One of the primary goals of the Rust project is to enable safe systems programming. Systems programming usually implies imperative programming, which in turns often implies side-effects, reasoning about shared state, et cetera.

At the same time, to provide safety, Rust programs and data types must be structured in a way that allows static checking to ensure soundness. Rust has features and restrictions that operate in tandem to ease writing programs that can pass these checks and thus ensure safety. For example, Rust incorporates the notion of ownership deeply into the language.

Rust’s match expression is a construct that offers an interesting combination of such features and restrictions. A match expression takes an input value, classifies it, and then jumps to code written to handle the identified class of data.

In this post we explore how Rust processes such data via match. The crucial elements that match and its counterpart enum tie together are:

  • Structural pattern matching: case analysis with ergonomics vastly improved over a C or Java style switch statement.

  • Exhaustive case analysis: ensures that no case is omitted when processing an input.

  • match embraces both imperative and functional styles of programming: you can continue using break statements, assignments, et cetera, rather than being forced to adopt an expression-oriented mindset.

  • match “borrows” or “moves”, as needed: Rust encourages the developer to think carefully about ownership and borrowing. To ensure that one is not forced to yield ownership of a value prematurely, match is designed with support for merely borrowing substructure (as opposed to always moving such substructure).

We cover each of the items above in detail below, but first we establish a foundation for the discussion: What does match look like, and how does it work?

The Basics of match

The match expression in Rust has this form:


where each of the PATTERNS_i contains at least one pattern. A pattern describes a subset of the possible values to which INPUT_EXPRESSION could evaluate. The syntax PATTERNS => RESULT_EXPRESSION is called a “match arm”, or simply “arm”.

Patterns can match simple values like integers or characters; they can also match user-defined symbolic data, defined via enum.

The below code demonstrates generating the next guess (poorly) in a number guessing game, given the answer from a previous guess.

enum Answer {

fn suggest_guess(prior_guess: u32, answer: Answer) {
    match answer {
        Answer::Higher => println!("maybe try {} next", prior_guess + 10),
        Answer::Lower  => println!("maybe try {} next", prior_guess - 1),
        Answer::Bingo  => println!("we won with {}!", prior_guess),

fn demo_suggest_guess() {
    suggest_guess(10, Answer::Higher);
    suggest_guess(20, Answer::Lower);
    suggest_guess(19, Answer::Bingo);

(Incidentally, nearly all the code in this post is directly executable; you can cut-and-paste the code snippets into a file, compile the file with --test, and run the resulting binary to see the tests run.)

Patterns can also match structured data (e.g. tuples, slices, user-defined data types) via corresponding patterns. In such patterns, one often binds parts of the input to local variables; those variables can then be used in the result expression.

The special _ pattern matches any single value, and is often used as a catch-all; the special .. pattern generalizes this by matching any series of values or name/value pairs.

Also, one can collapse multiple patterns into one arm by separating the patterns by vertical bars (|); thus that arm matches either this pattern, or that pattern, et cetera.

These features are illustrated in the following revision to the guessing-game answer generation strategy:

struct GuessState {
    guess: u32,
    answer: Answer,
    low: u32,
    high: u32,

fn suggest_guess_smarter(s: GuessState) {
    match s {
        // First arm only fires on Bingo; it binds `p` to last guess.
        GuessState { answer: Answer::Bingo, guess: p, .. } => {
     // ~~~~~~~~~~   ~~~~~~~~~~~~~~~~~~~~~  ~~~~~~~~  ~~
     //     |                 |                 |     |
     //     |                 |                 |     Ignore remaining fields
     //     |                 |                 |
     //     |                 |      Copy value of field `guess` into local variable `p`
     //     |                 |
     //     |   Test that `answer field is equal to `Bingo`
     //     |
     //  Match against an instance of the struct `GuessState`

            println!("we won with {}!", p);

