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Functional Language Features: Iterators and Closures

Rust’s design has taken inspiration from many existing languages and techniques, and one significant influence is functional programming. Programming in a functional style often includes using functions as values by passing them in arguments, returning them from other functions, assigning them to variables for later execution, and so forth.

In this chapter, we won’t debate the issue of what functional programming is or isn’t but will instead discuss some features of Rust that are similar to features in many languages often referred to as functional.

More specifically, we’ll cover:

Closures, a function-like construct you can store in a variable
Iterators, a way of processing a series of elements
How to use closures and iterators to improve the I/O project in Chapter 12
The performance of closures and iterators (spoiler alert: they’re faster than you might think!)

We’ve already covered some other Rust features, such as pattern matching and enums, that are also influenced by the functional style. Because mastering closures and iterators is an important part of writing idiomatic, fast Rust code, we’ll devote this entire chapter to them.

Closures: Anonymous Functions That Capture Their Environment

Rust’s closures are anonymous functions you can save in a variable or pass as arguments to other functions. You can create the closure in one place and then call the closure elsewhere to evaluate it in a different context. Unlike functions, closures can capture values from the scope in which they’re defined. We’ll demonstrate how these closure features allow for code reuse and behavior customization.

Capturing the Environment with Closures

We’ll first examine how we can use closures to capture values from the environment they’re defined in for later use. Here’s the scenario: every so often, our T-shirt company gives away an exclusive, limited-edition shirt to someone on our mailing list as a promotion. People on the mailing list can optionally add their favorite color to their profile. If the person chosen for a free shirt has their favorite color set, they get that color shirt. If the person hasn’t specified a favorite color, they get whatever color the company currently has the most of.

There are many ways to implement this. For this example, we’re going to use an enum called ShirtColor that has the variants Red and Blue (limiting the number of colors available for simplicity). We represent the company’s inventory with an Inventory struct that has a field named shirts that contains a Vec<ShirtColor> representing the shirt colors currently in stock. The method giveaway defined on Inventory gets the optional shirt color preference of the free-shirt winner, and returns the shirt color the person will get. This setup is shown in Listing 13-1.

Filename: src/main.rs

#[derive(Debug, PartialEq, Copy, Clone)]
enum ShirtColor {
    Red,
    Blue,
}

struct Inventory {
    shirts: Vec<ShirtColor>,
}

impl Inventory {
    fn giveaway(
        &self,
        user_preference: Option<ShirtColor>,
    ) -> ShirtColor {
      1 user_preference.unwrap_or_else(|| self.most_stocked())
    }

    fn most_stocked(&self) -> ShirtColor {
        let mut num_red = 0;
        let mut num_blue = 0;

        for color in &self.shirts {
            match color {
                ShirtColor::Red => num_red += 1,
                ShirtColor::Blue => num_blue += 1,
            }
        }
        if num_red > num_blue {
            ShirtColor::Red
        } else {
            ShirtColor::Blue
        }
    }
}

fn main() {
    let store = Inventory {
      2 shirts: vec![
            ShirtColor::Blue,
            ShirtColor::Red,
            ShirtColor::Blue,
        ],
    };

    let user_pref1 = Some(ShirtColor::Red);
  3 let giveaway1 = store.giveaway(user_pref1);
    println!(
        "The user with preference {:?} gets {:?}",
        user_pref1, giveaway1
    );

    let user_pref2 = None;
  4 let giveaway2 = store.giveaway(user_pref2);
    println!(
        "The user with preference {:?} gets {:?}",
        user_pref2, giveaway2
    );
}

Listing 13-1: Shirt company giveaway situation

The store defined in main has two blue shirts and one red shirt remaining to distribute for this limited-edition promotion [2]. We call the giveaway method for a user with a preference for a red shirt [3] and a user without any preference [4].

Again, this code could be implemented in many ways, and here, to focus on closures, we’ve stuck to concepts you’ve already learned, except for the body of the giveaway method that uses a closure. In the giveaway method, we get the user preference as a parameter of type Option<ShirtColor> and call the unwrap_or_else method on user_preference [1]. The unwrap_or_else method on Option<T> is defined by the standard library. It takes one argument: a closure without any arguments that returns a value T (the same type stored in the Some variant of the Option<T>, in this case ShirtColor). If the Option<T> is the Some variant, unwrap_or_else returns the value from within the Some. If the Option<T> is the None variant, unwrap_or_else calls the closure and returns the value returned by the closure.

We specify the closure expression || self.most_stocked() as the argument to unwrap_or_else. This is a closure that takes no parameters itself (if the closure had parameters, they would appear between the two vertical pipes). The body of the closure calls self.most_stocked(). We’re defining the closure here, and the implementation of unwrap_or_else will evaluate the closure later if the result is needed.

