Concept of MapReduce in BigData

Last updated on May 30 2022
Sanjay Grover

Table of Contents

Concept of MapReduce in BigData

MapReduce – Introduction

MapReduce may be a programming model for writing applications which will process Big Data in parallel on multiple nodes. MapReduce provides analytical capabilities for analyzing huge volumes of complex data.

What is Big Data?

Big Data may be a collection of huge datasets that can’t be processed using traditional computing techniques. for instance, the quantity of knowledge Facebook or YouTube need require it to gather and manage on a day to day , can fall into the category of massive Data. However, Big Data isn’t only about scale and volume, it also involves one or more of the subsequent aspects − Velocity, Variety, Volume, and Complexity.

Why MapReduce?

Traditional Enterprise Systems normally have a centralized server to store and process data. the subsequent illustration depicts a schematic view of a standard enterprise system. Traditional model is never suitable to process huge volumes of scalable data and can’t be accommodated by standard database servers. Moreover, the centralized system creates an excessive amount of a bottleneck while processing multiple files simultaneously.

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bigData

Google solved this bottleneck issue using an algorithm called MapReduce. MapReduce divides a task into small parts and assigns them to several computers. Later, the results are collected at one place and integrated to make the result dataset.

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How MapReduce Works?

The MapReduce algorithm contains two important tasks, namely Map and Reduce.
• The Map task takes a set of data and converts it into another set of data, where individual elements are broken down into tuples (key-value pairs).
• The Reduce task takes the output from the Map as an input and combines those data tuples (key-value pairs) into a smaller set of tuples.
The reduce task is always performed after the map job.
Let us now take an in depth check out each of the phases and check out to know their significance.

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Input Phase − Here we’ve a Record Reader that translates each record in an input data and sends the parsed data to the mapper within the sort of key-value pairs.
Map − Map may be a user-defined function, which takes a series of key-value pairs and processes all of them to get zero or more key-value pairs.
Intermediate Keys − They key-value pairs generated by the mapper are referred to as intermediate keys.
Combiner − A combiner may be a sort of local Reducer that groups similar data from the map phase into identifiable sets. It takes the intermediate keys from the mapper as input and applies a user-defined code to aggregate the values during a small scope of 1 mapper. it’s not a neighborhood of the most MapReduce algorithm; it’s optional.
Shuffle and Sort − The Reducer task starts with the Shuffle and type step. It downloads the grouped key-value pairs onto the local machine, where the Reducer is running. The individual key-value pairs are sorted by key into a bigger data list. the info list groups the equivalent keys together in order that their values are often iterated easily within the Reducer task.
Reducer − The Reducer takes the grouped key-value paired data as input and runs a Reducer function on all of them. Here, the info are often aggregated, filtered, and combined during a number of the way , and it requires a good range of processing. Once the execution is over, it gives zero or more key-value pairs to the ultimate step.
Output Phase − within the output phase, we’ve an output formatter that translates the ultimate key-value pairs from the Reducer function and writes them onto a file employing a record writer.
• Let us attempt to understand the 2 tasks Map &f Reduce with the assistance of a little diagram –

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MapReduce-Example

Let us take a real-world example to grasp the facility of MapReduce. Twitter receives around 500 million tweets per day, which is almost 3000 tweets per second. the subsequent illustration shows how Tweeter manages its tweets with the assistance of MapReduce.

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As shown within the illustration, the MapReduce algorithm performs the subsequent actions −
Tokenize − Tokenizes the tweets into maps of tokens and writes them as key-value pairs.
Filter − Filters unwanted words from the maps of tokens and writes the filtered maps as key-value pairs.
Count − Generates a token counter per word.
Aggregate Counters − Prepares an aggregate of similar counter values into small manageable units.
So, this brings us to the end of blog. This Tecklearn ‘Concept of MapReduce in BigData’ helps you with commonly asked questions if you are looking out for a job in Big Data and Hadoop Domain.
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Hadoop Security
• Why Hadoop Security Is Important
• Hadoop’s Security System Concepts
• What Kerberos Is and How it Works
• Securing a Hadoop Cluster with Kerberos
Managing and Scheduling Jobs
• Managing Running Jobs
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Cluster Maintenance
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Cluster Monitoring and Troubleshooting
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Processing Complex Data with Pig
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Multi-Dataset Operations with Pig
• Techniques for Combining Data Sets
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Pig Troubleshooting and Optimization
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Introduction to Hive and Impala
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Querying with Hive and Impala
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• Data Types
• Differences Between Hive and Impala Query Syntax
• Using Hue to Execute Queries
• Using the Impala Shell
Data Management
• Data Storage
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Data Storage and Performance
• Partitioning Tables
• Choosing a File Format
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Relational Data Analysis with Hive and Impala
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• Aggregation and Windowing
Working with Impala
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Analyzing Text and Complex Data with Hive
• Complex Values in Hive
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Hive Optimization
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Importing Relational Data with Apache Sqoop
• Sqoop Overview
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• Limiting Results
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• Sqoop 2
Introduction to Impala and Hive
• Introduction to Impala and Hive
• Why Use Impala and Hive?
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Modelling and Managing Data with Impala and Hive
• Data Storage Overview
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Spark Basics
• What is Apache Spark?
• Using the Spark Shell
• RDDs (Resilient Distributed Datasets)
• Functional Programming in Spark
Working with RDDs in Spark
• A Closer Look at RDDs
• Key-Value Pair RDDs
• MapReduce
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• Machine Learning
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Preview: Spark SQL
• Spark SQL and the SQL Context
• Creating DataFrames
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Hadoop Testing
• Hadoop Application Testing
• Roles and Responsibilities of Hadoop Testing Professional
• Framework MRUnit for Testing of MapReduce Programs
• Unit Testing
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Big Data Testing
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