Julia 1.8 Documentation

    Please read the release notes to see what has changed since the last release.

    Note

    The documentation is also available in PDF format: .

    Because Julia’s compiler is different from the interpreters used for languages like Python or R, you may find that Julia’s performance is unintuitive at first. If you find that something is slow, we highly recommend reading through the section before trying anything else. Once you understand how Julia works, it’s easy to write code that’s nearly as fast as C.

    Julia features optional typing, multiple dispatch, and good performance, achieved using type inference and just-in-time (JIT) compilation, implemented using . It is multi-paradigm, combining features of imperative, functional, and object-oriented programming. Julia provides ease and expressiveness for high-level numerical computing, in the same way as languages such as R, MATLAB, and Python, but also supports general programming. To achieve this, Julia builds upon the lineage of mathematical programming languages, but also borrows much from popular dynamic languages, including Lisp), ), Python), ), and Ruby).

    The most significant departures of Julia from typical dynamic languages are:

    • The core language imposes very little; Julia Base and the standard library are written in Julia itself, including primitive operations like integer arithmetic
    • A rich language of types for constructing and describing objects, that can also optionally be used to make type declarations
    • The ability to define function behavior across many combinations of argument types via
    • Automatic generation of efficient, specialized code for different argument types

    While the casual programmer need not explicitly use types or multiple dispatch, they are the core unifying features of Julia: functions are defined on different combinations of argument types, and applied by dispatching to the most specific matching definition. This model is a good fit for mathematical programming, where it is unnatural for the first argument to “own” an operation as in traditional object-oriented dispatch. Operators are just functions with special notation – to extend addition to new user-defined data types, you define new methods for the function. Existing code then seamlessly applies to the new data types.

    Partly because of run-time type inference (augmented by optional type annotations), and partly because of a strong focus on performance from the inception of the project, Julia’s computational efficiency exceeds that of other dynamic languages, and even rivals that of statically-compiled languages. For large scale numerical problems, speed always has been, continues to be, and probably always will be crucial: the amount of data being processed has easily kept pace with Moore’s Law over the past decades.

    Julia aims to create an unprecedented combination of ease-of-use, power, and efficiency in a single language. In addition to the above, some advantages of Julia over comparable systems include:

    • Free and open source (MIT licensed)
    • User-defined types are as fast and compact as built-ins
    • No need to vectorize code for performance; devectorized code is fast
    • Designed for parallelism and distributed computation
    • Lightweight “green” threading ()
    • Elegant and extensible conversions and promotions for numeric and other types
    • Efficient support for Unicode, including but not limited to
    • Call C functions directly (no wrappers or special APIs needed)
    • Powerful shell-like capabilities for managing other processes
    • Lisp-like macros and other metaprogramming facilities