Reliability & Observability
Most vibe coders consider a feature done when it's deployed. Real engineers know that deployment is when the engineering starts. Does it work under load? Is it logging the right information to debug when it fails? Are there alerts that will wake someone up when it degrades? What's the SLO, and is the system currently meeting it? These questions are the domain of reliability engineering, and they're what separate engineers who can ship from engineers who can ship and maintain. This module covers the full observability stack: structured logging that's queryable and searchable, metrics that tell you what your system is doing, distributed traces that follow a request across microservices, and dashboards that surface problems before users notice them. You'll understand the RED method (Rate, Errors, Duration) and how to build dashboards that answer the three questions every on-call engineer needs: is it broken, how broken is it, and where is the problem? You'll also go deeper into chaos engineering — the practice of deliberately injecting failures to verify that your resilience mechanisms work before an unplanned outage tests them for you. Netflix's Chaos Monkey became famous for a reason: systems that are regularly chaos-tested fail more gracefully when real failures happen. This mindset — proactively finding and fixing reliability gaps — is the mark of an engineer who takes production ownership seriously.
What You'll Learn
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1
SLIs, SLOs, and SLAs — Error budgets, reliability vocabulary, engineering decisions
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2
Structured Logging — JSON logs, what to log, correlation IDs, what not to log
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3
Metrics — Counters, gauges, histograms, Prometheus, Grafana, RED method
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4
Distributed Tracing — OpenTelemetry, trace context propagation, sampling strategies
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5
Alerting Strategies — Symptoms vs causes, runbooks, on-call rotation, alert fatigue
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6
Chaos Engineering — Fault injection, GameDay exercises, chaos tools
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7
Capacity Planning — Forecasting, autoscaling, growth curves, over vs under-provisioning
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8
Failure Modes and Incident Management — Cascading failures, postmortems, on-call
Capstone Project: Instrument an Application with Full Observability — Logs, Metrics, Traces, Dashboards, and Alerts
Take a multi-service application and instrument it with complete observability — structured JSON logs with correlation IDs, Prometheus metrics exposing the RED signals for each service, OpenTelemetry distributed traces that span all services, a Grafana dashboard showing service health, and alerting rules for SLO breaches. Then inject failures using chaos engineering to verify that the observability stack catches each failure and the alerts fire correctly before users are impacted.
Why This Matters for Your Career
Production incidents are inevitable. The question isn't whether your system will fail — it's how quickly you can detect the failure, understand its scope, diagnose the root cause, and restore service. Engineers who have built observability into their systems can do this in minutes; engineers who haven't are flying blind and restoring service by restarting things and hoping. Error budgets — the SRE concept that defines how much unreliability is acceptable — transform reliability from a vague aspiration into an engineering constraint. When you have a 99.9% SLO, you have 43 minutes of downtime per month in your error budget. Every deployment decision, every feature flag rollout, every infrastructure change is made in the context of that budget. This is the framework that enables teams to move fast without ignoring reliability. On-call engineering is a reality for most production systems, and the quality of the observability stack determines whether on-call is manageable or miserable. Alert fatigue — being paged for non-actionable issues until you start ignoring alerts — is a real and dangerous failure mode. Engineers who design alerting from the principle of 'every alert should be actionable' build on-call rotations that are sustainable and effective.