# Types¶

Type systems have traditionally fallen into two quite different camps: static type systems, where every program expression must have a type computable before the execution of the program, and dynamic type systems, where nothing is known about types until run time, when the actual values manipulated by the program are available. Object orientation allows some flexibility in statically typed languages by letting code be written without the precise types of values being known at compile time. The ability to write code that can operate on different types is called polymorphism. All code in classic dynamically typed languages is polymorphic: only by explicitly checking types, or when objects fail to support operations at run-time, are the types of any values ever restricted.

Julia’s type system is dynamic, but gains some of the advantages of static type systems by making it possible to indicate that certain values are of specific types. This can be of great assistance in generating efficient code, but even more significantly, it allows method dispatch on the types of function arguments to be deeply integrated with the language. Method dispatch is explored in detail in Methods, but is rooted in the type system presented here.

The default behavior in Julia when types are omitted is to allow values to be of any type. Thus, one can write many useful Julia programs without ever explicitly using types. When additional expressiveness is needed, however, it is easy to gradually introduce explicit type annotations into previously “untyped” code. Doing so will typically increase both the performance and robustness of these systems, and perhaps somewhat counterintuitively, often significantly simplify them.

Describing Julia in the lingo of type systems, it is: dynamic, nominative and parametric. Generic types can be parameterized, and the hierarchical relationships between types are explicitly declared, rather than implied by compatible structure. One particularly distinctive feature of Julia’s type system is that concrete types may not subtype each other: all concrete types are final and may only have abstract types as their supertypes. While this might at first seem unduly restrictive, it has many beneficial consequences with surprisingly few drawbacks. It turns out that being able to inherit behavior is much more important than being able to inherit structure, and inheriting both causes significant difficulties in traditional object-oriented languages. Other high-level aspects of Julia’s type system that should be mentioned up front are:

• There is no division between object and non-object values: all values in Julia are true objects having a type that belongs to a single, fully connected type graph, all nodes of which are equally first-class as types.
• There is no meaningful concept of a “compile-time type”: the only type a value has is its actual type when the program is running. This is called a “run-time type” in object-oriented languages where the combination of static compilation with polymorphism makes this distinction significant.
• Only values, not variables, have types — variables are simply names bound to values.
• Both abstract and concrete types can be parameterized by other types. They can also be parameterized by symbols, by values of any type for which isbits() returns true (essentially, things like numbers and bools that are stored like C types or structs with no pointers to other objects), and also by tuples thereof. Type parameters may be omitted when they do not need to be referenced or restricted.

Julia’s type system is designed to be powerful and expressive, yet clear, intuitive and unobtrusive. Many Julia programmers may never feel the need to write code that explicitly uses types. Some kinds of programming, however, become clearer, simpler, faster and more robust with declared types.

## Type Declarations¶

The :: operator can be used to attach type annotations to expressions and variables in programs. There are two primary reasons to do this:

1. As an assertion to help confirm that your program works the way you expect,
2. To provide extra type information to the compiler, which can then improve performance in some cases

When appended to an expression computing a value, the :: operator is read as “is an instance of”. It can be used anywhere to assert that the value of the expression on the left is an instance of the type on the right. When the type on the right is concrete, the value on the left must have that type as its implementation — recall that all concrete types are final, so no implementation is a subtype of any other. When the type is abstract, it suffices for the value to be implemented by a concrete type that is a subtype of the abstract type. If the type assertion is not true, an exception is thrown, otherwise, the left-hand value is returned:

julia> (1+2)::AbstractFloat
ERROR: TypeError: typeassert: expected AbstractFloat, got Int64
...

julia> (1+2)::Int
3


This allows a type assertion to be attached to any expression in-place.

When appended to a variable on the left-hand side of an assignment, or as part of a local declaration, the :: operator means something a bit different: it declares the variable to always have the specified type, like a type declaration in a statically-typed language such as C. Every value assigned to the variable will be converted to the declared type using convert():

julia> function foo()
x::Int8 = 100
x
end
foo (generic function with 1 method)

julia> foo()
100

julia> typeof(ans)
Int8


This feature is useful for avoiding performance “gotchas” that could occur if one of the assignments to a variable changed its type unexpectedly.

This “declaration” behavior only occurs in specific contexts:

local x::Int8  # in a local declaration
x::Int8 = 10   # as the left-hand side of an assignment


and applies to the whole current scope, even before the declaration. Currently, type declarations cannot be used in global scope, e.g. in the REPL, since Julia does not yet have constant-type globals.

Declarations can also be attached to function definitions:

function sinc(x)::Float64
if x == 0
return 1
end
return sin(pi*x)/(pi*x)
end


Returning from this function behaves just like an assignment to a variable with a declared type: the value is always converted to Float64.

## Abstract Types¶

Abstract types cannot be instantiated, and serve only as nodes in the type graph, thereby describing sets of related concrete types: those concrete types which are their descendants. We begin with abstract types even though they have no instantiation because they are the backbone of the type system: they form the conceptual hierarchy which makes Julia’s type system more than just a collection of object implementations.

