# Frequently Asked Questions

## General

### Is Julia named after someone or something?

No.

### Why don't you compile Matlab/Python/R/… code to Julia?

Since many people are familiar with the syntax of other dynamic languages, and lots of code has already been written in those languages, it is natural to wonder why we didn't just plug a Matlab or Python front-end into a Julia back-end (or “transpile” code to Julia) in order to get all the performance benefits of Julia without requiring programmers to learn a new language. Simple, right?

The basic issue is that there is nothing special about Julia's compiler: we use a commonplace compiler (LLVM) with no “secret sauce” that other language developers don't know about. Indeed, Julia's compiler is in many ways much simpler than those of other dynamic languages (e.g. PyPy or LuaJIT). Julia's performance advantage derives almost entirely from its front-end: its language semantics allow a well-written Julia program to give more opportunities to the compiler to generate efficient code and memory layouts. If you tried to compile Matlab or Python code to Julia, our compiler would be limited by the semantics of Matlab or Python to producing code no better than that of existing compilers for those languages (and probably worse). The key role of semantics is also why several existing Python compilers (like Numba and Pythran) only attempt to optimize a small subset of the language (e.g. operations on Numpy arrays and scalars), and for this subset they are already doing at least as well as we could for the same semantics. The people working on those projects are incredibly smart and have accomplished amazing things, but retrofitting a compiler onto a language that was designed to be interpreted is a very difficult problem.

Julia's advantage is that good performance is not limited to a small subset of “built-in” types and operations, and one can write high-level type-generic code that works on arbitrary user-defined types while remaining fast and memory-efficient. Types in languages like Python simply don't provide enough information to the compiler for similar capabilities, so as soon as you used those languages as a Julia front-end you would be stuck.

For similar reasons, automated translation to Julia would also typically generate unreadable, slow, non-idiomatic code that would not be a good starting point for a native Julia port from another language.

On the other hand, language interoperability is extremely useful: we want to exploit existing high-quality code in other languages from Julia (and vice versa)! The best way to enable this is not a transpiler, but rather via easy inter-language calling facilities. We have worked hard on this, from the built-in `ccall` intrinsic (to call C and Fortran libraries) to JuliaInterop packages that connect Julia to Python, Matlab, C++, and more.

## Sessions and the REPL

### How do I delete an object in memory?

Julia does not have an analog of MATLAB's `clear` function; once a name is defined in a Julia session (technically, in module `Main`), it is always present.

If memory usage is your concern, you can always replace objects with ones that consume less memory. For example, if `A` is a gigabyte-sized array that you no longer need, you can free the memory with `A = nothing`. The memory will be released the next time the garbage collector runs; you can force this to happen with `GC.gc()`. Moreover, an attempt to use `A` will likely result in an error, because most methods are not defined on type `Nothing`.

### How can I modify the declaration of a type in my session?

Perhaps you've defined a type and then realize you need to add a new field. If you try this at the REPL, you get the error:

    ERROR: invalid redefinition of constant MyType

Types in module `Main` cannot be redefined.

While this can be inconvenient when you are developing new code, there's an excellent workaround. Modules can be replaced by redefining them, and so if you wrap all your new code inside a module you can redefine types and constants. You can't import the type names into `Main` and then expect to be able to redefine them there, but you can use the module name to resolve the scope. In other words, while developing you might use a workflow something like this:

    include("mynewcode.jl")              # this defines a module MyModule
    obj1 = MyModule.ObjConstructor(a, b)
    obj2 = MyModule.somefunction(obj1)
    # Got an error. Change something in "mynewcode.jl"
    include("mynewcode.jl")              # reload the module
    obj1 = MyModule.ObjConstructor(a, b) # old objects are no longer valid, must reconstruct
    obj2 = MyModule.somefunction(obj1)   # this time it worked!
    obj3 = MyModule.someotherfunction(obj2, c)
    ...

## Scripting

### How do I check if the current file is being run as the main script?

When a file is run as the main script using `julia file.jl` one might want to activate extra functionality like command line argument handling. A way to determine that a file is run in this fashion is to check if `abspath(PROGRAM_FILE) == @__FILE__` is `true`.

### How do I catch CTRL-C in a script?

Running a Julia script using `julia file.jl` does not throw `InterruptException` when you try to terminate it with CTRL-C (SIGINT). To run a certain code before terminating a Julia script, which may or may not be caused by CTRL-C, use `atexit`. Alternatively, you can use `julia -e 'include(popfirst!(ARGS))' file.jl` to execute a script while being able to catch `InterruptException` in the `try` block.

