-
I started by creating a mapper.py file like this, In the mapper i am reading one line from input at a time and then splitting it into pieces and writing it to output in
(word,1)
format. In the mapper whatever i write in output gets passed back to Hadoop, so i could not use standard output for writing debug statements. So i configured file logger that generates debug.log in the current directory -
Next i created a reducer.py program that reads one line at a time and splits it on tab character. In the split first part is word and second is the count. Now one difference between java reducer and streaming reducer is in Java your reduce method gets input like this
(key, [value1, value2,value3]),(key1, [value1, value2,value3])
. In streaming it gets called with one key and value every time like this(key,value1),(key,value2),(key,value3),(key1,value),(key1,value2),(key1,value3)
, so you will have to remember what key your processing and handle the change in key. In my reducer i am keeping track of current key, and for every value of the current key i keep accumulating it, when the key changes i use that opportunity to dump the old key and count -
One good part with developing using scripting is that you can test your code without hadoop as well. In this case once my mapper and reducer are ready i can test it on command line using
data | mapper | sort | reducer
format. In my case the mapper and reducer files are in /home/user/workspace/HadoopPython/streaming/ directory. and i have a sample file in home directory so i could test my program by executing it like thiscat ~/sample.txt | /home/user/workspace/HadoopPython/streaming/mapper.py | sort | /home/user/workspace/HadoopPython/streaming/reducer.py
-
After working through bugs i copied aesop.txt in in root of my HDFS and then i could use following command to execute my map reduce program.
hadoop jar /usr/local/hadoop/share/hadoop/tools/lib/hadoop-streaming-2.4.0.jar -input aesop.txt -output output/wordcount -mapper /home/user/workspace/HadoopPython/streaming/mapper.py -reducer /home/user/workspace/HadoopPython/streaming/reducer.py
-
Once the program is done executing i could see the output generated by it using following command
hdfs dfs -cat output/wordcount/part-00000
WordCount MapReduce program using Hadoop streaming and python
I wanted to learn how to use Hadoop Streaming, which allows us to use scripting language such as Python, Ruby,.. etc for developing Map Reduce program. The idea is instead of writing Java classes for Mapper and Reducer you develop 2 script files (something that can be executed from command line) one for mapper and other for reducer and pass it to Hadoop. Hadoop will communicate to the script files using standard input/output, which means for both mapper and reducer hadoop will pass input on standard input and your script file will read it from standard input. Once your script is done processing the data in either mapper or reducer it will write output to standard output that will get sent back to hadoop.
I decided to create Word Count program that takes a file as input and counts occurrence of every word in the file and writes it in output. I followed these steps
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1. Mapper Script (mapper.py):
This script reads lines of text from standard input (STDIN) and emits each word as a key-value pair. Here's an example:
Python
#!/usr/bin/env python
import sys
for line in sys.stdin:
# Clean and split line into words
words = line.strip().lower().split()
# Emit each word with a count of 1
for word in words:
print(f"{word}\t1")
Use code with caution.
Explanation:
#!/usr/bin/env python specifies the interpreter for running the script.
import sys provides access to system features like standard input.
The loop iterates over each line read from STDIN.
Text cleaning:
strip() removes leading and trailing whitespace.
lower() converts all characters to lowercase.
split() splits the line into individual words.
We iterate over each word and print it as the key with a value of 1 (representing its initial count).
The tab (\t) separates the key and value.
2. Reducer Script (reducer.py):
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This script receives key-value pairs (word and its count) from the mapper and sums the counts for each unique word. Here's an example:
Python
#!/usr/bin/env python
from collections import defaultdict
import sys
# Use a dictionary to store word counts
word_counts = defaultdict(int)
# Read key-value pairs from standard input
for line in sys.stdin:
word, count = line.strip().split('\t', 1)
# Convert count to integer
word_counts[word] += int(count)
# Emit final word counts
for word, count in word_counts.items():
print(f"{word}\t{count}")
Use code with caution.
Explanation:
Similar shebang line for specifying the interpreter.
from collections import defaultdict imports a dictionary that sets default values to 0 when keys are not found.
An empty dictionary word_counts is created.
The loop reads key-value pairs from STDIN.
split('\t', 1) splits the line by the first tab, assigning the first element to word and the second to count (with a maximum of 1 split).
The count is converted from string to integer.
word_counts[word] += int(count) increments the count for the specific word in the dictionary.
Finally, we iterate through the dictionary and print the final word counts.
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Thank you for sharing such valuable knowledge! I found your tips practical and applicable to my own life. I’m eager to implement what I’ve learned.
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Thank you for this informative article on using Hadoop Streaming with Python for a Word Count MapReduce program. Your clear explanation of how to create and execute mapper and reducer scripts is incredibly helpful for those new to this approach. I appreciate the effort you've put into sharing this knowledge!
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The WordCount program using Hadoop Streaming and Python is a foundational MapReduce. By splitting the input data into lines and counting occurrences of each word, it efficiently handles large datasets. Hadoop Streaming enables using Python scripts for mapping and reducing, making it accessible for non-Java users. The mapper outputs words with counts, and the reducer aggregates these counts, producing the final word frequency. This program showcases the power of parallelism in big data processing.
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Such a well-written and insightful post! You really know your stuff, and I appreciate how you shared it in an easy-to-digest way
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Fantastic post! The information shared is very insightful and helpful. I appreciate the clear explanations and practical tips. Looking forward to more articles like this!
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Exploring Hadoop Streaming with Python for a WordCount program is a great way to simplify MapReduce development! The blend of scripting ease with big data power is fascinating. 🚀
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Great post! The detailed explanation of WordCount MapReduce program using Hadoop streaming and python is really helpful, especially for python web developers. The step-by-step guidance makes the process clear and easy to understand. Thanks for sharing this valuable info. Investment Banking Course
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Excellent explanation of the WordCount MapReduce program using Hadoop! Your step-by-step breakdown makes it easy to understand the concepts and implementation. Thanks for sharing this helpful guide!
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