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System Design Masterclass · Reliability Resilience
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Netflix serves 230+ million subscribers across 190 countries. Their infrastructure spans thousands of , millions of containers, and multiple cloud regions. Individual components fail constantly. Servers crash, networks partition, deployments go wrong. Yet users rarely notice. That is reliability.
Reliability is not the absence of failure. It is correct operation despite failures. A reliable system continues to serve correct results at acceptable performance even when hardware fails, software has bugs, operators make mistakes, or traffic spikes beyond expected levels.
Martin Kleppmann's "Designing Data-Intensive Applications" defines reliability as the system continuing to work correctly even when things go wrong. The things that go wrong are called faults, and systems that tolerate faults are called fault-tolerant.
| Metric | Definition | Target Example |
|---|---|---|
| Availability | Uptime / (Uptime + Downtime) | 99.99% = 52 min downtime/year |
| Durability | Probability data is not lost | 99.999999999% (11 nines for S3) |
| MTBF | Mean Time Between Failures | Hours to months |
| MTTR | Mean Time To Recovery | Minutes to hours |
| Error rate | Failed requests / Total requests | < 0.1% |
The nines table:
| Availability | Annual Downtime |
|---|---|
| 99% (two nines) | 3.65 days |
| 99.9% (three nines) | 8.77 hours |
| 99.99% (four nines) | 52.6 minutes |
| 99.999% (five nines) | 5.26 minutes |
Each additional nine is exponentially harder to achieve. Going from 99.9% to 99.99% often requires redundant infrastructure, automated failover, and eliminating single points of failure.