Making Arc more atomic

This is a story of a tiny feature I was missing in Rust… so I created it (partly because I like the feature, because it felt wrong for Rust not to have it, but mostly for the practice and fun of beating a hard and interesting problem). You can read the story if you are interested about the behind the scenes, about the feature itself, how to use it or just for fun ☺.

The missing feature

There are two smart pointers that allow having a shared ownership in Rust, std::rc::Rc and std::sync::Arc . Rc means „reference counted“ ‒ it keeps a pointer where both the value and a reference count lives. When the pointer is cloned, the reference count is increased, when one pointer is dropped, the reference is decreased. If it reaches 0 (nobody points to it any more), the value is destroyed. There are some more details (cycles, weak counts ‒ you can read the documentation).

The A in Arc stands for „atomic“. It allows all these increments and decrements to work correctly across threads ( Rc plain out refuses to have anything to do with more than one thread) ‒ so more than one thread at a time can point to the same value and no matter which thread drops the last pointer, the value gets destroyed then and only then correctly.

From this description, it looks a lot like std::shared_ptr from C++ . However, the shared_ptr has one extra capability the Rust’s Arc doesn’t. In Rust, only the reference count is atomic (and safe to manipulate from multiple different threads), but the Arc itself isn’t. That means that if we want to change where one given Arc points (not just clone it or have it in multiple threads), it needs to live under a Mutex or something similar. In C++ there are std::atomic_load and std::atomic_store functions that have specializations for shared_ptr and allow treating the pointer itself atomically.

I decided it would not do for Rust to lack such a great and needed functionality.

What good is it?

Sure, if C++ has the ability, so Rust should not stay behind, but what is the actual use case? I myself know of two.

First, a niche one, is that Mutex es are not allowed inside Unix signal handlers and one needs to synchronize data, because signal handlers can be run in whatever thread the OS likes (well, yes, there are ways to have a say in that, but still). Therefore, having something that can be used atomically instead of behind a mutex from within a signal handler is of some help ‒ it doesn’t make handling signals in non-trivial ways easy, but at least possible (more on that one in a future writeup).

The second one is form of RCU-like (Read-Copy-Update) data structure. It can be implemented by other means, but one way is with an Arc that changes from time to time.

Imagine a server that answers queries about some data set (maybe a DNS server, or maybe it’s not even a data set, it’s a configuration of your program). The data set is in the RAM, so the answering is fast. But from time to time, the data needs to be updated ‒ let’s say read from a file. We could lock the data structure and update it ‒ but we could not answer queries while we are updating it, creating a short downtime for the server. Depending on the kind of server, it might or might not be a problem.

Or, we could have the data set inside an Mutex<Arc<D>> . The query would lock the mutex, clone the Arc and unlock. Similarly, the update would prepare the new data set, lock the mutex and exchange the pointer and unlock it. This makes the downtime really short, the heavy updating is done outside of the lock. We could actually use RwLock instead of Mutex , which allows multiple queries to make the copy of the Arc at the same time. That’s pretty good (probably good enough for 99% practical uses), but it still has some downsides. It places locking onto the fast path of queries, whenever there’s an update, the queries have to wait for a short time and a steady stream of queries can block updates forever in some implementations of RwLock . The code could look something like this:

lazy_static! { static ref DATA : RwLock < Arc < Data >> = RwLock :: new ( Arc :: new ( Data :: load ())); } fn main () { for _ in 0 .. WORKER_THREAD_CNT { thread :: spawn (|| { loop { let query = read_query (); let data = Arc :: clone ( & DATA .read () .unwrap ()); let reply = data .answer ( query ); send_reply ( reply ); } }); } thread :: spawn (|| { loop { thread :: sleep ( Duration :: from_secs ( 900 )); let data = Arc :: new ( Data :: load ()); * DATA .write () .unwrap () = data ; } }); }

If we had an Arc that could be loaded atomically (without locking or waiting) and changed atomically, we could do even a bit better, avoiding all the locks on the hot path and nothing could ever get blocked.

Ingredients

We have an Arc as mentioned above. That one can track how many pointers point to our data. But it can’t be loaded or stored atomically.

There’s also AtomicPtr , which allows loading and storing atomically. But it does not track how many pointers point to the data, therefore we don’t know when to free the data. We are not in a garbage collected language, this is a problem.

I’ve decided to crossbreed them. The result is the arc-swap crate and the ArcSwap type, which can store an Arc into itself and provide a copy, both in a lock-less fashion.

Challenges

I wanted the atomic storage to work with ordinary Arc s from the standard library, not to build my own, incompatible, smart pointer ‒ too many things in Rust operate on the standard one. Therefore, I had to work with whatever the public interface gives me.

It has the into_raw and from_raw pair of methods. When storing the Arc, we can turn it into a raw pointer and that in turn can be stored inside a AtomicPtr . So far so good.

The into_raw method does not decrease the reference count ‒ even in the raw form, the pointer still holds its shared ownership ‒ and so does the AtomicPtr . Basically, the AtomicPtr is a frozen storage for the Arc . We just need to build our operations so they preserve the correct reference counts. Creation of the ArcSwap is easy, because there’s no chance of concurrency yet.

Let’s do swap ‒ exchange the content with a provided Arc and return the original. The swap preserves the reference counts, so all is well. And it can serve as the building block for store ‒ it swaps and throws the original away (in the Arc form, which will take care of all reference count decreasing for us).

The problem lies within load . Because we would like load to return a new Arc with the pointer, but also keep the original stored, we need to increment the reference count somehow ‒ from_raw claims ownership of the one reference inside the AtomicPtr , we need to generate a new one for it. We can do it by cloning and turning one clone of the Arc back into the raw pointer (and throwing it away).

