink.table.runtime.operators.window.TimeWindow; +import org.apache.flink.table.runtime.operators.window.assigners.SlidingWindowAssigner; +import org.apache.flink.table.runtime.operators.window.assigners.WindowAssigner; +import org.apache.flink.table.runtime.utils.PassThroughStreamGroupWindowAggregatePythonFunctionRunner; +import org.apache.flink.table.runtime.utils.PythonTestUtils; +import org.apache.flink.table.types.AtomicDataType; +import org.apache.flink.table.types.logical.BigIntType; +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.flink.table.types.logical.VarCharType; +import org.apache.flink.types.RowKind; + +import org.junit.Test; + +import java.io.IOException; +import java.time.Duration; +import java.util.ArrayList; +import java.util.Arrays; +import java.util.Collection; +import java.util.HashMap; +import java.util.LinkedList; +import java.util.List; +import java.util.Map; +import java.util.concurrent.ConcurrentLinkedQueue; +import java.util.concurrent.LinkedBlockingQueue; +import java.util.function.Function; +import java.util.stream.Collectors; + +import scala.Some; + +import static org.apache.flink.table.expressions.ApiExpressionUtils.intervalOfMillis; + +/** + * Test for {@link PythonStreamGroupWindowAggregateOperator}. These test that: + * + * <ul> + * <li>Retraction flag is handled correctly + * <li>FinishBundle is called when checkpoint is encountered + * <li>FinishBundle is called when bundled element count reach to max bundle size + * <li>FinishBundle is called when bundled time reach to max bundle time + * <li>Watermarks are buffered and only sent to downstream when finishedBundle is triggered + * </ul> + */ +public class PythonStreamGroupWindowAggregateOperatorTest + extends AbstractPythonStreamAggregateOperatorTest { + @Test + public void testGroupWindowAggregateFunction() throws Exception { + OneInputStreamOperatorTestHarness<RowData, RowData> testHarness = + getTestHarness(new Configuration()); + long initialTime = 0L; + ConcurrentLinkedQueue<Object> expectedOutput = new ConcurrentLinkedQueue<>(); + testHarness.open(); + testHarness.processElement( + new StreamRecord<>(newRow(true, "c1", "c2", 0L, 0L), initialTime + 1)); + testHarness.processElement( + new StreamRecord<>(newRow(true, "c1", "c4", 1L, 6000L), initialTime + 2)); + testHarness.processElement( + new StreamRecord<>(newRow(true, "c1", "c6", 2L, 10000L), initialTime + 3)); + testHarness.processElement( + new StreamRecord<>(newRow(true, "c2", "c8", 3L, 0L), initialTime + 4)); + testHarness.processElement( + new StreamRecord<>(newRow(true, "c3", "c8", 3L, 0L), initialTime + 5)); + testHarness.processElement( + new StreamRecord<>(newRow(false, "c3", "c8", 3L, 0L), initialTime + 6)); + testHarness.processWatermark(Long.MAX_VALUE); + testHarness.close(); + + expectedOutput.add( + new StreamRecord<>( + newRow( + true, + "c1", + 0L, + TimestampData.fromEpochMillis(-5000L), + TimestampData.fromEpochMillis(5000L)))); + + expectedOutput.add( + new StreamRecord<>( + newRow( + true, + "state_cleanup_triggered: c1 : TimeWindow{start=-5000, end=5000}", + 0L, + TimestampData.fromEpochMillis(-5000L), + TimestampData.fromEpochMillis(5000L)))); + + expectedOutput.add( + new StreamRecord<>( + newRow( + true, + "state_cleanup_triggered: c3 : TimeWindow{start=-5000, end=5000}", + 0L, + TimestampData.fromEpochMillis(-5000L), + TimestampData.fromEpochMillis(5000L)))); + + expectedOutput.add( + new StreamRecord<>( + newRow( + true, + "c2", + 3L, + TimestampData.fromEpochMillis(-5000L), + TimestampData.fromEpochMillis(5000L)))); + + expectedOutput.add( + new StreamRecord<>( + newRow( + true, + "state_cleanup_triggered: c2 : TimeWindow{start=-5000, end=5000}", + 