### How do I pass options to `julia` using `#!/usr/bin/env`?

Passing options to `julia` in so-called shebang by, e.g., `#!/usr/bin/env julia --startup-file=no` may not work in some platforms such as Linux. This is because argument parsing in shebang is platform-dependent and not well-specified. In a Unix-like environment, a reliable way to pass options to `julia` in an executable script would be to start the script as a `bash` script and use `exec` to replace the process to `julia`:

    #!/bin/bash
    #=
    exec julia --color=yes --startup-file=no "${BASH_SOURCE[0]}" "$@"
    =#

    @show ARGS  # put any Julia code here

In the example above, the code between `#=` and `=#` is run as a `bash` script. Julia ignores this part since it is a multi-line comment for Julia. The Julia code after `=#` is ignored by `bash` since it stops parsing the file once it reaches to the `exec` statement.

Note

In order to catch CTRL-C in the script you can use

    #!/bin/bash
    #=
    exec julia --color=yes --startup-file=no -e 'include(popfirst!(ARGS))' \
        "${BASH_SOURCE[0]}" "$@"
    =#

    @show ARGS  # put any Julia code here

instead. Note that with this strategy `PROGRAM_FILE` will not be set.

## Functions

### I passed an argument `x` to a function, modified it inside that function, but on the outside, the variable `x` is still unchanged. Why?

Suppose you call a function like this:

    julia> x = 10
    10

    julia> function change_value!(y)
               y = 17
           end
    change_value! (generic function with 1 method)

    julia> change_value!(x)
    17

    julia> x # x is unchanged!
    10

In Julia, the binding of a variable `x` cannot be changed by passing `x` as an argument to a function. When calling `change_value!(x)` in the above example, `y` is a newly created variable, bound initially to the value of `x`, i.e. `10`; then `y` is rebound to the constant `17`, while the variable `x` of the outer scope is left untouched.

However, if `x` is bound to an object of type `Array` (or any other mutable type). From within the function, you cannot "unbind" `x` from this Array, but you can change its content. For example:

    julia> x = [1,2,3]
    3-element Vector{Int64}:
     1
     2
     3

    julia> function change_array!(A)
               A[1] = 5
           end
    change_array! (generic function with 1 method)

    julia> change_array!(x)
    5

    julia> x
    3-element Vector{Int64}:
     5
     2
     3

Here we created a function `change_array!`, that assigns `5` to the first element of the passed array (bound to `x` at the call site, and bound to `A` within the function). Notice that, after the function call, `x` is still bound to the same array, but the content of that array changed: the variables `A` and `x` were distinct bindings referring to the same mutable `Array` object.

### Can I use `using` or `import` inside a function?

No, you are not allowed to have a `using` or `import` statement inside a function. If you want to import a module but only use its symbols inside a specific function or set of functions, you have two options:

1. Use `import`:

       import Foo
       function bar(...)
           # ... refer to Foo symbols via Foo.baz ...
       end

   This loads the module `Foo` and defines a variable `Foo` that refers to the module, but does not import any of the other symbols from the module into the current namespace. You refer to the `Foo` symbols by their qualified names `Foo.bar` etc.

2. Wrap your function in a module:

       module Bar
       export bar
       using Foo
       function bar(...)
           # ... refer to Foo.baz as simply baz ....
       end
       end
       using Bar

   This imports all the symbols from `Foo`, but only inside the module `Bar`.

### What does the `...` operator do?

### The two uses of the `...` operator: slurping and splatting

Many newcomers to Julia find the use of `...` operator confusing. Part of what makes the `...` operator confusing is that it means two different things depending on context.

### `...` combines many arguments into one argument in function definitions

In the context of function definitions, the `...` operator is used to combine many different arguments into a single argument. This use of `...` for combining many different arguments into a single argument is called slurping:

    julia> function printargs(args...)
               println(typeof(args))
               for (i, arg) in enumerate(args)
                   println("Arg #$i = $arg")
               end
           end
    printargs (generic function with 1 method)

    julia> printargs(1, 2, 3)
    Tuple{Int64, Int64, Int64}
    Arg #1 = 1
    Arg #2 = 2
    Arg #3 = 3

If Julia were a language that made more liberal use of ASCII characters, the slurping operator might have been written as `<-...` instead of `...`.

