Where database blog posts get flame-broiled to perfection
Alright, settle down, class. Alex is here. I just finished reading this... optimistic take on deterministic databases, and I have to say, it’s adorable. It has all the wide-eyed wonder of a fresh computer science grad who's never had their soul crushed by a PagerDuty alert at 3:17 AM on New Year's Day. Every few years a paper like this comes along, promising a beautiful, frictionless future. I've got a laptop lid covered in the vendor stickers of those futures. They don't stick around long.
Let's break down this masterpiece of academic abstraction, shall we?
First, we have the "Pristine Production" fantasy. The entire premise is built on studying open-source web applications using clean ORMs. That’s not the real world. My world is a seventeen-year-old enterprise monolith that communicates with a dozen microservices through a baroque messaging queue, all while the finance department runs hand-written, table-locking queries to "reconcile the numbers." Your neat-and-tidy transaction patterns are a statistical anomaly. The bulk of my workload is chaotic, convoluted cruft that was written by a contractor in 2011 who now lives off-grid in Montana. This paper studied a petting zoo and is now trying to sell me a tiger-handling manual.
Then there's the "minimal code changes" myth. I love this part. The authors wave their hands and suggest that most "interactive" transactions can be easily converted to a one-shot model. Easily. Let me translate that from academic jargon into operations English: that means a six-month, cross-team death march to refactor a critical payment processing module that has zero documentation and a bus factor of one. The original developer, Dave, thought comments were for cowards. Good luck convincing my product manager to halt all feature development so we can chase a theoretical performance gain from a database nobody has ever run at scale.
My personal favorite is the casual dismissal of the "CDA Mismatch." Their solution? Just run a "lightweight reconnaissance query" first to find the right key. Brilliant! Let’s solve a performance problem by introducing a Doubled-Up Database Dip for 27% of our transactions. We'll just add an extra network round-trip and a delightful new race condition where the data can change between the recon query and the actual transaction. I can already picture the emergency Slack channel: “The scout query saw the row, but the write failed with a key violation! Is the database possessed?!” No, it’s not possessed; it was just designed in a lab.
And the grand finale: dismissing the 0.5% of "Strictly Interactive" transactions. I will bet my entire on-call bonus that this 0.5% represents the single most important, revenue-generating, and horrifyingly complex transaction in any given application. It's the one that calls three external APIs, holds a lock for an eternity, and has more conditional branching than a choose-your-own-adventure novel. It’s the transaction that will inevitably wedge the entire deterministic scheduler, bringing the system to a grinding halt while everyone is on a holiday break. But don't worry, it's only half a percent.
Fascinating theory. Now, if you'll excuse me, I need to clear a spot on my graveyard laptop for your sticker, right between RethinkDB and CoreOS. My pager’s going off.