Use MapReduce with Apache Hadoop on HDInsight with SSH

Learn how to submit MapReduce jobs from a Secure Shell (SSH) connection to HDInsight.


If you are already familiar with using Linux-based Apache Hadoop servers, but you are new to HDInsight, see Linux-based HDInsight tips.


An Apache Hadoop cluster on HDInsight. See Create Apache Hadoop clusters using the Azure portal.

Use Hadoop commands

  1. Use ssh command to connect to your cluster. Edit the command below by replacing CLUSTERNAME with the name of your cluster, and then enter the command:

  2. After you are connected to the HDInsight cluster, use the following command to start a MapReduce job:

    yarn jar /usr/hdp/current/hadoop-mapreduce-client/hadoop-mapreduce-examples.jar wordcount /example/data/gutenberg/davinci.txt /example/data/WordCountOutput

    This command starts the wordcount class, which is contained in the hadoop-mapreduce-examples.jar file. It uses the /example/data/gutenberg/davinci.txt document as input, and output is stored at /example/data/WordCountOutput.


    For more information about this MapReduce job and the example data, see Use MapReduce in Apache Hadoop on HDInsight.

    The job emits details as it processes, and it returns information similar to the following text when the job completes:

    File Input Format Counters
    Bytes Read=1395666
    File Output Format Counters
    Bytes Written=337623
  3. When the job completes, use the following command to list the output files:

    hdfs dfs -ls /example/data/WordCountOutput

    This command display two files, _SUCCESS and part-r-00000. The part-r-00000 file contains the output for this job.


    Some MapReduce jobs may split the results across multiple part-r-##### files. If so, use the ##### suffix to indicate the order of the files.

  4. To view the output, use the following command:

    hdfs dfs -cat /example/data/WordCountOutput/part-r-00000

    This command displays a list of the words that are contained in the wasbs://example/data/gutenberg/davinci.txt file and the number of times each word occurred. The following text is an example of the data that is contained in the file:

    wreathed        3
    wreathing       1
    wreaths         1
    wrecked         3
    wrenching       1
    wretched        6
    wriggling       1

Next steps

As you can see, Hadoop commands provide an easy way to run MapReduce jobs in an HDInsight cluster and then view the job output. For information about other ways you can work with Hadoop on HDInsight: