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  1. data/
  2. src/
  3. tests/
  4. .cargo-checksum.json
  5. Cargo.toml
  9. rustfmt.toml
  10. TODO


A low level regular expression library that uses deterministic finite automata. It supports a rich syntax with Unicode support, has extensive options for configuring the best space vs time trade off for your use case and provides support for cheap deserialization of automata for use in no_std environments.

Build status

Dual-licensed under MIT or the UNLICENSE.



Add this to your Cargo.toml:

regex-automata = "0.1"

and this to your crate root (if you're using Rust 2015):

extern crate regex_automata;

Example: basic regex searching

This example shows how to compile a regex using the default configuration and then use it to find matches in a byte string:

use regex_automata::Regex;

let re = Regex::new(r"[0-9]{4}-[0-9]{2}-[0-9]{2}").unwrap();
let text = b"2018-12-24 2016-10-08";
let matches: Vec<(usize, usize)> = re.find_iter(text).collect();
assert_eq!(matches, vec![(0, 10), (11, 21)]);

For more examples and information about the various knobs that can be turned, please see the docs.

Support for no_std

This crate comes with a std feature that is enabled by default. When the std feature is enabled, the API of this crate will include the facilities necessary for compiling, serializing, deserializing and searching with regular expressions. When the std feature is disabled, the API of this crate will shrink such that it only includes the facilities necessary for deserializing and searching with regular expressions.

The intended workflow for no_std environments is thus as follows:

  • Write a program with the std feature that compiles and serializes a regular expression. Serialization should only happen after first converting the DFAs to use a fixed size state identifier instead of the default usize. You may also need to serialize both little and big endian versions of each DFA. (So that's 4 DFAs in total for each regex.)
  • In your no_std environment, follow the examples above for deserializing your previously serialized DFAs into regexes. You can then search with them as you would any regex.

Deserialization can happen anywhere. For example, with bytes embedded into a binary or with a file memory mapped at runtime.

Note that the ucd-generate tool will do the first step for you with its dfa or regex sub-commands.

Cargo features

  • std - Enabled by default. This enables the ability to compile finite automata. This requires the regex-syntax dependency. Without this feature enabled, finite automata can only be used for searching (using the approach described above).
  • transducer - Disabled by default. This provides implementations of the Automaton trait found in the fst crate. This permits using finite automata generated by this crate to search finite state transducers. This requires the fst dependency.

Differences with the regex crate

The main goal of the regex crate is to serve as a general purpose regular expression engine. It aims to automatically balance low compile times, fast search times and low memory usage, while also providing a convenient API for users. In contrast, this crate provides a lower level regular expression interface that is a bit less convenient while providing more explicit control over memory usage and search times.

Here are some specific negative differences:

  • Compilation can take an exponential amount of time and space in the size of the regex pattern. While most patterns do not exhibit worst case exponential time, such patterns do exist. For example, [01]*1[01]{N} will build a DFA with 2^(N+1) states. For this reason, untrusted patterns should not be compiled with this library. (In the future, the API may expose an option to return an error if the DFA gets too big.)
  • This crate does not support sub-match extraction, which can be achieved with the regex crate's “captures” API. This may be added in the future, but is unlikely.
  • While the regex crate doesn't necessarily sport fast compilation times, the regexes in this crate are almost universally slow to compile, especially when they contain large Unicode character classes. For example, on my system, compiling \w{3} with byte classes enabled takes just over 1 second and almost 5MB of memory! (Compiling a sparse regex takes about the same time but only uses about 500KB of memory.) Conversly, compiling the same regex without Unicode support, e.g., (?-u)\w{3}, takes under 1 millisecond and less than 5KB of memory. For this reason, you should only use Unicode character classes if you absolutely need them!
  • This crate does not support regex sets.
  • This crate does not support zero-width assertions such as ^, $, \b or \B.
  • As a lower level crate, this library does not do literal optimizations. In exchange, you get predictable performance regardless of input. The philosophy here is that literal optimizations should be applied at a higher level, although there is no easy support for this in the ecosystem yet.
  • There is no &str API like in the regex crate. In this crate, all APIs operate on &[u8]. By default, match indices are guaranteed to fall on UTF-8 boundaries, unless RegexBuilder::allow_invalid_utf8 is enabled.

