For parsing in Haskell it is quite common to use a family of libraries known as parser combinators which let us compose higher order functions to generate parsers. Parser combinators are a particularly expressive pattern that allows us to quickly prototype language grammars in an small embedded domain language inside of Haskell itself. Most notably we can embed custom Haskell logic inside of the parser.
So now let's build our own toy parser combinator library which we'll call NanoParsec just to get the feel of how these things are built.
Structurally a parser is a function which takes an input stream of characters and yields a parse tree by applying the parser logic over sections of the character stream (called lexemes) to build up a composite data structure for the AST.
Running the function will result in traversing the stream of characters
yielding a value of type a that usually represents the AST for the
parsed expression, or failing with a parse
error for malformed input, or failing by not consuming the entire stream of
input. A more robust implementation would track the position information of
failures for error reporting.
Recall that in Haskell the String type is defined to be a list of
Char values, so the following are equivalent forms of the same data.
"1+2*3"
['1', '+', '2', '*', '3']We advance the parser by extracting a single character from the parser stream and returning in a tuple containing itself and the rest of the stream. The parser logic will then scrutinize the character and either transform it in some portion of the output or advance the stream and proceed.
A bind operation for our parser type will take one parse operation and compose it over the result of second parse function. Since the parser operation yields a list of tuples, composing a second parser function simply maps itself over the resulting list and concat's the resulting nested list of lists into a single flat list in the usual list monad fashion. The unit operation injects a single pure value as the result, without reading from the parse stream.
As the terminology might have indicated this is indeed a Monad (also Functor and Applicative).
Of particular importance is that this particular monad has a zero value
(failure), namely the function which halts reading the stream and returns
the empty stream. Together this forms a monoidal structure with a secondary
operation (combine) which applies two parser functions over the same stream
and concatenates the result. Together these give rise to both the Alternative
and MonadPlus class instances which encode the logic for trying multiple parse
functions over the same stream and handling failure and rollover.
The core operator introduced here is the (<|>) operator for combining two
optional paths of parser logic, switching to the second path if the first fails
with the zero value.
Derived automatically from the Alternative typeclass definition are the many
and some functions. Many takes a single function argument and repeatedly
applies it until the function fails and then yields the collected results up to
that point. The some function behaves similar except that it will fail
itself if there is not at least a single match.
-- | One or more.
some :: f a -> f [a]
some v = some_v
where
many_v = some_v <|> pure []
some_v = (:) <$> v <*> many_v
-- | Zero or more.
many :: f a -> f [a]
many v = many_v
where
many_v = some_v <|> pure []
some_v = (:) <$> v <*> many_vOn top of this we can add functionality for checking whether the current character in the stream matches a given predicate ( i.e is it a digit, is it a letter, a specific word, etc).
Essentially this 50 lines code encodes the entire core of the parser combinator machinery. All higher order behavior can be written on top of just this logic. Now we can write down several higher level functions which operate over sections of the stream.
chainl1 parses one or more occurrences of p, separated by op and
returns a value obtained by a recursing until failure on the left hand side of
the stream. This can be used to parse left-recursive grammar.
Using satisfy we can write down several combinators for detecting the
presence of specific common patterns of characters ( numbers, parenthesized
expressions, whitespace, etc ).
And that's about it! In a few hundred lines we have enough of a parser library to write down a simple parser for a calculator grammar. In the formal Backus–Naur Form our grammar would be written as:
number = [ "-" ] digit { digit }.
digit = "0" | "1" | ... | "8" | "9".
expr = term { addop term }.
term = factor { mulop factor }.
factor = "(" expr ")" | number.
addop = "+" | "-".
mulop = "*".The direct translation to Haskell in terms of our newly constructed parser combinator has the following form:
Now we can try out our little parser.
$ runhaskell parsec.hs
> 1+2
3
> 1+2*3
7Generalizing String
The limitations of the String type are well-known, but what is particularly nice
about this approach is that it adapts to different stream types simply by adding
an additional parameter to the Parser type which holds the stream type. In its
place a more efficient string data structure (Text, ByteString) can be
used.
newtype Parser s a = Parser { parse :: s -> [(a,s)] }For the first couple of simple parsers we will use the String type for
simplicity's sake, but later we will generalize our parsers to use the Text
type. The combinators and parsing logic will not change, only the lexer and
language definition types will change slightly to a generalized form.
Now that we have the feel for parser combinators work, we can graduate to the full Parsec library. We'll effectively ignore the gritty details of parsing and lexing from now on. Although an interesting subject parsing is effectively a solved problem and the details are not terribly important for our purposes.
