A library for turning unstructured data into structured data, with a focus on composition, performance, generality, and invertibility:
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Composition: Ability to break large, complex parsing problems down into smaller, simpler ones. And the ability to take small, simple parsers and easily combine them into larger, more complex ones.
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Performance: Parsers that have been composed of many smaller parts should perform as well as highly-tuned, hand-written parsers.
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Generality: Ability to parse any kind of input into any kind of output. This allows you to choose which abstraction levels you want to work on based on how much performance you need or how much correctness you want guaranteed. For example, you can write a highly tuned parser on collections of UTF-8 code units, and it will automatically plug into parsers of strings, arrays, unsafe buffer pointers and more.
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Invertibility: Ability to invert your parsers so that they are printers. This allows you to transform your well-structured data back into unstructured data, which is useful for serialization, sending data over the network, URL routing, and more.
This library was designed over the course of many episodes on Point-Free, a video series exploring functional programming and the Swift language, hosted by Brandon Williams and Stephen Celis. You can watch all of the episodes here.
Parsing is a surprisingly ubiquitous problem in programming. We can define parsing as trying to transform unstructured data into structured data. The Swift standard library comes with a number of parsers that we reach for every day. For example, there are initializers on Int, Double, and even Bool, that attempt to parse numbers and booleans from strings:
Int("42") // 42
Int("Hello") // nil
Double("123.45") // 123.45
Double("Goodbye") // nil
Bool("true") // true
Bool("0") // nilAnd there are types like JSONDecoder and PropertyListDecoder that attempt to parse Decodable-conforming types from data:
try JSONDecoder().decode(User.self, from: data)
try PropertyListDecoder().decode(Settings.self, from: data)While parsers are everywhere in Swift, Swift has no holistic story for parsing. Instead, we typically parse data in an ad hoc fashion using a number of unrelated initializers, methods, and other means. And this typically leads to less maintainable, less reusable code.
This library aims to write such a story for parsing in Swift. It introduces a single unit of parsing that can be combined in interesting ways to form large, complex parsers that can tackle the programming problems you need to solve in a maintainable way.
This is an abridged version of the "Getting Started" article in the library's documentation.
Suppose you have a string that holds some user data that you want to parse into an array of Users:
var input = """
1,Blob,true
2,Blob Jr.,false
3,Blob Sr.,true
"""
struct User {
var id: Int
var name: String
var isAdmin: Bool
}A naive approach to this would be a nested use of .split(separator:), and then a little bit of extra work to convert strings into integers and booleans:
let users = input
.split(separator: "\n")
.compactMap { row -> User? in
let fields = row.split(separator: ",")
guard
fields.count == 3,
let id = Int(fields[0]),
let isAdmin = Bool(String(fields[2]))
else { return nil }
return User(id: id, name: String(fields[1]), isAdmin: isAdmin)
}Not only is this code a little messy, but it is also inefficient since we are allocating arrays for the .split and then just immediately throwing away those values.
It would be more straightforward and efficient to instead describe how to consume bits from the beginning of the input and convert that into users. This is what this parser library excels at 😄.
