A simple way to write parsers: using the State monad

June 15, 2018

Sometimes, we just want to parse and process lines from a text file without relying on a third-party library, and not writing tons of if ourself because the structure is a bit too dynamic.

Sometimes, regexes are enough but they can be obscure and way too complicated or maintenable.

We remember parsers as huge auto-generated files with some grammar as entry point and we don’t want to get there. Hopefully, we know the State monad (because shared state is evil) or not (we’ll quickly present it). We’ll see how “easy” it is to write parsers using the State monad.

We’ll slowly implement an arithmetic parser from scratch, then we’ll see a Whitespace (the programming language, composed only of… whitespaces!) parser implementation using this technique.

Finally, we’ll take a look around some battle-tested parsing libraries, to get serious.

This article is shamelessly inspired by Leif Battermann’s article: Parsers in Scala built upon existing abstractions. Thanks!


Parsers generality

Grammar needed

In this section, we’ll quickly explain parsers logic from a high-level. Feel free to dive into Introduction to parsers which is a very nice post going more deeply around all the different layers and techniques.

A parser works with a grammar, to recognize the language used. For instance, one arithmetic grammar is:

<Exp> ::= <Exp> + <Term> | <Exp> - <Term> | <Term>
<Term> ::= <Term> * <Factor> | <Term> / <Factor> | <Factor>
<Factor> ::= x | y | ... | ( <Exp> ) | - <Factor>

This is a grammar under the Backus–Naur form (BNF).

Each symbol <Exp> <Term> <Factor> is defined (ie: "::=") by an expression (symbols, literals, or a combination).

Where it hurts the brain is that it’s all recursive! (Exp uses Term, which uses Factor, which uses Exp and Factor, …)

Here are the steps parsing 5 + ( -3 / 2 ):

<Exp> + <Term>
<Term> + <Factor>
<Factor> + ( <Expr> )
5 + ( <Term> )
5 + ( <Term> / <Factor> )
5 + ( <Factor> / 2 )
5 + ( - <Factor> / 2 )
5 + ( - 3 / 2 )

There are tons of frameworks able to read a BNF file to generate a parser in the language of our choice. In Java, we have ANTLR for instance.

A parser consumes a text then it tries to match its grammar. If it successfully matches some expressions and symbols, we can then interpret them (do something, like setting some value into the memory, here, we’ll .modify our State monad!).


Parser combinators are a powerful way of writing parsers. We work on “small parsers” not aware of the “grand schema of things”, then we combine them into bigger ones.

If we have 2 parsers:

  • A understands digits [0-9]+;
  • B understands letters [a-zA-Z]+.

We can combine them to understand: [0-9]+|[a-zA-Z]+.

Some pseudo-code would give:

val A: Parser[String] = makeParser("[0-9]+")
val B: Parser[String] = makeParser("[a-zA-Z]+")
val AorB: Parser[String] = A <+> B

// A
A.parse("123") = "123"
A.parse("toto") = ERR
// B
B.parse("123") = ERR
B.parse("toto") = "toto"
// (A | B)
AorB.parse("123") = "123"
AorB.parse("toto") = "toto"

Here, we are creating 2 parsers A and B, then we create another one AB from their combination using <+> (let’s say it’s an operator available on Parser).

A, B, and AorB are all typed the same Parser[String] because they return a String as result. A parser can be seen as a function: String => Option[A]. We give it an input, it outputs a value or not (a parser is not total: when it can’t match its input, then we have an error or a fallback). It’s easy to combine such parsers using PartialFunction’s orElse:

val A: PartialFunction[String, Int] = { case s if s.matches("[0-9]+") => s.toInt }
val B: PartialFunction[String, Int] = PartialFunction(_.length)
val AB = A orElse B

// A("toto"): MatchError!
AB("123")  // 123
AB("toto") // 4

Et voilà, parser combinators FTW!

We’ll do exactly the same when combining our “parsers” (represented by a StateT monad), except that we’ll use proper operators available thanks to some typeclasses and laws.

Inversible parsers

A quick note on inversible parsers because it’s quite powerful.

When we parse a language, we provide the parser some input. It matches and converts the input into some objects in-memory. What if we want to serialize the result back into to JSON or to the original format (altered, optimized maybe) for instance? We need to write the inverse operation of the parsing, the serialization.

