# Parsing

## Parser Combinators

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.

## NanoParsec

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.

{-# OPTIONS_GHC -fno-warn-unused-do-bind #-}

module NanoParsec where

import Data.Char
import Control.Applicative


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.

newtype Parser a = Parser { parse :: String -> [(a,String)] }

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.

runParser :: Parser a -> String -> a
runParser m s =
case parse m s of
[(res, [])] -> res
[(_, rs)]   -> error "Parser did not consume entire stream."
_           -> error "Parser error."


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.

item :: Parser Char
item = Parser $\s -> case s of [] -> [] (c:cs) -> [(c,cs)]  A bind operation for our parser type will take one parse operation and compose it over the result of second parse function. Since the parser operation yields a list of tuples, composing a second parser function simply maps itself over the resulting list and concat's the resulting nested list of lists into a single flat list in the usual list monad fashion. The unit operation injects a single pure value as the result, without reading from the parse stream. bind :: Parser a -> (a -> Parser b) -> Parser b bind p f = Parser$ \s -> concatMap (\(a, s') -> parse (f a) s') $parse p s unit :: a -> Parser a unit a = Parser (\s -> [(a,s)])  As the terminology might have indicated this is indeed a Monad (also Functor and Applicative). instance Functor Parser where fmap f (Parser cs) = Parser (\s -> [(f a, b) | (a, b) <- cs s]) instance Applicative Parser where pure = return (Parser cs1) <*> (Parser cs2) = Parser (\s -> [(f a, s2) | (f, s1) <- cs1 s, (a, s2) <- cs2 s1]) instance Monad Parser where return = unit (>>=) = bind  Of particular importance is that this particular monad has a zero value (failure), namely the function which halts reading the stream and returns the empty stream. Together this forms a monoidal structure with a secondary operation (combine) which applies two parser functions over the same stream and concatenates the result. Together these give rise to both the Alternative and MonadPlus class instances which encode the logic for trying multiple parse functions over the same stream and handling failure and rollover. The core operator introduced here is the (<|>) operator for combining two optional paths of parser logic, switching to the second path if the first fails with the zero value. instance MonadPlus Parser where mzero = failure mplus = combine instance Alternative Parser where empty = mzero (<|>) = option combine :: Parser a -> Parser a -> Parser a combine p q = Parser (\s -> parse p s ++ parse q s) failure :: Parser a failure = Parser (\cs -> []) option :: Parser a -> Parser a -> Parser a option p q = Parser$ \s ->
case parse p s of
[]     -> parse q s
res    -> res


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_v

On 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).

satisfy :: (Char -> Bool) -> Parser Char
satisfy p = item bind \c ->
if p c
then unit c
else (Parser (\cs -> []))


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.

oneOf :: [Char] -> Parser Char
oneOf s = satisfy (flip elem s)

chainl :: Parser a -> Parser (a -> a -> a) -> a -> Parser a
chainl p op a = (p chainl1 op) <|> return a

chainl1 :: Parser a -> Parser (a -> a -> a) -> Parser a
p chainl1 op = do {a <- p; rest a}
where rest a = (do f <- op
b <- p
rest (f a b))
<|> return a


Using satisfy we can write down several combinators for detecting the presence of specific common patterns of characters ( numbers, parenthesized expressions, whitespace, etc ).

char :: Char -> Parser Char
char c = satisfy (c ==)

natural :: Parser Integer
natural = read <$> some (satisfy isDigit) string :: String -> Parser String string [] = return [] string (c:cs) = do { char c; string cs; return (c:cs)} token :: Parser a -> Parser a token p = do { a <- p; spaces ; return a} reserved :: String -> Parser String reserved s = token (string s) spaces :: Parser String spaces = many$ oneOf " \n\r"

