
[TOC]

# Smart Pointers

A *pointer* is a general concept for a variable that contains an address in
memory. This address refers to, or “points at,” some other data. The most
common kind of pointer in Rust is a reference, which you learned about in
Chapter 4. References are indicated by the `&` symbol and borrow the value they
point to. They don’t have any special capabilities other than referring to
data. Also, they don’t have any overhead and are the kind of pointer we use
most often.

*Smart pointers*, on the other hand, are data structures that act like a
pointer but also have additional metadata and capabilities. The concept of
smart pointers isn’t unique to Rust: smart pointers originated in C++ and exist
in other languages as well. In Rust, the different smart pointers defined in
the standard library provide extra functionality beyond that provided by
references. One example that we’ll explore in this chapter is the *reference
counting* smart pointer type. This pointer enables you to have multiple owners
of data by keeping track of the number of owners and, when no owners remain,
taking care of cleaning up the data.

In Rust, where we have the concept of ownership and borrowing, an additional
difference between references and smart pointers is that references are
pointers that only borrow data; in contrast, in many cases, smart pointers
*own* the data they point to.

We’ve already encountered a few smart pointers in this book, such as `String`
and `Vec<T>` in Chapter 8, although we didn’t call them smart pointers at the
time. Both these types count as smart pointers because they own some memory and
allow you to manipulate it. They also have metadata (such as their capacity)
and extra capabilities or guarantees (such as with `String` ensuring its data
will always be valid UTF-8).

Smart pointers are usually implemented using structs. The characteristic that
distinguishes a smart pointer from an ordinary struct is that smart pointers
implement the `Deref` and `Drop` traits. The `Deref` trait allows an instance
of the smart pointer struct to behave like a reference so we can write code
that works with either references or smart pointers. The `Drop` trait allows us
to customize the code that is run when an instance of the smart pointer goes
out of scope. In this chapter, we’ll discuss both traits and demonstrate why
they’re important to smart pointers.

Given that the smart pointer pattern is a general design pattern used
frequently in Rust, this chapter won’t cover every existing smart pointer. Many
libraries have their own smart pointers, and you can even write your own. We’ll
cover the most common smart pointers in the standard library:

* `Box<T>` for allocating values on the heap
* `Rc<T>`, a reference counted type that enables multiple ownership
* `Ref<T>` and `RefMut<T>`, accessed through `RefCell<T>`, a type that enforces
  the borrowing rules at runtime instead of compile time

In addition, we’ll cover the *interior mutability* pattern where an immutable
type exposes an API for mutating an interior value. We’ll also discuss
*reference cycles*: how they can leak memory and how to prevent them.

Let’s dive in!

## `Box<T>` Points to Data on the Heap and Has a Known Size

The most straightforward smart pointer is a *box*, whose type is written
`Box<T>`. Boxes allow you to store data on the heap rather than the stack. What
remains on the stack is the pointer to the heap data. Refer to Chapter 4 to
review the difference between the stack and the heap.

Boxes don’t have performance overhead, other than storing their data on the
heap instead of on the stack. But they don’t have many extra capabilities
either. You’ll use them most often in these situations:

* When you have a type whose size can’t be known at compile time, and you want
  to use a value of that type in a context that needs to know an exact size
* When you have a large amount of data and you want to transfer ownership but
  ensure the data won’t be copied when you do so
* When you want to own a value and only care that it’s a type that implements a
  particular trait rather than knowing the concrete type

We’ll demonstrate the first situation in this section. But before we do so,
we’ll elaborate on the other two situations a bit more: in the second case,
transferring ownership of a large amount of data can take a long time because
the data is copied around on the stack. To improve performance in this
situation, we can store the large amount of data on the heap in a box. Then,
only the small amount of pointer data is copied around on the stack, and the
data stays in one place on the heap. The third case is known as a *trait
object*, and Chapter 17 devotes an entire section just to that topic. So what
you learn here you’ll apply again in Chapter 17!

### Using a `Box<T>` to Store Data on the Heap

Before we discuss this use case for `Box<T>`, we’ll cover the syntax and how to
interact with values stored within a `Box<T>`.

Listing 15-1 shows how to use a box to store an `i32` value on the heap:

Filename: src/main.rs

```
fn main() {
    let b = Box::new(5);
    println!("b = {}", b);
}
```

Listing 15-1: Storing an `i32` value on the heap using a box

We define the variable `b` to have the value of a `Box` that points to the
value `5`, which is allocated on the heap. This program will print `b = 5`; in
this case, we can access the data in the box in a similar way as we would if
this data was on the stack. Just like any owned value, when a box goes out of
scope like `b` does at the end of `main`, it will be deallocated. The
deallocation happens for the box (stored on the stack) and the data it points
to (stored on the heap).

Putting a single value on the heap isn’t very useful, so you won’t use boxes by
themselves in this way very often. Having values like a single `i32` on the
stack, where they’re stored by default, is more appropriate in the majority of
situations. Let’s look at a case where boxes allow us to define types that we
wouldn’t be allowed to if we didn’t have boxes.

### Boxes Enable Recursive Types

At compile time, Rust needs to know how much space a type takes up. One type
whose size can’t be known at compile time is a *recursive type*, where a value
can have as part of itself another value of the same type. Because this nesting
of values could theoretically continue infinitely, Rust doesn’t know how much
space a value of a recursive type needs. However, boxes have a known size, so
by inserting a box in a recursive type definition, we can have recursive types.

Let’s explore the *cons list*, which is a data type common in functional
programming languages, as an example of a recursive type. The cons list type
we’ll define is straightforward except for the recursion; therefore, the
concepts in the example we’ll work with will be useful any time you get into
more complex situations involving recursive types.

#### More Information About the Cons List

A *cons list* is a data structure that comes from the Lisp programming language
and its dialects. In Lisp, the `cons` function (short for “construct function”)
constructs a new pair from its two arguments, which usually are a single value
and another pair. These pairs containing pairs form a list.

The cons function concept has made its way into more general functional
programming jargon: “to cons x onto y” informally means to construct a new
container instance by putting the element x at the start of this new container,
followed by the container y.

Each item in a cons list contains two elements: the value of the current item
and the next item. The last item in the list contains only a value called `Nil`
without a next item. A cons list is produced by recursively calling the `cons`
function. The canonical name to denote the base case of the recursion is `Nil`.
Note that this is not the same as the “null” or “nil” concept in Chapter 6,
which is an invalid or absent value.

Although functional programming languages use cons lists frequently, it isn’t a
commonly used data structure in Rust. Most of the time when you have a list of
items in Rust, `Vec<T>` is a better choice to use. Other, more complex
recursive data types *are* useful in various situations, but by starting with
the cons list, we can explore how boxes let us define a recursive data type
without much distraction.

Listing 15-2 contains an enum definition for a cons list. Note that this code
won’t compile yet because the `List` type doesn’t have a known size, which
we’ll demonstrate:

Filename: src/main.rs

```
enum List {
    Cons(i32, List),
    Nil,
}
```

Listing 15-2: The first attempt at defining an enum to represent a cons list
data structure of `i32` values

> Note: We’re implementing a cons list that only holds `i32` values for the
> purposes of this example. We could have implemented it using generics, as we
> discussed in Chapter 10, to define a cons list type that could store values of
> any type.

Using the `List` type to store the list `1, 2, 3` would look like the code in
Listing 15-3:

Filename: src/main.rs

```
use List::{Cons, Nil};

fn main() {
    let list = Cons(1, Cons(2, Cons(3, Nil)));
}
```

Listing 15-3: Using the `List` enum to store the list `1, 2, 3`

The first `Cons` value holds `1` and another `List` value. This `List` value is
another `Cons` value that holds `2` and another `List` value. This `List` value
is one more `Cons` value that holds `3` and a `List` value, which is finally
`Nil`, the non-recursive variant that signals the end of the list.

If we try to compile the code in Listing 15-3, we get the error shown in
Listing 15-4:

```
error[E0072]: recursive type `List` has infinite size
 --> src/main.rs:1:1
  |
1 | enum List {
  | ^^^^^^^^^ recursive type has infinite size
2 |     Cons(i32, List),
  |               ----- recursive without indirection
  |
  = help: insert indirection (e.g., a `Box`, `Rc`, or `&`) at some point to
  make `List` representable
```

Listing 15-4: The error we get when attempting to define a recursive enum

The error shows this type “has infinite size.” The reason is that we’ve defined
`List` with a variant that is recursive: it holds another value of itself
directly. As a result, Rust can’t figure out how much space it needs to store a
`List` value. Let’s break down why we get this error a bit: first, let’s look
at how Rust decides how much space it needs to store a value of a non-recursive
type.

#### Computing the Size of a Non-Recursive Type

Recall the `Message` enum we defined in Listing 6-2 when we discussed enum
definitions in Chapter 6:

```
enum Message {
    Quit,
    Move { x: i32, y: i32 },
    Write(String),
    ChangeColor(i32, i32, i32),
}
```

To determine how much space to allocate for a `Message` value, Rust goes
through each of the variants to see which variant needs the most space. Rust
sees that `Message::Quit` doesn’t need any space, `Message::Move` needs enough
space to store two `i32` values, and so forth. Because only one variant will be
used, the most space a `Message` value will need is the space it would take to
store the largest of its variants.

