The Rust Programming Language

Shared-State Concurrency

Message passing is a fine way of handling concurrency, but it’s not the only one. Another method would be for multiple threads to access the same shared data. Consider this part of the slogan from the Go language documentation again: “do not communicate by sharing memory.”

What would communicating by sharing memory look like? In addition, why would message-passing enthusiasts caution not to use memory sharing?

In a way, channels in any programming language are similar to single ownership, because once you transfer a value down a channel, you should no longer use that value. Shared memory concurrency is like multiple ownership: multiple threads can access the same memory location at the same time. As you saw in Chapter 15, where smart pointers made multiple ownership possible, multiple ownership can add complexity because these different owners need managing. Rust’s type system and ownership rules greatly assist in getting this management correct. For an example, let’s look at mutexes, one of the more common concurrency primitives for shared memory.

Using Mutexes to Allow Access to Data from One Thread at a Time

Mutex is an abbreviation for mutual exclusion, as in, a mutex allows only one thread to access some data at any given time. To access the data in a mutex, a thread must first signal that it wants access by asking to acquire the mutex’s lock. The lock is a data structure that is part of the mutex that keeps track of who currently has exclusive access to the data. Therefore, the mutex is described as guarding the data it holds via the locking system.

Mutexes have a reputation for being difficult to use because you have to remember two rules:

• You must attempt to acquire the lock before using the data.
• When you’re done with the data that the mutex guards, you must unlock the data so other threads can acquire the lock.

For a real-world metaphor for a mutex, imagine a panel discussion at a conference with only one microphone. Before a panelist can speak, they have to ask or signal that they want to use the microphone. When they get the microphone, they can talk for as long as they want to and then hand the microphone to the next panelist who requests to speak. If a panelist forgets to hand the microphone off when they’re finished with it, no one else is able to speak. If management of the shared microphone goes wrong, the panel won’t work as planned!

Management of mutexes can be incredibly tricky to get right, which is why so many people are enthusiastic about channels. However, thanks to Rust’s type system and ownership rules, you can’t get locking and unlocking wrong.

The API of Mutex<T>

As an example of how to use a mutex, let’s start by using a mutex in a single-threaded context, as shown in Listing 16-12:

use std::sync::Mutex;

fn main() {
    let m = Mutex::new(5);

    {
        let mut num = m.lock().unwrap();
        *num = 6;
    }

    println!("m = {m:?}");
}

As with many types, we create a Mutex<T> using the associated function new. To access the data inside the mutex, we use the lock method to acquire the lock. This call will block the current thread so it can’t do any work until it’s our turn to have the lock.

The call to lock would fail if another thread holding the lock panicked. In that case, no one would ever be able to get the lock, so we’ve chosen to unwrap and have this thread panic if we’re in that situation.

After we’ve acquired the lock, we can treat the return value, named num in this case, as a mutable reference to the data inside. The type system ensures that we acquire a lock before using the value in m. The type of m is Mutex<i32>, not i32, so we must call lock to be able to use the i32 value. We can’t forget; the type system won’t let us access the inner i32 otherwise.

As you might suspect, Mutex<T> is a smart pointer. More accurately, the call to lock returns a smart pointer called MutexGuard, wrapped in a LockResult that we handled with the call to unwrap. The MutexGuard smart pointer implements Deref to point at our inner data; the smart pointer also has a Drop implementation that releases the lock automatically when a MutexGuard goes out of scope, which happens at the end of the inner scope. As a result, we don’t risk forgetting to release the lock and blocking the mutex from being used by other threads, because the lock release happens automatically.

After dropping the lock, we can print the mutex value and see that we were able to change the inner i32 to 6.

