Communication in timely dataflow starts from the timely_communication crate. This crate includes not only communication, but is actually where we start up the various worker threads and establish their identities. As in timely dataflow, everything starts by providing a per-worker closure, but this time we are given only a channel allocator as an argument.

Before continuing, I want to remind you that this is the internals section; you could write your code against this crate if you really want, but one of the nice features of timely dataflow is that you don't have to. You can use a nice higher level layer, as discussed previously in the document.

That being said, let's take a look at the example from the timely_communication documentation, which is not brief but shouldn't be wildly surprising either.

extern crate timely_communication;

fn main() {

    // extract the configuration from user-supplied arguments, initialize the computation.
    let config = timely_communication::Configuration::from_args(std::env::args()).unwrap();
    let guards = timely_communication::initialize(config, |mut allocator| {

        println!("worker {} of {} started", allocator.index(), allocator.peers());

        // allocates a pair of senders list and one receiver.
        let (mut senders, mut receiver) = allocator.allocate();

        // send typed data along each channel
        for i in 0 .. allocator.peers() {
            senders[i].send(format!("hello, {}", i));

        // no support for termination notification,
        // we have to count down ourselves.
        let mut received = 0;
        while received < allocator.peers() {
            if let Some(message) = receiver.recv() {
                println!("worker {}: received: <{}>", allocator.index(), message);
                received += 1;


    // computation runs until guards are joined or dropped.
    if let Ok(guards) = guards {
        for guard in guards.join() {
            println!("result: {:?}", guard);
    else { println!("error in computation"); }

There are a few steps here, and we'll talk through the important parts in each of them.


There is only a limited amount of configuration you can currently do in a timely dataflow computation, and it all lives in the initialize::Configuration type. This type is a simple enumeration of three ways a timely computation could run:

pub enum Configuration {
    Cluster(usize, usize, Vec<String>, bool)

The first variant Thread indicates that we will simply have one worker thread. This is a helpful thing to know because it means that all of our exchange channels can be dramatically simplified, just down to simple queues. The second variant Process corresponds to multiple worker threads within one process. The number indicates the parameters. The third variant Cluster is how we indicate that this process will participate in a larger clustered computation; we supply the number of threads, this process' identifier, a list of addresses of all participants, and a boolean for whether we would like some diagnostics about the established connections.

The configuration is important because it determines how we build the channel allocator allocator that we eventually provide to each worker: allocator will be responsible for building communication channels to other workers, and it will need to know where these other workers are.

Channel Allocators

The allocator reference bound by the worker closure is the only handle a worker has to the outside world (other than any values you move into the closure). It wraps up all the information we have about this workers place in the world, and provides the ability to assemble channels to the other workers.

There are a few implementations of the Allocate trait, which is defined as

pub trait Allocate {
    fn index(&self) -> usize;
    fn peers(&self) -> usize;
    fn allocate<T: Data>(&mut self) -> (Vec<Box<Push<T>>>, Box<Pull<T>>);

These methods are the only functionality provided by allocator. A worker can ask for its own index, which is a number from zero up to the number of total peer workers (including itself), which it can also ask for. Perhaps most importantly, the worker can also request the allocation of a typed channel, which is returned as a pair of (i) a list of Push endpoints into which it can send data, and (ii) a single Pull endpoint from which it can extract data. The list has length equal to the number of peers, and data sent into push endpoint i will eventually be received by the worker with index i, if it keeps pulling on its pull endpoint.

The channels are various and interesting, but should be smartly arranged. The channel from the worker back to itself is just a queue, the channels within the same process are Rust's inter-thread channels, and the channels between processes will automatically serialize and deserialize the type T for you (this is part of the T: Data requirement).

One crucial assumption made in this design is that the channels can be identified by their order of creation. If two workers start executing in different processes, allocating multiple channels, the only way we will know how to align these channels is by identifiers handed out as the channels are allocated. I strongly recommend against non-deterministic channel construction, or "optimizing out" some channels from some workers.

