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prefix function is an example of a “parser combinator”: it’s a function that produces brand new parsers out of thin air given a little configuration. This
prefix parser combinator unlocks some extra power because we can scan off as many characters as we want off the beginning of a string, based on a predicate, and then do something later with it.
Although this is the first time we’ve called something a “parser combinator,” it’s not the first parser combinator that we’ve seen:
flatMap are all parser combinators, because they’re functions that take parsers as input and produce parsers as output. Even the
literal parser is a parser combinator because it takes some configuration up front (a literal string to parse) and returns a brand new parser that uses that configuration.
We’re beginning to see how parser combinators can enter our code and clean up things in the process: they allow us to solve more and more precise parsing problems by moving the lower-level, trickier parsing logic to these combinators. And then we can use them to build more specific kinds of parsers.
prefix combinator was handy enough to clean up the
zeroOrMoreSpaces parsers, but it’ll definitely help us build plenty more parsers in the future. In fact, that kind of
prefix checking is being done in a bunch of the parsers we’ve already defined.
We quickly added a
separatedBy argument to
zeroOrMore, but it can be very useful to parse out an array of values without a separator. Give
separatedBy a default parser for this behavior. Is this a parser we’ve already encountered?
until parser argument to
oneOrMore) that parses a number of values until the given parser succeeds.
until parser argument optional by providing a default parser value. Is this a parser we’ve already encountered?
Define a parser combinator,
oneOf, that takes an array of
Parser<A>s as input and produces a single parser of
Parser<A>. What can/should this parser do?
Daniel gives a wonderful overview of how the idea of “combinators” infiltrates many common programming tasks.
Just as with OO, one of the keys to a functional style of programming is to write very small bits of functionality that can be combined to create powerful results. The glue that combines the small bits are called Combinators. In this talk we’ll motivate the topic with a look at Swift Sets before moving on to infinite sets, random number generators, parser combinators, and Peter Henderson’s Picture Language. Combinators allow you to provide APIs that are friendly to non-functional programmers.
In the first ever try! Swift conference, Yasuhiro Inami gives a broad overview of parsers and parser combinators, and shows how they can accomplish very complex parsing.
Parser combinators are one of the most awesome functional techniques for parsing strings into trees, like constructing JSON. In this talk from try! Swift, Yasuhiro Inami describes how they work by combining small parsers together to form more complex and practical ones.
This library for parsing regular expression strings into a Swift data type uses many of the ideas developed in our series of episodes on parsers. It’s a great example of how to break a very large, complex problem into many tiny parsers that glue back together.
In this article, Soroush Khanlou applies parser combinators to a real world problem: parsing notation for a music app. He found that parser combinators improved on regular expressions not only in readability, but in performance!
A wonderful article that explains parser combinators from start to finish. The article assumes you are already familiar with Rust, but it is possible to look past the syntax and see that there are many shapes in the code that are similar to what we have covered in our episodes on parsers.
A parser library built in Swift that uses many of the concepts we cover in our series of episodes on parsers.
Sparse is a simple parser-combinator library written in Swift.
Parsec is one of the first and most widely used parsing libraries, built in Haskell. It’s built on many of the same ideas we have covered in our series of episodes on parsers, but using some of Haskell’s most powerful type-level features.
This article demonstrates that parsing can be a great alternative to validating. When validating you often check for certain requirements of your values, but don’t have any record of that check in your types. Whereas parsing allows you to upgrade the types to something more restrictive so that you cannot misuse the value later on.
In this free episode of Swift talk, Chris and Florian discuss various techniques for parsing strings as a means to process a ledger file. It contains a good overview of various parsing techniques, including parser grammars.