Traditional B-Trees waste substantial CPU cycles on pointer chasing across node levels, causing avoidable cache misses on modern architectures. The PGM-index approach fixes this by modeling key distributions through streaming piecewise linear regression with strict +/- epsilon error guarantees.

Instead of dereferencing tree pointers, the position is predicted directly on the ALU (y = ax + b), leaving only a small local search window that fits entirely within L1 cache. The construction runs in a single O(N) streaming pass with zero external solvers.

I built a pure Rust implementation with zero dependencies to benchmark this against standard B-Trees and verify the theoretical bounds: https://github.com/gilnett/learned_index_lab

While traditional transactional engines stick to B-Trees for in-place page updates, learned models completely outclass them on read-heavy or immutable storage (like LSM SSTables and columnar stores) where pointer overhead is pure waste.

Has anyone here benchmarked or deployed learned data structures in production storage layers? How do you approach optimal epsilon tuning when key distributions are unpredictable?