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Kafka Streams in Action, Second Edition
1. About_this_book
2. Acknowledgements
3. Preface
4. PART_1:_INTRODUCTION
5. 1_Welcome_to_the_Kafka_Event_Streaming_Platform
6. 2_Kafka_Brokers
7. PART_2:_GETTING_DATA_INTO_KAFKA
8. 3_Schema_Registry
9. 4_Kafka_Clients
10. 5_Kafka_Connect
11. PART_3:_EVENT_STREAM_PROCESSING_DEVELOPMENT
12. 6_Developing_Kafka_Streams
13. 7_Streams_and_State
14. 8_The_KTable_API
15. 9_Windowing_and_Timestamps
16. 10_The_Processor_API
17. 11_ksqlDB
18. 12_Spring_Kafka
19. 13_Kafka_Streams_interactive_queries
20. 14_Testing
21. Appendix_A._Schema_Compatibility_Workshop
22. Appendix_B._Working_with_Avro,_Protobuf_and_JSON_Schema
23. Appendix_C._Understanding_Kafka_Streams_architecture
24. Appendix_D._Confluent_Resources
25. index
About this book
I wrote the 2nd edition of Kafka Streams in Action to teach you how to build
event streaming applications in Kafka Streams and include other components
of the Kafka ecosystem, Producer and Consumer clients, Connect, and
Schema Registry. I took this approach because for your event-streaming
application to be as effective as possible, you’ll need not just Kafka Streams
but other essential tools. My approach to writing this book is a pair-
programming perspective; I imagine myself sitting next to you as you write
the code and learn the API. You’ll learn about the Kafka broker and how the
producer and consumer clients work. Then, you’ll see how to manage
schemas, their role with Schema Registry, and how Kafka Connect bridges
external components and Kafka. From there, you’ll dive into Kafka Streams,
first building a simple application, then adding more complexity as you dig
deeper into Kafka Streams API. You’ll also learn about ksqlDB, testing, and,
finally, integrating Kafka with the popular Spring framework.

Who should read this book

Kafka Streams in Action is for any developer wishing to get into stream
processing. While not strictly required, knowledge of distributed
programming will help understand Kafka and Kafka Streams. Knowledge of
Kafka is beneficial but not required; I’ll teach you what you need to know.
Experienced Kafka developers and those new to Kafka will learn how to
develop compelling stream-processing applications with Kafka Streams.
Intermediate-to-advanced Java developers familiar with topics like
serialization will learn how to use their skills to build a Kafka Streams
application. The book’s source code is written in Java 17 and extensively
uses Java lambda syntax, so experience with lambdas (even from another
language) will be helpful.
How this book is organized: a roadmap
This book has three parts spread over 14 chapters. While the book’s title is
“Kafka Streams in Action”, it covers the entire Kafka event streaming
platform. As a result, the first five chapters cover the different components:
Kafka brokers, consumer and producer clients, Schema Registry, and Kafka
Connect. This approach makes sense, especially considering that Kafka
Streams is an abstraction over the consumer and producer clients. So, if
you’re already familiar with Kafka, Connect, and Schema Registry or if
you’re excited to get going with Kafka Streams, then by all means, skip
directly to Part 3.

Part 1 introduces event streaming and describes the different parts of the
Kafka ecosystem to show you the big-picture view of how it all works and
fits together. These chapters also provide the basics of the Kafka broker for
those who need them or want a review:

1. Chapter 1 provides some context on what is an event and event-

streaming and why it’s vital for working with real-time data. It also
presents the mental model of the different components we’ll cover: the
broker, clients, Kafka Connect, Schema Registry, and, of course, Kafka
Streams. I don’t go over any code but describe how they all work.
2. Chapter 2 is a primer for developers who are new to Kafka, and it covers
the role of the broker, topics, partitions, and some monitoring. Those
with more experience with Kafka can skip this chapter.

