完成了第一个mapReduce例子，记录一下。
实验环境:
hadoop在三台ubuntu机器上部署 开发在window7上进行 hadoop版本2.2.0
下载了hadoop-eclipse-plugin-2.2.0.jar放入eclipse的plugin文件夹中，重启后有如下标识
下方右击: add hadoop location
此时，eclipse 左侧会有
上图即简单的实现了一个嵌于eclipse中的用于访问hdfs系统的client端，其中可以增删改查文件。
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下面研究一下编程吧...
1 新建一个map/Reduce Project，本例中由于eclpise与hadoop不在一台主机上，着实费了一些时间
如下图，此时应该选择第二项(specify hadoop library locaiton), 导入的应该是hadoop目录下的lib/native文件夹，这里如果选错了，project是建不起来的！
2. 取出hadoop-2.2.0sharehadoopmapreducesourceshadoop-mapreduce-examples-2.2.0-sources.jar下，解压后导入eclipse，好了，可以开始研究代码了。.
基本流程如下
(input) <k1, v1> -> map -> <k2, v2> -> combine -> <k2, v2> -> reduce -> <k3, v3> (output)
/** * 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.hadoop.examples; 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 { /*Mapper(14-26行)中的map方法(18-25行)通过指定的 TextInputFormat(49行)一次处理一行。然后，它通过StringTokenizer 以空格为分隔符将一行切分为若干tokens，之后，输出< <word>, 1> 形式的键值对。*/ 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); } } }
/*
做Combiner: 每次map运行之后，会对输出按照key进行排序，然后把输出传递给本地的combiner（按照作业的配置与Reducer一样），进行本地聚合
做Reducer中的reduce方法 仅是将每个key（本例中就是单词）出现的次数求和。
*/ 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); } Job job = new Job(conf, "word count"); job.setJarByClass(WordCount.class); job.setMapperClass(TokenizerMapper.class); job.setCombinerClass(IntSumReducer.class); // combiner与reducer用的类一样，减轻reducer负担 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); } }
3. wordCount执行效果如下
root@kali:/data/hadoop/share/hadoop# hadoop jar ./mapreduce/hadoop-mapreduce-examples-2.2.0.jar wordcount /abc/start-all.sh /abd/out 14/03/09 21:24:43 INFO Configuration.deprecation: session.id is deprecated. Instead, use dfs.metrics.session-id 14/03/09 21:24:43 INFO jvm.JvmMetrics: Initializing JVM Metrics with processName=JobTracker, sessionId= 14/03/09 21:24:44 INFO input.FileInputFormat: Total input paths to process : 1 14/03/09 21:24:44 INFO mapreduce.JobSubmitter: number of splits:1 14/03/09 21:24:44 INFO Configuration.deprecation: user.name is deprecated. Instead, use mapreduce.job.user.name 14/03/09 21:24:44 INFO Configuration.deprecation: mapred.jar is deprecated. Instead, use mapreduce.job.jar 14/03/09 21:24:44 INFO Configuration.deprecation: mapred.output.value.class is deprecated. Instead, use mapreduce.job.output.value.class 14/03/09 21:24:44 INFO Configuration.deprecation: mapreduce.combine.class is deprecated. Instead, use mapreduce.job.combine.class 14/03/09 21:24:44 INFO Configuration.deprecation: mapreduce.map.class is deprecated. Instead, use mapreduce.job.map.class
http://phz50.iteye.com/blog/932373
http://www.ibm.com/developerworks/cn/java/j-javadev2-15/
http://www.cnblogs.com/flyoung2008/archive/2011/12/09/2281400.html
http://f.dataguru.cn/thread-167597-1-1.html hadoop在eclipse中上传文件size为0
http://cs.smith.edu/dftwiki/index.php/Hadoop_Tutorial_1_--_Running_WordCount step by step!