system-design

Part 8 — Observability & Operations

You can’t debug, secure, or improve what you can’t see. And you can’t run a reliable service without the practices for when — not if — it breaks.

Time for this part: Week 10 on the Standard track. Observability comes up in most senior design interviews as “how would you operate/debug this?” — and it’s the difference between a design that works on a whiteboard and one that survives production.

The unifying idea: operability is a design property, not an afterthought. A system you can’t observe, alert on, or recover from is not done, however elegant the architecture.

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Chapters

Seeing inside the system

#	Chapter	Time	The problem it solves
1	The Three Pillars: Logs, Metrics, Traces	20 min	Each answers a different question; you need all three
4	Distributed Tracing	18 min	Following one request across fifty services

Knowing when something’s wrong

#	Chapter	Time	The problem it solves
2	Monitoring & Alerting That Doesn’t Suck	18 min	Alerts that mean “act now,” not noise
3	SLIs, SLOs, SLAs, and Error Budgets	18 min	Turning “reliable” into a number, and a decision framework

When it breaks

#	Chapter	Time	The problem it solves
5	Incident Response & Blameless Postmortems	16 min	Recovering fast and learning, not preventing every incident
6	Chaos Engineering	16 min	Validating resilience before it’s tested for real

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The things to remember

1. Metrics tell you something’s wrong, traces tell you where, logs tell you why. The trace ID ties all three together — that’s the debugging workflow.
2. Alert on symptoms (user impact), not causes (CPU). You can’t enumerate every cause, but you can measure the failure. Causes go on dashboards.
3. SLI = measurement, SLO = target, SLA = promise. The SLA is looser than the SLO. The error budget turns velocity-vs-stability from a political fight into a data-driven decision.
4. Mitigate before you diagnose. Restore service first (roll back, fail over); understand the root cause later. Recovering fast beats failing never.
5. Postmortems are blameless — a good person made a mistake, so the system allowed it. Fix the system, not the person. “Human error” is never a root cause.
6. Untested failure handling is broken failure handling. Chaos engineering validates resilience by breaking things deliberately, when everyone’s watching.

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Before moving on

You should be able to answer these without notes:

• Explain what question each pillar answers and how the trace ID connects them.
• Explain why you alert on symptoms not causes, and what alert fatigue is.
• Distinguish SLI/SLO/SLA and explain error budgets.
• Explain why the SLA should be looser than the SLO, and why not to target 100%.
• Explain trace context propagation and why one un-instrumented service breaks traces.
• Explain tail-based vs head-based sampling and why tail-based keeps the interesting traces.
• Explain “mitigate before diagnose” and the Incident Commander role.
• Explain what “blameless” means and why blame prevents learning.
• Explain what chaos engineering validates and what you need before doing it.

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Next: Part 9 — Deployment & Infrastructure — how the systems you’ve designed actually get built, shipped, and run.

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