Inversible parsers kill two birds with one stone, and ensure the isomorphism of both forms.

There is a Scala project: invertible-syntax, demonstrating this feature:

import invertible._, Syntax._
val ints = new Syntax[List[BigInt]] {
    def apply[P[_]: Transcriber] = int sepBy1 sepSpace

ints.parse("1 20 30")
// \/-(List(1, 20, 30))

ints.print(List(1, 20, 300))
// Some(1 20 300)

From the String we get the List, and vice-versa, without custom serialization code.

The State monad in a nutshell

Let’s dive into our main topic: the State monad!

As we know, we use it to avoid to depends on some global mutable state.

A really good documentation about State in general can be found on cats website. I’ll just quote their introduction because it’s clear and concise:

State is a structure that provides a functional approach to handling application state. State[S, A] is basically a function S => (S, A), where S is the type that represents your state and A is the result the function produces. In addition to returning the result of type A, the function returns a new S value, which is the updated state.

For instance, we can rely on State like this:

val c = State[String, Char](s => (s.tail, s.head))
c.run("re").value // res0: (String, Char) = (e,r)
c.run("are").value // res0: (String, Char) = (re,a)

Ok, this is just a plain function, what else? Well, it’s a monad!

val head = State[String, Char](s => (s.tail, s.head))
val toInt = State[String, Int](s => ("", s.toInt))
val product: State[String, Int] = for {
  h <- head
  i <- toInt
} yield h * i

val initialState = "23"
product.run(initialState).value // "2" * 3 = 50 * 3 = 150

Clear and concise, right? Also easy to test! No need to think of anything else, all is “contained”.

What does it bring on the table compared to a function? Ouch…

val head: String => (String, Char) = s => (s.tail, s.head)
val toInt: String => (String, Int) = s => ("", s.toInt)
val product: String => (String, Int) = s => {
  val (state, ch) = head(s)
  val (state2, i) = toInt(state)
  (state2, ch * i)

val initialState = "23"
product(initialState) // "150" ("2" * 3 = 50 * 3)

Clearly, the State monad removes tons of boilerplate and even provides way more than just state and value management. (more on this in cats documentation)

It also encapsulates the value into an effect! This is why we used product.run(...).value. We need to add .value because State is an alias for StateT[Eval, S, A]: therefore it returns an Eval (an Eval is a monad containing a computation or value, strict or lazy).

Down to the rabbit hole, StateT is also an alias for IndexedStateT[F, S, S, A] which can have a different state type in input and output. More on this later (or not).

The State object provides some functions to easily identify some patterns, such as modify used to alter the state, without outputting any value (Unit).

// excerpt!
object State {
    def modify[S](f: S => S): State[S, Unit] = State(s => (f(s), ()))

val toInt = State[String, Int](s => ("", s.toInt))
val tail: State[String, Unit] = State.modify[String](_.tail)
val product: State[String, Int] = for {
  _ <- tail // we ignore the value, it's ()
  i <- toInt
} yield i

product.run("23").value // "3"

Working with State and for-comprehension (flatMap), it’s important to remember 2 things:

  • the new state is automatically given to the next State, it’s not magic (it’s part of the flatMap implementation of State);
  • according to the inner monad of StateT (Eval for State but could be anything like Option), it’s possible to short-circuit the rest of the for-comprehension

    • None.flatMap(x => ???) is still None, it does nothing;
    • same for Left("boom").flatMap(x => ???).

Back to our parsers: we’ll create mini-parsers to parse digits, letters, words etc. We’ll combine them to form the language we want.

State to “parser”

How to deal with errors

Back to our initial parser:

val c = State[String, Char](s => (s.tail, s.head))

// BOOM!
// Exception in thread "main" java.lang.UnsupportedOperationException: empty.tail

We don’t handle errors yet. It would be better to tail only if we have something to tail.

First, let’s expand and use a for-comprehension to do the same exact thing, but more chunked:

val c: State[String, Char] = for {
  state <- State.get[String] // alias for: State(s => (s, s)), we want the current state
  _ <- State.modify[String](_.tail) // we alter the State's state
} yield state.head // the value of the State

We can add a condition to check if the state is empty before processing:

val c: State[String, Char] = for {
  state <- State.get[String]
  _ <- if (state.isEmpty)
    // ???
    // We need to short-circuit here to not evaluate yield state.head, how?
    // ().raiseError[State[String, ?], String] // ERR: no ApplicativeError instance available :(
} yield state.head

The only is to short-circuit the flatMapping to not evaluate yield state.head.

