Wordcount on YARN 一个MapReduce示例

Hadoop YARN版本：2.2.0

关于hadoop yarn的环境搭建可以参考这篇博文：Hadoop 2.0安装以及不停集群加datanode

 

hadoop hdfs yarn伪分布式运行，有如下进程

1320 DataNode
1665 ResourceManager 1771 NodeManager 1195 NameNode 1487 SecondaryNameNode

 

写一个mapreduce示例，在yarn上跑，wordcount数单词示例

代码在github上：https://github.com/huahuiyang/yarn-demo

步骤一

我们要处理的输入如下，每行包含一个或多个单词，空格分开。可以用hadoop fs -put ... 把本地文件放到hdfs上去，方便mapreduce程序读取

hadoop yarn mapreduce hello redis java hadoop hello world here we go

wordcount程序希望完成数单词任务，输出格式是 <单词  出现次数>

 

步骤二

新建一个工程，工程结构如下，这个是个maven管理的工程

源代码如下：

pom.xml文件<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd"><modelVersion>4.0.0</modelVersion><groupId>hadoop-yarn</groupId><artifactId>hadoop-demo</artifactId><version>0.0.1-SNAPSHOT</version><dependencies><dependency><groupId>org.apache.hadoop</groupId><artifactId>hadoop-mapreduce-client-core</artifactId><version>2.1.1-beta</version></dependency><dependency><groupId>org.apache.hadoop</groupId><artifactId>hadoop-common</artifactId><version>2.1.1-beta</version></dependency><dependency><groupId>org.apache.hadoop</groupId><artifactId>hadoop-mapreduce-client-common</artifactId><version>2.1.1-beta</version></dependency><dependency><groupId>org.apache.hadoop</groupId><artifactId>hadoop-mapreduce-client-jobclient</artifactId><version>2.1.1-beta</version></dependency></dependencies> </project>

 

package com.yhh.mapreduce.wordcount; import java.io.IOException;import org.apache.hadoop.io.*; import org.apache.hadoop.mapred.*;public class WordCountMapper extends MapReduceBase implements Mapper<LongWritable, Text, Text,IntWritable> {@Overridepublic void map(LongWritable key, Text value,OutputCollector<Text, IntWritable> output, Reporter reporter)throws IOException {String line = value.toString();if(line != null) {String[] words = line.split(" ");for(String word:words) {output.collect(new Text(word), new IntWritable(1));}}}}

 

package com.yhh.mapreduce.wordcount;import java.io.IOException; import java.util.Iterator;import org.apache.hadoop.io.*; import org.apache.hadoop.mapred.*;public class WordCountReducer extends MapReduceBase implements Reducer<Text, IntWritable, Text, IntWritable>{@Overridepublic void reduce(Text key, Iterator<IntWritable> values,OutputCollector<Text, IntWritable> output, Reporter reporter)throws IOException {int count = 0;while(values.hasNext()) {values.next();count++;}output.collect(key, new IntWritable(count));}}

 

package com.yhh.mapreduce.wordcount;import java.io.IOException;import org.apache.hadoop.fs.Path; import org.apache.hadoop.io.IntWritable; import org.apache.hadoop.io.Text; import org.apache.hadoop.mapred.JobConf; import org.apache.hadoop.mapred.FileInputFormat; import org.apache.hadoop.mapred.FileOutputFormat; import org.apache.hadoop.mapred.JobClient;public class WordCount {public static void main(String[] args) throws IOException {if(args.length != 2) {System.err.println("Error!");System.exit(1);}JobConf conf = new JobConf(WordCount.class);conf.setJobName("word count mapreduce demo");conf.setMapperClass(WordCountMapper.class);conf.setReducerClass(WordCountReducer.class);conf.setOutputKeyClass(Text.class);conf.setOutputValueClass(IntWritable.class);FileInputFormat.addInputPath(conf, new Path(args[0]));FileOutputFormat.setOutputPath(conf, new Path(args[1]));JobClient.runJob(conf);}}

 

步骤三

打包发布成jar，右击java工程，选择Export...，然后选择jar file生成目录，这边发布成wordcount.jar，然后上传到hadoop集群

[root@hadoop-namenodenew ~]# ll wordcount.jar -rw-r--r--. 1 root root 4401 6月 1 22:05 wordcount.jar

运行mapreduce任务。命令如下

hadoop jar ~/wordcount.jar com.yhh.mapreduce.wordcount.WordCount data.txt /wordcount/result

可以用hadoop job -list看任务运行情况，运行成功大概会有如下输出

14/06/01 22:06:25 INFO mapreduce.Job: The url to track the job: http://hadoop-namenodenew:8088/proxy/application_1401631066126_0003/ 14/06/01 22:06:25 INFO mapreduce.Job: Running job: job_1401631066126_0003 14/06/01 22:06:33 INFO mapreduce.Job: Job job_1401631066126_0003 running in uber mode : false 14/06/01 22:06:33 INFO mapreduce.Job: map 0% reduce 0% 14/06/01 22:06:40 INFO mapreduce.Job: map 50% reduce 0% 14/06/01 22:06:41 INFO mapreduce.Job: map 100% reduce 0% 14/06/01 22:06:47 INFO mapreduce.Job: map 100% reduce 100% 14/06/01 22:06:48 INFO mapreduce.Job: Job job_1401631066126_0003 completed successfully 14/06/01 22:06:49 INFO mapreduce.Job: Counters: 43

 

然后mapreduce输出的任务结果如下，单词按照字典序排序

hadoop fs -cat /wordcount/result/part-00000go 1 hadoop 2 hello 2 here 1 java 1 mapreduce 1 redis 1 we 1 world 1 yarn 1

 

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