zhuzhurk commented on code in PR #25552:
URL: https://github.com/apache/flink/pull/25552#discussion_r1910075634
##########
flink-runtime/src/test/java/org/apache/flink/runtime/scheduler/adaptivebatch/VertexInputInfoComputerTestUtil.java:
##########
@@ -86,22 +90,55 @@ public static List<BlockingInputInfo>
createBlockingInputInfos(
return blockingInputInfos;
}
- public static void checkJobVertexInputInfo(
+ public static void checkParallelism(
int targetParallelism,
- List<BlockingInputInfo> inputInfos,
+ Map<IntermediateDataSetID, JobVertexInputInfo> vertexInputInfoMap)
{
+ vertexInputInfoMap
+ .values()
+ .forEach(
+ info ->
+
assertThat(info.getExecutionVertexInputInfos().size())
+ .isEqualTo(targetParallelism));
+ }
+
+ public static void checkConsumedSubpartitionGroups(
List<Map<IndexRange, IndexRange>> targetConsumedSubpartitionGroups,
+ List<BlockingInputInfo> inputInfos,
Map<IntermediateDataSetID, JobVertexInputInfo> vertexInputInfoMap)
{
JobVertexInputInfo vertexInputInfo =
checkAndGetJobVertexInputInfo(inputInfos, vertexInputInfoMap);
List<ExecutionVertexInputInfo> executionVertexInputInfos =
vertexInputInfo.getExecutionVertexInputInfos();
-
assertThat(executionVertexInputInfos.size()).isEqualTo(targetParallelism);
- for (int i = 0; i < targetParallelism; i++) {
+ for (int i = 0; i < executionVertexInputInfos.size(); i++) {
assertThat(executionVertexInputInfos.get(i).getConsumedSubpartitionGroups())
.isEqualTo(targetConsumedSubpartitionGroups.get(i));
}
}
+ public static void checkConsumedDataVolumePerSubtask(
+ long[] targetConsumedDataVolume,
+ List<BlockingInputInfo> inputInfos,
+ Map<IntermediateDataSetID, JobVertexInputInfo> vertexInputs) {
+ long[] consumedDataVolume = new long[targetConsumedDataVolume.length];
+ for (BlockingInputInfo inputInfo : inputInfos) {
+ JobVertexInputInfo vertexInputInfo =
vertexInputs.get(inputInfo.getResultId());
+ List<ExecutionVertexInputInfo> executionVertexInputInfos =
+ vertexInputInfo.getExecutionVertexInputInfos();
+ for (int i = 0; i < executionVertexInputInfos.size(); ++i) {
+ ExecutionVertexInputInfo executionVertexInputInfo =
+ executionVertexInputInfos.get(i);
+ consumedDataVolume[i] +=
+
executionVertexInputInfo.getConsumedSubpartitionGroups().entrySet().stream()
+ .mapToLong(
+ entry ->
+ inputInfo.getNumBytesProduced(
+ entry.getKey(),
entry.getValue()))
+ .sum();
+ }
+ }
+ assertThat(consumedDataVolume).isEqualTo(targetConsumedDataVolume);
+ }
+
public static JobVertexInputInfo checkAndGetJobVertexInputInfo(
Review Comment:
can be private
##########
flink-runtime/src/test/java/org/apache/flink/runtime/scheduler/adaptivebatch/VertexInputInfoComputerTestUtil.java:
##########
@@ -86,22 +90,55 @@ public static List<BlockingInputInfo>
createBlockingInputInfos(
return blockingInputInfos;
}
- public static void checkJobVertexInputInfo(
+ public static void checkParallelism(
Review Comment:
can be private
##########
flink-runtime/src/test/java/org/apache/flink/runtime/scheduler/adaptivebatch/VertexInputInfoComputerTestUtil.java:
##########
@@ -0,0 +1,357 @@
+/*
+ * 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.runtime.scheduler.adaptivebatch;
+
+import org.apache.flink.runtime.executiongraph.ExecutionVertexInputInfo;
+import org.apache.flink.runtime.executiongraph.IndexRange;
+import org.apache.flink.runtime.executiongraph.JobVertexInputInfo;
+import org.apache.flink.runtime.jobgraph.IntermediateDataSetID;
+
+import java.util.ArrayList;
+import java.util.HashMap;
+import java.util.HashSet;
+import java.util.List;
+import java.util.Map;
+import java.util.Set;
