Overview
Even though the Hadoop framework is written in Java, but we can use other languages like python and C++, to write MapReduce for Hadoop. However, Hadoop’s documentation suggest that your must translate your code to java jar file using jython. which is not very convenient and can even be problematic if you depend on Python features not provided by Jython.Example
We will write simple WordCount MapReduce program using pure python. input is text files and output is file with words and thier count. you can use other languages like perl.Prerequisites
You should have hadoop cluster running if still not have cluster ready Try this to start with single node cluster.MapReduce
Idea behind python code is that we will use hadoop streaming API to transfer data/Result between our Map and Reduce code using STDIN(sys.stdin)/ STDOUT(sys.stdout). We will use STDIN to read datafrom input and print output to STDOUT.
mapper.py
import sys
for line in sys.stdin:
line = line.strip()
words = line.split()
for word in words:
print '%s\t%s' % (word, 1)
reducer.py
from operator import itemgetter
import sys
current_word = None
current_count = 0
word = None
for line in sys.stdin:
line = line.strip()
word, count = line.split('\t', 1)
try:
count = int(count)
except ValueError:
continue
if current_word == word:
current_count += count
else:
if current_word:
print '%s\t%s' % (current_word, current_count)
current_count = count
current_word = word
if current_word == word:
print '%s\t%s' % (current_word, current_count)
Running Hadoop's Job
Download Example Data to home directory like /home/elite/Downloads/examples/Book1
Book2
Book3
Start Cluster
$ bin/start-all.shCopy Data from Local to dfs File System
$ bin/hadoop dfs -mkdir /wordscount
$ bin/hadoop dfs -copyFromLocal /home/elite/Downloads/examples/ /home/hdpuser/wordscount/
Here we have created directory in hadoop file system named wordcount and moved our local directory containing our test data to hadoop hdfs. We can check if files have been copied properly to hadoop directory by listing its content as presented below.
Check files on dfs
$ bin/hadoop dfs -ls /home/hdpuser/wordscount
Run MapReduce Job
I have both mapper.py and reducer.py and /home/hdpuser/ here is command to run job.$ bin/hadoop jar contrib/streaming/hadoop-streaming-1.2.1.jar \
-file /home/hduser/mapper.py -mapper /home/hduser/mapper.py \
-file /home/hduser/reducer.py -reducer /home/hduser/reducer.py \
-input /home/hdpuser/wordscount/* -output /home/hdpuser/wordscount.out
You Can check status from terminal or web page http://localhost:50030/ configured in your cluster setup. after job is complete we can get results back by coping output file from hadoop file system to local
$ bin/hadoop dfs -copyToLocal /home/hdpuser/wordscount.out /home/hdpuser/
