一，介绍
1，旧API中有 org.apache.hadoop.mapred.lib.MultipleOutputFormat和org.apache.hadoop.mapred.lib.MultipleOutputs
MultipleOutputFormat allowing to write the output data to different output files.
MultipleOutputs creates multiple OutputCollectors. Each OutputCollector can have its own OutputFormat and types for the key/value pair. Your MapReduce program will decide what to output to each OutputCollector.
2，新API中 org.apache.hadoop.mapreduce.lib.output.MultipleOutputs
整合了上面旧API两个的功能，没有了MultipleOutputFormat。
The MultipleOutputs class simplifies writing output data to multiple outputs
Case one: writing to additional outputs other than the job default output. Each additional output, or named output, may be configured with its own OutputFormat, with its own key class and with its own value class.
Case two: to write data to different files provided by user
下面这段话来自Hadoop：The.Definitive.Guide(3rd,Early.Release)P251
“In the old MapReduce API there are two classes for producing multiple outputs: MultipleOutputFormat and MultipleOutputs. In a nutshell, MultipleOutputs is more fully featured, but MultipleOutputFormat has more control over the output directory structure and file naming. MultipleOutputs in the new API combines the best features of the two multiple output classes in the old API.”
二，应用
1，输出到多个文件或多个文件夹：
驱动中不需要额外改变，只需要在MapClass或Reduce类中加入如下代码
private MultipleOutputs<Text,IntWritable> mos;
public void setup(Context context) throws IOException,InterruptedException {
mos = new MultipleOutputs(context);
}
public void cleanup(Context context) throws IOException,InterruptedException {
mos.close();
}
然后就可以用mos.write(Key key,Value value,String baseOutputPath)代替context.write(key, value);
在MapClass或Reduce中使用，输出时也会有默认的文件part-m-00*或part-r-00*，不过这些文件是无内容的，大小为0. 而且只有part-m-00*会传给Reduce。
2，以多种格式输出：
public class TestwithMultipleOutputs extends Configured implements Tool {
public static class MapClass extends Mapper<LongWritable,Text,Text,IntWritable> {
private MultipleOutputs<Text,IntWritable> mos;
protected void setup(Context context) throws IOException,InterruptedException {
mos = new MultipleOutputs<Text,IntWritable>(context);
}
public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException{
String line = value.toString();
String[] tokens = line.split("-");
mos.write("MOSInt",new Text(tokens[0]), new IntWritable(Integer.parseInt(tokens[1]))); //（第一处）
mos.write("MOSText", new Text(tokens[0]),tokens[2]); //（第二处）
mos.write("MOSText", new Text(tokens[0]),line,tokens[0]+"/"); //（第三处）同时也可写到指定的文件或文件夹中
}
protected void cleanup(Context context) throws IOException,InterruptedException {
mos.close();
}
}
public int run(String[] args) throws Exception {
Configuration conf = getConf();
Job job = new Job(conf,"word count with MultipleOutputs");
job.setJarByClass(TestwithMultipleOutputs.class);
Path in = new Path(args[0]);
Path out = new Path(args[1]);
FileInputFormat.setInputPaths(job, in);
FileOutputFormat.setOutputPath(job, out);
job.setMapperClass(MapClass.class);
job.setNumReduceTasks(0);
MultipleOutputs.addNamedOutput(job,"MOSInt",TextOutputFormat.class,Text.class,IntWritable.class);
MultipleOutputs.addNamedOutput(job,"MOSText",TextOutputFormat.class,Text.class,Text.class);
System.exit(job.waitForCompletion(true)?0:1);
return 0;
}
public static void main(String[] args) throws Exception {
int res = ToolRunner.run(new Configuration(), new TestwithMultipleOutputs(), args);
System.exit(res);
}
}
测试的数据：
abc-1232-hdf
abc-123-rtd
ioj-234-grjth
ntg-653-sdgfvd
kju-876-btyun
bhm-530-bhyt
hfter-45642-bhgf
bgrfg-8956-fmgh
jnhdf-8734-adfbgf
ntg-68763-nfhsdf
ntg-98634-dehuy
hfter-84567-drhuk
结果截图：（结果输出到/test/testMOSout）
遇到的一个问题：
如果没有mos.close(), 程序运行中会出现异常：
12/05/21 20:12:47 WARN hdfs.DFSClient: DataStreamer Exception:
org.apache.hadoop.ipc.RemoteException:org.apache.hadoop.hdfs.server.namenode.LeaseExpiredException: No lease on
/test/mosreduce/_temporary/_attempt_local_0001_r_000000_0/h-r-00000 File does not exist. [Lease. Holder: DFSClient_-352105532, pendingcreates: 5]