Recall that in Integers and Floating-Point Numbers, we introduced a variety of concrete types of numeric values: Int8, UInt8, Int16, UInt16, Int32, UInt32, Int64, UInt64, Int128, UInt128, Float16, Float32, and Float64. Although they have different representation sizes, Int8, Int16, Int32, Int64 and Int128 all have in common that they are signed integer types. Likewise UInt8, UInt16, UInt32, UInt64 and UInt128 are all unsigned integer types, while Float16, Float32 and Float64 are distinct in being floating-point types rather than integers. It is common for a piece of code to make sense, for example, only if its arguments are some kind of integer, but not really depend on what particular kind of integer. For example, the greatest common denominator algorithm works for all kinds of integers, but will not work for floating-point numbers. Abstract types allow the construction of a hierarchy of types, providing a context into which concrete types can fit. This allows you, for example, to easily program to any type that is an integer, without restricting an algorithm to a specific type of integer.

Abstract types are declared using the abstract keyword. The general syntaxes for declaring an abstract type are:

abstract «name»
abstract «name» <: «supertype»


The abstract keyword introduces a new abstract type, whose name is given by «name». This name can be optionally followed by <: and an already-existing type, indicating that the newly declared abstract type is a subtype of this “parent” type.

When no supertype is given, the default supertype is Any — a predefined abstract type that all objects are instances of and all types are subtypes of. In type theory, Any is commonly called “top” because it is at the apex of the type graph. Julia also has a predefined abstract “bottom” type, at the nadir of the type graph, which is written as Union{}. It is the exact opposite of Any: no object is an instance of Union{} and all types are supertypes of Union{}.

Let’s consider some of the abstract types that make up Julia’s numerical hierarchy:

abstract Number
abstract Real     <: Number
abstract AbstractFloat <: Real
abstract Integer  <: Real
abstract Signed   <: Integer
abstract Unsigned <: Integer


The Number type is a direct child type of Any, and Real is its child. In turn, Real has two children (it has more, but only two are shown here; we’ll get to the others later): Integer and AbstractFloat, separating the world into representations of integers and representations of real numbers. Representations of real numbers include, of course, floating-point types, but also include other types, such as rationals. Hence, AbstractFloat is a proper subtype of Real, including only floating-point representations of real numbers. Integers are further subdivided into Signed and Unsigned varieties.

The <: operator in general means “is a subtype of”, and, used in declarations like this, declares the right-hand type to be an immediate supertype of the newly declared type. It can also be used in expressions as a subtype operator which returns true when its left operand is a subtype of its right operand:

julia> Integer <: Number
true

julia> Integer <: AbstractFloat
false


An important use of abstract types is to provide default implementations for concrete types. To give a simple example, consider:

function myplus(x,y)
x+y
end


The first thing to note is that the above argument declarations are equivalent to x::Any and y::Any. When this function is invoked, say as myplus(2,5), the dispatcher chooses the most specific method named myplus that matches the given arguments. (See Methods for more information on multiple dispatch.)

Assuming no method more specific than the above is found, Julia next internally defines and compiles a method called myplus specifically for two Int arguments based on the generic function given above, i.e., it implicitly defines and compiles:

function myplus(x::Int,y::Int)
x+y
end


and finally, it invokes this specific method.

Thus, abstract types allow programmers to write generic functions that can later be used as the default method by many combinations of concrete types. Thanks to multiple dispatch, the programmer has full control over whether the default or more specific method is used.

An important point to note is that there is no loss in performance if the programmer relies on a function whose arguments are abstract types, because it is recompiled for each tuple of argument concrete types with which it is invoked. (There may be a performance issue, however, in the case of function arguments that are containers of abstract types; see Performance Tips.)

## Bits Types¶

A bits type is a concrete type whose data consists of plain old bits. Classic examples of bits types are integers and floating-point values. Unlike most languages, Julia lets you declare your own bits types, rather than providing only a fixed set of built-in bits types. In fact, the standard bits types are all defined in the language itself:

bitstype 16 Float16 <: AbstractFloat
bitstype 32 Float32 <: AbstractFloat
bitstype 64 Float64 <: AbstractFloat

bitstype 8  Bool <: Integer
bitstype 32 Char

bitstype 8  Int8     <: Signed
bitstype 8  UInt8    <: Unsigned
bitstype 16 Int16    <: Signed
bitstype 16 UInt16   <: Unsigned
bitstype 32 Int32    <: Signed
bitstype 32 UInt32   <: Unsigned
bitstype 64 Int64    <: Signed
bitstype 64 UInt64   <: Unsigned
bitstype 128 Int128  <: Signed
bitstype 128 UInt128 <: Unsigned


The general syntaxes for declaration of a bitstype are:

bitstype «bits» «name»
bitstype «bits» «name» <: «supertype»


The number of bits indicates how much storage the type requires and the name gives the new type a name. A bits type can optionally be declared to be a subtype of some supertype. If a supertype is omitted, then the type defaults to having Any as its immediate supertype. The declaration of Bool above therefore means that a boolean value takes eight bits to store, and has Integer as its immediate supertype. Currently, only sizes that are multiples of 8 bits are supported. Therefore, boolean values, although they really need just a single bit, cannot be declared to be any smaller than eight bits.

