Where database blog posts get flame-broiled to perfection
Alright team, Iāve reviewed the latest proposal for our database infrastructure, complete with thisā¦ inspirational blog post about achieving millisecond performance. It's a compelling story. A real rags-to-riches tale of a query that went from a sluggish collection scan to a lean, mean, index-only machine. Iām touched. But since my bonus is tied to our EBITDA and not to how many documents we can avoid examining, letās add a few line items they conveniently left out of their performance report.
First, we have the "Just Rethink Your Entire Data Model" initiative. They present this as a simple toggle switch from slow to fast. On my P&L, this "rethink" looks suspiciously like a six-month, five-engineer project to refactor every service that touches an order. Letās do some quick math: five senior engineers at a blended rate of $150k/year is $750k. For half a year, thatās $375,000 in salary, not including benefits, overhead, or the opportunity cost of them not building features that, you know, generate revenue. All to embed some customer data into an order document. What a bargain.
My personal favorite claim is this little gem:
Duplicated data isnāt a concern hereādocuments are compressed on diskā¦ Oh, it isn't a concern? Wonderful. So when marketing wants to A/B test a new product title, weāre just going to leave the old one permanently etched into a million historical order documents? That sounds like a data integrity problem that will require an expensive cleanup script later. But let's focus on the now. Duplicating customer and product data into every single order document means our storage footprint will balloon. They whisper "compression" like it's magic pixie dust, but I see a direct multiplier on our cloud storage bill. It's the buy-one-get-ten-free deal where we pay for all eleven.
Then there's the "Index for Your Query" strategy. It's pitched as precision engineering, but it sounds more like a full-employment act for database administrators. Each new business question, each new filter in the analytics dashboard, apparently requires its own bespoke, artisanal compound index. These indexes aren't free; they consume RAM and storage, adding to our monthly bill. More importantly, this creates a bottleneck where every new feature is waiting on a database guru to craft the perfect index so the query doesn't bring the whole system to its knees. We're not building a database; we're curating an art collection of fragile, high-maintenance indexes.
This whole exercise is a masterclass in vendor lock-in. They show you how terrible performance is using a standard, portable, relational model. Then, they guide you to their "optimized" embedded model. Once your entire application is hard-coded to expect a denormalized document with everything nested inside, how do you ever leave? Migrating off this platform won't be a refactor; it'll be a complete rewrite from the ground up. The cost to leave becomes so astronomically high that we're stuck paying their "flexible" consumption-based pricing until the end of time. It's the Hotel California of data platforms.
So, let's calculate the "True Cost of Ownership." We have the $375k migration project, a conservative 20% increase in storage costs year-over-year, and let's budget another $200k for the inevitable "optimization consultant" we'll need to hire when our developers create a query that doesn't have its own personal index. We're looking at a first-year cost of over half a million dollars just to get a single query to run in zero milliseconds instead of 500.
This isn't a performance strategy; it's a leveraged buyout of our engineering department, paid for with our money. Denied.