Microservices promise scalability and team autonomy. But they also introduce distributed systems complexity. Here's how we build microservices that don't wake anyone up at 3 AM.
The Circuit Breaker Pattern
When a downstream service fails, don't keep hammering it. Implement circuit breakers that:
- Detect failure — Track error rates over a sliding window
- Open the circuit — Stop sending requests after threshold is reached
- Allow recovery — Periodically test if the service has recovered
Bulkhead Isolation
Isolate critical paths from non-critical ones. If your recommendation engine fails, product listings should still work. We achieve this with:
- Separate thread pools per integration
- Independent database connections per service
- Queue-based decoupling for async workflows
Graceful Degradation
Every feature should have a fallback:
- Cache-first reads — Serve stale data rather than no data
- Default responses — Show generic recommendations if personalization fails
- Feature flags — Disable non-essential features under load
Observability
You can't fix what you can't see. Our observability stack includes:
- Distributed tracing (OpenTelemetry) — Follow requests across services
- Structured logging — Every log line is queryable JSON
- Custom dashboards — Business metrics alongside system metrics
- Alerting — Based on SLOs, not raw thresholds
Testing for Resilience
We practice chaos engineering in staging:
- Random service shutdowns
- Network latency injection
- Database connection pool exhaustion
- Disk space and memory pressure tests
The goal isn't to prevent failures — it's to ensure they don't cascade.