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MapReduce streaming example will help you running word count program using Hadoop streaming. We use Python for writing mapper and reducer logic. Data is stored as sample.txt file. mapper, reducer and data can be downloaded in a bundle from the link provided. Prerequisites:

• Hadoop 2.6.5
• Python 2.7
• Log in with your Hadoop user
• Working directory should be set to /usr/local/hadoop.

Steps:

1. Make sure you’re in /usr/local/hadoop, if not use:

   cd /usr/local/hadoop

2. Start HDFS:

   start-dfs.sh

3. Start YARN:

   start-yarn.sh

4. Check if everything is up (6 services should be running):

   jps

5. Download data and code files to be used in this tutorial from here.

6. Unzip contents of streaming.zip:

   unzip streaming.zip

7. Move mapper and reducer Python files to /home/$USER/:

   mv streaming/mapper.py /home/$USER/mapper.py
   mv streaming/reducer.py /home/$USER/reducer.py

8. Create working directories for storing data and downloading output when the Hadoop job finishes:

   mkdir /tmp/wordcount/
   mkdir /tmp/wordcount-output/

9. Move sample.txt to /tmp

   mv streaming/sample.txt /tmp/wordcount/

10. Upload data to HDFS:

hdfs dfs -copyFromLocal /tmp/wordcount /user/hduser/wordcount

11. Submit the job:

yarn jar share/hadoop/tools/lib/hadoop-streaming-2.6.5.jar 
-file /home/$USER/mapper.py -mapper /home/$USER/mapper.py
-file /home/$USER/reducer.py -reducer /home/$USER/reducer.py
-input /user/$USER/wordcount/\*
-output /user/$USER/wordcount-output

12. When the job finishes, download output data:

hdfs dfs -getmerge /user/$USER/wordcount-output /tmp/wordcount-output/output.txt

13. See word count output on terminal:

cat /tmp/wordcount-output/output.txt

Note: Many common errors are documented in comments section. Please see comments section for help. References

1. Hadoop Streaming
2. Hadopp Installation