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Julia

AKA JuliaLang

Julia logo

Summary

Julia is a high-level, high-performance dynamic programming language for technical computing. It combines the ease of use of Python and R with the speed of C, featuring a JIT compiler, multiple dispatch, and support for a range of programming paradigms.

Paradigms

imperative, functional, object-oriented

Domains

scientific computing, data science, numerical analysis, statistics, machine learning frameworks

Key Features

first-class functions, type inference, meta-programming, macros, operator overloading, modules, interactive development, REPL, native FFI, string interpolation, coroutines, variadic functions, named arguments

Typing

dynamic system, non-structural typing, strong typing, partial inference, runtime checking, conservative type coercion, optional type annotations

Compilation

compiled, interpreted with JIT compilation

Influenced By

Python, R, MATLAB, Lisp, Ruby, C, Lua, Mathematica

Ratings

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Compilation speed
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Maturity
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Community size
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Learning resources
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Job market demand
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Learning curve
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Cognitive load
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Syntax complexity
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Semantic complexity
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Memory safety
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Concurrency ease of use
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Std lib maturity
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Std lib size
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Backwards compatibility
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Documentation quality
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File extensions

jl

External Links

Status active
Type programming
Created 2009
Designed by Jeff Bezanson
Stefan Karpinski
Viral B. Shah
Alan Edelman
Developed by MIT
Julia Computing (now JuliaHub)
PyPL Index #25
TIOBE Index #35
GitHub rank #43

Popularity [PyPL]

Code Example

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