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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
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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

Common message consuming patterns

Here are a few of the common message consuming patterns:

  • Consumer group - continuous data processing: In this pattern, once consumer is created and subscribes to a topic, it starts receiving messages from the current offset. The consumer commits the latest offsets based on the count of messages received in a batch at a regular, configured interval. The consumer checks whether it's time to commit, and if it is, it will commit the offsets. Offset commit can happen synchronously or asynchronously. It uses the auto-commit feature of the consumer API.

The key point to understand in this pattern is that consumer is not controlling the message flows. It is driven by the current offset of the partition in a consumer group. It receives messages from that current offset and commits the offsets as and when messages are received by it after regular...

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