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Mastering Hadoop 3

You're reading from  Mastering Hadoop 3

Product type Book
Published in Feb 2019
Publisher Packt
ISBN-13 9781788620444
Pages 544 pages
Edition 1st Edition
Languages
Authors (2):
Chanchal Singh Chanchal Singh
Profile icon Chanchal Singh
Manish Kumar Manish Kumar
Profile icon Manish Kumar
View More author details

Table of Contents (23) Chapters

Title Page
Dedication
About Packt
Foreword
Contributors
Preface
Journey to Hadoop 3 Deep Dive into the Hadoop Distributed File System YARN Resource Management in Hadoop Internals of MapReduce SQL on Hadoop Real-Time Processing Engines Widely Used Hadoop Ecosystem Components Designing Applications in Hadoop Real-Time Stream Processing in Hadoop Machine Learning in Hadoop Hadoop in the Cloud Hadoop Cluster Profiling Who Can Do What in Hadoop Network and Data Security Monitoring Hadoop Other Books You May Enjoy Index

Common MapReduce patterns


The design patterns are the solution templates for solving specific problems. Developers can reuse templates for similar problems across domains so that they save time in solving problems. If you are a programmer, you would have used the abstract factory pattern, builder pattern, observer pattern, and so on before. These patterns are discovered by people who have been solving similar problems for many years. The MapReduce framework has existed for almost a decade now. Let's look into a few of the commonly used MapReduce design patterns across industries.

Summarization patterns

Summarization problems use the pattern widely across domains. It's all about grouping similar data together and then performing an operation such as calculating a minimum, maximum, count, average, median-standard deviation, building an index, or just simply counting based on key. For example, we might want to calculate the total amount of money our website has made by country. As another example...

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