Julia
A dynamic language for numerical and scientific computing, compiled to near-native speed.
27 steps across 1 tours, from the basics to complete programs.
The tours
- First released
- 2012
- Created by
- Jeff Bezanson, Stefan Karpinski, Viral Shah, and Alan Edelman
- Typing
- Dynamic with JIT compilation; multiple dispatch shapes everything
- Snippets target
- 1.12
- File extensions
- .jl
What it is for
Julia exists because scientific computing lived with a split: prototype in a friendly dynamic language, then rewrite the slow parts in C or Fortran. Julia JIT-compiles dynamic, math-first code to machine speed, so the exploration language and the production language are the same file. Climate models (CliMA), pharmaceutical simulation (Pumas), the Federal Reserve's DSGE models, and a wide swath of astronomy and optimization research run on it.
The signature idea is multiple dispatch: a function is an open family of methods, and the runtime picks one by the types of every argument. It sounds academic and turns out to be the practical reason Julia packages compose — a differential-equation solver, an automatic-differentiation library, and a unit-checking package that have never heard of each other routinely work together on the same numbers.
The ecosystem centers on science: DifferentialEquations.jl is arguably the strongest solver suite in any language, Flux does machine learning in pure Julia, and the plotting, statistics, and optimization stacks are deep. The trade-offs are honest ones: first-call latency while the JIT warms (much improved since 1.9), and a general-purpose library shelf thinner than Python's.
Where it came from
Julia began in 2009 at MIT, announced to the world in a 2012 blog post titled 'Why We Created Julia' that read as a manifesto: greedy authors wanting the speed of C, the dynamism of Ruby, the math of Matlab, and the usability of Python in one language. Jeff Bezanson's dispatch-centered design, with Stefan Karpinski, Viral Shah, and Alan Edelman, made the greed plausible.
Version 1.0 landed at JuliaCon 2018 with a stability commitment the young community badly wanted, and the language settled into steady annual releases. The 'time to first plot' latency complaint — the JIT recompiling the world on every fresh session — was answered incrementally, then decisively in 1.9 and 1.10 with native code caching in packages.
The founders' company, Julia Computing (now JuliaHub), and an active open ecosystem carried the language into regulated industries — clinical pharmacology, aerospace, finance — while JuliaCon grew into the annual gathering of a community that skews unusually heavy on scientists writing their own code. The language remains MIT-licensed, community-governed, and pointedly version-stable.
What it is like to type
Julia types like a scripting language with math manners: no braces, blocks closing on the word end, functions defined in one line with a bare =. The broadcasting dot is the signature keystroke — sqrt.(xs), mags .<= 6 — turning any function elementwise, and ! trails every mutating name like push! and sort!. Macros open with @, ranges write as 1:10, and => builds Dict pairs. Unicode is idiomatic in the community but this corpus stays ASCII, where the feel is clean lowercase words, colons, and that ever-present dot.
27 steps across 1 tours, from the basics to complete programs.