Part 2 moves on and covers getting data into and out of Kafka and managing
schemas: . Chapter 3 covers using Schema Registry to help you manage the
evolution of your data’s schemas. Spoiler alert: you’re always using a
schema-if not explicitly, then it’s implicitly there. . Chapter 4 discusses the
Kafka producer and consumer clients. The clients are how you get data into
and out of Kafka and provide the building blocks for Kafka Connect and
Kafka Streams. . Chapter 5 is about Kafka Connect. Kafka Connect provides
the ability to get data into Kafka via source connectors and export it to
external systems with sink connectors.
Part 3 gets to the book’s heart and covers developing Kafka Streams
applications. In this section, you’ll also learn about ksqlDB and testing your
event-streaming application, and it concludes with integrating Kafka with the
Spring Framework . Chapter 6 is your introduction to Kafka Streams, where
you’ll build a Hello World application and, from there, build a more realistic
application for a fictional retailer. Along the way, you’ll learn about the
Kafka Streams DSL. . Chapter 7 continues your Kafka Streams learning path,
where we discuss application state and why it’s required for streaming
applications. In this chapter, some of the things you’ll learn about are
aggregating data and joins. . Chapter 8: You’ll learn about the KTable API.
Whereas a KStream is a stream of events, a KTable is a stream of related
events or an update stream. . Chapter 9 covers windowed operations and
timestamps. Windowing an aggregation allows you to bucket results by time,
and the timestamps on the records drive the action. . Chapter 10 dives into the
Kafka Streams Processor API. Up to this point, you’ve been working with the
high-level DSL, but here, you’ll learn how to use the Processor API when
you need more control. . Chapter 11 takes you further into the development
stack, where you’ll learn about ksqlDB. ksqlDB allows you to write event-
streaming applications without any code but using SQL. . Chapter 12
discusses using the Spring Framework with Kafka clients and Kafka Streams.
Spring allows you to write more modular and testable code by providing a
dependency injection framework for wiring up your applications. . Chapter
13 introduces you to Kafka Streams Interactive Queries or IQ. IQ is the
ability to directly query the state store of a state operation in Kafka Streams.
You’ll use what you learned in Chapter 12 to build a Spring-enabled IQ web
application. . Chapter 14 covers the all-important topic of testing. You’ll
learn how to test client applications with a Kafka Streams topology, the
difference between unit testing and integration testing, and when to apply
them. . Appendix A contains a workshop on Schema Registry to get hands-on
experience with the different schema compatibility modes. . Appendix B is a
survey of working with the different schema types Avro, Protobuf, and JSON
Schema. . Appendix C covers the architecture and internals of Kafka Streams.
. Appendix D presents information on using Confluent Cloud to help develop
your event streaming applications.

About the code

This book contains many examples of source code both in numbered listings
andinline with normal text. In both cases, source code is formatted in a
fixed-width font like this to separate it from ordinary text.

In many cases, the original source code has been reformatted; we’ve added
linebreaks and reworked indentation to accommodate the available page
space in the book. In rare cases, even this was not enough, and listings
include line-continuationmarkers (➥).

Additionally, comments in the source code have often been removed from the
list-ings when the code is described in the text. Code annotations accompany
many of thelistings, highlighting important concepts.

Finally, it’s important to note that many of the code examples aren’t meant
tostand on their own: they’re excerpts containing only the most relevant parts
of what is currently under discussion. You’ll find all the examples from the
book in the accompanying source code in their complete form.

Source code for the book’s examples is available from GitHub at

https://github.com/bbejeck/KafkaStreamsInAction2ndEdition and the
publisher’s website at www.manning.com/books/kafka-streams-in-action-
second-edition. The source code for the book is an all-encompassing project
using the build tool Gradle (https://gradle.org). You can import the project
into either IntelliJ or Eclipse using the appropriate commands. Full
instructions for using and navigating the sourcecode can be found in the
accompanying README.md file.