Running this code prints the following:

The user with preference Some(Red) gets Red
The user with preference None gets Blue

One interesting aspect here is that we’ve passed a closure that calls self.most_stocked() on the current Inventory instance. The standard library didn’t need to know anything about the Inventory or ShirtColor types we defined, or the logic we want to use in this scenario. The closure captures an immutable reference to the self Inventory instance and passes it with the code we specify to the unwrap_or_else method. Functions, on the other hand, are not able to capture their environment in this way.

Closure Type Inference and Annotation

There are more differences between functions and closures. Closures don’t usually require you to annotate the types of the parameters or the return value like fn functions do. Type annotations are required on functions because the types are part of an explicit interface exposed to your users. Defining this interface rigidly is important for ensuring that everyone agrees on what types of values a function uses and returns. Closures, on the other hand, aren’t used in an exposed interface like this: they’re stored in variables and used without naming them and exposing them to users of our library.

Closures are typically short and relevant only within a narrow context rather than in any arbitrary scenario. Within these limited contexts, the compiler can infer the types of the parameters and the return type, similar to how it’s able to infer the types of most variables (there are rare cases where the compiler needs closure type annotations too).

As with variables, we can add type annotations if we want to increase explicitness and clarity at the cost of being more verbose than is strictly necessary. Annotating the types for a closure would look like the definition shown in Listing 13-2. In this example, we’re defining a closure and storing it in a variable rather than defining the closure in the spot we pass it as an argument, as we did in Listing 13-1.

Filename: src/main.rs

let expensive_closure = |num: u32| -> u32 {
    println!("calculating slowly...");
    thread::sleep(Duration::from_secs(2));
    num
};

Listing 13-2: Adding optional type annotations of the parameter and return value types in the closure

With type annotations added, the syntax of closures looks more similar to the syntax of functions. Here, we define a function that adds 1 to its parameter and a closure that has the same behavior, for comparison. We’ve added some spaces to line up the relevant parts. This illustrates how closure syntax is similar to function syntax except for the use of pipes and the amount of syntax that is optional:

fn  add_one_v1   (x: u32) -> u32 { x + 1 }
let add_one_v2 = |x: u32| -> u32 { x + 1 };
let add_one_v3 = |x|             { x + 1 };
let add_one_v4 = |x|               x + 1  ;

The first line shows a function definition and the second line shows a fully annotated closure definition. In the third line, we remove the type annotations from the closure definition. In the fourth line, we remove the curly brackets, which are optional because the closure body has only one expression. These are all valid definitions that will produce the same behavior when they’re called. The add_one_v3 and add_one_v4 lines require the closures to be evaluated to be able to compile because the types will be inferred from their usage. This is similar to let v = Vec::new(); needing either type annotations or values of some type to be inserted into the Vec for Rust to be able to infer the type.

For closure definitions, the compiler will infer one concrete type for each of their parameters and for their return value. For instance, Listing 13-3 shows the definition of a short closure that just returns the value it receives as a parameter. This closure isn’t very useful except for the purposes of this example. Note that we haven’t added any type annotations to the definition. Because there are no type annotations, we can call the closure with any type, which we’ve done here with String the first time. If we then try to call example_closure with an integer, we’ll get an error.

Filename: src/main.rs

let example_closure = |x| x;

let s = example_closure(String::from("hello"));
let n = example_closure(5);

Listing 13-3: Attempting to call a closure whose types are inferred with two different types

The compiler gives us this error:

error[E0308]: mismatched types
 --> src/main.rs:5:29
  |
5 |     let n = example_closure(5);
  |                             ^- help: try using a conversion method:
`.to_string()`
  |                             |
  |                             expected struct `String`, found integer

The first time we call example_closure with the String value, the compiler infers the type of x and the return type of the closure to be String. Those types are then locked into the closure in example_closure, and we get a type error when we next try to use a different type with the same closure.

Capturing References or Moving Ownership

Closures can capture values from their environment in three ways, which directly map to the three ways a function can take a parameter: borrowing immutably, borrowing mutably, and taking ownership. The closure will decide which of these to use based on what the body of the function does with the captured values.

In Listing 13-4, we define a closure that captures an immutable reference to the vector named list because it only needs an immutable reference to print the value.

Filename: src/main.rs

fn main() {
    let list = vec![1, 2, 3];
    println!("Before defining closure: {:?}", list);

  1 let only_borrows = || println!("From closure: {:?}", list);

    println!("Before calling closure: {:?}", list);
  2 only_borrows();
    println!("After calling closure: {:?}", list);
}

Listing 13-4: Defining and calling a closure that captures an immutable reference

This example also illustrates that a variable can bind to a closure definition [1], and we can later call the closure by using the variable name and parentheses as if the variable name were a function name [2].

Because we can have multiple immutable references to list at the same time, list is still accessible from the code before the closure definition, after the closure definition but before the closure is called, and after the closure is called. This code compiles, runs, and prints:

Before defining closure: [1, 2, 3]
Before calling closure: [1, 2, 3]
From closure: [1, 2, 3]
After calling closure: [1, 2, 3]

Next, in Listing 13-5, we change the closure body so that it adds an element to the list vector. The closure now captures a mutable reference.