The Parsec library defines a set of common combinators much like the operators we defined in our toy library.
Combinator Description
char Match the given character.
string Match the given string.
<|> The choice operator tries to parse the first argument before proceeding to the second. Can be chained sequentially to generate a sequence of options.
many Consumes an arbitrary number of patterns matching the given pattern and returns them as a list.
many1 Like many but requires at least one match.
sepBy Match a arbitrary length sequence of patterns, delimited by a given pattern.
optional Optionally parses a given pattern returning its value as a Maybe.
try Backtracking operator will let us parse ambiguous matching expressions and restart with a different pattern.
parens Parses the given pattern surrounded by parentheses.
Tokens
To create a Parsec lexer we must first specify several parameters about how individual characters are handled and converted into tokens. For example some tokens will be handled as comments and simply omitted from the parse stream. Other parameters include indicating what characters are to be handled as keyword identifiers or operators.
langDef :: Tok.LanguageDef ()
langDef = Tok.LanguageDef
{ Tok.commentStart = "{-"
, Tok.commentEnd = "-}"
, Tok.commentLine = "--"
, Tok.nestedComments = True
, Tok.identStart = letter
, Tok.identLetter = alphaNum <|> oneOf "_'"
, Tok.opStart = oneOf ":!#$%&*+./<=>?@\\^|-~"
, Tok.opLetter = oneOf ":!#$%&*+./<=>?@\\^|-~"
, Tok.reservedNames = reservedNames
, Tok.reservedOpNames = reservedOps
, Tok.caseSensitive = True
}Lexer
Given the token definition we can create the lexer functions.
Abstract Syntax Tree
In a separate module we'll now define the abstract syntax for our language as a datatype.
Parser
Much like before our parser is simply written in monadic blocks, each mapping a
set of patterns to a construct in our Expr type. The toplevel entry point
to our parser is the expr function which we can parse with by using the
Parsec function parse.
The toplevel function we'll expose from our Parse module is parseExpr
which will be called as the entry point in our REPL.
Our small language gives rise to two syntactic classes, values and expressions.
Values are in normal form and cannot be reduced further. They consist of
True and False values and literal numbers.
The evaluation of our languages uses the Maybe applicative to accommodate
the fact that our reduction may halt at any level with a Nothing if the
expression being reduced has reached a normal form or cannot proceed because the
reduction simply isn't well-defined. The rules for evaluation are a single step
by which an expression takes a single small step from one form to another by a
given rule.
At the toplevel we simply apply eval' repeatedly until either a value is
reached or we're left with an expression that has no well-defined way to
proceed. The term is "stuck" and the program is in an undefined state.
The driver for our simple language simply invokes all of the parser and evaluation logic in a loop feeding the resulting state to the next iteration. We will use the haskeline library to give us readline interactions for the small REPL. Behind the scenes haskeline is using readline or another platform-specific system library to manage the terminal input. To start out we just create the simplest loop, which only parses and evaluates expressions and prints them to the screen. We'll build on this pattern in each chapter, eventually ending up with a more full-featured REPL.
The two functions of note are the operations for the InputT monad
transformer.
runInputT :: Settings IO -> InputT IO a -> IO a
getInputLine :: String -> InputT IO (Maybe String)When the user enters an EOF or sends a SIGQUIT to input, getInputLine
will yield Nothing and can handle the exit logic.
process :: String -> IO ()
process line = do
let res = parseExpr line
case res of
Left err -> print err
Right ex -> print $ runEval ex
main :: IO ()
main = runInputT defaultSettings loop
where
loop = do
minput <- getInputLine "Repl> "
case minput of
Nothing -> outputStrLn "Goodbye."
Just input -> (liftIO $ process input) >> loopGreat, now let's test our little interpreter and indeed we see that it behaves as expected.
Arith> succ 0
succ 0
Arith> succ (succ 0)
succ (succ 0)
Arith> iszero 0
true
Arith> if false then true else false
false
Arith> iszero (pred (succ (succ 0)))
false
Arith> pred (succ 0)
0
Arith> iszero false
Cannot evaluate
Arith> if 0 then true else false
Cannot evaluateOh no, our calculator language allows us to evaluate terms which are syntactically valid but semantically meaningless. We'd like to restrict the existence of such terms since when we start compiling our languages later into native CPU instructions these kind errors will correspond to all sorts of nastiness (segfaults, out of bounds errors, etc). How can we make these illegal states unrepresentable to begin with?
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