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MapReduce >> mail # dev >> hadoop streaming using java as mapper & reducer

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Re: hadoop streaming using java as mapper & reducer
I'm confused.  First, most people don't use streaming in conjunction with Java since Hadoop supports Java directly...although I think you were saying in your parenthetical comment at the top of your post that you may have a legitimate reason for doing that.  Then, in your code, you appear to be creating a "classic" MapReduce program, not a streaming one.  I say this because your Mapper is a typical mapper, extending the Mapper from the Hadoop API.  I would expect a streaming Mapper to be a top-level class, not a derivation from the API.  Furthermore, it would gather its inputs not through the overridden map() method but rather from stdin.  Likewise, it would send output to stdout.

Am I completely misunderstanding your situation?

On May 27, 2012, at 23:50 , HU Wenjing A wrote:

> Hi all,
> I am a new learner of hadoop, and recently I want to use hadoop streaming to run java program as mapper and reducer.
> (because I want to use hadoop streaming to transplant some existing java programs to process xml file).
>  To have a try, first I use the hadoop wordcount example (as follows):
>     Countm.java:
>     import java.io.IOException;
> import java.util.StringTokenizer;
> import org.apache.hadoop.io.IntWritable;
> import org.apache.hadoop.io.LongWritable;
> import org.apache.hadoop.io.Text;
> import org.apache.hadoop.mapreduce.Mapper;
> public class countm extends 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, Context context) throws IOException, InterruptedException {
>           String line = value.toString();
>           StringTokenizer tokenizer = new StringTokenizer(line);
>           while (tokenizer.hasMoreTokens()) {
>                word.set(tokenizer.nextToken());
>                context.write(word, one);
>                }
>             }
> }
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