If You’ve By no means Damaged It, You Don’t Actually Know It – O’Reilly

The next article initially appeared on Medium and is being republished right here with the creator’s permission.

There’s a pretend confidence you may carry round while you’re studying a brand new know-how. You watch a number of movies, skim some docs, get a toy instance working, and inform your self, “Yeah, I’ve obtained this.” I’ve performed that. It by no means lasts. A troublesome lesson typically accompanies the one expertise that issues.

You be taught by failure—falling flat in your face, trying on the mess, and determining why it broke. Something that feels too straightforward? It most likely was, and also you didn’t exit the method with something price studying.

Ask About Failure: Failure === Expertise

After I’m hiring somebody who claims relational database experience, I ask a “trick” query:

Inform me concerning the worst database schema you ever created. What did it train you to keep away from?

It’s probably not a trick. Anybody who’s been knee‑deep in relational databases is aware of there’s no good schema. There are competing use instances that always pull in opposition to one another. You design for transaction workloads, however inevitably, somebody tries to make use of it for reporting, then everybody wonders why queries crawl. One other developer on the workforce inadvertently optimizes the schema (normally years later) for the reporting use case solely to make the transactional workload unworkable.

The right reply normally seems like:

We constructed for transactional throughput—one of many founders of the corporate thought MySQL was a database, which was our first mistake. The enterprise then used it for reporting functions. The system modified palms a number of instances over the course of a number of years. Joins turned gnarly, indices didn’t match the entry patterns, and nightly jobs began interfering with consumer visitors. We needed to cut up learn replicas, ultimately introduce a warehouse, and after 5–6 years, we ended up simplifying the transactions and transferring them over to Cassandra.

That’s an individual who has lived the trade-offs. They’ve skilled a drawn-out existential failure associated to operating a database. Whereas they won’t know the right way to resolve a few of the foolish logic questions which can be more and more widespread in job interviews, that is the form of expertise that carries way more weight with me.

The Schema That Almost Broke Me

I as soon as shipped a transactional schema that seemed high-quality on paper: normalized, neat, every part in its correct place.

Then analytics confirmed up with “simply a few fast dashboards.” Subsequent factor you already know, my fairly 3NF mannequin, now linked to each elementary classroom in America, was getting used like a million-row Excel spreadsheet to summarize an accounting report. For a number of months, it was high-quality till it wasn’t, and the database had made a sluggish‑movement faceplant as a result of it was spending 80% of its time updating an index. It wasn’t as if I may repair something, as a result of that may imply a number of days of downtime coupled with a rewrite for a undertaking whose contract was nearly up.

And the way have been we attempting to repair it? In case you’ve been on this state of affairs, you’ll perceive that what I’m about to write down is the signal that you’ve got reached a brand new stage of determined failure. As an alternative of contemplating a rational method to reform the schema or separating what had develop into a “web-scale” workload in 2007 from a NoSQL database, we have been attempting to determine the right way to buy sooner arduous drives with greater IOPS.

I discovered loads of issues:

• I discovered that upgrading {hardware} (shopping for a sooner machine or dropping 1,000,000 {dollars} on arduous drives) will solely delay your disaster. The actual repair is unavoidable—large horizontal scaling is incompatible with relational databases.
• I discovered the that means of “question plan from hell.” We band‑aided it with materialized views and skim replicas. Then we did what we must always’ve performed from day one: arrange an precise reporting path.
• In case you are having to optimize for a question plan each week? Your database is sending you an necessary sign, which it’s best to translate to, “It’s time to start out in search of an alternate.”

Lesson burned in: Design for the use case you even have, not the one you hope to have—and assume the use case will change.

What Does This Need to Do with Cursor and Copilot?

I’m seeing lots of people writing on LinkedIn and different websites about how superb vibe coding is. These celebratory posts reveal extra concerning the folks posting them than they notice, as they hardly ever acknowledge the fact of the method—it’s not all enjoyable and video games. Whereas it’s astonishing how a lot progress one could make in a day or every week, these of us who’re really utilizing these instruments to write down code are the primary to inform you that we’re studying loads of troublesome classes.

It’s not “straightforward.” There’s nothing “vibey” concerning the course of, and in case you are doing it proper, you’re beginning to use curse phrases in your prompts. For instance, a few of my prompts in response to a Cursor Agent yesterday have been: “You’ve got to be kidding me, I’ve a rule that acknowledged that I by no means wished you to try this, and also you simply ignored it?”

At any time when I see folks get excited concerning the newest, biggest fad factor that’s altering the world, I’m additionally the primary to note that possibly they aren’t utilizing all of it. In the event that they have been, they’d perceive that it’s not as “straightforward” as they’re reporting.

The failure muscle you construct with databases is identical one you want with AI coding instruments. You’ll be able to’t tiptoe in. You must push till one thing breaks. Then you determine the right way to method a brand new know-how as knowledgeable.

• Ask an agent to refactor one file—nice.
• Ask it to coordinate modifications throughout 20 recordsdata, rethink error dealing with, and preserve assessments passing—now we’re studying.
• Watch the place it stumbles, and be taught to body the work so it might succeed subsequent time.
• Spend a whole weekend on a “wild goose chase” as a result of your agentic coder determined to disregard your Cursor guidelines fully. ← That is costly, nevertheless it’s the way you be taught.

The trick isn’t avoiding failure. It’s failing in a managed, reversible means.

The Meta Lesson

In case you’ve by no means damaged it, you don’t actually understand it. That is true for coding, budgeting, managing, cooking, and snowboarding. In case you haven’t failed, you don’t understand it. And most people speaking about “vibe coding” haven’t.

The folks I belief most as engineers can inform me why one thing failed and how they adjusted their method in consequence. That’s the complete sport with AI coding instruments. The sooner you may run the loop—attempt → break → examine → refine—the higher you get.