Where database blog posts get flame-broiled to perfection
Alright, let's pull up a chair and our Q3 budget spreadsheet. Iâve just skimmed this⊠fascinating dissertation on a problem I believe my engineers solved years ago with something they called a "code review." It seems someone has spent a great deal of time and money trying to sell us a fire truck to put out a birthday candle. My thoughts, for the record:
First, Iâm being told about a terrifying monster called the âConnection Trap.â Apparently, itâs what happens when you write a bad query. The proposed solution in the SQL world is to⊠add another table. The proposed solution in the MongoDB world is to⊠rewrite your entire data model. I just did some quick math on a cocktail napkin. The cost of a senior engineer spending 15 minutes to fix a bad JOIN is about $45. The cost to migrate our entire infrastructure to a new "document model" to prevent this theoretical mistake is, let's see... carry the one... roughly the GDP of a small island nation. I'm not seeing the ROI here.
The "elegant solution" proposed is to just embed data everywhere. They call this a "domain-driven design" within a "bounded context." I call it "making a thousand expensive copies of the same file and hoping no one ever has to update them." They even have the gall to admit it might create some slight issues:
It may look like data duplication... and indeed this would be undesirable in a fully normalized model... You donât say. So, we trade a simple, well-understood relational model for one where our storage costs balloon, and every time a supplier changes their name, we have to launch a search-and-rescue mission across millions of documents. This isnât a feature; it's a future line item on my budget titled "Emergency Data Cleanup Consultants."
And how do we handle those updates? With a query so complex it looks like an incantation to summon a technical debt demon. This updateMany with $set and arrayFilters is presented as an efficient solution. Efficient for whom? Certainly not for our balance sheet when we have to hire three specialist developers and a part-time philosopher just to manage data consistency. The article breezily mentions the update is "not atomic across documents," which is a wonderfully creative way of saying, "good luck ensuring your data is ever actually correct across the entire system."
Letâs calculate the âTrue Cost of Ownershipâ for this paradigm shift, shall we? We start with the six-figure licensing and support contract. Then we add the cost of retraining our entire engineering department to forget decades of sensible data modeling. We'll factor in the migration project, which will inevitably be 18 months late and 200% over budget. Then comes the recurring operational overhead of bloated storage and compute costs. And finally, the seven-figure emergency fund for when we discover that "eventual consistency" was corporate-speak for "frequently wrong." My napkin math shows this "solution" will have us filing for Chapter 11 by the end of next fiscal year.
Ultimately, this entire article is a masterclass in vendor lock-in disguised as academic theory. It redefines a basic coding error as a fundamental flaw in a technology they compete with, then presents a "solution" that requires you to structure your entire business logic around their proprietary model. Once you've tangled your data into this web of aggregates and embedded documents, extracting it will be more painful and expensive than a corporate divorce. Youâre not just buying a database; youâre buying an ideology, and the subscription fees are perpetual.
Anyway, thanks for the read. I'll be sure to file this under "Things That Will Never Get Budget Approval." I have a P&L statement that needs my attention. I will not be returning to this blog.