# Frequently Asked Questions¶

## 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 = 0`

. The memory will be released the next time the garbage
collector runs; you can force this to happen with `gc()`

.

### How can I modify the declaration of a type/immutable 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obj1 = MyModule.ObjConstructor(a, b) # old objects are no longer valid, must reconstruct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)
...
```

## 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.

But here is a thing you should pay attention to: suppose `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 Array{Int64,1}:
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 Array{Int64,1}:
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 refering 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:

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.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...)
@printf("%s\n", typeof(args))
for (i, arg) in enumerate(args)
@printf("Arg %d = %s\n", i, arg)
end
end
printargs (generic function with 1 method)
julia> printargs(1, 2, 3)
(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)
@printf("a = %s::%s\n", a, typeof(a))
@printf("b = %s::%s\n", b, typeof(b))
@printf("c = %s::%s\n", c, typeof(c))
end
threeargs (generic function with 1 method)
julia> vec = [1, 2, 3]
3-element Array{Int64,1}:
1
2
3
julia> threeargs(vec...)
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 `...`

.

## 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:

```
function unstable(flag::Bool)
if flag
return 1
else
return 1.0
end
end
```

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 will have to guard against both
types possibly occurring, making generation of fast machine code difficult.

### 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
in sqrt at math.jl:128
julia> 2^-5
ERROR: DomainError
in power_by_squaring at intfuncs.jl:70
in ^ at intfuncs.jl:84
```

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
julia> 2.0^-5
0.03125
```

### 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> typemax(Int)
9223372036854775807
julia> ans+1
-9223372036854775808
julia> -ans
-9223372036854775808
julia> 2*ans
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,(Int,))
.section __TEXT,__text,regular,pure_instructions
Filename: none
Source line: 1
push RBP
mov RBP, RSP
Source line: 1
lea RAX, QWORD PTR [RDI + 4*RDI - 1]
pop RBP
ret
```

The actual body of the function is a single `lea`

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 2 methods)
julia> code_native(g,(Int,Int))
.section __TEXT,__text,regular,pure_instructions
Filename: none
Source line: 3
push RBP
mov RBP, RSP
test RSI, RSI
jle 22
mov EAX, 1
Source line: 3
lea RDI, QWORD PTR [RDI + 4*RDI - 1]
inc RAX
cmp RAX, RSI
Source line: 2
jle -17
Source line: 5
mov RAX, RDI
pop RBP
ret
```

Since the call to `f`

gets inlined, the loop body ends up being just a
single `lea`

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,))
.section __TEXT,__text,regular,pure_instructions
Filename: none
Source line: 3
push RBP
mov RBP, RSP
Source line: 3
imul RAX, RDI, 9765625
add RAX, -2441406
Source line: 5
pop RBP
ret
```

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.

## Packages and Modules¶

### What is the difference between “using” and “importall”?¶

There is only one difference, and on the surface (syntax-wise) it may seem very minor.
The difference between `using`

and `importall`

is that with `using`

you need to say
`function Foo.bar(..`

to extend module Foo’s function bar with a new method, but with
`importall`

or `import Foo.bar`

, you only need to say `function bar(...`

and it
automatically extends module Foo’s function bar.

If you use `importall`

, then `function Foo.bar(...`

and `function bar(...`

become
equivalent. If you use `using`

, then they are different.

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 `importall`

, 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” or “nothingness” work in Julia?¶

Unlike many languages (for example, C and Java), Julia does not have a
“null” value. 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`

function.

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 `Void`

. 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 exists only sometimes (for example, missing
statistical data), it is best to use the `Nullable{T}`

type, which allows
specifying the type of a missing value.

The empty tuple (`()`

) is another form of nothingness. But, it should not
really be thought of as nothing but rather a tuple of zero values.

In code written for Julia prior to version 0.4 you may occasionally see `None`

,
which is quite different. It is the empty (or “bottom”) type, a type with no values
and no subtypes (except itself). This is now written as `Union{}`

(an empty union
type). 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.

The following:

```
@sync for i in 1:3
@async write(STDOUT, string(i), " Foo ", " Bar ")
end
```

results in:

```
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:

```
1 Foo Bar
2 Foo Bar
3 Foo Bar
```

You can lock your writes with a `ReentrantLock`

like this:

```
l = ReentrantLock()
@sync for i in 1:3
@async begin
lock(l)
try
write(STDOUT, string(i), " Foo ", " Bar ")
finally
unlock(l)
end
end
end
```

## Julia Releases¶

### Do I want to use a release, beta, or nightly version of Julia?¶

You may prefer the release version of Julia if you are looking for a stable code base. Releases generally occur every 6 months, giving you a stable platform for writing code.

You may prefer the beta version of Julia if you don’t mind being slightly behind the latest bugfixes and changes, but find the slightly faster rate of changes more appealing. Additionally, these binaries are tested before they are published to ensure they are fully functional.

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.

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.

### When are deprecated functions removed?¶

Deprecated functions are removed after the subsequent release. For example, functions marked as deprecated in the 0.1 release will not be available starting with the 0.2 release.