One of the value propositions most frequently lauded by Rust developers is its freedom from data races. The compiler will literally not allow you to build code that could ever produce a situation where two threads can mutate the same data. This teaches us two things:

Loathing of the Rust compiler and its borrow checker (until we make things work) The extent to which we have written unsafe, race-prone code in other languages

I’ve learned a great deal from the second bullet point. I look back at some of the code I’ve written in the past and I realize that the only reason it worked is because some other library had guard rails in place, or the lack of data races was an accident. In other words, my lack of failure was more attributable to random chance than skill.

Rust applications are also praised for their performance. This performance comes from its ability to help developers write safe, multi-threaded code. While starting out on my Rust learning curve, I tried concurrency a few times and the error messages and ceaseless barrage of red console output from the compiler had me running away in search of a security blanket (Ah, Go, my old friend, you’ll never let me down, will you? You’re so soft and friendly).

Every language has its own way of dealing with concurrency. In Go, we can spin up goroutines (also called “green” or lightweight threads), and get data in and out of them, simplifying synchronization with channels. In Elixir, we create a module with functions that communicate with a process (the Erlang BEAM version of a green thread, of which we can create millions). In Java and C#, we have OS threads and data access is synchronized through mutex and atomic counting guards around critical sections. Additionally, we have deeply integrated concurrency models like those used by Android, where you can start an activity as a background service and communicate with it by sending and subscribing to events. Event dispatch to the Android activity is always single-threaded, freeing the developer from the complexities of critical sections and mutexes.

Rust has heavyweight threads, mutexes, atomic reference counting, and channels. For me, this wide range of choices led to a lot of confusion on how I should or should not deal with concurrency.

Let’s take an example where I’ve got two background threads contributing to mutable state. One thread is reading bank transactions from a file source while the other is reading them from mobile applications (simulated to keep the sample simple). To make this work, each of the threads needs a thread-safe reference to the bank account being mutated. Here’s the account struct that allows for thread-safe, multi-threaded mutation:

#[derive(Debug)]

struct Transaction {

amount: isize,

timestamp: u64,

txid: String,

} #[derive(Debug)]

struct Account {

account_number: String,

transactions: Mutex<Vec<Transaction>>,

acct_type: String,

}

Unlike some other languages, where the mutex and the data it protects are isolated and it’s left to the developer to guard the right code, in Rust the mutex contains (owns) the data it protects. Calling lock on a mutex will block all other threads until the produced guard is dropped. This is how we can add transactions to a shared account from multiple threads:

let my_savings = Arc::new(Account::new("0001"));

let feed_account = my_savings.clone(); // clones the ref, not the item

let mobile_account = my_savings.clone();



let file_feed = spawn(move || {



let mut tx_guard = feed_account.transactions.lock().unwrap();



tx_guard.push(Transaction {

amount: 500,

timestamp: 12,

txid: "tx-001".to_owned(),

});



tx_guard.push( Transaction {

amount: 750,

timestamp: 4,

txid: "tx-002".to_owned(),

})

});



let mobile_feed = spawn(move || {



mobile_account.transactions.lock().unwrap().push(Transaction {

amount: 50,

timestamp: 7,

txid: "tx-003".to_owned(),

});

});



file_feed.join().unwrap();

mobile_feed.join().unwrap();

I had an “a-ha!” moment when I realized that cloning an Arc actually just creates a new reference to the original heap-stored value, it doesn’t duplicate the underlying data. This creation of a copy of a heap reference is something that many other languages do implicitly. In the first thread I store the guard in a variable tx_guard because I need it to survive long enough to handle both push calls. In the second one I can do it inline because it only lasts until the end of the function call. Finally, I call join to wait for both threads to complete.

I can then inspect the transactions field from the original account and see that it contains my transactions in an unpredictable order:

println!("mutating from bg threads:

\t{:?}", my_savings.transactions);

This compiles and runs and we have guaranteed thread-safe access to the vector of transactions in the single shared savings account. This is excellent, but it feels a little off to me. Mutexes are powerful but they’re not as safe as we like to think. Even in Rust, you can still produce deadlocks waiting for a mutex to be dropped. My exposure to Elixir also makes me chafe at the idea of multiple threads mutating a shared component, regardless of how “safe” it might seem to the compiler.

I think we can do better. Obviously everyone has their own opinions here and some prefer the flexibility and straightforward nature of mutexes, others prefer channels.

If I were to modify my sample such that the background threads are emitting data on sender channels, then the background threads are no longer mutating shared data. I can write them in such a style as they are emitting immutable messages or events, and this feels more natural to me. Removing the ability to mutate data cross-thread, I can simplify my account struct so it contains no mutexes:

#[derive(Debug)]

struct Account2 {

account_number: String,

transactions: Vec<Transaction>,

acct_type: String,

}

I also don’t need an Arc because I’m not sharing a value of this type across threads — I’m keeping it private to whatever thread is responsible for maintaining this data. Now, instead of cloning an Arc to give multiple threads access to the data, I can just clone a Sender channel and give a separate sender to each thread while those channels all still send to the same receiver:

let (tx, rx) = mpsc::channel();



let tx2 = mpsc::Sender::clone(&tx);



let file_feed2 = spawn(move || {

tx.send(Transaction {

amount: 500,

timestamp: 12,

txid: "ch-tx-001".to_owned(),

}).unwrap();

tx.send(Transaction {

amount: 750,

timestamp: 4,

txid: "ch-tx-002".to_owned(),

}).unwrap();

});



let mobile_feed2 = spawn(move || {

tx2.send(Transaction {

amount: 50,

timestamp: 7,

txid: "ch-tx-003".to_owned(),

}).unwrap();

});



file_feed2.join().unwrap();

mobile_feed2.join().unwrap();

One interesting side effect here is that while these channels are doing their background work, the mutable savings account value may not even exist yet. Now the main thread (or a third processing thread, etc) can gather up the values from these channels and do thread-private mutation:

let mut tl_savings = Account2 {

acct_type: "Savings".to_owned(),

account_number: "0001".to_owned(),

transactions: Vec::new(),

};



for transaction in rx {

tl_savings.transactions.push(transaction);

}

The last for loop works because receiver channels can be treated like iterators, where the next function blocks until there’s a value available on the channel. The for loop stops when the sender channel is dropped, which could happen if the thread holding the sender goes away. In the case of my sample, this for loop completes because even before I start, the senders have both dropped due to the calls to join .

You’re free to use mutexes if you like, but I’ve also found that giving a thread the ability to directly mutate shared state, even under the umbrella of mutex safety, creates too tight a coupling. What if I want to change the shape of the Account struct? If I change the thing that is wrapped in a mutex, this could then cause cascading changes to all of my worker thread code. I can hide the transactions field behind functions like add_transaction as a start, but channels allow even more loose coupling and as long as I treat the immutable messages being passed as a strong contract, my code can stay more loosely coupled and, in my opinion, more easily tested.

In conclusion, the only parting advice I have is that Rust has a steep learning curve, and shared mutation across threads is pretty high up on that curve. I had to spend some time getting familiar with building apps that don’t do this for a while before diving into the deep end of the thread pool (get it? thread pool??). When you are ready for concurrency in Rust, I highly recommend trying a “channel-first” approach to finding concurrency solutions and only using mutexes if channels become unwieldy.