With some of the downsides out of the way, here are some positive differences:

  • Both dense and sparse DFAs can be serialized to raw bytes, and then cheaply deserialized. Deserialization always takes constant time since searching can be performed directly on the raw serialized bytes of a DFA.
  • This crate was specifically designed so that the searching phase of a DFA has minimal runtime requirements, and can therefore be used in no_std environments. While no_std environments cannot compile regexes, they can deserialize pre-compiled regexes.
  • Since this crate builds DFAs ahead of time, it will generally out-perform the regex crate on equivalent tasks. The performance difference is likely not large. However, because of a complex set of optimizations in the regex crate (like literal optimizations), an accurate performance comparison may be difficult to do.
  • Sparse DFAs provide a way to build a DFA ahead of time that sacrifices search performance a small amount, but uses much less storage space. Potentially even less than what the regex crate uses.
  • This crate exposes DFAs directly, such as DenseDFA and SparseDFA, which enables one to do less work in some cases. For example, if you only need the end of a match and not the start of a match, then you can use a DFA directly without building a Regex, which always requires a second DFA to find the start of a match.
  • Aside from choosing between dense and sparse DFAs, there are several options for configuring the space usage vs search time trade off. These include things like choosing a smaller state identifier representation, to premultiplying state identifiers and splitting a DFA's alphabet into equivalence classes. Finally, DFA minimization is also provided, but can increase compilation times dramatically.

Future work

  • Look into being smarter about generating NFA states for large Unicode character classes. These can create a lot of additional work for both the determinizer and the minimizer, and I suspect this is the key thing we‘ll want to improve if we want to make DFA compile times faster. I believe it’s possible to potentially build minimal or nearly minimal NFAs for the special case of Unicode character classes by leveraging Daciuk's algorithms for building minimal automata in linear time for sets of strings. See for more details. The key adaptation I think we need to make is to modify the algorithm to operate on byte ranges instead of enumerating every codepoint in the set. Otherwise, it might not be worth doing.
  • Add support for regex sets. It should be possible to do this by “simply” introducing more match states. I think we can also report the positions at each match, similar to how Aho-Corasick works. I think the long pole in the tent here is probably the API design work and arranging it so that we don't introduce extra overhead into the non-regex-set case without duplicating a lot of code. It seems doable.
  • Stretch goal: support capturing groups by implementing “tagged” DFA (transducers). Laurikari‘s paper is the usual reference here, but Trofimovich has a much more thorough treatment here: I’ve only read the paper once. I suspect it will require at least a few more read throughs before I understand it. See also:
  • Possibly less ambitious goal: can we select a portion of Trofimovich's work to make small fixed length look-around work? It would be really nice to support ^, $ and \b, especially the Unicode variant of \b and CRLF aware $.
  • Experiment with code generating Rust code. There is an early experiment in src/ that is thoroughly bit-rotted. At the time, I was experimenting with whether or not codegen would significant decrease the size of a DFA, since if you squint hard enough, it‘s kind of like a sparse representation. However, it didn’t shrink as much as I thought it would, so I gave up. The other problem is that Rust doesn‘t support gotos, so I don’t even know whether the “match on each state” in a loop thing will be fast enough. Either way, it's probably a good option to have. For one thing, it would be endian independent where as the serialization format of the DFAs in this crate are endian dependent (so you need two versions of every DFA, but you only need to compile one of them for any given arch).
  • Experiment with unrolling the match loops and fill out the benchmarks.
  • Add some kind of streaming API. I believe users of the library can already implement something for this outside of the crate, but it would be good to provide an official API. The key thing here is figuring out the API. I suspect we might want to support several variants.
  • Make a decision on whether or not there is room for literal optimizations in this crate. My original intent was to not let this crate sink down into that very very very deep rabbit hole. But instead, we might want to provide some way for literal optimizations to hook into the match routines. The right path forward here is to probably build something outside of the crate and then see about integrating it. After all, users can implement their own match routines just as efficiently as what the crate provides.
  • A key downside of DFAs is that they can take up a lot of memory and can be quite costly to build. Their worst case compilation time is O(2^n), where n is the number of NFA states. A paper by Yang and Prasanna (2011) actually seems to provide a way to character state blow up such that it is detectable. If we could know whether a regex will exhibit state explosion or not, then we could make an intelligent decision about whether to ahead-of-time compile a DFA. See: