Reactive Patterns

Building systems that respond to events and data streams.

Context

You are designing reactive systems. Understand the reactive manifesto and stream processing.

Domain Context

• Streams: Asynchronous sequences of events/values
• Observables: Producers of streams; observers consume them
• Backpressure: Consumer can't keep up; producer must slow down
• Reactive Manifesto: Responsive, resilient, elastic, message-driven

Instructions

1. Model Data as Streams: Think of updates as event sequences
2. Use Operators: Map, filter, reduce; transform streams
3. Handle Backpressure: If consumer is slow, producer must slow down
4. Error Handling: Streams can fail; plan error propagation
5. Lifecycle: When does stream start/stop? Clean up resources
6. Test: Use test schedulers; control time in tests

Anti-Patterns

• Nested observables without flattening (FlatMap confusion)
• Ignoring backpressure; leads to memory exhaustion
• Silent failures in streams; explicitly handle errors
• Hot observables when you need cold; hot ones replay, cold ones don't
• Over-complicated operator chains; keep it readable

Further Reading

• Reactive Manifesto (Ellis, Kuhn, Meyarivan, Vinoski)
• RxJS documentation (Reactive Extensions)
• Erik Meijer, "The Reactive Manifesto" (video)