When Optimization Becomes a Problem

I used to think optimization was the sign of a good engineer — the smarter and more efficient my code, the better I was doing.

But over time, I learned that over-optimizing isn't just wasted effort. It's how you create invisible complexity that haunts you for years.

The Database That Taught Me Humility

Back in 2008, I was a junior developer at Advision Development working on a contest platform. We were responsible for everything — front end, back end, databases, deployment — and I wanted to make my mark.

So I decided to "optimize" our database by storing timestamps as integers instead of strings. In theory, it would save a few bytes per row. In practice, it cost me five years of headaches.

You couldn't read the dates with your eyes. Queries became painful. Every time we debugged a production issue, I was decoding integers just to understand what happened. It was a classic rookie move: optimizing for a problem that didn't exist yet.

Then I did it again — I partitioned the database by year. My logic was that one day we'd have hundreds of thousands of contests, and I'd be ready. Instead, every January, the app broke because the new year didn't have a partition. We'd scramble to patch it before users noticed.

At the time, I thought I was being smart. Now I see it for what it was: over-engineering born out of insecurity and inexperience.

The API That Nobody Could Read

Years later, I repeated the same mistake — this time at scale.

We were building a microservice backend for our odds data. Each feed had parameters like eventId, lineId, and timestamp. I thought I could save bandwidth by shortening everything — eid, lid, ts. Maybe it would save a few bucks on AWS transfer costs.

It didn't.

Compression made the difference negligible, but the real cost came later:

• No one knew what the fields meant. Every new engineer had to learn a new dialect just to understand the data.
• I'd optimized for the computer, not the human.

The Cache That Hid the Real Problem

The worst over-optimization I ever made wasn't in code — it was architectural.

At one point, we had three layers of caching: in the app, in the backend, and on the CDN. It felt efficient. It wasn't.

Each cache made debugging harder. Each layer hid symptoms instead of fixing causes. Our pages looked fast in tests but crawled in production. When Google tried to index our hundreds of thousands of pages, half weren't warm — and each cold page took three seconds to load. So we built "cache warmers" and "cache monitors," piling on more complexity to support a system that shouldn't have needed it.

In hindsight, caching wasn't the problem — it was the crutch. We used it to mask bad architecture instead of fixing the root.

What I Learned About Optimization

Optimization should be a response to evidence, not a substitute for it.

Here's what I know now:

• Don't optimize until you measure a problem.
• Every layer of caching is a tradeoff in clarity.
• Compression makes most micro-optimizations irrelevant.
• Premature optimization creates future debt — always.
• The best systems are optimized for humans, not machines.

The Real Lesson

Optimization is valuable. But over-optimization is just fear wearing an engineer's badge.

If you can't explain the benefit in numbers, it's not optimization — it's avoidance.