It should be possible if Eval (the monad inside State) is short-circuitable. Is it? To be so, the monad needs to be a MonadError. It doesn’t look like Eval is. ;(

Let’s try to implement it nonetheless!

ApplicativeError to the rescue?

We’ll run into troubles if we try, because we can’t set any value to the State when an error is raised:

implicit val stateAE = new ApplicativeError[State[String, ?], Unit] {
  override def raiseError[A](e: Unit): State[String, A] =
    State[String, A](s => ("", ???))
    // We can't construct any State because we can't construct any A!

().raiseError[State[String, ?], Unit].run("hey").value
// Exception scala.NotImplementedError

All this is because State depends upon Eval, which is not a MonadError as we said. It can’t represent an error state. We need to change our inner monad. :sunglasses:

Distinguish empty result from an error

We’ll need to go deeper and use StateT to change it. We’ll naturally switch to Option which implements MonadError with None as “error” hence short-circuitable.

type Parser[A] = StateT[Option, String, A]

val c: Parser[Char] = for {
  state <- StateT.get[Option, String]
  _ <- if (state.isEmpty)
    ().raiseError[Parser, String]
    // same: ApplicativeError[Parser, Unit].raiseError(())
    // same: StateT.liftF[Option, String, Char](None)
    StateT.modify[Option, String](_.tail)
} yield state.head

// Some((oto,t))

// None

It’s working! Now, we can short-circuit our parsers when they can’t match the content.

Note: we use () as error, because Option implements only the Unit type as “error”: MonadError[Option, Unit]. It makes sense, because None can’t contain any value, therefore, we can’t provide any message or anything.

This tiny parser is the root of everything. It is the one that consumes a Char if it can. Let’s build more specific parsers on top of it and build a real parser.

Implementing an arithmetic parser


We can use the same technique to build parsers that only recognize special characters (digits, letters etc.).

To make it reusable, we’ll use a predicate and will rely on our first parser:

def matchChar(p: Char => Boolean): Parser[Char] = for {
  char <- c
  _ <- if (p(char))
    StateT.get[Option, String] // we don't use it, but we need to return a StateT!
    ().raiseError[Parser, Unit]
} yield char

def digit = matchChar(_.isDigit)
def letter = matchChar(_.isLetter)
def space = matchChar(_.isWhitespace)

// We can create a parser that matches a specific character
def customChar(c: Char): Parser[Char] = matchChar(_ == c)

That gives:

digit.run("01") // Some(("1", "0"))
digit.run("ab") // None
letter.run("ab") // Some(("b", "a"))

Next, more than one character parsers:

def string(word: String) = StateT[Option, String, String](s =>
    if (s.startsWith(word))
        Some(s.drop(word.length), word)
// We can also reuse "customChar" to deal with errors (non-match)
def string2(s: String): Parser[String] = s.map(customChar).toList.sequence.map(_.mkString)

// same for numbers
def number = StateT[Option, String, String](s =>
    if (s.headOption.exists(_.isDigit)) // dealing with empty string


string("toto").run("toto") // Some((,toto))
string("toto").run("titi") // None
number.run("123to") // Some((to,123))

Combination using Applicatives

We need a way to combine them, in the sense of “concatenation” (not fallback). This is where the Applicative magic shines, because it’s its purpose to combine stuff.

Monoid also combines stuff. The Applicative has a monoidal structure: def ap[A, B](ff: F[A => B])(fa: F[A]): F[B]. It gives: F -> F -> F.

cats’s Applicative offers us multiple functions to combine them (all of them rely on ap):

  • map2[A, B, Z](fa: F[A], fb: F[B])(f: (A, B) => Z): F[Z] to combine two of them and chose what to do;
  • forEffect alias <* alias productL: discard the right value;
  • followedBy alias *> alias productR: discard the left value;
  • (...).mapN((A, B, C, ...) => Z): syntax sugar available on any tuples.