+import java.util.stream.Collectors;
+import java.util.stream.IntStream;
+
+import static
org.apache.flink.runtime.executiongraph.IndexRangeUtil.mergeIndexRanges;
+import static org.assertj.core.api.Assertions.assertThat;
+
+/** Utils for vertex input info computer test. */
+public class VertexInputInfoComputerTestUtil {
+ public static List<BlockingInputInfo> createBlockingInputInfos(
+ int typeNumber,
+ int numInputInfos,
+ int numPartitions,
+ int numSubpartitions,
+ boolean existIntraInputKeyCorrelation,
+ boolean existInterInputsKeyCorrelation,
+ int defaultSize,
+ double skewedFactor,
+ List<Integer> skewedPartitionIndex,
+ List<Integer> skewedSubpartitionIndex,
+ boolean isPointwise) {
+ List<BlockingInputInfo> blockingInputInfos = new ArrayList<>();
+ for (int i = 0; i < numInputInfos; i++) {
+ Map<Integer, long[]> subpartitionBytesByPartitionIndex = new
HashMap<>();
+ for (int j = 0; j < numPartitions; j++) {
+ long[] subpartitionBytes = new long[numSubpartitions];
+ for (int k = 0; k < numSubpartitions; k++) {
+ if (skewedSubpartitionIndex.contains(k) ||
skewedPartitionIndex.contains(j)) {
+ subpartitionBytes[k] = (long) (defaultSize *
skewedFactor);
+ } else {
+ subpartitionBytes[k] = defaultSize;
+ }
+ }
+ subpartitionBytesByPartitionIndex.put(j, subpartitionBytes);
+ }
+ BlockingResultInfo resultInfo;
+ if (isPointwise) {
+ resultInfo =
+ new PointwiseBlockingResultInfo(
+ new IntermediateDataSetID(),
+ numPartitions,
+ numSubpartitions,
+ subpartitionBytesByPartitionIndex);
+ } else {
+ resultInfo =
+ new AllToAllBlockingResultInfo(
+ new IntermediateDataSetID(),
+ numPartitions,
+ numSubpartitions,
+ false,
+ subpartitionBytesByPartitionIndex);
+ }
+ blockingInputInfos.add(
+ new BlockingInputInfo(
+ resultInfo,
+ typeNumber,
+ existInterInputsKeyCorrelation,
+ existIntraInputKeyCorrelation));
+ }
+ return blockingInputInfos;
+ }
+
+ public static void checkParallelism(
+ int targetParallelism,
+ Map<IntermediateDataSetID, JobVertexInputInfo> vertexInputInfoMap)
{
+ vertexInputInfoMap
+ .values()
+ .forEach(
+ info ->
+
assertThat(info.getExecutionVertexInputInfos().size())
+ .isEqualTo(targetParallelism));
+ }
+
+ public static void checkConsumedSubpartitionGroups(
+ List<Map<IndexRange, IndexRange>> targetConsumedSubpartitionGroups,
+ List<BlockingInputInfo> inputInfos,
+ Map<IntermediateDataSetID, JobVertexInputInfo> vertexInputInfoMap)
{
+ JobVertexInputInfo vertexInputInfo =
+ checkAndGetJobVertexInputInfo(inputInfos, vertexInputInfoMap);
+ List<ExecutionVertexInputInfo> executionVertexInputInfos =
+ vertexInputInfo.getExecutionVertexInputInfos();
+ for (int i = 0; i < executionVertexInputInfos.size(); i++) {
+
assertThat(executionVertexInputInfos.get(i).getConsumedSubpartitionGroups())
+ .isEqualTo(targetConsumedSubpartitionGroups.get(i));
+ }
+ }
+
+ public static void checkConsumedDataVolumePerSubtask(
+ long[] targetConsumedDataVolume,
+ List<BlockingInputInfo> inputInfos,
+ Map<IntermediateDataSetID, JobVertexInputInfo> vertexInputs) {
+ long[] consumedDataVolume = new long[targetConsumedDataVolume.length];
+ for (BlockingInputInfo inputInfo : inputInfos) {
+ JobVertexInputInfo vertexInputInfo =
vertexInputs.get(inputInfo.getResultId());
+ List<ExecutionVertexInputInfo> executionVertexInputInfos =
+ vertexInputInfo.getExecutionVertexInputInfos();
+ for (int i = 0; i < executionVertexInputInfos.size(); ++i) {
+ ExecutionVertexInputInfo executionVertexInputInfo =
+ executionVertexInputInfos.get(i);
+ consumedDataVolume[i] +=
+
executionVertexInputInfo.getConsumedSubpartitionGroups().entrySet().stream()
+ .mapToLong(
+ entry ->
