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From fhueske <...@git.apache.org>
Subject [GitHub] flink pull request: [FLINK-7] [Runtime] Enable Range Partitioner.
Date Mon, 26 Oct 2015 15:10:28 GMT
Github user fhueske commented on a diff in the pull request:

    https://github.com/apache/flink/pull/1255#discussion_r43005310
  
    --- Diff: flink-java/src/main/java/org/apache/flink/api/java/functions/AssignRangeIndex.java
---
    @@ -0,0 +1,88 @@
    +/*
    + * 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.api.java.functions;
    +
    +import org.apache.flink.api.common.distributions.DataDistribution;
    +import org.apache.flink.api.common.functions.RichMapPartitionFunction;
    +import org.apache.flink.api.java.tuple.Tuple2;
    +import org.apache.flink.configuration.Configuration;
    +import org.apache.flink.util.Collector;
    +
    +import java.util.ArrayList;
    +import java.util.List;
    +
    +/**
    + * This mapPartition function require a DataSet with DataDistribution as broadcast input,
it read
    + * target parallelism from parameter, build partition boundaries with input DataDistribution,
then
    + * compute the range index for each record.
    + *
    + * @param <IN> The original data type.
    + * @param <K> The key type.
    + */
    +public class AssignRangeIndex<IN, K extends Comparable<K>>
    +	extends RichMapPartitionFunction<Tuple2<K, IN>, Tuple2<Integer, IN>>
{
    +
    +	private List<K> partitionBoundaries;
    +	private int numberChannels;
    +
    +	@Override
    +	public void open(Configuration parameters) throws Exception {
    +		this.numberChannels = parameters.getInteger("TargetParallelism", 1);
    +	}
    +
    +	@Override
    +	public void mapPartition(Iterable<Tuple2<K, IN>> values, Collector<Tuple2<Integer,
IN>> out) throws Exception {
    +
    +		List<Object> broadcastVariable = getRuntimeContext().getBroadcastVariable("DataDistribution");
    +		if (broadcastVariable == null || broadcastVariable.size() != 1) {
    +			throw new RuntimeException("AssignRangePartition require a single DataDistribution
as broadcast input.");
    +		}
    +		DataDistribution<K> dataDistribution = (DataDistribution<K>) broadcastVariable.get(0);
    +
    +		partitionBoundaries = new ArrayList<>(numberChannels);
    +		for (int i=0; i<numberChannels - 1; i++) {
    --- End diff --
    
    Each Assigner will independently initialize the boundaries and have its own copy of it.
It would be better if you could do that only once in a single operator (I believe you had
an AllReduce, i.e., a reduce without groupBy() before) and broadcast the result of this operator.
The benefit is that the boundaries are only built once and all tasks on a TaskManager share
the same broadcast variable, i.e., there are not multiple copies of the boundaries.


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