Navigating Programming Paradigms in Rust

Rust is a multi-paradigm programming language, accommodating imperative, object-oriented, and functional programming styles. The choice of style often depends on a developer’s background and the specific problem they’re addressing.

With Rust attracting developers from varied backgrounds such as C++, Java, Python, and Haskell, it has shaped its own unique set of styles and idioms. This diversity is a strength, but it also leads to uncertainty about which style to use in various scenarios.

As the Rust Book explains:

Many competing definitions describe what Object-Oriented Programming is, and by some of these definitions Rust is object-oriented.

However, it also states:

Rust’s design has taken inspiration from many existing languages and techniques, and one significant influence is functional programming.

These statements are not contradictory, but they do leave a lot of room for interpretation and personal preference.

Guiding Principles For Choosing The Right Paradigm

Rust is certainly influenced by object-oriented programming concepts. One factor that sets it apart from other object-oriented languages is its composition-based nature, as opposed to being inheritance-based. Its trait system is a core component of this object-oriented design, a concept absent in languages like C++ and Java.

Similarly, Rust’s design encourages patterns that align closely with functional programming principles: immutability, iterator patterns, algebraic data types, and pattern matching.

Just as Rust adopts certain object-oriented principles without being a purely object-oriented language, it similarly embraces functional programming concepts without being a purely functional language. It allows side effects everywhere and does not strictly enforce referential transparency — the ability to replace an expression with its value without changing the program’s behavior.

In conclusion, providing some guidance on using different paradigms in Rust might be helpful, especially for developers transitioning from other languages. This article explores my personal decision-making process when choosing between different paradigms in Rust, a process that has by now become almost second nature.

A Small Example

There is nothing wrong with simple for loops in Rust.

let mut sum = 0;
for i in 0..10 {
    sum += i;
}

But even in such a short example, we can see a discrepancy between the problem we’re trying to solve and the code we’re writing: The intermediate values of sum are irrelevant! We only care about the final result.

Compare that to a more functional version:

let sum: u32 = (0..10).sum();

In small examples like this, it might not matter much, but when we start working with nested loops, we see that in the imperative approach, more lines are dedicated to bookkeeping than to the actual problem. This causes the code’s accidental complexity (the unnecessary complexity we introduce ourselves) to increase. Complexity, no matter how small, costs attention.

A Slightly Bigger Example: Nested Loops

Let’s consider a slightly bigger example. Imagine we had a list of programming languages, their supported paradigms, and the number of production users for each language. The task is to find the top five languages that support functional programming and have the most users.

// All data is made up for the sake of this example! We love you, Haskell.
let languages = vec![
    Language::new("Rust", vec![Paradigm::Functional, Paradigm::ObjectOriented], 100_000),
    Language::new("Go", vec![Paradigm::ObjectOriented], 200_000),
    Language::new("Haskell", vec![Paradigm::Functional], 5_000),
    Language::new("Java", vec![Paradigm::ObjectOriented], 1_000_000),
    Language::new("C++", vec![Paradigm::ObjectOriented], 1_000_000),
    Language::new("Python", vec![Paradigm::ObjectOriented, Paradigm::Functional], 1_000_000),
];

Here is a painfully explicit solution using nested for loops:

// Filter languages to keep only the functional ones
let mut functional_languages = vec![];
for language in languages {
    if language.paradigms.contains(&Paradigm::Functional) {
        functional_languages.push(language);
    }
}

// Sort the functional languages by the number of users in descending order
for i in 1..functional_languages.len() {
    let mut j = i;
    while j > 0 && functional_languages[j].users > functional_languages[j - 1].users {
        functional_languages.swap(j, j - 1);
        j -= 1;
    }
}

// Keep only the top 5 languages
while functional_languages.len() > 5 {
    functional_languages.pop();
}

(Rust Playground)

This is a very verbose, imperative solution. We mutate the vector in-place and destroy the intermediate results in the process. While it’s not incorrect, I would argue that it’s not the most idiomatic Rust code either.

