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From "ASF GitHub Bot (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (FLINK-2997) Support range partition with user customized data distribution.
Date Tue, 22 Mar 2016 10:11:25 GMT

    [ https://issues.apache.org/jira/browse/FLINK-2997?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15206109#comment-15206109
] 

ASF GitHub Bot commented on FLINK-2997:
---------------------------------------

Github user fhueske commented on a diff in the pull request:

    https://github.com/apache/flink/pull/1776#discussion_r56961903
  
    --- 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 --
    
    This is not a common distribution for composite range partition keys. It should look similar
to this one:
    ```
    ( (1, 2), (1, 7) ]
    ( (1, 7), (3, 2) ]
    ( (3, 2), (4, 8) ]
    ...
    ```
    This would result in 4 partitions:
    
    - Partition 1: `(0,2), (1,1), (0,10), ...`
    - Partition 2: `(1,3), (2,20), (1,8), ...`
    - Partition 3: `(4,10), (3,5), ...`
    - Partition 4: `(5,3), (8,20), (4,12), ...`


> Support range partition with user customized data distribution.
> ---------------------------------------------------------------
>
>                 Key: FLINK-2997
>                 URL: https://issues.apache.org/jira/browse/FLINK-2997
>             Project: Flink
>          Issue Type: New Feature
>            Reporter: Chengxiang Li
>
> This is a followup work of FLINK-7, sometime user have better knowledge of the source
data, and they can build customized data distribution to do range partition more efficiently.



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