Contrast this to what happens when Rust tries to determine how much space a
recursive type like the `List` enum in Listing 15-2 needs. The compiler starts
by looking at the `Cons` variant, which holds a value of type `i32` and a value
of type `List`. Therefore, `Cons` needs an amount of space equal to the size of
an `i32` plus the size of a `List`. To figure out how much memory the `List`
type needs, the compiler looks at the variants, starting with the `Cons`
variant. The `Cons` variant holds a value of type `i32` and a value of type
`List`, and this process continues infinitely, as shown in Figure 15-1:

An infinite Cons list

Figure 15-1: An infinite `List` consisting of infinite `Cons` variants

#### Using `Box<T>` to Get a Recursive Type with a Known Size

Rust can’t figure out how much space to allocate for recursively defined types,
so the compiler gives the error in Listing 15-4. But the error does include
this helpful suggestion:

```
  = help: insert indirection (e.g., a `Box`, `Rc`, or `&`) at some point to
  make `List` representable
```

In this suggestion, “indirection” means that instead of storing a value
directly, we’ll change the data structure to store the value indirectly by
storing a pointer to the value instead.

Because a `Box<T>` is a pointer, Rust always knows how much space a `Box<T>`
needs: a pointer’s size doesn’t change based on the amount of data it’s
pointing to. This means we can put a `Box<T>` inside the `Cons` variant instead
of another `List` value directly. The `Box<T>` will point to the next `List`
value that will be on the heap rather than inside the `Cons` variant.
Conceptually, we still have a list, created with lists “holding” other lists,
but this implementation is now more like the items being next to one another
rather than inside one another.

We can change the definition of the `List` enum in Listing 15-2 and the usage
of the `List` in Listing 15-3 to the code in Listing 15-5, which will compile:

Filename: src/main.rs

```
enum List {
    Cons(i32, Box<List>),
    Nil,
}

use List::{Cons, Nil};

fn main() {
    let list = Cons(1,
        Box::new(Cons(2,
            Box::new(Cons(3,
                Box::new(Nil))))));
}
```

Listing 15-5: Definition of `List` that uses `Box<T>` in order to have a known
size

The `Cons` variant will need the size of an `i32` plus the space to store the
box’s pointer data. The `Nil` variant stores no values, so it needs less space
than the `Cons` variant. We now know that any `List` value will take up the
size of an `i32` plus the size of a box’s pointer data. By using a box, we’ve
broken the infinite, recursive chain, so the compiler can figure out the size
it needs to store a `List` value. Figure 15-2 shows what the `Cons` variant
looks like now:

A finite Cons list

Figure 15-2: A `List` that is not infinitely sized because `Cons` holds a `Box`

Boxes only provide the indirection and heap allocation; they don’t have any
other special capabilities, like those we’ll see with the other smart pointer
types. They also don’t have any performance overhead that these special
capabilities incur, so they can be useful in cases like the cons list where the
indirection is the only feature we need. We’ll look at more use cases for boxes
in Chapter 17, too.

The `Box<T>` type is a smart pointer because it implements the `Deref` trait,
which allows `Box<T>` values to be treated like references. When a `Box<T>`
value goes out of scope, the heap data that the box is pointing to is cleaned
up as well because of the `Drop` trait implementation. Let’s explore these two
traits in more detail. These two traits will be even more important to the
functionality provided by the other smart pointer types we’ll discuss in the
rest of this chapter.

## Treating Smart Pointers Like Regular References with the `Deref` Trait

Implementing the `Deref` trait allows us to customize the behavior of the
*dereference operator*, `*` (as opposed to the multiplication or glob
operator). By implementing `Deref` in such a way that a smart pointer can be
treated like a regular reference, we can write code that operates on references
and use that code with smart pointers too.

Let’s first look at how `*` works with regular references, and then try to
define our own type like `Box<T>` and see why `*` doesn’t work like a reference
on our newly defined type. We’ll explore how implementing the `Deref` trait
makes it possible for smart pointers to work in a similar way as references.
Then we’ll look at Rust’s *deref coercion* feature and how it lets us work with
either references or smart pointers.

### Following the Pointer to the Value with `*`

A regular reference is a type of pointer, and one way to think of a pointer is
as an arrow to a value stored somewhere else. In Listing 15-6, we create a
reference to an `i32` value and then use the dereference operator to follow the
reference to the data:

Filename: src/main.rs

```
fn main() {
    let x = 5;
    let y = &x;

    assert_eq!(5, x);
    assert_eq!(5, *y);
}
```

Listing 15-6: Using the dereference operator to follow a reference to an `i32`
value

The variable `x` holds an `i32` value, `5`. We set `y` equal to a reference to
`x`. We can assert that `x` is equal to `5`. However, if we want to make an
assertion about the value in `y`, we have to use `*y` to follow the reference
to the value it’s pointing to (hence *dereference*). Once we dereference `y`,
we have access to the integer value `y` is pointing to that we can compare with
`5`.

If we tried to write `assert_eq!(5, y);` instead, we would get this compilation
error:

```
error[E0277]: the trait bound `{integer}: std::cmp::PartialEq<&{integer}>` is
not satisfied
 --> src/main.rs:6:5
  |
6 |     assert_eq!(5, y);
  |     ^^^^^^^^^^^^^^^^^ can't compare `{integer}` with `&{integer}`
  |
  = help: the trait `std::cmp::PartialEq<&{integer}>` is not implemented for
  `{integer}`
```

Comparing a number and a reference to a number isn’t allowed because they’re
different types. We must use `*` to follow the reference to the value it’s
pointing to.

### Using `Box<T>` Like a Reference

We can rewrite the code in Listing 15-6 to use a `Box<T>` instead of a
reference, and the dereference operator will work the same way as shown in
Listing 15-7:

Filename: src/main.rs

```
fn main() {
    let x = 5;
    let y = Box::new(x);

    assert_eq!(5, x);
    assert_eq!(5, *y);
}
```

Listing 15-7: Using the dereference operator on a `Box<i32>`

The only difference between Listing 15-7 and Listing 15-6 is that here we set
`y` to be an instance of a box pointing to the value in `x` rather than a
reference pointing to the value of `x`. In the last assertion, we can use the
dereference operator to follow the box’s pointer in the same way that we did
when `y` was a reference. Next, we’ll explore what is special about `Box<T>`
that enables us to use the dereference operator by defining our own box type.

### Defining Our Own Smart Pointer

Let’s build a smart pointer similar to the `Box<T>` type provided by the
standard library to experience how smart pointers behave differently to
references by default. Then we’ll look at how to add the ability to use the
dereference operator.

The `Box<T>` type is ultimately defined as a tuple struct with one element, so
Listing 15-8 defines a `MyBox<T>` type in the same way. We’ll also define a
`new` function to match the `new` function defined on `Box<T>`:

Filename: src/main.rs

```
struct MyBox<T>(T);

impl<T> MyBox<T> {
    fn new(x: T) -> MyBox<T> {
        MyBox(x)
    }
}
```

Listing 15-8: Defining a `MyBox<T>` type

We define a struct named `MyBox` and declare a generic parameter `T`, because
we want our type to hold values of any type. The `MyBox` type is a tuple struct
with one element of type `T`. The `MyBox::new` function takes one parameter of
type `T` and returns a `MyBox` instance that holds the value passed in.

Let’s try adding the `main` function in Listing 15-7 to Listing 15-8 and
changing it to use the `MyBox<T>` type we’ve defined instead of `Box<T>`. The
code in Listing 15-9 won’t compile because Rust doesn’t know how to dereference
`MyBox`:

Filename: src/main.rs

```
fn main() {
    let x = 5;
    let y = MyBox::new(x);

    assert_eq!(5, x);
    assert_eq!(5, *y);
}
```

Listing 15-9: Attempting to use `MyBox<T>` in the same way we used references
and `Box<T>`

Here’s the resulting compilation error:

```
error[E0614]: type `MyBox<{integer}>` cannot be dereferenced
  --> src/main.rs:14:19
   |
14 |     assert_eq!(5, *y);
   |                   ^^
```

Our `MyBox<T>` type can’t be dereferenced because we haven’t implemented that
ability on our type. To enable dereferencing with the `*` operator, we
implement the `Deref` trait.

### Treating a Type Like a Reference by Implementing the `Deref` Trait

As discussed in Chapter 10, to implement a trait, we need to provide
implementations for the trait’s required methods. The `Deref` trait, provided
by the standard library, requires us to implement one method named `deref` that
borrows `self` and returns a reference to the inner data. Listing 15-10
contains an implementation of `Deref` to add to the definition of `MyBox`:

Filename: src/main.rs

```
use std::ops::Deref;

impl<T> Deref for MyBox<T> {
    type Target = T;

    fn deref(&self) -> &T {
        &self.0
    }
}
```

Listing 15-10: Implementing `Deref` on `MyBox<T>`

The `type Target = T;` syntax defines an associated type for the `Deref` trait
to use. Associated types are a slightly different way of declaring a generic
parameter, but you don’t need to worry about them for now; we’ll cover them in
more detail in Chapter 19.

We fill in the body of the `deref` method with `&self.0` so `deref` returns a
reference to the value we want to access with the `*` operator. The `main`
function in Listing 15-9 that calls `*` on the `MyBox<T>` value now compiles
and the assertions pass!

