Building Reliable Systems in an Unreliable World
Networks fail. Databases slow down. APIs break. Here’s a practical way to design systems that handle failure calmly and predictably.
Who this is for
This post is for engineers and early-stage founders building real production systems — systems that rely on networks, databases, and third-party APIs.
In other words: systems that will fail.
In production, failure isn’t an edge case.
It’s the default.
Networks timeout. Databases slow down. APIs return errors. And somehow, all of this happens during your busiest hour.
The real question isn’t if something will fail.
It’s how your system behaves when it does.
Reliable systems don’t avoid failure.
They expect it and handle it calmly.
The Reality of Production Systems
After working on real products, a few things become obvious very quickly:
- Network calls will timeout — sometimes without a clear reason
- Databases will have bad days — even managed ones
- Third-party APIs will rate-limit or break — usually at the worst time
- Retries can make things worse — if done carelessly
If your system assumes everything works perfectly, it’s already broken.
1. Fail Fast, Recover Faster
A slow dependency shouldn’t slow down your entire system.
Set clear timeouts and always define fallback behavior.
await withTimeout(fetchUser(userId), {
timeout: 3000,
fallback: () => getCachedUser(userId),
});
Failing fast keeps your system responsive. Fallbacks keep it useful.
The goal isn’t to hide failure — it’s to contain it.
2. Use Circuit Breakers
If a service is failing, stop calling it.
Continuing to hammer a broken dependency usually turns a small issue into a system-wide outage.
A simple circuit breaker:
- tracks failure rates
- temporarily blocks calls when failures spike
- periodically checks if the service has recovered
This gives downstream systems time to breathe and protects your own infrastructure.
3. Prefer Queues Over Synchronous Calls
If something doesn’t need to happen immediately, don’t block on it.
Queues give you:
- retries
- natural rate limiting
- protection during downstream outages
They turn outages into delays instead of disasters.
If losing a synchronous call breaks your system, it probably shouldn’t have been synchronous in the first place.
4. Make Everything Idempotent
Retries are unavoidable.
Networks fail. Clients retry. Messages get delivered twice.
Your APIs should be safe to call more than once.
That means:
- the same request produces the same result
- side effects happen only once
- system state stays consistent
If retries scare you, your system isn’t ready for production.
5. Observability Is Not Optional
You can’t fix what you can’t see.
At a minimum, every system should have:
- Structured logs with request or correlation IDs
- Basic metrics like error rate and latency
- Clear alerts when things break
If users tell you something is broken before your monitoring does, you’re already late.
The Cost of Reliability
Reliable systems take more effort upfront.
You have to:
- handle failure paths explicitly
- think about edge cases
- add monitoring and alerts
But the trade-off is worth it.
The cost of a single production outage usually exceeds the cost of building reliability into the system from the start.
Start Small
You don’t need to implement everything on day one.
Start with:
- Timeouts on all external calls
- Retries with exponential backoff
- Basic health checks
- Structured logging
Add complexity only when real pain demands it.
Related Reading
If you're building production systems, you might also find these useful:
- Why I Choose Boring Technology — my approach to picking stable, predictable tools
- How I Build MVPs That Don't Need a Rewrite — building for flexibility from day one
- My Services — if you need help building reliable systems
Final Thought
Reliability isn’t something you bolt on later.
It’s a mindset you build with from the start.
The best systems aren’t clever. They’re predictable, boring, and calm under pressure.
And that’s exactly what you want when things go wrong.