This section is devoted to introduce smoothly the different PEG combinators through a tutorial presenting Calc: a small language with arithmetic expressions and variable bindings. If you want to test the code while reading this tutorial, a skeleton project is available in the section Getting Started. Before diving into the details, we present a program written in Calc:
let a = -10 - 2 in let b = a / 2 in a + 3 * (b / 2)
It declares two local variables a and b initialized with arithmetic expressions and usable within the scope of the let-binding, which is everything after the in. Let-bindings can be composed in cascade but must terminates with an arithmetic expression, such as a + 3 * (b / 2) in our example.
A parser is a bridge between meaning-less sequence of characters and structured representation of data. It tries to give meanings to raw characters by constructing an Abstract Syntax Tree (AST) that will be processed by subsequent compilation phases. We expect a parser to transform 7 - 1 into a structure such as Minus(i32, i32). As a side note, you should avoid to compute the actual result of 7 - 1 in the parsing step, it works for simple language but tends to entangle syntactic and semantic analysis later. Invalid programs such as let a = 8 in a * b will still be correctly parsed, the semantic analysis is responsible for detecting that b is undeclared.
This tutorial will not cover the semantic analysis part and will only describe the grammar used for parsing Calc. Our parser will thus produce an AST and will not evaluate expressions.
CalcWhen it comes to elaborate a grammar, we usually start by identifying atoms of the language, e.g. syntactic constructions that can not be divided into smaller ones. These atoms are called tokens and are often processed during a lexical analysis happening before the parsing. Oak is based on Parsing Expression Grammar (PEG) and works directly on a stream of characters instead of a stream of tokens. An advantage is to have a unique and coherent grammar syntax which is helpful for composing grammars that do not necessarily expect the same set of tokens. Before continuing reading, try to find out what are the atoms of Calc.
The keywords let and in, the binding operator =, parenthesis () and arithmetic operators +, -, *, / form the unvalued atoms of the language. Calc has two valued atoms which are identifiers and integers. Unvalued atoms give a shape to the AST but they do not carry any specific data retrieved from the stream of characters. The following grammar parses the atoms of Calc:
grammar! calc { #![show_api] let_kw = "let" in_kw = "in" bind_op = "=" add_op = "+" sub_op = "-" mul_op = "*" div_op = "/" identifier = ["a-zA-Z0-9_"]+ integer = ["0-9"]+ }
A grammar is introduced with the macro grammar! <name> where <name> is the name of the grammar but also the name of the module in which generated functions will lie. A grammar is a set of rules of the form <name> = <expr> where <name> is the rule name and <expr> is a parsing expression.
The rules describing keywords and operators use string literals expressions of the form "<literal>", it expects the input to match exactly the sequence of characters given.
Identifiers and integers are recognized with character classes where a class is a single character or a character range. A range r has the form <char>-<char> inside a set ["r1r2..rN"]. Since - is used to denote a range, it must be placed before or after all the ranges such as in ["-a-z"] to be recognized as an accepted character. Character classes will succeed and "eat" one character if it is present in the set, so b, 8, _ are all accepted by ["a-zA-Z0-9_"] but é, - or ] are not.
For both string literals and character classes, any Unicode characters are interpreted following the same requirements as string literals in the Rust specification. The only other parsing expression consuming a character is the . expression, it matches any character and can only fail if we reached the end of input.
The remaining parsing expressions are combinators, they must be composed with sub-expressions. Identifiers and integers are sequences of one or more characters and we use the combinator e+ to repeat e while it succeeds. For example identifier matches "x_1" from the input "x_1 x_2" by successively applying ["a-zA-Z0-9_"] to the input; it parses x, _ and 1 and then fails on the space character. It however succeeds, even if the match is partial, and identifier returns the remaining input " x_2" and the data read. A requirement of e+ is that e must be repeated at least once. The e* expression does not impose this constraint and allow e to be repeated zero or more times. e* and e+ will consume as much input as they can and are said to be greedy operators.
Before explaining the others combinators, we take a glimpse at the generated code and how to use it. Oak will generate two functions per rule, a recognizer and a parser. A recognizer only matches the input against a specific rule but does not build any value from it. A parser matches and builds the corresponding AST (possibly with the help of user-specific function called semantic actions). For example, the functions parse_identifier and recognize_identifier will be generated for rule identifier. The #![show_api] attribute tells Oak to output, as a compilation note, the signatures of all the generated functions. We obtain the following from the Calc grammar:
// `ParseState` and `CharStream` are prefixed by `oak_runtime::`. // It is removed from this snippet for clarity. note: pub mod calc { pub fn parse_let_kw<S>(mut stream: S) -> ParseState<S, ()> where S: CharStream; pub fn recognize_let_kw<S>(mut stream: S) -> ParseState<S, ()> where S: CharStream; pub fn parse_identifier<S>(mut stream: S) -> ParseState<S, Vec<char>> where S: CharStream; pub fn recognize_identifier<S>(mut stream: S) -> ParseState<S, ()> where S: CharStream; pub fn parse_integer<S>(mut stream: S) -> ParseState<S, Vec<char>> where S: CharStream; // ... // Rest of the output truncated for the tutorial. }
We can already use these functions in our main:
fn main() { let let_kw = "let"; let state = calc::recognize_let_kw(let_kw.stream()); assert!(state.is_successful()); let ten = "10"; let state = calc::parse_integer(ten.stream()); assert_eq!(state.unwrap_data(), vec!['1', '0']); }fn main() { let let_kw = "let"; let state = calc::recognize_let_kw(let_kw.stream()); assert!(state.is_successful()); let ten = "10"; let state = calc::parse_integer(ten.stream()); assert_eq!(state.unwrap_data(), vec!['1', '0']); }
First of all, there is a documentation of the runtime available, but please, be aware that it also contains functions and structures used by the generated code and that you will probably not need.