We can start by describing what it means to parse a single row, first by parsing an integer off the front of the string, and then parsing a comma. We can do this by using the Parse type, which acts as an entry point into describing a list of parsers that you want to run one after the other to consume from an input:
let user = Parse {
Int.parser()
","
}Already this can consume the beginning of the input:
try user.parse("1,") // 1Next we want to take everything up until the next comma for the user's name, and then consume the comma:
let user = Parse {
Int.parser()
","
Prefix { $0 != "," }
","
}And then we want to take the boolean at the end of the row for the user's admin status:
let user = Parse {
Int.parser()
","
Prefix { $0 != "," }
","
Bool.parser()
}Currently this will parse a tuple (Int, Substring, Bool) from the input, and we can .map on that to turn it into a User:
let user = Parse {
Int.parser()
","
Prefix { $0 != "," }
","
Bool.parser()
}
.map { User(id: $0, name: String($1), isAdmin: $2) }To make the data we are parsing to more prominent, we can instead pass the transform closure as the first argument to Parse:
let user = Parse {
User(id: $0, name: String($1), isAdmin: $2)
} with: {
Int.parser()
","
Prefix { $0 != "," }
","
Bool.parser()
}Or we can pass the User initializer to Parse in a point-free style by transforming the Prefix parser's output from a Substring to String first:
let user = Parse(User.init(id:name:isAdmin:)) {
Int.parser()
","
Prefix { $0 != "," }.map(String.init)
","
Bool.parser()
}That is enough to parse a single user from the input string:
try user.parse("1,Blob,true")
// User(id: 1, name: "Blob", isAdmin: true)To parse multiple users from the input we can use the Many parser to run the user parser many times:
let users = Many {
user
} separator: {
"\n"
}
try users.parse(input)
// [User(id: 1, name: "Blob", isAdmin: true), ...]Now this parser can process an entire document of users, and the code is simpler and more straightforward than the version that uses .split and .compactMap.
Even better, it's more performant. We've written benchmarks for these two styles of parsing, and the .split-style of parsing is more than twice as slow:
name time std iterations
------------------------------------------------------------------
README Example.Parser: Substring 3426.000 ns ± 63.40 % 385395
README Example.Ad hoc 7631.000 ns ± 47.01 % 169332
Program ended with exit code: 0
Further, if you are willing write your parsers against UTF8View instead of Substring, you can eke out even more performance, more than doubling the speed:
name time std iterations
------------------------------------------------------------------
README Example.Parser: Substring 3693.000 ns ± 81.76 % 349763
README Example.Parser: UTF8 1272.000 ns ± 128.16 % 999150
README Example.Ad hoc 8504.000 ns ± 59.59 % 151417
We can also compare these times to a tool that Apple's Foundation gives us: Scanner. It's a type that allows you to consume from the beginning of strings in order to produce values, and provides a nicer API than using .split:
var users: [User] = []
while scanner.currentIndex != input.endIndex {
guard
let id = scanner.scanInt(),
let _ = scanner.scanString(","),
let name = scanner.scanUpToString(","),
let _ = scanner.scanString(","),
let isAdmin = scanner.scanBool()
else { break }
users.append(User(id: id, name: name, isAdmin: isAdmin))
_ = scanner.scanString("\n")
}However, the Scanner style of parsing is more than 5 times as slow as the substring parser written above, and more than 15 times slower than the UTF-8 parser:
name time std iterations
-------------------------------------------------------------------
README Example.Parser: Substring 3481.000 ns ± 65.04 % 376525
README Example.Parser: UTF8 1207.000 ns ± 110.96 % 1000000
README Example.Ad hoc 8029.000 ns ± 44.44 % 163719
README Example.Scanner 19786.000 ns ± 35.26 % 62125
We can take things even further. With one small change we can turn the parser into a printer.
-let user = Parse(User.init(id:name:isAdmin:)) {
+let user = ParsePrint(.memberwise(User.init(id:name:isAdmin:))) {
Int.parser()
","
Prefix { $0 != "," }.map(String.init)
","
Bool.parser()
}
let users = Many {
user
} separator: {
"\n"
}With this one change we can now print an array of users back into a string:
users.print([
User(id: 1, name: "Blob", isAdmin: true),
User(id: 2, name: "Blob Jr.", isAdmin: false),
User(id: 3, name: "Blob Sr.", isAdmin: true),
])
// 1,Blob,true
// 2,Blob Jr.,false
// 3,Blob Sr.,trueThat's the basics of parsing and printing a simple string format, but there's a lot more operators and tricks to learn in order to performantly parse larger inputs. Read the documentation to dive more deeply into the concepts of parser-printers, and view the benchmarks for more examples of real life parsing scenarios.