Let’s play with them:

val totoAndTiti = string("toto") *> string("titi")
// Some((,titi)): has consumed "toto" then "titi"; kept the second value

val totoAndTiti2 = string("toto") <* string("titi")
// Some((,toto)): has consumed "toto" then "titi"; kept the first value

(string("toto") <* string("titi") *> string("a")).run("tototitia")
// Some((,toto)): consumed everything; kept the first

((string("toto") <* string("titi")) *> string("a")).run("tototitia")
// Some((,a)): not the same! be careful of operators precedence

// Some((,a)): equivalent, but with a fluent code

val totoAndTiti3 = string("toto") *> string("titi") <* string("a")
totoAndTiti3.run("tototitia") // Some((,titi)): consumed everything; kept the middle

It’s sometimes clearer to combine Applicative’s using mapN:

val array = (string("toto"), string("["), number, string("]")).mapN { (a,b,c,d) => (a,c) }
// Some((,(toto,42)))

Fallback using SemigroupK

In the same way we previously used orElse to “fallback” on another PartialFunction when the first one couldn’t handle the input, it’s possible to provide a fallback to our parsers (StateT). This is called backtracking: the 1st parser fails to match, then the 2nd parser starts from the same beginning.

val totoOrTiti = string("A") <+> string("B")
totoOrTiti.run("Axxx") // Some((xxx,A))
totoOrTiti.run("Bxxx") // Some((xxx,B))
totoOrTiti.run("C")    // None

<+> is syntax sugar coming from SemigroupK. It is defined as def <+>(y : F[A]) : F[A]. It’s just a synonym of combineK: combining two F[A]s to get an F[A]. (Semigroup combines two A only)

List is a ubiquitous example: List(1) <+> List(2, 3) is List(1, 2, 3).

Without using the SemigroupK thing, we could also configure a fallback using Option.orElse (because our F is Option, that’s not always the case!):

val aOrB = StateT[Option, String, String](s => string("A").run(s) orElse string("B").run(s))
aOrB.run("B") // Some((,B))

If the parsing for "A" fails (it will raiseError, ie: return None), then it will try with the other one.

Recursion is at stake

For the sake of genericity, let’s say we want to parse multiple digits without our custom number parser. We know how to match a digit, so why not reuse this?

   val oneDigit = digit.map(_.toInt)
  val twoDigits = digit.flatMap(a => digit.map(b => s"$a$b".toInt))
val threeDigits = digit.flatMap(a => digit.flatMap(b => digit.map(c => s"$a$b$c".toInt)))

threeDigits.run("123") // Some((,123))
threeDigits.run("12f") // None

This is nice, but those parsers have a fixed length. Let’s try to make one more dynamic (at most N digits):

// We can't do that, because there is no such thing as an "empty" Char
// val atMostTwoDigits = for {
//     a <- digit
//     b <- digit <+> ''.pure[Parser] // '' doesn't compile
//   } yield List(a, b).mkString.toInt

We don’t want to short-circuit the whole thing if we don’t find the 2nd digit. Hence we need an “empty” Parser if the 2nd digit is not found (to handle the error that would lead to short-circuiting).

We can use a List[Char] that has a empty state: List():

val atMostTwoDigits = for {
  a <- digit
  b <- digit.map(List(_)) <+> StateT.pure(List())
} yield s"$a${b.headOption.getOrElse("")}".toInt

// Some((,1))

// Some((3,12))

Clearly, something is recursive around here. Let’s make a function we’ll call while we find digits:

val digits: StateT[Option, String, Int] = for {
  head <- digit
  rest <- digits <+> StateT.pure(0)
} yield head.toString.toInt * math.pow(10, nbDigits(rest)).toInt + rest

def nbDigits(rest: Int) = if (rest == 0) 0 else math.ceil(math.log10(rest))

digits.run("123a") // Some((a,123))

If we don’t find a 2nd digit, we fallback to 0. In the end, everything is summed up so 0 has no impact.

Because we decided that our State would be Int-based, we must do some maths to find back the number from a Char (head) + a number (rest). Here, 123 = (1 * 100) + (2 * 10) + (3 * 1) + 0.