However, while load from the AtomicPtr is atomic, we now have a short time before we read the pointer and increase the reference count (there are some papers how this could be done atomically with double-word atomics, but it would not work with the standard library Arc even if we had the double-word atomics). If some other thread managed to swap the original pointer out and drop it before we increase the reference count, we could get to 0 prematurely, and get our value freed. That’s not good.

Intermezzo with C++ standard library

I went to read the C++ standard library code (the one coming with gcc), because, after all, they already implemented it. I wanted inspiration.

Leaving aside that Rust’s standard library is much more readable than the C++ one (imagine a forest of templates and everything named with two leading underscores), I did not find my inspiration there. Instead, I found several things with mutex in their name around shared_ptr and its atomic_load / atomic_store function specializations. As it turns out, C++ standard does not mandate that these functions are lock-free.

Protecting the dangerous area

The next step was patching the dangerous area where we owe the reference. The initial fix was to add another atomic variable ( AtomicUsize ) inside the ArcSwap that would count how many threads there are inside load . The swap could then wait until the count drops to 0 before returning the old pointer to its caller. That way it would make sure no load owes the reference to the old value (another future load could own, but to the new value). And bad things can only happen when we drop the Arc , which happens outside of swap .

We prevented a crash, but it’s still not good enough. The problem is, if the ArcSwap is hammered enough with load s, it would never drop to 0 and the swap would wait forever more (and busy-wait on reading the counter).

The trick is, the swap doesn’t have to wait for no load s to be present in the dangerous section. No old load s that have seen the previous value is enough. Tracking the load s by which exact version of the pointer they’ve seen would be expensive. But we can make an error in the safe direction, by allowing to recognize it a bit later.

Therefore, we have two counters for the tracking of the load s and a switch to route new ones to either one or the other counter. If they are routed to the first, the second will eventually drop to zero. A waiting swap is allowed to turn the switch if it sees 0 in one, so the other one might get empty later on. Having glimpsed a zero in both (not necessarily at the same time) means any old load that saw the previous value must have gone away ‒ it must have been in either one or the other counter.

This way, the swap still has to wait for the load s to clear, but there’s guarantee at least one swap makes a progress each time there’s a zero (possibly more, but that much is not guaranteed). And multiple swap s can wait at the same time (same as multiple load s can happen at the same time). Seeing the generation increase by at least 2 should be enough too in theory (some other thread seeing the 0 also means it happened) but I have yet to implement it in a way this is faster in practice.

A load is always the same sequence of instructions. It will still slow down a bit if multiple loads happen at the same time, though, because the CPUs need to fight over the same memory location (there’s no lock-contention on the program level, but there’s contention on the HW level).

In the jargon, load is lock-free and wait-free. The swap (and therefore store ) is only lock-free, but that still allows it to be used inside a signal handler (however, signal handlers have memory allocation and deallocation forbidden too, so making an actual use of that is hard) and in massively-parallel workloads.

Edit: It turns out swap is not lock-free and can’t be safely put inside a signal handler (without some special care) ‒ swap s won’t block each other, but a interrupted load can block it. I’ll see if something can be done about that, but at least load can still go into the signal handler (which is what my signal use case is about).

Doing the true RCU

The example above assumed the whole data set was loaded completely anew each time and simply replaced the old value. Sometimes, we want a true update ‒ take the old data, make modifications to it and store the update. There are two reasonable ways to do that.

The simpler one is probably making sure there’s only one thread doing updates. That way, this thread can read the old value, create the updated version and swap it without fear it changed in meantime. This can be inside a mutex or by having a dedicated update thread.

If there could be multiple update threads, one would generally do the same, but retry the update (or relevant part) if the pointer was changed by another thread in the meantime. The ArcSwap has the needed compare_and_swap method to implement this and an rcu helper method doing these retries.

There’s also a little devil in the details. The last thread holding the original value is responsible for dropping the whole data set. If the data is expensive to drop, it could slow down the query and we are doing this thing to have fast queries. The updater thread could make sure it is the last one holding it (it can afford the slowdown), by repeatedly doing try_unwrap on the Arc , until it succeeds (there’s a helper method on ArcSwap for this thing too). The example code above does not suffer this problem, because the answer is sent before the drop.

The update might be done in several ways:

Cloning the old value and modifying it. This is simple, but slow and consumes twice as much memory (in case of large data sets).

If operating in the single thread, it could hold one „production“ copy and one „standby“ copy. It makes the update to the standby and swaps them. Then it makes sure it is the only owner (by the try_unwrap trick from above) and makes the same update to it too. It is now ready to accept a new update. This still makes use of twice as much memory, but the updates can be much faster, because there’s no need for the cloning.

trick from above) and makes the same update to it too. It is now ready to accept a new update. This still makes use of twice as much memory, but the updates can be much faster, because there’s no need for the cloning. Using persistent data structures. These are usually some kind of trees built from Arc s. An update needs to clone only the one traversed path of the tree, making both the update and drop of the old version much cheaper ‒ they share a lot of unchanged data that is not touched. But the tree data structures are often more CPU and memory hungry than some flat data structure like HashMap or Vec , so you may want to measure what results in better performance and memory consumption for you. I know of the rpds and im crates implementing some persistent data structures.

Alternatives

I’ve done some minimal benchmarks. I’ve found none where the ArcSwap would perform worse than RwLock<Arc<T>> . Some other benchmarks could find such a situation and benchmarks are not a real-word load, so it does not have to be a clear win for ArcSwap over some conventional means, even with the extra guarantees about being lock-free.