Python MapReduce Code

The “trick” behind the following Python code is that we will use the Hadoop Streaming API (see also the corresponding wiki entry) for helping us passing data between our Map and Reduce code via STDIN (standard input) and STDOUT (standard output). We will simply use Python’s sys.stdin to read input data and print our own output to sys.stdout. That’s all we need to do because Hadoop Streaming will take care of everything else!

Map step: mapper.py

Save the following code in the file /home/hduser/mapper.py. It will read data from STDIN, split it into words and output a list of lines mapping words to their (intermediate) counts to STDOUT. The Map script will not compute an (intermediate) sum of a word’s occurrences though. Instead, it will output <word> 1 tuples immediately – even though a specific word might occur multiple times in the input. In our case we let the subsequent Reduce step do the final sum count. Of course, you can change this behavior in your own scripts as you please, but we will keep it like that in this tutorial because of didactic reasons. :-)

Make sure the file has execution permission (chmod +x /home/hduser/mapper.py should do the trick) or you will run into problems.

#!/usr/bin/env python
"""mapper.py"""import sys# input comes from STDIN (standard input)
for line in sys.stdin:
    # remove leading and trailing whitespace
    line = line.strip()
    # split the line into words
    words = line.split()
    # increase counters
    for word in words:
        # write the results to STDOUT (standard output);
        # what we output here will be the input for the
        # Reduce step, i.e. the input for reducer.py
        #
        # tab-delimited; the trivial word count is 1
        print '%s\t%s' % (word, 1)

Reduce step: reducer.py

Save the following code in the file /home/hduser/reducer.py. It will read the results of mapper.py from STDIN (so the output format of mapper.py and the expected input format of reducer.py must match) and sum the occurrences of each word to a final count, and then output its results to STDOUT.

Make sure the file has execution permission (chmod +x /home/hduser/reducer.py should do the trick) or you will run into problems.

#!/usr/bin/env python
"""reducer.py"""from operator import itemgetter
import syscurrent_word = None
current_count = 0
word = None# input comes from STDIN
for line in sys.stdin:
    # remove leading and trailing whitespace
    line = line.strip()    # parse the input we got from mapper.py
    word, count = line.split('\t', 1)    # convert count (currently a string) to int
    try:
        count = int(count)
    except ValueError:
        # count was not a number, so silently
        # ignore/discard this line
        continue    # this IF-switch only works because Hadoop sorts map output
    # by key (here: word) before it is passed to the reducer
    if current_word == word:
        current_count += count
    else:
        if current_word:
            # write result to STDOUT
            print '%s\t%s' % (current_word, current_count)
        current_count = count
        current_word = word# do not forget to output the last word if needed!
if current_word == word:
    print '%s\t%s' % (current_word, current_count)

Test your code (cat data | map | sort | reduce)

I recommend to test your mapper.py and reducer.py scripts locally before using them in a MapReduce job. Otherwise your jobs might successfully complete but there will be no job result data at all or not the results you would have expected. If that happens, most likely it was you (or me) who screwed up.

Here are some ideas on how to test the functionality of the Map and Reduce scripts.

# Test mapper.py and reducer.py locally first# very basic test
hduser@ubuntu:~$ echo "foo foo quux labs foo bar quux" | /home/hduser/mapper.py
foo     1
foo     1
quux    1
labs    1
foo     1
bar     1
quux    1hduser@ubuntu:~$ echo "foo foo quux labs foo bar quux" | /home/hduser/mapper.py | sort -k1,1 | /home/hduser/reducer.py
bar     1
foo     3
labs    1
quux    2# using one of the ebooks as example input
# (see below on where to get the ebooks)
hduser@ubuntu:~$ cat /tmp/gutenberg/20417-8.txt | /home/hduser/mapper.py
 The     1
 Project 1
 Gutenberg       1
 EBook   1
 of      1
 [...]
 (you get the idea)

Running the Python Code on Hadoop

Download example input data

We will use three ebooks from Project Gutenberg for this example:

Download each ebook as text files in Plain Text UTF-8 encoding and store the files in a local temporary directory of choice, for example /tmp/gutenberg.

hduser@ubuntu:~$ ls -l /tmp/gutenberg/
total 3604
-rw-r--r-- 1 hduser hadoop  674566 Feb  3 10:17 pg20417.txt
-rw-r--r-- 1 hduser hadoop 1573112 Feb  3 10:18 pg4300.txt
-rw-r--r-- 1 hduser hadoop 1423801 Feb  3 10:18 pg5000.txt
hduser@ubuntu:~$

Copy local example data to HDFS

Before we run the actual MapReduce job, we must first copy the files from our local file system to Hadoop’s HDFS.

hduser@ubuntu:/usr/local/hadoop$ bin/hadoop dfs -copyFromLocal /tmp/gutenberg /user/hduser/gutenberg
hduser@ubuntu:/usr/local/hadoop$ bin/hadoop dfs -ls
Found 1 items
drwxr-xr-x   - hduser supergroup          0 2010-05-08 17:40 /user/hduser/gutenberg
hduser@ubuntu:/usr/local/hadoop$ bin/hadoop dfs -ls /user/hduser/gutenberg
Found 3 items
-rw-r--r--   3 hduser supergroup     674566 2011-03-10 11:38 /user/hduser/gutenberg/pg20417.txt
-rw-r--r--   3 hduser supergroup    1573112 2011-03-10 11:38 /user/hduser/gutenberg/pg4300.txt
-rw-r--r--   3 hduser supergroup    1423801 2011-03-10 11:38 /user/hduser/gutenberg/pg5000.txt
hduser@ubuntu:/usr/local/hadoop$

other command to copy is : hadoop fs -put /localpath/filename /hdfspath

Run the MapReduce job

Now that everything is prepared, we can finally run our Python MapReduce job on the Hadoop cluster. As I said above, we leverage the Hadoop Streaming API for helping us passing data between our Map and Reduce code via STDIN and STDOUT.

hduser@ubuntu:/usr/local/hadoop$ bin/hadoop jar contrib/streaming/hadoop-*streaming*.jar \
-file /home/hduser/mapper.py    -mapper /home/hduser/mapper.py \
-file /home/hduser/reducer.py   -reducer /home/hduser/reducer.py \
-input /user/hduser/gutenberg/* -output /user/hduser/gutenberg-output

If you want to modify some Hadoop settings on the fly like increasing the number of Reduce tasks, you can use the -D option:

hduser@ubuntu:/usr/local/hadoop$ bin/hadoop jar contrib/streaming/hadoop-*streaming*.jar -D mapred.reduce.tasks=16 ...

Get the streaming jar file that matches your hadoop version: https://drive.google.com/file/d/1Y64SpxsrmwOi9c-yZECTyiJPGI82EbKn/view?usp=sharing (version 3.3.0)