🔥 The DB Grill 🔥

Oh, wonderful. Another blog post that's going to "inspire" me. Let me guess, it's a groundbreaking technique that will revolutionize our query performance, and all it requires is a little bit of... manual rewriting of critical production queries. Fantastic. I haven't had a good PagerDuty-induced panic attack in at least a week.

So, let me get this straight. AWS, in its infinite wisdom, has solved the age-old problem of correlated subqueries inside their proprietary, closed-source fork of Postgres. And their gift to us, the humble peasants using the open-source version, is not the code, but a blog post showing us how to do their magic tricks by hand. How generous. It's like a magician showing you how a trick works but refusing to sell you the props, instead suggesting you can probably build a similar-looking box out of cardboard and hope for the best.

I love this part: "the database, not the developer, manages this complexity." That's the dream, isn't it? The same dream we were sold with the last three databases we migrated to. The reality is the developer—that's me, at 3 AM, mainlining cold brew—is the one who ends up "managing the complexity" when the planner makes a galaxy-brained decision to do a full table scan on a terabyte of data because someone looked at it funny.

The article dives right into the deep end with this gem:

It is important to understand the transformation carefully to ensure it does not change the results.

You don't say. I have that phrase tattooed on my eyelids from the "Simple Sharding Project of 2022," which only resulted in a 12-hour outage and the discovery of three new ways NULL can be interpreted by a distributed transaction coordinator. My entire career is built on the scar tissue from not understanding something "carefully" enough.

And of course, it immediately gets into the weeds with AVG() versus COUNT(). This is my favorite part of any "simple" database trick. The tiny, razor-sharp edge case that seems trivial in a blog post but will silently corrupt your analytics data for six months before anyone notices.

• AVG() returns NULL on an empty set? Great, that makes sense.
• COUNT() returns 0? Also makes sense.
• My manual LEFT JOIN with a GROUP BY returns NULL for a count? Oh, right.

So now I have to remember to sprinkle COALESCE(..., 0) on every COUNT transformation. What about SUM()? What about MAX()? What about some custom aggregate function our data science team cooked up in a fever dream? I'm already picturing the JIRA ticket: "Monthly recurring revenue is NULL. Urgent."

And the proposed solution for this minefield?

Even if you don’t use Aurora in production, you can spin up a small Aurora Serverless instance to validate your manual rewrites.

Oh, for the love of—YES. Let me just "spin up" another piece of infrastructure, get it through security review, expense the cost, and create a whole new validation pipeline just so I can double-check the work that their database does automatically. This isn't a solution; it's a sales pitch for an Aurora-based QA environment. "Our car has automatic transmission, but you can get the same experience in your manual car by hiring a guy to sit in the passenger seat and shout 'Shift now!' at you. For a small fee, of course."

This whole article is a perfect microcosm of my life. It presents a problem we all have, dangles a tantalizingly simple automated solution that we can't have, and then gives us a "just as good" manual workaround that is riddled with semantic traps. It's an instruction manual for building a time bomb.

I can see it now. Some bright-eyed junior dev is going to read this, get "inspired," and rewrite a 500-line legacy query that powers our main dashboard. They'll use the AVG() example, everything will look fine, and they'll get a round of applause for the performance gains. Six months later, we'll find out our user activity counts have been NULL for any customer who signed up on a Tuesday in a leap year, all because they forgot one little COALESCE. And guess who's going to get paged to fix it?

No, thank you. I'm going to take this "inspiration," print it out, and file it in the shredder under "Future Incidents." I'll just stick with the slow, predictable, nested-loop subquery. It may be inefficient, but at least its failures are ones I already know how to fix in my sleep. And believe me, I have plenty of experience with that.