Clean code examples: Servo
Open-source Rust codebases (series)
The Rust programming language powers Servo (https://servo.org/). Rust emphasizes three key points: safety, speed, and concurrency. These qualities make Rust a perfect match for Servo, as a browser engine requires robust performance while handling multiple tasks safely and efficiently. Rust achieves safety by preventing memory errors such as null pointer dereferencing and buffer overflows at compile time, without sacrificing performance.
We'll now take a closer look at the Servo Rust codebase (https://github.com/servo/servo) to see how it exemplifies clean, idiomatic Rust code. The intent is to assess just how elegantly and effectively Servo uses Rust's features, with concrete examples to guide our exploration.
To understand the integrity of Servo's codebase, consider this snippet as an example:
In this function `run`, Rust's error handling is on full display. Rather than unwrapping potential errors, the question mark (`?`) operator is used. This operator either yields the value inside the `Ok` variant of a `Result`, or it returns from the function early with the `Err` variant. By using the `?` operator, the code adheres to Rust's conventions for error handling, promoting clarity and robust error management.
The `Box<dyn std::error::Error>` return type is another example of idiomatic Rust. It allows the function to return any error that implements the `std::error::Error` trait, giving the function flexibility in what kinds of errors it can handle. This is made possible by Rust's trait objects, which enable dynamic dispatch at runtime.
Next, consider Rust's safety in the context of concurrency. Servo uses Rust's ownership and borrowing rules to manage access to data in a multitasked environment.
For instance, Rust's type system prevents data races—a type of concurrency error—by ensuring that mutable access to a data structure is exclusive. Here's how Rust's borrow checker works to guarantee safety in concurrent code:
This example illustrates Rust's strict borrowing rules that prevent concurrent mutable access, thanks to the compile-time checks of the borrow checker.
In handling errors, Servo reaps the benefits of Rust's `Result` and `Option` types. These constructs ensure that error handling is integral to the flow of the codebase and is not an afterthought. Below is a simplified example of how Servo might use these to handle potential errors gracefully:
fn get_page(url: &str) -> Result<Page, Box<dyn std::error::Error>> {
let response = fetch(url)?;
if response.status().is_success() {
Ok(parse_page(&response))
} else {
Err(Box::new(PageError::HttpError(response.status())))
}
}
In the above function, fetching a web page could fail, but the `?` operator succinctly propagates the error upwards, allowing the caller of `get_page` to deal with it. This clarity in error propagation is akin to a startup iteratively addressing obstacles on the path to creating something groundbreaking.
The power of Rust's documentation tools is not lost in the Servo codebase. By using `rustdoc`, the project maintains up-to-date and accessible documentation. Consider the following illustration:
/// Parses the HTML content of a web page.
///
/// # Arguments
///
/// * `response` - The HTTP response with HTML content.
///
/// # Returns
///
/// This function returns a `Page` on success.
///
/// # Errors
///
/// Will return `PageError::ParseError` if the page content could not be parsed.
fn parse_page(response: &HttpResponse) -> Result<Page, PageError> {
// ...
}
This doc comment explains the purpose, arguments, return value, and possible errors of the `parse_page` function. It's a hallmark of well-documented code that enables developers to understand functionality at a glance, fostering an environment where innovation thrives, much like the rapid exchange of ideas in a successful startup.
When we dive into the Servo engine, we find ample evidence that its developers aren't just using Rust; they're using it skillfully, in the manner that Rustaceans—avid Rust programmers—naturally do. This involves very explicit, focused manipulation of data and control flow that might be likened to the precision needed to ace a complex problem within a startup's critical path.