Now, for everything to work we need some helpers to make our infrastructure work.

First of all, we need a way to get the id of an available thread.

fn main() {
   fn get_available_thread(&mut self) -> usize {
        match self.available_threads.pop() {
            Some(thread_id) => thread_id,
            // We would normally return None and not panic!
            None => panic!("Out of threads."),

As you see, we take one huge shortcut here. If we run out of threads, we panic!. This is not good, and we should rather implement logic to queue these requests and run them as soon as a thread is available. However, our code is already getting long, and it's not very important for our goal of learning about async.

Maybe this implementing such a queue is a good reader-exercise? Feel free to fork the repository and go ahead :)

The next thing we need to do is to create an unique identity for our callbacks.

fn main() {
/// If we hit max we just wrap around
fn generate_identity(&mut self) -> usize {
    self.identity_token = self.identity_token.wrapping_add(1);

fn generate_cb_identity(&mut self) -> usize {
    let ident = self.generate_identity();
    let taken = self.callback_queue.contains_key(&ident);

    // if there is a collision or the identity is already there, we loop until we
    // find a new one. We don't cover the case where there are `usize::max_value()`
    // callbacks waiting, since if we're fast and queue a new event
    // every nanosecond, that would still take 585 years on a 64 bit system.
    if !taken {
    } else {
        loop {
            let possible_ident = self.generate_identity();
            if self.callback_queue.contains_key(&possible_ident) {
                break possible_ident;

The function generate_cb_identity is where it all happens, genereate_identity is just a small function so we try to avoid the long functions we had in the introduction.

Now, there are some important considerations to be aware of. Even though we use several threads, we use a regular usize here and the reason for that is that it's only one thread that will be generating Id's. This could cause problems if several threads tried to read and generate new Id's at the same time.

We use the wrapping_add method on usize to get the next Id, this means that when we reach 18446744073709551615 we wrap around to 0 again.

We do check of our callback_queue contains our key (even though that is unlikely by design), and if it's taken we just generate a new one until we find a available one.

Next up is the method we use to add a callback to our callback_queue:

fn main() {
/// Adds a callback to the queue and returns the key
fn add_callback(&mut self, ident: usize, cb: impl FnOnce(Js) + 'static) {
    let boxed_cb = Box::new(cb);
    self.callback_queue.insert(ident, boxed_cb);

If you haven't seen the signature cb: impl FnOnce(Js) + 'static before I'll explain it briefly here.

The impl ... means that we accept an arguments that implements the trait FnOnce(Js) with a 'static lifetime. FnOnce is a trait implemented by closures. There are three main traits a closure can implement in Rust and FnOnce is the one you'll use if you plan on consume an instance from the environment.

Since you consume the variable a closure implementing FnOnce can only be called once. Our closure will take ownership over resources we create in our main thread and consume it. We want this since once consumed, the resources we used will be cleaned up as a result of Rusts RAII pattern. It's implicit that FnOnce returns () in this case so we don't have to write FnOnce(Js) -> ().

Since callbacks are meant to only be called once, this is a perfectly fine bound for us to use here.

Now, traits doesn't have a size so for the compiler to be able to allocate space for it on the stack we either need to take a reference &FnOnce(Js) or place it on the heap using Box. We do the latter since that's the only thing that makes sense for our use case. Box is a pointer to a heap allocated variable which we do know the size of so we store that reference in our callback_queue HashMap.

What makes a closure? A function in rust can be defined as easily as || { }. If this is all we write it's the same as a function pointer, equivalent to just referencing my_method (without parenthesis). It becomes a closure as soon as you "close" over your environment by referencing variables that's not owned by the function.

Fn traits are automatically implemented, and whether it implements Fn, FnMut or FnOnce depend whether you take ownership over a non-copy variable, take a shared reference & or an exclusive reference &mut (often called a mutable reference).

Now that we got some closure basics out of the way we can move on. The next method is how we register I/O work. This is how we register an epoll event with our runtime:

fn main() {
pub fn register_event_epoll(&mut self, token: usize, cb: impl FnOnce(Js) + 'static) {
    self.add_callback(token, cb);

    print(format!("Event with id: {} registered.", token));
    self.pending_events += 1;
    self.epoll_pending_events += 1;

The first thing we do is to add the callback to our callback_queue, calling the method we explained previously. Next we do a print statement, just since we want to print out the flow of our program we need to add this at strategic places.

