Playing with the new async

I’m quite a fan of the asynchronous programming styles (not spawning too many threads, using some kind of epoll instead to coordinate what to do next). I’ve written few event loops manually in C. My first Rust project was an attempt to write an event loop (luckily, I’ve never released that one; while it did work, the API turned out just terrible). I’ve done some development with old tokio-core, the bit newer tokio and wrote a coroutine library on top of tokio as an attempt to make the working with async in Rust more convenient while not having to wait for the full compiler support.

So, the Rust’s async-await is an area of interest for me. However, due to all the churn around it, I was mostly staying with the tokio 0.1 and waiting it out to see how everything develops.

But I’ve decided it’s about time to have a closer look at the new stuff and play with it a bit. Partly because I feel I want to see it before it is released „for good“ (eg. in stable 1.39), but partly because I wanted to measure some network throughputs, but in a different way than iperf3 does (well, you can call it an excuse to play with Rust over the weekend instead of reason, if you want). So I’ve decided to write my own tool and see if the async-await and all the libraries around it are up to it. And I finally found out a bit of time. The result can be found here.

The task

In my job, I work with kind of a smart firewall thing that sits in an embedded device, small enough to be put to users’ homes. And while we have some performance measurements, these mostly concentrate at not doing visible impact to normal home traffic usage.

But the thing with benchmarking is, you never run out of ideas what to measure.

The question I’m asking right now is how many new connections per second can I open before the device starts sweating. We know the number is large enough to not happen during normal browsing, but we don’t know the exact number yet.

So I’ve decided to write this tool that’ll keep opening many new connections and sending only little data over each. Then it closes the connection and measures the latency of the whole exchange. That’s easy in principle, except I’m dry-running it on a gigabit ethernet link without anything in between the two computers and I want the program to keep up with that too 😈. Just to make sure I can give the device enough traffic to start sweating.

While simple, having configurable of data to be sent in each direction also allows me to test in many scenarios ‒ empty connections, connections with some unrecognized content, big ones, small ones, http connections (this can be faked by just placing the request and response into a file and sending them), etc.

Initial research

I knew about the async-await notation and that I need nightly to compile that. The question was, what libraries are out there to provide the actual primitives to await on. Luckily, I wanted only the basic networking stuff ‒ only TCP connections, no fancy protocols on top, no encryption.

I’ve discovered three libraries that provide that:

tokio (in its alpha releases of version 0.2)

async-std

runtime

This was both good and bad news. On one side, it means there are multiple projects working on the problem and therefore there’s life around the problem. There must be something that’ll fit my needs in that, right? The community must be thriving if it can feed three concurrent implementations.

On the other hand, there’s one synchronous TcpStream in the standard library. If I’m counting the tokio-v0.1 and tokio-core, there are 5 pairwise-incompatible asynchronous TcpStream s. Libraries built on top of these will either need to be generic, using traits, or will depend on specific one of them. If you happen to need two in the same program, you have to bring both dependencies in. Furthermore, it feels like duplicating the effort and that there’ll be further time needed before the dust settles and compatibility layers appear ☹. It’ll mean triple the amount to work to bring the async support for signal-hook up to date.

Anyway, back to my problem of choosing the library. The runtime went off list pretty fast. The library seems to be great for examples and tutorials, but seems to be lacking some kind of flexibility to meet real-world needs. In particular, I wasn’t able to find a way to have only part of my program asynchronous. The library is all or nothing ‒ the whole main must be marked as #[runtime::main] (and I can’t place the attribute at differently named function). However, I wanted to do a synchronous startup, then run something asynchronous, wait for it to finish. On the client side, I actually wanted to have multiple rounds of this. I probably could invest some mental effort and shoehorn my idea of what I wanted to do into all-async code, but I didn’t want to. After all, some real world projects I’ve worked on do have some synchronous threads and some asynchronous ones. I wanted to be able to switch between the modes and not being able to feels limiting.

I’ve decided to go with async-std, for two reasons. First, it tries to reflect the std API as closely as possible, which I find a nice idea. Second, I already worked with tokio, so I was curious about the alternatives.