        // Second arm fires if answer was too low or too high.
        // We want to find a new guess in the range (l..h), where:
        // - If it was too low, then we want something higher, so we
        //   bind the guess to `l` and use our last high guess as `h`.
        // - If it was too high, then we want something lower; bind
        //   the guess to `h` and use our last low guess as `l`.
        GuessState { answer: Answer::Higher, low: _, guess: l, high: h } |
        GuessState { answer: Answer::Lower,  low: l, guess: h, high: _ } => {
     // ~~~~~~~~~~   ~~~~~~~~~~~~~~~~~~~~~   ~~~~~~  ~~~~~~~~  ~~~~~~~
     //     |                 |                 |        |        |
     //     |                 |                 |        |    Copy or ignore
     //     |                 |                 |        |    field `high`,
     //     |                 |                 |        |    as appropriate
     //     |                 |                 |        |
     //     |                 |                 |  Copy field `guess` into
     //     |                 |                 |  local variable `l` or `h`,
     //     |                 |                 |  as appropriate
     //     |                 |                 |
     //     |                 |    Copy value of field `low` into local
     //     |                 |    variable `l`, or ignore it, as appropriate
     //     |                 |
     //     |   Test that `answer field is equal
     //     |   to `Higher` or `Lower`, as appropriate
     //     |
     //  Match against an instance of the struct `GuessState`

            let mid = l + ((h - l) / 2);
            println!("lets try {} next", mid);

fn demo_guess_state() {
    suggest_guess_smarter(GuessState {
        guess: 20, answer: Answer::Lower, low: 10, high: 1000

This ability to simultaneously perform case analysis and bind input substructure leads to powerful, clear, and concise code, focusing the reader’s attention directly on the data relevant to the case at hand.

That is match in a nutshell.

So, what is the interplay between this construct and Rust’s approach to ownership and safety in general?

Exhaustive case analysis

…when you have eliminated all which is impossible, then whatever remains, however improbable, must be the truth.

– Sherlock Holmes (Arthur Conan Doyle, “The Blanched Soldier”)

One useful way to tackle a complex problem is to break it down into individual cases and analyze each case individually. For this method of problem solving to work, the breakdown must be collectively exhaustive; all of the cases you identified must actually cover all possible scenarios.

Using enum and match in Rust can aid this process, because match enforces exhaustive case analysis: Every possible input value for a match must be covered by the pattern in a least one arm in the match.

This helps catch bugs in program logic and ensures that the value of a match expression is well-defined.

So, for example, the following code is rejected at compile-time.

fn suggest_guess_broken(prior_guess: u32, answer: Answer) {
    let next_guess = match answer {
        Answer::Higher => prior_guess + 10,
        Answer::Lower  => prior_guess - 1,
        // ERROR: non-exhaustive patterns: `Bingo` not covered
    println!("maybe try {} next", next_guess);

Many other languages offer a pattern matching construct (ML and various macro-based match implementations in Scheme both come to mind), but not all of them have this restriction.

Rust has this restriction for these reasons:

  • First, as noted above, dividing a problem into cases only yields a general solution if the cases are exhaustive. Exhaustiveness-checking exposes logical errors.

  • Second, exhaustiveness-checking can act as a refactoring aid. During the development process, I often add new variants for a particular enum definition. The exhaustiveness-check helps points out all of the match expressions where I only wrote the cases from the prior version of the enum type.

  • Third, since match is an expression form, exhaustiveness ensures that such expressions always either evaluate to a value of the correct type, or jump elsewhere in the program.

Jumping out of a match

The following code is a fixed version of the suggest_guess_broken function we saw above; it directly illustrates “jumping elsewhere”:

fn suggest_guess_fixed(prior_guess: u32, answer: Answer) {
    let next_guess = match answer {
        Answer::Higher => prior_guess + 10,
        Answer::Lower  => prior_guess - 1,
        Answer::Bingo  => {
            println!("we won with {}!", prior_guess);
    println!("maybe try {} next", next_guess);

fn demo_guess_fixed() {
    suggest_guess_fixed(10, Answer::Higher);
    suggest_guess_fixed(20, Answer::Lower);
    suggest_guess_fixed(19, Answer::Bingo);

The suggest_guess_fixed function illustrates that match can handle some cases early (and then immediately return from the function), while computing whatever values are needed from the remaining cases and letting them fall through to the remainder of the function body.

We can add such special case handling via match without fear of overlooking a case, because match will force the case analysis to be exhaustive.

