This crate provides a fast implementation of ordered sets and maps using finite state machines. In particular, it makes use of finite state transducers to map keys to values as the machine is executed. Using finite state machines as data structures enables us to store keys in a compact format that is also easily searchable. For example, this crate leverages memory maps to make range queries very fast.

Check out my blog post Index 1,600,000,000 Keys with Automata and Rust for extensive background, examples and experiments.

Dual-licensed under MIT or the UNLICENSE.

https://docs.rs/fst

The regex-automata crate provides implementations of the fst::Automata trait when its transducer feature is enabled. This permits using DFAs compiled by regex-automata to search finite state transducers produced by this crate.

Simply add a corresponding entry to your Cargo.toml dependency list:

[dependencies]
fst = "0.4"

This example demonstrates building a set in memory and executing a fuzzy query against it. You'll need fst = "0.4" with the levenshtein feature enabled in your Cargo.toml.

use fst::{IntoStreamer, Set};
use fst::automaton::Levenshtein;
fn main() -> Result<(), Box<dyn std::error::Error>> {
  // A convenient way to create sets in memory.
  let keys = vec!["fa", "fo", "fob", "focus", "foo", "food", "foul"];
  let set = Set::from_iter(keys)?;
  // Build our fuzzy query.
  let lev = Levenshtein::new("foo", 1)?;
  // Apply our fuzzy query to the set we built.
  let stream = set.search(lev).into_stream();
  let keys = stream.into_strs()?;
  assert_eq!(keys, vec!["fo", "fob", "foo", "food"]);
  Ok(())
}

Check out the documentation for a lot more examples!

• levenshtein - Disabled by default. This adds the Levenshtein automaton to the automaton sub-module. This includes an additional dependency on utf8-ranges.