The types Bool, Int8 and UInt8 all have identical representations: they are eight-bit chunks of memory. Since Julia’s type system is nominative, however, they are not interchangeable despite having identical structure. Another fundamental difference between them is that they have different supertypes: Bool‘s direct supertype is Integer, Int8‘s is Signed, and UInt8‘s is Unsigned. All other differences between Bool, Int8, and UInt8 are matters of behavior — the way functions are defined to act when given objects of these types as arguments. This is why a nominative type system is necessary: if structure determined type, which in turn dictates behavior, then it would be impossible to make Bool behave any differently than Int8 or UInt8.

## Composite Types¶

Composite types are called records, structures (structs in C), or objects in various languages. A composite type is a collection of named fields, an instance of which can be treated as a single value. In many languages, composite types are the only kind of user-definable type, and they are by far the most commonly used user-defined type in Julia as well.

In mainstream object oriented languages, such as C++, Java, Python and Ruby, composite types also have named functions associated with them, and the combination is called an “object”. In purer object-oriented languages, such as Ruby or Smalltalk, all values are objects whether they are composites or not. In less pure object oriented languages, including C++ and Java, some values, such as integers and floating-point values, are not objects, while instances of user-defined composite types are true objects with associated methods. In Julia, all values are objects, but functions are not bundled with the objects they operate on. This is necessary since Julia chooses which method of a function to use by multiple dispatch, meaning that the types of all of a function’s arguments are considered when selecting a method, rather than just the first one (see Methods for more information on methods and dispatch). Thus, it would be inappropriate for functions to “belong” to only their first argument. Organizing methods into function objects rather than having named bags of methods “inside” each object ends up being a highly beneficial aspect of the language design.

Since composite types are the most common form of user-defined concrete type, they are simply introduced with the type keyword followed by a block of field names, optionally annotated with types using the :: operator:

julia> type Foo
bar
baz::Int
qux::Float64
end


Fields with no type annotation default to Any, and can accordingly hold any type of value.

New objects of composite type Foo are created by applying the Foo type object like a function to values for its fields:

julia> foo = Foo("Hello, world.", 23, 1.5)
Foo("Hello, world.",23,1.5)

julia> typeof(foo)
Foo


When a type is applied like a function it is called a constructor. Two constructors are generated automatically (these are called default constructors). One accepts any arguments and calls convert() to convert them to the types of the fields, and the other accepts arguments that match the field types exactly. The reason both of these are generated is that this makes it easier to add new definitions without inadvertently replacing a default constructor.

Since the bar field is unconstrained in type, any value will do. However, the value for baz must be convertible to Int:

julia> Foo((), 23.5, 1)
ERROR: InexactError()
in Foo(::Tuple{}, ::Float64, ::Int64) at ./none:2
...


You may find a list of field names using the fieldnames function.

julia> fieldnames(foo)
3-element Array{Symbol,1}:
:bar
:baz
:qux


You can access the field values of a composite object using the traditional foo.bar notation:

julia> foo.bar
"Hello, world."

julia> foo.baz
23

julia> foo.qux
1.5


You can also change the values as one would expect:

julia> foo.qux = 2
2

julia> foo.bar = 1//2
1//2


Composite types with no fields are singletons; there can be only one instance of such types:

type NoFields
end

julia> is(NoFields(), NoFields())
true


The is function confirms that the “two” constructed instances of NoFields are actually one and the same. Singleton types are described in further detail below.

There is much more to say about how instances of composite types are created, but that discussion depends on both Parametric Types and on Methods, and is sufficiently important to be addressed in its own section: Constructors.

## Immutable Composite Types¶

It is also possible to define immutable composite types by using the keyword immutable instead of type:

immutable Complex
real::Float64
imag::Float64
end


Such types behave much like other composite types, except that instances of them cannot be modified. Immutable types have several advantages:

• They are more efficient in some cases. Types like the Complex example above can be packed efficiently into arrays, and in some cases the compiler is able to avoid allocating immutable objects entirely.
• It is not possible to violate the invariants provided by the type’s constructors.
• Code using immutable objects can be easier to reason about.

An immutable object might contain mutable objects, such as arrays, as fields. Those contained objects will remain mutable; only the fields of the immutable object itself cannot be changed to point to different objects.

A useful way to think about immutable composites is that each instance is associated with specific field values — the field values alone tell you everything about the object. In contrast, a mutable object is like a little container that might hold different values over time, and so is not identified with specific field values. In deciding whether to make a type immutable, ask whether two instances with the same field values would be considered identical, or if they might need to change independently over time. If they would be considered identical, the type should probably be immutable.

To recap, two essential properties define immutability in Julia:

• An object with an immutable type is passed around (both in assignment statements and in function calls) by copying, whereas a mutable type is passed around by reference.
• It is not permitted to modify the fields of a composite immutable type.