### `...` splits one argument into many different arguments in function calls

In contrast to the use of the `...` operator to denote slurping many different arguments into one argument when defining a function, the `...` operator is also used to cause a single function argument to be split apart into many different arguments when used in the context of a function call. This use of `...` is called splatting:

    julia> function threeargs(a, b, c)
               println("a = $a::$(typeof(a))")
               println("b = $b::$(typeof(b))")
               println("c = $c::$(typeof(c))")
           end
    threeargs (generic function with 1 method)

    julia> x = [1, 2, 3]
    3-element Vector{Int64}:
     1
     2
     3

    julia> threeargs(x...)
    a = 1::Int64
    b = 2::Int64
    c = 3::Int64

If Julia were a language that made more liberal use of ASCII characters, the splatting operator might have been written as `...->` instead of `...`.

### What is the return value of an assignment?

The operator `=` always returns the right-hand side, therefore:

    julia> function threeint()
               x::Int = 3.0
               x # returns variable x
           end
    threeint (generic function with 1 method)

    julia> function threefloat()
               x::Int = 3.0 # returns 3.0
           end
    threefloat (generic function with 1 method)

    julia> threeint()
    3

    julia> threefloat()
    3.0

and similarly:

    julia> function threetup()
               x, y = [3, 3]
               x, y # returns a tuple
           end
    threetup (generic function with 1 method)

    julia> function threearr()
               x, y = [3, 3] # returns an array
           end
    threearr (generic function with 1 method)

    julia> threetup()
    (3, 3)

    julia> threearr()
    2-element Vector{Int64}:
     3
     3

## Types, type declarations, and constructors

### What does "type-stable" mean?

It means that the type of the output is predictable from the types of the inputs. In particular, it means that the type of the output cannot vary depending on the values of the inputs. The following code is not type-stable:

    julia> function unstable(flag::Bool)
               if flag
                   return 1
               else
                   return 1.0
               end
           end
    unstable (generic function with 1 method)

It returns either an `Int` or a `Float64` depending on the value of its argument. Since Julia can't predict the return type of this function at compile-time, any computation that uses it must be able to cope with values of both types, which makes it hard to produce fast machine code.

### Why does Julia give a `DomainError` for certain seemingly-sensible operations?

Certain operations make mathematical sense but result in errors:

    julia> sqrt(-2.0)
    ERROR: DomainError with -2.0:
    sqrt will only return a complex result if called with a complex argument. Try sqrt(Complex(x)).
    Stacktrace:
    [...]

This behavior is an inconvenient consequence of the requirement for type-stability. In the case of `sqrt`, most users want `sqrt(2.0)` to give a real number, and would be unhappy if it produced the complex number `1.4142135623730951 + 0.0im`. One could write the `sqrt` function to switch to a complex-valued output only when passed a negative number (which is what `sqrt` does in some other languages), but then the result would not be type-stable and the `sqrt` function would have poor performance.

In these and other cases, you can get the result you want by choosing an input type that conveys your willingness to accept an output type in which the result can be represented:

    julia> sqrt(-2.0+0im)
    0.0 + 1.4142135623730951im

### How can I constrain or compute type parameters?

The parameters of a parametric type can hold either types or bits values, and the type itself chooses how it makes use of these parameters. For example, `Array{Float64, 2}` is parameterized by the type `Float64` to express its element type and the integer value `2` to express its number of dimensions. When defining your own parametric type, you can use subtype constraints to declare that a certain parameter must be a subtype (`<:`) of some abstract type or a previous type parameter. There is not, however, a dedicated syntax to declare that a parameter must be a value of a given type — that is, you cannot directly declare that a dimensionality-like parameter `isa` `Int` within the `struct` definition, for example. Similarly, you cannot do computations (including simple things like addition or subtraction) on type parameters. Instead, these sorts of constraints and relationships may be expressed through additional type parameters that are computed and enforced within the type's constructors.

As an example, consider

    struct ConstrainedType{T,N,N+1} # NOTE: INVALID SYNTAX
        A::Array{T,N}
        B::Array{T,N+1}
    end

where the user would like to enforce that the third type parameter is always the second plus one. This can be implemented with an explicit type parameter that is checked by an inner constructor method (where it can be combined with other checks):

    struct ConstrainedType{T,N,M}
        A::Array{T,N}
        B::Array{T,M}
        function ConstrainedType(A::Array{T,N}, B::Array{T,M}) where {T,N,M}
            N + 1 == M || throw(ArgumentError("second argument should have one more axis" ))
            new{T,N,M}(A, B)
        end
    end

This check is usually costless, as the compiler can elide the check for valid concrete types. If the second argument is also computed, it may be advantageous to provide an outer constructor method that performs this calculation:

    ConstrainedType(A) = ConstrainedType(A, compute_B(A))

### Why does Julia use native machine integer arithmetic?

Julia uses machine arithmetic for integer computations. This means that the range of `Int` values is bounded and wraps around at either end so that adding, subtracting and multiplying integers can overflow or underflow, leading to some results that can be unsettling at first:

    julia> x = typemax(Int)
    9223372036854775807

    julia> y = x+1
    -9223372036854775808

    julia> z = -y
    -9223372036854775808

    julia> 2*z
    0

Clearly, this is far from the way mathematical integers behave, and you might think it less than ideal for a high-level programming language to expose this to the user. For numerical work where efficiency and transparency are at a premium, however, the alternatives are worse.