0L, + TimestampData.fromEpochMillis(-5000L), + TimestampData.fromEpochMillis(5000L)))); + + expectedOutput.add( + new StreamRecord<>( + newRow( + true, + "c1", + 0L, + TimestampData.fromEpochMillis(0L), + TimestampData.fromEpochMillis(10000L)))); + + expectedOutput.add( + new StreamRecord<>( + newRow( + true, + "state_cleanup_triggered: c1 : TimeWindow{start=0, end=10000}", + 0L, + TimestampData.fromEpochMillis(0L), + TimestampData.fromEpochMillis(10000L)))); + + expectedOutput.add( + new StreamRecord<>( + newRow( + true, + "c2", + 3L, + TimestampData.fromEpochMillis(0L), + TimestampData.fromEpochMillis(10000L)))); + + expectedOutput.add( + new StreamRecord<>( + newRow( + true, + "state_cleanup_triggered: c2 : TimeWindow{start=0, end=10000}", + 0L, + TimestampData.fromEpochMillis(0L), + TimestampData.fromEpochMillis(10000L)))); + + expectedOutput.add( + new StreamRecord<>( + newRow( + true, + "state_cleanup_triggered: c3 : TimeWindow{start=0, end=10000}", + 0L, + TimestampData.fromEpochMillis(0L), + TimestampData.fromEpochMillis(10000L)))); + + expectedOutput.add( + new StreamRecord<>( + newRow( + true, + "c1", + 1L, + TimestampData.fromEpochMillis(5000L), + TimestampData.fromEpochMillis(15000L)))); + expectedOutput.add( + new StreamRecord<>( + newRow( + true, + "state_cleanup_triggered: c1 : TimeWindow{start=5000, end=15000}", + 0L, + TimestampData.fromEpochMillis(5000L), + TimestampData.fromEpochMillis(15000L)))); + + expectedOutput.add( + new StreamRecord<>( + newRow( + true, + "c1", + 2L, + TimestampData.fromEpochMillis(10000L), + TimestampData.fromEpochMillis(20000L)))); + + expectedOutput.add( + new StreamRecord<>( + newRow( + true, + "state_cleanup_triggered: c1 : TimeWindow{start=10000, end=20000}", + 0L, + TimestampData.fromEpochMillis(10000L), + TimestampData.fromEpochMillis(20000L)))); + + expectedOutput.add(new Watermark(Long.MAX_VALUE)); + + assertOutputEquals("Output was not correct.", expectedOutput, testHarness.getOutput()); + } + + @Test + public void testFinishBundleTriggeredOnCheckpoint() throws Exception { + Configuration conf = new Configuration(); + conf.setInteger(PythonOptions.MAX_BUNDLE_SIZE, 10); + OneInputStreamOperatorTestHarness<RowData, RowData> testHarness = getTestHarness(conf); + + long initialTime = 0L; + ConcurrentLinkedQueue<Object> expectedOutput = new ConcurrentLinkedQueue<>(); + + testHarness.open(); + + testHarness.processElement( + new StreamRecord<>(newRow(true, "c1", "c2", 0L, 0L), initialTime + 1)); + testHarness.processElement( + new StreamRecord<>(newRow(true, "c1", "c4", 1L, 6000L), initialTime + 2)); + testHarness.processElement( + new StreamRecord<>(newRow(true, "c1", "c6", 2L, 10000L), initialTime + 3)); + testHarness.processElement( + new StreamRecord<>(newRow(true, "c2", "c8", 3L, 0L), initialTime + 4)); + testHarness.processWatermark(new Watermark(10000L)); + // checkpoint trigger finishBundle + testHarness.prepareSnapshotPreBarrier(0L); + + expectedOutput.add( + new StreamRecord<>( + newRow( + true, + "c1", + 0L, + TimestampData.fromEpochMillis(-5000L), + TimestampData.fromEpochMillis(5000L)))); + + expectedOutput.add( + new StreamRecord<>( + newRow( + true, + "state_cleanup_triggered: c1 : TimeWindow{start=-5000, end=5000}", + 0L, + TimestampData.fromEpochMillis(-5000L), + TimestampData.fromEpochMillis(5000L)))); + + expectedOutput.add( + new StreamRecord<>( + newRow( + true, + "c2", + 3L, + TimestampData.fromEpochMillis(-5000L), + TimestampData.fromEpochMillis(5000L)))); + + expectedOutput.add( + new StreamRecord<>( + newRow( + true, + "state_cleanup_triggered: c2 : TimeWindow{start=-5000, end=5000}", + 0L, + TimestampData.fromEpochMillis(-5000L), + TimestampData.fromEpochMillis(5000L)))); + + expectedOutput.add( + new