Sharing a Mutex<T> Between Multiple Threads

Now, let’s try to share a value between multiple threads using Mutex<T>. We’ll spin up 10 threads and have them each increment a counter value by 1, so the counter goes from 0 to 10. The next example in Listing 16-13 will have a compiler error, and we’ll use that error to learn more about using Mutex<T> and how Rust helps us use it correctly.

use std::sync::Mutex;
use std::thread;

fn main() {
    let counter = Mutex::new(0);
    let mut handles = vec![];

    for _ in 0..10 {
        let handle = thread::spawn(move || {
            let mut num = counter.lock().unwrap();

            *num += 1;
        });
        handles.push(handle);
    }

    for handle in handles {
        handle.join().unwrap();
    }

    println!("Result: {}", *counter.lock().unwrap());
}

We create a counter variable to hold an i32 inside a Mutex<T>, as we did in Listing 16-12. Next, we create 10 threads by iterating over a range of numbers. We use thread::spawn and give all the threads the same closure: one that moves the counter into the thread, acquires a lock on the Mutex<T> by calling the lock method, and then adds 1 to the value in the mutex. When a thread finishes running its closure, num will go out of scope and release the lock so another thread can acquire it.

In the main thread, we collect all the join handles. Then, as we did in Listing 16-2, we call join on each handle to make sure all the threads finish. At that point, the main thread will acquire the lock and print the result of this program.

We hinted that this example wouldn’t compile. Now let’s find out why!

$ cargo run
   Compiling shared-state v0.1.0 (file:///projects/shared-state)
error[E0382]: borrow of moved value: `counter`
  --> src/main.rs:21:29
   |
5  |     let counter = Mutex::new(0);
   |         ------- move occurs because `counter` has type `Mutex<i32>`, which does not implement the `Copy` trait
...
8  |     for _ in 0..10 {
   |     -------------- inside of this loop
9  |         let handle = thread::spawn(move || {
   |                                    ------- value moved into closure here, in previous iteration of loop
...
21 |     println!("Result: {}", *counter.lock().unwrap());
   |                             ^^^^^^^ value borrowed here after move
   |
help: consider moving the expression out of the loop so it is only moved once
   |
8  ~     let mut value = counter.lock();
9  ~     for _ in 0..10 {
10 |         let handle = thread::spawn(move || {
11 ~             let mut num = value.unwrap();
   |

For more information about this error, try `rustc --explain E0382`.
error: could not compile `shared-state` (bin "shared-state") due to 1 previous error

The error message states that the counter value was moved in the previous iteration of the loop. Rust is telling us that we can’t move the ownership of counter into multiple threads. Let’s fix the compiler error with a multiple-ownership method we discussed in Chapter 15.

Multiple Ownership with Multiple Threads

In Chapter 15, we gave a value multiple owners by using the smart pointer Rc<T> to create a reference counted value. Let’s do the same here and see what happens. We’ll wrap the Mutex<T> in Rc<T> in Listing 16-14 and clone the Rc<T> before moving ownership to the thread.

use std::rc::Rc;
use std::sync::Mutex;
use std::thread;

fn main() {
    let counter = Rc::new(Mutex::new(0));
    let mut handles = vec![];

    for _ in 0..10 {
        let counter = Rc::clone(&counter);
        let handle = thread::spawn(move || {
            let mut num = counter.lock().unwrap();

            *num += 1;
        });
        handles.push(handle);
    }

    for handle in handles {
        handle.join().unwrap();
    }

    println!("Result: {}", *counter.lock().unwrap());
}

Once again, we compile and get… different errors! The compiler is teaching us a lot.

$ cargo run
   Compiling shared-state v0.1.0 (file:///projects/shared-state)
error[E0277]: `Rc<Mutex<i32>>` cannot be sent between threads safely
  --> src/main.rs:11:36
   |
11 |           let handle = thread::spawn(move || {
   |                        ------------- ^------
   |                        |             |
   |  ______________________|_____________within this `{closure@src/main.rs:11:36: 11:43}`
   | |                      |
   | |                      required by a bound introduced by this call
12 | |             let mut num = counter.lock().unwrap();
13 | |
14 | |             *num += 1;
15 | |         });
   | |_________^ `Rc<Mutex<i32>>` cannot be sent between threads safely
   |
   = help: within `{closure@src/main.rs:11:36: 11:43}`, the trait `Send` is not implemented for `Rc<Mutex<i32>>`, which is required by `{closure@src/main.rs:11:36: 11:43}: Send`
note: required because it's used within this closure
  --> src/main.rs:11:36
   |
11 |         let handle = thread::spawn(move || {
   |                                    ^^^^^^^
note: required by a bound in `spawn`
  --> /rustc/eeb90cda1969383f56a2637cbd3037bdf598841c/library/std/src/thread/mod.rs:688:1

For more information about this error, try `rustc --explain E0277`.
error: could not compile `shared-state` (bin "shared-state") due to 1 previous error

Wow, that error message is very wordy! Here’s the important part to focus on: `Rc<Mutex<i32>>` cannot be sent between threads safely. The compiler is also telling us the reason why: the trait `Send` is not implemented for `Rc<Mutex<i32>>` . We’ll talk about Send in the next section: it’s one of the traits that ensures the types we use with threads are meant for use in concurrent situations.

