When comparing APL vs Julia, the Slant community recommends Julia for most people. In the question“What is the best programming language to learn first?” Julia is ranked 25th while APL is ranked 54th. The most important reason people chose Julia is:
Julia runs almost as fast as C code.
Ranked in these QuestionsQuestion Ranking
No complicated loop processing to apply a function to a array of arrays. Functions are defined in a way that they will typically operate the same way on any number of array dimensions. This, along with the clear syntax, leads to very compact code that can be comprehended in a single line, rather than spread out over many pages.
Pro Clear syntax
There is no operator precedence to memorize, as everything is evaluated right-to-left. E.g., in APL 3*10+3 = 39. You do have to type in some otherwise unusual characters, such as ↓ and ∊, but those are easy enough to pick up -- and they have the advantage of being easily remembered once understood, as they often have some connection to common mathematical symbols.
You can seriously implement Conway's Game of Life in one line. There's a reason we do algebra with symbols instead of story problems. APL is good as a language of thought, since you can hold entire algorithms in your head at once.
Pro Almost fast as C
Julia runs almost as fast as C code.
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 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 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 Function overloading
You can have multiple functions with the same name, but doing different things depending on function arguments and argument types.
Pro Function and operator broadcasting
You can perform operations on scalars, for example 2^2 or [1, 2, 3].^2.
Pro Powerfull n-dimensional arrays
Julia has built in n-dimensional arrays similar in functionality as Python's numpy.
APL symbols are only used by APL. You have to learn how to type them and how to read them. It doesn't work well with standard text editors , version control systems, search engines, or web forums. This makes it difficult for a beginner to find help.
Con Does not prepare you for most of the practical programming languages of today
While APL does have a strong use in certain areas (mostly mathematically intensive applications), it is a Domain-Specific language. That along with the fact that its syntax is not similar to C-like or other common syntax forms means that learning APL and expecting it to help you with learning other languages is like learning Calculus and expecting it to make English easier.
Con Write-only language
Maybe you can learn to read it with experience. And an interpreter. Reading APL is like reading a college math book. You might have to study a single line for fifteen minutes to understand what it's doing. And that's if you're an expert at APL. This also applies if you wrote it yourself more than a month ago. hopefully you have comments.
Con Flawless diamond
You can't extend the language itself. (J does this better.) Of course, what's built in is quite powerful.
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 come from Matlab, but makes it unnatural to interface C and C++ and python.