hadoop初学之WordCount程序一步一步运行

时间：2010-09-16 来源：flying5

虽说现在用Eclipse下开发hadoop程序很方便了，但是命令行方式对于小程序开发验证很方便。这是初学hadoop时的笔记，记录下来以备查。
1. 经典的WordCound程序（WordCount.java），见 hadoop0.18文档

import java.io.IOException;
import java.util.*;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.conf.*;
import org.apache.hadoop.io.*;
import org.apache.hadoop.mapred.*;
import org.apache.hadoop.util.*;

public class WordCount {
public static class Map extends MapReduceBase implements Mapper<LongWritable, Text, Text, IntWritable> {
    private final static IntWritable one = new IntWritable(1);
    private Text word = new Text();
    public void map(LongWritable key, Text value, OutputCollector<Text, IntWritable> output, Reporter reporter) throws IOException {
        String line = value.toString();
        StringTokenizer tokenizer = new StringTokenizer(line);
        while (tokenizer.hasMoreTokens()) {
            word.set(tokenizer.nextToken());
            output.collect(word, one);
        }
    }
}

public static class Reduce extends MapReduceBase implements Reducer<Text, IntWritable, Text, IntWritable> {
    public void reduce(Text key, Iterator<IntWritable> values, OutputCollector<Text, IntWritable> output, Reporter reporter) throws IOException {
        int sum = 0;
        while (values.hasNext()) {
            sum += values.next().get();
        }
        output.collect(key, new IntWritable(sum));
    }
}
public static void main(String[] args) throws Exception {
    JobConf conf = new JobConf(WordCount.class);
    conf.setJobName("wordcount");

    conf.setOutputKeyClass(Text.class);
    conf.setOutputValueClass(IntWritable.class);

    conf.setMapperClass(Map.class);
    conf.setCombinerClass(Reduce.class);
    conf.setReducerClass(Reduce.class);

    conf.setInputFormat(TextInputFormat.class);
    conf.setOutputFormat(TextOutputFormat.class);

    FileInputFormat.setInputPaths(conf, new Path(args[0]));
    FileOutputFormat.setOutputPath(conf, new Path(args[1]));

    JobClient.runJob(conf);
}
}

2. 保证hadoop集群是配置好了的，单机的也好。新建一个目录，比如 /home/admin/WordCount 编译WordCount.java程序。

javac -classpath /home/admin/hadoop/hadoop-0.19.1-core.jar WordCount.java -d /home/admin/WordCount

3. 编译完后在/home/admin/WordCount目录会发现三个class文件 WordCount.class，WordCount$Map.class，WordCount$Reduce.class。 cd 进入 /home/admin/WordCount目录,然后执行：

jar cvf WordCount.jar *.class

就会生成 WordCount.jar 文件。
4. 构造一些输入数据
input1.txt和input2.txt的文件里面是一些单词。如下：

[admin@host WordCount]$ cat input1.txt
Hello, i love china
are you ok?
[admin@host WordCount]$ cat input2.txt
hello, i love word
You are ok

在hadoop上新建目录，和put程序运行所需要的输入文件:

hadoop fs -mkdir /tmp/input
hadoop fs -mkdir /tmp/output
hadoop fs -put input1.txt /tmp/input/
hadoop fs -put input2.txt /tmp/input/

5. 运行程序,会显示job运行时的一些信息。

[admin@host WordCount]$ hadoop jar WordCount.jar WordCount /tmp/input /tmp/output

10/09/16 22:49:43 WARN mapred.JobClient: Use GenericOptionsParser for parsing the arguments. Applications should implement Tool for the same.
10/09/16 22:49:43 INFO mapred.FileInputFormat: Total input paths to process :2
10/09/16 22:49:43 INFO mapred.JobClient: Running job: job_201008171228_76165
10/09/16 22:49:44 INFO mapred.JobClient: map 0% reduce 0%
10/09/16 22:49:47 INFO mapred.JobClient: map 100% reduce 0%
10/09/16 22:49:54 INFO mapred.JobClient: map 100% reduce 100%
10/09/16 22:49:55 INFO mapred.JobClient: Job complete: job_201008171228_76165
10/09/16 22:49:55 INFO mapred.JobClient: Counters: 16
10/09/16 22:49:55 INFO mapred.JobClient: File Systems
10/09/16 22:49:55 INFO mapred.JobClient: HDFS bytes read=62
10/09/16 22:49:55 INFO mapred.JobClient: HDFS bytes written=73
10/09/16 22:49:55 INFO mapred.JobClient: Local bytes read=152
10/09/16 22:49:55 INFO mapred.JobClient: Local bytes written=366
10/09/16 22:49:55 INFO mapred.JobClient: Job Counters
10/09/16 22:49:55 INFO mapred.JobClient: Launched reduce tasks=1
10/09/16 22:49:55 INFO mapred.JobClient: Rack-local map tasks=2
10/09/16 22:49:55 INFO mapred.JobClient: Launched map tasks=2
10/09/16 22:49:55 INFO mapred.JobClient: Map-Reduce Framework
10/09/16 22:49:55 INFO mapred.JobClient: Reduce input groups=11
10/09/16 22:49:55 INFO mapred.JobClient: Combine output records=14
10/09/16 22:49:55 INFO mapred.JobClient: Map input records=4
10/09/16 22:49:55 INFO mapred.JobClient: Reduce output records=11
10/09/16 22:49:55 INFO mapred.JobClient: Map output bytes=118
10/09/16 22:49:55 INFO mapred.JobClient: Map input bytes=62
10/09/16 22:49:55 INFO mapred.JobClient: Combine input records=14
10/09/16 22:49:55 INFO mapred.JobClient: Map output records=14
10/09/16 22:49:55 INFO mapred.JobClient: Reduce input records=14

6. 查看运行结果

[admin@host WordCount]$ hadoop fs -ls /tmp/output/
Found 2 items
drwxr-x--- - admin admin 0 2010-09-16 22:43 /tmp/output/_logs
-rw-r----- 1 admin admin 102 2010-09-16 22:44 /tmp/output/part-00000
[admin@host WordCount]$ hadoop fs -cat /tmp/output/part-00000
Hello, 1
You 1
are 2
china 1
hello, 1
i 2
love 2
ok 1
ok? 1
word 1
you 1

ok，结束了。