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So, what are we to do? Parsing this string with
flatMap gets the job done, but it isn’t super nice. Luckily, there’s a third operation that sits right in between
flatMap. We talked about this very topic over the course of 3 entire episodes on Point-Free (1, 2, 3), and it naturally encompasses the idea of “context independence” computation, and it is none other than
zip. It allows you to take multiple generic values and combine them into a single generic value. The Swift standard library defines
zip on arrays, but in our previous episodes we showed that it makes sense to define
zip on many more types, such as optionals, results, validated values, lazy values, and asynchronous values.
Define an alternate parser that parses coordinates formatted as decimal degree minutes, like
"40° 26.767′ N 79° 58.933′ W".
Define an alternate parser that parses coordinates formatted as decimal degree minutes and seconds, like
"40° 26′ 46″ N 79° 58′ 56″ W".
Build an ISO-8601 parser that can parse the date string
Create a parser,
oneOrMoreSpaces, that parses one or more spaces off the beginning of a string. Why can’t this parser be defined using
Create a parser,
zeroOrMoreSpaces, that parses zero or more spaces off the beginning of a string. How does it differ from
Define a function that shares the common parsing logic of
zeroOrMoreSpaces. It should have the signature
((Character) -> Bool) -> Parser<Substring>. Redefine
zeroOrMoreSpacesin terms of this function.
Parserin terms of
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.
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.