Programming language: Rust
License: The Unlicense
Tags: Index     Text     Search     Suffix    
Latest version: v1.2.0

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

Build status

Dual-licensed under MIT or the UNLICENSE.



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.


This crate works with Cargo and is on crates.io. The package is regularly updated. Add it to your Cargo.toml like so:

suffix = "1.2"


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

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:

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

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.


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.

*Note that all licence references and agreements mentioned in the suffix README section above are relevant to that project's source code only.