Cloud Event Driven Architectures with Spring Cloud Stream 2.0

Event-driven have never left the main stage, but with the advent of cloud-native, distributed architectures (such as microservices) it has become more popular than ever. But it is a broad category that covers a variety of use cases, ranging from enterprise integration to data streaming. So, how to make sense of them all, and especially, what are the best tool for the task? In this presentation, we will provide an overview of the various types of event-driven architectures, and how the different message-oriented components of the Spring portfolio (Spring Kafka, Spring AMQP, Spring Integration, Spring Cloud Stream) fit into the picture.

Transcript

1. Spring Cloud Stream Spring Cloud Task Spring Cloud Skipper Spring Flo Spring Cloud Data Flow Sabby Anandan | @sabbyanandan
2. Event-centric Thinking Spring and Events Events in Action! 2
3. Profound Customer Engagement Event-centric Thinking 3
4. Real-time Matching Efficient Pickups/Drops Lower Prices Convenient Costs Less to Ride Still Reach on Time Customer: Rider(s) Improved Ride Opportunities More Income More Tips Effective Use of Time Customer: Driver 4
5. Events A State Change: Something already happened —- it’s past Decouple senders and receivers in terms of identity System’s interaction with outside world Provide measurability and intent 5
6. Connectivity to external world Cross-platform/service event visibility Imperative vs. Functional programming models Operating on events (content negotiation; transformation; etc.) In Developer View 6
7. Spring Boot App Low level abstraction on top of Apache Kafka. Spring Kafka CLI Input KafkaListener Topic = s1p Apache Kafka payload=“hello world” 7
8. Spring Boot App Enterprise Integration Patterns with Spring. Spring Integration Kafka CLI Input KafkaListener Apache Kafka Integration Flow Topic = s1pIntInput Topic = s1pIntOutput payload=“hello” payload=“HELLO -— HELLO” 8
9. Spring Boot App Spring Boot App A event-driven microservices framework. Spring Cloud Stream Kafka Binder CLI Input Sink Apache Kafka / RabbitMQ / Amazon Kinesis / Google PubSub / Azure Event Hubs … Processor Topic = s1p.dest.in Topic = s1p.dest.out payload=“hello world” payload=“HELLO WORLD” 9
10. Spring Kafka Spring Integration Kafka Spring Cloud Stream Kafka Binder Pluggable Binder Abstraction Partitioning Consumer Groups Message Headers Programming Model: - Imperative (eg: SI Channels) - Functional (eg: Kafka Streams, Reactor Flux) Content-type Negotiation Unit/IT Testing Framework Spring Cloud Stream 10
11. Events in Action! Domain Driven Design; CQRS; Event Sourcing 11
12. Vaughn Vernon, Domain-Driven Design Distilled 12
13. Bounded Context 13
14. Event Sourcing John Doe: User :Address region: US-CA 14
15. John Doe: User :Address region: US-NY change my address 15
16. John Doe: User :Address region: US-NY change my address :Event address changed :Event address changed 16
17. Application State Append-only Log 17
18. A Social Graph 18 :UserCreated John Doe :UserCreated Jane Cool :UserNameChanged Michelle Bryan :UserActivated Yulia Zack
19. Customer Engagement 19 Friends Nearby Basketball Event Nearby New Movie!
20. Analytics on Real-time Events Top-5 States with “New Users” in a 30 seconds Tumbling Window CA HI IL PA NY 20
21. CA HI IL PA NY 21 Spring Cloud Data Flow
22. Resources SK, SIK, SCStK -> https://github.com/sabbyanandan/springonetour2018 EDA -> https://github.com/sabbyanandan/eda State or Events? Which shall I keep? (Kenny B.; Jakub P.) Deconstructing Monoliths with DDD (Rohit K.;David T.; Rohit S.) Experimentation Using Event-based Systems (Martin F.; Toby C.) 22