Create ridiculously fast Lexers.
Logos works by:
- Resolving all logical branching of token definitions into a state machine.
- Optimizing complex patterns into Lookup Tables.
- Avoiding backtracking, unwinding loops, and batching reads to minimize bounds checking.
In practice it means that for most grammars the lexing performance is virtually unaffected by the number of tokens defined in the grammar. Or, in other words, it is really fast.
Example
use Logos;
Callbacks
Logos can also call arbitrary functions whenever a pattern is matched, which can be used to put data into a variant:
use ;
// Note: callbacks can return `Option` or `Result`
Logos can handle callbacks with following return types:
Return type | Produces |
---|---|
() |
Token::Unit |
bool |
Token::Unit or <Token as Logos>::ERROR |
Result<(), _> |
Token::Unit or <Token as Logos>::ERROR |
T |
Token::Value(T) |
Option<T> |
Token::Value(T) or <Token as Logos>::ERROR |
Result<T, _> |
Token::Value(T) or <Token as Logos>::ERROR |
Skip |
skips matched input |
Filter<T> |
Token::Value(T) or skips matched input |
Callbacks can be also used to do perform more specialized lexing in place
where regular expressions are too limiting. For specifics look at
Lexer::remainder
and
Lexer::bump
.
Token disambiguation
Rule of thumb is:
- Longer beats shorter.
- Specific beats generic.
If any two definitions could match the same input, like fast
and [a-zA-Z]+
in the example above, it's the longer and more specific definition of Token::Fast
that will be the result.
This is done by comparing numeric priority attached to each definition. Every consecutive, non-repeating single byte adds 2 to the priority, while every range or regex class adds 1. Loops or optional blocks are ignored, while alternations count the shortest alternative:
[a-zA-Z]+
has a priority of 1 (lowest possible), because at minimum it can match a single byte to a class.foobar
has a priority of 12.(foo|hello)(bar)?
has a priority of 6,foo
being it's shortest possible match.