Filename: src/main.rs

fn main() {
    let mut list = vec![1, 2, 3];
    println!("Before defining closure: {:?}", list);

    let mut borrows_mutably = || list.push(7);

    borrows_mutably();
    println!("After calling closure: {:?}", list);
}

Listing 13-5: Defining and calling a closure that captures a mutable reference

This code compiles, runs, and prints:

Before defining closure: [1, 2, 3]
After calling closure: [1, 2, 3, 7]

Note that there’s no longer a println! between the definition and the call of the borrows_mutably closure: when borrows_mutably is defined, it captures a mutable reference to list. We don’t use the closure again after the closure is called, so the mutable borrow ends. Between the closure definition and the closure call, an immutable borrow to print isn’t allowed because no other borrows are allowed when there’s a mutable borrow. Try adding a println! there to see what error message you get!

If you want to force the closure to take ownership of the values it uses in the environment even though the body of the closure doesn’t strictly need ownership, you can use the move keyword before the parameter list.

This technique is mostly useful when passing a closure to a new thread to move the data so that it’s owned by the new thread. We’ll discuss threads and why you would want to use them in detail in Chapter 16 when we talk about concurrency, but for now, let’s briefly explore spawning a new thread using a closure that needs the move keyword. Listing 13-6 shows Listing 13-4 modified to print the vector in a new thread rather than in the main thread.

Filename: src/main.rs

use std::thread;

fn main() {
    let list = vec![1, 2, 3];
    println!("Before defining closure: {:?}", list);

  1 thread::spawn(move || {
      2 println!("From thread: {:?}", list)
    }).join().unwrap();
}

Listing 13-6: Using move to force the closure for the thread to take ownership of list

We spawn a new thread, giving the thread a closure to run as an argument. The closure body prints out the list. In Listing 13-4, the closure only captured list using an immutable reference because that’s the least amount of access to list needed to print it. In this example, even though the closure body still only needs an immutable reference [2], we need to specify that list should be moved into the closure by putting the move keyword [1] at the beginning of the closure definition. The new thread might finish before the rest of the main thread finishes, or the main thread might finish first. If the main thread maintains ownership of list but ends before the new thread and drops list, the immutable reference in the thread would be invalid. Therefore, the compiler requires that list be moved into the closure given to the new thread so the reference will be valid. Try removing the move keyword or using list in the main thread after the closure is defined to see what compiler errors you get!

Moving Captured Values Out of Closures and the Fn Traits

Once a closure has captured a reference or captured ownership of a value from the environment where the closure is defined (thus affecting what, if anything, is moved into the closure), the code in the body of the closure defines what happens to the references or values when the closure is evaluated later (thus affecting what, if anything, is moved out of the closure).

A closure body can do any of the following: move a captured value out of the closure, mutate the captured value, neither move nor mutate the value, or capture nothing from the environment to begin with.

The way a closure captures and handles values from the environment affects which traits the closure implements, and traits are how functions and structs can specify what kinds of closures they can use. Closures will automatically implement one, two, or all three of these Fn traits, in an additive fashion, depending on how the closure’s body handles the values:

FnOnce applies to closures that can be called once. All closures implement at least this trait because all closures can be called. A closure that moves captured values out of its body will only implement FnOnce and none of the other Fn traits because it can only be called once.
FnMut applies to closures that don’t move captured values out of their body, but that might mutate the captured values. These closures can be called more than once.
Fn applies to closures that don’t move captured values out of their body and that don’t mutate captured values, as well as closures that capture nothing from their environment. These closures can be called more than once without mutating their environment, which is important in cases such as calling a closure multiple times concurrently.

Let’s look at the definition of the unwrap_or_else method on Option<T> that we used in Listing 13-1:

impl<T> Option<T> {
    pub fn unwrap_or_else<F>(self, f: F) -> T
    where
        F: FnOnce() -> T
    {
        match self {
            Some(x) => x,
            None => f(),
        }
    }
}

Recall that T is the generic type representing the type of the value in the Some variant of an Option. That type T is also the return type of the unwrap_or_else function: code that calls unwrap_or_else on an Option<String>, for example, will get a String.

Next, notice that the unwrap_or_else function has the additional generic type parameter F. The F type is the type of the parameter named f, which is the closure we provide when calling unwrap_or_else.

The trait bound specified on the generic type F is FnOnce() -> T, which means F must be able to be called once, take no arguments, and return a T. Using FnOnce in the trait bound expresses the constraint that unwrap_or_else is only going to call f one time, at most. In the body of unwrap_or_else, we can see that if the Option is Some, f won’t be called. If the Option is None, f will be called once. Because all closures implement FnOnce, unwrap_or_else accepts the largest variety of closures and is as flexible as it can be.