Other online resources

1. Apache Kafka documentation: https://kafka.apache.org
2. Confluent documentation: https://docs.confluent.io/current
3. Kafka Streams documentation:
https://docs.confluent.io/current/streams/index.html#kafka-streams
4. ksqlDB documentation: https://ksqldb.io/
5. Spring Framework: https://spring.io/
Acknowledgements
I want to thank my wife, Beth, for supporting my signing up for a second
edition. Writing the first edition of a book is very time-consuming, so you’d
think the second edition would be more straightforward, just making
adjustments for API changes. But in this case, I wanted more from my
previous work and decided to do an entire rewrite. Beth never questioned my
decision and fully supported my new direction, and as before, I couldn’t have
completed this without her support. Beth, you are fantastic, and I’m very
grateful to have you as my wife. I’d also like to thank my three children for
having great attitudes and supporting me in doing a second edition.

Next, I thank my editor at Manning, Frances Lefkowitz, whose continued

expert guidance and patience made the writing process fun this time. I also
thank John Guthrie for his excellent, precise technical feedback and Karsten
Strøbæk, the technical proofer, for his superb work reviewing the code. I’d
also like to thank the Kafka Streams developers and community for being so
engaging and brilliant in making Kafka Streams the best stream processing
library available. I want to acknowledge all the Kafka developers for building
such high-quality software, especially Jay Kreps, Neha Narkhede, and Jun
Rao, not only for starting Kafka in the first place but for creating such a great
place to work in Confluent.

Last but certainly not least, I thank the reviewers for their hard work and
invaluable feedback in making the quality of this book better for all readers.
Preface
After completing the first edition of Kafka Streams in Action, I thought that I
had accomplished everything I had set out to do. But as time went on, my
understanding of the Kafka ecosystem and my appreciation for Kafka
Streams grew. I saw that Kafka Streams was more powerful than I had
initially thought. Additionally, I noticed other important pieces in building
event-streaming applications; Kafka Streams is still a key player but not the
only requirement. I realized that Apache Kafka could be considered the
central nervous system for an organization’s data. If Kafka is the central
nervous system, then Kafka Streams is a vital organ performing some
necessary operations.

But Kafka Streams relies on other components to bring events into Kafka or
export them to the outside world where its results and calculations can be put
to good use. I’m talking about the producer and consumer clients and Kafka
Connect. As I put the pieces together, I realized you need these other
components to complete the event-streaming picture. Couple all this with
some significant improvements to Kafka Streams since 2018, and I knew I
wanted to write a second edition.

But I didn’t just want to brush up on the previous edition; I wanted to express
my improved understanding and add complete coverage of the entire Kafka
ecosystem. This meant expanding the scope of some subjects from sections
of chapters to whole chapters (like the producer and consumer clients), or
adding entirely new chapters (such as the new chapters on Connect and
Schema Registry). For the existing Kafka Streams chapters, writing a second
edition meant updating and improving the existing material to clarify and
communicate my deeper understanding.

Taking on the second edition with this new focus during the pandemic was
not easy and not without some serious personal challenges along the way. But
in the end, it was worth every minute of it, and if I were to go back in time, I
would make the same decision. I hope that new readers of Kafka Streams in
Action will find the book an essential resource and that readers from the first
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edition will enjoy and apply the improvements as well.
PART 1: INTRODUCTION
In part one, you’ll learn about events and event streaming in general. Event
streaming is a software development approach that considers events as an
application’s primary input and output. But to develop an effective event
streaming application, you’ll first need to learn what an event is (spoiler alert:
it’s everything!). Then you’ll read about what use cases are good candidates
for event-streaming applications and which are not.