Without the `Deref` trait, the compiler can only dereference `&` references.
The `deref` method gives the compiler the ability to take a value of any type
that implements `Deref` and call the `deref` method to get a `&` reference that
it knows how to dereference.

When we entered `*y` in Listing 15-9, behind the scenes Rust actually ran this
code:

```
*(y.deref())
```

Rust substitutes the `*` operator with a call to the `deref` method and then a
plain dereference so as programmers we don’t have to think about whether or not
we need to call the `deref` method. This Rust feature lets us write code that
functions identically whether we have a regular reference or a type that
implements `Deref`.

The reason the `deref` method returns a reference to a value and that the plain
dereference outside the parentheses in `*(y.deref())` is still necessary is due
to the ownership system. If the `deref` method returned the value directly
instead of a reference to the value, the value would be moved out of `self`. We
don’t want to take ownership of the inner value inside `MyBox<T>` in this case
and in most cases where we use the dereference operator.

Note that the `*` is replaced with a call to the `deref` method and then a call
to `*` just once, each time we type a `*` in our code. Because the substitution
of `*` does not recurse infinitely, we end up with data of type `i32`, which
matches the `5` in `assert_eq!` in Listing 15-9.

### Implicit Deref Coercions with Functions and Methods

*Deref coercion* is a convenience that Rust performs on arguments to functions
and methods. Deref coercion converts a reference to a type that implements
`Deref` into a reference to a type that `Deref` can convert the original type
into. Deref coercion happens automatically when we pass a reference to a
particular type’s value as an argument to a function or method that doesn’t
match the parameter type in the function or method definition. A sequence of
calls to the `deref` method converts the type we provided into the type the
parameter needs.

Deref coercion was added to Rust so that programmers writing function and
method calls don’t need to add as many explicit references and dereferences
with `&` and `*`. The deref coercion feature also lets us write more code that
can work for either references or smart pointers.

To see deref coercion in action, let’s use the `MyBox<T>` type we defined in
Listing 15-8 as well as the implementation of `Deref` that we added in Listing
15-10. Listing 15-11 shows the definition of a function that has a string slice
parameter:

Filename: src/main.rs

```
fn hello(name: &str) {
    println!("Hello, {}!", name);
}
```

Listing 15-11: A `hello` function that has the parameter `name` of type `&str`

We can call the `hello` function with a string slice as an argument, such as
`hello("Rust");` for example. Deref coercion makes it possible to call `hello`
with a reference to a value of type `MyBox<String>`, as shown in Listing 15-12:

Filename: src/main.rs

```
fn main() {
    let m = MyBox::new(String::from("Rust"));
    hello(&m);
}
```

Listing 15-12: Calling `hello` with a reference to a `MyBox<String>` value,
which works because of deref coercion

Here we’re calling the `hello` function with the argument `&m`, which is a
reference to a `MyBox<String>` value. Because we implemented the `Deref` trait
on `MyBox<T>` in Listing 15-10, Rust can turn `&MyBox<String>` into `&String`
by calling `deref`. The standard library provides an implementation of `Deref`
on `String` that returns a string slice, which is in the API documentation for
`Deref`. Rust calls `deref` again to turn the `&String` into `&str`, which
matches the `hello` function’s definition.

If Rust didn’t implement deref coercion, we would have to write the code in
Listing 15-13 instead of the code in Listing 15-12 to call `hello` with a value
of type `&MyBox<String>`:

Filename: src/main.rs

```
fn main() {
    let m = MyBox::new(String::from("Rust"));
    hello(&(*m)[..]);
}
```

Listing 15-13: The code we would have to write if Rust didn’t have deref
coercion

The `(*m)` dereferences the `MyBox<String>` into a `String`. Then the `&` and
`[..]` take a string slice of the `String` that is equal to the whole string to
match the signature of `hello`. The code without deref coercions is harder to
read, write, and understand with all of these symbols involved. Deref coercion
allows Rust to handle these conversions for us automatically.

When the `Deref` trait is defined for the types involved, Rust will analyze the
types and use `Deref::deref` as many times as necessary to get a reference to
match the parameter’s type. The number of times that `Deref::deref` needs to be
inserted is resolved at compile time, so there is no runtime penalty for taking
advantage of deref coercion!

### How Deref Coercion Interacts with Mutability

Similar to how we use the `Deref` trait to override `*` on immutable
references, Rust provides a `DerefMut` trait for overriding `*` on mutable
references.

Rust does deref coercion when it finds types and trait implementations in three
cases:

* From `&T` to `&U` when `T: Deref<Target=U>`
* From `&mut T` to `&mut U` when `T: DerefMut<Target=U>`
* From `&mut T` to `&U` when `T: Deref<Target=U>`

The first two cases are the same except for mutability. The first case states
that if you have a `&T`, and `T` implements `Deref` to some type `U`, you can
get a `&U` transparently. The second case states that the same deref coercion
happens for mutable references.

The third case is trickier: Rust will also coerce a mutable reference to an
immutable one. But the reverse is *not* possible: immutable references will
never coerce to mutable references. Because of the borrowing rules, if you have
a mutable reference, that mutable reference must be the only reference to that
data (otherwise, the program wouldn’t compile). Converting one mutable
reference to one immutable reference will never break the borrowing rules.
Converting an immutable reference to a mutable reference would require that
there is only one immutable reference to that data, and the borrowing rules
don’t guarantee that. Therefore, Rust can’t make the assumption that converting
an immutable reference to a mutable reference is possible.

## The `Drop` Trait Runs Code on Cleanup

The second trait important to the smart pointer pattern is `Drop`, which lets
us customize what happens when a value is about to go out of scope. We can
provide an implementation for the `Drop` trait on any type, and the code we
specify can be used to release resources like files or network connections.
We’re introducing `Drop` in the context of smart pointers because the
functionality of the `Drop` trait is almost always used when implementing a
smart pointer. For example, `Box<T>` customizes `Drop` to deallocate the space
on the heap that the box points to.

In some languages, the programmer must call code to free memory or resources
every time they finish using an instance of a smart pointer. If they forget,
the system might become overloaded and crash. In Rust, we can specify that a
particular bit of code should be run whenever a value goes out of scope, and
the compiler will insert this code automatically. As a result, we don’t need to
be careful about placing cleanup code everywhere in a program that an instance
of a particular type is finished with, but we still won’t leak resources!

We specify the code to run when a value goes out of scope by implementing the
`Drop` trait. The `Drop` trait requires us to implement one method named `drop`
that takes a mutable reference to `self`. To see when Rust calls `drop`, let’s
implement `drop` with `println!` statements for now.

Listing 15-14 shows a `CustomSmartPointer` struct whose only custom
functionality is that it will print `Dropping CustomSmartPointer!` when the
instance goes out of scope. This example demonstrates when Rust runs the `drop`
function:

Filename: src/main.rs

```
struct CustomSmartPointer {
    data: String,
}

impl Drop for CustomSmartPointer {
    fn drop(&mut self) {
        println!("Dropping CustomSmartPointer with data `{}`!", self.data);
    }
}

fn main() {
    let c = CustomSmartPointer { data: String::from("my stuff") };
    let d = CustomSmartPointer { data: String::from("other stuff") };
    println!("CustomSmartPointers created.");
}
```

Listing 15-14: A `CustomSmartPointer` struct that implements the `Drop` trait
where we would put our cleanup code

The `Drop` trait is included in the prelude, so we don’t need to import it. We
implement the `Drop` trait on `CustomSmartPointer` and provide an
implementation for the `drop` method that calls `println!`. The body of the
`drop` function is where you would place any logic that you wanted to run when
an instance of your type goes out of scope. We’re printing some text here to
demonstrate when Rust will call `drop`.

In `main`, we create two instances of `CustomSmartPointer` and then print
`CustomSmartPointers created.`. At the end of `main`, our instance of
`CustomSmartPointer` will go out of scope, and Rust will call the code we put
in the `drop` method, printing our final message. Note that we didn’t need to
call the `drop` method explicitly.

When we run this program, we’ll see the following output:

```
CustomSmartPointers created.
Dropping CustomSmartPointer with data `other stuff`!
Dropping CustomSmartPointer with data `my stuff`!
```

Rust automatically called `drop` for us when our instance went out of scope,
calling the code we specified. Variables are dropped in the reverse order of
the order in which they were created, so `d` was dropped before `c`. This
example just gives you a visual guide to how the `drop` method works, but
usually you would specify the cleanup code that your type needs to run rather
than a print message.

### Dropping a Value Early with `std::mem::drop`

Unfortunately, it’s not straightforward to disable the automatic `drop`
functionality. Disabling `drop` isn’t usually necessary; the whole point of the
`Drop` trait is that it’s taken care of automatically. Occasionally, you might
want to clean up a value early. One example is when using smart pointers that
manage locks: you might want to force the `drop` method that releases the lock
to run so other code in the same scope can acquire the lock. Rust doesn’t let
us call the `Drop` trait’s `drop` method manually; instead we have to call the
`std::mem::drop` function provided by the standard library if we want to force
a value to be dropped before the end of its scope.

Let’s see what happens when we try to call the `Drop` trait’s `drop` method
manually by modifying the `main` function in Listing 15-14, as shown in Listing
15-15:

Filename: src/main.rs

```
fn main() {
    let c = CustomSmartPointer { data: String::from("some data") };
    println!("CustomSmartPointer created.");
    c.drop();
    println!("CustomSmartPointer dropped before the end of main.");
}
```

Listing 15-15: Attempting to call the `drop` method from the `Drop` trait
manually to clean up early

When we try to compile this code, we’ll get this error:

```
error[E0040]: explicit use of destructor method
  --> src/main.rs:14:7
   |
14 |     c.drop();
   |       ^^^^ explicit destructor calls not allowed
```

This error message states that we’re not allowed to explicitly call `drop`. The
error message uses the term *destructor*, which is the general programming term
for a function that cleans up an instance. A *destructor* is analogous to a
*constructor* that creates an instance. The `drop` function in Rust is one
particular destructor.

Rust doesn’t let us call `drop` explicitly because Rust would still
automatically call `drop` on the value at the end of `main`. This would be a
*double free* error because Rust would be trying to clean up the same value
twice.

We can’t disable the automatic insertion of `drop` when a value goes out of
scope, and we can’t call the `drop` method explicitly. So, if we need to force
a value to be cleaned up early, we can use the `std::mem::drop` function.

The `std::mem::drop` function is different than the `drop` method in the `Drop`
trait. We call it by passing the value we want to force to be dropped early as
an argument. The function is in the prelude, so we can modify `main` in Listing
15-14 to call the `drop` function, as shown in Listing 15-16:

Filename: src/main.rs

```
fn main() {
    let c = CustomSmartPointer { data: String::from("some data") };
    println!("CustomSmartPointer created.");
    drop(c);
    println!("CustomSmartPointer dropped before the end of main.");
}
```

Listing 15-16: Calling `std::mem::drop` to explicitly drop a value before it
goes out of scope

Running this code will print the following:

```
CustomSmartPointer created.
Dropping CustomSmartPointer with data `some data`!
CustomSmartPointer dropped before the end of main.
```

The text ```Dropping CustomSmartPointer with data `some data`!``` is printed
between the `CustomSmartPointer created.` and `CustomSmartPointer dropped
before the end of main.` text, showing that the `drop` method code is called to
drop `c` at that point.

We can use code specified in a `Drop` trait implementation in many ways to make
cleanup convenient and safe: for instance, we could use it to create our own
memory allocator! With the `Drop` trait and Rust’s ownership system, we don’t
have to remember to clean up because Rust does it automatically.

We also don’t have to worry about accidentally cleaning up values still in use
because that would cause a compiler error: the ownership system that makes sure
references are always valid also ensures that `drop` gets called only once when
the value is no longer being used.

Now that we’ve examined `Box<T>` and some of the characteristics of smart
pointers, let’s look at a few other smart pointers defined in the standard
library.

## `Rc<T>`, the Reference Counted Smart Pointer

In the majority of cases, ownership is clear: you know exactly which variable
owns a given value. However, there are cases when a single value might have
multiple owners. For example, in graph data structures, multiple edges might
point to the same node, and that node is conceptually owned by all of the edges
that point to it. A node shouldn’t be cleaned up unless it doesn’t have any
edges pointing to it.

To enable multiple ownership, Rust has a type called `Rc<T>`. Its name is an
abbreviation for *reference counting*, which keeps track of the number of
references to a value to know whether or not a value is still in use. If there
are zero references to a value, the value can be cleaned up without any
references becoming invalid.

Imagine `Rc<T>` as a TV in a family room. When one person enters to watch TV,
they turn it on. Others can come into the room and watch the TV. When the last
person leaves the room, they turn off the TV because it’s no longer being used.
If someone turns off the TV while others are still watching it, there would be
uproar from the remaining TV watchers!

We use the `Rc<T>` type when we want to allocate some data on the heap for
multiple parts of our program to read, and we can’t determine at compile time
which part will finish using the data last. If we knew which part would finish
last, we could just make that part the data’s owner and the normal ownership
rules enforced at compile time would take effect.

Note that `Rc<T>` is only for use in single-threaded scenarios. When we discuss
concurrency in Chapter 16, we’ll cover how to do reference counting in
multithreaded programs.

### Using `Rc<T>` to Share Data

Let’s return to our cons list example in Listing 15-5. Recall that we defined
it using `Box<T>`. This time, we’ll create two lists that both share ownership
of a third list, which conceptually will look similar to Figure 15-3:

Two lists that share ownership of a third list

Figure 15-3: Two lists, `b` and `c`, sharing ownership of a third list, `a`

We’ll create list `a` that contains 5 and then 10. Then we’ll make two more
lists: `b` that starts with 3 and `c` that starts with 4. Both `b` and `c`
lists will then continue on to the first `a` list containing 5 and 10. In other
words, both lists will share the first list containing 5 and 10.

Trying to implement this scenario using our definition of `List` with `Box<T>`
won’t work, as shown in Listing 15-17:

Filename: src/main.rs

```
enum List {
    Cons(i32, Box<List>),
    Nil,
}

use List::{Cons, Nil};

fn main() {
    let a = Cons(5,
        Box::new(Cons(10,
            Box::new(Nil))));
    let b = Cons(3, Box::new(a));
    let c = Cons(4, Box::new(a));
}
```

Listing 15-17: Demonstrating we’re not allowed to have two lists using `Box<T>`
that try to share ownership of a third list

When we compile this code, we get this error:

```
error[E0382]: use of moved value: `a`
  --> src/main.rs:13:30
   |
12 |     let b = Cons(3, Box::new(a));
   |                              - value moved here
13 |     let c = Cons(4, Box::new(a));
   |                              ^ value used here after move
   |
   = note: move occurs because `a` has type `List`, which does not implement
   the `Copy` trait
```

The `Cons` variants own the data they hold, so when we create the `b` list, `a`
is moved into `b` and `b` owns `a`. Then, when we try to use `a` again when
creating `c`, we’re not allowed to because `a` has been moved.

We could change the definition of `Cons` to hold references instead, but then
we would have to specify lifetime parameters. By specifying lifetime
parameters, we would be specifying that every element in the list will live at
least as long as the entire list. The borrow checker wouldn’t let us compile
`let a = Cons(10, &Nil);` for example, because the temporary `Nil` value would
be dropped before `a` could take a reference to it.

Instead, we’ll change our definition of `List` to use `Rc<T>` in place of
`Box<T>`, as shown in Listing 15-18. Each `Cons` variant will now hold a value
and an `Rc<T>` pointing to a `List`. When we create `b`, instead of taking
ownership of `a`, we’ll clone the `Rc<List>` that `a` is holding, which
increases the number of references from one to two and lets `a` and `b` share
ownership of the data in that `Rc<List>`. We’ll also clone `a` when creating
`c`, which increases the number of references from two to three. Every time we
call `Rc::clone`, the reference count to the data within the `Rc<List>` will
increase, and the data won’t be cleaned up unless there are zero references to
it:

Filename: src/main.rs

```
enum List {
    Cons(i32, Rc<List>),
    Nil,
}

use List::{Cons, Nil};
use std::rc::Rc;

fn main() {
    let a = Rc::new(Cons(5, Rc::new(Cons(10, Rc::new(Nil)))));
    let b = Cons(3, Rc::clone(&a));
    let c = Cons(4, Rc::clone(&a));
}
```

Listing 15-18: A definition of `List` that uses `Rc<T>`

We need to add a `use` statement to bring `Rc<T>` into scope because it’s not
in the prelude. In `main`, we create the list holding 5 and 10 and store it in
a new `Rc<List>` in `a`. Then when we create `b` and `c`, we call the
`Rc::clone` function and pass a reference to the `Rc<List>` in `a` as an
argument.

We could have called `a.clone()` rather than `Rc::clone(&a)`, but Rust’s
convention is to use `Rc::clone` in this case. The implementation of
`Rc::clone` doesn’t make a deep copy of all the data like most types’
implementations of `clone` do. The call to `Rc::clone` only increments the
reference count, which doesn’t take much time. Deep copies of data can take a
lot of time. By using `Rc::clone` for reference counting, we can visually
distinguish between the deep copy kinds of clones and the kinds of clones that
increase the reference count. When looking for performance problems in the
code, we only need to consider the deep copy clones and can disregard calls to
`Rc::clone`.

### Cloning an `Rc<T>` Increases the Reference Count

Let’s change our working example in Listing 15-18 so we can see the reference
counts changing as we create and drop references to the `Rc<List>` in `a`.

In Listing 15-19, we’ll change `main` so it has an inner scope around list `c`;
then we can see how the reference count changes when `c` goes out of scope. At
each point in the program where the reference count changes, we’ll print the
reference count, which we can get by calling the `Rc::strong_count` function.
This function is named `strong_count` rather than `count` because the `Rc<T>`
type also has a `weak_count`; we’ll see what `weak_count` is used for in the
“Preventing Reference Cycles” section.

