Understanding Observability

I've been on-call for long enough to know that the worst feeling in engineering isn't the alert itself, but when everything looks fine and something clearly isn't.

The dashboards are green, the metrics look healthy, and yet your users are complaining that things are slow, broken, or worse - systems are silently failing.

You start tailing logs, flipping through dashboards, and convincing yourself it’s just a fluke. But deep down, you know something’s wrong. You just can’t see it.

And then, there’s the opposite kind of nightmare.

The dashboards are red, the alerts won’t stop, and every metric looks like it’s screaming for attention. CPU is spiking, latency’s through the roof, error rates are flashing, but somehow, you still don’t know why. The noise is deafening but not informative. Every graph is shouting symptoms, and none are pointing to a cause.

That is what poor observability feels like. And once you have lived through it, you will never want to go back.

🌍 What Observability Really Means

Observability is often confused with monitoring, but the two are not the same thing. Monitoring tells you something broke. Observability helps you understand why!

To me, observability is the ability to look at the data your system emits (logs, metrics, traces) and answer questions without writing new code. It's what turns chaos into clarity.

It's being able to say, "Oh, that latency spike is only for users in Mumbai hitting API version 2 after yesterday's deploy," as opposed to simply "latency is high".

The three pillars-metrics, logs, and traces-are well known, but the real power shows only when they all work together.

Metrics let you know that something is wrong. Logs provide the context on what happened. Traces connect the dots across services to show why. Debugging stops feeling like detective work and starts feeling like diagnosis when all three are stitched together.

⚙️ Building Observability That Actually Helps

When I was younger, I thought it was enough to add a few metrics and error logs. It wasn't. We had data everywhere: CloudWatch, custom dashboards, random log files on EC2 instances. Nothing was connected to anything else. When an incident hit, I'd spend more time finding the right dashboard than actually fixing the problem.

Real observability starts with consistency. Every service, no matter how small, should speak the same telemetry language. That means using tools like OpenTelemetry for standardized instrumentation, tagging everything with environment, version and request identifiers, and collecting it all in one place.

Centralization is key. You can't debug a distributed system with distributed data. No matter if you are using Grafana, Datadog, or something developed in-house, being able to jump from a high-level metric into a single trace and then into an exact log line is priceless. This is the kind of correlation we should aim for.

💔 The Pain of Bad Observability

Bad observability isn't just an inconvenience, it's expensive in every sense!

It costs time, money, and energy. It leads to alert fatigue, longer outages, slower deployments, and constant second-guessing. When you can't see what's happening, you make defensive decisions: over-provisioning infrastructure, delaying releases, hesitating to change anything because "something might break again."

At one company, our alerts were so noisy we started ignoring them. One night, a real incident slipped through—and by the time we noticed, users were already hurting.

That's when I realized: alert fatigue is worse than no alerts at all.

Every alert must mean something. It must demand action. Everything else is noise, and noise kills focus.

💡The Simple Things That Made a Huge Difference

If I could go back and give my younger self one piece of advice about observability, it would be this: start small, but start.

Instrument everything early, don't wait for incidents to tell you what to measure.

Make sure each log line includes context like trace_id or request_id. You'd be surprised how much easier debugging becomes when you can correlate across services. Keep your dashboards simple and purposeful. One dashboard showing latency, error rate, and throughput is worth more than ten fancy ones nobody looks at.

After every incident, take ten minutes to improve observability. Add one new metric, one new trace, one clearer log message. Those small improvements compound into a system that tells its own story.

💸 The Real Cost of Flying Blind

Poor observability is more than a hindrance to speed; it's an organizational drain. It turns engineers into detectives, and deploys into anxiety triggers. It's the reason people fear being on-call. It quietly eats into your bottom line through inefficiency, wasted time, and preventable downtime.

Good observability, is the complete opposite. It is magic. Deploys become less scary. Incidents become calm and focused.Engineers fix issues confidently without relying on tribal knowledge. You spend less time guessing and more time building. When observability is done right, you can feel it in the culture. People are less defensive, more curious. When something breaks, you already know how to find it. When an alert fires, you trust it.

That’s the real win.

Observability isn't about collecting data; it's about trusting your systems.

It is what turns panic into understanding, chaos into clarity, and firefighting into engineering.

If you take one thing away from this: "You can't fix what you can't see."

So begin small. Choose a service. Add tracing. Clean up your dashboards. Prune noisy alerts. And after every incident, make your system a little easier to understand than it was yesterday. Because one day, when your phone buzzes at 2 A.M., you’ll be grateful for every bit of visibility you built.