First, you’ll discover what a Kafka broker is and how it’s at the heart of the
Kafka ecosystem, and the various jobs it performs. Then you’ll learn what
Schema Registry, producer and consumer clients, Connect, and Kafka
Streams are and their different roles. Then you’ll learn about the Apache
Kafka event streaming platform; although this book focuses on Kafka
Streams, it’s part of a larger whole that allows you to develop event-
streaming applications. If this first part leaves you with more questions than
answers, don’t fret; I’ll explain them all in subsequent chapters.
1 Welcome to the Kafka Event
Streaming Platform
This chapter covers
Defining event streaming and events
Introducing the Kafka event streaming platform
Applying the platform to a concrete example

While the constant influx of data creates more entertainment and

opportunities for the consumer, increasingly, the users of this information are
software systems using other software systems. Think, for example, of the
fundamental interaction of watching a movie from your favorite movie
streaming application. You log into the application, search for and select a
film, then watch it, and afterward, you may provide a rating or some
indication of how you enjoyed the movie. Just this simple interaction
generates several events captured by the movie streaming service. But this
information needs analysis if it’s to be of use to the business. That’s where all
the other software comes into play.

First, the software systems consume and store all the information obtained
from your interaction and the interactions of other subscribers. Then,
additional software systems use that information to make recommendations
to you and to provide the streaming service with insight on what
programming to provide in the future. Now, consider that this process occurs
hundreds of thousands or even millions of times per day, and you can see the
massive amount of information that businesses need to harness and that their
software needs to make sense of to meet customer demands and expectations
and stay competitive.

Another way to think of this process is that everything modern-day

consumers do, from streaming a movie online to purchasing a pair of shoes at
a brick-and-mortar store, generates an event. For an organization to survive
and excel in our digital economy, it must have an efficient way of capturing
and acting on these events. In other words, businesses must find ways to keep
up with the demand of this endless flow of events if they want to satisfy
customers and maintain a robust bottom line. Developers call this constant
flow an event stream. And, increasingly, they are meeting the demands of this
endless digital activity with an event-streaming platform, which utilizes a
series of event-streaming applications.

An event-streaming platform is analogous to our central nervous system,

which processes millions of events (nerve signals) and, in response, sends out
messages to the appropriate parts of the body. Our conscious thoughts and
actions generate some of these responses. When we are hungry and open the
refrigerator, the central nervous system gets the message and sends out
another one, telling the arm to reach for a nice red apple on the first shelf.
Other actions, such as your heart rate increasing in anticipation of exciting
news, are handled unconsciously.

An event-streaming platform captures events generated from mobile devices,

customer interaction with websites, online activity, shipment tracking, and
other business transactions. But the platform, like the nervous system, does
more than capture events. It also needs a mechanism to reliably transfer and
store the information from those events in the order in which they occurred.
Then, other applications can process or analyze the events to extract different
bits of that information.

Processing the event stream in real time is essential for making time-sensitive
decisions. For example, Does this purchase from customer X seem
suspicious? Are the signals from this temperature sensor indicating
something has gone wrong in a manufacturing process? Has the routing
information been sent to the appropriate department of a business?

But the value of an event-streaming platform goes beyond gaining immediate

information. Providing durable storage allows us to go back and look at
event-stream data in its raw form, perform some manipulation of the data for
more insight, or replay a sequence of events to try and understand what led to
a particular outcome. For example, an e-commerce site offers a fantastic deal
on several products on the weekend after a big holiday. The response to the
sale is so strong that it crashes a few servers and brings the business down for
a few minutes. By replaying all customer events, engineers can better
understand what caused the breakdown and how to fix the system so it can
handle a large, sudden influx of activity.

So, where do you need event-streaming applications?

Since everything in life can be considered an event, any problem domain will
benefit from processing event streams. But there are some areas where it’s
more important to do so. Here are some typical examples

Credit card fraud — A credit card owner may be unaware of

unauthorized use. By reviewing purchases as they happen against
established patterns (location, general spending habits), you may be able
to detect a stolen credit card and alert the owner.
Intrusion detection — The ability to monitor aberrant behavior in real-
time is critical for the protection of sensitive data and the well-being of
an organization.
The Internet of Things - With IoT, sensors are located in all kinds of
places and send back data frequently. The ability to quickly capture and
process this data meaningfully is essential; anything less diminishes the
effect of deploying these sensors.
The financial industry — The ability to track market prices and direction
in real-time is essential for brokers and consumers to make effective
decisions about when to sell or buy.
Sharing data in real-time - Large organizations, like corporations or
conglomerates, that have many applications need to share data in a
standard, accurate, and real-time way

Bottom line: If the event stream provides essential and actionable

information, businesses and organizations need event-driven applications to
capitalize on the information provided.

