🔥 The DB Grill 🔥

Alright team, gather 'round the virtual water cooler. I’ve just finished reading another one of those blog posts—the kind written by an engineer who’s clearly never had to justify a Q3 budget overrun to the board. It’s a masterful piece of technical misdirection, designed to make us feel inadequate about our perfectly functional, already paid for SQL database. Let's break down this masterpiece of fiscal irresponsibility, shall we?

• First, we have the classic "Manufactured Crisis." The entire premise hinges on the terrifying prospect that our data might be larger than a fixed-page size. Oh, the humanity! They talk about "CPU overhead" and "random IO" as if these are apocalyptic events, rather than minor performance characteristics our current systems have handled for years. This isn't a technical problem; it's a solution desperately searching for a problem, wrapped in fear, uncertainty, and a conveniently vague definition of "large" that they literally wave their hands to define.

• Then comes the sales pitch disguised as a technical revelation. “Perhaps by luck, perhaps it was fate, but WiredTiger is a great fit for MongoDB…” Fate had nothing to do with it, sweetheart. That was a product manager’s carefully crafted strategy to create a wedge issue. They're selling us a "copy-on-write random b-tree" not because it's inherently superior for every use case, but because it’s different enough to force a full-scale migration. It’s the enterprise software version of convincing you to renovate your entire kitchen because your toaster has a crumb tray that opens to the left instead of the right.

• Naturally, this post conveniently forgets to mention the "True Cost of Ownership," so let me do the math on the back of this now-useless purchase order. Let's see: a full migration to this new system for, say, a team of 20 engineers.

  • Six months of two senior engineers' time just to plan and execute the data migration: $300,000.
  • Mandatory training for the other 18 engineers to learn the new ecosystem's quirks: $90,000.
  • The inevitable, white-glove, $750/hour consultants we'll need when the migration invariably goes sideways two weeks before launch: $150,000.
  • Lost productivity and project delays while everyone is distracted by this shiny new database: I'll be conservative and call it $500,000. That’s over $1 million to solve a "problem" that might save us a few milliseconds on a query we run twice a day. The ROI is not just negative; it's a black hole.

• My favorite part is the casual dismissal of existing, proven technology.

  This approach is far from optimal as there will be more CPU overhead, more random IO and might be more wasted space. You know what else is far from optimal? Tossing out decades of stability and institutional knowledge built around SQL for a system that will lock us into a single vendor's ecosystem forever. Their "sub-optimal" is our "predictable and budgeted." Their "flexible" is my "impossible to hire for." This isn't an upgrade; it's a hostage situation with a higher monthly burn rate.

• And the grand finale: a call to create a whole new industry of expensive benchmarking. "Should it be TPC-LOB or TPC-BLOB?" How about TPC-NO? Let's not invent another meaningless acronym that vendors can use to print money and produce charts where—surprise!—their product is always at the top right. We don't need another standardized test; we need vendors to standardize their pricing so it doesn't read like a high-fantasy novel.

Honestly, it's exhausting. Every time a new data type gets popular, the database vendors circle like buzzards, trying to convince us our entire infrastructure is obsolete.

Sigh. I'm going to go approve an expense report for a new coffee machine. At least that ROI is immediate.