Anyone can port a library. Point a translator at the source, clean up the output, get it to compile, and you have something that looks like a port. The actual engineering problem is different and much harder: proving that the new code means the same thing as the old code, across thirty algorithms, hundreds of edge cases, and a test suite written by people who were not thinking about you.
This is the story of porting textdistance, a Python library for measuring string similarity, to Rust. The result is textdistance-rs. The porting took a fraction of the time. Everything else: the differential fuzzing, the 140 divergences, the 35 year old threshold I violated, the floating-point drift at the 15th decimal place, is what this writeup is actually about.
Thirty algorithms and one architectural bet
The original textdistance covers a lot of ground: edit-based distances (Levenshtein, Damerau-Levenshtein, Hamming, Jaro-Winkler), token-based measures (Jaccard, Sørensen-Dice, cosine, Tversky), sequence-based methods (LCS, Ratcliff-Obershelp), phonetic algorithms (MRA, Editex), and compression-based distances built on normalized compression distance. Over thirty algorithms in total, all reimplemented in Rust.