The first implementation

I know the infrastructure around the std::future and such is young. I expected problems to appear.

However, the initial implementation went much smoother than expected. Due to the familiarity of the APIs I could write most of the code from memory, only with occasional check and glimpse at an example how to put some bits together. I had both the server and the client ready in few hours (with most of the bells and whistles like command line parsing with help, etc; did anyone say Rust is not good for prototyping?). The only thing I’ve noticed was that async-std didn’t contain a channel, which in my experience is quite often used tool in many applications that also need async. This wasn’t a big problem, since the futures-preview is already a dependency of async-std and it provides one.

But when running it, with both server and client on localhost , sometimes the experiment never finished, even with low rates of new connection creation. This seemed odd. I mean, on a real network one can expect a packet to get lost, but it gets retransmitted and the problem should resolve itself in a short time. Furthermore, this was a localhost , not a real network. Maybe I was doing something wrong?

Timeouts

Nevermind, I wanted an easy way out. So I’ve decided to add a timeout to each connection and get rid of the broken ones. Or to prove the problem was somewhere else.

OK, there’s the timeout function. But it says it is supported only on unstable ‒ what does that mean and how do I turn it on? The docs don’t say. Trying to use it didn’t provoke the compiler to issue an useful hint either.

But after using the old futures, I could do such thing by hand, of course. One only needs to create an independent time-based future and select which one happens first. Dropping the other will cancel it. async-std offers such future, it’s called sleep.

And a roadblock appeared. All kinds of select things expect the futures to be Unpin. My connection future wasn’t. That wasn’t too unexpected. What surprised me that even the (probably quite stateless) sleep wasn’t. Maybe some basic futures provided by the library eventually will become Unpin, to make them more widely usable.

The problem can be solved, and quite easily once the concept of pinning is absorbed. There’s the pin_mut macro which will take care of everything needed. But even though I watched the development of async-await a bit and knew about pins, I had to spend some time searching for the right incantations. Previously, selecting over two futures was a no-brainer, currently it poses additional friction. The code is not really short either (I’ve preferred the function over the macro version to avoid rightward drift of match in match -like macro).

async fn connect ( server : SocketAddr , content : Arc < [ u8 ] > , mut results : Sender < Duration > ) { let connect = connect_inner ( server , content ); pin_mut! ( connect ); let timeout = task :: sleep ( TIMEOUT ); pin_mut! ( timeout ); match future :: select ( connect , timeout ) .await { Either :: Left (( Ok ( duration ), _ )) => results .send ( duration ) .await .expect ( "Channel prematurely closed" ), Either :: Left (( Err ( e ), _ )) => error! ( "Connection failed: {}" , e ), Either :: Right ( _ ) => { warn! ( "Connection timed out" ); results .send ( TIMEOUT ) .await .expect ( "Channel prematurely closed" ); } } }

Switching to tokio

I’ve used 5 seconds timeout. That is an ethernity when it comes to solving some connectivity issues on localhost. Still, even with rates like 100 new connections per second, some of the connections were timing out. But only very few of them, so it didn’t look like I was doing something terribly wrong myself. I suspect the async-std is missing some wakeup registration in one of its primitives. I’ll try to pin the problem down further and report the bug later on, but I want to have more data for that first (bugreports in the style of „Something doesn’t work as expected are not useful).

Anyway, I wasn’t even sure this was the fault of the library. So I’ve decided to try switching to tokio and see if it goes away.

The API is mostly similar, with some exceptions. For example, there’s no free-standing block_on function. It seems tokio::run is gone too from the 0.2 branch ☹. There’s the [tokio::main] attribute, but once again, I don’t want my whole program to be asynchronous. Luckily, I can build a Runtime manually and that one does have block_on (did the old Runtime had some more tweaking knobs, though?). It even allows me to reuse the runtime between the multiple asynchronous parts, something I’m not sure the previous free standing block_on of async-std did (maybe it did, but the docs don’t say much about what happens under the hood; It doesn’t allow much room for tweaking too, but maybe the crate is just too young and there’ll be a way in the future).

