Events

Sources

https://microservices.io/patterns/data/saga.html coursehunter https://www.youtube.com/watch?v=SbL3a9YOW7s https://eventuate.io/docs/manual/eventuate-tram/latest/about-eventuate-tram.html https://www.youtube.com/watch?v=gA2-eqDVSng https://aws.amazon.com/blogs/architecture/lets-architect-designing-event-driven-architectures/ https://aws.amazon.com/messaging/ https://aws.amazon.com/blogs/compute/choosing-between-messaging-services-for-serverless-applications/ https://aws.amazon.com/event-driven-architecture/ https://www.youtube.com/watch?v=28B4L1fnnGM https://www.youtube.com/@CodeOpinion/

https://www.youtube.com/watch?v=A_mstzRGfIE https://www.developertoarchitect.com/lessons/lesson165.html

https://medium.com/wix-engineering/6-event-driven-architecture-patterns-part-1-93758b253f47

event tips: https://www.youtube.com/watch?v=9r9WDzzTcr0 https://serverlessland.com/event-driven-architecture/visuals/good-and-hard-parts-of-event-architectures https://www.aklivity.io/post/the-continued-rise-of-event-driven-architectures https://www.infoq.com/presentations/event-driven-arch-challenges/ https://www.equalexperts.com/blog/tech-focus/event-driven-architecture-the-good-the-bad-and-the-ugly/ https://serverlessland.com/event-driven-architecture/visuals/common-issued-with-eda

common https://www.developertoarchitect.com/resources.html

topics: tools: Kafka, RabbitMQ, SQS, SNS, EventBridge models: pub/sub, … managing distributed models frameworks: https://eventuate.io/

patterns

Common

Pros: refactor v isolation delivery semantics ordering replaying or repeated consuming (by persistence mechanishm) content-based filtering retention scalability? In Kafka there is distributing partitions over brokers (horizontal scalability).

Events used to:

• Event Sourcing
• Event Carried State Transfer
• Domain Events
• Integration Events
• Workflow Events

event schemas idempotency

Architectural Charateristics:

• MQ should be configurable to high throughput or low-latency

Terms

• Integration (definition?) (protocol, format, data schema & evolution)
• Data Stream - continuous flow of data. Some examples of data streams include sensor data, activity logs from web browsers, and financial transaction logs. General Characteristics of Data Streams: time sensitive, continuous, heterogeneous, imperfect.

Messaging models

The most popular messaging models are point-to-point and publish-subscribe.

Point-to-point#

Point-to-point model is common for traditional message queues. In this model, a message sent to a queue can be consumed by one and only one consumer.

Pub/Sub pattern#

Before discussing the specifics of Apache Kafka, it is important for us to understand the concept of publish/subscribe messaging and why it is a critical component of data-driven applications. Publish/subscribe (pub/sub) messaging is a pattern that is characterized by the sender (publisher) of a piece of data (message) not specifically directing it to a receiver. Instead, the publisher classifies the message somehow, and that receiver (subscriber) subscribes to receive certain classes of messages. Pub/sub systems often have a broker, a central point where messages are published, to facilitate this pattern.

ISR (In-Sync Replicas)

Delivery Semantics

implementing delivery semantics in Kafka

Retry composition

Parameters and Limits

Event-Sourcing

ES is about state, about persisting state. It’s an iternal implementation detail.