🔥 The DB Grill 🔥

Alright, let’s pull up a chair. I’ve just been sent another one of these… thought leadership pieces. This one’s a real page-turner. "Think PostgreSQL with JSONB can replace a document database?" Oh, honey, that’s adorable. It’s like asking if my son’s lemonade stand can replace the Coca-Cola Company. It’s a tempting idea, sure, if your goal is to go bankrupt with extra steps.

Let's dig into this fiscal tragedy masquerading as a technical deep-dive. They start with a "straightforward example." That’s vendor-speak for, “Here’s a scenario so sterilized and perfect it will never happen in the real world, but it makes our charts look pretty.” They load up a hundred thousand orders, each with ten items, and what's this? They’re generating random data with /dev/urandom piped through base64. Fantastic. We're not just wasting CPU cycles, we're doing it with panache. I can already see the AWS bill for this little science fair project.

And look at this wall of text they call a query result. What am I looking at? The encrypted launch codes for a defunct Soviet satellite? This isn’t data; it’s a cry for help. I’m paying for storage on this, by the way. Every single one of these gibberish characters is a tiny debit against my Q4 earnings.

Now for the juicy part, the part they always gloss over in the sales pitch: the execution plan. The first query, the "good" relational one, reads eight pages. Eight pages. In my world, that’s not a performance metric; it's an itemized receipt for wasted resources. Four for the index, four for the table. Simple enough. But then they get clever. They decide to "improve" things by cramming everything into a JSONB column to get that sweet, sweet data locality. They want to be just like MongoDB, isn't that cute?

So they run their little update and vacuum commands—cha-ching, cha-ching, that’s the sound of billable compute hours—and what happens? To get the same data out, the page count goes from eight… to ten.

Let me repeat that for the MBAs in the back. Their "optimization" resulted in a 25% increase in I/O for a single lookup. If one of my department heads came to me with a 25% cost overrun on a core business function, they wouldn't be optimizing a database; they’d be optimizing their LinkedIn profile.

But it gets better. They reveal the dark secret behind this magic trick: a mechanism called TOAST. It sounds warm and comforting, doesn't it? Let me tell you what TOAST is. TOAST is the hidden resort fee on your hotel bill. It's the "convenience charge" for using your own credit card. It’s a system designed to take something that should be simple—storing data—and turn it into a byzantine nightmare of hidden tables, secret indexes (pg_toast_10730420_index, really rolls off the tongue), and extra lookups. You thought you bought a single, elegant solution, but you actually bought a timeshare in a relational database pretending to be something it's not.

This execution plan reveals the actual physical access to the JSONB document... no data locality at all.

There it is. The whole premise is a lie. It's the Fyre Festival of database architectures. You're promised luxury villas on the beach, and you end up with relational tables in a leaky tent.

Now, let's do some real CFO math, the back-of-the-napkin kind they don’t teach you at Stanford.

• Migration Cost: They casually mention alter table and update. For one hundred thousand records. Do you know what that looks like on our multi-terabyte production database? That’s not a script; that’s a three-week project requiring two senior DBAs, a project manager to tell them they’re behind schedule, and a catering budget for all the late-night pizza. Estimate: $85,000.
• Consultant Fees: When this inevitably grinds to a halt because a developer tries to query for the second item in the array and accidentally reads the entire database into memory—which they helpfully demonstrate reads half a million pages—who do we call? The PostgreSQL gurus. The ones who charge $500 an hour to look at our EXPLAIN ANALYZE output and say, "Yep, you’re TOASTed." Estimate: A recurring $150,000 per year, forever.
• Training & Rework: Every engineer who was told "it's just like a document database" now has to unlearn that and learn the thirty-seven exceptions and hidden gotchas of the TOAST protocol. That’s productivity down the drain. Estimate: Lost opportunity cost of $250,000 in the first year alone.

So the "true" cost of this "free" optimization is a cool half-a-million dollars just to get worse performance. The ROI on this project isn't just negative; it's a black hole that sucks money out of the budget and light out of my soul.

They conclude with this masterpiece of corporate doublespeak: "PostgreSQL’s JSONB offers a logical data embedding, but not physical, while MongoDB provides physical data locality." Translation: "Our product can wear a costume of the thing you actually want, but underneath, it’s still the same old thing, just slower and more confusing." Then they have the audacity to plug a conference. Sell me the problem, then sell me a ticket to the solution. That's a business model I can almost respect.

So, no. We will not be replacing our document database with a relational database in a cheap Halloween costume. I’ve seen better-structured data in my grandma’s recipe box.

My budget is closed.