Neha Narkhede - Kafka: The Definitive Guide, 2nd Edition
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by Gwen Shapira , Todd Palino , Rajini Sivaram , and Neha Narkhede
Copyright 2022 Gwen Shapira, Todd Palino, Rajini Sivaram, and Neha Narkhede. All rights reserved.
Printed in the United States of America.
Published by OReilly Media, Inc. , 1005 Gravenstein Highway North, Sebastopol, CA 95472.
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- October 2021: Second Edition
- 2020-05-22: First Release
See http://oreilly.com/catalog/errata.csp?isbn=9781492043089 for release details.
The OReilly logo is a registered trademark of OReilly Media, Inc. Kafka: The Definitive Guide, the cover image, and related trade dress are trademarks of OReilly Media, Inc.
While the publisher and the authors have used good faith efforts to ensure that the information and instructions contained in this work are accurate, the publisher and the authors disclaim all responsibility for errors or omissions, including without limitation responsibility for damages resulting from the use of or reliance on this work. Use of the information and instructions contained in this work is at your own risk. If any code samples or other technology this work contains or describes is subject to open source licenses or the intellectual property rights of others, it is your responsibility to ensure that your use thereof complies with such licenses and/or rights.
978-1-492-04301-0
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This will be the 1st chapter of the final book.
If you have comments about how we might improve the content and/or examples in this book, or if you notice missing material within this chapter, please reach out to the author at .
Every enterprise is powered by data. We take information in, analyze it, manipulate it, and create more as output. Every application creates data, whether it is log messages, metrics, user activity, outgoing messages, or something else. Every byte of data has a story to tell, something of importance that will inform the next thing to be done. In order to know what that is, we need to get the data from where it is created to where it can be analyzed. We see this every day on websites like Amazon, where our clicks on items of interest to us are turned into recommendations that are shown to us a little later.
The faster we can do this, the more agile and responsive our organizations can be. The less effort we spend on moving data around, the more we can focus on the core business at hand. This is why the pipeline is a critical component in the data-driven enterprise. How we move the data becomes nearly as important as the data itself.
Any time scientists disagree, its because we have insufficient data. Then we can agree on what kind of data to get; we get the data; and the data solves the problem. Either Im right, or youre right, or were both wrong. And we move on.
Neil deGrasse Tyson
Before discussing the specifics of Apache Kafka, it is important for us to understand the concept of publish/subscribe messaging and why it is important. Publish/subscribe messaging is a pattern that is characterized by the sender (publisher) of a piece of data (message) not specifically directing it to a receiver. Instead, the publisher classifies the message somehow, and that receiver (subscriber) subscribes to receive certain classes of messages. Pub/sub systems often have a broker, a central point where messages are published, to facilitate this.
.
This is a simple solution to a simple problem that works when you are getting started with monitoring. Before long, you decide you would like to analyze your metrics over a longer term, and that doesnt work well in the dashboard. You start a new service that can receive metrics, store them, and analyze them. In order to support this, you modify your application to write metrics to both systems. By now you have three more applications that are generating metrics, and they all make the same connections to these two services. Your coworker thinks it would be a good idea to do active polling of the services for alerting as well, so you add a server on each of the applications to provide metrics on request. After a while, you have more applications that are using those servers to get individual metrics and use them for various purposes. This architecture can look much like , with connections that are even harder to trace.
The technical debt built up here is obvious, so you decide to pay some of it back. You set up a single application that receives metrics from all the applications out there, and provide a server to query those metrics for any system that needs them. This reduces the complexity of the architecture to something similar to . Congratulations, you have built a publish-subscribe messaging system!
shows such an infrastructure, with three separate pub/sub systems.
This is certainly a lot better than utilizing point-to-point connections (as in
Apache Kafka is a publish/subscribe messaging system designed to solve this problem. It is often described as a distributed commit log or more recently as a distributing streaming platform. A filesystem or database commit log is designed to provide a durable record of all transactions so that they can be replayed to consistently build the state of a system. Similarly, data within Kafka is stored durably, in order, and can be read deterministically. In addition, the data can be distributed within the system to provide additional protections against failures, as well as significant opportunities for scaling performance.
The unit of data within Kafka is called a
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