+ inputInfo.getNumBytesProduced(
+ entry.getKey(),
entry.getValue()))
+ .sum();
+ }
+ }
+ assertThat(consumedDataVolume).isEqualTo(targetConsumedDataVolume);
+ }
+
+ public static JobVertexInputInfo checkAndGetJobVertexInputInfo(
+ List<BlockingInputInfo> inputInfos,
+ Map<IntermediateDataSetID, JobVertexInputInfo> vertexInputInfoMap)
{
+ List<JobVertexInputInfo> vertexInputInfos =
+ inputInfos.stream()
+ .map(inputInfo ->
vertexInputInfoMap.get(inputInfo.getResultId()))
+ .collect(Collectors.toList());
+ assertThat(vertexInputInfos.size()).isEqualTo(inputInfos.size());
+ JobVertexInputInfo baseVertexInputInfo = vertexInputInfos.get(0);
+ for (int i = 1; i < vertexInputInfos.size(); i++) {
+ assertThat(vertexInputInfos.get(i)).isEqualTo(baseVertexInputInfo);
+ }
+ return baseVertexInputInfo;
+ }
+
+ public static void checkCorrectnessForNonCorrelatedInput(
+ Map<IntermediateDataSetID, JobVertexInputInfo> vertexInputInfoMap,
+ BlockingInputInfo inputInfo,
+ int targetParallelism) {
+ checkParallelism(targetParallelism, vertexInputInfoMap);
+ Map<Integer, List<IndexRange>> consumedPartitionToSubpartitionRanges =
new HashMap<>();
+ vertexInputInfoMap
+ .get(inputInfo.getResultId())
+ .getExecutionVertexInputInfos()
+ .forEach(
+ info ->
+ info.getConsumedSubpartitionGroups()
+ .forEach(
+ (partitionRange,
subpartitionRange) -> {
+ for (int i =
partitionRange.getStartIndex();
+ i <=
partitionRange.getEndIndex();
+ ++i) {
+
consumedPartitionToSubpartitionRanges
+
.computeIfAbsent(
+ i, k
-> new ArrayList<>())
+
.add(subpartitionRange);
+ }
+ }));
+ Set<Integer> partitionIndex =
+ IntStream.rangeClosed(0, inputInfo.getNumPartitions() - 1)
+ .boxed()
+ .collect(Collectors.toSet());
+ IndexRange subpartitionRange = new IndexRange(0,
inputInfo.getNumSubpartitions(0) - 1);
+
assertThat(consumedPartitionToSubpartitionRanges.keySet()).isEqualTo(partitionIndex);
+ consumedPartitionToSubpartitionRanges
+ .values()
+ .forEach(
+ subpartitionRanges -> {
+ List<IndexRange> mergedRange =
mergeIndexRanges(subpartitionRanges);
+ assertThat(mergedRange.size()).isEqualTo(1);
+
assertThat(mergedRange.get(0)).isEqualTo(subpartitionRange);
+ });
+ }
+
+ public static void checkCorrectnessForCorrelatedInputs(
+ Map<IntermediateDataSetID, JobVertexInputInfo> vertexInputInfoMap,
+ List<BlockingInputInfo> inputInfos,
+ int targetParallelism,
+ int numSubpartitions) {
+ checkParallelism(targetParallelism, vertexInputInfoMap);
+ Map<Integer, List<BlockingInputInfo>> inputInfosGroupByTypeNumber =
+ inputInfos.stream()
+
.collect(Collectors.groupingBy(BlockingInputInfo::getInputTypeNumber));
+
+ Map<Integer, List<JobVertexInputInfo>>
vertexInputInfosGroupByTypeNumber =
+ inputInfosGroupByTypeNumber.entrySet().stream()
+ .collect(
+ Collectors.toMap(
+ Map.Entry::getKey,
+ e ->
+ e.getValue().stream()
+ .map(
+ v ->
+
vertexInputInfoMap.get(
+
v.getResultId()))
+
.collect(Collectors.toList())));
+
+ Map<JobVertexInputInfo, Integer> vertexInputInfoToNumPartitionsMap =
+ inputInfosGroupByTypeNumber.values().stream()
+ .flatMap(List::stream)
+ .collect(
+ Collectors.toMap(
+ v ->
vertexInputInfoMap.get(v.getResultId()),
+ BlockingInputInfo::getNumPartitions));
+ processPairs(
+ new ArrayList<>(vertexInputInfosGroupByTypeNumber.values()),
+ 0,
+ new ArrayList<>(),
+ vertexInputInfoToNumPartitionsMap,
+ numSubpartitions);
+ }
+
+ private static void processPairs(
Review Comment:
IIUC, it only does verification if there are two type numbers, i.e. when
lists.size() == 2.
In other cases, the verification in
`checkCorrectnessForConsumedSubpartitionRanges` will never be invoked.
