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Mahmoud Parsian - Data Algorithms: Recipes for Scaling Up with Hadoop and Spark

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Mahmoud Parsian Data Algorithms: Recipes for Scaling Up with Hadoop and Spark
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If you are ready to dive into the MapReduce framework for processing large datasets, this practical book takes you step by step through the algorithms and tools you need to build distributed MapReduce applications with Apache Hadoop or Apache Spark. Each chapter provides a recipe for solving a massive computational problem, such as building a recommendation system. Youll learn how to implement the appropriate MapReduce solution with code that you can use in your projects.

Dr. Mahmoud Parsian covers basic design patterns, optimization techniques, and data mining and machine learning solutions for problems in bioinformatics, genomics, statistics, and social network analysis. This book also includes an overview of MapReduce, Hadoop, and Spark.

Topics include:

  • Market basket analysis for a large set of transactions
  • Data mining algorithms (K-means, KNN, and Naive Bayes)
  • Using huge genomic data to sequence DNA and RNA
  • Naive Bayes theorem and Markov chains for data and market prediction
  • Recommendation algorithms and pairwise document similarity
  • Linear regression, Cox regression, and Pearson correlation
  • Allelic frequency and mining DNA
  • Social network analysis (recommendation systems, counting triangles, sentiment analysis)

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Data Algorithms

Mahmoud Parsian

Data Algorithms

by Mahmoud Parsian

Copyright 2015 Mahmoud Parsian. All rights reserved.

Printed in the United States of America.

Published by OReilly Media, Inc. , 1005 Gravenstein Highway North, Sebastopol, CA 95472.

OReilly books may be purchased for educational, business, or sales promotional use. Online editions are also available for most titles (http://safaribooksonline.com). For more information, contact our corporate/institutional sales department: 800-998-9938 or corporate@oreilly.com .

  • Editors: Ann Spencer and Marie Beaugureau
  • Production Editor: Matthew Hacker
  • Copyeditor: Rachel Monaghan
  • Proofreader: Rachel Head
  • Indexer: Judith McConville
  • Interior Designer: David Futato
  • Cover Designer: Ellie Volckhausen
  • Illustrator: Rebecca Demarest
  • July 2015: First Edition
Revision History for the First Edition
  • 2015-07-10: First Release

See http://oreilly.com/catalog/errata.csp?isbn=9781491906187 for release details.

While the publisher and the author have used good faith efforts to ensure that the information and instructions contained in this work are accurate, the publisher and the author disclaim all responsibility for errors or omissions, including without limitation responsibility for damages resulting from the use of or reliance on this work. Use of the information and instructions contained in this work is at your own risk. If any code samples or other technology this work contains or describes is subject to open source licenses or the intellectual property rights of others, it is your responsibility to ensure that your use thereof complies with such licenses and/or rights.

978-1-491-90618-7

[LSI]

Dedication

This book is dedicated to my dear family:

wife, Behnaz,

daughter, Maral,

son, Yaseen

Foreword

Unlocking the power of the genome is a powerful notion one that intimates knowledge, understanding, and the ability of science and technology to be transformative. But transformation requires alignment and synergy, and synergy almost always requires deep collaboration. From scientists to software engineers, and from academia into the clinic, we will need to work together to pave the way for our genetically empowered future.

The creation of data algorithms that analyze the information generated from large-scale genetic sequencing studies is key. Genetic variations are diverse; they can be complex and novel, compounded by a need to connect them to an individuals physical presentation in a meaningful way for clinical insights to be gained and applied. Accelerating our ability to do this at scale, across populations of individuals, is critical. The methods in this book serve as a compass for the road ahead.

MapReduce, Hadoop, and Spark are key technologies that will help us scale the use of genetic sequencing, enabling us to store, process, and analyze the big data of genomics. Mahmouds book covers these topics in a simple and practical manner. Data Algorithms illuminates the way for data scientists, software engineers, and ultimately clinicians to unlock the power of the genome, helping to move human health into an era of precision, personalization, and transformation.

Jay Flatley

CEO, Illumina Inc.

Preface

With the development of massive search engines (such as Google and Yahoo!), genomic analysis (in DNA sequencing, RNA sequencing, and biomarker analysis), and social networks (such as Facebook and Twitter), the volumes of data being generated and processed have crossed the petabytes threshold. To satisfy these massive computational requirements, we need efficient, scalable, and parallel algorithms. One framework to tackle these problems is the MapReduce paradigm.

MapReduce is a software framework for processing large (giga-, tera-, or petabytes) data sets in a parallel and distributed fashion, and an execution framework for large-scale data processing on clusters of commodity servers. There are many ways to implement MapReduce, but in this book our primary focus will be Apache Spark and MapReduce/Hadoop. You will learn how to implement MapReduce in Spark and Hadoop through simple and concrete examples.

This book provides essential distributed algorithms (implemented in MapReduce, Hadoop, and Spark) in the following areas, and the chapters are organized accordingly :

  • Basic design patterns
  • Data mining and machine learning
  • Bioinformatics, genomics, and statistics
  • Optimization techniques
What Is MapReduce?

] implementation is a proprietary solution and has not yet been released to the public.

A simple view of the MapReduce process is illustrated in . Simply put, MapReduce is about scalability. Using the MapReduce paradigm, you focus on writing two functions:

map()
Filters and aggregates data
reduce()
Reduces, groups, and summarizes by keys generated by map()
Figure P-1 The simple view of the MapReduce process These two functions can - photo 1
Figure P-1. The simple view of the MapReduce process

These two functions can be defined as follows:

map() function
The master node takes the input, partitions it into smaller data chunks, and distributes them to worker (slave) nodes. The worker nodes apply the same transformation function to each data chunk, then pass the results back to the master node. In MapReduce, the programmer defines a mapper with the following signature :
map():(Key1,Value1)[(Key2,Value2)]
reduce() function
The master node shuffles and clusters the received results based on unique key-value pairs; then, through another redistribution to the workers/slaves, these values are combined via another type of transformation function. In MapReduce, the programmer defines a reducer with the following signature:
reduce():(Key2,[Value2])[(Key3,Value3)]

Note
In informal presentations of the map() and reduce() functions throughout this book, Ive used square brackets, [], to denote a list.

In .

Table P-1. Mappers output
KeyValue
K1V11
K2V21
K1V12
K2V22
K2V23

In this example, all mappers generate only two unique keys: {K1, K2}. When all mappers are completed, the keys are sorted, shuffled, grouped, and sent to reducers. Finally, the reducers generate the desired outputs. For this example, we have two reducers identified by {K1, K2} keys (illustrated by ).

Table P-2. Reducers input
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