Book Image

Learning Apache Flink

By : Tanmay Deshpande
Book Image

Learning Apache Flink

By: Tanmay Deshpande

Overview of this book

<p>With the advent of massive computer systems, organizations in different domains generate large amounts of data on a real-time basis. The latest entrant to big data processing, Apache Flink, is designed to process continuous streams of data at a lightning fast pace.</p> <p>This book will be your definitive guide to batch and stream data processing with Apache Flink. The book begins with introducing the Apache Flink ecosystem, setting it up and using the DataSet and DataStream API for processing batch and streaming datasets. Bringing the power of SQL to Flink, this book will then explore the Table API for querying and manipulating data. In the latter half of the book, readers will get to learn the remaining ecosystem of Apache Flink to achieve complex tasks such as event processing, machine learning, and graph processing. The final part of the book would consist of topics such as scaling Flink solutions, performance optimization and integrating Flink with other tools such as ElasticSearch.</p> <p>Whether you want to dive deeper into Apache Flink, or want to investigate how to get more out of this powerful technology, you’ll find everything you need inside.</p>
Table of Contents (17 chapters)
Learning Apache Flink
Credits
About the Author
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface

Iterative graph processing


Gelly enhances Flink's iterative processing capabilities to support large scale graph processing. Currently it supports implementation of the following models:

  • Vertex-Centric

  • Scatter-Gather

  • Gather-Sum-Apply

Let's start by understanding these models in the context of Gelly.

Vertex-Centric iterations

As the name suggest, these iterations are built thinking the vertex is in the center. Here each Vertex processes the same user-defined function in parallel. Each step of execution is called a superset. A vertex can send a message to another vertex as long as it knows its unique ID. This message would be used as input to the next superset.

To use Vertex-Centric iterations, the user needs to provide a ComputeFunction. We can also define an optional MessageCombiner to reduce the cost of communication. We can solve problems, such as Single Source Shortest Path in which we need to find the shortest path from source vertex to all other vertices.

Note

Single Source Shortest Path is...