Advanced System design concept · Distributed Systems & Data · 27 mins read
Service Communication Patterns
Pick how services hand work to each other without turning every request into a brittle chain.
Event Streaming Basics
Treat events as a durable log that many consumers can read at their own pace.
Intuition
Point-to-point queues are awkward when many teams need the same business history. Event streaming keeps a durable ordered log that many consumers can read independently. Treat events as a durable log that many consumers can read at their own pace.
Mental Model
Kafka-style streams fit order pipelines, click tracking, audit trails, and analytics because billing, search, monitoring, and fulfillment can each replay the same events.
Examples
- Kafka: Kafka-style streams fit order pipelines, click tracking, audit trails, and analytics because billing, search, monitoring, and fulfillment can each replay the same events.
When to Avoid
Streams improve reuse and replay, but add schema governance, consumer lag, and heavier ops than a simple work queue. Use them when shared event history matters.
Practice service communication patterns in PRISM
Concepts stick when you watch them fail. Build an architecture that depends on service communication patterns, push traffic through it in the PRISM simulator, and see the latency and error rates change as you adjust the design.