Is Your Scaling Strategy Designed—or Just Assumed?¶
Type: DeepDive
Category: Performance
Audience: Engineers preparing for growth, traffic spikes, or multi-tenant architectures
🔍 What This Perspective Asks¶
- What happens when load triples overnight?
- Which parts of the system become bottlenecks?
- Do you scale up? Scale out? Degrade gracefully?
Most systems are “scalable”
—until they actually scale.
⚠️ What Breaks¶
- One database serves multiple high-traffic features
- Horizontal scaling assumed, but stateful logic blocks it
- Feature flags load config on every request
- Per-tenant bottlenecks invisible in global metrics
- Load testing ignores cold-start conditions
✅ Healthier Scaling Strategy¶
- Explicitly model resource ownership: CPU, DB, IOPS, mem
- Plan per-feature and per-tenant scaling paths
- Separate config loads from hot-paths
- Test for hot-boot, cold-start, and partial-dependency performance
- Define when degradation is acceptable—and what gets dropped first
🧠 Design Philosophy¶
Scalability isn’t about infrastructure.
It’s about knowing which limits come first—and who they’ll hurt.
❓ FAQ¶
-
Q: But we’re on Kubernetes. Doesn’t it scale?
A: Pods scale. Architectural limits don’t move. -
Q: Should we optimize now or later?
A: Design the escape hatch now. Use it later.