StreamRecord<>( + newRow( + true, + "c2", + 3L, + TimestampData.fromEpochMillis(0L), + TimestampData.fromEpochMillis(10000L)))); + expectedOutput.add( + new StreamRecord<>( + newRow( + true, + "state_cleanup_triggered: c2 : TimeWindow{start=0, end=10000}", + 0L, + TimestampData.fromEpochMillis(0L), + TimestampData.fromEpochMillis(10000L)))); + + expectedOutput.add( + new StreamRecord<>( + newRow( + true, + "c1", + 0L, + TimestampData.fromEpochMillis(0L), + TimestampData.fromEpochMillis(10000L)))); + + expectedOutput.add( + new StreamRecord<>( + newRow( + true, + "state_cleanup_triggered: c1 : TimeWindow{start=0, end=10000}", + 0L, + TimestampData.fromEpochMillis(0L), + TimestampData.fromEpochMillis(10000L)))); + + expectedOutput.add(new Watermark(10000L)); + + assertOutputEquals("Output was not correct.", expectedOutput, testHarness.getOutput()); + + testHarness.processWatermark(20000L); + + testHarness.close(); + + expectedOutput.add( + new StreamRecord<>( + newRow( + true, + "c1", + 1L, + TimestampData.fromEpochMillis(5000L), + TimestampData.fromEpochMillis(15000L)))); + + expectedOutput.add( + new StreamRecord<>( + newRow( + true, + "state_cleanup_triggered: c1 : TimeWindow{start=5000, end=15000}", + 0L, + TimestampData.fromEpochMillis(5000L), + TimestampData.fromEpochMillis(15000L)))); + + expectedOutput.add( + new StreamRecord<>( + newRow( + true, + "c1", + 2L, + TimestampData.fromEpochMillis(10000L), + TimestampData.fromEpochMillis(20000L)))); + expectedOutput.add( + new StreamRecord<>( + newRow( + true, + "state_cleanup_triggered: c1 : TimeWindow{start=10000, end=20000}", + 0L, + TimestampData.fromEpochMillis(10000L), + TimestampData.fromEpochMillis(20000L)))); + + expectedOutput.add(new Watermark(20000L)); + + assertOutputEquals("Output was not correct.", expectedOutput, testHarness.getOutput()); + } + + @Test + public void testFinishBundleTriggeredByCount() throws Exception { + Configuration conf = new Configuration(); + conf.setInteger(PythonOptions.MAX_BUNDLE_SIZE, 4); + OneInputStreamOperatorTestHarness<RowData, RowData> testHarness = getTestHarness(conf); + + long initialTime = 0L; + ConcurrentLinkedQueue<Object> expectedOutput = new ConcurrentLinkedQueue<>(); + + testHarness.open(); + + testHarness.processElement( + new StreamRecord<>(newRow(true, "c1", "c2", 0L, 0L), initialTime + 1)); + testHarness.processElement( + new StreamRecord<>(newRow(true, "c1", "c4", 1L, 6000L), initialTime + 2)); + testHarness.processElement( + new StreamRecord<>(newRow(true, "c1", "c6", 2L, 10000L), initialTime + 3)); + testHarness.processElement( + new StreamRecord<>(newRow(true, "c2", "c8", 3L, 0L), initialTime + 4)); + + testHarness.processWatermark(new Watermark(10000L)); + + expectedOutput.add( + new StreamRecord<>( + newRow( + true, + "c1", + 0L, + TimestampData.fromEpochMillis(-5000L), + TimestampData.fromEpochMillis(5000L)))); + + expectedOutput.add( + new StreamRecord<>( + newRow( + true, + "state_cleanup_triggered: c1 : TimeWindow{start=-5000, end=5000}", + 0L, + TimestampData.fromEpochMillis(-5000L), + TimestampData.fromEpochMillis(5000L)))); + + expectedOutput.add( + new StreamRecord<>( + newRow( + true, + "c2", + 3L, + TimestampData.fromEpochMillis(-5000L), + TimestampData.fromEpochMillis(5000L)))); + + expectedOutput.add( + new StreamRecord<>( + newRow( + true, + "state_cleanup_triggered: c2 : TimeWindow{start=-5000, end=5000}", + 0L, + TimestampData.fromEpochMillis(-5000L), + TimestampData.fromEpochMillis(5000L)))); + + expectedOutput.add( + new StreamRecord<>( + newRow( + true, + "c2", + 3L, + TimestampData.fromEpochMillis(0L), + TimestampData.fromEpochMillis(10000L)))); + + expectedOutput.add( + new