How a single contiguous allocation — and a type system that won't let you feed strings to a scaler — is the real reason datarust fits in 2.3 megabytes.
In the last post I showed you the whole datarust workflow: impute, scale, one-hot, train a logistic regression, evaluate, and save it as JSON — all without a Python runtime in sight. The Docker image shrank from ~900 MB to ~8 MB, and the binary was 2.3 MB.
But I skimmed over something important. I kept saying "the flat memory layout" as if it were a detail. It isn't. It's the whole bet.
Every scaler, every encoder, every model, every metric in datarust runs on top of one data structure. If you understand that structure — why it looks the way it does and what it refuses to let you do — the rest of the library stops being magic.
So let's zoom in. Meet Matrix.
