You shipped the migration. But can you see what's happening inside it?
A clean architecture means nothing if you're flying blind in production.
Here's what every Spring Boot system needs to expose
1. Latency = Revenue on the line Chase p95/p99, not means. One endpoint — /checkout — crept from 420ms to 1.2s post-release. Conversion fell 8% within a day. Slow code isn't a tech problem. It's a business problem.
2. RPS & Load Limits = Are you ready to grow? 300 RPS became 900. CPU maxed out. Autoscaling kicked in too late. Your system will hit its ceiling — the question is whether you discover it in a load test or a production incident.
3. Error Rate = The number that keeps your SLA honest A jump from 0.3% to 3% looks like a rounding error on a chart. It's 200 broken transactions in 60 minutes. Track it as a ratio. Alert on change, not absolute count.
4. JVM Signals = Early warning system Heap that won't flatten is a leak. GC pauses stretching from 50ms to 400ms are your app struggling to breathe. Today it's sluggish. Next week it's down. The signal is always there before the failure.
5. Queries & Connection Pools = Where time actually goes A single query ballooning from 20ms to 900ms will back up your entire connection pool and cascade into timeouts everywhere. Stop debugging the controller. Start watching the database.
6. Business Layer Metrics = The real health check System uptime was 99.9%. Payment failure rate climbed from 1% to 4%. Every dashboard said green. Revenue said otherwise. Technical metrics without business context are incomplete.
Stack that makes this real:
Actuator → Micrometer → Prometheus + Grafana → Elastic StackRefactoring the code is step one. Instrumenting it is what makes the migration actually worth it.
Traffic doubles tomorrow. Will you know before your users do?
Great breakdown. I especially like the emphasis on p95/p99 and business metrics—those are often ignored but matter the most. Technical health doesn’t always reflect business health, and that gap is where real problems hide.