Author here. I wrote this up a couple of weeks ago after finishing a year-long project rebuilding a core search system, and I've been reflecting on the lessons ever since.
As an experienced architect, I went in with a lot of core assumptions, and the project forced me to refine or even break many of them. The biggest was my old belief in "correctness from day one."
I learned (the hard way) that in a complex, user-facing system like search, velocity is the only path to correctness. A scrappy but live end-to-end pipeline in production is a better teacher than a perfect component in a lab.
The post covers this and four other key "refinements," including:
* Treating search as a Data & Product problem first (not just an Algo/Infra one).
* Tying every single experiment to a Business KPI (not just offline metrics like nDCG).
* Blurring the lines between DS, BE, and Platform to break bottlenecks.
I know this isn't "new," but the lessons feel timeless. I'm here to discuss and answer any questions about the process or the tech stack.
sesigl89•2h ago
As an experienced architect, I went in with a lot of core assumptions, and the project forced me to refine or even break many of them. The biggest was my old belief in "correctness from day one."
I learned (the hard way) that in a complex, user-facing system like search, velocity is the only path to correctness. A scrappy but live end-to-end pipeline in production is a better teacher than a perfect component in a lab.
The post covers this and four other key "refinements," including: * Treating search as a Data & Product problem first (not just an Algo/Infra one). * Tying every single experiment to a Business KPI (not just offline metrics like nDCG). * Blurring the lines between DS, BE, and Platform to break bottlenecks.
I know this isn't "new," but the lessons feel timeless. I'm here to discuss and answer any questions about the process or the tech stack.