Event-Driven Architecture with Kafka

Event-driven architecture decouples services by communicating through events rather than direct calls.

Core Concepts

Topic

A named channel where events are published.

Producer

Publishes events to a topic.

Consumer

Subscribes to topics and processes events.

Partition

Topics are split into partitions for parallelism.

Topic: orders
  Partition 0: [event1, event2, event3]
  Partition 1: [event4, event5, event6]
  Partition 2: [event7, event8, event9]

Why Kafka?

• 1M+ messages/second throughput
• Durable (messages stored on disk)
• Replayable (consumers can re-read)
• Ordered within partition

Common Patterns

Event Sourcing

Store all state changes as events:

OrderCreated → OrderPaid → OrderShipped

CQRS

Separate write (command) and read (query) models.

Saga Pattern

Coordinate distributed transactions via events:

Order Service → Payment Service → Inventory Service
     ↑ Compensate on failure

Consumer Groups

Multiple consumers in a group each read from a subset of partitions — enables parallel processing.

Retention

Kafka retains messages for a configurable duration (default 7 days), enabling replay and debugging.

Conclusion

Kafka is the backbone of modern event-driven systems — enabling async communication, fault tolerance, and high throughput.