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
About the Author
About the Reviewers
Customer Feedback


Flink 1.X's architecture consists of various components such as deploy, core processing, and APIs. We can easily compare the latest architecture with Stratosphere's architecture and see its evolution. The following diagram shows the components, APIs, and libraries:

Flink has a layered architecture where each component is a part of a specific layer. Each layer is built on top of the others for clear abstraction. Flink is designed to run on local machines, in a YARN cluster, or on the cloud. Runtime is Flink's core data processing engine that receives the program through APIs in the form of JobGraph. JobGraph is a simple parallel data flow with a set of tasks that produce and consume data streams.

The DataStream and DataSet APIs are the interfaces programmers can use for defining the Job. JobGraphs are generated by these APIs when the programs are compiled. Once compiled, the DataSet API allows the optimizer to generate the optimal execution plan while DataStream API uses a stream build for efficient execution plans.

The optimized JobGraph is then submitted to the executors according to the deployment model. You can choose a local, remote, or YARN mode of deployment. If you have a Hadoop cluster already running, it is always better to use a YARN mode of deployment.