This document summarizes Jan Svoboda's presentation on building event streaming microservices with Spring Boot and Apache Kafka. Jan discusses his motivation for learning Kafka and microservices development. He outlines a typical journey from strangling monoliths to building event-driven architectures using Kafka. Jan explains how Spring and Kafka integrate well together. The remainder of the presentation demonstrates building a sample application using these techniques and dives into common architecture patterns like event sourcing, CQRS, scaling stateful services, and reverse proxy routing.

1. Building Event Streaming
Microservices with Spring Boot
and Apache Kafka
Jan Svoboda, Solution Engineer, May 2021

2. Jan Svoboda
https://www.linkedin.com/in/svobodajan/
• Confluent, Solutions Engineer 2020 - now
• Event Streaming, Event-Driven Microservices
• Apache Kafka, Kafka Streams
• Pivotal, Platform Architect, 2016 - 2020
• PaaS, Microservices
• Cloud Foundry, Spring, Rabbit MQ
• IBM, Solutions Architect, 2011 - 2016
• DevOps, Agile
• Bluemix, Rational

3. Agenda
3
Motivation
My journey to learning Kafka
Spring Microservices Architecture
Why Spring + Kafka = ♥
Events Streaming Microservices Concepts
Look Ma No Hands!
Event Sourcing and Data Liberation
Removing Database Dependency
Microservices Decoupling
Integration Pattern
State Caching
Storing State of a Microservice
Demo
See it to believe it...

4. My Motivation for Kafka and
Microservices
Why should a microservices developer care about learning Kafka?

5. Building Microservices aka Cloud Native Applications
Modernizing Legacy Applications with New Technology Stack
• 3-Layer Model-View-Controller Pattern has been very popular last 15 years
• Organizations look for migration shortcuts (no budget=no fun)
• New Toy Syndrome (just buy new technology and we are done…)
5
App
PaaS
D
B
UI
UI
App
Service Reg.
LB
My session from Cloud Foundry Summit https://www.youtube.com/watch?v=9moTgBiEauE

6. What is your Microservices Journey?
6
James Watters (Pivotal SVP), Kafka Summit 2019 Keynote, https://youtu.be/9I3CDfHKfNY?t=582
● Stage 1 -> Strangling the Monolith
○ DB based integration
● Stage 2-> Provide Independent
Data Sources
○ REST based integration
● Stage 3 -> Build Event Streaming
○ Kafka based integration

7. Spring ♥Kafka
Like Peanut Butter and Jelly...

8. Spring? Spring Kafka? Spring Cloud Services?
8
● Spring Kafka -> provides a
core Kafka libraries
● Spring Cloud Stream +
Kafka Binders -> provides
an abstraction for building
Kafka pipelines
● Spring Integration ->
provides Kafka as an
integration adapter (EIP)
https://spring.io/projects/spring-cloud-stream
https://spring.io/blog/2015/11/29/how-not-to-
hate-spring-in-2016

9. (Re)Building Spring based
Microservices with Kafka
Look Ma, No Hands!

10. Demo Application - Architecture and Dataflow
• Spring Boot (2 jars)
• Apache Kafka (2 topics)
• Spring Cloud Streams + Kafka
Streams Binder (RocksDB as a
state store)
Jan:Google,http://google.com
Jan:Google,http://google.com
JanGoogle: Google, http://google.com
JanGoogle,http://google.com
https://github.com/griga23/bookmarksDemo

11. Event Streaming Microservices
Architecture Patterns
Getting hands dirty...

12. 1. Event Sourcing - Removing Database
Dependency
My Data Liberation
● From Data sourcing to Event sourcing
● From synchronous Request/Reply to asynchronous Publish/Subscribe
● Store the latest key/values in Kafka Streams State Store
● Use Kafka Streams Interactive Queries to query the State Store
● Make data free for everyone to read
https://docs.confluent.io/platform/current/streams/developer-guide/interactive-queries.html
=>

13. 2. CQRS - Microservices Decoupling
Command Query Responsibility
Segregation
● Pattern separates read and
update operations for a data
store.
● Implementing CQRS can
maximize performance,
scalability, and security.
● Allows a system to better
evolve over time and prevents
update commands from causing
merge conflicts at the domain
level.
https://docs.microsoft.com/en-
us/azure/architecture/patterns/cqrs

14. 3. Scaling – Stateful Microservices
• Multiple State Stores with
different partitions
• HTTP REST calls to provide State
Stores inter communication
Jan:Google,http://google.com
Jan:Yahoo,http://yahoo.com
JanGoogle:Google,http://google.com
JanYahoo:Yahoo,http://yahoo.com
JanGoogle:Google,http://google.co
m
JanYahoo:Yahoo,http://yahooo.co
m
https://docs.confluent.io/platform/current/streams/developer-guide/interactive-queries.html#querying-remote-state-stores-for-the-entire-app

15. 4. Reverse Proxy – Routing to the Right State Store
https://medium.com/bakdata/queryable-kafka-topics-with-kafka-streams-8d2cca9de33f
• Spring Cloud Gateway for
incoming HTTP traffic to redirect
to proper State Store
• State Store Host Awareness
Jan:Google,http://google.com
Jan:Google
JanYahoo:Yahoo,http://yahoo.com

16. Demo
https://github.com/griga23/bookmarksDemo

17. #CONFLUENTSKO2021
Thank you!
jsvoboda@confliuent.io