One alternative to consider would be to check each integer operation for overflow and promote results to bigger integer types such as `Int128` or `BigInt` in the case of overflow. Unfortunately, this introduces major overhead on every integer operation (think incrementing a loop counter) – it requires emitting code to perform run-time overflow checks after arithmetic instructions and branches to handle potential overflows. Worse still, this would cause every computation involving integers to be type-unstable. As we mentioned above, type-stability is crucial for effective generation of efficient code. If you can't count on the results of integer operations being integers, it's impossible to generate fast, simple code the way C and Fortran compilers do.

A variation on this approach, which avoids the appearance of type instability is to merge the `Int` and `BigInt` types into a single hybrid integer type, that internally changes representation when a result no longer fits into the size of a machine integer. While this superficially avoids type-instability at the level of Julia code, it just sweeps the problem under the rug by foisting all of the same difficulties onto the C code implementing this hybrid integer type. This approach can be made to work and can even be made quite fast in many cases, but has several drawbacks. One problem is that the in-memory representation of integers and arrays of integers no longer match the natural representation used by C, Fortran and other languages with native machine integers. Thus, to interoperate with those languages, we would ultimately need to introduce native integer types anyway. Any unbounded representation of integers cannot have a fixed number of bits, and thus cannot be stored inline in an array with fixed-size slots – large integer values will always require separate heap-allocated storage. And of course, no matter how clever a hybrid integer implementation one uses, there are always performance traps – situations where performance degrades unexpectedly. Complex representation, lack of interoperability with C and Fortran, the inability to represent integer arrays without additional heap storage, and unpredictable performance characteristics make even the cleverest hybrid integer implementations a poor choice for high-performance numerical work.

An alternative to using hybrid integers or promoting to BigInts is to use saturating integer arithmetic, where adding to the largest integer value leaves it unchanged and likewise for subtracting from the smallest integer value. This is precisely what Matlab™ does:

    >> int64(9223372036854775807)

    ans =

      9223372036854775807

    >> int64(9223372036854775807) + 1

    ans =

      9223372036854775807

    >> int64(-9223372036854775808)

    ans =

     -9223372036854775808

    >> int64(-9223372036854775808) - 1

    ans =

     -9223372036854775808

At first blush, this seems reasonable enough since 9223372036854775807 is much closer to 9223372036854775808 than -9223372036854775808 is and integers are still represented with a fixed size in a natural way that is compatible with C and Fortran. Saturated integer arithmetic, however, is deeply problematic. The first and most obvious issue is that this is not the way machine integer arithmetic works, so implementing saturated operations requires emitting instructions after each machine integer operation to check for underflow or overflow and replace the result with `typemin(Int)` or `typemax(Int)` as appropriate. This alone expands each integer operation from a single, fast instruction into half a dozen instructions, probably including branches. Ouch. But it gets worse – saturating integer arithmetic isn't associative. Consider this Matlab computation:

    >> n = int64(2)^62
    4611686018427387904

    >> n + (n - 1)
    9223372036854775807

    >> (n + n) - 1
    9223372036854775806

This makes it hard to write many basic integer algorithms since a lot of common techniques depend on the fact that machine addition with overflow is associative. Consider finding the midpoint between integer values `lo` and `hi` in Julia using the expression `(lo + hi) >>> 1`:

    julia> n = 2^62
    4611686018427387904

    julia> (n + 2n) >>> 1
    6917529027641081856

See? No problem. That's the correct midpoint between 2^62 and 2^63, despite the fact that `n + 2n` is -4611686018427387904. Now try it in Matlab:

    >> (n + 2*n)/2

    ans =

      4611686018427387904

Oops. Adding a `>>>` operator to Matlab wouldn't help, because saturation that occurs when adding `n` and `2n` has already destroyed the information necessary to compute the correct midpoint.

