Hadoop如何打包和运行MapReduce程序

本篇内容主要讲解"Hadoop如何打包和运行MapReduce程序"，感兴趣的朋友不妨来看看。本文介绍的方法操作简单快捷，实用性强。下面就让小编来带大家学习"Hadoop如何打包和运行MapReduce程序"吧!

主要内容：将 MapReduce 代码通过命令行打包成 jar 包，然后提交给 Hadoop 集群运行。示例的 WordCount.java、WordCount.txt 见最后面。

一、编译 Hadoop 的应用程序需要将所需的依赖包添加到 CLASSPATH，可以添加到 .bashrc 或者 /etc/profile。

# javac 编译相关包依赖HADOOP_CLASSPATH=$($HADOOP_HOME/bin/hadoop classpath)# 将 HADOOP_CLASSPATH 添加到 CLASSPATHexport CLASSPATH=.:$HADOOP_CLASSPATH:$CLASSPATH

二、编译源代码

# 编译 没有设置 CLASSPATH 通过 -cp $($HADOOP_HOME/bin/hadoop classpath)javac WordCount.java# 打包jar -cvf WordCount.jar  *.class

三、提交到 Hadoop

# 上传 WordCount.txt 到 Hadoophdfs dfs -mkdir inputhdfs dfs -put WordCount.txt input# 提交任务 jar 包、main 所在的类、输入文件夹、输出文件夹hadoop jar WordCount.jar WordCount input output# 查看运行结果hdfs dfs -cat output/*# 删除输出结果目录hdfs dfs -rm -r output

四、运行结果

and   1bigdata 2hadoop  2hello   4world   1

附录：

WordCount.txt，单词使用空格分隔

hello worldhello hadoophello bigdatahello hadoop  and bigdata

WordCount.java

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;public class WordCount {    public static class TokenizerMapper extends Mapper {        private final static IntWritable ONE = new IntWritable(1);        private final Text word = new Text();        @Override        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 {        private final IntWritable result = new IntWritable();        @Override        public void reduce(Text key, Iterable values, Context context)                throws IOException, InterruptedException {            int sum = 0;            for (IntWritableval : values) {                sum += val.get();            }            result.set(sum);            context.write(key, result);        }    }    public static void main(String[] args) throws Exception {        Configuration conf = new Configuration();        Job job = Job.getInstance(conf, "WordCount");        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(args[0]));        FileOutputFormat.setOutputPath(job, new Path(args[1]));        System.exit(job.waitForCompletion(true) ? 0 : 1);    }}

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