mapreduce程序编写(WordCount)

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折腾了半天。终于编写成功了第一个自己的mapreduce程序，并通过打jar包的方式运行起来了。

运行环境：

windows 64bit

eclipse 64bit

jdk6.0 64bit

一、工程准备

1、新建java project

2、导入jar包

新建一个user library 把hadoop文件夹里的hadoop-core和lib包里的所有包都导入进来，以免出错。

二、编码

1、主要是计算单词的小程序，测试用

package com.hirra;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 {
//嵌套类 Mapper
//Mapper<keyin,valuein,keyout,valueout>
public static class WordCountMapper extends Mapper<Object, Text, Text, IntWritable>{
private final static IntWritable one = new IntWritable(1);
private Text word = new Text(); @Override
protected 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);//Context机制
}
}
} //嵌套类Reducer
//Reduce<keyin,valuein,keyout,valueout>
//Reducer的valuein类型要和Mapper的va lueout类型一致,Reducer的valuein是Mapper的valueout经过shuffle之后的值
public static class WordCountReducer extends Reducer<Text, IntWritable, Text, IntWritable>{
private IntWritable result = new IntWritable(); @Override
protected void reduce(Text key, Iterable<IntWritable> values,
Context context)
throws IOException, InterruptedException {
int sum = 0;
for(IntWritable i:values){
sum += i.get();
}
result.set(sum);
context.write(key,result);//Context机制
} } public static void main(String[] args) throws Exception{
Configuration conf = new Configuration();//获得Configuration配置 Configuration: core-default.xml, core-site.xml
//很关键
conf.set("mapred.job.tracker", "hadoopmaster:9001");
String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs();//获得输入参数[hdfs://localhost:9000/user/dat/input, hdfs://localhost:9000/user/dat/output]
if(otherArgs.length != 2){//判断输入参数个数，不为两个异常退出
System.err.println("Usage:wordcount <in> <out>");
System.exit(2);
} ////设置Job属性
Job job = new Job(conf,"word count");
job.setJarByClass(WordCount.class);
job.setMapperClass(WordCountMapper.class);
job.setCombinerClass(WordCountReducer.class);//将结果进行局部合并
job.setReducerClass(WordCountReducer.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class); FileInputFormat.addInputPath(job, new Path(otherArgs[0]));//传入input path
FileOutputFormat.setOutputPath(job, new Path(otherArgs[1]));//传入output path，输出路径应该为空，否则报错org.apache.hadoop.mapred.FileAlreadyExistsException。 System.exit(job.waitForCompletion(true)?0:1);//是否正常退出
}
}