Note: Functions can implement all three of the Fn traits too. If what we want to do doesn’t require capturing a value from the environment, we can use the name of a function rather than a closure where we need something that implements one of the Fn traits. For example, on an Option<Vec<T>> value, we could call unwrap_or_else(Vec::new) to get a new, empty vector if the value is None.

Now let’s look at the standard library method sort_by_key, defined on slices, to see how that differs from unwrap_or_else and why sort_by_key uses FnMut instead of FnOnce for the trait bound. The closure gets one argument in the form of a reference to the current item in the slice being considered, and returns a value of type K that can be ordered. This function is useful when you want to sort a slice by a particular attribute of each item. In Listing 13-7, we have a list of Rectangle instances and we use sort_by_key to order them by their width attribute from low to high.

Filename: src/main.rs

#[derive(Debug)]
struct Rectangle {
    width: u32,
    height: u32,
}

fn main() {
    let mut list = [
        Rectangle { width: 10, height: 1 },
        Rectangle { width: 3, height: 5 },
        Rectangle { width: 7, height: 12 },
    ];

    list.sort_by_key(|r| r.width);
    println!("{:#?}", list);
}

Listing 13-7: Using sort_by_key to order rectangles by width

This code prints:

[
    Rectangle {
        width: 3,
        height: 5,
    },
    Rectangle {
        width: 7,
        height: 12,
    },
    Rectangle {
        width: 10,
        height: 1,
    },
]

The reason sort_by_key is defined to take an FnMut closure is that it calls the closure multiple times: once for each item in the slice. The closure |r| r.width doesn’t capture, mutate, or move anything out from its environment, so it meets the trait bound requirements.

In contrast, Listing 13-8 shows an example of a closure that implements just the FnOnce trait, because it moves a value out of the environment. The compiler won’t let us use this closure with sort_by_key.

Filename: src/main.rs

--snip--

fn main() {
    let mut list = [
        Rectangle { width: 10, height: 1 },
        Rectangle { width: 3, height: 5 },
        Rectangle { width: 7, height: 12 },
    ];

    let mut sort_operations = vec![];
    let value = String::from("by key called");

    list.sort_by_key(|r| {
        sort_operations.push(value);
        r.width
    });
    println!("{:#?}", list);
}

Listing 13-8: Attempting to use an FnOnce closure with sort_by_key

This is a contrived, convoluted way (that doesn’t work) to try and count the number of times sort_by_key gets called when sorting list. This code attempts to do this counting by pushing value—a String from the closure’s environment—into the sort_operations vector. The closure captures value and then moves value out of the closure by transferring ownership of value to the sort_operations vector. This closure can be called once; trying to call it a second time wouldn’t work because value would no longer be in the environment to be pushed into sort_operations again! Therefore, this closure only implements FnOnce. When we try to compile this code, we get this error that value can’t be moved out of the closure because the closure must implement FnMut:

error[E0507]: cannot move out of `value`, a captured variable in an `FnMut`
closure
  --> src/main.rs:18:30
   |
15 |       let value = String::from("by key called");
   |           ----- captured outer variable
16 |
17 |       list.sort_by_key(|r| {
   |  ______________________-
18 | |         sort_operations.push(value);
   | |                              ^^^^^ move occurs because `value` has
type `String`, which does not implement the `Copy` trait
19 | |         r.width
20 | |     });
   | |_____- captured by this `FnMut` closure

The error points to the line in the closure body that moves value out of the environment. To fix this, we need to change the closure body so that it doesn’t move values out of the environment. Keeping a counter in the environment and incrementing its value in the closure body is a more straightforward way to count the number of times sort_by_key is called. The closure in Listing 13-9 works with sort_by_key because it is only capturing a mutable reference to the num_sort_operations counter and can therefore be called more than once.

Filename: src/main.rs

--snip--

fn main() {
    --snip--

    let mut num_sort_operations = 0;
    list.sort_by_key(|r| {
        num_sort_operations += 1;
        r.width
    });
    println!(
        "{:#?}, sorted in {num_sort_operations} operations",
        list
    );
}

Listing 13-9: Using an FnMut closure with sort_by_key is allowed.

The Fn traits are important when defining or using functions or types that make use of closures. In the next section, we’ll discuss iterators. Many iterator methods take closure arguments, so keep these closure details in mind as we continue!

Processing a Series of Items with Iterators

The iterator pattern allows you to perform some task on a sequence of items in turn. An iterator is responsible for the logic of iterating over each item and determining when the sequence has finished. When you use iterators, you don’t have to reimplement that logic yourself.

In Rust, iterators are lazy, meaning they have no effect until you call methods that consume the iterator to use it up. For example, the code in Listing 13-10 creates an iterator over the items in the vector v1 by calling the iter method defined on Vec<T>. This code by itself doesn’t do anything useful.

let v1 = vec![1, 2, 3];

let v1_iter = v1.iter();

Listing 13-10: Creating an iterator

The iterator is stored in the v1_iter variable. Once we’ve created an iterator, we can use it in a variety of ways. In Listing 3-5, we iterated over an array using a for loop to execute some code on each of its items. Under the hood, this implicitly created and then consumed an iterator, but we glossed over how exactly that works until now.