Filename: src/main.rs

```
fn main() {
    let a = Rc::new(Cons(5, Rc::new(Cons(10, Rc::new(Nil)))));
    println!("count after creating a = {}", Rc::strong_count(&a));
    let b = Cons(3, Rc::clone(&a));
    println!("count after creating b = {}", Rc::strong_count(&a));
    {
        let c = Cons(4, Rc::clone(&a));
        println!("count after creating c = {}", Rc::strong_count(&a));
    }
    println!("count after c goes out of scope = {}", Rc::strong_count(&a));
}
```

Listing 15-19: Printing the reference count

This code prints the following:

```
count after creating a = 1
count after creating b = 2
count after creating c = 3
count after c goes out of scope = 2
```

We can see that the `Rc<List>` in `a` has an initial reference count of one;
then each time we call `clone`, the count goes up by one. When `c` goes out of
scope, the count goes down by one. We don’t have to call a function to decrease
the reference count like we have to call `Rc::clone` to increase the reference
count: the implementation of the `Drop` trait decreases the reference count
automatically when an `Rc<T>` value goes out of scope.

What we can’t see in this example is that when `b` and then `a` go out of scope
at the end of `main`, the count is then 0, and the `Rc<List>` is cleaned up
completely at that point. Using `Rc<T>` allows a single value to have
multiple owners, and the count ensures that the value remains valid as long as
any of the owners still exist.

Via immutable references, `Rc<T>` allows us to share data between multiple
parts of our program for reading only. If `Rc<T>` allowed us to have multiple
mutable references too, we might violate one of the borrowing rules discussed
in Chapter 4: multiple mutable borrows to the same place can cause data races
and inconsistencies. But being able to mutate data is very useful! In the next
section, we’ll discuss the interior mutability pattern and the `RefCell<T>`
type that we can use in conjunction with an `Rc<T>` to work with this
immutability restriction.

## `RefCell<T>` and the Interior Mutability Pattern

*Interior mutability* is a design pattern in Rust that allows you to mutate
data even when there are immutable references to that data: normally, this
action is disallowed by the borrowing rules. To do so, the pattern uses
`unsafe` code inside a data structure to bend Rust’s usual rules that govern
mutation and borrowing. We haven’t yet covered unsafe code; we will in Chapter
19. We can use types that use the interior mutability pattern when we can
ensure that the borrowing rules will be followed at runtime, even though the
compiler can’t guarantee that. The `unsafe` code involved is then wrapped in a
safe API, and the outer type is still immutable.

Let’s explore this concept by looking at the `RefCell<T>` type that follows the
interior mutability pattern.

### Enforcing Borrowing Rules at Runtime with `RefCell<T>`

Unlike `Rc<T>`, the `RefCell<T>` type represents single ownership over the data
it holds. So, what makes `RefCell<T>` different than a type like `Box<T>`?
Recall the borrowing rules you learned in Chapter 4:

* At any given time, you can have *either* but not both of the following: one
  mutable reference or any number of immutable references.
* References must always be valid.

With references and `Box<T>`, the borrowing rules’ invariants are enforced at
compile time. With `RefCell<T>`, these invariants are enforced *at runtime*.
With references, if you break these rules, you’ll get a compiler error. With
`RefCell<T>`, if you break these rules, your program will `panic!` and exit.

The advantages of checking the borrowing rules at compile time are that errors
will be caught sooner in the development process, and there is no impact on
runtime performance because all the analysis is completed beforehand. For those
reasons, checking the borrowing rules at compile time is the best choice in the
majority of cases, which is why this is Rust’s default.

The advantage of checking the borrowing rules at runtime instead is that
certain memory safe scenarios are then allowed, whereas they are disallowed by
the compile time checks. Static analysis, like the Rust compiler, is inherently
conservative. Some properties of code are impossible to detect by analyzing the
code: the most famous example is the Halting Problem, which is beyond the scope
of this book but is an interesting topic to research.

Because some analysis is impossible, if the Rust compiler can’t be sure the
code complies with the ownership rules, it might reject a correct program; in
this way, it’s conservative. If Rust accepted an incorrect program, users
wouldn’t be able to trust in the guarantees Rust makes. However, if Rust
rejects a correct program, the programmer will be inconvenienced, but nothing
catastrophic can occur. The `RefCell<T>` type is useful when you’re sure your
code follows the borrowing rules, but the compiler is unable to understand and
guarantee that.

Similar to `Rc<T>`, `RefCell<T>` is only for use in single-threaded scenarios
and will give you a compile time error if you try using it in a multithreaded
context. We’ll talk about how to get the functionality of `RefCell<T>` in a
multithreaded program in Chapter 16.

Here is a recap of the reasons to choose `Box<T>`, `Rc<T>`, or `RefCell<T>`:

* `Rc<T>` enables multiple owners of the same data; `Box<T>` and `RefCell<T>`
  have single owners.
* `Box<T>` allows immutable or mutable borrows checked at compile time; `Rc<T>`
  only allows immutable borrows checked at compile time; `RefCell<T>` allows
  immutable or mutable borrows checked at runtime.
* Because `RefCell<T>` allows mutable borrows checked at runtime, we can mutate
  the value inside the `RefCell<T>` even when the `RefCell<T>` is immutable.

Mutating the value inside an immutable value is the *interior mutability*
pattern. Let’s look at a situation in which interior mutability is useful and
examine how it’s possible.

### Interior Mutability: A Mutable Borrow to an Immutable Value

A consequence of the borrowing rules is that when we have an immutable value,
we can’t borrow it mutably. For example, this code won’t compile:

```
fn main() {
    let x = 5;
    let y = &mut x;
}
```

When we try to compile this code, we’ll get the following error:

```
error[E0596]: cannot borrow immutable local variable `x` as mutable
 --> src/main.rs:3:18
  |
2 |     let x = 5;
  |         - consider changing this to `mut x`
3 |     let y = &mut x;
  |                  ^ cannot borrow mutably
```

However, there are situations in which it would be useful for a value to mutate
itself in its methods, but to other code, the value would appear immutable.
Code outside the value’s methods would not be able to mutate the value. Using
`RefCell<T>` is one way to get the ability to have interior mutability. But
`RefCell<T>` doesn’t get around the borrowing rules completely: the borrow
checker in the compiler allows this interior mutability, and the borrowing
rules are checked at runtime instead. If we violate the rules, we’ll get a
`panic!` instead of a compiler error.

Let’s work through a practical example where we can use `RefCell<T>` to mutate
an immutable value and see why that is useful.

#### A Use Case for Interior Mutability: Mock Objects

A *test double* is the general programming concept for a type used in place of
another type during testing. *Mock objects* are specific types of test doubles
that record what happens during a test so we can assert that the correct
actions took place.

Rust doesn’t have objects in the same sense as other languages have objects,
and Rust doesn’t have mock object functionality built into the standard library
like some other languages do. However, we can definitely create a struct that
will serve the same purposes as a mock object.

Here’s the scenario we’ll test: we’ll create a library that tracks a value
against a maximum value and sends messages based on how close to the maximum
value the current value is. This library could be used for keeping track of a
user’s quota for the number of API calls they’re allowed to make, for example.

Our library will only provide the functionality of tracking how close to the
maximum a value is and what the messages should be at what times. Applications
that use our library will be expected to provide the mechanism for sending the
messages: the application could put a message in the application, send an
email, send a text message, or something else. The library doesn’t need to know
that detail. All it needs is something that implements a trait we’ll provide
called `Messenger`. Listing 15-20 shows the library code:

Filename: src/lib.rs

```
pub trait Messenger {
    fn send(&self, msg: &str);
}

pub struct LimitTracker<'a, T: 'a + Messenger> {
    messenger: &'a T,
    value: usize,
    max: usize,
}

impl<'a, T> LimitTracker<'a, T>
    where T: Messenger {
    pub fn new(messenger: &T, max: usize) -> LimitTracker<T> {
        LimitTracker {
            messenger,
            value: 0,
            max,
        }
    }

    pub fn set_value(&mut self, value: usize) {
        self.value = value;

        let percentage_of_max = self.value as f64 / self.max as f64;

        if percentage_of_max >= 0.75 && percentage_of_max < 0.9 {
            self.messenger.send("Warning: You've used up over 75% of your
quota!");
        } else if percentage_of_max >= 0.9 && percentage_of_max < 1.0 {
            self.messenger.send("Urgent warning: You've used up over 90% of
your quota!");
        } else if percentage_of_max >= 1.0 {
            self.messenger.send("Error: You are over your quota!");
        }
    }
}
```

Listing 15-20: A library to keep track of how close to a maximum value a value
is and warn when the value is at certain levels

One important part of this code is that the `Messenger` trait has one method
called `send` that takes an immutable reference to `self` and text of the
message. This is the interface our mock object needs to have. The other
important part is that we want to test the behavior of the `set_value` method
on the `LimitTracker`. We can change what we pass in for the `value` parameter,
but `set_value` doesn’t return anything for us to make assertions on. We want
to be able to say that if we create a `LimitTracker` with something that
implements the `Messenger` trait and a particular value for `max`, when we pass
different numbers for `value`, the messenger is told to send the appropriate
messages.