Algebraic Data Types and Structural Invariants

Algebraic data types succinctly describe classes of data and allow one to encode rich structural invariants. Rust uses enum and struct definitions for this purpose.

An enum type allows one to define mutually-exclusive classes of values. The examples shown above used enum for simple symbolic tags, but in Rust, enums can define much richer classes of data.

For example, a binary tree is either a leaf, or an internal node with references to two child trees. Here is one way to encode a tree of integers in Rust:

enum BinaryTree {
    Node(Box<BinaryTree>, i32, Box<BinaryTree>)

(The Box<V> type describes an owning reference to a heap-allocated instance of V; if you own a Box<V>, then you also own the V it contains, and can mutate it, lend out references to it, et cetera. When you finish with the box and let it fall out of scope, it will automatically clean up the resources associated with the heap-allocated V.)

The above enum definition ensures that if we are given a BinaryTree, it will always fall into one of the above two cases. One will never encounter a BinaryTree::Node that does not have a left-hand child. There is no need to check for null.

One does need to check whether a given BinaryTree is a Leaf or is a Node, but the compiler statically ensures such checks are done: you cannot accidentally interpret the data of a Leaf as if it were a Node, nor vice versa.

Here is a function that sums all of the integers in a tree using match.

fn tree_weight_v1(t: BinaryTree) -> i32 {
    match t {
        BinaryTree::Leaf(payload) => payload,
        BinaryTree::Node(left, payload, right) => {
            tree_weight_v1(*left) + payload + tree_weight_v1(*right)

/// Returns tree that Looks like:
///      +----(4)---+
///      |          |
///   +-(2)-+      [5]
///   |     |   
///  [1]   [3]
fn sample_tree() -> BinaryTree {
    let l1 = Box::new(BinaryTree::Leaf(1));
    let l3 = Box::new(BinaryTree::Leaf(3));
    let n2 = Box::new(BinaryTree::Node(l1, 2, l3));
    let l5 = Box::new(BinaryTree::Leaf(5));

    BinaryTree::Node(n2, 4, l5)

fn tree_demo_1() {
    let tree = sample_tree();
    assert_eq!(tree_weight_v1(tree), (1 + 2 + 3) + 4 + 5);

Algebraic data types establish structural invariants that are strictly enforced by the language. (Even richer representation invariants can be maintained via the use of modules and privacy; but let us not digress from the topic at hand.)

Both expression- and statement-oriented

Unlike many languages that offer pattern matching, Rust embraces both statement- and expression-oriented programming.

Many functional languages that offer pattern matching encourage one to write in an “expression-oriented style”, where the focus is always on the values returned by evaluating combinations of expressions, and side-effects are discouraged. This style contrasts with imperative languages, which encourage a statement-oriented style that focuses on sequences of commands executed solely for their side-effects.

Rust excels in supporting both styles.

Consider writing a function which maps a non-negative integer to a string rendering it as an ordinal (“1st”, “2nd”, “3rd”, …).

The following code uses range patterns to simplify things, but also, it is written in a style similar to a switch in a statement-oriented language like C (or C++, Java, et cetera), where the arms of the match are executed for their side-effect alone:

fn num_to_ordinal(x: u32) -> String {
    let suffix;
    match (x % 10, x % 100) {
        (1, 1) | (1, 21...91) => {
            suffix = "st";
        (2, 2) | (2, 22...92) => {
            suffix = "nd";
        (3, 3) | (3, 23...93) => {
            suffix = "rd";
        _                     => {
            suffix = "th";
    return format!("{}{}", x, suffix);

fn test_num_to_ordinal() {
    assert_eq!(num_to_ordinal(   0),    "0th");
    assert_eq!(num_to_ordinal(   1),    "1st");
    assert_eq!(num_to_ordinal(  12),   "12th");
    assert_eq!(num_to_ordinal(  22),   "22nd");
    assert_eq!(num_to_ordinal(  43),   "43rd");
    assert_eq!(num_to_ordinal(  67),   "67th");
    assert_eq!(num_to_ordinal(1901), "1901st");

The Rust compiler accepts the above program. This is notable because its static analyses ensure both:

  • suffix is always initialized before we run the format! at the end of the function, and

  • suffix is assigned at most once during the function’s execution (because if we could assign suffix multiple times, the compiler would force us to mark suffix as mutable).