In practice, you would probably reach for a few more helper methods from the standard library:

let mut top_languages = vec![];
for language in languages {
    if language.paradigms.contains(&Paradigm::Functional) {
        top_languages.push(language);
    }
}

// Sort our languages in descending order of popularity.
// This line is already somewhat functional in nature.
top_languages.sort_by_key(|lang| std::cmp::Reverse(lang.users));

top_languages.truncate(5);

Since we’re consuming languages anyway, we might as well be a little more concise when filtering:

let mut top_languages = languages;
top_languages.retain(|language| language.paradigms.contains(&Paradigm::Functional));

We still use a mutable variable, but now the code looks more succinct. retain is a higher-order method that takes a closure as an argument, so the code naturally became a little more functional. Let’s continue down this path and see where it takes us next.

let mut top_languages = languages.clone();
top_languages.sort();

let top_languages: Vec<Language> = top_languages
    .into_iter()
    // Only keep functional languages
    .filter(|language| language.paradigms.contains(&Paradigm::Functional))
    // Keep only the top 5 languages
    .take(5)
    // Collect the results into a vector
    .collect();

Or, if external crates are an option, we could use sorted_by_key from itertools to chain all intermediate operations:

let top_languages: Vec<Language> = languages
    .iter()
    // Only keep functional languages
    .filter(|language| language.paradigms.contains(&Paradigm::Functional))
    // Sort our languages in descending order of popularity.
    .sorted_by_key(|lang| Reverse(lang.users))
    // Keep only the top 5 languages
    .take(5)
    // Collect the results into a new vector
    .collect();

(Rust Playground)

Sorting the entire list (even if it’s filtered) to extract just the top 5 elements seems somewhat inefficient. This highlights a limitation in Rust compared to C++, which offers a partial_sort function in its standard library. While Rust doesn’t have an equivalent in std, there are third-party crates. Alternatively, a BinaryHeap can be used.

To me, this solution is easier to reason about. The operations are neatly aligned below each other, and the code reads like a description of what we’re trying to achieve. I do admit, however, that it takes some getting used to, especially if you’re not familiar with functional programming patterns.

One could say that I hand-picked a problem that is well-suited for functional programming, and that is certainly the case. The truth is, that this way of method chaining just feels natural after a while — especially for ad-hoc transformations on immutable data structures.

There are a few reasons for this:

• Readability: The steps are easy to follow.
• Library Support: The Rust standard library and external crates provide many helpful combinators for iterators, which play nicely with immutable data structures.
• Efficiency: Under the hood, methods like map and filter create new iterators that operate on the previous iterator and do not incur any allocations. The actual computations (like adding 1 or filtering even numbers) are only executed when the final iterator is consumed, in this case by the collect method. The collect method makes a single allocation to store the results in a new vector. Our higher-level abstractions incur no runtime overhead.
• Parallelism: The functional approach lends itself to parallel computation. Each chain of operations is independent of the others, allowing them to be executed simultaneously on modern hardware.

The result is clean, readable, and efficient code, which is why you’ll see this pattern a lot.

No matter what language you work in, programming in a functional style provides benefits. You should do it whenever it is convenient, and you should think hard about the decision when it isn’t convenient. — John Carmack

Carmack talks about convenience here. What is the tipping point where functional programming becomes inconvenient? Let’s explore that with a more realistic example.

Real-World Example: Filtering a List of Files

Here is a little Rust exercise: how would you list all XML files in a directory? Before you continue, feel free to try this yourself. See which style you would naturally lean towards. Why not try different approaches and see which one you prefer?

Imperative Style

Here is my imperative solution:

fn xml_files(p: &Path) -> Result<Vec<PathBuf>> {
    let mut files = Vec::new();
    for f in fs::read_dir(p)? {
        // This line is necessary, because the file could have
        // been deleted since the call to `read_dir`.
        let f = f?;
        if f.path().extension() == Some(OsStr::new("xml")) {
            files.push(f.path());
        }
    }
    Ok(files)
}

(Rust Playground)

Not great, not terrible.

We have to do some bookkeeping, and there are some minor paper cuts like let f = f?; and the bit about OsStr::to_str, but overall it’s fine. The paper cuts are due to the inherent complexity of the problem: dealing with the possibility of errors and the fact that the file extension might not be valid UTF-8 on all platforms.

As the documentation for OsStr explains:

• On Unix systems, strings are often arbitrary sequences of non-zero bytes, in many cases interpreted as UTF-8.
• On Windows, strings are often arbitrary sequences of non-zero 16-bit values, interpreted as UTF-16 when it is valid to do so.

The astute reader might have noticed that we don’t check if the path is actually a file before we check the extension. This is done in the interest of brevity.