We need a mock object that instead of sending an email or text message when we
call `send` will only keep track of the messages it’s told to send. We can
create a new instance of the mock object, create a `LimitTracker` that uses the
mock object, call the `set_value` method on `LimitTracker`, and then check that
the mock object has the messages we expect. Listing 15-21 shows an attempt of
implementing a mock object to do just that but that the borrow checker won’t
allow:

Filename: src/lib.rs

```
#[cfg(test)]
mod tests {
    use super::*;

    struct MockMessenger {
        sent_messages: Vec<String>,
    }

    impl MockMessenger {
        fn new() -> MockMessenger {
            MockMessenger { sent_messages: vec![] }
        }
    }

    impl Messenger for MockMessenger {
        fn send(&self, message: &str) {
            self.sent_messages.push(String::from(message));
        }
    }

    #[test]
    fn it_sends_an_over_75_percent_warning_message() {
        let mock_messenger = MockMessenger::new();
        let mut limit_tracker = LimitTracker::new(&mock_messenger, 100);

        limit_tracker.set_value(80);

        assert_eq!(mock_messenger.sent_messages.len(), 1);
    }
}
```

Listing 15-21: An attempt to implement a `MockMessenger` that isn’t allowed by
the borrow checker

This test code defines a `MockMessenger` struct that has a `sent_messages`
field with a `Vec` of `String` values to keep track of the messages it’s told
to send. We also define an associated function `new` to make it convenient to
create new `MockMessenger` values that start with an empty list of messages. We
then implement the `Messenger` trait for `MockMessenger` so we can give a
`MockMessenger` to a `LimitTracker`. In the definition of the `send` method, we
take the message passed in as a parameter and store it in the `MockMessenger`
list of `sent_messages`.

In the test, we’re testing what happens when the `LimitTracker` is told to set
`value` to something that is more than 75 percent of the `max` value. First, we
create a new `MockMessenger`, which will start with an empty list of messages.
Then we create a new `LimitTracker` and give it a reference to the new
`MockMessenger` and a `max` value of 100. We call the `set_value` method on the
`LimitTracker` with a value of 80, which is more than 75 percent of 100. Then
we assert that the list of messages that the `MockMessenger` is keeping track
of should now have one message in it.

However, there’s one problem with this test, as shown here:

```
error[E0596]: cannot borrow immutable field `self.sent_messages` as mutable
  --> src/lib.rs:52:13
   |
51 |         fn send(&self, message: &str) {
   |                 ----- use `&mut self` here to make mutable
52 |             self.sent_messages.push(String::from(message));
   |             ^^^^^^^^^^^^^^^^^^ cannot mutably borrow immutable field
```

We can’t modify the `MockMessenger` to keep track of the messages because the
`send` method takes an immutable reference to `self`. We also can’t take the
suggestion from the error text to use `&mut self` instead because then the
signature of `send` wouldn’t match the signature in the `Messenger` trait
definition (feel free to try and see what error message you get).

This is a situation in which interior mutability can help! We’ll store the
`sent_messages` within a `RefCell<T>`, and then the `send` message will be
able to modify `sent_messages` to store the messages we’ve seen. Listing 15-22
shows what that looks like:

Filename: src/lib.rs

```
#[cfg(test)]
mod tests {
    use super::*;
    use std::cell::RefCell;

    struct MockMessenger {
        sent_messages: RefCell<Vec<String>>,
    }

    impl MockMessenger {
        fn new() -> MockMessenger {
            MockMessenger { sent_messages: RefCell::new(vec![]) }
        }
    }

    impl Messenger for MockMessenger {
        fn send(&self, message: &str) {
            self.sent_messages.borrow_mut().push(String::from(message));
        }
    }

    #[test]
    fn it_sends_an_over_75_percent_warning_message() {
        // --snip--

        assert_eq!(mock_messenger.sent_messages.borrow().len(), 1);
    }
}
```

Listing 15-22: Using `RefCell<T>` to mutate an inner value while the outer
value is considered immutable

The `sent_messages` field is now of type `RefCell<Vec<String>>` instead of
`Vec<String>`. In the `new` function, we create a new `RefCell<Vec<String>>`
instance around the empty vector.

For the implementation of the `send` method, the first parameter is still an
immutable borrow of `self`, which matches the trait definition. We call
`borrow_mut` on the `RefCell<Vec<String>>` in `self.sent_messages` to get a
mutable reference to the value inside the `RefCell<Vec<String>>`, which is
the vector. Then we can call `push` on the mutable reference to the vector to
keep track of the messages sent during the test.

The last change we have to make is in the assertion: to see how many items are
in the inner vector, we call `borrow` on the `RefCell<Vec<String>>` to get an
immutable reference to the vector.

Now that you’ve seen how to use `RefCell<T>`, let’s dig into how it works!

#### `RefCell<T>` Keeps Track of Borrows at Runtime

When creating immutable and mutable references, we use the `&` and `&mut`
syntax, respectively. With `RefCell<T>`, we use the `borrow` and `borrow_mut`
methods, which are part of the safe API that belongs to `RefCell<T>`. The
`borrow` method returns the smart pointer type `Ref<T>`, and `borrow_mut`
returns the smart pointer type `RefMut<T>`. Both types implement `Deref` so
we can treat them like regular references.

The `RefCell<T>` keeps track of how many `Ref<T>` and `RefMut<T>` smart
pointers are currently active. Every time we call `borrow`, the `RefCell<T>`
increases its count of how many immutable borrows are active. When a `Ref<T>`
value goes out of scope, the count of immutable borrows goes down by one. Just
like the compile time borrowing rules, `RefCell<T>` lets us have many immutable
borrows or one mutable borrow at any point in time.

If we try to violate these rules, rather than getting a compiler error like we
would with references, the implementation of `RefCell<T>` will `panic!` at
runtime. Listing 15-23 shows a modification of the implementation of `send` in
Listing 15-22. We’re deliberately trying to create two mutable borrows active
for the same scope to illustrate that `RefCell<T>` prevents us from doing this
at runtime:

Filename: src/lib.rs

```
impl Messenger for MockMessenger {
    fn send(&self, message: &str) {
        let mut one_borrow = self.sent_messages.borrow_mut();
        let mut two_borrow = self.sent_messages.borrow_mut();

        one_borrow.push(String::from(message));
        two_borrow.push(String::from(message));
    }
}
```

Listing 15-23: Creating two mutable references in the same scope to see that
`RefCell<T>` will panic

We create a variable `one_borrow` for the `RefMut<T>` smart pointer returned
from `borrow_mut`. Then we create another mutable borrow in the same way in the
variable `two_borrow`. This makes two mutable references in the same scope,
which isn’t allowed. When we run the tests for our library, the code in Listing
15-23 will compile without any errors, but the test will fail:

```
---- tests::it_sends_an_over_75_percent_warning_message stdout ----
	thread 'tests::it_sends_an_over_75_percent_warning_message' panicked at
    'already borrowed: BorrowMutError', src/libcore/result.rs:906:4
note: Run with `RUST_BACKTRACE=1` for a backtrace.
```

Notice that the code panicked with the message `already borrowed:
BorrowMutError`. This is how `RefCell<T>` handles violations of the borrowing
rules at runtime.

Catching borrowing errors at runtime rather than compile time means that we
would find a mistake in our code later in the development process and possibly
not even until our code was deployed to production. Also, our code will incur a
small runtime performance penalty as a result of keeping track of the borrows
at runtime rather than compile time. However, using `RefCell<T>` makes it
possible for us to write a mock object that can modify itself to keep track of
the messages it has seen while we’re using it in a context where only immutable
values are allowed. We can use `RefCell<T>` despite its trade-offs to get more
functionality than regular references give us.

### Having Multiple Owners of Mutable Data by Combining `Rc<T>` and `RefCell<T>`

A common way to use `RefCell<T>` is in combination with `Rc<T>`. Recall that
`Rc<T>` lets us have multiple owners of some data, but it only gives us
immutable access to that data. If we have an `Rc<T>` that holds a `RefCell<T>`,
we can get a value that can have multiple owners *and* that we can mutate!

For example, recall the cons list example in Listing 15-18 where we used
`Rc<T>` to let us have multiple lists share ownership of another list. Because
`Rc<T>` only holds immutable values, we can’t change any of the values in the
list once we’ve created them. Let’s add in `RefCell<T>` to gain the ability to
change the values in the lists. Listing 15-24 shows that by using a
`RefCell<T>` in the `Cons` definition, we can modify the value stored in all
the lists:

Filename: src/main.rs

```
#[derive(Debug)]
enum List {
    Cons(Rc<RefCell<i32>>, Rc<List>),
    Nil,
}

use List::{Cons, Nil};
use std::rc::Rc;
use std::cell::RefCell;

fn main() {
    let value = Rc::new(RefCell::new(5));

    let a = Rc::new(Cons(Rc::clone(&value), Rc::new(Nil)));

    let b = Cons(Rc::new(RefCell::new(6)), Rc::clone(&a));
    let c = Cons(Rc::new(RefCell::new(10)), Rc::clone(&a));

    *value.borrow_mut() += 10;

    println!("a after = {:?}", a);
    println!("b after = {:?}", b);
    println!("c after = {:?}", c);
}
```

Listing 15-24: Using `Rc<RefCell<i32>>` to create a `List` that we can mutate

We create a value that is an instance of `Rc<RefCell<i32>` and store it in a
variable named `value` so we can access it directly later. Then we create a
`List` in `a` with a `Cons` variant that holds `value`. We need to clone
`value` so both `a` and `value` have ownership of the inner `5` value rather
than transferring ownership from `value` to `a` or having `a` borrow from
`value`.