If so, maybe simplify its logical to
`checkCorrectnessForCorrelatedInputs(inputsWithTypeNumber1,
inputsWithTypeNumber2, vertexInputInfoToNumPartitionsMap, numSubpartitions)`?
##########
flink-runtime/src/test/java/org/apache/flink/runtime/scheduler/adaptivebatch/util/PointwiseVertexInputInfoComputerTest.java:
##########
@@ -0,0 +1,186 @@
+/*
+ * 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.runtime.scheduler.adaptivebatch.util;
+
+import org.apache.flink.runtime.executiongraph.IndexRange;
+import org.apache.flink.runtime.executiongraph.JobVertexInputInfo;
+import org.apache.flink.runtime.jobgraph.IntermediateDataSetID;
+import org.apache.flink.runtime.scheduler.adaptivebatch.BlockingInputInfo;
+import
org.apache.flink.runtime.scheduler.adaptivebatch.VertexInputInfoComputerTestUtil;
+
+import org.junit.jupiter.api.Test;
+
+import java.util.ArrayList;
+import java.util.List;
+import java.util.Map;
+
+import static
org.apache.flink.runtime.scheduler.adaptivebatch.VertexInputInfoComputerTestUtil.checkConsumedDataVolumePerSubtask;
+import static
org.apache.flink.runtime.scheduler.adaptivebatch.VertexInputInfoComputerTestUtil.checkConsumedSubpartitionGroups;
+import static
org.apache.flink.runtime.scheduler.adaptivebatch.VertexInputInfoComputerTestUtil.checkCorrectnessForNonCorrelatedInput;
+
+/** Tests for {@link PointwiseVertexInputInfoComputer}. */
+class PointwiseVertexInputInfoComputerTest {
+
+ @Test
+ void testComputeNormalInput() {
+ PointwiseVertexInputInfoComputer computer =
createPointwiseVertexInputInfoComputer();
+ List<BlockingInputInfo> inputInfos = createBlockingInputInfos(2,
List.of(), false);
+ Map<IntermediateDataSetID, JobVertexInputInfo> vertexInputs =
+ computer.compute(inputInfos, 2, 10);
+ checkCorrectnessForNonCorrelatedInput(vertexInputs, inputInfos.get(0),
2);
+ checkConsumedDataVolumePerSubtask(new long[] {3L, 3L}, inputInfos,
vertexInputs);
+
+ // with different parallelism
+ Map<IntermediateDataSetID, JobVertexInputInfo> vertexInputs2 =
+ computer.compute(inputInfos, 3, 10);
+ checkCorrectnessForNonCorrelatedInput(vertexInputs2,
inputInfos.get(0), 3);
+ checkConsumedDataVolumePerSubtask(new long[] {2L, 2L, 2L}, inputInfos,
vertexInputs2);
+ }
+
+ @Test
+ void testComputeSkewedInputsWithDifferentSkewedPartitions() {
+ PointwiseVertexInputInfoComputer computer =
createPointwiseVertexInputInfoComputer();
+ List<BlockingInputInfo> inputInfosWithDifferentSkewedPartitions = new
ArrayList<>();
+ BlockingInputInfo inputInfo1 = createBlockingInputInfo(3, 3,
List.of(0), false);
+ BlockingInputInfo inputInfo2 = createBlockingInputInfo(3, 3,
List.of(1), false);
+ inputInfosWithDifferentSkewedPartitions.add(inputInfo1);
+ inputInfosWithDifferentSkewedPartitions.add(inputInfo2);
+ Map<IntermediateDataSetID, JobVertexInputInfo> vertexInputs =
+ computer.compute(inputInfosWithDifferentSkewedPartitions, 3,
10);
+ checkCorrectnessForNonCorrelatedInput(vertexInputs, inputInfo1, 3);
+ checkConsumedDataVolumePerSubtask(
+ new long[] {10L, 10L, 16L}, List.of(inputInfo1), vertexInputs);
+
+ checkCorrectnessForNonCorrelatedInput(vertexInputs, inputInfo2, 3);
+ checkConsumedDataVolumePerSubtask(
+ new long[] {13L, 10L, 13L}, List.of(inputInfo2), vertexInputs);
+ }
+
+ @Test
+ void testComputeSkewedInputsWithDifferentNumPartitions() {
+ PointwiseVertexInputInfoComputer computer =
createPointwiseVertexInputInfoComputer();
+ List<BlockingInputInfo> inputInfosWithDifferentNumPartitions = new
ArrayList<>();
+ BlockingInputInfo inputInfo1 = createBlockingInputInfo(3, 3,
