Hadoop Streaming Using Python – Word Count Problem

Hadoop Streaming is a feature that comes with Hadoop and allows users or developers to use various different languages for writing MapReduce programs like Python, C++, Ruby, etc. It supports all the languages that can read from standard input and write to standard output. We will be implementing Python with Hadoop Streaming and will observe how it works. We will implement the word count problem in python to understand Hadoop Streaming. We will be creating mapper.py and reducer.py to perform map and reduce tasks.

Let’s create one file which contains multiple words that we can count. 

Step 1: Create a file with the name word_count_data.txt  and add some data to it.

cd Documents/                                  # to change the directory to /Documents
touch word_count_data.txt               # touch is used to create an empty file    
nano word_count_data.txt               # nano is a command line editor to edit the file    
cat word_count_data.txt                        # cat is used to see the content of the file

Create a file with the name word_count_data.txt

Step 2: Create a mapper.py file that implements the mapper logic. It will read the data from STDIN and will split the lines into words, and will generate an output of each word with its individual count. 



cd Documents/                                   # to change the directory to /Documents
touch mapper.py                    # touch is used to create an empty file    
cat mapper.py                    # cat is used to see the content of the file

Copy the below code to the mapper.py file.

Python3

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#!/usr/bin/env python
  
# import sys because we need to read and write data to STDIN and STDOUT
import sys
  
# reading entire line from STDIN (standard input)
for line in sys.stdin:
    # to remove leading and trailing whitespace
    line = line.strip()
    # split the line into words
    words = line.split()
      
    # we are looping over the words array and printing the word
    # with the count of 1 to the STDOUT
    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
        print '%s\t%s' % (word, 1)

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Here in the above program #! is known as shebang and used for interpreting the script. The file will be run using the command we are specifying.

mapper.py

Let’s test our mapper.py locally that it is working fine or not.

Syntax:

cat <text_data_file> | python <mapper_code_python_file>

Command(in my case)

cat word_count_data.txt | python mapper.py

The output of the mapper is shown below.



Step 3: Create a reducer.py file that implements the reducer logic. It will read the output of mapper.py from STDIN(standard input) and will aggregate the occurrence of each word and will write the final output to STDOUT. 

cd Documents/                                   # to change the directory to /Documents
touch reducer.py                     # touch is used to create an empty file 

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#!/usr/bin/env python
  
from operator import itemgetter
import sys
  
current_word = None
current_count = 0
word = None
  
# read the entire line from STDIN
for line in sys.stdin:
    # remove leading and trailing whitespace
    line = line.strip()
    # slpiting the data on the basis of tab we have provided in 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)

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Now let’s check our reducer code reducer.py with mapper.py is it working properly or not with the help of the below command.

cat word_count_data.txt | python mapper.py | sort -k1,1 | python reducer.py

We can see that our reducer is also working fine in our local system. 

Step 4: Now let’s start all our Hadoop daemons with the below command.

start-dfs.sh

start-yarn.sh

Now make a directory word_count_in_python in our HDFS in the root directory that will store our word_count_data.txt file with the below command.

hdfs dfs -mkdir /word_count_in_python

Copy word_count_data.txt to this folder in our HDFS with help of copyFromLocal command.



Syntax to copy a file from your local file system to the HDFS is given below:

hdfs dfs -copyFromLocal /path 1 /path 2 .... /path n /destination

Actual command(in my case)

hdfs dfs -copyFromLocal /home/dikshant/Documents/word_count_data.txt /word_count_in_python

Now our data file has been sent to HDFS successfully. we can check whether it sends or not by using the below command or by manually visiting our HDFS. 

hdfs dfs -ls /       # list down content of the root directory

hdfs dfs -ls /word_count_in_python    # list down content of /word_count_in_python directory

Let’s give executable permission to our mapper.py and reducer.py with the help of below command.

cd Documents/

chmod 777 mapper.py reducer.py     # changing the permission to read, write, exectute for user, group and others

In below image,Then we can observe that we have changed the file permission.

Step 5: Now download the latest hadoop-streaming jar file from this Link. Then place, this Hadoop,-streaming jar file to a place from you can easily access it. In my case, I am placing it to /Documents folder where mapper.py and reducer.py file is present.

Now let’s run our python files with the help of the Hadoop streaming utility as shown below.

hadoop jar /home/dikshant/Documents/hadoop-streaming-2.7.3.jar \

> -input /word_count_in_python/word_count_data.txt \

> -output /word_count_in_python/output \

> -mapper /home/dikshant/Documents/mapper.py \

> -reducer /home/dikshant/Documents/reducer.py

In the above command in -output, we will specify the location in HDFS where we want our output to be stored. So let’s check our output in output file at location /word_count_in_python/output/part-00000 in my case. We can check results by manually vising the location in HDFS or with the help of cat command as shown below.

hdfs dfs -cat /word_count_in_python/output/part-00000

Basic options that we can use with Hadoop Streaming

Option

Description

-mapper The command to be run as the mapper
-reducer The command to be run as the reducer
-input The DFS input path for the Map step
-output The DFS output directory for the Reduce step



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