It is instructive, particularly for readers whose background is C/C++, to consider why these two properties go hand in hand. If they were separated, i.e., if the fields of objects passed around by copying could be modified, then it would become more difficult to reason about certain instances of generic code. For example, suppose x is a function argument of an abstract type, and suppose that the function changes a field: x.isprocessed = true. Depending on whether x is passed by copying or by reference, this statement may or may not alter the actual argument in the calling routine. Julia sidesteps the possibility of creating functions with unknown effects in this scenario by forbidding modification of fields of objects passed around by copying.

## Declared Types¶

The three kinds of types discussed in the previous three sections are actually all closely related. They share the same key properties:

• They are explicitly declared.
• They have names.
• They have explicitly declared supertypes.
• They may have parameters.

Because of these shared properties, these types are internally represented as instances of the same concept, DataType, which is the type of any of these types:

julia> typeof(Real)
DataType

julia> typeof(Int)
DataType


A DataType may be abstract or concrete. If it is concrete, it has a specified size, storage layout, and (optionally) field names. Thus a bits type is a DataType with nonzero size, but no field names. A composite type is a DataType that has field names or is empty (zero size).

Every concrete value in the system is an instance of some DataType.

## Type Unions¶

A type union is a special abstract type which includes as objects all instances of any of its argument types, constructed using the special Union function:

julia> IntOrString = Union{Int,AbstractString}
Union{AbstractString,Int64}

julia> 1 :: IntOrString
1

julia> "Hello!" :: IntOrString
"Hello!"

julia> 1.0 :: IntOrString
ERROR: type: typeassert: expected Union{AbstractString,Int64}, got Float64


The compilers for many languages have an internal union construct for reasoning about types; Julia simply exposes it to the programmer.

## Parametric Types¶

An important and powerful feature of Julia’s type system is that it is parametric: types can take parameters, so that type declarations actually introduce a whole family of new types — one for each possible combination of parameter values. There are many languages that support some version of generic programming, wherein data structures and algorithms to manipulate them may be specified without specifying the exact types involved. For example, some form of generic programming exists in ML, Haskell, Ada, Eiffel, C++, Java, C#, F#, and Scala, just to name a few. Some of these languages support true parametric polymorphism (e.g. ML, Haskell, Scala), while others support ad-hoc, template-based styles of generic programming (e.g. C++, Java). With so many different varieties of generic programming and parametric types in various languages, we won’t even attempt to compare Julia’s parametric types to other languages, but will instead focus on explaining Julia’s system in its own right. We will note, however, that because Julia is a dynamically typed language and doesn’t need to make all type decisions at compile time, many traditional difficulties encountered in static parametric type systems can be relatively easily handled.

All declared types (the DataType variety) can be parameterized, with the same syntax in each case. We will discuss them in the following order: first, parametric composite types, then parametric abstract types, and finally parametric bits types.

### Parametric Composite Types¶

Type parameters are introduced immediately after the type name, surrounded by curly braces:

type Point{T}
x::T
y::T
end


This declaration defines a new parametric type, Point{T}, holding two “coordinates” of type T. What, one may ask, is T? Well, that’s precisely the point of parametric types: it can be any type at all (or a value of any bits type, actually, although here it’s clearly used as a type). Point{Float64} is a concrete type equivalent to the type defined by replacing T in the definition of Point with Float64. Thus, this single declaration actually declares an unlimited number of types: Point{Float64}, Point{AbstractString}, Point{Int64}, etc. Each of these is now a usable concrete type:

julia> Point{Float64}
Point{Float64}

julia> Point{AbstractString}
Point{AbstractString}


The type Point{Float64} is a point whose coordinates are 64-bit floating-point values, while the type Point{AbstractString} is a “point” whose “coordinates” are string objects (see Strings). However, Point itself is also a valid type object:

julia> Point
Point{T}


Here the T is the dummy type symbol used in the original declaration of Point. What does Point by itself mean? It is a type that contains all the specific instances Point{Float64}, Point{AbstractString}, etc.:

julia> Point{Float64} <: Point
true

julia> Point{AbstractString} <: Point
true


Other types, of course, are not subtypes of it:

julia> Float64 <: Point
false

julia> AbstractString <: Point
false


Concrete Point types with different values of T are never subtypes of each other:

julia> Point{Float64} <: Point{Int64}
false

julia> Point{Float64} <: Point{Real}
false


This last point is very important:

• Even though Float64 <: Real we DO NOT have Point{Float64} <: Point{Real}.

In other words, in the parlance of type theory, Julia’s type parameters are invariant, rather than being covariant (or even contravariant). This is for practical reasons: while any instance of Point{Float64} may conceptually be like an instance of Point{Real} as well, the two types have different representations in memory:

• An instance of Point{Float64} can be represented compactly and efficiently as an immediate pair of 64-bit values;
• An instance of Point{Real} must be able to hold any pair of instances of Real. Since objects that are instances of Real can be of arbitrary size and structure, in practice an instance of Point{Real} must be represented as a pair of pointers to individually allocated Real objects.

The efficiency gained by being able to store Point{Float64} objects with immediate values is magnified enormously in the case of arrays: an Array{Float64} can be stored as a contiguous memory block of 64-bit floating-point values, whereas an Array{Real} must be an array of pointers to individually allocated Real objects — which may well be boxed 64-bit floating-point values, but also might be arbitrarily large, complex objects, which are declared to be implementations of the Real abstract type.

