Squaring

As you could see if you did the previous set of exercises, we have already covered enough Rust to start doing some actually useful computations.

There is still one important building block that we are missing to make the most of Rust iterator adapters, however, and that is anonymous functions, also known as lambda functions or lexical closures in programming language theory circles.

In this chapter, we will introduce this language feature, and show how it can be used, along with Rust traits and the higher-order function pattern, to compute the square of every element of an array using fully idiomatic Rust code.

Meet the lambda

In Rust, you can define a function anywhere, including inside of another function¹. Parameter types are specified using the same syntax as variable type ascription, and return types can be specified at the end after a -> arrow sign:

#![allow(unused)]
fn main() {
fn outsourcing(x: u8, y: u8) {
    fn sum(a: u8, b: u8) -> u8 {
        // Unlike in C/++, return statements are unnecessary in simple cases.
        // Just write what you want to return as a trailing expression.
        a + b
    }
    println!("{}", sum(x, y));
}
}

However, Rust is not Python, and inner function definitions cannot capture variables from the scope of outer function definitions. In other words, the following code does not compile:

#![allow(unused)]
fn main() {
fn outsourcing(x: u8, y: u8) {
    fn sum() -> u8 {
        // ERROR: There are no "x" and "y" variables in this scope.
        x + y
    }
    println!("{}", sum());
}
}

Rust provides a slightly different abstraction for this, namely anonymous functions aka lambdas aka closures. In addition to being able to capture surrounding variables, these also come with much lighter-weight syntax for simple use cases…

#![allow(unused)]
fn main() {
fn outsourcing(x: u8, y: u8) {
    let sum = || x + y;  // Notice that the "-> u8" return type is inferred.
                         // If you have parameters, their type is also inferred.
    println!("{}", sum());
}
}

…while still supporting the same level of type annotation sophistication as full function declarations, should you need it to guide type inference or improve clarity:

#![allow(unused)]
fn main() {
fn outsourcing(x: u8, y: u8) {
    let sum = |a: u8, b: u8| -> u8 { a + b };
    println!("{}", sum(x, y));
}
}

The main use case for lambda functions, however, is interaction with higher-order functions: functions that take other functions as inputs and/or return other functions as output.

A glimpse of Rust traits

We have touched upon the notion of traits several time before in this course, without taking the time to really explain it. That’s because Rust traits are a complex topic, which we do not have the luxury of covering in depth in this short course.

But now that we are getting to higher-order functions, we are going to need to interact a little bit more with Rust traits, so this is a good time to expand a bit more on what Rust traits do.

Traits are the cornerstone of Rust’s genericity and polymorphism system. They let you define a common protocol for interacting with several different types in a homogeneous way. If you are familiar with C++, traits in Rust can be used to replace any of these C++ features:

• Virtual methods and overrides
• Templates and C++20 concepts, with first-class support for the “type trait” pattern
• Function and method overloading
• Implicit conversions

The main advantage of having one single complex general-purpose language feature like this, instead of many simpler narrow-purpose features, is that you do not need to deal with interactions between the narrow-purpose features. As C++ practicioners know, these can be result in quite surprising behavior and getting their combination right is a very subtle art.

Another practical advantage is that you will less often hit a complexity wall, where you hit the limits of the particular language feature that you were using and must rewrite large chunks code in terms of a completely different language feature.

Finally, Rust traits let you do things that are impossible in C++. Such as adding methods to third-party types, or verifying that generic code follows its intended API contract.

Higher order functions

One of Rust’s most important traits is the Fn trait, which is implemented for types that can be called like a function. It also has a few cousins that we will cover later on.

Thanks to special treatment by the compiler³, the Fn trait is actually a family of traits that can be written like a function signature, without parameter names. So for example, an object of a type that implements the Fn(i16, f32) -> usize trait can be called like a function, with two parameters of type i16 and f32, and the call will return a result of type usize.

You can write a generic function that accepts any object of such a type like this…

#![allow(unused)]
fn main() {
fn outsourcing(op: impl Fn(i16, f32) -> usize) {
    println!("The result is {}", op(42, 4.2));
}
}

…and it will accept any matching callable object, including both regular functions, and closures:

#![allow(unused)]
fn main() {
fn outsourcing(op: impl Fn(i16, f32) -> usize) {
    println!("The result is {}", op(42, 6.66));
}

// Example with a regular function
fn my_op(x: i16, y: f32) -> usize {
    (x as f32 + 1.23 * y) as usize
}
outsourcing(my_op);

// Example with a closure
outsourcing(|x, y| {
    println!("x may be {x}, y may be {y}, but there is only one answer");
    42
});
}

As you can see, closures shine in this role by keeping the syntax lean and the code more focused on the task at hand. Their ability to capture environment can also be very powerful in this situation, as we will see in later chapters.

You can also use the impl Trait syntax as the return type of a function, in order to state that you are returning an object of a type that implements a certain trait, without specifying what the trait is.

This is especially useful when working with closures, because the type of a closure object is a compiler-internal secret that cannot be named by the programmer:

#![allow(unused)]
fn main() {
/// Returns a function object with the signature that we have seen so far
fn make_op() -> impl Fn(i16, f32) -> usize {
    |x, y| (x as f32 + 1.23 * y) as usize
}
}

By combining these two features, Rust programmers can very easily implement any higher-order function that takes a function as a parameter or returns a function as a result. And because the code of these higher-order functions is specialized for the specific function type that you’re dealing with at compile time, runtime performance can be much better than when using dynamically dispatched higher-order function abstractions in other languages, like std::function in C++⁴.

Squaring numbers at last

The Iterator trait provides a number of methods that are actually higher-order functions. The simpler of them is the map method, which consumes the input iterator, takes a user-provided function, and produces an output iterator whose elements are the result of applying the user-provided function to each element of the input iterator:

#![allow(unused)]
fn main() {
let numbers = [1.2f32, 3.4, 5.6];
let squares = numbers.into_iter()
                     .map(|x| x.powi(2))
                     .collect::<Vec<_>>();
println!("{numbers:?} squared is {squares:?}");
}

And thanks to good language design and heroic optimization work by the Rust compiler team, the result will be just as fast as hand-optimized assembly for all but the smallest input sizes⁵.

Exercise

Now, go to your code editor, open the examples/04-square-one.rs source file, and address the TODOs in it. The code should compile and runs successfully at the end.

To check this, you may use the following command in your development environment’s terminal:

cargo run --example 04-square-one