When comparing Julia vs Reason ML, the Slant community recommends Julia for most people. In the question“What are the best languages for learning functional programming?” Julia is ranked 17th while Reason ML is ranked 19th. The most important reason people chose Julia is:
Julia runs almost as fast as (and in fact in some cases faster than) C code.
Specs
Ranked in these QuestionsQuestion Ranking
Pros
Pro Almost as fast as C
Julia runs almost as fast as (and in fact in some cases faster than) C code.
Pro Strong dynamic typing
Dynamic and high level, but does not isolate the user from properly thinking about types. Can do explicit type signatures which is great for teaching structured thinking.
Pro Great standard REPL
Out of the box Julia has a very good Read-Eval-Print-Loop, which both completes functions and types, as well as completion based on history of previous statements. It integrates well with the shell and has an excellent online help system.
Pro Nice regular syntax
Julia code is easy to read and avoid a lot of unnecessary special symbols and fluff. It uses newline to end statements and "end" to end blocks so there is no need for lots of semicolons and curly braces. It is regular in that unless it is a variable assignment, function name always comes first. No need to be confused about whether something is a method on an object or a free function.
Unlike Python and Ruby, since you can annotate the types a function operates on, you can overload function names, so that you can use the same function name for many data types. So you can keep simple descriptive function names and not have to invent artificial function names to separate them from the type they operate on.
Pro Written in itself
The Julia language is written in itself to a much larger extent than most other languages, so a budding programmer can read through the depths of the standard library and learn exactly how things work all the way down to the low-level bit-twiddling details, which can be englightening.
Pro Powerful n-dimensional arrays
Julia has built in n-dimensional arrays similar in functionality as Python's numpy.
Pro Function overloading
You can have multiple functions with the same name, but doing different things depending on function arguments and argument types.
Pro Amazing learning curve
Julia requires no boilerplate code – a beginner needs to write only the parts they care about. This combined with the REPL provides the best learning experience available.
Pro High-level code
Julia provides a high level of abstraction, making platform-independent programming trivial and easing the learning curve.
Pro Function and operator broadcasting
You can perform operations on scalars, for example 2^2 or [1, 2, 3].^2.
Pro Strong Metaprogramming
Julia allows you to edit Julia code in the language itself and write powerful macros. It is a great introduction to metaprogramming features
Pro REPL-based
The Julia REPL allows quickly testing how some code behaves and gives access to documentation and package management immediately in the REPL.
Pro Uses the excellent Bucklescript Ocaml to Javascript transpiler
Pro Superior type inference
Ocaml type inference is so smart that you never have to repeat yourself and keep code very clean, type errors also are very pleasant.
Pro Aims to make the transition from Javascript easier
Despite being a completely different language Javascript programmer will find that the syntax of ReasonML has many familiarities with Javascript.
Pro Uses established compiler technology from Ocaml with a tweaked syntax that leans more towards Javascript
Pro Removes JavaScript "bad parts" but sticks to it's design philosophy
unlike other js-targetting languages that are thought as a way to have a language that pleases community X run in a browser, reason is really designed with JavaScript community in mind. it removes the bad parts but keeps its syntax and its best design principles (from Scheme): simplicity, minimalism, and functions as building block.
Pro Immutability with escape hatches
reason includes true immutability, but it has escape hatches to let you use mutations in exceptional cases.
Pro Compiles to JavaScript or assembly (ocaml)
The same reasonml code can compile to js (eg. run on browsers or node.js, use any lib in npm), or compile to assembly thru ocaml (unless of course you load js externals), running on any device, with C-comparable (or better) performance.
Pro JSX syntax natively supported
Reason was created by the creator of react, for developers already using JSX to template web or native UIs this results very familiar.
Cons
Con Young language with limited support
Julia was released in 2012. Due to its short existence, there is a limited amount of support for the language. Very few libraries exist as of yet, and the community is still quite small (though growing quickly).
Con 1-based array and column major
This design probably comes from Matlab, but makes it unnatural to interface C and C++ and python.
Con A standard async syntax is pending
Async syntax is not standard across native/js projects and in both cases a bit awkward for non-ocaml devs. Currently this is reasonml most voted issue in their GitHub repo so hopefully, there's news soon.
Con Ecosystem is a mess
A wonderful language, but a user is required to use multiple different package managers for many things (esy, bsb, npm). Some standard templates from bsb (e.g. react-starter) are not installable out of the box.