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Building Data Streaming Applications with Apache Kafka

You're reading from   Building Data Streaming Applications with Apache Kafka Design, develop and streamline applications using Apache Kafka, Storm, Heron and Spark

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Product type Paperback
Published in Aug 2017
Publisher Packt
ISBN-13 9781787283985
Length 278 pages
Edition 1st Edition
Tools
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Authors (2):
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Chanchal Singh Chanchal Singh
Author Profile Icon Chanchal Singh
Chanchal Singh
Manish Kumar Manish Kumar
Author Profile Icon Manish Kumar
Manish Kumar
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Table of Contents (14) Chapters Close

Preface 1. Introduction to Messaging Systems FREE CHAPTER 2. Introducing Kafka the Distributed Messaging Platform 3. Deep Dive into Kafka Producers 4. Deep Dive into Kafka Consumers 5. Building Spark Streaming Applications with Kafka 6. Building Storm Applications with Kafka 7. Using Kafka with Confluent Platform 8. Building ETL Pipelines Using Kafka 9. Building Streaming Applications Using Kafka Streams 10. Kafka Cluster Deployment 11. Using Kafka in Big Data Applications 12. Securing Kafka 13. Streaming Application Design Considerations

Message processing semantics

Exactly-once delivery is the holy grail of streaming analytics. Having duplicates of events processed in a streaming job is inconvenient and often undesirable, depending on the nature of the application. For example, if billing applications miss an event or process an event twice, they could lose revenue or overcharge customers. Guaranteeing that such scenarios never happen is difficult; any project seeking such a property will need to make some choices with respect to availability and consistency. One main difficulty stems from the fact that a streaming pipeline might have multiple stages, and exactly-once delivery needs to happen at each stage. Another difficulty is that intermediate computations could potentially affect the final computation. Once results are exposed, retracting them causes problems.

It is useful to provide exactly-once guarantees...

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