Customizing asyncio Event Loop Implementations

The first question to ask yourself is why. Why would you want to replace something as fundamental as the asyncio event loop? It’s the engine of the entire library. For almost everyone, the answer should be that you don’t. The loops that ship with Python are fast, stable, and have been tested by thousands of developers in production. Tinkering with the event loop is like deciding to forge your own engine block because you think you can improve on what Honda does. You probably can’t.

So when is it not a crazy idea? The most common and legitimate reason is integration. You’re building an application with a graphical user interface, using a framework like PyQT, Tkinter, or wxPython. These frameworks all have their own event loops. They need to listen for mouse clicks, key presses, and window resize events. You can’t just run the asyncio loop in one thread and the GUI loop in another and hope they’ll cooperate. It leads to a world of pain. The only clean solution is to have a single, unified event loop that understands both asyncio’s awaitables and the GUI’s events. Someone has to teach the loop how to listen to both file descriptors and Qt signals at the same time.

If you’ve ever tried to mix asyncio with a library that has its own main loop, you’ve felt this pain. You end up with awkward workarounds. You might run the asyncio loop for a few milliseconds, then yield control back to the GUI loop, then run the asyncio loop again. It’s a polling mechanism, and it feels wrong because it is. It’s inefficient and prone to latency spikes. The right way is to have one loop manage all sources of events. Some libraries, like quamash for Qt, do exactly this. They provide a custom asyncio event loop that is, under the hood, powered by the Qt event loop.

# The wrong way: trying to "pump" one loop from another
def update_gui_and_run_asyncio():
    # Process some GUI events
    app.processEvents()
    # Manually run asyncio for a short burst
    loop.call_soon(loop.stop)
    loop.run_forever()
    # Schedule this to run again
    QTimer.singleShot(10, update_gui_and_run_asyncio)

# The right way: a loop that understands both
# (conceptual)
class QtAsyncioLoop(asyncio.AbstractEventLoop):
    # ... implementation that integrates with QCoreApplication ...

asyncio.set_event_loop(QtAsyncioLoop())

Another reason, though far less common, is specialized scheduling. The default asyncio scheduler is fair. It tries to give every ready task a chance to run. But what if you don’t want fair? What if one type of task is vastly more important than another? Imagine a control system where a task monitoring a pressure valve must run with near-zero latency, while a task logging data to a remote server can wait. The default loop doesn’t have a concept of task priority. If you need that, you might have to build it yourself by writing a custom loop with a priority-based ready queue.

The most interesting reason might be for debugging and instrumentation. When an asyncio application misbehaves, it can be maddeningly difficult to figure out why. Which task is stuck? Which callback is blocking the loop? By implementing your own loop, you can wrap every single operation. You can log when a task is created, when it becomes ready, when it starts running, and when it finishes. You can measure the exact time spent in every callback. It’s the ultimate printf debugging for the asynchronous world. You’re not just observing the system; you’re building the observation tools into its very foundation. This is often a temporary measure, a tool you use for development rather than something you ship to production, but it can be invaluable.

A common temptation is to write a custom loop for performance. This is almost always a mistake. Unless you are the author of uvloop and have found a new way to interact with epoll or kqueue, you are unlikely to write a faster general-purpose event loop in Python than the C-based ones that already exist. The performance bottlenecks in most Python applications are in the application code itself, not in the nanoseconds the loop spends polling for I/O. Chasing performance by rewriting the loop is a form of premature optimization that distracts from where the real gains are.

And finally, there’s the reason of learning. You write a custom loop for the same reason you’d write a toy Lisp interpreter. Not because the world needs another one, but because you want to understand what’s really going on. What does call_soon actually do? How does the loop know a socket is ready for reading? How are futures and tasks connected? Implementing a simple selector-based loop from scratch is one of the best ways to answer these questions. It demystifies the magic and turns it into concrete, understandable code.

What a Loop Must Do

At its heart, an event loop is just a while loop that doesn’t end until you tell it to. But what does it do inside that loop? It does two main things: it checks for I/O and it runs scheduled callbacks. That’s it. Everything else is built on top of these two primitives.

If you look at the asyncio.AbstractEventLoop class, you’ll see a long list of methods you’re supposed to implement. It looks intimidating. But many of them are helpers or conveniences. The core machinery is surprisingly small. You need a way to manage time and a way to manage I/O.

