Friday, October 18, 2013

Hadoop WordCount with new map reduce api

There are so many version of WordCount hadoop example flowing around the web. However, a lot of them are using the older version of hadoop api. Following are example of word count using the newest hadoop map reduce api. The new map reduce api reside in org.apache.hadoop.mapreduce package instead of org.apache.hadoop.mapred.

WordMapper.java

import java.io.IOException;
import java.util.StringTokenizer;

import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;
public class WordMapper extends Mapper<Object, Text, Text, IntWritable> {
 private Text word = new Text();
 private final static IntWritable one = new IntWritable(1);
 
 @Override
 public void map(Object key, Text value,
   Context contex) throws IOException, InterruptedException {
  // Break line into words for processing
  StringTokenizer wordList = new StringTokenizer(value.toString());
  while (wordList.hasMoreTokens()) {
   word.set(wordList.nextToken());
   contex.write(word, one);
  }
 }
}

SumReducer.java

import java.io.IOException;
import java.util.Iterator;

import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;



public class SumReducer extends Reducer<Text, IntWritable, Text, IntWritable> {
 
 private IntWritable totalWordCount = new IntWritable();
 
 @Override
 public void reduce(Text key, Iterable<IntWritable> values, Context context)
            throws IOException, InterruptedException {
  int wordCount = 0;
  Iterator<IntWritable> it=values.iterator();
  while (it.hasNext()) {
   wordCount += it.next().get();
  }
  totalWordCount.set(wordCount);
  context.write(key, totalWordCount);
 }
}

WordCount.java (Driver)

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
public class WordCount {
 public static void main(String[] args) throws Exception {
        if (args.length != 2) {
          System.out.println("usage: [input] [output]");
          System.exit(-1);
        }
  
  
        Job job = Job.getInstance(new Configuration());
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(IntWritable.class);

        job.setMapperClass(WordMapper.class); 
        job.setReducerClass(SumReducer.class);  

        job.setInputFormatClass(TextInputFormat.class);
        job.setOutputFormatClass(TextOutputFormat.class);

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

        job.setJarByClass(WordCount.class);

        job.submit();
        
        
        
  

  
 }
}

165 comments:

  1. please add the build and packaging process to make it easy to use, especially for newbies. thx

    ReplyDelete
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  2. when I run this I just ger usage : [input] [output[
    How do I give a input file to this pogram

    ReplyDelete
  3. hadoop jar wordcount.jar inputpathhere outputpathhere

    ReplyDelete
  4. for hadoop 2.2.

    bin/hadoop jar share/hadoop/mapreduce/hadoop-mapreduce-examples-2.2.0.jar wordcount /map2 /map2output

    ReplyDelete
  5. Like Urvir, I had to put the full path to examples jar.

    Now that we've come this far, are there any suggestions for running apps in eclipse (with or with out the plugin, I've struggled to get the plugin working with no luck, even rebuilt with newer jars)? I'm on eclipse 4.2 and hadoop 2.2

    ReplyDelete
  6. Would you please write some steps for creating a Jar and Running with Hadoop? It will help me a lot. I count not find any sample code with Hadoop-2.4 version from Web.

    ReplyDelete
  7. Yes, pls.. step by step for creating jar???

    ReplyDelete
    Replies
    1. $ javac -classpath /librarypath/X.jar:/librarypath/X.jar -d /desiredpath/ /javafilepath/X.java
      $ $JAVA_HOME/bin/jar -cvf /desiredpath/Y.jar -C /desiredpath/ .

      Delete
  8. Thank you so much for publishing an updated version using the new API. I've been struggling for a day and a half getting my code to work. I could not figure out why the reducer was not being called. It is because the method signature of reduce() now takes an Iterable instead of an Iterator! This subtle change meant that my reduce class was not actually overriding the base implementation in Reducer. Thanks again!

    ReplyDelete

  9. Is there any other way to get answer like this? I tried with out success. Any way thanks for your help.
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  10. This comment has been removed by a blog administrator.

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  11. This comment has been removed by a blog administrator.

    ReplyDelete
  12. good night
    You can generate the jar file and running on Amazon AWS? How do I send the parameters?
    Thank you.

    ReplyDelete
  13. This comment has been removed by a blog administrator.

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  14. This comment has been removed by a blog administrator.

    ReplyDelete
    Replies
    1. This comment has been removed by a blog administrator.

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  16. 15/03/09 20:09:17 INFO client.RMProxy: Connecting to ResourceManager at /0.0.0.0:8032
    15/03/09 20:09:17 INFO mapreduce.JobSubmitter: Cleaning up the staging area /tmp/hadoop-yarn/staging/hduser/.staging/job_1425911430560_0002
    15/03/09 20:09:17 ERROR security.UserGroupInformation: PriviledgedActionException as:hduser (auth:SIMPLE) cause:org.apache.hadoop.mapreduce.lib.input.InvalidInputException: Input path does not exist: hdfs://localhost:9000/map2
    org.apache.hadoop.mapreduce.lib.input.InvalidInputException: Input path does not exist: hdfs://localhost:9000/map2
    at org.apache.hadoop.mapreduce.lib.input.FileInputFormat.listStatus(FileInputFormat.java:285)
    at org.apache.hadoop.mapreduce.lib.input.FileInputFormat.getSplits(FileInputFormat.java:340)
    at org.apache.hadoop.mapreduce.JobSubmitter.writeNewSplits(JobSubmitter.java:491)
    at org.apache.hadoop.mapreduce.JobSubmitter.writeSplits(JobSubmitter.java:508)
    at org.apache.hadoop.mapreduce.JobSubmitter.submitJobInternal(JobSubmitter.java:392)
    at org.apache.hadoop.mapreduce.Job$10.run(Job.java:1268)
    at org.apache.hadoop.mapreduce.Job$10.run(Job.java:1265)
    at java.security.AccessController.doPrivileged(Native Method)
    at javax.security.auth.Subject.doAs(Subject.java:415)
    at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1491)
    at org.apache.hadoop.mapreduce.Job.submit(Job.java:1265)
    at org.apache.hadoop.mapreduce.Job.waitForCompletion(Job.java:1286)
    at org.apache.hadoop.examples.WordCount.main(WordCount.java:84)
    at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
    at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
    at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
    at java.lang.reflect.Method.invoke(Method.java:606)
    at org.apache.hadoop.util.ProgramDriver$ProgramDescription.invoke(ProgramDriver.java:72)
    at org.apache.hadoop.util.ProgramDriver.run(ProgramDriver.java:144)
    at org.apache.hadoop.examples.ExampleDriver.main(ExampleDriver.java:74)
    at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
    at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
    at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
    at java.lang.reflect.Method.invoke(Method.java:606)
    at org.apache.hadoop.util.RunJar.main(RunJar.java:212)

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