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More tricks up in the ArcSwap’s sleeve

This is a continuation of the Making Arc more atomic post. In short, ArcSwap is a place where you can atomically store and load an Arc, similar to RwLock<Arc<T>> but without the locking. It’s a good tool if you have some data that is very frequently read but infrequently modified, like configuration or an in-memory database that answers millions of queries per second, but is replaced only every 5 minutes. The canonical example for this is routing tables ‒ you want to read them with every passing packet, but you change them only when routing changes.

I’ve been asked a few times how it works inside and I don’t want to repeat myself too much, so here we go. This is the high level overview of what happens under the hood, you’ll have to read the code for the fine details, proofs of correctness, etc.

I’ll be talking mostly about Arc, but the thing is generic and can be made to work with other things ‒ notably, Option<Arc<T>> is supported by the library, and if you have your own version of Arc, you can implement a trait on it and there you go too.

The devil is in the details

In the heart of the thing there lies an AtomicPtr. As Arc can be turned into a raw pointer and created from one, it can be stored in there. That conversion keeps the reference counts. The swap method is therefore easy ‒ one pointer with its reference goes in, another gets out. Just make sure the atomic orderings are correct. store is just a swap that throws the result out. That’s easy.

What does some kind of load look like? It needs to read the pointer, and because it returns one Arc, while keeping another „virtual“ Arc inside the storage, it needs to increment the reference count. But here lies the problem, as by the time we get to incrementing it, some other thread might have swapped the pointer out of the storage, dropped its copy, reached zero refcount and deleted the data, including the reference count itself. Ouch. Not good. We can’t increment non-existing reference count.

So, the original solution was a kind of uni-directional short-term lock which prevented the writer (the one doing swap) from completing before we increment the refcount. Read the original article about the details, it is all about that lock.

Properties of the lock

The original one (now called generation lock) had some good properties. Specifically, the read operation was wait-free, which is great. Literally, a reader stopped for nobody, performed its fixed sequence of operations and continued with a copy of the Arc in its hand.

There were just three downsides:

Sharding the lock

For this library, read performance is much more important than write performance. Therefore, the first changes I’ve made were to use multiple instances of the lock. Each reader would pick just one of them and lock it, while a writer would have to wait for a permission from all of them. By making sure the locks align to a multiple of cache lines size, the chance of two reader threads colliding on the same cache line is lowered (while making it slower for writers).

However, this doesn’t solve the contention on the reference count. If the only thing I want to do is to read one integer from configuration behind that pointer, incrementing and immediately afterwards decrementing the shared reference count is a big deal.

So I’ve made another method, the peek, available. Instead of locking, incrementing the reference count and unlocking, it returns the lock guard with access to the data. The caller reads that i64 from there and unlocks, never getting the actual Arc created (but keeping the data alive for the whole time) ‒ and never touching the reference count.

This solves the problem for very short reads, but runs the risk of making the writer wait in a busy loop for longer if my peek into the data is not so short. I could still take the load, which creates the Arc with its reference, but that feels wrong.

Making the lock configurable

By default the storage uses a global set of locks. The idea is, if a thread is busy locking one ArcSwap, it won’t be trying to lock a different one at the same time. That way, ArcSwap itself has the same size as a pointer, but runs the risk of blocking writers on unrelated instances of ArcSwap.

It is possible to fine-tune the behaviour by a type parameter. The library contains implementation of private locks for each separate instance. This is even used inside the signal-hook crate. There, this lock is the only allowed possibility due to restrictions what can be done inside a signal handler. However, the signal handler may run for a longish time and it would not do to block unrelated processing. With a private lock instance, only adding or removing signal handlers is affected (which usually happens just on application startup).

What about hazard pointers

As you can see, the lock approach isn’t perfect.

I’m certainly not the only person to take an interest in a similar problem. I’ve came across something called hazard pointers and one specific implementation, the AtomicArc was certainly a very interesting read. Unfortunately, as far as I know, it’s not yet finished and released.

On a very very high level, we have a global „don’t you dare to free this, I’m using it right now“ registry. If I want to start doing something with a pointer, I read it, write it down into the registry and check by reading it once more (to avoid races). Only after this I can actually access the memory. When done, I remove it from the registry.

If I want to free a pointer, I first remove it from the storage (so nobody else can read it any more), then walk the whole registry. If I don’t see the pointer mentioned there, fine, I can safely free it. If it is there, I mark it in the registry with a note „you’re now responsible for freeing it“, passing the problem onto some other unfortunate thread. That thread in turn will try to free it (with the same check through the whole registry) when removing its entry from the registry.

This is all very nice and shiny, but I didn’t like two details about it:

The hybrid approach

This is the implementation of the third loading method, lease. This one should probably be considered the primary one for most use cases.

Eventually I’ve decided to trim the hazard pointers down to a bare minimum. If anything at all doesn’t go according to the best-case scenario, it falls back to a full load instead. That one is still formally wait-free and by making it rare to happen, the problem with contention on the reference count should be effectively gone.

Furthermore, the responsibility for freeing something is not passed through the registry. A record in the registry means there’s an Arc somewhere which owes one reference (that is fine as long as we don’t reach 0). For this reason, it is no longer called hazard pointers in the code, they are debts. So instead of passing the responsibility to walk the registry, the writer takes the debts onto itself (removes it from the registry) and pays them (by incrementing the number). Actually, it pre-pays one upfront and removes it at the end, to be on the safe side.

The read operation therefore tries this, in order:

Just not to confuse people reading through the code, the Lease guard does not store the full Arc as an Arc, only as the raw pointer and that Arc is only virtual (eg. there’s the reference count for it).

When dropping the guard, we check if we owe a reference. If we do, we actually have nothing to do (except for removing it from the registry) ‒ we simply make it even by stopping to exist. If we don’t (either because we’ve done a full load or because someone paid our debt), we decrement the reference count.

As long as the current thread doesn’t hold more leases than there are slots, this is pretty fast ‒ comparable to lock & unlock of uncontended Mutex (Muteces are slow only if locked, because then they have to wait in line, and go to the OS to put the thread to sleep…), but with the ability for arbitrary number concurrent readers ‒ no risk of being locked or slowed down.


The repository contains some benchmarks. The ones I consider the most relevant are doing the measured operation (eg. peek) while some other threads do „background noise“ traffic ‒ like writing new values all the time. There are many combinations of the measured operation and how many threads do the background traffic and what kind of background traffic. This is done with several alternatives to compare with [ArcSwap].

However, benchmarks are not the real thing, for many reasons. First and foremost, real applications do other stuff than just access one shared piece of data, therefore the differences might be hidden in the costs of whatever else the application does. Apart from that, it turns out CPUs are sensitive to mostly whatever. Like, spinning up a parallel thread that doesn’t access anything near our data nevertheless may slow our core down because of the temperature gets higher or because the power subsystem is not able to feed both. So while interesting, benchmarks are not to be taken too seriously.

Areas of improvement

I’m open to ideas what and how to improve. In particular, I believe there is room for improvement in the writer performance (that one is rather slow) ‒ but I want to do that only if reader performance doesn’t get hurt. This crate is biased towards readers, if you need something „balanced“, there’s a place for some other implementation.

Also, discovering places where the documentation is not perfect (and sending a PR) would be highly appreciated. Or coming up with a nicer API.

If you feel like reading the proofs and the code and double-checking my reasoning is sound and that I didn’t overlook something would also be very valuable.

Implementation for other languages

I’ve already been asked if I would like to port this to another language (specifically, C++). My answer to that is no, I wouldn’t. This code is already quite challenging even with all the help and checks I get from the Rust compiler (which is a lot even if the code is using unsafe extensively). And I don’t have the free time for that. Besides, I don’t think C++ shared_ptr can be turned into a single pointer.

However, if you want to go ahead and implement it in some other language, I’ll be more than happy to answer any questions and walk you through the design or just talk about it.