Not only is lack of associativity unfortunate for programmers who cannot rely it for techniques like this, but it also defeats almost anything compilers might want to do to optimize integer arithmetic. For example, since Julia integers use normal machine integer arithmetic, LLVM is free to aggressively optimize simple little functions like `f(k) = 5k-1`. The machine code for this function is just this:

    julia> code_native(f, Tuple{Int})
      .text
    Filename: none
      pushq %rbp
      movq  %rsp, %rbp
    Source line: 1
      leaq  -1(%rdi,%rdi,4), %rax
      popq  %rbp
      retq
      nopl  (%rax,%rax)

The actual body of the function is a single `leaq` instruction, which computes the integer multiply and add at once. This is even more beneficial when `f` gets inlined into another function:

    julia> function g(k, n)
               for i = 1:n
                   k = f(k)
               end
               return k
           end
    g (generic function with 1 methods)

    julia> code_native(g, Tuple{Int,Int})
      .text
    Filename: none
      pushq %rbp
      movq  %rsp, %rbp
    Source line: 2
      testq %rsi, %rsi
      jle L26
      nopl  (%rax)
    Source line: 3
    L16:
      leaq  -1(%rdi,%rdi,4), %rdi
    Source line: 2
      decq  %rsi
      jne L16
    Source line: 5
    L26:
      movq  %rdi, %rax
      popq  %rbp
      retq
      nop

Since the call to `f` gets inlined, the loop body ends up being just a single `leaq` instruction. Next, consider what happens if we make the number of loop iterations fixed:

    julia> function g(k)
               for i = 1:10
                   k = f(k)
               end
               return k
           end
    g (generic function with 2 methods)

    julia> code_native(g,(Int,))
      .text
    Filename: none
      pushq %rbp
      movq  %rsp, %rbp
    Source line: 3
      imulq $9765625, %rdi, %rax    # imm = 0x9502F9
      addq  $-2441406, %rax         # imm = 0xFFDABF42
    Source line: 5
      popq  %rbp
      retq
      nopw  %cs:(%rax,%rax)

Because the compiler knows that integer addition and multiplication are associative and that multiplication distributes over addition – neither of which is true of saturating arithmetic – it can optimize the entire loop down to just a multiply and an add. Saturated arithmetic completely defeats this kind of optimization since associativity and distributivity can fail at each loop iteration, causing different outcomes depending on which iteration the failure occurs in. The compiler can unroll the loop, but it cannot algebraically reduce multiple operations into fewer equivalent operations.

The most reasonable alternative to having integer arithmetic silently overflow is to do checked arithmetic everywhere, raising errors when adds, subtracts, and multiplies overflow, producing values that are not value-correct. In this blog post, Dan Luu analyzes this and finds that rather than the trivial cost that this approach should in theory have, it ends up having a substantial cost due to compilers (LLVM and GCC) not gracefully optimizing around the added overflow checks. If this improves in the future, we could consider defaulting to checked integer arithmetic in Julia, but for now, we have to live with the possibility of overflow.

In the meantime, overflow-safe integer operations can be achieved through the use of external libraries such as SaferIntegers.jl. Note that, as stated previously, the use of these libraries significantly increases the execution time of code using the checked integer types. However, for limited usage, this is far less of an issue than if it were used for all integer operations. You can follow the status of the discussion here.

### What are the possible causes of an `UndefVarError` during remote execution?

As the error states, an immediate cause of an `UndefVarError` on a remote node is that a binding by that name does not exist. Let us explore some of the possible causes.

    julia> module Foo
               foo() = remotecall_fetch(x->x, 2, "Hello")
           end

    julia> Foo.foo()
    ERROR: On worker 2:
    UndefVarError: Foo not defined
    Stacktrace:
    [...]

The closure `x->x` carries a reference to `Foo`, and since `Foo` is unavailable on node 2, an `UndefVarError` is thrown.

Globals under modules other than `Main` are not serialized by value to the remote node. Only a reference is sent. Functions which create global bindings (except under `Main`) may cause an `UndefVarError` to be thrown later.

    julia> @everywhere module Foo
               function foo()
                   global gvar = "Hello"
                   remotecall_fetch(()->gvar, 2)
               end
           end

    julia> Foo.foo()
    ERROR: On worker 2:
    UndefVarError: gvar not defined
    Stacktrace:
    [...]

In the above example, `@everywhere module Foo` defined `Foo` on all nodes. However the call to `Foo.foo()` created a new global binding `gvar` on the local node, but this was not found on node 2 resulting in an `UndefVarError` error.

