# Fuzzies
**Fuzzies** is a fast, friendly integration layer that bridges the gap between low-level finite state transducers (`fst`) and Levenshtein automata, saving you from writing tedious boilerplate.
[](https://crates.io/crates/fuzzies)
[](https://docs.rs/fuzzies)
[](https://github.com/Isvane/fuzzies/blob/main/LICENSE)
More information about this crate can be found in the [crate documentation](https://docs.rs/fuzzies)
---
## Installation
```bash
cargo add fuzzies
```
---
## Example
```rust, no_run
use fuzzies::{Dictionary, DictionaryError};
fn main() -> Result<(), DictionaryError> {
// Prepare your raw text file (must be sorted lexicographically)
// Fuzzies provides a handy in-place sorter for convenience:
Dictionary::sort("words.txt")?;
// Build the immutable binary FST from the sorted text file
Dictionary::build("words.txt", "words.fst")?;
// Load the dictionary (memory-mapped from disk)
let dict = Dictionary::open("words.fst")?;
// Check for exact matches instantly
if dict.contains("banana") {
println!("Exact match found!");
}
// Perform a fuzzy search with a max typo distance of 2 and limit of 5 results
let results = dict.search("banaan")
.distance(2)
.transposition(true) // Handles adjacent swaps (e.g., "teh" -> "the")
.prefix(false) // Set to true for prefix fuzzy lookups
// .ge("a").lt("e") // Optionally restrict search bounds (e.g., 'a' <= key < 'e')
.limit(5)
.execute()?;
for result in results {
println!("Found: {}", result);
}
// Batch search (multithreaded, defaults to a distance of 1)
let queries = vec!["aple", "baxana", "cherri"];
let batch_results = dict.batch_search(&queries).execute();
for (query, result) in queries.iter().zip(batch_results) {
match result {
Ok(matches) => println!("Query '{}' found {} matches", query, matches.len()),
Err(e) => eprintln!("Error searching for '{}': {}", query, e),
}
}
Ok(())
}
```
### Embedding Data
If you don't want to manage external `.fst` files on disk, embed the dataset directly into your application:
```rust, ignore
static DICT_DATA: &[u8] = include_bytes!("../assets/words.fst");
let dict = Dictionary::from_embedded(DICT_DATA)?;
```
---
## Built with Fuzzies
Check out real-world projects utilizing `fuzzies`:
* **[Mamoru](https://github.com/Isvane/mamoru)**: A blazing-fast Git `commit-msg` hook that embeds a compiled dictionary of over 106,000 words to instantly catch and block typos before they make it into your version control history.
---
## 🎈 Performance
The following benchmarks were gathered using Criterion on an **Intel Core i5-10300H** (4 cores / 8 threads). You can re-run these on your hardware with `cargo bench`.
### Metadata & Operations
```ignore
Dictionary Operations/len 448.12 ps/iter (+/- 2.10 ps)
Dictionary Operations/contains (Hit) 34.60 ns/iter (+/- 0.15 ns)
Dictionary Operations/contains (Miss) 11.08 ns/iter (+/- 0.08 ns)
```
### Loading & Creation
```ignore
Dictionary Setup/from_embedded 11.14 ns/iter (+/- 0.05 ns)
Dictionary Setup/open (Mmap) 3.68 µs/iter (+/- 0.02 µs)
Dictionary Setup/sort (In-place) 109.05 µs/iter (+/- 0.81 µs)
Dictionary Setup/build 192.88 µs/iter (+/- 1.12 µs)
```
### Search Performance
```ignore
Dictionary Search/Exact (dist = 0) 2.18 µs/iter (+/- 0.01 µs)
Dictionary Search/Range Bounded 4.35 µs/iter (+/- 0.02 µs)
Dictionary Search/Prefix 5.53 µs/iter (+/- 0.03 µs)
Dictionary Search/Fuzzy (dist = 1) 7.97 µs/iter (+/- 0.04 µs)
```
### Parallel Batch Search (Rayon)
```ignore
Rayon Parallel Batch/100 queries 429.00 µs/iter (+/- 1.82 µs) [~4.29 µs/query]
Rayon Parallel Batch/500 queries 2.00 ms/iter (+/- 0.01 ms) [~4.01 µs/query]
Rayon Parallel Batch/1000 queries 4.02 ms/iter (+/- 0.02 ms) [~4.02 µs/query]
```
---
## License
This project is licensed under the [MIT license.](LICENSE)