In the example in Listing 13-11, we separate the creation of the iterator from the use of the iterator in the for loop. When the for loop is called using the iterator in v1_iter, each element in the iterator is used in one iteration of the loop, which prints out each value.

let v1 = vec![1, 2, 3];

let v1_iter = v1.iter();

for val in v1_iter {
    println!("Got: {val}");
}

Listing 13-11: Using an iterator in a for loop

In languages that don’t have iterators provided by their standard libraries, you would likely write this same functionality by starting a variable at index 0, using that variable to index into the vector to get a value, and incrementing the variable value in a loop until it reached the total number of items in the vector.

Iterators handle all of that logic for you, cutting down on repetitive code you could potentially mess up. Iterators give you more flexibility to use the same logic with many different kinds of sequences, not just data structures you can index into, like vectors. Let’s examine how iterators do that.

The Iterator Trait and the next Method

All iterators implement a trait named Iterator that is defined in the standard library. The definition of the trait looks like this:

pub trait Iterator {
    type Item;

    fn next(&mut self) -> Option<Self::Item>;

    // methods with default implementations elided
}

Notice that this definition uses some new syntax: type Item and Self::Item, which are defining an associated type with this trait. We’ll talk about associated types in depth in Chapter 19. For now, all you need to know is that this code says implementing the Iterator trait requires that you also define an Item type, and this Item type is used in the return type of the next method. In other words, the Item type will be the type returned from the iterator.

The Iterator trait only requires implementors to define one method: the next method, which returns one item of the iterator at a time, wrapped in Some, and, when iteration is over, returns None.

We can call the next method on iterators directly; Listing 13-12 demonstrates what values are returned from repeated calls to next on the iterator created from the vector.

Filename: src/lib.rs

#[test]
fn iterator_demonstration() {
    let v1 = vec![1, 2, 3];

    let mut v1_iter = v1.iter();

    assert_eq!(v1_iter.next(), Some(&1));
    assert_eq!(v1_iter.next(), Some(&2));
    assert_eq!(v1_iter.next(), Some(&3));
    assert_eq!(v1_iter.next(), None);
}

Listing 13-12: Calling the next method on an iterator

Note that we needed to make v1_iter mutable: calling the next method on an iterator changes internal state that the iterator uses to keep track of where it is in the sequence. In other words, this code consumes, or uses up, the iterator. Each call to next eats up an item from the iterator. We didn’t need to make v1_iter mutable when we used a for loop because the loop took ownership of v1_iter and made it mutable behind the scenes.

Also note that the values we get from the calls to next are immutable references to the values in the vector. The iter method produces an iterator over immutable references. If we want to create an iterator that takes ownership of v1 and returns owned values, we can call into_iter instead of iter. Similarly, if we want to iterate over mutable references, we can call iter_mut instead of iter.

Methods That Consume the Iterator

The Iterator trait has a number of different methods with default implementations provided by the standard library; you can find out about these methods by looking in the standard library API documentation for the Iterator trait. Some of these methods call the next method in their definition, which is why you’re required to implement the next method when implementing the Iterator trait.

Methods that call next are called consuming adapters because calling them uses up the iterator. One example is the sum method, which takes ownership of the iterator and iterates through the items by repeatedly calling next, thus consuming the iterator. As it iterates through, it adds each item to a running total and returns the total when iteration is complete. Listing 13-13 has a test illustrating a use of the sum method.

Filename: src/lib.rs

#[test]
fn iterator_sum() {
    let v1 = vec![1, 2, 3];

    let v1_iter = v1.iter();

    let total: i32 = v1_iter.sum();

    assert_eq!(total, 6);
}

Listing 13-13: Calling the sum method to get the total of all items in the iterator

We aren’t allowed to use v1_iter after the call to sum because sum takes ownership of the iterator we call it on.

Methods That Produce Other Iterators

Iterator adapters are methods defined on the Iterator trait that don’t consume the iterator. Instead, they produce different iterators by changing some aspect of the original iterator.

Listing 13-14 shows an example of calling the iterator adapter method map, which takes a closure to call on each item as the items are iterated through. The map method returns a new iterator that produces the modified items. The closure here creates a new iterator in which each item from the vector will be incremented by 1.

Filename: src/main.rs

let v1: Vec<i32> = vec![1, 2, 3];

v1.iter().map(|x| x + 1);

Listing 13-14: Calling the iterator adapter map to create a new iterator

However, this code produces a warning:

warning: unused `Map` that must be used
 --> src/main.rs:4:5
  |
4 |     v1.iter().map(|x| x + 1);
  |     ^^^^^^^^^^^^^^^^^^^^^^^^^
  |
  = note: `#[warn(unused_must_use)]` on by default
  = note: iterators are lazy and do nothing unless consumed

The code in Listing 13-14 doesn’t do anything; the closure we’ve specified never gets called. The warning reminds us why: iterator adapters are lazy, and we need to consume the iterator here.

