ML language style utilities in Julia that provide (generalized) algebraic data types abstraction, highly optimized and extensible pattern matching, and killer tools for meta-programming.
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What's MLStyle.jl?

MLStyle.jl is a Julia package that provides multiple productivity tools from ML(Meta Language) like pattern matching that're statically generated and extensible, ADTs/GADTs(Algebraic Data Type, Generalized Algebraic Data Type) and Active Patterns.

If you still have problems with the scoping of MLStyle.jl, treat it as FP.jl.


The people who're used to so-called functional programming could become retarded when there're no pattern matching and ADTs, and of course I'm one of them.

However, I don't want to take a trade-off here to use some available alternatives that miss features or are not well-optimized. Just like why those greedy people created Julia, I'm also so greedy that I want to integrate all those useful features into one language and, make all of them convenient, efficient and extensible.

On the other side, during recent years I was addicted to extend Python with metaprogramming and even internal mechanisms. Although I made something interesting like pattern-matching, goto, ADTs, constexpr, macros, etc., most of these implementations are so disgustingly evil. Furtunately, in Julia, all of them could be achieved straightforwardly without any black magic, at last, some of these ideas come into the existence of MLStyle.jl.

Finally, we finish such a library that provides extensible pattern matching in such an efficient language.

Why to use MLStyle.jl

  • Straightforward

    I think there is no need to talk about why we should use pattern mathing instead of manually writing something like conditional branches and nested visitors for datatypes.

  • Performance Gain

    When dealing with complex conditional logics and visiting nested datatypes, the codes compiled via MLStyle.jl could always match the handwritten. You can check Benchmark for details.

  • Extensibility and Hygienic Scoping

    You can define your own patterns via the interfaces def_pattern, def_app_pattern and def_gapp_pattern. Almost all built-in patterns are defined at Pervasives.jl.

    Once you define a pattern, you're to be asked to give some qualifiers to your own patterns to prevent visiting them from unexpected modules.

  • * Modern Ways about AST Manipulations

    MLStyle.jl is not a superset of MacroToos.jl, but it provides something useful for AST manipulations. Furthermore, in terms of extracting sub-structures from a given AST, using expr patterns and AST patterns could make a orders of magnitude speed up.

Installation, Documentations and Tutorials

Rich features are provided by MLStyle.jl and you can check documents to get started.

For installation, open package manager mode in Julia shell and add MLStyle.

For more examples or tutorials, check this project which will be frequently updated to present some interesting uses of MLStyle.jl.


In this README I'm glad to share some non-trivial code snippets.

Homoiconic pattern matching for Julia ASTs

rmlines =begin
    e :: Expr           -> Expr(e.head, filter(x -> x !== nothing, map(rmlines, e.args))...)
      :: LineNumberNode -> nothing
    a                   -> a
expr = quote
    struct S{T}
        a :: Int
        b :: T
end |> rmlines

@match expr begin
        struct $name{$tvar}
            $f1 :: $t1
            $f2 :: $t2
    end =>
        struct $name{$tvar}
            $f1 :: $t1
            $f2 :: $t2
    end |> rmlines == expr

Generalized Algebraic Data Types

@use GADT

@data public Exp{T} begin
   Sym       :: Symbol => Exp{A} where {A}
   Val{A}    :: A => Exp{A}
   App{A, B} :: (Exp{Fun{A, B}}, Exp{A_}) => Exp{B} where {A_ <: A}
   Lam{A, B} :: (Symbol, Exp{B}) => Exp{Fun{A, B}}
   If{A}     :: (Exp{Bool}, Exp{A}, Exp{A}) => Exp{A}

A simple intepreter implementation using GADTs could be found at test/untyped_lam.jl.

Active Patterns

Currently, in MLStyle it's not a full featured one, but even a subset with parametric active pattern could be super useful.

@active Re{r :: Regex}(x) begin
    match(r, x)

@match "123" begin
    Re{r"\d+"}(x) => x
    _ => @error ""
end # RegexMatch("123")



Recently the rudimentary benchmarks have been finished, which turns out that MLStyle.jl could be extremely fast when matching cases are complicated, while in terms of some very simple cases(straightforward destruct shallow tuples, arrays and datatypes without recursive invocations), Match.jl could be faster.

All benchmark scripts are provided at directory Matrix-Benchmark.

To run these cross-implementation benchmarks, some extra dependencies should be installed:

  • (v1.1) pkg> add for making cross-implementation benchmark methods and plotting.

  • (v1.1) pkg> add Gadfly MacroTools Match BenchmarkTools StatsBase Statistics ArgParse DataFrames.

  • (v1.1) pkg> add MLStyle#base for a specific version of MLStyle.jl is required.

After installing dependencies, you can directly benchmark them with julia matrix_benchmark.jl hw-tuple hw-array match macrotools match-datatype at the root directory.

The benchmarks presented here are made by Julia v1.1 on Fedora 28. For reports made on Win10, check stats/windows/ directory.


Time Overhead

In x-axis, after the name of test-case is the least time-consuming one's index, the unit is ns).

The y-label is the ratio of the implementation's time cost to that of the least time-consuming.


In x-axis, after the name of test-case is the least allocted one's index, the unit is _ -> (_ + 1) bytes).

The y-label is the ratio of the implementation's allocation cost to that of the least allocted.


The benchmark results in dataframe format are available at this directory.

There are still some performamce issues with array patterns.

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Contributing to MLStyle

Thanks to all individuals referred in Acknowledgements!

Feel free to ask questions about usage, development or extensions about MLStyle at Gitter Room.