We wrap the list `a` in an `Rc<T>` so when we create lists `b` and `c`, they
can both refer to `a`, which is what we did in Listing 15-18.

After we’ve created the lists in `a`, `b`, and `c`, we add 10 to the value in
`value`. We do this by calling `borrow_mut` on `value`, which uses the
automatic dereferencing feature we discussed in Chapter 5 (see the section
“Where’s the `->` Operator?”) to dereference the `Rc<T>` to the inner
`RefCell<T>` value. The `borrow_mut` method returns a `RefMut<T>` smart
pointer, and we use the dereference operator on it and change the inner value.

When we print `a`, `b`, and `c`, we can see that they all have the modified
value of 15 rather than 5:

```
a after = Cons(RefCell { value: 15 }, Nil)
b after = Cons(RefCell { value: 6 }, Cons(RefCell { value: 15 }, Nil))
c after = Cons(RefCell { value: 10 }, Cons(RefCell { value: 15 }, Nil))
```

This technique is pretty neat! By using `RefCell<T>`, we have an outwardly
immutable `List`. But we can use the methods on `RefCell<T>` that provide
access to its interior mutability so we can modify our data when we need to.
The runtime checks of the borrowing rules protect us from data races, and it’s
sometimes worth trading a bit of speed for this flexibility in our data
structures.

The standard library has other types that provide interior mutability, such as
`Cell<T>`, which is similar except that instead of giving references to the
inner value, the value is copied in and out of the `Cell<T>`. There’s also
`Mutex<T>`, which offers interior mutability that’s safe to use across threads;
we’ll discuss its use in Chapter 16. Check out the standard library docs for
more details on the differences between these types.

## Reference Cycles Can Leak Memory

Rust’s memory safety guarantees make it *difficult* but not impossible to
accidentally create memory that is never cleaned up (known as a *memory leak*).
Preventing memory leaks entirely is not one of Rust’s guarantees in the same
way that disallowing data races at compile time is, meaning memory leaks are
memory safe in Rust. We can see that Rust allows memory leaks by using `Rc<T>`
and `RefCell<T>`: it’s possible to create references where items refer to each
other in a cycle. This creates memory leaks because the reference count of each
item in the cycle will never reach 0, and the values will never be dropped.

### Creating a Reference Cycle

Let’s look at how a reference cycle might happen and how to prevent it,
starting with the definition of the `List` enum and a `tail` method in Listing
15-25:

Filename: src/main.rs

```
use std::rc::Rc;
use std::cell::RefCell;
use List::{Cons, Nil};

#[derive(Debug)]
enum List {
    Cons(i32, RefCell<Rc<List>>),
    Nil,
}

impl List {
    fn tail(&self) -> Option<&RefCell<Rc<List>>> {
        match *self {
            Cons(_, ref item) => Some(item),
            Nil => None,
        }
    }
}
```

Listing 15-25: A cons list definition that holds a `RefCell<T>` so we can
modify what a `Cons` variant is referring to

We’re using another variation of the `List` definition in Listing 15-5. The
second element in the `Cons` variant is now `RefCell<Rc<List>>`, meaning that
instead of having the ability to modify the `i32` value like we did in Listing
15-24, we want to modify which `List` a `Cons` variant is pointing to. We’re
also adding a `tail` method to make it convenient for us to access the second
item if we have a `Cons` variant.

In Listing 15-26, we’re adding a `main` function that uses the definitions in
Listing 15-25. This code creates a list in `a` and a list in `b` that points to
the list in `a`, and then modifies the list in `a` to point to `b`, which
creates a reference cycle. There are `println!` statements along the way to
show what the reference counts are at various points in this process:

Filename: src/main.rs

```
fn main() {
    let a = Rc::new(Cons(5, RefCell::new(Rc::new(Nil))));

    println!("a initial rc count = {}", Rc::strong_count(&a));
    println!("a next item = {:?}", a.tail());

    let b = Rc::new(Cons(10, RefCell::new(Rc::clone(&a))));

    println!("a rc count after b creation = {}", Rc::strong_count(&a));
    println!("b initial rc count = {}", Rc::strong_count(&b));
    println!("b next item = {:?}", b.tail());

    if let Some(link) = a.tail() {
        *link.borrow_mut() = Rc::clone(&b);
    }

    println!("b rc count after changing a = {}", Rc::strong_count(&b));
    println!("a rc count after changing a = {}", Rc::strong_count(&a));

    // Uncomment the next line to see that we have a cycle; it will
    // overflow the stack
    // println!("a next item = {:?}", a.tail());
}
```

Listing 15-26: Creating a reference cycle of two `List` values pointing to each
other

We create an `Rc<List>` instance holding a `List` value in the variable `a`
with an initial list of `5, Nil`. We then create an `Rc<List>` instance
holding another `List` value in the variable `b` that contains the value 10 and
then points to the list in `a`.

We modify `a` so it points to `b` instead of `Nil`, which creates a cycle. We
do that by using the `tail` method to get a reference to the
`RefCell<Rc<List>>` in `a`, which we put in the variable `link`. Then we use
the `borrow_mut` method on the `RefCell<Rc<List>>` to change the value inside
from an `Rc<List>` that holds a `Nil` value to the `Rc<List>` in `b`.

When we run this code, keeping the last `println!` commented out for the
moment, we’ll get this output:

```
a initial rc count = 1
a next item = Some(RefCell { value: Nil })
a rc count after b creation = 2
b initial rc count = 1
b next item = Some(RefCell { value: Cons(5, RefCell { value: Nil }) })
b rc count after changing a = 2
a rc count after changing a = 2
```

The reference count of the `Rc<List>` instances in both `a` and `b` are 2
after we change the list in `a` to point to `b`. At the end of `main`, Rust
will try to drop `b` first, which will decrease the count in each of the
`Rc<List>` instances in `a` and `b` by one.

However, because `a` is still referencing the `Rc<List>` that was in `b`,
that `Rc<List>` has a count of 1 rather than 0, so the memory the
`Rc<List>` has on the heap won’t be dropped. The memory will just sit there
with a count of one, forever. To visualize this reference cycle, we’ve created
a diagram in Figure 15-4:

Reference cycle of lists

Figure 15-4: A reference cycle of lists `a` and `b` pointing to each other

If you uncomment the last `println!` and run the program, Rust will try to
print this cycle with `a` pointing to `b` pointing to `a` and so forth until it
overflows the stack.

In this case, right after we create the reference cycle, the program ends. The
consequences of this cycle aren’t very dire. If a more complex program
allocates lots of memory in a cycle and holds onto it for a long time, the
program would use more memory than it needs and might overwhelm the system,
causing it to run out of available memory.

Creating reference cycles is not easily done, but it’s not impossible either.
If you have `RefCell<T>` values that contain `Rc<T>` values or similar nested
combinations of types with interior mutability and reference counting, you must
ensure that you don’t create cycles; you can’t rely on Rust to catch them.
Creating a reference cycle would be a logic bug in your program that you should
use automated tests, code reviews, and other software development practices to
minimize.

Another solution for avoiding reference cycles is reorganizing your data
structures so that some references express ownership and some references don’t.
As a result, you can have cycles made up of some ownership relationships and
some non-ownership relationships, and only the ownership relationships affect
whether or not a value can be dropped. In Listing 15-25, we always want `Cons`
variants to own their list, so reorganizing the data structure isn’t possible.
Let’s look at an example using graphs made up of parent nodes and child nodes
to see when non-ownership relationships are an appropriate way to prevent
reference cycles.

### Preventing Reference Cycles: Turn an `Rc<T>` into a `Weak<T>`

So far, we’ve demonstrated that calling `Rc::clone` increases the
`strong_count` of an `Rc<T>` instance, and an `Rc<T>` instance is only
cleaned up if its `strong_count` is 0. We can also create a *weak reference* to
the value within an `Rc<T>` instance by calling `Rc::downgrade` and passing a
reference to the `Rc<T>`. When we call `Rc::downgrade`, we get a smart
pointer of type `Weak<T>`. Instead of increasing the `strong_count` in the
`Rc<T>` instance by one, calling `Rc::downgrade` increases the `weak_count`
by one. The `Rc<T>` type uses `weak_count` to keep track of how many
`Weak<T>` references exist, similar to `strong_count`. The difference is the
`weak_count` doesn’t need to be 0 for the `Rc<T>` instance to be cleaned up.

Strong references are how we can share ownership of an `Rc<T>` instance. Weak
references don’t express an ownership relationship. They won’t cause a
reference cycle because any cycle involving some weak references will be broken
once the strong reference count of values involved is 0.

Because the value that `Weak<T>` references might have been dropped, to do
anything with the value that a `Weak<T>` is pointing to, we must make sure the
value still exists. We do this by calling the `upgrade` method on a `Weak<T>`
instance, which will return an `Option<Rc<T>>`. We’ll get a result of `Some` if
the `Rc<T>` value has not been dropped yet and a result of `None` if the
`Rc<T>` value has been dropped. Because `upgrade` returns an `Option<T>`, Rust
will ensure that we handle the `Some` case and the `None` case, and there won’t
be an invalid pointer.

As an example, rather than using a list whose items know only about the next
item, we’ll create a tree whose items know about their children items *and*
their parent items.