Since Point{Float64} is not a subtype of Point{Real}, the following method can’t be applied to arguments of type Point{Float64}:

function norm(p::Point{Real})
sqrt(p.x^2 + p.y^2)
end


The correct way to define a method that accepts all arguments of type Point{T} where T is a subtype of Real is:

function norm{T<:Real}(p::Point{T})
sqrt(p.x^2 + p.y^2)
end


More examples will be discussed later in Methods.

How does one construct a Point object? It is possible to define custom constructors for composite types, which will be discussed in detail in Constructors, but in the absence of any special constructor declarations, there are two default ways of creating new composite objects, one in which the type parameters are explicitly given and the other in which they are implied by the arguments to the object constructor.

Since the type Point{Float64} is a concrete type equivalent to Point declared with Float64 in place of T, it can be applied as a constructor accordingly:

julia> Point{Float64}(1.0,2.0)
Point{Float64}(1.0,2.0)

julia> typeof(ans)
Point{Float64}


For the default constructor, exactly one argument must be supplied for each field:

julia> Point{Float64}(1.0)
ERROR: MethodError: Cannot convert an object of type Float64 to an object of type Point{Float64}
This may have arisen from a call to the constructor Point{Float64}(...),
since type constructors fall back to convert methods.
in Point{Float64}(::Float64) at ./sysimg.jl:53
...

julia> Point{Float64}(1.0,2.0,3.0)
ERROR: MethodError: no method matching Point{Float64}(::Float64, ::Float64, ::Float64)
Closest candidates are:
Point{Float64}{T}(::Any, ::Any) at none:3
Point{Float64}{T}(::Any) at sysimg.jl:53
...


Only one default constructor is generated for parametric types, since overriding it is not possible. This constructor accepts any arguments and converts them to the field types.

In many cases, it is redundant to provide the type of Point object one wants to construct, since the types of arguments to the constructor call already implicitly provide type information. For that reason, you can also apply Point itself as a constructor, provided that the implied value of the parameter type T is unambiguous:

julia> Point(1.0,2.0)
Point{Float64}(1.0,2.0)

julia> typeof(ans)
Point{Float64}

julia> Point(1,2)
Point{Int64}(1,2)

julia> typeof(ans)
Point{Int64}


In the case of Point, the type of T is unambiguously implied if and only if the two arguments to Point have the same type. When this isn’t the case, the constructor will fail with a MethodError:

julia> Point(1,2.5)
ERROR: MethodError: no method matching Point{T}(::Int64, ::Float64)
...


Constructor methods to appropriately handle such mixed cases can be defined, but that will not be discussed until later on in Constructors.

### Parametric Abstract Types¶

Parametric abstract type declarations declare a collection of abstract types, in much the same way:

abstract Pointy{T}


With this declaration, Pointy{T} is a distinct abstract type for each type or integer value of T. As with parametric composite types, each such instance is a subtype of Pointy:

julia> Pointy{Int64} <: Pointy
true

julia> Pointy{1} <: Pointy
true


Parametric abstract types are invariant, much as parametric composite types are:

julia> Pointy{Float64} <: Pointy{Real}
false

julia> Pointy{Real} <: Pointy{Float64}
false


Much as plain old abstract types serve to create a useful hierarchy of types over concrete types, parametric abstract types serve the same purpose with respect to parametric composite types. We could, for example, have declared Point{T} to be a subtype of Pointy{T} as follows:

type Point{T} <: Pointy{T}
x::T
y::T
end


Given such a declaration, for each choice of T, we have Point{T} as a subtype of Pointy{T}:

julia> Point{Float64} <: Pointy{Float64}
true

julia> Point{Real} <: Pointy{Real}
true

julia> Point{AbstractString} <: Pointy{AbstractString}
true


This relationship is also invariant:

julia> Point{Float64} <: Pointy{Real}
false


What purpose do parametric abstract types like Pointy serve? Consider if we create a point-like implementation that only requires a single coordinate because the point is on the diagonal line x = y:

type DiagPoint{T} <: Pointy{T}
x::T
end


Now both Point{Float64} and DiagPoint{Float64} are implementations of the Pointy{Float64} abstraction, and similarly for every other possible choice of type T. This allows programming to a common interface shared by all Pointy objects, implemented for both Point and DiagPoint. This cannot be fully demonstrated, however, until we have introduced methods and dispatch in the next section, Methods.

There are situations where it may not make sense for type parameters to range freely over all possible types. In such situations, one can constrain the range of T like so:

abstract Pointy{T<:Real}


With such a declaration, it is acceptable to use any type that is a subtype of Real in place of T, but not types that are not subtypes of Real:

julia> Pointy{Float64}
Pointy{Float64}

julia> Pointy{Real}
Pointy{Real}

julia> Pointy{AbstractString}
ERROR: TypeError: Pointy: in T, expected T<:Real, got Type{AbstractString}
...

julia> Pointy{1}
ERROR: TypeError: Pointy: in T, expected T<:Real, got Int64
...


