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Large-Scale Graph Processing Using Apache Giraph

Authors: Sakr, S., Orakzai, F.M., Abdelaziz, I., Khayyat, Z.

  • Describes the fundamental abstractions of the Apache Giraph, its programming models and various techniques
  • Offers step-by-step coverage of the implementation of several popular and advanced graph analytics algorithms, including related optimization details
  • All source code presented in the book is available for download from an associated github repository
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Buy this book

eBook $39.99
price for USA (gross)
  • ISBN 978-3-319-47431-1
  • Digitally watermarked, DRM-free
  • Included format: EPUB, PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $49.99
price for USA
  • ISBN 978-3-319-47430-4
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
About this book

This book takes its reader on a journey through Apache Giraph, a popular distributed graph processing platform designed to bring the power of big data processing to graph data. Designed as a step-by-step self-study guide for everyone interested in large-scale graph processing, it describes the fundamental abstractions of the system, its programming models and various techniques for using the system to process graph data at scale, including the implementation of several popular and advanced graph analytics algorithms.

The book is organized as follows: Chapter 1 starts by providing a general background of the big data phenomenon and a general introduction to the Apache Giraph system, its abstraction, programming model and design architecture. Next, chapter 2 focuses on Giraph as a platform and how to use it. Based on a sample job, even more advanced topics like monitoring the Giraph application lifecycle and different methods for monitoring Giraph jobs are explained.  Chapter 3 then provides an introduction to Giraph programming, introduces the basic Giraph graph model and explains how to write Giraph programs. In turn, Chapter 4 discusses in detail the implementation of some popular graph algorithms including PageRank, connected components, shortest paths and triangle closing. Chapter 5 focuses on advanced Giraph programming, discussing common Giraph algorithmic optimizations, tunable Giraph configurations that determine the system’s utilization of the underlying resources, and how to write a custom graph input and output format. Lastly, chapter 6 highlights two systems that have been introduced to tackle the challenge of large scale graph processing, GraphX and GraphLab, and explains the main commonalities and differences between these systems and Apache Giraph.

This book serves as an essential reference guide for students, researchers and practitioners in the domain of large scale graph processing. It offers step-by-step guidance, with several code examples and the complete source code available in the related github repository. Students will find a comprehensive introduction to and hands-on practice with tackling large scale graph processing problems using the Apache Giraph system, while researchers will discover thorough coverage of the emerging and ongoing advancements in big graph processing systems.

About the authors

Sherif Sakr is currently a professor of computer and information science in the Health Informatics department at King Saud bin Abdulaziz University for Health Sciences. He is also affiliated with the University of New South Wales and DATA61/CSIRO (formerly NICTA). He had held visiting appointments in several academic and research institutes including Microsoft Research (2011), Alcatel-Lucent Bell Labs (2012), Humboldt University of Berlin (2015), University of Zurich (2016) and TU Dresden (2016). In 2013, Sherif has been awarded the Stanford Innovation and Entrepreneurship Certificate.

Faisal Moeen Orakzai is a joint PhD candidate at Université Libre de Bruxelles (ULB) Belgium and Aalborg University (AAU) Denmark. In addition to doing research, he works as a consultant and helps companies setting up their distributed data processing architectures and pipelines. He is a Big Data management and analytics enthusiast and currently working on a Giraph based framework for spatio-temporal pattern mining.

Ibrahim Abdelaziz is a Computer Science PhD candidate at King Abdullah University of Science and Technology (KAUST). Prior to joining KAUST, he used to work on pattern recognition and information retrieval in several research organizations in Egypt. His current research interests are Data Mining over large scale graphs, Distributed Systems and Machine Learning.

Zuhair Khayyat is a PhD candidate in the InfoCloud group at King Abdullah University of Science and Technology (KAUST) focusing on Big Data, Analytics and Graphs.

Table of contents (6 chapters)

  • Introduction

    Sakr, Sherif (et al.)

    Pages 1-33

  • Installing and Getting Giraph Ready to Use

    Sakr, Sherif (et al.)

    Pages 35-86

  • Getting Started with Giraph Programming

    Sakr, Sherif (et al.)

    Pages 87-117

  • Popular Graph Algorithms on Giraph

    Sakr, Sherif (et al.)

    Pages 119-139

  • Advanced Giraph Programming

    Sakr, Sherif (et al.)

    Pages 141-173

Buy this book

eBook $39.99
price for USA (gross)
  • ISBN 978-3-319-47431-1
  • Digitally watermarked, DRM-free
  • Included format: EPUB, PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $49.99
price for USA
  • ISBN 978-3-319-47430-4
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
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Bibliographic Information

Bibliographic Information
Book Title
Large-Scale Graph Processing Using Apache Giraph
Authors
Copyright
2016
Publisher
Springer International Publishing
Copyright Holder
Springer International Publishing AG
eBook ISBN
978-3-319-47431-1
DOI
10.1007/978-3-319-47431-1
Hardcover ISBN
978-3-319-47430-4
Edition Number
1
Number of Pages
XXV, 197
Number of Illustrations and Tables
15 b/w illustrations, 87 illustrations in colour
Topics