The other part I was missing was an async port of the std::io module. I wanted to read and throw away all the data from the connection, which can be done in the async-std easily:

io :: copy ( & mut connection , & mut io :: sink ()) .await ? ;

Tokio has a .copy method on the TCP connection (this might be coming from futures-preview, actually), but no blackhole to write into ☹. Writing one is quite straight-forward, but still the amount of code feels like it’s more than the problem deserves. I guess I’ll just post a pull request to either futures-preview or tokio, someone might find it useful (with a read counterpart that’s always empty).

struct Sink ; impl AsyncWrite for Sink { fn poll_write ( self : Pin <& mut Self > , _ : & mut Context , buf : & [ u8 ], ) -> Poll < Result < usize , IoError >> { Poll :: Ready ( Ok ( buf .len ())) } fn poll_flush ( self : Pin <& mut Self > , _ : & mut Context ) -> Poll < Result < (), IoError >> { Poll :: Ready ( Ok (())) } fn poll_shutdown ( self : Pin <& mut Self > , _ : & mut Context ) -> Poll < Result < (), IoError >> { Poll :: Ready ( Ok (())) } }

Edit: It turn out this is already in master of futures-preview, just not yet released.

However, when I run it again after the switch, the experiment was running for much longer than it should have. At that time the hardcoded time of one run was 10 seconds (plus waiting for all the connections to finish, but the maxima were in terms of milliseconds). It felt much longer.

The problem turned out to be tokio’s timer inaccuracy (which is a feature). I always started a new connection and then computed the new time at which I’d like the next one to start by adding a pause interval. However, the timer’s accuracy is one millisecond and with rates over 1000 connections per second, this was not enough, it always slept for that one millisecond. This was an easy fix, just changing from adding one „pause“ between connections to the previous time, I count n pauses from the base time. This has the slight disadvantage of „batching“ the connections, though ‒ if I run at a rate of 25k, it creates 25 connections and then sleeps a millisecond, because all 25 times round to the same millisecond.

Anyway, the timeouts went away. As a bonus, the program seemed to use slightly less CPU, but I didn’t do much scientific measurement of that, I’ve simply eyeballed top ‒ so it might as well an impression or the change of sleeping algorithm (executing in batches could mean less context switching or something).

Performance tuning

Actually getting to a point when the connections and data sent over them saturates the gigabit ethernet (connecting my desktop and laptop, both quite ordinary machines) took some effort. Surprisingly, most of it was tuning of the kernel and OS, not of the code in the program. The biggest problem was running out of client source address/port pairs (see the repo for the details).

I’ve done some tweaks in the code too, like having multiple sockets accepting on the same port, running in independent tasks (therefore able to run in parallel threads) or tuning the listen queue length. I might eventually add support for multiple source IPs too.

Even with all the tuning, at high rates (tens of thousands of new connections per second), the behaviour is a bit glitchy ‒ sometimes, connections are reset by either side. Sometimes, on the client, something in the kernel goes mad and starts eating a lot of (all of the) CPU while dropping performance significantly. So there is still space for more research. I’m probably not investing the time, though, for the purpose of the tool these rates are likely several orders of magnitude higher than needed.

And I’ve noticed one weird thing. With growing rate, the mean latency tends to shring. Well, up to a point. But it seems the thread pools are faster when they are kept a bit busy.

Conclusions

I know I’m a picky person and I find problems wherever I look. So I’m not surprised to have found them here. Actually, my feeling about the state of the async-await is quite positive. It’s definitely more comfortable to use than the original combinators way, though I still feel like the combinators are not completely dead ‒ some idioms are simply easier to express with them, eg buffer_unordered .

Obviously, many things need some more love and polishing ‒ I’ve seen dead links in documentation, functionality that I was used to but went away, bugs… but that’s to be expected at this state. The stuff I used did say „alpha“.

As mentioned above, I’m a bit worried about the schism of libraries.

I’m quite impressed in how little work it took to write a high-performance implementation. I mean, if the first part that gives performance-wise is the kernel, then Rust has nothing to be ashamed of.

And, by the way, I haven’t yet measured the device yet, only spent part of the weekend playing with Rust.