digit :: Parser Char
digit = satisfy isDigit

number :: Parser Int
number = do
s <- string "-" <|> return []
cs <- some digit
return $read (s ++ cs) parens :: Parser a -> Parser a parens m = do reserved "(" n <- m reserved ")" return n  And that's about it! In a few hundred lines we have enough of a parser library to write down a simple parser for a calculator grammar. In the formal Backus–Naur Form our grammar would be written as: number = [ "-" ] digit { digit }. digit = "0" | "1" | ... | "8" | "9". expr = term { addop term }. term = factor { mulop factor }. factor = "(" expr ")" | number. addop = "+" | "-". mulop = "*". The direct translation to Haskell in terms of our newly constructed parser combinator has the following form: data Expr = Add Expr Expr | Mul Expr Expr | Sub Expr Expr | Lit Int deriving Show eval :: Expr -> Int eval ex = case ex of Add a b -> eval a + eval b Mul a b -> eval a * eval b Sub a b -> eval a - eval b Lit n -> n int :: Parser Expr int = do n <- number return (Lit n) expr :: Parser Expr expr = term chainl1 addop term :: Parser Expr term = factor chainl1 mulop factor :: Parser Expr factor = int <|> parens expr infixOp :: String -> (a -> a -> a) -> Parser (a -> a -> a) infixOp x f = reserved x >> return f addop :: Parser (Expr -> Expr -> Expr) addop = (infixOp "+" Add) <|> (infixOp "-" Sub) mulop :: Parser (Expr -> Expr -> Expr) mulop = infixOp "*" Mul run :: String -> Expr run = runParser expr main :: IO () main = forever$ do
putStr "> "
a <- getLine
print $eval$ run a

Now we can try out our little parser.

, Tok.opLetter        = oneOf ":!#$%&*+./<=>?@\\^|-~" , Tok.reservedNames = reservedNames , Tok.reservedOpNames = reservedOps , Tok.caseSensitive = True } Lexer Given the token definition we can create the lexer functions. lexer :: Tok.TokenParser () lexer = Tok.makeTokenParser langDef parens :: Parser a -> Parser a parens = Tok.parens lexer reserved :: String -> Parser () reserved = Tok.reserved lexer semiSep :: Parser a -> Parser [a] semiSep = Tok.semiSep lexer reservedOp :: String -> Parser () reservedOp = Tok.reservedOp lexer prefixOp :: String -> (a -> a) -> Ex.Operator String () Identity a prefixOp s f = Ex.Prefix (reservedOp s >> return f)  Abstract Syntax Tree In a separate module we'll now define the abstract syntax for our language as a datatype. module Syntax where data Expr = Tr | Fl | Zero | IsZero Expr | Succ Expr | Pred Expr | If Expr Expr Expr deriving (Eq, Show) 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. prefixOp s f = Ex.Prefix (reservedOp s >> return f) -- Prefix operators table :: Ex.OperatorTable String () Identity Expr table = [ [ prefixOp "succ" Succ , prefixOp "pred" Pred , prefixOp "iszero" IsZero ] ] -- if/then/else ifthen :: Parser Expr ifthen = do reserved "if" cond <- expr reservedOp "then" tr <- expr reserved "else" fl <- expr return (If cond tr fl) -- Constants true, false, zero :: Parser Expr true = reserved "true" >> return Tr false = reserved "false" >> return Fl zero = reservedOp "0" >> return Zero expr :: Parser Expr expr = Ex.buildExpressionParser table factor factor :: Parser Expr factor = true <|> false <|> zero <|> ifthen <|> parens expr contents :: Parser a -> Parser a contents p = do Tok.whiteSpace lexer r <- p eof return r  The toplevel function we'll expose from our Parse module is parseExpr which will be called as the entry point in our REPL. parseExpr s = parse (contents expr) "<stdin>" s ## Evaluation 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. isNum Zero = True isNum (Succ t) = isNum t isNum _ = False isVal :: Expr -> Bool isVal Tr = True isVal Fl = True isVal t | isNum t = True isVal _ = False  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. eval' x = case x of IsZero Zero -> Just Tr IsZero (Succ t) | isNum t -> Just Fl IsZero t -> IsZero <$> (eval' t)
Succ t                    -> Succ <$> (eval' t) Pred Zero -> Just Zero Pred (Succ t) | isNum t -> Just t Pred t -> Pred <$> (eval' t)
If Tr  c _                -> Just c
If Fl _ a                 -> Just a
If t c a                  -> (\t' -> If t' c a) <$> eval' t _ -> Nothing  At the toplevel we simply apply eval' repeatedly until either a value is reached or we're left with an expression that has no well-defined way to proceed. The term is "stuck" and the program is in an undefined state. nf x = fromMaybe x (nf <$> eval' x)

eval :: Expr -> Maybe Expr
eval t = case nf t of
nft | isVal nft -> Just nft
| otherwise -> Nothing -- term is "stuck"

## REPL

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) >> loop

## Soundness

Great, 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 evaluate

Oh 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?