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Learning Apache Apex

You're reading from   Learning Apache Apex Real-time streaming applications with Apex

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Product type Paperback
Published in Nov 2017
Publisher
ISBN-13 9781788296403
Length 290 pages
Edition 1st Edition
Languages
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Authors (5):
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Munagala V. Ramanath Munagala V. Ramanath
Author Profile Icon Munagala V. Ramanath
Munagala V. Ramanath
David Yan David Yan
Author Profile Icon David Yan
David Yan
Ananth Gundabattula Ananth Gundabattula
Author Profile Icon Ananth Gundabattula
Ananth Gundabattula
Thomas Weise Thomas Weise
Author Profile Icon Thomas Weise
Thomas Weise
Kenneth Knowles Kenneth Knowles
Author Profile Icon Kenneth Knowles
Kenneth Knowles
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Toc

Table of Contents (11) Chapters Close

Preface 1. Introduction to Apex FREE CHAPTER 2. Getting Started with Application Development 3. The Apex Library 4. Scalability, Low Latency, and Performance 5. Fault Tolerance and Reliability 6. Example Project – Real-Time Aggregation and Visualization 7. Example Project – Real-Time Ride Service Data Processing 8. Example Project – ETL Using SQL 9. Introduction to Apache Beam 10. The Future of Stream Processing

Fault-tolerance components and mechanism in Apex


In Chapter 2, Getting Started with Application Development, we looked at the deployment of an Apex application when it is executing on a YARN cluster. Let's revisit the diagram to see which type of failures may occur and how they are handled by the system:

The client is only required for launching the application; it is not involved in the execution of the DAG on the cluster, and failure of the client node does not affect the pipeline. Since Apex is running on YARN, let's first see how YARN supports resilient applications (from a user's perspective).

YARN consists of a resource manager (RM) and node managers (NM). Each YARN cluster node has a node manager service running, which communicates with the resource manager.

  • Failure of an NM: When a node manager fails (regardless of software or hardware failure), the RM will detect this and ensure that the affected containers can be allocated on different machines. The RM itself cannot recover those...
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