Keeping a List[Char] as value is easier because we can aggregate at the end only:

val digits: StateT[Option, String, Int] = {
  def digits0: StateT[Option, String, List[Char]] = for {
    head <- digit
    tail <- digits0 <+> StateT.pure(List())
  } yield head :: tail
  digits0.map(_.mkString.toInt) // we don't mind the overflow!

Our digits0 function can be made generic and reused for any parser. We use a List[A] (A depends of the result of the given parser) to aggregate the results:

def many[A](s: Parser[A]): Parser[List[A]] = for {
  head <- s
  tail <- many(s) <+> StateT.pure(List())
} yield head :: tail

val digits: Parser[Int] = many(digit).map(_.mkString.toInt)

digits.run("123a") // Some((a, 123))

We may want to provide a “many or none” parser (like spaces, to ignore them between tokens):

def manyOrNone[A](s: Parser[A]): Parser[List[A]] =
    many(s) <+> StateT.pure(List())

val spaces: Parser[Unit] = manyOrNone(space).map(_ => ())
def ignoreSpaces[A](s: Parser[A]): Parser[A] =
    (spaces, s, spaces).mapN((_, v, _) => v)

// we make our digits parser to ignore space between and after numbers
val digits: Parser[Int] = ignoreSpaces(many(digit).map(_.mkString.toInt))

spaces.run("   a")   // Some((a,()))
digits.run("1a")     // Some((a,1))
digits.run("12 ")    // Some((,12))
digits.run("  123 ") // Some((,123))

Again, we can combine them:

(digits *> digits).run(" 123 456  ")
// Some((,456))

(digits *> word).run(" 123 toto  "))
// Some((,toto))

// :digits: "+" :digits:
val plus: Parser[Int] = (digits, customChar('+'), digits).mapN((lhs, _, rhs) => lhs + rhs)
// Some((,6))

It’s getting there, our first operation!

We can generalize our plus with some recursion, to handle any amount of +:

// expr := digits [ '+' expr ]

def parseOrElse[A](s: Parser[A], default: A) = s <+> StateT.pure(default)

def plus(lhs: Int) = (customChar('+'), expr).mapN((_, rhs) => lhs + rhs)
def expr: Parser[Int] = for {
  lhs <- digits
  res <- parseOrElse(plus(lhs), lhs) // if we can't match "+ digits", we return the lhs only
} yield res

expr.run("1+5+8 + 13") // Some((,27))

Time to handle another operation: the multiplication!


With the multiplication, we must be careful. A lazy and wrong implementation is to just fallback on '*':

def expr: Parser[Int] = for {
  lhs <- digits
  res <- parseOrElse(
    (customChar('+'), expr).mapN((_, rhs) => lhs + rhs) <+>
    (customChar('*'), expr).mapN((_, rhs) => lhs * rhs),
  } yield res

expr.run("1+2+3") // Some((,6)): OK!
expr.run("2*5")   // Some((,10)): OK!
expr.run("2*5+8") // Some((,26)): KO! It did: 2*(5+8)
expr.run("2*5+8*2") // Some((,42)): KO! It did: 2*(5 + (8*2)) = 2*(5 + 16) = 2*21

The arithmetic operator precedence is not respected. Our implementation tries to do + first, then *. Inversing both lines won’t do a thing neither: the fact is that the processing (the + or *) is done “backward” (recursion goes the deepest it can, then goes up).

A working implementation is to prioritize the multiplications first: we want to finish the * recursion first, to compute the value, before getting back to the other parsers (+):

// - product := digits [ '*' product ]
// - expr := product [ '+' expr ]

def expr: Parser[Int] = for {
  lhs <- product
  res <- parseOrElse((customChar('+'), expr).mapN((_, rhs) => lhs + rhs), lhs)
} yield res

def product: Parser[Int] = for {
  lhs <- digits
  res <- parseOrElse((customChar('*'), product).mapN((_, rhs) => lhs * rhs), lhs)
} yield res

expr.runA("1+2+3")     // Some(6)
expr.runA("2*5")       // Some(10)
expr.runA("2*5*2+8")   // Some(28)
expr.runA("2*5+8*2*2") // Some(42)


We didn’t introduce ( or ) in our grammar yet. Easy? Let’s find out:

// - expr := product [ '+' expr ]
// - product := parens [ '*' product ]
// - parens := '(' + expr + ')' | digits