Type parameters for parametric composite types can be restricted in the same manner:

type Point{T<:Real} <: Pointy{T}
x::T
y::T
end


To give a real-world example of how all this parametric type machinery can be useful, here is the actual definition of Julia’s Rational immutable type (except that we omit the constructor here for simplicity), representing an exact ratio of integers:

immutable Rational{T<:Integer} <: Real
num::T
den::T
end


It only makes sense to take ratios of integer values, so the parameter type T is restricted to being a subtype of Integer, and a ratio of integers represents a value on the real number line, so any Rational is an instance of the Real abstraction.

### Tuple Types¶

Tuples are an abstraction of the arguments of a function — without the function itself. The salient aspects of a function’s arguments are their order and their types. Therefore a tuple type is similar to a parameterized immutable type where each parameter is the type of one field. For example, a 2-element tuple type resembles the following immutable type:

immutable Tuple2{A,B}
a::A
b::B
end


However, there are three key differences:

• Tuple types may have any number of parameters.
• Tuple types are covariant in their parameters: Tuple{Int} is a subtype of Tuple{Any}. Therefore Tuple{Any} is considered an abstract type, and tuple types are only concrete if their parameters are.
• Tuples do not have field names; fields are only accessed by index.

Tuple values are written with parentheses and commas. When a tuple is constructed, an appropriate tuple type is generated on demand:

julia> typeof((1,"foo",2.5))
Tuple{Int64,String,Float64}


Note the implications of covariance:

julia> Tuple{Int,AbstractString} <: Tuple{Real,Any}
true

julia> Tuple{Int,AbstractString} <: Tuple{Real,Real}
false

julia> Tuple{Int,AbstractString} <: Tuple{Real,}
false


Intuitively, this corresponds to the type of a function’s arguments being a subtype of the function’s signature (when the signature matches).

### Vararg Tuple Types¶

The last parameter of a tuple type can be the special type Vararg, which denotes any number of trailing elements:

julia> isa(("1",), Tuple{AbstractString,Vararg{Int}})
true

julia> isa(("1",1), Tuple{AbstractString,Vararg{Int}})
true

julia> isa(("1",1,2), Tuple{AbstractString,Vararg{Int}})
true

julia> isa(("1",1,2,3.0), Tuple{AbstractString,Vararg{Int}})
false


Notice that Vararg{T} corresponds to zero or more elements of type T. Vararg tuple types are used to represent the arguments accepted by varargs methods (see Varargs Functions).

The type Vararg{T,N} corresponds to exactly N elements of type T. NTuple{N,T} is a convenient alias for Tuple{Vararg{T,N}}, i.e. a tuple type containing exactly N elements of type T.

#### Singleton Types¶

There is a special kind of abstract parametric type that must be mentioned here: singleton types. For each type, T, the “singleton type” Type{T} is an abstract type whose only instance is the object T. Since the definition is a little difficult to parse, let’s look at some examples:

julia> isa(Float64, Type{Float64})
true

julia> isa(Real, Type{Float64})
false

julia> isa(Real, Type{Real})
true

julia> isa(Float64, Type{Real})
false


In other words, isa(A,Type{B}) is true if and only if A and B are the same object and that object is a type. Without the parameter, Type is simply an abstract type which has all type objects as its instances, including, of course, singleton types:

julia> isa(Type{Float64},Type)
true

julia> isa(Float64,Type)
true

julia> isa(Real,Type)
true


Any object that is not a type is not an instance of Type:

julia> isa(1,Type)
false

julia> isa("foo",Type)
false


Until we discuss Parametric Methods and conversions, it is difficult to explain the utility of the singleton type construct, but in short, it allows one to specialize function behavior on specific type values. This is useful for writing methods (especially parametric ones) whose behavior depends on a type that is given as an explicit argument rather than implied by the type of one of its arguments.

A few popular languages have singleton types, including Haskell, Scala and Ruby. In general usage, the term “singleton type” refers to a type whose only instance is a single value. This meaning applies to Julia’s singleton types, but with that caveat that only type objects have singleton types.

### Parametric Bits Types¶

Bits types can also be declared parametrically. For example, pointers are represented as boxed bits types which would be declared in Julia like this:

# 32-bit system:
bitstype 32 Ptr{T}

# 64-bit system:
bitstype 64 Ptr{T}


The slightly odd feature of these declarations as compared to typical parametric composite types, is that the type parameter T is not used in the definition of the type itself — it is just an abstract tag, essentially defining an entire family of types with identical structure, differentiated only by their type parameter. Thus, Ptr{Float64} and Ptr{Int64} are distinct types, even though they have identical representations. And of course, all specific pointer types are subtype of the umbrella Ptr type:

julia> Ptr{Float64} <: Ptr
true

julia> Ptr{Int64} <: Ptr
true


## Type Aliases¶

Sometimes it is convenient to introduce a new name for an already expressible type. For such occasions, Julia provides the typealias mechanism. For example, UInt is type aliased to either UInt32 or UInt64 as is appropriate for the size of pointers on the system:

# 32-bit system:
julia> UInt
UInt32

# 64-bit system:
julia> UInt
UInt64


This is accomplished via the following code in base/boot.jl:

if is(Int,Int64)
typealias UInt UInt64
else
typealias UInt UInt32
end


Of course, this depends on what Int is aliased to — but that is predefined to be the correct type — either Int32 or Int64.

