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System Design Masterclass · Observability Monitoring
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"We have 99% uptime" sounds impressive until you do the math. 99% uptime means your service is down for 3.65 days per year. That's almost a full work week of outages. Your users would experience roughly 7 hours of downtime every month.
Now look at 99.99%. "four nines." That's 52 minutes of allowed downtime per year. Total. Across all incidents, all maintenance windows, all deployment hiccups. The distance between two nines and four nines isn't a small incremental improvement. It's a fundamentally different engineering discipline.
Every additional nine roughly costs 10x more in engineering effort. This is why understanding availability metrics matters: they set the target that determines your architecture, your on-call rotation, your deployment process, and ultimately your budget.
Availability is expressed as a percentage of time the service is operational:
| Availability | Annual Downtime | Monthly Downtime | Common Name |
|---|---|---|---|
| 99% | 3.65 days | 7.3 hours | Two nines |
| 99.9% | 8.77 hours | 43.8 minutes | Three nines |
| 99.95% | 4.38 hours | 21.9 minutes | Three and a half nines |
| 99.99% | 52.6 minutes | 4.38 minutes | Four nines |
| 99.999% | 5.26 minutes | 26.3 seconds | Five nines |
The formula is straightforward: Availability = (Total Time - Downtime) / Total Time * 100
But here's where it gets tricky. What counts as "down"? If 10% of your users can't reach your service due to a regional issue, are you down? If the homepage loads but checkout is broken, are you down? If response times are 10x slower than normal, are you down?
Most mature teams define availability in terms of successful requests: Availability = Successful Requests / Total Requests * 100. This captures partial failures, slow responses that timeout, and regional issues, not just complete outages.