This library comes with a benchmark executable that not only demonstrates the performance of the library, but also provides a wide variety of parsing examples:
- Hex color
- Simplified CSV
- Simplified JSON
- ISO8601 date
- HTTP request
- DNS header
- Arithmetic grammar
- Xcode test logs
- and more
These are the times we currently get when running the benchmarks:
MacBook Pro (16-inch, 2021)
Apple M1 Pro (10 cores, 8 performance and 2 efficiency)
32 GB (LPDDR5)
name time std iterations
----------------------------------------------------------------------------------
Arithmetic.Parser 8042.000 ns ± 5.91 % 174657
BinaryData.Parser 42.000 ns ± 56.81 % 1000000
Bool.Bool.init 41.000 ns ± 60.69 % 1000000
Bool.Bool.parser 42.000 ns ± 57.28 % 1000000
Bool.Scanner.scanBool 1041.000 ns ± 25.98 % 1000000
Color.Parser 209.000 ns ± 13.68 % 1000000
CSV.Parser 4047750.000 ns ± 1.18 % 349
CSV.Ad hoc mutating methods 898604.000 ns ± 1.49 % 1596
Date.Parser 6416.000 ns ± 2.56 % 219218
Date.DateFormatter 25625.000 ns ± 2.19 % 54110
Date.ISO8601DateFormatter 35125.000 ns ± 1.71 % 39758
HTTP.HTTP 9709.000 ns ± 3.81 % 138868
JSON.Parser 32292.000 ns ± 3.18 % 41890
JSON.JSONSerialization 1833.000 ns ± 8.58 % 764057
Numerics.Int.init 41.000 ns ± 84.54 % 1000000
Numerics.Int.parser 42.000 ns ± 72.17 % 1000000
Numerics.Scanner.scanInt 125.000 ns ± 20.26 % 1000000
Numerics.Comma separated: Int.parser 8096459.000 ns ± 0.44 % 173
Numerics.Comma separated: Scanner.scanInt 49178770.500 ns ± 0.24 % 28
Numerics.Comma separated: String.split 14922583.500 ns ± 0.67 % 94
Numerics.Double.init 42.000 ns ± 72.61 % 1000000
Numerics.Double.parser 125.000 ns ± 58.57 % 1000000
Numerics.Scanner.scanDouble 167.000 ns ± 18.84 % 1000000
Numerics.Comma separated: Double.parser 11313395.500 ns ± 0.96 % 124
Numerics.Comma separated: Scanner.scanDouble 50431521.000 ns ± 0.19 % 28
Numerics.Comma separated: String.split 18744125.000 ns ± 0.46 % 75
PrefixUpTo.Parser: Substring 249958.000 ns ± 0.88 % 5595
PrefixUpTo.Parser: UTF8 13250.000 ns ± 2.96 % 105812
PrefixUpTo.String.range(of:) 43084.000 ns ± 1.57 % 32439
PrefixUpTo.Scanner.scanUpToString 47500.000 ns ± 1.27 % 29444
Race.Parser 34417.000 ns ± 2.73 % 40502
README Example.Parser: Substring 4000.000 ns ± 3.79 % 347868
README Example.Parser: UTF8 1125.000 ns ± 7.92 % 1000000
README Example.Ad hoc 3542.000 ns ± 4.13 % 394248
README Example.Scanner 14292.000 ns ± 2.82 % 97922
String Abstractions.Substring 934167.000 ns ± 0.60 % 1505
String Abstractions.UTF8 158750.000 ns ± 1.36 % 8816
UUID.UUID.init 209.000 ns ± 15.02 % 1000000
UUID.UUID.parser 208.000 ns ± 24.17 % 1000000
Xcode Logs.Parser 3768437.500 ns ± 0.56 % 372
The documentation for releases and main are available here:
If you want to discuss this library or have a question about how to use it to solve a particular problem, there are a number of places you can discuss with fellow Point-Free enthusiasts:
- For long-form discussions, we recommend the discussions tab of this repo.
- For casual chat, we recommend the Point-Free Community Slack.
There are a few other parsing libraries in the Swift community that you might also be interested in:
The printing functionality in this library is inspired by the paper "Invertible syntax descriptions: Unifying parsing and pretty printing", by Tillmann Rendel and Klaus Ostermann.
This library is released under the MIT license. See LICENSE for details.