Note that this does not apply to globals created under module `Main`. Globals under module `Main` are serialized and new bindings created under `Main` on the remote node.

    julia> gvar_self = "Node1"
    "Node1"

    julia> remotecall_fetch(()->gvar_self, 2)
    "Node1"

    julia> remotecall_fetch(varinfo, 2)
    name          size summary
    ––––––––– –––––––– –––––––
    Base               Module
    Core               Module
    Main               Module
    gvar_self 13 bytes String

This does not apply to `function` or `struct` declarations. However, anonymous functions bound to global variables are serialized as can be seen below.

    julia> bar() = 1
    bar (generic function with 1 method)

    julia> remotecall_fetch(bar, 2)
    ERROR: On worker 2:
    UndefVarError: #bar not defined
    [...]

    julia> anon_bar  = ()->1
    (::#21) (generic function with 1 method)

    julia> remotecall_fetch(anon_bar, 2)
    1

### Why does Julia use `*` for string concatenation? Why not `+` or something else?

The main argument against `+` is that string concatenation is not commutative, while `+` is generally used as a commutative operator. While the Julia community recognizes that other languages use different operators and `*` may be unfamiliar for some users, it communicates certain algebraic properties.

Note that you can also use `string(...)` to concatenate strings (and other values converted to strings); similarly, `repeat` can be used instead of `^` to repeat strings. The interpolation syntax is also useful for constructing strings.

## Packages and Modules

### What is the difference between "using" and "import"?

There is only one difference, and on the surface (syntax-wise) it may seem very minor. The difference between `using` and `import` is that with `using` you need to say `function Foo.bar(..` to extend module Foo's function bar with a new method, but with `import Foo.bar`, you only need to say `function bar(...` and it automatically extends module Foo's function bar.

The reason this is important enough to have been given separate syntax is that you don't want to accidentally extend a function that you didn't know existed, because that could easily cause a bug. This is most likely to happen with a method that takes a common type like a string or integer, because both you and the other module could define a method to handle such a common type. If you use `import`, then you'll replace the other module's implementation of `bar(s::AbstractString)` with your new implementation, which could easily do something completely different (and break all/many future usages of the other functions in module Foo that depend on calling bar).

## Nothingness and missing values

### How does "null", "nothingness" or "missingness" work in Julia?

Unlike many languages (for example, C and Java), Julia objects cannot be "null" by default. When a reference (variable, object field, or array element) is uninitialized, accessing it will immediately throw an error. This situation can be detected using the `isdefined` or `isassigned` functions.

Some functions are used only for their side effects, and do not need to return a value. In these cases, the convention is to return the value `nothing`, which is just a singleton object of type `Nothing`. This is an ordinary type with no fields; there is nothing special about it except for this convention, and that the REPL does not print anything for it. Some language constructs that would not otherwise have a value also yield `nothing`, for example `if false; end`.

For situations where a value `x` of type `T` exists only sometimes, the `Union{T, Nothing}` type can be used for function arguments, object fields and array element types as the equivalent of `Nullable`, `Option` or `Maybe` in other languages. If the value itself can be `nothing` (notably, when `T` is `Any`), the `Union{Some{T}, Nothing}` type is more appropriate since `x == nothing` then indicates the absence of a value, and `x == Some(nothing)` indicates the presence of a value equal to `nothing`. The `something` function allows unwrapping `Some` objects and using a default value instead of `nothing` arguments. Note that the compiler is able to generate efficient code when working with `Union{T, Nothing}` arguments or fields.

To represent missing data in the statistical sense (`NA` in R or `NULL` in SQL), use the `missing` object. See the `Missing Values` section for more details.

In some languages, the empty tuple (`()`) is considered the canonical form of nothingness. However, in julia it is best thought of as just a regular tuple that happens to contain zero values.

The empty (or "bottom") type, written as `Union{}` (an empty union type), is a type with no values and no subtypes (except itself). You will generally not need to use this type.

## Memory

### Why does `x += y` allocate memory when `x` and `y` are arrays?

In Julia, `x += y` gets replaced during parsing by `x = x + y`. For arrays, this has the consequence that, rather than storing the result in the same location in memory as `x`, it allocates a new array to store the result.

While this behavior might surprise some, the choice is deliberate. The main reason is the presence of immutable objects within Julia, which cannot change their value once created. Indeed, a number is an immutable object; the statements `x = 5; x += 1` do not modify the meaning of `5`, they modify the value bound to `x`. For an immutable, the only way to change the value is to reassign it.

