Book Image

Kafka Streams with Spring Cloud Stream [Video]

By : Prashant Kumar Pandey
5 (1)
Book Image

Kafka Streams with Spring Cloud Stream [Video]

5 (1)
By: Prashant Kumar Pandey

Overview of this book

Kafka Streams with Spring Cloud Streams will help you understand stream processing in general and apply it to Kafka Streams Programming using Spring Boot. This course uses the Kafka Streams library compatible with Spring Cloud 2020. All the source code and examples used in this course have been tested by the author on Confluent Platform 6.0.0, which is compatible with Apache Kafka 2.6 open-source distribution. This is a fully example-driven course, and you will be working with multiple examples during the entire session. We will be making extensive use of IntelliJ IDEA as the preferred development IDE and Apache Maven and Gradle as the preferred build tool. However, based on your prior experience, you should be able to work with any other IDE designed for Spring application development and any other build tool designed for Java applications. This course also makes use of Log4J2 to teach you industry-standard log implementation in your application. We will be using JUnit5, which is the latest version of JUnit, to implement unit test cases. Working examples and exercises are the most critical tool to sharpen your skills. This course consists of some programming assignments as and when appropriate. These exercises will help you validate and check your concepts and apply your learning to solve programming problems. The code bundles for this course is available in https://github.com/PacktPublishing/Kafka-Streams-with-Spring-Cloud-Stream

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Kafka Streams with Spring Cloud Stream [Video]
Prashant Kumar Pandey
Jul, 2021 | 7 hours 26 minutes
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Table of Contents (12 chapters)
Free Chapter
1
Before You Begin
About the Course
Course Prerequisite
Setting Up Your Environment
Creating Your Starter Project
2
Environment Setup on Window 10 Machine
Installing Confluent Kafka - Windows WSL
Creating Your First Kafka Streams Project – Windows.
3
Environment Setup on Mac Machine
Installing Confluent Kafka - Mac
Creating Your First Kafka Streams Project – Mac
4
Understanding the Technology Stack
Understanding Kafka Support in Spring
Introduction to Spring Cloud Streams
Introduction to Kafka Streams
5
Producing Data to Kafka
Simple RESTful Kafka Producer
Creating Retail POS Simulator
Producing JSON Messages
Producing AVRO Messages
6
Processing Kafka Streams
Real-Time Stream Processing – Requirement
Processing JSON Message Stream
Real-Life Serialization Scenarios
Processing AVRO Message Stream
Understanding Record Serialization
KStream Methods
7
Working with KStream
Kafka Streams Exactly Once Implementation
Implementing Exactly Once
Let's Practice - a Complex Problem Statement
Working with XML Inputs
Handling Errors and Exceptions
Mixed Branching of a KStream
Handling Poisson Pills
8
KTable and Aggregations
Introducing KTable
Deep Dive into KTable
Computing Streaming Aggregates
Aggregation Concepts
Reducing a Kafka Stream
Aggregating a Kafka Stream
Aggregation Challenges
KTable Aggregation
9
Timestamp and Windowing Aggregates
Kafka Time Semantics
Windowing Aggregates
Tumbling Window Versus Hopping Time Window
Session Windows
10
Joins in Kafka Streams
Joins in Kafka Stream
KStream to KStream Joins
KTable to KTable Join
KStream to KTable Join
Implementing Complex Aggregation
11
Kafka Streams in Functional Style and Unit Testing
Stream Listener Manual Testing
Stream Listeners Automating Test Cases
Functional Style of Converting Stream Listeners
12
Keep Learning
Final Word
Chapter 1
Before You Begin
Section 1
About the Course
Kafka has become de facto for designing event stream applications powered by the Kafka platform and the Kafka APIs. Kafka Streams API is one of the most powerful components of the Kafka stack. It allows you to implement an event stream processor model. In this model, you will read a stream of events from an inbound Kafka topic, apply business processing, transform events, and write them to an outbound topic. All this can be easily done using Kafka Streams DSL. On the other side, Spring Boot transformed the way we develop production-grade Spring-based microservices. In the recent past, Spring Boot was put together with the Spring Integration to create a new project: Spring Cloud Streams. Spring Cloud extends Spring Boot’s capabilities to apply a microservice architecture pattern to create event-centric applications. Now you can use it to create stateless and stateful event stream processing microservices. That’s what this course is all about.
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