Hadoop实例WordCount程序一步一步运行

import java.io.IOException;
import java.util.ArrayList;
import java.util.Iterator;
import java.util.List;
import java.util.StringTokenizer;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapred.FileInputFormat;
import org.apache.hadoop.mapred.FileOutputFormat;
import org.apache.hadoop.mapred.JobClient;
import org.apache.hadoop.mapred.JobConf;
import org.apache.hadoop.mapred.MapReduceBase;
import org.apache.hadoop.mapred.Mapper;
import org.apache.hadoop.mapred.OutputCollector;
import org.apache.hadoop.mapred.Reducer;
import org.apache.hadoop.mapred.Reporter;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;

public class WordCount extends Configured implements Tool {

public static class MapClass 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 itr = new StringTokenizer(line);
while (itr.hasMoreTokens()) {
word.set(itr.nextToken());
output.collect(word, one);
}
}
}

/**
* A reducer class that just emits the sum of the input values.
*/
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));
}
}

static int printUsage() {
System.out.println( " wordcount [-m <maps>] [-r <reduces>] <input> <output> " );
ToolRunner.printGenericCommandUsage(System.out);
return - 1 ;
}

/**
* The main driver for word count map/reduce program. Invoke this method to
* submit the map/reduce job.
*
* @throws IOException
* When there is communication problems with the job tracker.
*/
public int run(String[] args) throws Exception {
JobConf conf = new JobConf(getConf(), WordCount. class );
conf.setJobName( " wordcount " );

// the keys are words (strings)
conf.setOutputKeyClass(Text. class );
// the values are counts (ints)
conf.setOutputValueClass(IntWritable. class );

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

List < String > other_args = new ArrayList < String > ();
for ( int i = 0 ; i < args.length; ++ i) {
try {
if ( " -m " .equals(args[i])) {
conf.setNumMapTasks(Integer.parseInt(args[ ++ i]));
} else if ( " -r " .equals(args[i])) {
conf.setNumReduceTasks(Integer.parseInt(args[ ++ i]));
} else {
other_args.add(args[i]);
}
} catch (NumberFormatException except) {
System.out.println( " ERROR: Integer expected instead of "
+ args[i]);
return printUsage();
} catch (ArrayIndexOutOfBoundsException except) {
System.out.println( " ERROR: Required parameter missing from "
+ args[i - 1 ]);
return printUsage();
}
}

// Make sure there are exactly 2 parameters left.
if (other_args.size() != 2 ) {
System.out.println( " ERROR: Wrong number of parameters: "
+ other_args.size() + " instead of 2. " );
return printUsage();
}
FileInputFormat.setInputPaths(conf, other_args.get( 0 ));
FileOutputFormat.setOutputPath(conf, new Path(other_args.get( 1 )));

JobClient.runJob(conf);
return 0 ;
}

public static void main(String[] args) throws Exception {
int res = ToolRunner.run( new Configuration(), new WordCount(), args);
System.exit(res);
}

}

javac - classpath / home / admin / hadoop / hadoop - 0.19 . 1 - core.jar WordCount.java - d / home / admin / WordCount

jar cvf WordCount.jar * . class

[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

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

[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

[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