🔥 The DB Grill 🔥

Ah, yes. A veritable bildungsroman of the modern developer. One must commend the author for their candor in documenting, with such painstaking detail, a journey from blissful ignorance to what now passes for competence. It reads like a charming parable on the perils of eschewing a formal education for the fleeting wisdom of a blog post.

It is particularly delightful to see the author’s first “mistake” was, in fact, attempting to apply the foundational principles of database normalization.

I built my schema like I was still working in SQL– every entity in its own collection, always referencing instead of embedding, and absolutely no data duplication. It felt safe because it was familiar.

Familiar? My dear boy, it felt “safe” because it was the result of Dr. Codd’s revolutionary work to eliminate data redundancy and the ensuing update, insertion, and deletion anomalies! To cast aside decades of established relational theory as mere “old habits” is… well, it’s a bold choice. He then discovers “embedding,” which he hails as a “cheat code.” A cheat code, it seems, that deactivates the ‘C’ in ACID. He was astonished to find that duplicating data everywhere led to consistency issues. One imagines Archimedes being similarly surprised when, upon jumping into his tub, the water level rose. Eureka, indeed.

Then we come to the performance section, a truly harrowing account of one man’s battle with a query planner. He bravely admits to scattering indexes about his collections like a toddler flinging paint at a canvas, hoping a masterpiece might emerge by sheer chance. His great epiphany? That an index must actually match the query it is intended to accelerate. Groundbreaking. Clearly they’ve never read Stonebraker’s seminal work on query optimization; I suppose that’s not covered in a lunch-break Skill Badge. His subsequent discovery of the aggregation framework—the idea that one might perform data transformations within the database itself—is treated with the reverence of discovering fire. It is a concept so radical, so utterly foreign, that one can only assume his prior experience involved piping raw data through a labyrinth of shell scripts.

The chapter on reliability is perhaps my favorite. His initial strategy was, and I quote, to “wait for something to break, then figure out why.” An approach he later enhanced by turning the server “off and on again.” One is left breathless by the sheer audacity. We have wrestled with Brewer’s CAP Theorem for over two decades, meticulously balancing consistency, availability, and partition tolerance in distributed systems, and this brave pioneer’s contribution is a power cycle. To learn, years into his journey, that one should monitor latency and replication lag is not a sign of growing wisdom; it is a sign that he has finally found the dashboard of the car he has been driving blindfolded.

And now, with the “fundamentals” apparently mastered, he is free to explore Vector Search and gen AI. It’s a bit like a student who, having finally learned that dividing by zero is problematic, immediately declares themselves ready to tackle Riemannian geometry. The confidence is admirable, if profoundly misplaced.

In the end, this whole saga serves as a rather depressing validation of my deepest fears. We have replaced rigorous, principled computer science education with a series of digital merit badges one can earn while chewing on a sandwich. We’ve swapped Codd’s twelve rules for a dozen bullet points in a blog post. This entire journey of “discovery” is little more than a slow, painful, and entirely avoidable rediscovery of problems solved a half-century ago.

Ah, well. At least the résumés will look impressive. One more for the pile.