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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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 Gundabattula Gundabattula
Author Profile Icon Gundabattula
Gundabattula
Thomas Weise Thomas Weise
Author Profile Icon Thomas Weise
Thomas Weise
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
Kenneth Knowles Kenneth Knowles
Author Profile Icon Kenneth Knowles
Kenneth Knowles
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Table of Contents (17) Chapters Close

Title Page
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Customer Feedback
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

Processing guarantees


The Apex engine by default guarantees that data is processed at-least-once and that state updates within the DAG occur exactly-once. With respect to state mutation through interaction with external systems, the results depend on the connector (refer to Chapter 3, The Apex Library). Connectors that support the exactly-once results include Files, Kafka, JDBC, Cassandra, and all others where the write operations are, or can be made, idempotent. We will look at an example application in the next section.

In distributed systems, a guarantee of the exactly-once processing is not really possible since nodes may go down at any time and when they are restored, some reprocessing of prior data, however minimal, must occur in order to guarantee correctness (or we have to accept data loss, which yields at-most-once processing). So, when we see exactly-once in published feature matrices of stream processors, it really means at-least-once along with the often implicit assumption of...

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