本文演示如何在Eclipse中开发一个Map/Reduce项目：
1、环境说明
- Hadoop2.2.0
- Eclipse Juno SR2
- Hadoop2.x-eclipse-plugin 插件的编译安装配置的过程参考：http://www.micmiu.com/bigdata/hadoop/hadoop2-x-eclipse-plugin-build-install/
2、新建MR工程
依次点击 File → New → Ohter… 选择 “Map/Reduce Project”，然后输入项目名称:micmiu_MRDemo，创建新项目:
3、创建Mapper和Reducer
依次点击 File → New → Ohter… 选择Mapper，自动继承Mapper<KEYIN, VALUEIN, KEYOUT, VALUEOUT>
创建Reducer的过程同Mapper，具体的业务逻辑自己实现即可。
本文就以官方自带的WordCount为例进行测试：
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package com.micmiu.mr; /** * 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. */ import java.io.IOException; import java.util.StringTokenizer; import org.apache.hadoop.conf.Configuration; import org.apache.hadoop.fs.Path; import org.apache.hadoop.io.IntWritable; import org.apache.hadoop.io.Text; import org.apache.hadoop.mapreduce.Job; import org.apache.hadoop.mapreduce.Mapper; import org.apache.hadoop.mapreduce.Reducer; import org.apache.hadoop.mapreduce.lib.input.FileInputFormat; import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat; import org.apache.hadoop.util.GenericOptionsParser; public class WordCount { public static class TokenizerMapper extends Mapper<Object, Text, Text, IntWritable>{ private final static IntWritable one = new IntWritable(1); private Text word = new Text(); public void map(Object key, Text value, Context context ) throws IOException, InterruptedException { StringTokenizer itr = new StringTokenizer(value.toString()); while (itr.hasMoreTokens()) { word.set(itr.nextToken()); context.write(word, one); } } } public static class IntSumReducer extends Reducer<Text,IntWritable,Text,IntWritable> { private IntWritable result = new IntWritable(); public void reduce(Text key, Iterable<IntWritable> values, Context context ) throws IOException, InterruptedException { int sum = 0; for (IntWritable val : values) { sum += val.get(); } result.set(sum); context.write(key, result); } } public static void main(String[] args) throws Exception { Configuration conf = new Configuration(); String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs(); if (otherArgs.length != 2) { System.err.println("Usage: wordcount <in> <out>"); System.exit(2); } //conf.set("fs.defaultFS", "hdfs://192.168.6.77:9000"); Job job = new Job(conf, "word count"); job.setJarByClass(WordCount.class); job.setMapperClass(TokenizerMapper.class); job.setCombinerClass(IntSumReducer.class); job.setReducerClass(IntSumReducer.class); job.setOutputKeyClass(Text.class); job.setOutputValueClass(IntWritable.class); FileInputFormat.addInputPath(job, new Path(otherArgs[0])); FileOutputFormat.setOutputPath(job, new Path(otherArgs[1])); System.exit(job.waitForCompletion(true) ? 0 : 1); } } |
4、准备测试数据
micmiu-01.txt：
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Hi Michael welcome to Hadoop more see micmiu.com |
micmiu-02.txt：
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Hi Michael welcome to BigData more see micmiu.com |
micmiu-03.txt：
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Hi Michael welcome to Spark more see micmiu.com |
把 micmiu 打头的三个文件上传到hdfs：
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micmiu-mbp:Downloads micmiu$ hdfs dfs -copyFromLocal micmiu-*.txt /user/micmiu/test/input micmiu-mbp:Downloads micmiu$ hdfs dfs -ls /user/micmiu/test/input Found 3 items -rw-r--r-- 1 micmiu supergroup 50 2014-04-15 14:53 /user/micmiu/test/input/micmiu-01.txt -rw-r--r-- 1 micmiu supergroup 50 2014-04-15 14:53 /user/micmiu/test/input/micmiu-02.txt -rw-r--r-- 1 micmiu supergroup 49 2014-04-15 14:53 /user/micmiu/test/input/micmiu-03.txt micmiu-mbp:Downloads micmiu$ |
5、配置运行参数
Run As → Run Configurations… ，在Arguments中配置运行参数，例如程序的输入参数：
6、运行
Run As -> Run on Hadoop ，执行完成后可以看到如下信息：
到此Eclipse中调用Hadoop2x本地伪分布式模式执行MR演示成功。
ps：调用集群环境MR运行一直失败，暂时没有找到原因。
—————– EOF @Michael Sun —————–
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