One important thing to note here. Our token in this case is already guaranteed to be unique. We generate it in the Http module (which is the only one registering events by using this method in our example). The reason for this will become clear in a few short chapters. Just note that we don't need to call generate_cb_identity here.

We increase the counters on both pending_events and epoll_pending_events.

Our next method registers work for the thread pool

fn main() {
pub fn register_event_threadpool(
    &mut self,
    task: impl Fn() -> Js + Send + 'static,
    kind: ThreadPoolTaskKind,
    cb: impl FnOnce(Js) + 'static,
) {
    let callback_id = self.generate_cb_identity();
    self.add_callback(callback_id, cb);

    let event = Task {
        task: Box::new(task),

    // we are not going to implement a real scheduler here, just a LIFO queue
    let available = self.get_available_thread();
    self.thread_pool[available].sender.send(event).expect("register work");
    self.pending_events += 1;

Let's first have a look at the arguments to this function (aside from &mut self).

task: impl Fn() -> Js + Send + 'static is a task we want to run on a separate thread. This closure has the bond: Fn() -> Js + Send + 'static which means it's a closure that takes no arguments, but returns a type of Js. It needs to be Send since we're sending this task to another thread.

kind: ThreadPoolTaskKind lets us know what kind of task this. We do this for two reasons:

  1. We need to be able to signal a Close event to our threads
  2. We want to be able to print the kind of task each event received.

As you understand, we don't have to create a Kind for every task, but since we want to print out what the thread received we need some way of judging what kind of task each thread received.

The last argument cb: impl FnOnce(Js) + 'static is our callback. It's not a coincidence that our task returns a type of Js and our callback takes a Js as an argument. The result of the work we do in our thread is the input to our callback. This closure doesn't need to be Send since we don't pass the callback itself to the thread pool.

Next we generate a new identity with self.generate_cb_identity() and we add the callback to our callback queue.

Then we construct a new Event, and as I have shown earlier, we need to Box the closure.

Now, the last part could be made arbitrarily complex. This is where you decide how you want to schedule your work to the thread pool. In our case we just get an available thread (and panic! if we're out of thread - ouch), and we send our task to the thread which then runs it until it's finished.

You could make priorities based on TaskKind, you could try to decide which tasks are short and which are long and prioritize them based on load. A lot of exciting things could be done here. We will choose the simplest possible one though, and just push them directly to a thread in the order they come.

The last part of the "infrastructure" is a function to set a timeout.

fn main() {
    fn set_timeout(&mut self, ms: u64, cb: impl Fn(Js) + 'static) {
        // Is it theoretically possible to get two equal instants? If so we'll have a bug...
        let now = Instant::now();
        let cb_id = self.generate_cb_identity();
        self.add_callback(cb_id, cb);
        let timeout = now + Duration::from_millis(ms);
        self.timers.insert(timeout, cb_id);
        self.pending_events += 1;
        print(format!("Registered timer event id: {}", cb_id));

Set timeout uses std::time::Instant to get a representation of "now". It's the first thing we do since the user expects the timeout to be calculated from "now", and some of our operations here might take a little time.

We generate an identity for the callback cb passed in to set_timeout and add that callback to our callback queue.

We add the duration in milliseconds to our Instant so we know at what time our timeout times out.

We insert the callback_id instant to our BtreeMap with the calculated Instant as the key.

We increase the counter for pending_events and print out a message for us to be able to follow the flow of our program.

This might be a good time to talk briefly about our choice of a BTreeMap as the collection we store timers in.

From the documentation we can read "In theory, a binary search tree (BST) is the optimal choice for a sorted map, as a perfectly balanced BST performs the theoretical minimum amount of comparisons necessary to find an element (log2n)." Now, this isn't a Binary Tree but a BTree. While a BST allocates one node for each value, a BTree allocates a small Vec of values for each node. Modern computers reads much more data than we normally ask for into its caches, and thats one reason they love contiguous parts of memory. A BTree will result in a more optimal "cache efficiency" which often trumps the gains of the theoretically more optimal algorithm in a true BST.

Lastly, since we're talking about searching sorted collections here, and timeouts, is a perfect example of such, we'll of course use this when it's so readily available to us in Rusts standard library.