To amplify a bit further, consider the following function:

    function power_by_squaring(x, n::Int)
        ispow2(n) || error("This implementation only works for powers of 2")
        while n >= 2
            x *= x
            n >>= 1
        end
        x
    end

After a call like `x = 5; y = power_by_squaring(x, 4)`, you would get the expected result: `x == 5 && y == 625`. However, now suppose that `*=`, when used with matrices, instead mutated the left hand side. There would be two problems:

- For general square matrices, `A = A*B` cannot be implemented without temporary storage: `A[1,1]` gets computed and stored on the left hand side before you're done using it on the right hand side.
- Suppose you were willing to allocate a temporary for the computation (which would eliminate most of the point of making `*=` work in-place); if you took advantage of the mutability of `x`, then this function would behave differently for mutable vs. immutable inputs. In particular, for immutable `x`, after the call you'd have (in general) `y != x`, but for mutable `x` you'd have `y == x`.

Because supporting generic programming is deemed more important than potential performance optimizations that can be achieved by other means (e.g., using explicit loops), operators like `+=` and `*=` work by rebinding new values.

## Asynchronous IO and concurrent synchronous writes

### Why do concurrent writes to the same stream result in inter-mixed output?

While the streaming I/O API is synchronous, the underlying implementation is fully asynchronous.

Consider the printed output from the following:

    julia> @sync for i in 1:3
               @async write(stdout, string(i), " Foo ", " Bar ")
           end
    123 Foo  Foo  Foo  Bar  Bar  Bar

This is happening because, while the `write` call is synchronous, the writing of each argument yields to other tasks while waiting for that part of the I/O to complete.

`print` and `println` "lock" the stream during a call. Consequently changing `write` to `println` in the above example results in:

    julia> @sync for i in 1:3
               @async println(stdout, string(i), " Foo ", " Bar ")
           end
    1 Foo  Bar
    2 Foo  Bar
    3 Foo  Bar

You can lock your writes with a `ReentrantLock` like this:

    julia> l = ReentrantLock();

    julia> @sync for i in 1:3
               @async begin
                   lock(l)
                   try
                       write(stdout, string(i), " Foo ", " Bar ")
                   finally
                       unlock(l)
                   end
               end
           end
    1 Foo  Bar 2 Foo  Bar 3 Foo  Bar

## Arrays

### What are the differences between zero-dimensional arrays and scalars?

Zero-dimensional arrays are arrays of the form `Array{T,0}`. They behave similar to scalars, but there are important differences. They deserve a special mention because they are a special case which makes logical sense given the generic definition of arrays, but might be a bit unintuitive at first. The following line defines a zero-dimensional array:

    julia> A = zeros()
    0-dimensional Array{Float64,0}:
    0.0

In this example, `A` is a mutable container that contains one element, which can be set by `A[] = 1.0` and retrieved with `A[]`. All zero-dimensional arrays have the same size (`size(A) == ()`), and length (`length(A) == 1`). In particular, zero-dimensional arrays are not empty. If you find this unintuitive, here are some ideas that might help to understand Julia's definition.

- Zero-dimensional arrays are the "point" to vector's "line" and matrix's "plane". Just as a line has no area (but still represents a set of things), a point has no length or any dimensions at all (but still represents a thing).
- We define `prod(())` to be 1, and the total number of elements in an array is the product of the size. The size of a zero-dimensional array is `()`, and therefore its length is `1`.
- Zero-dimensional arrays don't natively have any dimensions into which you index – they’re just `A[]`. We can apply the same "trailing one" rule for them as for all other array dimensionalities, so you can indeed index them as `A[1]`, `A[1,1]`, etc; see Omitted and extra indices.

It is also important to understand the differences to ordinary scalars. Scalars are not mutable containers (even though they are iterable and define things like `length`, `getindex`, e.g. `1[] == 1`). In particular, if `x = 0.0` is defined as a scalar, it is an error to attempt to change its value via `x[] = 1.0`. A scalar `x` can be converted into a zero-dimensional array containing it via `fill(x)`, and conversely, a zero-dimensional array `a` can be converted to the contained scalar via `a[]`. Another difference is that a scalar can participate in linear algebra operations such as `2 * rand(2,2)`, but the analogous operation with a zero-dimensional array `fill(2) * rand(2,2)` is an error.

### Why are my Julia benchmarks for linear algebra operations different from other languages?

You may find that simple benchmarks of linear algebra building blocks like

    using BenchmarkTools
    A = randn(1000, 1000)
    B = randn(1000, 1000)
    @btime $A \ $B
    @btime $A * $B

can be different when compared to other languages like Matlab or R.

Since operations like this are very thin wrappers over the relevant BLAS functions, the reason for the discrepancy is very likely to be

1. the BLAS library each language is using,

2. the number of concurrent threads.

