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Practical Real-time Data Processing and Analytics

You're reading from Practical Real-time Data Processing and Analytics

Product type Book

Published in Sep 2017

Publisher Packt

ISBN-13 9781787281202

Pages 360 pages

Edition 1st Edition

Languages

Concepts

Table of Contents (20) Chapters

Title Page

Credits

About the Authors

About the Reviewers

www.PacktPub.com

Customer Feedback

Preface

1. Introducing Real-Time Analytics

2. Real Time Applications – The Basic Ingredients

3. Understanding and Tailing Data Streams

4. Setting up the Infrastructure for Storm

5. Configuring Apache Spark and Flink

6. Integrating Storm with a Data Source

7. From Storm to Sink

8. Storm Trident

9. Working with Spark

10. Working with Spark Operations

11. Spark Streaming

12. Working with Apache Flink

13. Case Study

Flink basic components and processes

As per the official documentation of Flink https://ci.apache.org/projects/flink/flink-docs-release-1.3/concepts/runtime.html and https://ci.apache.org/projects/flink/flink-docs-release-1.3/internals/components.html, the following are some of the processes and basic components of Flink.

Processes: There are two types of processes in Flink, as shown in the following figure:

JobManagers (Master): It is responsible for distributing the task among the TaskManagers, maintaining check points and for recovery in case of any failure. To achieve high availability, we need to set up more than one JobManager where one acts as a leader and the other as standby. The leader and standby JobManager are always in sync and in case of leader failure, the standby JobManager becomes the leader.
TaskManagers (workers): TaskManagers are responsible for executing the tasks, maintaining buffers for operators, and so on. TaskManager is a JVM process and subtasks are executed in multiple...

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