MapRedcue例程编译和执行

简介

例程的编译过程参考MapReduce Tutorial[1]，历程WordCount.java内容见附录。

Linux平台

开发环境

• JDK1.8.0
• Hadoop2.5.2

环境变量

• export JAVA_HOME=/usr/java/default
• export PATH=$JAVA_HOME/bin:\$PATH
• export HADOOP_CLASSPATH=$JAVA_HOME/lib/tools.jar

编译和打包

• hadoop com.sun.tools.javac.Main WordCount.java
• jar cf wc.jar WordCount*.class

准备输入文件

• echo Hello World Bye World>file01
• echo Hello Hadoop Goodbye Hadoop>file02
• hdfs dfs -mkdir -p /user/joe/wordcount/input
• hdfs dfs -put file0? /user/joe/wordcount/input/
• hdfs dfs -ls /user/joe/wordcount
• hdfs dfs -cat /user/joe/wordcount/input/file01
• hdfs dfs -cat /user/joe/wordcount/input/file02

执行并检查结果

• hadoop jar wc.jar WordCount /user/joe/wordcount/input /user/joe/wordcount/output
• hdfs dfs -cat /user/joe/wordcount/output/part-r-00000

Windows平台

Windows平台的只是完成例程编译和打包过程，仍需将程序包传回linux平台执行。

开发环境

• JDK1.8.0

环境变量

• export JAVA_HOME=/usr/java/default
• export PATH=$JAVA_HOME/bin:\$PATH

Hadoop引用包

• hadoop-common-2.7.3.2.5.0.0-1245.jar
• hadoop-mapreduce-client-core-2.7.3.2.5.0.0-1245.jar

编译和打包

• javac -cp hadoop-common-2.7.3.2.5.0.0-1245.jar;hadoop-mapreduce-client-core-2.7.3.2.5.0.0-1245.jar WordCount.java
• jar cf wc.jar WordCount*.class

附录

• 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<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();    Job job = Job.getInstance(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(args[0]));    FileOutputFormat.setOutputPath(job, new Path(args[1]));    System.exit(job.waitForCompletion(true) ? 0 : 1);  }}

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[1] http://hadoop.apache.org/docs/r2.5.2/hadoop-mapreduce-client/hadoop-mapreduce-client-core/MapReduceTutorial.html