To be clear, the above program certainly can be written in an expression-oriented style in Rust; for example, like so:

fn num_to_ordinal_expr(x: u32) -> String {
    format!("{}{}", x, match (x % 10, x % 100) {
        (1, 1) | (1, 21...91) => "st",
        (2, 2) | (2, 22...92) => "nd",
        (3, 3) | (3, 23...93) => "rd",
        _                     => "th"

Sometimes expression-oriented style can yield very succinct code; other times the style requires contortions that can be avoided by writing in a statement-oriented style. (The ability to return from one match arm in the suggest_guess_fixed function earlier was an example of this.)

Each of the styles has its use cases. Crucially, switching to a statement-oriented style in Rust does not sacrifice every other feature that Rust provides, such as the guarantee that a non-mut binding is assigned at most once.

An important case where this arises is when one wants to initialize some state and then borrow from it, but only on some control-flow branches.

fn sometimes_initialize(input: i32) {
    let string: String; // a dynamically-constructed string value
    let borrowed: &str; // a reference to string data
    match input {
        0...100 => {
            // Construct a String on the fly...
            string = format!("input prints as {}", input);
            // ... and then borrow from inside it.
            borrowed = &string[6..];
        _ => {
            // String literals are *already* borrowed references
            borrowed = "expected between 0 and 100";
    println!("borrowed: {}", borrowed);

    // Below would cause compile-time error if uncommented...

    // println!("string: {}", string);

    // ...namely: error: use of possibly uninitialized variable: `string`

fn demo_sometimes_initialize() {
    sometimes_initialize(23);  // this invocation will initialize `string`
    sometimes_initialize(123); // this one will not

The interesting thing about the above code is that after the match, we are not allowed to directly access string, because the compiler requires that the variable be initialized on every path through the program before it can be accessed. At the same time, we can, via borrowed, access data that may held within string, because a reference to that data is held by the borrowed variable when we go through the first match arm, and we ensure borrowed itself is initialized on every execution path through the program that reaches the println! that uses borrowed.

(The compiler ensures that no outstanding borrows of the string data could possibly outlive string itself, and the generated code ensures that at the end of the scope of string, its data is deallocated if it was previously initialized.)

In short, for soundness, the Rust language ensures that data is always initialized before it is referenced, but the designers have strived to avoid requiring artificial coding patterns adopted solely to placate Rust’s static analyses (such as requiring one to initialize string above with some dummy data, or requiring an expression-oriented style).

Matching without moving

Matching an input can borrow input substructure, without taking ownership; this is crucial for matching a reference (e.g. a value of type &T).

The “Algebraic Data Types” section above described a tree datatype, and showed a program that computed the sum of the integers in a tree instance.

That version of tree_weight has one big downside, however: it takes its input tree by value. Once you pass a tree to tree_weight_v1, that tree is gone (as in, deallocated).

fn tree_demo_v1_fails() {
    let tree = sample_tree();
    assert_eq!(tree_weight_v1(tree), (1 + 2 + 3) + 4 + 5);

    // If you uncomment this line below ...

    // assert_eq!(tree_weight_v1(tree), (1 + 2 + 3) + 4 + 5);

    // ... you will get: error: use of moved value: `tree`

This is not a consequence, however, of using match; it is rather a consequence of the function signature that was chosen:

fn tree_weight_v1(t: BinaryTree) -> i32 { 0 }
//                   ^~~~~~~~~~ this means this function takes ownership of `t`

In fact, in Rust, match is designed to work quite well without taking ownership. In particular, the input to match is an L-value expression; this means that the input expression is evaluated to a memory location where the value lives. match works by doing this evaluation and then inspecting the data at that memory location.

(If the input expression is a variable name or a field/pointer dereference, then the L-value is just the location of that variable or field/memory. If the input expression is a function call or other operation that generates an unnamed temporary value, then it will be conceptually stored in a temporary area, and that is the memory location that match will inspect.)

