Observability Studio is a practical place to learn observability — with a clear focus on
root-cause analysis for performance problems in modern systems.
What this site is for
Observability is often described with buzzwords — but in practice it’s simple:
it helps you answer why a system behaves the way it does, especially under load.
If you’ve ever stared at a p99 chart thinking “okay… but what exactly is causing this?”, you’re in the right place.
What you’ll learn here
- The fundamentals: APM vs observability, traces vs metrics vs logs, tail latency (p95/p99), and the minimum telemetry needed for RCA.
- Root-cause analysis: a repeatable workflow: scope → trace-first → metrics proof → classify → actions.
- Common performance patterns: pool exhaustion, CPU throttling, downstream bottlenecks, retry storms, GC-related spikes — and how to prove each one.
- Hands-on labs: practical exercises you can reproduce (often with OpenTelemetry-based demos) to build real debugging intuition.
The method (short version)
Most content on this site follows one simple idea:
don’t argue opinions — build evidence.
- Scope the problem (journey/endpoint, baseline vs regression, load profile, time window).
- Trace-first analysis (critical path, fan-out, waiting vs working, dominant spans).
- Prove or falsify hypotheses with metrics (correlation + breakdown by endpoint/version/pod/region).
- Classify the issue (downstream, saturation, pool/queue, contention, GC/memory churn, retry storms).
- Write it down as a short RCA summary (evidence + confidence + next actions).
Start learning
New to observability? Don’t start with tools. Start with questions.
The “Start Here” page gives you a simple path from fundamentals to practical RCA.