Kafka: The Definitive Guide: Real-Time Data and Stream Processing at Scale

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"O'Reilly Media, Inc.", 2017. 8. 31. - 322페이지

Every enterprise application creates data, whether it’s log messages, metrics, user activity, outgoing messages, or something else. And how to move all of this data becomes nearly as important as the data itself. If you’re an application architect, developer, or production engineer new to Apache Kafka, this practical guide shows you how to use this open source streaming platform to handle real-time data feeds.

Engineers from Confluent and LinkedIn who are responsible for developing Kafka explain how to deploy production Kafka clusters, write reliable event-driven microservices, and build scalable stream-processing applications with this platform. Through detailed examples, you’ll learn Kafka’s design principles, reliability guarantees, key APIs, and architecture details, including the replication protocol, the controller, and the storage layer.

  • Understand publish-subscribe messaging and how it fits in the big data ecosystem.
  • Explore Kafka producers and consumers for writing and reading messages
  • Understand Kafka patterns and use-case requirements to ensure reliable data delivery
  • Get best practices for building data pipelines and applications with Kafka
  • Manage Kafka in production, and learn to perform monitoring, tuning, and maintenance tasks
  • Learn the most critical metrics among Kafka’s operational measurements
  • Explore how Kafka’s stream delivery capabilities make it a perfect source for stream processing systems
 

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Chapter 1 Meet Kafka
1
Chapter 2 Installing Kafka
17
Writing Messages to Kafka
41
Reading Data from Kafka
63
Chapter 5 Kafka Internals
95
Chapter 6 Reliable Data Delivery
115
Chapter 7 Building Data Pipelines
135
Chapter 8 CrossCluster Data Mirroring
157
Chapter 9 Administering Kafka
181
Chapter 10 Monitoring Kafka
211
Chapter 11 Stream Processing
247
Appendix A Installing Kafka on Other Operating Systems
281
Index
287
About the Authors
298
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저자 정보 (2017)

Neha Narkhede is co-founder and CTO at Confluent, a company backing the popular Apache Kafka messaging system. Prior to founding Confluent, Neha led streams infrastructure at LinkedIn, where she was responsible for LinkedIn’s streaming infrastructure built on top of Apache Kafka and Apache Samza. She is one of the initial authors of Apache Kafka and a committer and PMC member on the project.

Gwen Shapira is a system architect at Confluent helping customers achieve success with their Apache Kafka implementation. She has 15 years of experience working with code and customers to build scalable data architectures, integrating relational and big data technologies. She currently specializes in building real-time reliable data processing pipelines using Apache Kafka. Gwen is an Oracle Ace Director, an author of "Hadoop Application Architectures", and a frequent presenter at data driven conferences. Gwen is also a committer on the Apache Kafka and Apache Sqoop projects.

Todd is a Staff Site Reliability Engineer at LinkedIn, tasked with keeping the largest deployment of Apache Kafka, Zookeeper, and Samza fed and watered. He is responsible for architecture, day-to-day operations, and tools development, including the creation of an advanced monitoring and notification system. Todd is the developer of the open source project Burrow, a Kafka consumer monitoring tool, and can be found sharing his experience on Apache Kafka at industry conferences and tech talks. Todd has spent over 20 years in the technology industryrunning infrastructure services, most recently as a Systems Engineer at Verisign, developing service management automation for DNS, networking, and hardware management, as well as managing hardware and software standards across the company.

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