Let’s start with time. The loop needs to know about two kinds of callbacks: those to run “as soon as possible” and those to run at a specific time in the future. This implies you need at least two data structures. For the “soon” callbacks, a simple queue or deque works fine. This is your _ready queue. When someone calls loop.call_soon(callback, arg1, arg2), you just package that up into a Handle object and append it to the deque.

# A simplified conceptual loop
import collections
import heapq
import time
import selectors

# asyncio.Handle is not public, so we'd need a simple substitute
class Handle:
    def __init__(self, callback, args):
        self._callback = callback
        self._args = args

    def run(self):
        self._callback(*self._args)

class MySimpleLoop:
    def __init__(self):
        self._ready = collections.deque()
        self._scheduled = [] # A min-heap
        self._stopping = False
        self._selector = selectors.DefaultSelector()

For callbacks scheduled at a specific time with loop.call_at(when, callback, ...), you need something more sophisticated. You can’t just put them in a list and scan it every time. That would be horribly inefficient. The right tool here is a min-heap, ordered by the scheduled run time. This way, the callback that needs to run next is always at the top of the heap. Your main loop only needs to peek at the first item to know how long it can afford to wait for I/O.

The central logic of your loop will live in a method that runs one “tick” of the loop. Let’s call it _run_once. On every tick, it does the following:

1. It calculates a timeout. How long can it wait for I/O? It looks at the top of the scheduled callbacks heap. If the next scheduled callback is in 50 milliseconds, the timeout is 0.05 seconds. If there are no scheduled callbacks, it can wait indefinitely. If there are callbacks in the _ready queue, the timeout should be zero—it should process them immediately without waiting for I/O.
2. It calls the selector. This is the part that talks to the operating system. selector.select(timeout) will block until either the timeout expires or a file descriptor becomes ready for reading or writing.
3. It processes I/O events. The selector returns a list of ready file descriptors. For each one, you look up the associated callback you stored earlier (when add_reader or add_writer was called) and move it to the _ready queue.
4. It processes scheduled callbacks. It checks the current time. It pops everything from the min-heap whose scheduled time has passed and moves those callbacks to the _ready queue.
5. It runs the ready callbacks. It works through everything currently in the _ready queue, calling them one by one. This is where the application’s logic actually executes.

The run_forever method is then just a while not self._stopping: loop that calls _run_once repeatedly.

Handling I/O means you need methods like add_reader(fd, callback, *args) and remove_reader(fd). When add_reader is called, all you’re really doing is storing the callback in a dictionary keyed by the file descriptor (fd) and then telling your selector object to watch that fd for readability. The selector is the key component that abstracts away the differences between epoll (Linux), kqueue (BSD/macOS), and select (Windows). Python’s selectors module gives you a high-level way to access the best one for the current platform.

# Inside our conceptual loop class
def add_reader(self, fd, callback, *args):
    # We unregister first in case it's already registered, to modify it
    try:
        self._selector.unregister(fd)
    except KeyError:
        pass
    self._selector.register(fd, selectors.EVENT_READ, Handle(callback, args))

def remove_reader(self, fd):
    try:
        self._selector.unregister(fd)
        return True
    except KeyError:
        return False

# Part of the _run_once logic
def _process_io_events(self):
    events = self._selector.select(timeout)
    for key, mask in events:
        handle = key.data
        self._ready.append(handle)

But what about await? What about Task and Future objects? This is where it gets interesting. The loop itself doesn’t know much about coroutines. It just runs callbacks. The magic happens in the Task object. A Task is what wraps your coroutine. When you await a future, the Task execution is suspended. The Task attaches a callback to the future it’s waiting on. The callback is simply a method on the task that resumes it. When the future completes (for example, when the I/O it was waiting for is done), it runs its callbacks. This puts the task’s resumption method into the loop’s _ready queue. When the loop executes that callback, the task resumes where it left off. So, the loop’s job isn’t to run coroutines directly, but to provide the call_soon mechanism that the Task and Future objects use to schedule their own resumption.

The create_task(coro) method on the loop is the entry point. It’s responsible for creating the Task object and scheduling its first run. It does this by simply calling self.call_soon(task._step), where _step is the internal method that runs the coroutine to its next await. This primes the pump. The task is now in the ready queue, and on the next iteration of the event loop, it will start executing the coroutine. The coroutine will run until it hits its first await. At that point, it yields control back to the loop, and the whole cycle of waiting for events and running ready callbacks continues.