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pointfreeco/swift-parsing 0.9.2
A library for turning nebulous data into well-structured data, with a focus on composition, performance, generality, and ergonomics.
⭐️ 590
🕓 8 weeks ago
.package(url: "https://github.com/pointfreeco/swift-parsing.git", from: "0.9.2")

swift-parsing

CI

A library for turning unstructured data into structured data, with a focus on composition, performance, generality, and invertibility:

  • 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.

  • Performance: Parsers that have been composed of many smaller parts should perform as well as highly-tuned, hand-written parsers.

  • 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.

  • 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.


Learn 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.

video poster image

Motivation

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")          // nil

And 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.

Getting started

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,") // 1

Next 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.,true

That'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.

Benchmarks

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:

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

Documentation

The documentation for releases and main are available here:

Other versions

Other libraries

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.

License

This library is released under the MIT license. See LICENSE for details.

GitHub

link
Stars: 590
Last commit: 1 hour ago
jonrohan Something's broken? Yell at me @ptrpavlik. Praise and feedback (and money) is also welcome.

Related Packages

Release Notes

0.9.2
8 weeks ago
  • Fixed: Added a missing ParserPrinter conformance to OptionalOneOf, which prevented parsers that used if statements in @OneOfBuilder blocks from being printers.

The following changes have been made to the more experimental _URLRouting module:

  • Added: A Body() initializer that takes no arguments. It simply parses the entire body as Data.
  • Infrastructure: documentation changes.

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