Functional Style

Let’s see how we can solve this problem in a more functional style:

fn xml_files(p: &Path) -> Result<Vec<PathBuf>> {
    let entries = fs::read_dir(p)?
        .filter_map(Result::ok)
        .map(|entry| entry.path())
        .filter(|path| path.extension() == Some(OsStr::new("xml")))
        .collect();

    Ok(entries)
}

(Rust Playground)

This implementation is arguably more streamlined. It maps directory entries to paths, filters out non-XML files, and collects the results, all without needing mutable variables or conditional branching.

That said, the solution also has its drawbacks. Most importantly, it is not equivalent to the imperative version. That is because filter_map(Result::ok) filters out all errors.

Whether we want to ignore errors depends on the use case; it is a tradeoff between correctness and ergonomics. In production code, we should at least log all errors, though. We can use inspect to do that:

fn xml_files(p: &Path) -> Result<Vec<PathBuf>> {
    let entries = fs::read_dir(p)?
        // Logs each element of the iterator to stderr for debugging, then passes the value on.
        .inspect(|entry| {
            if let Err(e) = entry {
                eprintln!("Error: {}", e);
            }
        })
        .filter_map(Result::ok)
        .map(|entry| entry.path())
        .filter(|path| path.extension() == Some(OsStr::new("xml")))
        .collect();

    Ok(entries)
}

(Rust Playground)

So far, I would still lean towards the functional version, but let’s see how both approaches hold up as we add more complexity.

Making Filtering More Generic

What if we wanted to filter by arbitrary file attributes? For instance, we might want to find all files with a given prefix or extension.

We could introduce a new parameter, valid, which would be a function that takes a Path and returns a bool. (This is also known as a predicate in functional programming.)

fn filter_files<F>(p: &Path, valid: &F) -> Result<Vec<PathBuf>>
where
    F: Fn(&Path) -> bool,
{
    Ok(fs::read_dir(p)?
        .filter_map(Result::ok)
        .map(|entry| entry.path())
        .filter(|path| valid(path))
        .collect())
}

This is a generic function that can be used for many different use cases. Higher-order functions like this are a typical pattern in functional programming and are also available in Rust.

The imperative version, while concise, now incorporates a higher-order function, demonstrating that the line between functional and imperative programming is often blurry:

fn filter_files<F>(p: &Path, valid: &F) -> Result<Vec<PathBuf>> 
where
    F: Fn(&Path) -> bool,
{
    let mut files = Vec::new();
    for f in fs::read_dir(p)? {
        let f = f?;
        if valid(&f.path()) {
            files.push(f.path());
        }
    }
    Ok(files)
}

(Rust Playground)

Recursively Filtering Directories For Files

Let’s go one more step further.

So far, our solution only works for a single directory. What if we wanted to recursively filter a directory and all its subdirectories for files?

First, the (mostly) imperative version with mutable state:

fn filter_files<F>(p: &Path, valid: &F) -> Result<Vec<PathBuf>>
where
    F: Fn(&Path) -> bool,
{
    let mut files = Vec::new();
    for f in fs::read_dir(p)? {
        let f = f?;
        if f.path().is_dir() {
            // Recursively filter the directory
            files.extend(filter_files(&f.path(), valid)?);
        } else if valid(&f.path()) {
            files.push(f.path());
        }
    }
    Ok(files)
}

(Rust Playground)

While we have one more level of nesting, the imperative version holds up reasonably well.

Next, the functional version:

fn filter_files<F>(p: &Path, valid: &F) -> Result<Vec<PathBuf>>
where
    F: Fn(&Path) -> bool,
{
    Ok(fs::read_dir(p)?
        .filter_map(Result::ok)
        .map(|entry| entry.path())
        .flat_map(|path| match path {
            p if p.is_dir() => filter_files(&p, valid).unwrap_or_default(),
            p if valid(&p) => vec![p],
            _ => vec![],
        })
        .collect())
}

We’re dealing with an iterator of iterators here, so we need to flatten it to get a single iterator of paths with the help of flat_map. However, this also means that we need to return a vector of paths in all cases, even if it’s empty. The unwrap_or_default is a symptom of this.

I will let you be the judge of which version you prefer.

Either way, this is where I feel the flow of logic is in need of improvement. What I want is better encapsulation and modularity to keep the complexity in check. Rust allows us to seamlessly transition to an object-oriented style to do just that.

Transitioning to Object-Oriented Rust

In contrast to the functional and imperative examples discussed earlier, let’s introduce a new struct, FileFilter, which encapsulates the logic for filtering files and file iteration.

pub struct FileFilter {
    predicates: Vec<Box<Predicate>>,
    start: Option<PathBuf>,
    stack: Vec<fs::ReadDir>,
}

Each FileFilter object carries its state: a collection of predicates for filtering, a starting path, and a stack of directories for iteration.