List.of(1), false);
+ BlockingInputInfo inputInfo2 = createBlockingInputInfo(2, 3,
List.of(1), false);
+ inputInfosWithDifferentNumPartitions.add(inputInfo1);
+ inputInfosWithDifferentNumPartitions.add(inputInfo2);
+ Map<IntermediateDataSetID, JobVertexInputInfo> vertexInputs =
+ computer.compute(inputInfosWithDifferentNumPartitions, 3, 10);
+ checkCorrectnessForNonCorrelatedInput(vertexInputs, inputInfo1, 3);
+ checkConsumedDataVolumePerSubtask(
+ new long[] {13L, 10L, 13L}, List.of(inputInfo1), vertexInputs);
+
+ checkCorrectnessForNonCorrelatedInput(vertexInputs, inputInfo2, 3);
+ checkConsumedDataVolumePerSubtask(
+ new long[] {13L, 10L, 10L}, List.of(inputInfo2), vertexInputs);
+ }
+
+ @Test
+ void testComputeSkewedInputsWithDifferentNumSubpartitions() {
+ PointwiseVertexInputInfoComputer computer =
createPointwiseVertexInputInfoComputer();
+ List<BlockingInputInfo> inputInfosWithDifferentNumSubpartitions = new
ArrayList<>();
+ BlockingInputInfo inputInfo1 = createBlockingInputInfo(3, 3,
List.of(1), false);
+ BlockingInputInfo inputInfo2 = createBlockingInputInfo(3, 5,
List.of(1), false);
+ inputInfosWithDifferentNumSubpartitions.add(inputInfo1);
+ inputInfosWithDifferentNumSubpartitions.add(inputInfo2);
+ Map<IntermediateDataSetID, JobVertexInputInfo> vertexInputs =
+ computer.compute(inputInfosWithDifferentNumSubpartitions, 3,
10);
+ checkCorrectnessForNonCorrelatedInput(vertexInputs, inputInfo1, 3);
+ checkConsumedDataVolumePerSubtask(
+ new long[] {13L, 10L, 13L}, List.of(inputInfo1), vertexInputs);
+
+ checkCorrectnessForNonCorrelatedInput(vertexInputs, inputInfo2, 3);
+ checkConsumedDataVolumePerSubtask(
+ new long[] {25L, 20L, 15L}, List.of(inputInfo2), vertexInputs);
+ }
+
+ @Test
+ void testComputeInputWithIntraCorrelation() {
+ PointwiseVertexInputInfoComputer computer =
createPointwiseVertexInputInfoComputer();
+ List<BlockingInputInfo> inputInfos = createBlockingInputInfos(3,
List.of(), true);
+ Map<IntermediateDataSetID, JobVertexInputInfo> vertexInputs =
+ computer.compute(inputInfos, 3, 10);
+ checkCorrectnessForNonCorrelatedInput(vertexInputs, inputInfos.get(0),
3);
+ checkConsumedSubpartitionGroups(
+ List.of(
+ Map.of(new IndexRange(0, 0), new IndexRange(0, 2)),
+ Map.of(new IndexRange(1, 1), new IndexRange(0, 2)),
+ Map.of(new IndexRange(2, 2), new IndexRange(0, 2))),
+ inputInfos,
+ vertexInputs);
+
+ // with different parallelism
+ Map<IntermediateDataSetID, JobVertexInputInfo> vertexInputs2 =
+ computer.compute(inputInfos, 2, 10);
+ checkCorrectnessForNonCorrelatedInput(vertexInputs2,
inputInfos.get(0), 2);
+ checkConsumedSubpartitionGroups(
+ List.of(
+ Map.of(new IndexRange(0, 1), new IndexRange(0, 2)),
+ Map.of(new IndexRange(2, 2), new IndexRange(0, 2))),
+ inputInfos,
+ vertexInputs2);
+
+ // with bigger parallelism and fallback to
+ // VertexInputInfoComputationUtils.computeVertexInputInfoForPointwise
+ Map<IntermediateDataSetID, JobVertexInputInfo> vertexInputs3 =
+ computer.compute(inputInfos, 4, 10);
+ checkCorrectnessForNonCorrelatedInput(vertexInputs3,
inputInfos.get(0), 4);
+ checkConsumedSubpartitionGroups(
+ List.of(
+ Map.of(new IndexRange(0, 0), new IndexRange(0, 0)),
+ Map.of(new IndexRange(0, 0), new IndexRange(1, 2)),
Review Comment:
The data of partition 0 should not be split if the input has intra key
correlation.
However, the case with a bigger parallelism and intra key correlation seems
to be an invalid case?
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