Julia compiles and uses its own copy of OpenBLAS, with threads currently capped at `8` (or the number of your cores).

Modifying OpenBLAS settings or compiling Julia with a different BLAS library, eg Intel MKL, may provide performance improvements. You can use MKL.jl, a package that makes Julia's linear algebra use Intel MKL BLAS and LAPACK instead of OpenBLAS, or search the discussion forum for suggestions on how to set this up manually. Note that Intel MKL cannot be bundled with Julia, as it is not open source.

## Computing cluster

### How do I manage precompilation caches in distributed file systems?

When using `julia` in high-performance computing (HPC) facilities, invoking n `julia` processes simultaneously creates at most n temporary copies of precompilation cache files. If this is an issue (slow and/or small distributed file system), you may:

1. Use `julia` with `--compiled-modules=no` flag to turn off precompilation.
2. Configure a private writable depot using `pushfirst!(DEPOT_PATH, private_path)` where `private_path` is a path unique to this `julia` process. This can also be done by setting environment variable `JULIA_DEPOT_PATH` to `$private_path:$HOME/.julia`.
3. Create a symlink from `~/.julia/compiled` to a directory in a scratch space.

## Julia Releases

### Do I want to use the Stable, LTS, or nightly version of Julia?

The Stable version of Julia is the latest released version of Julia, this is the version most people will want to run. It has the latest features, including improved performance. The Stable version of Julia is versioned according to SemVer as v1.x.y. A new minor release of Julia corresponding to a new Stable version is made approximately every 4-5 months after a few weeks of testing as a release candidate. Unlike the LTS version the a Stable version will not normally recieve bugfixes after another Stable version of Julia has been released. However, upgrading to the next Stable release will always be possible as each release of Julia v1.x will continue to run code written for earlier versions.

You may prefer the LTS (Long Term Support) version of Julia if you are looking for a very stable code base. The current LTS version of Julia is versioned according to SemVer as v1.0.x; this branch will continue to recieve bugfixes until a new LTS branch is chosen, at which point the v1.0.x series will no longer recieved regular bug fixes and all but the most conservative users will be advised to upgrade to the new LTS version series. As a package developer, you may prefer to develop for the LTS version, to maximize the number of users who can use your package. As per SemVer, code written for v1.0 will continue to work for all future LTS and Stable versions. In general, even if targetting the LTS, one can develop and run code in the latest Stable version, to take advantage of the improved performance; so long as one avoids using new features (such as added library functions or new methods).

You may prefer the nightly version of Julia if you want to take advantage of the latest updates to the language, and don't mind if the version available today occasionally doesn't actually work. As the name implies, releases to the nightly version are made roughly every night (depending on build infrastructure stability). In general nightly released are fairly safe to use—your code will not catch on fire. However, they may be occasional regressions and or issues that will not be found until more thorough pre-release testing. You may wish to test against the nightly version to ensure that such regressions that affect your use case are caught before a release is made.

Finally, you may also consider building Julia from source for yourself. This option is mainly for those individuals who are comfortable at the command line, or interested in learning. If this describes you, you may also be interested in reading our guidelines for contributing.

Links to each of these download types can be found on the download page at https://julialang.org/downloads/. Note that not all versions of Julia are available for all platforms.

### How can I transfer the list of installed packages after updating my version of Julia?

Each minor version of julia has its own default environment. As a result, upon installing a new minor version of Julia, the packages you added using the previous minor version will not be available by default. The environment for a given julia version is defined by the files `Project.toml` and `Manifest.toml` in a folder matching the version number in `.julia/environments/`, for instance, `.julia/environments/v1.3`.

If you install a new minor version of Julia, say `1.4`, and want to use in its default environment the same packages as in a previous version (e.g. `1.3`), you can copy the contents of the file `Project.toml` from the `1.3` folder to `1.4`. Then, in a session of the new Julia version, enter the "package management mode" by typing the key `]`, and run the command `instantiate`.

This operation will resolve a set of feasible packages from the copied file that are compatible with the target Julia version, and will install or update them if suitable. If you want to reproduce not only the set of packages, but also the versions you were using in the previous Julia version, you should also copy the `Manifest.toml` file before running the Pkg command `instantiate`. However, note that packages may define compatibility constraints that may be affected by changing the version of Julia, so the exact set of versions you had in `1.3` may not work for `1.4`.

Theme

This document was generated with Documenter.jl version 0.27.10 on Tuesday 19 July 2022. Using Julia version 1.6.7.