Created in 2009 by a four-person team and unveiled to the public in 2012, Julia is meant to address the shortcomings in Python and other languages and applications used for scientific computing and data processing. “We are greedy,” they wrote.
Here are some of the ways Julia implements those aspirations:
- Julia is compiled, not interpreted. For faster runtime performance, Julia is just-in-time (JIT) compiled using the LLVM compiler framework. At its best, Julia can approach or match the speed of C.
- Julia is interactive. Julia includes a REPL (read-eval-print loop), or interactive command line, similar to what Python offers. Quick one-off scripts and commands can be punched right in.
- Julia has a straightforward syntax. Julia’s syntax is similar to Python’s—terse, but also expressive and powerful.
- Julia combines the benefits of dynamic typing and static typing. You can specify types for variables, like “unsigned 32-bit integer.” But you can also create hierarchies of types to allow general cases for handling variables of specific types—for instance, to write a function that accepts integers without specifying the length or signing of the integer. You can even do without typing entirely if it isn’t needed in a particular context.
- Julia can call Python, C, and Fortran libraries. Julia can interface directly with external libraries written in C and Fortran. It’s also possible to interface with Python code by way of the PyCall library, and even share data between Python and Julia.
- Julia supports metaprogramming. Julia programs can generate other Julia programs, and even modify their own code, in a way that is reminiscent of languages like Lisp.
- Julia has a full-featured debugger. Julia 1.1 introduced a debugging suite, which executes code in a local REPL and allows you to step through the results, inspect variables, and add breakpoints in code. You can even perform fine-grained tasks like stepping through a function generated by code.