For parametric types, typealias can be convenient for providing names for cases where some of the parameter choices are fixed. Julia’s arrays have type Array{T,N} where T is the element type and N is the number of array dimensions. For convenience, writing Array{Float64} allows one to specify the element type without specifying the dimension:

julia> Array{Float64,1} <: Array{Float64} <: Array
true


However, there is no way to equally simply restrict just the dimension but not the element type. Yet, one often needs to ensure an object is a vector or a matrix (imposing restrictions on the number of dimensions). For that reason, the following type aliases are provided:

typealias Vector{T} Array{T,1}
typealias Matrix{T} Array{T,2}


Writing Vector{Float64} is equivalent to writing Array{Float64,1}, and the umbrella type Vector has as instances all Array objects where the second parameter — the number of array dimensions — is 1, regardless of what the element type is. In languages where parametric types must always be specified in full, this is not especially helpful, but in Julia, this allows one to write just Matrix for the abstract type including all two-dimensional dense arrays of any element type.

This declaration of Vector creates a subtype relation Vector{Int} <: Vector. However, it is not always the case that a parametric typealias statement creates such a relation; for example, the statement:

typealias AA{T} Array{Array{T,1},1}


does not create the relation AA{Int} <: AA. The reason is that Array{Array{T,1},1} is not an abstract type at all; in fact, it is a concrete type describing a 1-dimensional array in which each entry is an object of type Array{T,1} for some value of T.

## Operations on Types¶

Since types in Julia are themselves objects, ordinary functions can operate on them. Some functions that are particularly useful for working with or exploring types have already been introduced, such as the <: operator, which indicates whether its left hand operand is a subtype of its right hand operand.

The isa function tests if an object is of a given type and returns true or false:

julia> isa(1,Int)
true

julia> isa(1,AbstractFloat)
false


The typeof() function, already used throughout the manual in examples, returns the type of its argument. Since, as noted above, types are objects, they also have types, and we can ask what their types are:

julia> typeof(Rational)
DataType

julia> typeof(Union{Real,Float64,Rational})
DataType

julia> typeof(Union{Real,String})
Union


What if we repeat the process? What is the type of a type of a type? As it happens, types are all composite values and thus all have a type of DataType:

julia> typeof(DataType)
DataType

julia> typeof(Union)
DataType


DataType is its own type.

Another operation that applies to some types is supertype(), which reveals a type’s supertype. Only declared types (DataType) have unambiguous supertypes:

julia> supertype(Float64)
AbstractFloat

julia> supertype(Number)
Any

julia> supertype(AbstractString)
Any

julia> supertype(Any)
Any


If you apply supertype() to other type objects (or non-type objects), a MethodError is raised:

julia> supertype(Union{Float64,Int64})
ERROR: supertype has no method matching supertype(::Type{Union{Float64,Int64}})


## Custom pretty-printing¶

Often, one wants to customize how instances of a type are displayed. This is accomplished by overloading the show() function. For example, suppose we define a type to represent complex numbers in polar form:

type Polar{T<:Real} <: Number
r::T
Θ::T
end
Polar(r::Real,Θ::Real) = Polar(promote(r,Θ)...)


Here, we’ve added a custom constructor function so that it can take arguments of different Real types and promote them to a commmon type (see Constructors and Conversion and Promotion). (Of course, we would have to define lots of other methods, too, to make it act like a Number, e.g. +, *, one, zero, promotion rules and so on.) By default, instances of this type display rather simply, with information about the type name and the field values, as e.g. Polar{Float64}(3.0,4.0).

If we want it to display instead as 3.0 * exp(4.0im), we would define the following method to print the object to a given output object io (representing a file, terminal, buffer, etcetera; see Networking and Streams):

Base.show(io::IO, z::Polar) = print(io, z.r, " * exp(", z.Θ, "im)")


More fine-grained control over display of Polar objects is possible. In particular, sometimes one wants both a verbose multi-line printing format, used for displaying a single object in the REPL and other interactive environments, and also a more compact single-line format used for print() or for displaying the object as part of another object (e.g. in an array). Although by default the show(io, z) function is called in both cases, you can define a different multi-line format for displaying an object by overloading a three-argument form of show that takes the text/plain MIME type as its second argument (see Multimedia I/O), for example:

