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接上一篇《Ubuntu Kylin系统下安装Hadoop2.6.0》
通过上一篇，Hadoop伪分布式基本配好了。
下一步是运行一个MapReduce程序，以WordCount为例：
1. 构建实现类：
cd /usr/local/hadoop mkdir workspace
cd workspace
gedit WordCount.java
将代码复制粘贴。
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); } }
对于代码的具体分析，下一篇再详细讲解。
2. 编译
(1) 添加JAVA_HOME
export JAVA_HOME=/usr/lib/jvm/java-8u5-sun
忘记JAVA_HOME的可以使用：
echo $JAVA_HOME
(2) 将jdk目录下的bin文件夹添加到环境变量
export PATH=$JAVA_HOME/bin:$PATH
(3) 将hadoop_classpath添加到环境变量
export HADOOP_CLASSPATH=$JAVA_HOME/lib/tools.jar
编译WordCount.java文件
../bin/hadoop com.sun.tools.javac.Main WordCount.java
其中com.sun.tools.javac.Main是生成一个编译器的实例
上述语句生成三个class: WordCount.class Reducer.class TokenizerMapper.class
将上述三个class打包成.jar包
jar cf WordCount.jar WordCount*.class
生成WordCount.jar
3. 运行
bin/hdfs dfs -mkdir /user bin/hdfs dfs -mkdir /user/hadoop
构造输入文件：
bin/hdfs dfs -put etc/hadoop /input
其中，etc/hadoop是输入文件，可替换为其他文件
bin/hadoop jar /usr/local/hadoop/workspace/WordCount.jar /input /output
查看运行结果
bin/hdfs dfs -cat /output/*
4. 结束Hadoop
sbin/stop-dfs.sh
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原文地址：http://www.cnblogs.com/kingatnuaa/p/4174706.html
