Borrowing vs. Boxing in Async-heavy Code: How do you mentor juniors through lifetime hell?

[coderlegion]

We're porting our Python WebSocket service (10k msg/s) to Rust/Tokio and hit a recurring wall: juniors writing async fn process(&self) that fights the borrow checker when spawning tasks. Classic case:

struct Processor {
    cache: RefCell<HashMap<u64, Data>>, // 🤦
}

impl Processor {
    async fn handle_event(&self, event: Event) {
        let mut guard = self.cache.borrow_mut(); // Blocks entire struct
        // ...await point here = 💥
    }
}

The pain:

• Overusing Arc<Mutex<_>> kills throughput (contention up 300% in benchmarks)
• Box::pin + 'static lifetimes confuse those coming from GC langs
• Fear-driven clone() everywhere → memory bloat

Our stopgaps:

1. #![deny(clippy::await_holding_lock)]
2. Training sessions on Pin, Arc::clone discipline
3. Structured learning paths via platforms like CoderLegion for async fundamentals

Discussion points:
What's your threshold for unsafe when lifetimes fight back? (We drew line at 0.1% LOC)
How do you debug stuck futures without RUST_BACKTRACE=full crutches?
Are collaborative code reviews better than solo struggles for ownership mastery?
Could community learning resources help shorten the "Rust despair curve"?

(Production horror: Deadlock in our #[tokio::test] suite only triggered at 7k rps. Solution: tracing::instrument + tokio-console.)

[kpreid]

@coderlegion

Overusing Arc<Mutex<_>> kills throughput (contention up 300% in benchmarks)

Fundamentally, you have to be careful every time you introduce a Mutex, because uncautious use can introduce deadlocks, not just contention. If your “juniors” are reaching for it regularly, then you need to teach them patterns that don’t have Mutex. Design data that is shared or mutable but not shared and mutable.

For a cache, which is necessarily shared and mutable, probably a data structure meant for caching would be good. (I unfortunately don't yet have any library recommendations here.) If you don't have one, there's this simple approach:

struct Processor {
    cache: std::sync::Mutex<HashMap<u64, Arc<tokio::sync::OnceCell<Data>>>>,

let entry: Arc<OnceCell<Data>> = cache.entry(key).or_default().clone();
let value: &Data = entry.get_or_init(|| ...);

This way, the time spent computing one entry only affects other code which would be computing that specific entry anyway. Additionally, this can even be used recursively — there’s no way for it to deadlock as long as an individual Data entry never depends on itself.

@coderlegion

What's your threshold for unsafe when lifetimes fight back? (We drew line at 0.1% LOC)

This is, in my opinion, the wrong question to ask, twice.

• The amount of unsafe code does not matter. The soundness and maintainability of the unsafe code matters.
• Very many ideas people get for unsafe code while working in the frame of “fighting the borrow checker” are in fact unsound. Before starting to write the unsafe code, it is important to stop and think, and reframe the problem in terms of “What (unsafe-using) safe abstraction would solve my problem if it exists?” and write that, instead of writing a borrow-checker bypass for the particular application (which will very likely be unsound, or be easy to accidentally make unsound under maintenance).

The qualifications for introducing unsafe code should be:

1. Is there not any safe way to do the same thing with the same performance?
2. Is the unsafe code well isolated to a single module with a safe, sound API?
3. Is there a clearly written explanation of why this unsafe code is sound?

Aim for “high quality unsafe code”, and let low quantity be a natural consequence of that.