When to Go Async (And When to Resist the Urge)
Async patterns solve real problems -- bursty traffic, slow dependencies, decoupled teams. But the complexity tax is real. Lessons from building event-driven systems at Decloud.
Kafka
Definition
Kafka coverage in this archive spans 3 posts from Apr 2017 to Jul 2022 and centers on data correctness and operability under real production constraints. The strongest adjacent threads are architecture, rabbitmq, and async. Recurring title motifs include go, async, resist, and urge.
Key claims
- The common theme is that schema, ownership, and query shape drive most downstream outcomes.
- The consistent theme from 2017 to 2022 is disciplined execution over hype cycles.
- This topic repeatedly intersects with architecture, rabbitmq, and async, so design choices here rarely stand alone.
Practical checklist
- Define freshness, correctness, and latency targets before choosing storage or pipeline patterns.
- Start with the newest post to calibrate current constraints, then backtrack to older entries for first principles.
- When boundary questions appear, cross-read architecture and rabbitmq before committing implementation details.
Failure modes
- Scaling pipelines before locking down source-of-truth and reconciliation behavior.
- Optimizing single queries while ignoring data model drift and access patterns.
- Applying guidance from 2017 to 2022 without revisiting assumptions as context changed.
Suggested reading path
- Start here (current state): When to Go Async (And When to Resist the Urge)
- Then read (operating middle): Message Queues: The Patterns Nobody Tells You About Until 3 AM
- Finish with (foundational context): Why We Went Event-Driven (and What Nearly Broke)
Related posts
- When to Go Async (And When to Resist the Urge)
- Message Queues: The Patterns Nobody Tells You About Until 3 AM
- Why We Went Event-Driven (and What Nearly Broke)
References
Message Queues: The Patterns Nobody Tells You About Until 3 AM
Queues look simple on a whiteboard. Then you deploy them. Here are the messaging patterns I've learned the hard way across three startups, with Go code and real failure stories.
Why We Went Event-Driven (and What Nearly Broke)
Lessons from building event-driven systems at the fintech startup and Dropbyke -- what worked, what broke, and why I'd do it again.