Design systems you can debug, monitor, and run without heroics
A system you can’t observe is a system you can’t operate. Observability turns unknown failures into explainable failures.
All three matter. Logs tell you what, metrics tell you how often, traces tell you where.
Ask: “If this breaks at 3am, can we diagnose it quickly?”
If the answer is no, invest in:
Service Level Objectives define acceptable failure. Your architecture should aim to meet the SLO with margin.
Example SLO:
This drives redundancy, retries, and fallback decisions.
Q: Why are traces critical in distributed systems?
<details> <summary>💡 Reveal Answer</summary>Because latency and failures often occur across multiple services. Traces show the full request path and identify where time is spent or errors occur, which logs or metrics alone can’t reveal.
</details>Create an observability checklist for one endpoint:
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Scenario: On-call is blind: no dashboards, only raw logs. A production outage starts.
What’s the first architectural improvement you make after recovery?
Define one critical user journey (e.g., checkout) and build a minimal dashboard with success rate, latency, and error rate, plus traces. This creates an operational baseline and prevents future “blind” outages.
| Idea | Remember This |
|---|---|
| Observability | Logs + metrics + traces |
| Pager test | Design for 3am debugging |
| SLOs | Reliability targets that drive architecture |
| Operability | Runbooks and dashboards reduce MTTR |
Next: Performance & Latency Decision Patterns