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

Chapter 1. Introduction to Apache Flink

With distributed technologies evolving all the time, engineers are trying to push those technologies to their limits. Earlier, people were looking for faster, cheaper ways to process data. This need was satisfied when Hadoop was introduced. Everyone started using Hadoop, started replacing their ETLs with Hadoop-bound ecosystem tools. Now that this need has been satisfied and Hadoop is used in production at so many companies, another need arose to process data in a streaming manner, which gave rise to technologies such as Apache Spark and Flink. Features, such as fast processing engines, the ability to scale in no time, and support for machine learning and graph technologies, are popularizing these technologies among the developer community.

Some of you might have been already using Apache Spark in your day-to-day life and might have been wondering if I have Spark why I need to use Flink? The question is quite expected and the comparison is natural. Let me try to answer that in brief. The very first thing we need to understand here is Flink is based on the streaming first principle which means it is real streaming processing engine and not a fast processing engine that collects streams as mini batches. Flink considers batch processing as a special case of streaming whereas it is vice-versa in the case of Spark. Likewise we will discover more such differences throughout this book.

This book is about one of the most promising technologies--Apache Flink. In this chapter we are going to talk about the following topics:

  • History

  • Architecture

  • Distributed execution

  • Features

  • Quick start setup

  • Cluster setup

  • Running a sample application