To fix this warning and consume the iterator, we’ll use the collect method, which we used with env::args in Listing 12-1. This method consumes the iterator and collects the resultant values into a collection data type.

In Listing 13-15, we collect into a vector the results of iterating over the iterator that’s returned from the call to map. This vector will end up containing each item from the original vector, incremented by 1.

Filename: src/main.rs

let v1: Vec<i32> = vec![1, 2, 3];

let v2: Vec<_> = v1.iter().map(|x| x + 1).collect();

assert_eq!(v2, vec![2, 3, 4]);

Listing 13-15: Calling the map method to create a new iterator, and then calling the collect method to consume the new iterator and create a vector

Because map takes a closure, we can specify any operation we want to perform on each item. This is a great example of how closures let you customize some behavior while reusing the iteration behavior that the Iterator trait provides.

You can chain multiple calls to iterator adapters to perform complex actions in a readable way. But because all iterators are lazy, you have to call one of the consuming adapter methods to get results from calls to iterator adapters.

Using Closures That Capture Their Environment

Many iterator adapters take closures as arguments, and commonly the closures we’ll specify as arguments to iterator adapters will be closures that capture their environment.

For this example, we’ll use the filter method that takes a closure. The closure gets an item from the iterator and returns a bool. If the closure returns true, the value will be included in the iteration produced by filter. If the closure returns false, the value won’t be included.

In Listing 13-16, we use filter with a closure that captures the shoe_size variable from its environment to iterate over a collection of Shoe struct instances. It will return only shoes that are the specified size.

Filename: src/lib.rs

#[derive(PartialEq, Debug)]
struct Shoe {
    size: u32,
    style: String,
}

fn shoes_in_size(shoes: Vec<Shoe>, shoe_size: u32) -> Vec<Shoe> {
    shoes.into_iter().filter(|s| s.size == shoe_size).collect()
}

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

    #[test]
    fn filters_by_size() {
        let shoes = vec![
            Shoe {
                size: 10,
                style: String::from("sneaker"),
            },
            Shoe {
                size: 13,
                style: String::from("sandal"),
            },
            Shoe {
                size: 10,
                style: String::from("boot"),
            },
        ];

        let in_my_size = shoes_in_size(shoes, 10);

        assert_eq!(
            in_my_size,
            vec![
                Shoe {
                    size: 10,
                    style: String::from("sneaker")
                },
                Shoe {
                    size: 10,
                    style: String::from("boot")
                },
            ]
        );
    }
}

Listing 13-16: Using the filter method with a closure that captures shoe_size

The shoes_in_size function takes ownership of a vector of shoes and a shoe size as parameters. It returns a vector containing only shoes of the specified size.

In the body of shoes_in_size, we call into_iter to create an iterator that takes ownership of the vector. Then we call filter to adapt that iterator into a new iterator that only contains elements for which the closure returns true.

The closure captures the shoe_size parameter from the environment and compares the value with each shoe’s size, keeping only shoes of the size specified. Finally, calling collect gathers the values returned by the adapted iterator into a vector that’s returned by the function.

The test shows that when we call shoes_in_size, we get back only shoes that have the same size as the value we specified.

Improving Our I/O Project

With this new knowledge about iterators, we can improve the I/O project in Chapter 12 by using iterators to make places in the code clearer and more concise. Let’s look at how iterators can improve our implementation of the Config::build function and the search function.

Removing a clone Using an Iterator

In Listing 12-6, we added code that took a slice of String values and created an instance of the Config struct by indexing into the slice and cloning the values, allowing the Config struct to own those values. In Listing 13-17, we’ve reproduced the implementation of the Config::build function as it was in Listing 12-23.

Filename: src/lib.rs

impl Config {
    pub fn build(
        args: &[String]
    ) -> Result<Config, &'static str> {
        if args.len() < 3 {
            return Err("not enough arguments");
        }

        let query = args[1].clone();
        let file_path = args[2].clone();

        let ignore_case = env::var("IGNORE_CASE").is_ok();

        Ok(Config {
            query,
            file_path,
            ignore_case,
        })
    }
}

Listing 13-17: Reproduction of the Config::build function from Listing 12-23

At the time, we said not to worry about the inefficient clone calls because we would remove them in the future. Well, that time is now!

We needed clone here because we have a slice with String elements in the parameter args, but the build function doesn’t own args. To return ownership of a Config instance, we had to clone the values from the query and filename fields of Config so the Config instance can own its values.

With our new knowledge about iterators, we can change the build function to take ownership of an iterator as its argument instead of borrowing a slice. We’ll use the iterator functionality instead of the code that checks the length of the slice and indexes into specific locations. This will clarify what the Config::build function is doing because the iterator will access the values.

Once Config::build takes ownership of the iterator and stops using indexing operations that borrow, we can move the String values from the iterator into Config rather than calling clone and making a new allocation.