The Data Trait

The Data trait that we impose on all types that we exchange is a "marker trait": it wraps several constraints together, like so

pub trait Data : Send+Any+Serialize+Clone+'static { }
impl<T: Clone+Send+Any+Serialize+'static> Data for T { }

These traits are all Rust traits, except for Serialize, and they mostly just say that we can clone and send the data around. The Serialize trait is something we introduce, and asks for methods to get into and out of a sequence of bytes.

pub trait Serialize {
    fn into_bytes(&mut self, &mut Vec<u8>);
    fn from_bytes(&mut Vec<u8>) -> Self;

We have a blanket implementation of Serialize for any type that implements Abomonation. Ideally, you shouldn't have to worry about this, unless you are introducing a new type and need an Abomonation implementation or you are hoping to move some types containing fields that do not satisfy those Rust traits.

Push and Pull

The two traits Push and Pull are the heart of the communication underlying timely dataflow. They are very simple, but relatively subtle and interesting and perhaps even under-exploited.


The Push trait looks like so (with two helper methods elided):

pub trait Push<T> {
    fn push(&mut self, element: &mut Option<T>);

That's all of it.

The push method takes a mutable reference to an option wrapped around a thing. This is your way of telling the communication layer that, (i) if the reference points to a thing, you'd really like to push it into the channel, and (ii) if the reference doesn't point to a thing this is the cue that you might walk away for a while. It is important to send a None if you would like to ensure that whatever you've pushed in the past should be guaranteed to get through without further work on your part.

Now, we didn't need a mutable reference to do that; we could have just had the argument type be Option<T>, or had two methods send and done (those are the elided helper methods).

This framing allows for fairly natural and stable zero-copy communication. When you want to send a buffer of records, you wrap it up as Some(buffer) and call push. Once push returns, the channel has probably taken your buffer, but it has the opportunity to leave something behind for you. This is a very easy way for the communication infrastructure to return resources to you. In fact, even if you have finished sending messages, it may make sense to repeatedly send mutable references to None for as long as the channel has memory to hand you.

Although not used by timely at the moment, this is also designed to support zero copy networking where the communication layer below (e.g. something like RDMA) operates more efficiently if it allocates the buffers for you (e.g. in dedicated memory pinned by the hardware). In this case, push is a great way to request resources from the channel. Similarly, it can serve as a decent back-channel to return owned resources for the underlying typed data (e.g., you pushed a list of String elements, and once used they could be returned to you to be reused).


The Pull trait is the dual to Push: it allows someone on the other end of a channel to request whatever the channel has in store next, also as a mutable reference to an option wrapped around the type.

pub trait Pull<T> {
    fn pull(&mut self) -> &mut Option<T>;

As before, the mutable reference and option allow the two participants to communicate about the availability of data, and to return resources if appropriate. For example, it is very natural after the call to pull to claim any T made available with a ::std::mem::swap which puts something else in its place (either Some(other) or None). If the puller has some data to return, perhaps data it received from wherever it was pushing data at, this is a great opportunity to move it back up the communication chain.

I'm not aware of a circumstance where you might be obliged to call pull and set the result to None to signal that you may stop calling Pull. It seems like it could be important, if these methods really are dual, but I don't see how just yet.

Guarded Computation

The call to initialize returns a


which is Rust's approach to error handling: we either get some worker guards or a String explaining why things went wrong, perhaps because we weren't able to establish connections with all of the workers, or something like that. The WorkerGuards<T> is a list of thread join handles, ::std::thread::JoinHandle<T>, which is something that we can wait on and expect a T in return. Each of these handles allow us to wait on the local worker threads, and collect whatever they produce as output.

We've wrapped the handles up in a special type, WorkerGuards, because the default behavior otherwise should you just discard the result is for the threads to detach, which results in the main thread exiting and the workers just getting killed. This way, even if you ignore the result we will wait for the worker threads to complete. If you would like your main thread to exit and kill off the workers, you have other ways of doing this.