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），可参见 hadoop0.18文档
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);
}
}
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);
}
}
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
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 /
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
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
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