We use analytics cookies.We use cookies to analyze site traffic and improve your experience.
Loading...
System Design Masterclass · Observability Monitoring
Free preview: the first 2 parts of this 8-part lesson.
Already bought the course? Sign in to read the whole lesson.
this lesson is part of
System Design Masterclass
Unlock this lesson and the 751 other paid lessons
$25 once, by PayPal
See the System Design courseread the rest of this lesson in
System Design Masterclass
Unlock this lesson and the 751 other paid lessons
$25 once, by PayPal
See the System Design coursein Observability Monitoring
This lesson is part of the full System Design Masterclass. Unlock it along with 769 other lessons for a one-time payment.
One-time payment by card or PayPal
Get Lifetime AccessOne-time payment: UPI, card or netbanking
Get Lifetime Access18 Foundation lessons are free, no payment needed. Try them now
It is easy and good
“It is really nice and good Please join the course”
Two teams run services with identical MTTR, both recover from incidents in 30 minutes. But Team A's service fails once a quarter. Team B's fails twice a week.
Team A has a total annual downtime of 2 hours. Team B has 52 hours. Same recovery speed, wildly different reliability. The difference is MTBF, the average time between failures.
MTBF tells you how often your system breaks. Combined with MTTR, it gives you total expected downtime: Availability = MTBF / (MTBF + MTTR). A system with an MTBF of 720 hours (30 days) and an MTTR of 0.5 hours has an availability of 720 / 720.5 = 99.93%.
High MTBF means the system rarely fails. Increasing MTBF is harder than reducing MTTR because it requires preventing failures rather than just recovering from them faster. Prevention requires better testing, safer deployments, more strong architecture, and eliminating root causes, all of which are long-term investments.
| Factor | Higher MTBF (fewer failures) | Lower MTBF (more failures) |
|---|---|---|
| Deployment safety | Canary deploys, feature flags, automated rollback | Direct deploys to production, no gates |
| Testing | thorough , integration tests | Manual testing, no automated regression |
| Architecture | Redundancy, graceful degradation, circuit breakers | Single points of failure, tight coupling |
| Dependencies | Managed services, fallback mechanisms | Self-hosted, no redundancy |
| Change management | Small, frequent, reversible changes | Large, infrequent, risky releases |
Improving MTBF means answering: "Why did this fail, and how do we make sure this specific failure never happens again?"
That's what postmortems are for. Every incident should produce action items that either eliminate the root cause or add safeguards. If you have the same failure twice, your postmortem process isn't working.
Some teams track a related metric. Change Failure Rate (CFR), which measures what percentage of deployments cause incidents. DORA research considers a CFR below 15% to be "elite." If 30% of your deployments cause problems, your MTBF will be terrible regardless of everything else.