StreamRecord<>( + newRow( + true, + "state_cleanup_triggered: c2 : TimeWindow{start=0, end=10000}", + 0L, + TimestampData.fromEpochMillis(0L), + TimestampData.fromEpochMillis(10000L)))); + + expectedOutput.add( + new StreamRecord<>( + newRow( + true, + "c1", + 0L, + TimestampData.fromEpochMillis(0L), + TimestampData.fromEpochMillis(10000L)))); + + expectedOutput.add( + new StreamRecord<>( + newRow( + true, + "state_cleanup_triggered: c1 : TimeWindow{start=0, end=10000}", + 0L, + TimestampData.fromEpochMillis(0L), + TimestampData.fromEpochMillis(10000L)))); + + expectedOutput.add(new Watermark(10000L)); + + assertOutputEquals("Output was not correct.", expectedOutput, testHarness.getOutput()); + + testHarness.processWatermark(20000L); + testHarness.close(); + + expectedOutput.add( + new StreamRecord<>( + newRow( + true, + "c1", + 1L, + TimestampData.fromEpochMillis(5000L), + TimestampData.fromEpochMillis(15000L)))); + + expectedOutput.add( + new StreamRecord<>( + newRow( + true, + "state_cleanup_triggered: c1 : TimeWindow{start=5000, end=15000}", + 0L, + TimestampData.fromEpochMillis(5000L), + TimestampData.fromEpochMillis(15000L)))); + + expectedOutput.add( + new StreamRecord<>( + newRow( + true, + "c1", + 2L, + TimestampData.fromEpochMillis(10000L), + TimestampData.fromEpochMillis(20000L)))); + + expectedOutput.add( + new StreamRecord<>( + newRow( + true, + "state_cleanup_triggered: c1 : TimeWindow{start=10000, end=20000}", + 0L, + TimestampData.fromEpochMillis(10000L), + TimestampData.fromEpochMillis(20000L)))); + + expectedOutput.add(new Watermark(20000L)); + + assertOutputEquals("Output was not correct.", expectedOutput, testHarness.getOutput()); + } + + @Test + public void testFinishBundleTriggeredByTime() throws Exception { + Configuration conf = new Configuration(); + conf.setInteger(PythonOptions.MAX_BUNDLE_SIZE, 10); + conf.setLong(PythonOptions.MAX_BUNDLE_TIME_MILLS, 1000L); + OneInputStreamOperatorTestHarness<RowData, RowData> testHarness = getTestHarness(conf); + + long initialTime = 0L; + ConcurrentLinkedQueue<Object> expectedOutput = new ConcurrentLinkedQueue<>(); + + testHarness.open(); + + testHarness.processElement( + new StreamRecord<>(newRow(true, "c1", "c2", 0L, 0L), initialTime + 1)); + testHarness.processElement( + new StreamRecord<>(newRow(true, "c1", "c4", 1L, 6000L), initialTime + 2)); + testHarness.processElement( + new StreamRecord<>(newRow(true, "c1", "c6", 2L, 10000L), initialTime + 3)); + testHarness.processElement( + new StreamRecord<>(newRow(true, "c2", "c8", 3L, 0L), initialTime + 4)); + testHarness.processWatermark(new Watermark(20000L)); + assertOutputEquals( + "FinishBundle should not be triggered.", expectedOutput, testHarness.getOutput()); + + testHarness.setProcessingTime(1000L); + expectedOutput.add( + new StreamRecord<>( + newRow( + true, + "c1", + 0L, + TimestampData.fromEpochMillis(-5000L), + TimestampData.fromEpochMillis(5000L)))); + + expectedOutput.add( + new StreamRecord<>( + newRow( + true, + "state_cleanup_triggered: c1 : TimeWindow{start=-5000, end=5000}", + 0L, + TimestampData.fromEpochMillis(-5000L), + TimestampData.fromEpochMillis(5000L)))); + + expectedOutput.add( + new StreamRecord<>( + newRow( + true, + "c2", + 3L, + TimestampData.fromEpochMillis(-5000L), + TimestampData.fromEpochMillis(5000L)))); + + expectedOutput.add( + new StreamRecord<>( + newRow( + true, + "state_cleanup_triggered: c2 : TimeWindow{start=-5000, end=5000}", + 0L, + TimestampData.fromEpochMillis(-5000L), + TimestampData.fromEpochMillis(5000L)))); + + expectedOutput.add( + new StreamRecord<>( + newRow( + true, + "c2", + 3L, + TimestampData.fromEpochMillis(0L), + TimestampData.fromEpochMillis(10000L)))); + + expectedOutput.add( + new