This document discusses event-driven architectures using Spring technologies like Spring Cloud Stream and Spring Cloud Data Flow. It covers topics like event-driven microservices, processing events with Kafka and integrating systems using events. The document also discusses domain-driven design concepts like bounded contexts and event sourcing to store application state as a log of events. Real-time analytics on events is demonstrated to show insights from streaming data. Overall it promotes an event-centric approach using Spring tools for building reactive and responsive applications.

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 AppSpring 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”
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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
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15. John Doe: User
:Address
region: US-NY
changemy
address
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16. John Doe: User
:Address
region: US-NY
changemy
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
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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.)
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