// the parser: '(' expr ')' => expr (">>" is `flatMap` ignoring the lhs value)
def parens: Parser[Int] = (ch('(') >> expr << ch(')')) <+> digits

Notice the difference here: we are NOT using *> but >>. >> is lazy (by-name parameter). It’s just a flatMap ignoring the left value. This is important because we must short-circuit asap otherwise expr will be evaluated again and again and we’ll stackoverflow.

def product: Parser[Int] = for {
  lhs <- parens // we changed from "digits" to "parens" (which handles digits)
  res <- parseOrElse((ch('*'), product).mapN((_, rhs) => lhs * rhs), lhs)
} yield res

expr.run("(1)") // 1
expr.run("(2)*(5)") // 10
expr.run("(2*(4+1*1))") // 10
expr.run("(2*(3+2*1))+1*2") // 12

Yeah! Not that complicated, and we provided a great feature here.

Let’s dive into troubles now.

Minus troubles

To add "-", we could do something like this:

def expr: Parser[Int] = for {
  lhs <- products
  res <- parseOrElse(
    (customChar('+'), expr).mapN((_, rhs) => lhs + rhs) <+>
    (customChar('-'), expr).mapN((_, rhs) => lhs - rhs),
} yield res

expr.runA("10-2")    // Some(8)
expr.runA("2*5-2")   // Some(8)
expr.runA("2*5-2+2") // Some(6) KO!
expr.runA("3-2-1")   // Some(2) KO!

It’s more complicated because "-" is not associative. Right now, our parser evaluates "3-2-1" as (3-(2-1)) whereas it should be ((3-2)-1).

It’s because we have a right-recursion. So it goes the deepest it can to the right, compute, then sends the values up. It’s okay for ’+’ but not for ’-‘.

If we try to build the smallest parser, just to deal with "-", it would be:

lazy val expr: Parser[Int] = {
  for {
    d <- digits
    e <- (customChar('-') >> expr) <+> StateT.pure(0)
  } yield d - e

expr.runA("10-2-1") // Some(9) KO!

Using a left-recursion would be something like this:

lazy val expr: Parser[Int] = {
  for {
    d <- expr
    e <- (customChar('-') >> digits) <+> StateT.pure(0)
  } yield d - e

expr.runA("10-2-1") // Boom!

We want this to evaluate ((10-2)-1) but of course, this stackoverflows ad infinitum. We can’t really implement grammars with our classic recursion. (http://users.monash.edu/~lloyd/tildeProgLang/Grammar/Top-Down/)

If you have a simple way to deal with it, don’t hesitate to send me a message; maybe I forgot about something. ;-)


Another case of stackoverflow is with super long expression, because we are flatMapping the hell out of them.

// Boom! StackOverflowError
expr.runA("1" + ("+1" * 1000)))

We are in trouble because many calls many which calls many etc. until the head is empty but it can take a long time and the stacking continues in memory…

I let the reader exercises its talent over this issue (probably using IO, or any stack-safe monad).

A Whitespace parser

Whitespace is a terrible programming language dealingonly with whitespaces! Three of them actually: ” ”, “\t”, “\n”. It can print to the screen, do some arithmetic operation, keep values in memory, do condition, etc.

Here is its documentation: https://hackage.haskell.org/package/whitespace-0.4/src/docs/tutorial.html. As you can see, not that complicated as language!

I spent some time to write a parser using StateT to understand Whitespace, you can check it out here: https://github.com/sderosiaux/whitespace-parser

The main “loop” is exactly as we saw:

def imp: StateT[F, (String, Stack), String] = for {
   output <- stackCommands.all <+>
     ioCommands.all <+>
     arithmeticCommands.all <+>
     heapCommands.all <+>
   rest <- imp <+> StateT.pure[F, (String, Stack), String]("")
} yield output + rest

The State has:

  • as state: the program and a Stack (where it holds the values on the stack filled by the program);
  • as value: the output of the program.

