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https://github.com/BurntSushi/suffix

Fast suffix arrays for Rust (with Unicode support).
https://github.com/BurntSushi/suffix

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Fast suffix arrays for Rust (with Unicode support).

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suffix

======

Fast linear time & space suffix arrays for Rust. Supports Unicode!



[![Build status](https://github.com/BurntSushi/suffix/workflows/ci/badge.svg)](https://github.com/BurntSushi/suffix/actions)

[![](http://meritbadge.herokuapp.com/suffix)](https://crates.io/crates/suffix)



Dual-licensed under MIT or the [UNLICENSE](http://unlicense.org).



### Documentation



https://docs.rs/suffix



If you just want the details on how construction algorithm used, see the

documentation for the `SuffixTable` type. This is where you'll find info on

exactly how much overhead is required.



### Installation



This crate works with Cargo and is on

[crates.io](https://crates.io/crates/suffix). The package is regularly updated.

Add it to your `Cargo.toml` like so:



```toml

[dependencies]

suffix = "1.2"

```



### Examples



Usage is simple. Just create a suffix array and search:



```rust

use suffix::SuffixTable;



fn main() {

  let st = SuffixTable::new("the quick brown fox was quick.");

  assert_eq!(st.positions("quick"), &[4, 24]);

}

```



There is also a command line program, `stree`, that can be used to visualize

suffix trees:



```bash

git clone git://github.com/BurntSushi/suffix

cd suffix/stree_cmd

cargo build --release

./target/release/stree "banana" | dot -Tpng | xv -

```



And here's what it looks like:



!["banana" suffix tree](http://burntsushi.net/stuff/banana.png)



### Status of implementation



The big thing missing at the moment is a generalized suffix array. I started

out with the intention to build them into the construction algorithm, but this

has proved more difficult than I thought.



A kind-of-sort-of compromise is to append your distinct texts together, and

separate them with a character that doesn't appear in your document. (This is

technically incorrect, but maybe your documents don't contain any `NUL`

characters.) During construction of this one giant string, you should record

the offsets of where each document starts and stops. Then build a `SuffixTable`

with your giant string. After searching with the `SuffixTable`, you can find

the original document by doing a binary search on your list of documents.



I'm currently experimenting with different techniques to do this.



### Benchmarks



Here are some very rough benchmarks that compare suffix table searching with

searching in the using standard library functions. Note that these benchmarks

explicitly do not include the construction of the suffix table. The premise of

a suffix table is that you can afford to do that once---but you hope to gain

much faster queries once you do.



```

test search_scan_exists_many            ... bench:       2,964 ns/iter (+/- 180)

test search_scan_exists_one             ... bench:          19 ns/iter (+/- 1)

test search_scan_not_exists             ... bench:      84,645 ns/iter (+/- 3,558)

test search_suffix_exists_many          ... bench:         228 ns/iter (+/- 65)

test search_suffix_exists_many_contains ... bench:         102 ns/iter (+/- 10)

test search_suffix_exists_one           ... bench:         162 ns/iter (+/- 13)

test search_suffix_exists_one_contains  ... bench:           8 ns/iter (+/- 0)

test search_suffix_not_exists           ... bench:         177 ns/iter (+/- 21)

test search_suffix_not_exists_contains  ... bench:          50 ns/iter (+/- 6)

```



The "many" benchmarks test repeated queries that match. The "one" benchmarks

test a single query that matches. The "not_exists" benchmarks test a single

query that does *not* match. Finally, the "contains" benchmark test existence

rather finding all positions.



One thing you might take away from here is that you'll get a very large

performance boost if many of your queries don't match. A linear scan takes a

long time to fail!



And here are some completely useless benchmarks on suffix array construction.

They compare the linear time algorithm with the naive construction algorithm

(call `sort` on all suffixes, which is `O(n^2 * logn)`).



```

test naive_dna_medium                   ... bench:  22,307,313 ns/iter (+/- 939,557)

test naive_dna_small                    ... bench:   1,785,734 ns/iter (+/- 43,401)

test naive_small                        ... bench:         228 ns/iter (+/- 10)

test sais_dna_medium                    ... bench:   7,514,327 ns/iter (+/- 280,544)

test sais_dna_small                     ... bench:     712,938 ns/iter (+/- 34,730)

test sais_small                         ... bench:       1,038 ns/iter (+/- 58)

```



These benchmarks might make you say, "Whoa, the special algorithm isn't that

much faster." That's because the data just isn't big enough. And when it *is*

big enough, a micro benchmark is useless. Why? Because using the `naive`

algorithm will just burn your CPUs until the end of the time.



It would be more useful to compare this to other suffix array implementations,

but I haven't had time yet. Moreover, most (all?) don't support Unicode and

instead operate on bytes, which means they aren't paying the overhead of

decoding UTF-8.