Parsing functions accept a stream as input parameter which represents the data to be processed. A stream can be retrieved from type implementing Stream with the method stream() which is similar to iter() for retrieving an iterator. For example, Stream is implemented for the type &'a str and we can directly pass the result of stream() to the parsing function, as in calc::recognize_let_kw(let_kw.stream()). Basically, a stream must implement several operations described by the CharStream trait, it is generally an iterator that keeps a reference to the underlying data traversed. You can find a list of all types implementing Stream in the implementors list of Stream.
By looking at the signatures of parse_identifier and recognize_identifier we see that a value of type ParseState<S, T> is returned. T is the type of the data extracted during parsing. It is always equal to () in case of a recognizer since it does not produce data, and hence a recognizer is a particular case of a parser where the AST has type (). In the rest of this tutorial and when not specified, we consider the term parser to also include recognizer.
A state indicates if the parsing was successful, partial or erroneous. It carries information about which item was expected next and the AST built from the data read. Convenient functions such as unwrap_data() or is_successful() are available directly from ParseState. A more complete function is into_result() which transforms the state into a type Result that can be pattern matched. Here a full example:
fn analyse_state(state: ParseState<StrStream, Vec<char>>) { match state.into_result() { Ok((success, error)) => { if success.partial_read() { println!("Partial match: {:?} because: {}", success.data, error); } else { println!("Full match: {:?}", success.data); } } Err(error) => { println!("Error: {}", error); } } } fn main() { analyse_state(calc::parse_integer("10".stream())); // complete analyse_state(calc::parse_integer("10a".stream())); // partial analyse_state(calc::parse_integer("a".stream())); // erroneous } // Result: // Full match: ['1', '0'] // Partial match: ['1', '0'] because: 1:3: unexpected `a`, expecting `["0-9"]`. // Error: 1:1: unexpected `a`, expecting `["0-9"]`.
You are now able to efficiently use the code generated by Oak.
As you probably noticed, the rule integer produces a value of type Vec<char> which is not a usable representation of an integer. We must transform this value into a better type such as u32. To achieve this goal, we use a semantic action which gives meaning to the characters read. A semantic action is a Rust function taking the value produced by an expression and returning another one more suited for further processing. The grammar becomes:
grammar! calc { #![show_api] // ... previous rules truncated. integer = ["0-9"]+ > to_digit pub type Digit = u32; fn to_digit(raw_text: Vec<char>) -> Digit { use std::str::FromStr; let text: String = raw_text.into_iter().collect(); u32::from_str(&*text).unwrap() } }
The combinator e > f expects a PEG combinator on the left and a function name on the right, it works like a "reverse function call operator" in the sense that f is called with the result value of e. Semantic actions must be Rust functions declared inside the grammar! so we can examine its return type. You can call function from other modules or crates by wrapping it up inside a function local to the grammar. Any Rust code is accepted, here we use an extra type declaration Digit which will be accessible from outside with calc::Digit.
Oak gives a type to any parsing expression to help you constructing your AST more easily. Next sections explain how Oak gives a type to expressions and how you can help Oak to infer better types. For the moment, when you want to know the type of an expression, just creates a rule r = e, activates the attribute #[show_api] and consults the return type of the generated function from the compiler output. Note that a tuple type such as (T, U) is automatically unpacked into two function arguments, so we expect the function to be of type f(T, U) and not f((T, U)).
CalcWe can now build another part of our language: a simple arithmetic calculator where operands can be integers or variables (identifier). We start by extending our grammar to deal with addition and subtraction.
fn main() { grammar! calc { #![show_api] // ... previous rules truncated. integer = ["0-9"]+ > to_digit operand = integer / identifier pub type Digit = u32; fn to_digit(raw_text: Vec<char>) -> Digit { use std::str::FromStr; let text: String = raw_text.into_iter().collect(); u32::from_str(&*text).unwrap() } } }grammar! calc { #![show_api] // ... previous rules truncated. integer = ["0-9"]+ > to_digit operand = integer / identifier pub type Digit = u32; fn to_digit(raw_text: Vec<char>) -> Digit { use std::str::FromStr; let text: String = raw_text.into_iter().collect(); u32::from_str(&*text).unwrap() } }
(space, identifier that do not start with digit, ...).
Extend the grammar to support let-in anywhere in expressions. Note that you do not need to modify the AST structure.