Using the Returned Iterator Directly

Open your I/O project’s src/main.rs file, which should look like this:

Filename: src/main.rs

fn main() {
    let args: Vec<String> = env::args().collect();

    let config = Config::build(&args).unwrap_or_else(|err| {
        eprintln!("Problem parsing arguments: {err}");
        process::exit(1);
    });

    --snip--
}

We’ll first change the start of the main function that we had in Listing 12-24 to the code in Listing 13-18, which this time uses an iterator. This won’t compile until we update Config::build as well.

Filename: src/main.rs

fn main() {
    let config =
        Config::build(env::args()).unwrap_or_else(|err| {
            eprintln!("Problem parsing arguments: {err}");
            process::exit(1);
        });

    --snip--
}

Listing 13-18: Passing the return value of env::args to Config::build

The env::args function returns an iterator! Rather than collecting the iterator values into a vector and then passing a slice to Config::build, now we’re passing ownership of the iterator returned from env::args to Config::build directly.

Next, we need to update the definition of Config::build. In your I/O project’s src/lib.rs file, let’s change the signature of Config::build to look like Listing 13-19. This still won’t compile, because we need to update the function body.

Filename: src/lib.rs

impl Config {
    pub fn build(
        mut args: impl Iterator<Item = String>,
    ) -> Result<Config, &'static str> {
        --snip--

Listing 13-19: Updating the signature of Config::build to expect an iterator

The standard library documentation for the env::args function shows that the type of the iterator it returns is std::env::Args, and that type implements the Iterator trait and returns String values.

We’ve updated the signature of the Config::build function so the parameter args has a generic type with the trait bounds impl Iterator<Item = String> instead of &[String]. This usage of the impl Trait syntax we discussed in “Traits as Parameters” on page XX means that args can be any type that implements the Iterator type and returns String items.

Because we’re taking ownership of args and we’ll be mutating args by iterating over it, we can add the mut keyword into the specification of the args parameter to make it mutable.

Using Iterator Trait Methods Instead of Indexing

Next, we’ll fix the body of Config::build. Because args implements the Iterator trait, we know we can call the next method on it! Listing 13-20 updates the code from Listing 12-23 to use the next method.

Filename: src/lib.rs

impl Config {
    pub fn build(
        mut args: impl Iterator<Item = String>,
    ) -> Result<Config, &'static str> {
        args.next();

        let query = match args.next() {
            Some(arg) => arg,
            None => return Err("Didn't get a query string"),
        };

        let file_path = match args.next() {
            Some(arg) => arg,
            None => return Err("Didn't get a file path"),
        };

        let ignore_case = env::var("IGNORE_CASE").is_ok();

        Ok(Config {
            query,
            file_path,
            ignore_case,
        })
    }
}

Listing 13-20: Changing the body of Config::build to use iterator methods

Remember that the first value in the return value of env::args is the name of the program. We want to ignore that and get to the next value, so first we call next and do nothing with the return value. Then we call next to get the value we want to put in the query field of Config. If next returns Some, we use a match to extract the value. If it returns None, it means not enough arguments were given and we return early with an Err value. We do the same thing for the filename value.

Making Code Clearer with Iterator Adapters

We can also take advantage of iterators in the search function in our I/O project, which is reproduced here in Listing 13-21 as it was in Listing 12-19.

Filename: src/lib.rs

pub fn search<'a>(
    query: &str,
    contents: &'a str,
) -> Vec<&'a str> {
    let mut results = Vec::new();

    for line in contents.lines() {
        if line.contains(query) {
            results.push(line);
        }
    }

    results
}

Listing 13-21: The implementation of the search function from Listing 12-19

We can write this code in a more concise way using iterator adapter methods. Doing so also lets us avoid having a mutable intermediate results vector. The functional programming style prefers to minimize the amount of mutable state to make code clearer. Removing the mutable state might enable a future enhancement to make searching happen in parallel because we wouldn’t have to manage concurrent access to the results vector. Listing 13-22 shows this change.

Filename: src/lib.rs

pub fn search<'a>(
    query: &str,
    contents: &'a str,
) -> Vec<&'a str> {
    contents
        .lines()
        .filter(|line| line.contains(query))
        .collect()
}

Listing 13-22: Using iterator adapter methods in the implementation of the search function

Recall that the purpose of the search function is to return all lines in contents that contain the query. Similar to the filter example in Listing 13-16, this code uses the filter adapter to keep only the lines for which line.contains(query) returns true. We then collect the matching lines into another vector with collect. Much simpler! Feel free to make the same change to use iterator methods in the search_case_insensitive function as well.

Choosing Between Loops and Iterators

The next logical question is which style you should choose in your own code and why: the original implementation in Listing 13-21 or the version using iterators in Listing 13-22. Most Rust programmers prefer to use the iterator style. It’s a bit tougher to get the hang of at first, but once you get a feel for the various iterator adapters and what they do, iterators can be easier to understand. Instead of fiddling with the various bits of looping and building new vectors, the code focuses on the high-level objective of the loop. This abstracts away some of the commonplace code so it’s easier to see the concepts that are unique to this code, such as the filtering condition each element in the iterator must pass.