#### Creating a Tree Data Structure: a `Node` with Child Nodes

To start, we’ll build a tree with nodes that know about their child nodes.
We’ll create a struct named `Node` that holds its own `i32` value as well as
references to its children `Node` values:

Filename: src/main.rs

```
use std::rc::Rc;
use std::cell::RefCell;

#[derive(Debug)]
struct Node {
    value: i32,
    children: RefCell<Vec<Rc<Node>>>,
}
```

We want a `Node` to own its children, and we want to share that ownership with
variables so we can access each `Node` in the tree directly. To do this, we
define the `Vec<T>` items to be values of type `Rc<Node>`. We also want to
modify which nodes are children of another node, so we have a `RefCell<T>` in
`children` around the `Vec<Rc<Node>>`.

Next, we’ll use our struct definition and create one `Node` instance named
`leaf` with the value 3 and no children, and another instance named `branch`
with the value 5 and `leaf` as one of its children, as shown in Listing 15-27:

Filename: src/main.rs

```
fn main() {
    let leaf = Rc::new(Node {
        value: 3,
        children: RefCell::new(vec![]),
    });

    let branch = Rc::new(Node {
        value: 5,
        children: RefCell::new(vec![Rc::clone(&leaf)]),
    });
}
```

Listing 15-27: Creating a `leaf` node with no children and a `branch` node with
`leaf` as one of its children

We clone the `Rc<Node>` in `leaf` and store that in `branch`, meaning the
`Node` in `leaf` now has two owners: `leaf` and `branch`. We can get from
`branch` to `leaf` through `branch.children`, but there’s no way to get from
`leaf` to `branch`. The reason is that `leaf` has no reference to `branch` and
doesn’t know they’re related. We want `leaf` to know that `branch` is its
parent. We’ll do that next.

#### Adding a Reference from a Child to Its Parent

To make the child node aware of its parent, we need to add a `parent` field to
our `Node` struct definition. The trouble is in deciding what the type of
`parent` should be. We know it can’t contain an `Rc<T>` because that would
create a reference cycle with `leaf.parent` pointing to `branch` and
`branch.children` pointing to `leaf`, which would cause their `strong_count`
values to never be 0.

Thinking about the relationships another way, a parent node should own its
children: if a parent node is dropped, its child nodes should be dropped as
well. However, a child should not own its parent: if we drop a child node, the
parent should still exist. This is a case for weak references!

So instead of `Rc<T>`, we’ll make the type of `parent` use `Weak<T>`,
specifically a `RefCell<Weak<Node>>`. Now our `Node` struct definition looks
like this:

Filename: src/main.rs

```
use std::rc::{Rc, Weak};
use std::cell::RefCell;

#[derive(Debug)]
struct Node {
    value: i32,
    parent: RefCell<Weak<Node>>,
    children: RefCell<Vec<Rc<Node>>>,
}
```

Now a node will be able to refer to its parent node but doesn’t own its parent.
In Listing 15-28, we update `main` to use this new definition so the `leaf`
node will have a way to refer to its parent, `branch`:

Filename: src/main.rs

```
fn main() {
    let leaf = Rc::new(Node {
        value: 3,
        parent: RefCell::new(Weak::new()),
        children: RefCell::new(vec![]),
    });

    println!("leaf parent = {:?}", leaf.parent.borrow().upgrade());

    let branch = Rc::new(Node {
        value: 5,
        parent: RefCell::new(Weak::new()),
        children: RefCell::new(vec![Rc::clone(&leaf)]),
    });

    *leaf.parent.borrow_mut() = Rc::downgrade(&branch);

    println!("leaf parent = {:?}", leaf.parent.borrow().upgrade());
}
```

Listing 15-28: A `leaf` node with a `Weak` reference to its parent node `branch`

Creating the `leaf` node looks similar to how creating the `leaf` node looked
in Listing 15-27 with the exception of the `parent` field: `leaf` starts out
without a parent, so we create a new, empty `Weak<Node>` reference instance.

At this point, when we try to get a reference to the parent of `leaf` by using
the `upgrade` method, we get a `None` value. We see this in the output from the
first `println!` statement:

```
leaf parent = None
```

When we create the `branch` node, it will also have a new `Weak<Node>`
reference in the `parent` field, because `branch` doesn’t have a parent node.
We still have `leaf` as one of the children of `branch`. Once we have the
`Node` instance in `branch`, we can modify `leaf` to give it a `Weak<Node>`
reference to its parent. We use the `borrow_mut` method on the
`RefCell<Weak<Node>>` in the `parent` field of `leaf`, and then we use the
`Rc::downgrade` function to create a `Weak<Node>` reference to `branch` from
the `Rc<Node>` in `branch.`

When we print the parent of `leaf` again, this time we’ll get a `Some` variant
holding `branch`: now `leaf` can access its parent! When we print `leaf`, we
also avoid the cycle that eventually ended in a stack overflow like we had in
Listing 15-26: the `Weak<Node>` references are printed as `(Weak)`:

```
leaf parent = Some(Node { value: 5, parent: RefCell { value: (Weak) },
children: RefCell { value: [Node { value: 3, parent: RefCell { value: (Weak) },
children: RefCell { value: [] } }] } })
```

The lack of infinite output indicates that this code didn’t create a reference
cycle. We can also tell this by looking at the values we get from calling
`Rc::strong_count` and `Rc::weak_count`.

#### Visualizing Changes to `strong_count` and `weak_count`

Let’s look at how the `strong_count` and `weak_count` values of the `Rc<Node>`
instances change by creating a new inner scope and moving the creation of
`branch` into that scope. By doing so, we can see what happens when `branch` is
created and then dropped when it goes out of scope. The modifications are shown
in Listing 15-29:

Filename: src/main.rs

```
fn main() {
    let leaf = Rc::new(Node {
        value: 3,
        parent: RefCell::new(Weak::new()),
        children: RefCell::new(vec![]),
    });

    println!(
        "leaf strong = {}, weak = {}",
        Rc::strong_count(&leaf),
        Rc::weak_count(&leaf),
    );

    {
        let branch = Rc::new(Node {
            value: 5,
            parent: RefCell::new(Weak::new()),
            children: RefCell::new(vec![Rc::clone(&leaf)]),
        });

        *leaf.parent.borrow_mut() = Rc::downgrade(&branch);

        println!(
            "branch strong = {}, weak = {}",
            Rc::strong_count(&branch),
            Rc::weak_count(&branch),
        );

        println!(
            "leaf strong = {}, weak = {}",
            Rc::strong_count(&leaf),
            Rc::weak_count(&leaf),
        );
    }

    println!("leaf parent = {:?}", leaf.parent.borrow().upgrade());
    println!(
        "leaf strong = {}, weak = {}",
        Rc::strong_count(&leaf),
        Rc::weak_count(&leaf),
    );
}
```

Listing 15-29: Creating `branch` in an inner scope and examining strong and
weak reference counts

After `leaf` is created, its `Rc<Node>` has a strong count of 1 and a weak
count of 0. In the inner scope, we create `branch` and associate it with
`leaf`, at which point when we print the counts, the `Rc<Node>` in `branch`
will have a strong count of 1 and a weak count of 1 (for `leaf.parent` pointing
to `branch` with a `Weak<Node>`). When we print the counts in `leaf`, we’ll see
it will have a strong count of 2, because `branch` now has a clone of the
`Rc<Node>` of `leaf` stored in `branch.children` but will still have a weak
count of 0.

When the inner scope ends, `branch` goes out of scope and the strong count of
the `Rc<Node>` decreases to 0, so its `Node` is dropped. The weak count of 1
from `leaf.parent` has no bearing on whether or not `Node` is dropped, so we
don’t get any memory leaks!

If we try to access the parent of `leaf` after the end of the scope, we’ll get
`None` again. At the end of the program, the `Rc<Node>` in `leaf` has a strong
count of 1 and a weak count of 0, because the variable `leaf` is now the only
reference to the `Rc<Node>` again.

All of the logic that manages the counts and value dropping is built into
`Rc<T>` and `Weak<T>` and their implementations of the `Drop` trait. By
specifying that the relationship from a child to its parent should be a
`Weak<T>` reference in the definition of `Node`, we’re able to have parent
nodes point to child nodes and vice versa without creating a reference cycle
and memory leaks.

## Summary

This chapter covered how to use smart pointers to make different guarantees and
trade-offs than those Rust makes by default with regular references. The
`Box<T>` type has a known size and points to data allocated on the heap. The
`Rc<T>` type keeps track of the number of references to data on the heap, so
that data can have multiple owners. The `RefCell<T>` type with its interior
mutability gives us a type that we can use when we need an immutable type but
need to change an inner value of that type; it also enforces the borrowing
rules at runtime instead of at compile time.

Also discussed were the `Deref` and `Drop` traits that enable a lot of the
functionality of smart pointers. We explored reference cycles that can cause
memory leaks and how to prevent them using `Weak<T>`.

If this chapter has piqued your interest and you want to implement your own
smart pointers, check out “The Rustonomicon” at
*https://doc.rust-lang.org/stable/nomicon/* for more useful information.

Next, we’ll talk about concurrency in Rust. You’ll even learn about a few new
smart pointers.