Unfortunately, Rc<T> is not safe to share across threads. When Rc<T> manages the reference count, it adds to the count for each call to clone and subtracts from the count when each clone is dropped. But it doesn’t use any concurrency primitives to make sure that changes to the count can’t be interrupted by another thread. This could lead to wrong counts—subtle bugs that could in turn lead to memory leaks or a value being dropped before we’re done with it. What we need is a type exactly like Rc<T> but one that makes changes to the reference count in a thread-safe way.

Atomic Reference Counting with Arc<T>

Fortunately, Arc<T> is a type like Rc<T> that is safe to use in concurrent situations. The a stands for atomic, meaning it’s an atomically reference counted type. Atomics are an additional kind of concurrency primitive that we won’t cover in detail here: see the standard library documentation for std::sync::atomic for more details. At this point, you just need to know that atomics work like primitive types but are safe to share across threads.

You might then wonder why all primitive types aren’t atomic and why standard library types aren’t implemented to use Arc<T> by default. The reason is that thread safety comes with a performance penalty that you only want to pay when you really need to. If you’re just performing operations on values within a single thread, your code can run faster if it doesn’t have to enforce the guarantees atomics provide.

Let’s return to our example: Arc<T> and Rc<T> have the same API, so we fix our program by changing the use line, the call to new, and the call to clone. The code in Listing 16-15 will finally compile and run:

use std::sync::{Arc, Mutex};
use std::thread;

fn main() {
    let counter = Arc::new(Mutex::new(0));
    let mut handles = vec![];

    for _ in 0..10 {
        let counter = Arc::clone(&counter);
        let handle = thread::spawn(move || {
            let mut num = counter.lock().unwrap();

            *num += 1;
        });
        handles.push(handle);
    }

    for handle in handles {
        handle.join().unwrap();
    }

    println!("Result: {}", *counter.lock().unwrap());
}

This code will print the following:

Result: 10

We did it! We counted from 0 to 10, which may not seem very impressive, but it did teach us a lot about Mutex<T> and thread safety. You could also use this program’s structure to do more complicated operations than just incrementing a counter. Using this strategy, you can divide a calculation into independent parts, split those parts across threads, and then use a Mutex<T> to have each thread update the final result with its part.

Note that if you are doing simple numerical operations, there are types simpler than Mutex<T> types provided by the std::sync::atomic module of the standard library. These types provide safe, concurrent, atomic access to primitive types. We chose to use Mutex<T> with a primitive type for this example so we could concentrate on how Mutex<T> works.

Similarities Between RefCell<T>/Rc<T> and Mutex<T>/Arc<T>

You might have noticed that counter is immutable but we could get a mutable reference to the value inside it; this means Mutex<T> provides interior mutability, as the Cell family does. In the same way we used RefCell<T> in Chapter 15 to allow us to mutate contents inside an Rc<T>, we use Mutex<T> to mutate contents inside an Arc<T>.

Another detail to note is that Rust can’t protect you from all kinds of logic errors when you use Mutex<T>. Recall in Chapter 15 that using Rc<T> came with the risk of creating reference cycles, where two Rc<T> values refer to each other, causing memory leaks. Similarly, Mutex<T> comes with the risk of creating deadlocks. These occur when an operation needs to lock two resources and two threads have each acquired one of the locks, causing them to wait for each other forever. If you’re interested in deadlocks, try creating a Rust program that has a deadlock; then research deadlock mitigation strategies for mutexes in any language and have a go at implementing them in Rust. The standard library API documentation for Mutex<T> and MutexGuard offers useful information.

We’ll round out this chapter by talking about the Send and Sync traits and how we can use them with custom types.