But are the two implementations truly equivalent? The intuitive assumption might be that the lower-level loop will be faster. Let’s talk about performance.

Comparing Performance: Loops vs. Iterators

To determine whether to use loops or iterators, you need to know which implementation is faster: the version of the search function with an explicit for loop or the version with iterators.

We ran a benchmark by loading the entire contents of The Adventures of Sherlock Holmes by Sir Arthur Conan Doyle into a String and looking for the word the in the contents. Here are the results of the benchmark on the version of search using the for loop and the version using iterators:

test bench_search_for  ... bench:  19,620,300 ns/iter (+/- 915,700)
test bench_search_iter ... bench:  19,234,900 ns/iter (+/- 657,200)

The iterator version was slightly faster! We won’t explain the benchmark code here because the point is not to prove that the two versions are equivalent but to get a general sense of how these two implementations compare performance-wise.

For a more comprehensive benchmark, you should check using various texts of various sizes as the contents, different words and words of different lengths as the query, and all kinds of other variations. The point is this: iterators, although a high-level abstraction, get compiled down to roughly the same code as if you’d written the lower-level code yourself. Iterators are one of Rust’s zero-cost abstractions, by which we mean that using the abstraction imposes no additional runtime overhead. This is analogous to how Bjarne Stroustrup, the original designer and implementor of C++, defines zero-overhead in “Foundations of C++” (2012):

In general, C++ implementations obey the zero-overhead principle: What you don’t use, you don’t pay for. And further: What you do use, you couldn’t hand code any better.As another example, the following code is taken from an audio decoder. The decoding algorithm uses the linear prediction mathematical operation to estimate future values based on a linear function of the previous samples. This code uses an iterator chain to do some math on three variables in scope: a buffer slice of data, an array of 12 coefficients, and an amount by which to shift data in qlp_shift. We’ve declared the variables within this example but not given them any values; although this code doesn’t have much meaning outside of its context, it’s still a concise, real-world example of how Rust translates high-level ideas to low-level code.

let buffer: &mut [i32];
let coefficients: [i64; 12];
let qlp_shift: i16;

for i in 12..buffer.len() {
    let prediction = coefficients.iter()
                                 .zip(&buffer[i - 12..i])
                                 .map(|(&c, &s)| c * s as i64)
                                 .sum::<i64>() >> qlp_shift;
    let delta = buffer[i];
    buffer[i] = prediction as i32 + delta;
}

To calculate the value of prediction, this code iterates through each of the 12 values in coefficients and uses the zip method to pair the coefficient values with the previous 12 values in buffer. Then, for each pair, it multiplies the values together, sums all the results, and shifts the bits in the sum qlp_shift bits to the right.

Calculations in applications like audio decoders often prioritize performance most highly. Here, we’re creating an iterator, using two adapters, and then consuming the value. What assembly code would this Rust code compile to? Well, as of this writing, it compiles down to the same assembly you’d write by hand. There’s no loop at all corresponding to the iteration over the values in coefficients: Rust knows that there are 12 iterations, so it “unrolls” the loop. Unrolling is an optimization that removes the overhead of the loop controlling code and instead generates repetitive code for each iteration of the loop.

All of the coefficients get stored in registers, which means accessing the values is very fast. There are no bounds checks on the array access at runtime. All of these optimizations that Rust is able to apply make the resultant code extremely efficient. Now that you know this, you can use iterators and closures without fear! They make code seem like it’s higher level but don’t impose a runtime performance penalty for doing so.

Summary

Closures and iterators are Rust features inspired by functional programming language ideas. They contribute to Rust’s capability to clearly express high-level ideas at low-level performance. The implementations of closures and iterators are such that runtime performance is not affected. This is part of Rust’s goal to strive to provide zero-cost abstractions.

Now that we’ve improved the expressiveness of our I/O project, let’s look at some more features of cargo that will help us share the project with the world.

48 KiB Raw Permalink Blame History Unescape Escape

Functional Language Features: Iterators and Closures

Closures: Anonymous Functions That Capture Their Environment

Capturing the Environment with Closures

Closure Type Inference and Annotation

Capturing References or Moving Ownership

Moving Captured Values Out of Closures and the Fn Traits

Processing a Series of Items with Iterators

The Iterator Trait and the next Method

Methods That Consume the Iterator

Methods That Produce Other Iterators

Using Closures That Capture Their Environment

Improving Our I/O Project

Removing a clone Using an Iterator

Using the Returned Iterator Directly

Using Iterator Trait Methods Instead of Indexing

Making Code Clearer with Iterator Adapters

Choosing Between Loops and Iterators

Comparing Performance: Loops vs. Iterators

Summary

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