Base.show{T}(io::IO, ::MIME"text/plain", z::Polar{T}) =
print(io, "Polar{$T} complex number:\n ", z)  (Note that print(..., z) here will call the 2-argument show(io, z) method.) This results in: julia> Polar(3, 4.0) Polar{Float64} complex number: 3.0 * exp(4.0im) julia> [Polar(3, 4.0), Polar(4.0,5.3)] 2-element Array{Polar{Float64},1}: 3.0 * exp(4.0im) 4.0 * exp(5.3im)  where the single-line show(io, z) form is still used for an array of Polar values. Technically, the REPL calls display(z) to display the result of executing a line, which defaults to show(STDOUT, MIME("text/plain"), z), which in turn defaults to show(STDOUT, z), but you should not define new display() methods unless you are defining a new multimedia display handler (see Multimedia I/O). Moreover, you can also define show methods for other MIME types in order to enable richer display (HTML, images, etcetera) of objects in environments that support this (e.g. IJulia). For example, we can define formatted HTML display of Polar objects, with superscripts and italics, via: Base.show{T}(io::IO, ::MIME"text/html", z::Polar{T}) = println(io, "<code>Polar{$T}</code> complex number: ",
z.r, " <i>e</i><sup>", z.Θ, " <i>i</i></sup>")


A Polar object will then display automatically using HTML in an environment that supports HTML display, but you can call show manually to get HTML output if you want:

julia> show(STDOUT, "text/html", Polar(3.0,4.0))
<code>Polar{Float64}</code> complex number: 3.0 <i>e</i><sup>4.0 <i>i</i></sup>


An HTML renderer would display this as: Polar{Float64} complex number: 3.0 e4.0 i

## “Value types”¶

In Julia, you can’t dispatch on a value such as true or false. However, you can dispatch on parametric types, and Julia allows you to include “plain bits” values (Types, Symbols, Integers, floating-point numbers, tuples, etc.) as type parameters. A common example is the dimensionality parameter in Array{T,N}, where T is a type (e.g., Float64) but N is just an Int.

You can create your own custom types that take values as parameters, and use them to control dispatch of custom types. By way of illustration of this idea, let’s introduce a parametric type, Val{T}, which serves as a customary way to exploit this technique for cases where you don’t need a more elaborate hierarchy.

Val is defined as:

immutable Val{T}
end


There is no more to the implementation of Val than this. Some functions in Julia’s standard library accept Val types as arguments, and you can also use it to write your own functions. For example:

firstlast(::Type{Val{true}}) = "First"
firstlast(::Type{Val{false}}) = "Last"

julia> firstlast(Val{true})
"First"

julia> firstlast(Val{false})
"Last"


For consistency across Julia, the call site should always pass a Val type rather than creating an instance, i.e., use foo(Val{:bar}) rather than foo(Val{:bar}()).

It’s worth noting that it’s extremely easy to mis-use parametric “value” types, including Val; in unfavorable cases, you can easily end up making the performance of your code much worse. In particular, you would never want to write actual code as illustrated above. For more information about the proper (and improper) uses of Val, please read the more extensive discussion in the performance tips.

## Nullable Types: Representing Missing Values¶

In many settings, you need to interact with a value of type T that may or may not exist. To handle these settings, Julia provides a parametric type called Nullable{T}, which can be thought of as a specialized container type that can contain either zero or one values. Nullable{T} provides a minimal interface designed to ensure that interactions with missing values are safe. At present, the interface consists of four possible interactions:

• Construct a Nullable object.
• Check if a Nullable object has a missing value.
• Access the value of a Nullable object with a guarantee that a NullException will be thrown if the object’s value is missing.
• Access the value of a Nullable object with a guarantee that a default value of type T will be returned if the object’s value is missing.

### Constructing Nullable objects¶

To construct an object representing a missing value of type T, use the Nullable{T}() function:

julia> x1 = Nullable{Int64}()
Nullable{Int64}()

julia> x2 = Nullable{Float64}()
Nullable{Float64}()

julia> x3 = Nullable{Vector{Int64}}()
Nullable{Array{Int64,1}}()


To construct an object representing a non-missing value of type T, use the Nullable(x::T) function:

julia> x1 = Nullable(1)
Nullable{Int64}(1)

julia> x2 = Nullable(1.0)
Nullable{Float64}(1.0)

julia> x3 = Nullable([1, 2, 3])
Nullable{Array{Int64,1}}([1,2,3])


Note the core distinction between these two ways of constructing a Nullable object: in one style, you provide a type, T, as a function parameter; in the other style, you provide a single value of type T as an argument.

### Checking if a Nullable object has a value¶

You can check if a Nullable object has any value using isnull():

julia> isnull(Nullable{Float64}())
true

julia> isnull(Nullable(0.0))
false


### Safely accessing the value of a Nullable object¶

You can safely access the value of a Nullable object using get():

julia> get(Nullable{Float64}())
ERROR: NullException()
in get(::Nullable{Float64}) at ./nullable.jl:62
...

julia> get(Nullable(1.0))
1.0


If the value is not present, as it would be for Nullable{Float64}, a NullException error will be thrown. The error-throwing nature of the get() function ensures that any attempt to access a missing value immediately fails.

In cases for which a reasonable default value exists that could be used when a Nullable object’s value turns out to be missing, you can provide this default value as a second argument to get():

julia> get(Nullable{Float64}(), 0.0)
0.0

julia> get(Nullable(1.0), 0.0)
1.0


Tip

Make sure the type of the default value passed to get() and that of the Nullable object match to avoid type instability, which could hurt performance. Use convert() manually if needed.