Github user fhueske commented on a diff in the pull request:
https://github.com/apache/flink/pull/1776#discussion_r57078308
--- Diff:
flink-tests/src/test/java/org/apache/flink/test/javaApiOperators/CustomDistributionITCase.java
---
@@ -0,0 +1,184 @@
+/*
+ * 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.test.javaApiOperators;
+
+import org.apache.flink.api.common.distributions.DataDistribution;
+import org.apache.flink.api.common.functions.MapFunction;
+import org.apache.flink.api.common.functions.RichMapPartitionFunction;
+import org.apache.flink.api.common.typeinfo.BasicTypeInfo;
+import org.apache.flink.api.common.typeinfo.TypeInformation;
+import org.apache.flink.api.java.DataSet;
+import org.apache.flink.api.java.ExecutionEnvironment;
+import org.apache.flink.api.java.io.DiscardingOutputFormat;
+import org.apache.flink.api.java.tuple.Tuple3;
+import org.apache.flink.api.java.utils.DataSetUtils;
+import org.apache.flink.core.memory.DataInputView;
+import org.apache.flink.core.memory.DataOutputView;
+import org.apache.flink.test.javaApiOperators.util.CollectionDataSets;
+import org.apache.flink.util.Collector;
+import org.junit.Test;
+
+
+import java.io.IOException;
+
+import static org.junit.Assert.fail;
+
+
+public class CustomDistributionITCase {
+
+ @Test
+ public void testPartitionWithDistribution1() throws Exception{
+ /*
+ * Test the record partitioned rightly with one field according
to the customized data distribution
+ */
+
+ ExecutionEnvironment env =
ExecutionEnvironment.createLocalEnvironment();
+
+ DataSet<Tuple3<Integer, Long, String>> input1 =
CollectionDataSets.get3TupleDataSet(env);
+ final TestDataDist dist = new TestDataDist(1);
+
+ env.setParallelism(dist.getParallelism());
+
+ DataSet<Boolean> result = DataSetUtils.partitionByRange(input1,
dist, 0).mapPartition(new RichMapPartitionFunction<Tuple3<Integer, Long,
String>, Boolean>() {
+ @Override
+ public void mapPartition(Iterable<Tuple3<Integer, Long,
String>> values, Collector<Boolean> out) throws Exception {
+ int partitionIndex =
getRuntimeContext().getIndexOfThisSubtask();
+
+ for (Tuple3<Integer, Long, String> s : values) {
+ if ((s.f0 - 1) / 7 != partitionIndex) {
+ fail("Record was not correctly
partitioned: " + s.toString());
+ }
+ }
+ }
+ });
+
+ result.output(new DiscardingOutputFormat());
+ env.execute();
+ }
+
+ @Test
+ public void testRangeWithDistribution2() throws Exception{
+ /*
+ * Test the record partitioned rightly with two fields
according to the customized data distribution
+ */
+
+ ExecutionEnvironment env =
ExecutionEnvironment.createLocalEnvironment();
+
+ DataSet<Tuple3<Integer, Long, String>> input1 =
CollectionDataSets.get3TupleDataSet(env);
+ final TestDataDist dist = new TestDataDist(2);
+
+ env.setParallelism(dist.getParallelism());
+
+ DataSet<Boolean> result =
DataSetUtils.partitionByRange(input1.map(new MapFunction<Tuple3<Integer, Long,
String>, Tuple3<Integer, Integer, String>>() {
+ @Override
+ public Tuple3<Integer, Integer, String>
map(Tuple3<Integer, Long, String> value) throws Exception {
+ return new Tuple3<>(value.f0,
value.f1.intValue(), value.f2);
+ }
+ }), dist, 0, 1).mapPartition(new
RichMapPartitionFunction<Tuple3<Integer, Integer, String>, Boolean>() {
+ @Override
+ public void mapPartition(Iterable<Tuple3<Integer,
Integer, String>> values, Collector<Boolean> out) throws Exception {
+ int partitionIndex =
getRuntimeContext().getIndexOfThisSubtask();
+
+ for (Tuple3<Integer, Integer, String> s :
values) {
+ if (s.f0 <= partitionIndex *
(partitionIndex + 1) / 2 ||
+ s.f0 > (partitionIndex
+ 1) * (partitionIndex + 2) / 2 ||
+ s.f1 - 1 !=
partitionIndex) {
+ fail("Record was not correctly
partitioned: " + s.toString());
+ }
+ }
+ }
+ });
+
+ result.output(new DiscardingOutputFormat());
+ env.execute();
+ }
+
+ /**
+ * The class is used to do the tests of range partition with customed
data distribution.
+ */
+ public static class TestDataDist implements DataDistribution {
+
+ private int dim;
+
+ public TestDataDist() {}
+
+ /**
+ * Constructor of the customized distribution for range
partition.
+ * @param dim the number of the fields.
+ */
+ public TestDataDist(int dim) {
+ this.dim = dim;
+ }
+
+ public int getParallelism() {
+ if (dim == 1) {
+ return 3;
+ }
+ return 6;
+ }
+
+ @Override
+ public Object[] getBucketBoundary(int bucketNum, int
totalNumBuckets) {
+ if (dim == 1) {
+ /*
+ for the first test, the boundary is just like :
+ (0, 7]
+ (7, 14]
+ (14, 21]
+ */
+
+ return new Integer[]{(bucketNum + 1) * 7};
+ }
+ /*
+ for the second test, the boundary is just like :
+ (0, 1], (0, 1]
--- End diff --
Sure :-)
The value distribution of the two key attributes in the data set used for
the test is not optimal. Both attributes are increasing and hence somewhat
correlated. The buckets of the test distribution reflect this correlation
because the keys of both boundaries are increasing too. In fact, only a single
attribute can be used to determine the correct result partition. So the tests
do not show that both attributes are correctly evaluated to determine the
result partition.
In my opinion, it would be better to use a data set where the partition
keys are not correlated and have boundaries similar as the ones I posted before.
Did that clarify my previous comment?
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