So, if we want a version of tree_weight that merely borrows a tree rather than taking ownership of it, then we will need to make use of this feature of Rust’s match.

fn tree_weight_v2(t: &BinaryTree) -> i32 {
    //               ^~~~~~~~~~~ The `&` means we are *borrowing* the tree
    match *t {
        BinaryTree::Leaf(payload) => payload,
        BinaryTree::Node(ref left, payload, ref right) => {
            tree_weight_v2(left) + payload + tree_weight_v2(right)

fn tree_demo_2() {
    let tree = sample_tree();
    assert_eq!(tree_weight_v2(&tree), (1 + 2 + 3) + 4 + 5);

The function tree_weight_v2 looks very much like tree_weight_v1. The only differences are: we take t as a borrowed reference (the & in its type), we added a dereference *t, and, importantly, we use ref-bindings for left and right in the Node case.

The dereference *t, interpreted as an L-value expression, is just extracting the memory address where the BinaryTree is represented (since the t: &BinaryTree is just a reference to that data in memory). The *t here is not making a copy of the tree, nor moving it to a new temporary location, because match is treating it as an L-value.

The only piece left is the ref-binding, which is a crucial part of how destructuring bind of L-values works.

First, let us carefully state the meaning of a non-ref binding:

  • When matching a value of type T, an identifier pattern i will, on a successful match, move the value out of the original input and into i. Thus we can always conclude in such a case that i has type T (or more succinctly, “i: T”).

For some types T, known as copyable T (also pronounced “T implements Copy”), the value will in fact be copied into i for such identifier patterns. (Note that in general, an arbitrary type T is not copyable.)

Either way, such pattern bindings do mean that the variable i has ownership of a value of type T.

Thus, the bindings of payload in tree_weight_v2 both have type i32; the i32 type implements Copy, so the weight is copied into payload in both arms.

Now we are ready to state what a ref-binding is:

  • When matching an L-value of type T, a ref-pattern ref i will, on a successful match, merely borrow a reference into the matched data. In other words, a successful ref i match of a value of type T will imply that i has the type of a reference to T (or more succinctly, “i: &T”).

Thus, in the Node arm of tree_weight_v2, left will be a reference to the left-hand box (which holds a tree), and right will likewise reference the right-hand tree.

We can pass these borrowed references to trees into the recursive calls to tree_weight_v2, as the code demonstrates.

Likewise, a ref mut-pattern (ref mut i) will, on a successful match, borrow a mutable reference into the input: i: &mut T. This allows mutation and ensures there are no other active references to that data at the same time. A destructuring binding form like match allows one to take mutable references to disjoint parts of the data simultaneously.

This code demonstrates this concept by incrementing all of the values in a given tree.

fn tree_grow(t: &mut BinaryTree) {
    //          ^~~~~~~~~~~~~~~ `&mut`: we have exclusive access to the tree
    match *t {
        BinaryTree::Leaf(ref mut payload) => *payload += 1,
        BinaryTree::Node(ref mut left, ref mut payload, ref mut right) => {
            *payload += 1;

fn tree_demo_3() {
    let mut tree = sample_tree();
    tree_grow(&mut tree);
    assert_eq!(tree_weight_v2(&tree), (2 + 3 + 4) + 5 + 6);

Note that the code above now binds payload by a ref mut-pattern; if it did not use a ref pattern, then payload would be bound to a local copy of the integer, while we want to modify the actual integer in the tree itself. Thus we need a reference to that integer.

Note also that the code is able to bind left and right simultaneously in the Node arm. The compiler knows that the two values cannot alias, and thus it allows both &mut-references to live simultaneously.


Rust takes the ideas of algebraic data types and pattern matching pioneered by the functional programming languages, and adapts them to imperative programming styles and Rust’s own ownership and borrowing systems. The enum and match forms provide clean data definitions and expressive power, while static analysis ensures that the resulting programs are safe.

For more information on details that were not covered here, such as:

  • how to say Higher instead of Answer::Higher in a pattern,

  • defining new named constants,

  • binding via ident @ pattern, or

  • the potentially subtle difference between { let id = expr; ... } versus match expr { id => { ... } },

consult the Rust documentation, or quiz our awesome community (in #rust on IRC, or in the user group).

(Many thanks to those who helped review this post, especially Aaron Turon and Niko Matsakis, as well as Mutabah, proc, libfud, asQuirrel, and annodomini from #rust.)