But streaming applications are only a fit for some situations. Event-streaming
applications become necessary when you have data in different places or a
large volume of events requiring distributed data stores. So, if you can
manage with a single database instance, streaming is unnecessary. For
example, a small e-commerce business or a local government website with
primarily static data aren’t good candidates for building an event-streaming
solution.
In this book, you’ll learn about event-stream development, when and why it’s
essential, and how to use the Kafka event-streaming platform to build robust
and responsive applications. You’ll learn how to use the Kafka streaming
platform’s various components to capture events and make them available for
other applications. We’ll cover using the platform’s components for simple
actions such as writing (producing) or reading (consuming) events to
advanced stateful applications requiring complex transformations so you can
solve the appropriate business challenges with an event-streaming approach.
This book is suitable for any developer looking to get into building event-
streaming applications.

Although the title, "Kafka Streams in Action," focuses on Kafka Streams, this
book teaches the entire Kafka event-streaming platform, end to end. That
platform includes crucial components, such as producers, consumers, and
schemas, that you must work with before building your streaming apps,
which you’ll learn in Part 1. As a result, we don’t get into the subject of
Kafka Streams itself until later in the book, in Chapter 6. But the enhanced
coverage is worth it; Kafka Streams is an abstraction built on top of
components of the Kafka event streaming platform, so understanding them
gives you a better grasp of how you can use Kafka Streams.

1.1 What is an event ?

So we’ve defined an event stream, but what is an event? We’ll define an
event simply as "something that happens"[1]. While the term event probably
brings to mind something notable happening, like the birth of a child, or a
wedding or sporting event, we’re going to focus on smaller, more constant
events like a customer making a purchase (online or in-person), or clicking a
link on a web-page, or a sensor transmitting data. Either people or machines
can generate events. It’s the sequence of events and the constant flow of them
that make up an event stream.

Events conceptually contain three main components:

1. Key - an identifier for the event

2. Value - the event itself
3. timestamp - when the event occurred
Let’s discuss each of these parts of an event in more detail. The key could be
an identifier for the event, and as we’ll learn in later chapters, it plays a role
in routing and grouping events. Think of an online purchase, and using the
customer ID is an excellent example of the key. The value is the event
payload itself. The event value could be a trigger, such as activating a sensor
when someone opens a door or a result of some action like the item
purchased in the online sale. Finally, the timestamp is the date-time when
recording when the event occurred. As we go through the various chapters in
this book, we’ll encounter all three components of this "event trinity"
regularly.

I’ve used a lot of different terms in this introduction, so let’s wrap this section
up with a table of definitions:

Something that occurs and attributes

Event
about it recorded

A series of events captured in real-

Event Stream time from sources such as mobile or
IoT devices

Software to handle event streams -

capable of producing, consuming,
Event Streaming Platform
processing, and storage of event
streams

The premier event streaming

platform - it provides all the
Apache Kafka
components of an event streaming
platform in one battle-tested solution

The native event stream processing

Kafka Streams library for Kafka

1.2 An event stream example

Let’s say you’ve purchased a Flux Capacitor and are excited to receive your
new purchase. Let’s walk through the events leading up to the time you get
your brand new Flux Capacitor, using the following illustration as your
guide.

Figure 1.1 A sequence of events comprising an event stream starting with the online purchase of
the flux ch01capacitor
1. You complete the purchase on the retailer’s website, and the site
provides a tracking number.
2. The retailer’s warehouse receives the purchase event information and
puts the Flux Capacitor on a shipping truck, recording the date and time
your purchase left the warehouse.
3. The truck arrives at the airport, and the driver loads the Flux Capacitor
on a plane and scans a barcode recording the date and time.
4. The plane lands, and the package is loaded on a truck again headed for
the regional distribution center. The delivery service records the date
and time they loaded your Flux Capacitor.
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