StreamRecord<>( + newRow( + true, + "state_cleanup_triggered: c2 : TimeWindow{start=0, end=10000}", + 0L, + TimestampData.fromEpochMillis(0L), + TimestampData.fromEpochMillis(10000L)))); + + expectedOutput.add( + new StreamRecord<>( + newRow( + true, + "c1", + 0L, + TimestampData.fromEpochMillis(0L), + TimestampData.fromEpochMillis(10000L)))); + + expectedOutput.add( + new StreamRecord<>( + newRow( + true, + "state_cleanup_triggered: c1 : TimeWindow{start=0, end=10000}", + 0L, + TimestampData.fromEpochMillis(0L), + TimestampData.fromEpochMillis(10000L)))); + + expectedOutput.add( + new StreamRecord<>( + newRow( + true, + "c1", + 1L, + TimestampData.fromEpochMillis(5000L), + TimestampData.fromEpochMillis(15000L)))); + + expectedOutput.add( + new StreamRecord<>( + newRow( + true, + "state_cleanup_triggered: c1 : TimeWindow{start=5000, end=15000}", + 0L, + TimestampData.fromEpochMillis(5000L), + TimestampData.fromEpochMillis(15000L)))); + + expectedOutput.add( + new StreamRecord<>( + newRow( + true, + "c1", + 2L, + TimestampData.fromEpochMillis(10000L), + TimestampData.fromEpochMillis(20000L)))); + + expectedOutput.add( + new StreamRecord<>( + newRow( + true, + "state_cleanup_triggered: c1 : TimeWindow{start=10000, end=20000}", + 0L, + TimestampData.fromEpochMillis(10000L), + TimestampData.fromEpochMillis(20000L)))); + + expectedOutput.add(new Watermark(20000L)); + + assertOutputEquals("Output was not correct.", expectedOutput, testHarness.getOutput()); + + testHarness.close(); + } + + @Override + public LogicalType[] getOutputLogicalType() { + return new LogicalType[] { + DataTypes.STRING().getLogicalType(), DataTypes.BIGINT().getLogicalType() + }; + } + + @Override + public RowType getInputType() { + return new RowType( + Arrays.asList( + new RowType.RowField("f1", new VarCharType()), + new RowType.RowField("f2", new VarCharType()), + new RowType.RowField("f3", new BigIntType()), + new RowType.RowField("rowTime", new BigIntType()))); + } + + @Override + public RowType getOutputType() { + return new RowType( + Arrays.asList( + new RowType.RowField("f1", new VarCharType()), + new RowType.RowField("f2", new BigIntType()), + new RowType.RowField("windowStart", new TimestampType(3)), + new RowType.RowField("windowEnd", new TimestampType(3)))); + } + + @Override + OneInputStreamOperator getTestOperator(Configuration config) { + long size = 10000L; + long slide = 5000L; + SlidingWindowAssigner windowAssigner = + SlidingWindowAssigner.of(Duration.ofMillis(size), Duration.ofMillis(slide)) + .withEventTime(); + PlannerWindowReference windowRef = + new PlannerWindowReference("w$", new Some<>(new TimestampType(3))); + LogicalWindow window = + new SlidingGroupWindow( + windowRef, + new FieldReferenceExpression( + "rowtime", + new AtomicDataType( + new TimestampType(true, TimestampKind.ROWTIME, 3)), + 0, + 3), + intervalOfMillis(size), + intervalOfMillis(slide)); + return new PassThroughPythonStreamGroupWindowAggregateOperator( + config, + getInputType(), + getOutputType(), + new PythonAggregateFunctionInfo[] { + new PythonAggregateFunctionInfo( + PythonScalarFunctionOperatorTestBase.DummyPythonFunction.INSTANCE, + new Integer[] {2}, + -1, + false) + }, + getGrouping(), + -1, + false, + false, + 3, + windowAssigner, + window, + 0, + new int[] {0, 1}); + } + + /** PassThroughPythonStreamGroupWindowAggregateOperator. */ + public static class PassThroughPythonStreamGroupWindowAggregateOperator<K> Review comment: Yes, it is really bad for reading ---------------------------------------------------------------- This is an automated message from the Apache Git Service. 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