Use MapReduce in Apache Hadoop on HDInsight

Learn how to run MapReduce jobs on HDInsight clusters.

Example data

HDInsight provides various example data sets, which are stored in the /example/data and /HdiSamples directory. These directories are in the default storage for your cluster. In this document, we use the /example/data/gutenberg/davinci.txt file. This file contains the notebooks of Leonardo da Vinci.

Example MapReduce

An example MapReduce word count application is included with your HDInsight cluster. This example is located at /example/jars/hadoop-mapreduce-examples.jar on the default storage for your cluster.

The following Java code is the source of the MapReduce application contained in the hadoop-mapreduce-examples.jar file:

package org.apache.hadoop.examples;

import java.util.StringTokenizer;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.util.GenericOptionsParser;

public class WordCount {

    public static class TokenizerMapper
        extends Mapper<Object, Text, Text, IntWritable>{

    private final static IntWritable one = new IntWritable(1);
    private Text word = new Text();

    public void map(Object key, Text value, Context context
                    ) throws IOException, InterruptedException {
        StringTokenizer itr = new StringTokenizer(value.toString());
        while (itr.hasMoreTokens()) {
        context.write(word, one);

    public static class IntSumReducer
        extends Reducer<Text,IntWritable,Text,IntWritable> {
    private IntWritable result = new IntWritable();

    public void reduce(Text key, Iterable<IntWritable> values,
                        Context context
                        ) throws IOException, InterruptedException {
        int sum = 0;
        for (IntWritable val : values) {
        sum += val.get();
        context.write(key, result);

    public static void main(String[] args) throws Exception {
    Configuration conf = new Configuration();
    String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs();
    if (otherArgs.length != 2) {
        System.err.println("Usage: wordcount <in> <out>");
    Job job = new Job(conf, "word count");
    FileInputFormat.addInputPath(job, new Path(otherArgs[0]));
    FileOutputFormat.setOutputPath(job, new Path(otherArgs[1]));
    System.exit(job.waitForCompletion(true) ? 0 : 1);

For instructions to write your own MapReduce applications, see Develop Java MapReduce applications for HDInsight.

Run the MapReduce

HDInsight can run HiveQL jobs by using various methods. Use the following table to decide which method is right for you, then follow the link for a walkthrough.

Use this... do this ...from this client operating system
SSH Use the Hadoop command through SSH Linux, Unix, MacOS X, or Windows
Curl Submit the job remotely by using REST Linux, Unix, MacOS X, or Windows
Windows PowerShell Submit the job remotely by using Windows PowerShell Windows

Next steps

To learn more about working with data in HDInsight, see the following documents: