Last weeks were interesting for warmy, a crate I’ve been writing for several weeks / months now that enables you to hot load and reload scarce resources – e.g. textures, meshes, configuration, JSON parameters, dependency nodes, whatever. warmy received several interesting features, among:

Context passing: it is now possible to pass a mutable reference ( &mut ) to a typed context when loading and reloading a resource. This enables per-value loadings, which is neat if you need to add extra data when loading your resources (e.g. increment a counter).

) to a typed context when loading and reloading a resource. This enables per-value loadings, which is neat if you need to add extra data when loading your resources (e.g. increment a counter). Methods support: before version 0.7, you had to implement a trait, Load , in order to tell warmy how to load a given object of a given type. This is convenient but carries a bad drawback: if you want to represent your object with both JSON and XML for instance, you need to type wrap your type so that you can impl Load twice. This was annoying and the type system also handed you back an object which type was the wrapper type, not the wrapped type. This annoyance was removed in 0.7 as you now have an extra type variable to Load – it defaults to () though – that you can use to impl Load several times – think of that tag type variable as a type representing the encoding or the method to use to load your value.

, in order to tell warmy how to load a given object of a given type. This is convenient but carries a bad drawback: if you want to represent your object with both JSON and XML for instance, you need to type wrap your type so that you can twice. This was annoying and the type system also handed you back an object which type was the wrapper type, not the wrapped type. This annoyance was removed in 0.7 as you now have an extra type variable to – it defaults to though – that you can use to several times – think of that tag type variable as a type representing the encoding or the method to use to load your value. A VFS (Virtual FileSystem): the VFS makes it possible to write resource keys without caring about their real location – e.g. /splines/color_grading.json . Before that, you still had to provide a real filesystem path, which was both confusing and annoying (since you already give one when you create a Store , the object that holds your resource).

. Before that, you still had to provide a real filesystem path, which was both confusing and annoying (since you already give one when you create a , the object that holds your resource). Changelog here.

I posted on reddit in order to make people know of the new version, and interesting talks started to occur on both IRC and GitHub. What people seem to want the most now is asynchronous loading and reloading. I’ve been wanting that feature for a while too so I decided it could be a good idea to start working on it. However, after a day of toying around, I came to the realization that I should write a small blog post about it because I think it’s not trivial and it could help me shape my ideas.

Note: this post is about brainstorming and setting up the frame to why and how async warmy. You will find incomplete code, ideas and questions there. If you want to contribute to the discussion, you’re more than welcome!

Synchronous versus asynchronous

What does it mean to have a synchronous computation? What does it mean to have an asynchronous one? You might find it funny, but a lot of people are still confused with the exact definition, so I’ll try to give you more hindsight.

We talk about a synchronous task when we have to wait for it to finish before moving on to another task. We have to wait until its completion to get the control flow back and call other functions. We often talk about blocking computations, because you cannot do anything else while that computation is running – at least on the thread this computation is running on.

We talk about an asynchronous task when you can get the control flow back without having to wait for the task to finish. However, that doesn’t necessarily mean that the task is being executed in parallel or concurrently. At some extent, you could easily label generators as asynchronous primitives, and this is what actually happens in async / await code: you give back the control flow to the caller and the callee execution gets re-scheduled later. Hence, this is asynchronous programming, yet the scheduling execution could be implemented on a single thread – hence no parallel nor concurrency actually happen. Another example is when you perform a HTTP request: you can send the request and instead of blocking until the response arrive, you can give the control back, do something else, and then, at some time, handle the response. You don’t need parallelism to do this: you need asynchronous programming.

Note: a generalization of a generator is a coroutine, which hands the control flow back to another coroutine instead of the caller when wanted.

What about warmy?

At the time of writing this blog entry, warmy is completely synchronous. Consider the following example:

extern crate serde_json; extern crate warmy; use std::error; use std::fmt; use std::fs::File; use std::io; use std::thread; use std::time::Duration; use warmy::{FSKey, Load, Loaded, Res, Store, StoreOpt, Storage}; struct JSON; #[derive(Clone, Copy, Debug, PartialEq)] struct Foo { array: [f32; 4] } #[derive(Debug)] enum FooError { JsonError(serde_json::Error), IOError(io::Error) } impl fmt::Display for FooError { fn fmt(&self, f: &mut fmt::Formatter) -> Result<(), fmt::Error> { match *self { FooError::JsonError(ref e) => e.fmt(f), FooError::IOError(ref e) => e.fmt(f), } } } impl error::Error for FooError { fn description(&self) -> &str { match *self { FooError::JsonError(ref e) => e.description(), FooError::IOError(ref e) => e.description(), } } fn cause(&self) -> Option<&error::Error> { match *self { FooError::JsonError(ref e) => Some(e), FooError::IOError(ref e) => Some(e), } } } impl<C> Load<C, JSON> for Foo { type Key = FSKey; type Error = FooError; fn load( key: Self::Key, _: &mut Storage<C>, _: &mut C, ) -> Result<Loaded<Self>, Self::Error> { let file = File::open(key.as_path()).map_err(FooError::IOError)?; let array = serde_json::from_reader(file).map_err(FooError::JsonError)?; let foo = Foo { array }; Ok(foo.into()) } } fn main() { // read scarce resources in /tmp let ctx = &mut (); let opt = StoreOpt::default().set_root("/tmp"); let mut store: Store<()> = Store::new(opt).expect("store creation failed"); // get our resources via JSON decoding let foo: Res<Foo> = store.get_by(&FSKey::new("/foo.json"), ctx, JSON).expect("foo should be there"); let foo2: Res<Foo> = store.get_by(&FSKey::new("/foo2.json"), ctx, JSON).expect("foo should be there"); println!("{:#?}", foo); println!("{:#?}", foo2); loop { // sync the resources with the disk store.sync(ctx); thread::sleep(Duration::from_millis(100)); } }

Note: you can test that code by inserting warmy = "0.7" and serde_json = "1" in a Cargo.toml and by creating the JSON files containing 4D arrays of numbers, like [0, 1, 2, 3] in /tmp/foo{,2}.json .

If you look closely at that example, you can spot two important locations when we get resources: when we create the foo and foo2 bindings (search for store.get_by invocations). Here, it’s important to understand what’s really happening:

First, /foo.json is loading on the current thread. Then, when it’s loaded and cached, /foo2.json starts loading.

We can already see a pity here: both the files are completely disconnected from each other, yet, the second file must wait for the former to finish before starting loading. We’re not using all the cores / threads of our CPU. So we could perfectly imagine this new scenario:

First, ask to load /foo.json and immediately get control flow back. Then, ask to load /foo2.json and immediately get control flow back. Wait for both the resources to be available, then continue.

You could even do whatever you want between (2.) and (3.). The point here is that we can run tasks without having to wait for them to finish before starting others. The explicit waiting part could be a blocking call, such as:

let foo_task = store.get_by(…); let foo2_task = store.get_by(…); let foo = foo_task.wait(); let foo2 = foo2_task.wait();

Note: this is not the foreseen or planned interface. It’s just there to illustrate what we want to achieve there.

The goal of this blog entry is to explore possible implementations.

futures

The futures crate is a very promising and interesting crate. What it provides is basically a mean to express data that might get available in the future. For instance:

fn ping(host: &str) -> ???

Here, ping is a function that will perform an ICMP request to a given host, identified by the function argument. Because that function might take a while (at least several milliseconds, which is a lot), we have to make a decision:

Either we decide to block the current thread until a response gets available (the host responds to our ping and we get the packet back). Or either we decide to free the control flow from the ping function and do something else until the response arrive.

If you’ve followed all what I said since the beginning of this blog post, you might have noticed that (1.) is synchronous and (2.) is asynchronous (also notice that we haven’t talked about parallelism yet!).

With (1.), we would write ping ’s signature like this:

fn ping(host: &str) -> Result<PingResponse, NetworkError>

With (2.), we would write it like this, using the futures crate:

fn ping(host: &str) -> impl Future<Item = PingResponse, Error = NetworkError>

Note: the syntax impl Trait is called conservative impl trait and its description can be found in the corresponding RFC on impl Trait.

So what you basically do here is to send a non-blocking request and get its result in a non-blocking way. Sweet! How does that apply to warmy?

futures applied to warmy

Asynchronous warmy should have the following properties:

When you ask for a resource to load, you immediately get the control flow back.

Hot-reloading mustn’t block either, as you will do it in the Store::sync function.

function. Because of the asynchronous nature of those computations, the context passing now gets a bit tricky: we cannot handle a mutable reference on our context anymore, because otherwise we would block the main thread. We need to share the context in a smart way.

Finally, all parallel computations should be ran on some kind of green thread so that we don’t have to worry about destroying the OS threads amount.

Asynchronous loading

The current definition of the Store::get function is:

pub fn get<K, T>( &mut self, key: &K, ctx: &mut C ) -> Result<Res<T>, StoreErrorOr<T, C>> where T: Load<C>, K: Clone + Into<T::Key>

We want something like this:

pub fn async_get<K, T>( &mut self, key: &K, ctx: &mut C ) -> Result<impl Future<Item = AsyncRes<T>, Error = StoreErrorOr<T, C>>, StoreErrorOr<T, C>> where T: Load<C>, K: Clone + Into<T::Key>

Woosh. It gets hard to read – eventually, trait aliases will help us there. We can see the use of the futures crate here, in the return type:

impl Future<Item = AsyncRes<T>, Error = StoreErrorOr<T, C>>

Which reads as “An asynchronous resource of type T available in the future or an error due to the loading of T and C or due to the store”.

However, something important to notice is that we miss a crucial point here: we actually want a parallel execution. We could start try by defining a small state machine to step through the process of loading a resource. That could be:

enum LoadState<K, T, C> { Pending(K, Shared<C>), // resource loading pending Loading(Receiver<AsyncRes<T>>), // resource currently loading Done, // resource loaded }

This small state machine can be stepped through by the following process:

fn start_load<K, T, C>(key: K, ctx: &mut C) -> impl Future<Item = LoadState<K, T, C>> { LoadState::Pending(key, ctx.share_somehow()) } impl<K, T, C> Future for LoadState<K, T, C> { type Item = AsyncRes<T>; type Error = StoreErrorOr<T, C>; fn poll(&mut self, ctx: &mut Context) -> Result<Async<Self::Item>, Self::Error> { match *self { LoadState::Pending(..) => { // start the loading let (sx, rx) = oneshot::channel(); let task = Task::new(move || { // load the resource somehow let res = …; sx.send(res); }); // spawn a new task to load the resource and update the state machine ctx.spawn(task); *self = LoadState::Loading(rx); Ok(Async::NotReady) } LoadState::Loading(ref mut rx) => { match rx.map(|res| { *self = LoadState::Done; res }).poll()? } LoadState::Done => panic!("poll called after future has arrived") } } }

That code might be incomplete or not typecheck completely (not tested), but the idea is there. I still don’t know whether this is the good way to use futures or whether I should run a single, long computation without the small state machine. I just don’t know yet.

Asynchronous reloading

This part is tightly bound to how the Store::sync will behave. Now that I tink about it, maybe it’d be more ergonomic / elegant / simpler to have an event loop living on a separate thread that would benefit from epoll and all those fancy asynchronous primitives to deal with that. That would result in no more by-hand synchronization, since it’d be done by the event loop / reactor / whatever you want to name it.

Currently, I have not settled up on any decision regarding reloading.

Asynchronous context passing

This will be definitely a tricky part as well, because what was before:

store.sync(&mut ctx);

will then become something like:

store.sync(ctx.share());

Also, something that might happen is that because the synchronization will be done in an asynchronous way, you will need to ensure that you do not try to synchronize the same resource several times.

Finally, if you take into account the previous section, the user might not even synchronize by hand anymore, so the context will have to be shared anyway and moved to the event loop. Argh. That doesn’t seem simple at all! :)

Parallel execution

One final aspect of asynchronous warmy is obviously how we’re going to poll the various Future s generated by the get functions. One first, naive idea would be to run those in OS threads. However, imagine that you’ll be loading plenty of resources (on a big and release project, it’s easy to imagine several thousands of resources loading in parallel). You don’t want to allocate that many OS threads but instead rely on a green threading solution… which I haven’t found yet. People on IRC advised to have a look at futures-cpupool, but I’d like to have a complete solution set before deciding anything.

I will post this blog post on reddit with hope that people will come up with ideas and I’m sure enlighting ideas on how to cope with all of this asynchronous properties I want to push to warmy.

Thanks for having read me, and keep the vibes!