To understand the MapReduce algorithm, it’s essential to first grasp its challenges in Big Data environments. The rise of the digital age has created enormous amounts of data, and businesses now view it as a valuable resource for quickly gaining insights and predictions. However, managing this massive influx of data has become a major challenge. This is where Data Processing and Data Analytics come into play, allowing businesses to manage both structured and unstructured data to make better decisions.
To address these challenges, Google’s MapReduce algorithm was developed using a “Divide and Conquer” approach, integrated with the Hadoop Ecosystem. With tools like Hadoop Distributed File System (HDFS), MapReduce offers a scalable solution for distributed computing.
Understanding The MapReduce Algorithm
MapReduce, especially when used with Hadoop, is a game-changer for Big Data Analytics. It allows large-scale data processing by breaking down complex tasks into smaller, manageable chunks, processing them in parallel, and using key-value pairs as the basic unit of data. This approach is vital in Data Scalability, making it suitable for next-gen Hadoop applications and the evolving needs of Cloud Computing.
The MapReduce algorithm consists of two main steps: Map and Reduce.
- Map (Mapper): This step is responsible for splitting large datasets into smaller, equally-sized pieces. These chunks are then distributed across multiple computers or nodes, ensuring a balanced workload. It also handles errors and failures by rolling back and redistributing work as needed, which is especially important in distributed systems.
- Reduce (Reducer): Once the data has been processed in parallel, the Reduce step aggregates and combines the results from the Map step, producing the final output. This process is efficient for handling large-scale data processing in environments like Cloud Systems.
Hadoop MapReduce: Simplified Overview
Hadoop MapReduce is an implementation of the MapReduce algorithm developed by the Apache Hadoop project. It allows for parallel data processing across multiple CPU nodes, making it ideal for large-scale applications that require batch processing. The Hadoop framework also supports data storage solutions and integrates seamlessly with tools like Data Warehousing and Data Integration platforms.
The process is broken down into four key stages:
- Input Split: In this phase, the input file is located and divided into smaller chunks for processing by the Mapper. The file is split into fixed-sized pieces in the Hadoop Distributed File System (HDFS), and the splitting method depends on the input file format. The goal is to ensure that processing smaller chunks is faster than processing the entire dataset, and that the workload is evenly distributed across nodes in the cluster.
- Mapping: Once the data is split and formatted, each chunk is processed by a separate instance of the Mapper. The Mapper performs calculations on the data, generating key-value pairs. Each node in the Hadoop cluster processes its assigned data simultaneously, producing a list of key-value pairs.
- Shuffling and Sorting: Before the Reducer begins its work, the intermediate results from the Mappers are collected, shuffled, and sorted by a Partitioner. This step ensures the data is organized for optimal processing by the Reducer, particularly in the context of Data Transformation.
Reducing: Once the data is shuffled and sorted, the Reducer processes the intermediate results. The Reducer aggregates the key-value pairs to produce the final result. This stage can be customized with a user-defined function, leveraging Hadoop’s OutputFormat feature to transform data as needed.
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Mapreduce In Python
We aim to write a simple MapReduce program for Hadoop in Python that is meant to count words by value in a given input file.
We will make use of Hadoop Streaming API to be able to pass data between different phases of MapReduce through STDIN (Standard Input) and STDOUT (Standard Output).
- First of all, we need to create an example input file.
Create a text file named ‘dummytext.txt’ and copy the simple text in it:
Digixvalley introduces ML.
Digixvalley introduces BigData.
BigData facilitates ML.
- Now, create “mapper.py” to be executed in the Map phase.
Mapper.py will read data from standard input and will print on standard output a list of tuples for each word occuring in the input file.
“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()
for word in words:
# write the results to STDOUT (standard output)
# tab-delimited words
with default count 1
print ‘%s\t%s’ % (word, 1)
- Next, create a file named “reducer.py” to be executed in Reduce phase. Reducer.py will take the output of mapper.py as its input and will sum the occurrences of each word to a final count.
“reducer.py”
from operator import itemgetter
import sys
current_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
# prepare mapper.py output to be sorted by Hadoop
# by key 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
# to output the last word if needed
if current_word == word:
print ‘%s\t%s’ % (current_word, current_count)
- Make sure you make the two programs executable by using the following commands:
> chmod +x mapper.py
> chmod +x reducer.py
You can find the full code at a Digixvalley AI repository.
Running MapReduce Locally
> cat dummytext.txt | python mapper.py | sort -k1 | python reducer.py
Running MapReduce on Hadoop Cluster
We assume that the default user created in Hadoop is f3user.
- Firstly, we will copy local dummy file to Hadoop Distributed file system by running:
> hdfs dfs -put /src/dummytext.txt /user/f3user
- Finally, we run our MapReduce job on Hadoop cluster by leveraging the support of streaming API to support standard I/O.
> hadoop jar /usr/lib/hadoop-mapreduce/hadoop-streaming.jar \
-file /src/mapper.py -mapper “python mapper.py” \
-file /src/reducer.py -reducer “python reducer.py” \
-input /user/f3user/dummytext.txt -output /user/f3user/wordcount
The job will take input from ‘user/f3user/dummytext.txt’ and write output to ‘user/f3user/wordcount’.
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Connect with us for more information at info@digixvalley.com