Here is the “Hello, world!“:

val helloWorld = "   \t  \t   \n\t\n     \t\t  \t \t\n\t\n     \t\t \t\t  \n\t\n     \t\t \t\t  \n\t\n     \t\t \t\t\t\t\n\t\n     \t \t\t  \n\t\n     \t     \n\t\n     \t\t\t \t\t\t\n\t\n     \t\t \t\t\t\t\n\t\n     \t\t\t  \t \n\t\n     \t\t \t\t  \n\t\n     \t\t  \t  \n\t\n     \t    \t\n\t\n  \n\n\n"
val Some(((rest, stack), output)) = new WhitespaceParser[Option].eval(helloWorld)

println(s"Stack: $stack")
println(s"Output: $output")

// Stack: List(33, 100, 108, 114, 111, 119, 32, 44, 111, 108, 108, 101, 72)
// Output: Hello, world!

As we can see, the inner monad is generic and given at call-site. That complexifies a bit the code but it offers more latitude to use any MonadError as result, and forces us to use generic methods from the typeclasses, instead of relying on implementation details.

A Brainfuck parser

One could also implement a Brainfuck parser with the State monad… It’s already done! See: https://github.com/tomwadeson/brainfuck-scala.

Relying on atto for the parsing, this provides the same kind of combinators we wrote:

import atto._, Atto._
import cats.implicits._

scala> int.parseOnly("123abc")
res0: atto.ParseResult[Int] = Done(abc,123)

scala> letter.parse("xyz")
res4: atto.ParseResult[Char] = Done(yz,x)

scala> (letter ~ digit).parse("a1")
res13: atto.ParseResult[(Char, Char)] = Done(,(a,1))

Here, ~ is equivalent to our combinations like: (digits *> word).

When we need to write lightweight parsers, then atto is definitely a good solution (ported from the Haskell world attoparsec).

The difference (and advantage) is that it separates the parsing and the evaluation of the program (like any lexical analysis then compiling processing).

Remember, in our case, we did both at the same time (booh!):

val plus: Parser[Int] = (digits, ch('+'), digits).mapN((lhs, _, rhs) => lhs + rhs)

In the case of atto, we first parse then translate into some ADT and evaluate from the ADT:

val incrementPointer: Parser[Instruction] = char('>') >| IncrementPointer

// ...

def eval(i: Instruction): F[Unit] = i match {
    case IncrementPointer => incrementPointer()
    // ...

Splitting parsing and evaluation are better for tons of reasons:

  • do one thing and do it well;
  • more maintainable (less moving pieces);
  • operations processing can be optimized (because we have the “full” view of what they contain) instead of working piece by piece.


  • Surprisingly, Scala stdlib already embeds parser combinators code:
import scala.util.parsing.combinator._

class SimpleParser extends RegexParsers {
    def word: Parser[String]   = """[a-z]+""".r       ^^ { _.toString }
    def number: Parser[Int]    = """(0|[1-9]\d*)""".r ^^ { _.toInt }

object TestSimpleParser extends SimpleParser {
    def main(args: Array[String]) = {
        parse(word, "johnny") match {
            case Success(matched, _) => println(matched)
            case Failure(msg, _) => println("FAILURE: " + msg)
            case Error(msg, _) => println("ERROR: " + msg)

The project has been separated from the Scala stdlib only recently: https://github.com/scala/scala-parser-combinators.

  • atto: lightweight, and the syntax is quite elegant.
  • fastparser: according to the author, 100x faster than Scala stdlib combinators. It has many more features, handles streaming, positive & negative lookahead, “cuts” (marker to stop backtracking) etc.

Make your choice!


When we work with text files or any structured file, it can sometimes be easier to write a little parser to process it instead of trying your best with if or regexes.

  • You have something simple to parse? Use atto;
  • You want speed or special features? Use fastparse;
  • You don’t want any third-party, use the State monad! ;-)

The State monad can be used with tons of patterns, isolating the mutating state, making it easy to reason about, and to test.

Thanks to the monadic flow, we don’t have to think about passing the current state again and again. We just act as if it was “given” and keep on writing our code without boilerplate.

It’s important to be aware of the Applicative, MonadError, SemigroupK implications, because they provide great features around State to combine them and to handle errors. Always think which monad is contained inside the State, that will determine which “power” our State offers.

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Me, Stéphane Derosiaux

Hi, I'm Stéphane, CTO of Conduktor, a major Apache Kafka Platform. I live in France. Enthousiast and eager to learn, I'm always seeking for challenges. Find out more about me.

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