A predicate is defined like this:

type Predicate = dyn Fn(&Path) -> bool;

You might be surprised to see a dyn here. In Rust, no two closures, even if identical, have the same type!

A closure expression produces a closure value with a unique, anonymous type that cannot be written out. - The Rust Reference

To accommodate this in a collection like a Vec, we use a trait object with dynamic dispatch. By ‘boxing’ these closures, we create a Box<Predicate> (essentially Box<dyn Fn(&Path) -> bool>), which allows us to store different predicate closures in the same Vec despite their unique types.

Adding Filters

In functional programming, we leveraged the power of iterators and closures to filter files. In the imperative style, we directly manipulated vectors with loops and conditions. The FileFilter, however, abstracts these details away.

Consider the add_filter method:

pub fn add_filter(mut self, predicate: impl Fn(&Path) -> bool + 'static) -> Self {
    self.predicates.push(Box::new(predicate));
    self
}

This allows us to easily add multiple filters by chaining calls — something that was previously closely coupled with the iteration logic.

let filter = FileFilter::new()
    .add_filter(|path| {
        // check if path begins with "foo"
        path.file_name()
            .and_then(OsStr::to_str)
            .map(|name| name.starts_with("foo"))
            .unwrap_or(false)
    })  
    .add_filter(|path| path.extension() == Some(OsStr::new("xml")));

Iterator Implementation

What truly showcases the OOP approach in Rust is the implementation of the Iterator trait for FileFilter:

impl Iterator for FileFilter {
    type Item = Result<PathBuf>;

    fn next(&mut self) -> Option<Self::Item> {
        // Iteration logic to filter entries.
        // This is outside the scope of this article
        // Check out the full implementation on GitHub
        // or on the Rust Playground
    }
}

In doing so, FileFilter becomes a building block that neatly integrates with Rust’s powerful iterator ecosystem and can be used in all the same places as any other iterator. This design allows for complex iteration logic to be encapsulated within the object, abstracting away the details from the user.

You can find the full implementation of FileFilter on GitHub or on the Rust Playground. The code was closely modeled after the excellent Walkdir crate, which I recommend for production use.

Encapsulation and Reusability

The FileFilter example illustrates how OOP in Rust can lead to solid encapsulation and modularity. Unlike the earlier examples where the logic for filtering files was tightly coupled with iteration, we now separate the what (the predicates) from the how (the iteration and filtering logic). The trait system allows us to easily integrate our custom iterator with the rest of the ecosystem. Having these tools at our disposal makes the code more composable and reusable.

Summary

Mixing different styles is not only possible, but encouraged in Rust! This can also be seen by taking a look at Rust’s key influences on its language design. Influences as diverse as C++, Haskell, OCaml, and Erlang have shaped Rust’s design.

In the beginning, Rust was more functional in nature, but it has since evolved into a more balanced language, supporting a variety of styles. The question is where to draw the line between different programming paradigms.

Here are my personal rules of thumb:

• Embrace object-oriented patterns for organization. For organizing larger parts of your application, consider object-oriented constructs. Using structs or enums can encapsulate related data and functions, providing a clear structure without worrying about the details.
• Leverage functional patterns for data transformations. Especially within smaller scopes like functions and closures, functional methods such as mapping, filtering, or reducing can make your code both concise and clear. Use functional programming when you can phrase your problem as a series of transformations over some data.
• Use imperative style for granular control. In scenarios where you’re working close to the hardware, or when you need explicit step-by-step execution, the imperative style is often a necessity. It allows for precise control over operations, especially with mutable data. This style can be particularly useful in performance-critical sections or when interfacing with external systems where exact sequencing matters. However, always weigh its performance gains against potential readability trade-offs. If possible, encapsulate imperative code within a limited scope.
• Prioritize readability and maintainability. Regardless of your chosen paradigm, always write code that’s straightforward and easy to maintain. It benefits not only your future self, but also your colleagues who might work on the same codebase.
• Avoid premature optimization. Don’t prematurely optimize for performance at the cost of readability. The real bottleneck might be elsewhere. Measure first, then optimize. Elegant solutions can be turned into fast ones, but the reverse is not always true.

Lastly, avoid bias towards any particular paradigm. You can write better code if you test your assumptions every now and then.