Monitoring async Python

This post will cover some basics on how to monitor the health of a Python app making use of asyncio. I won’t go in to detail about async python in general as there are already a bunch of great tutorials out there. I’ll focus instead on an approach for monitoring the event loop. To quote the python docs:

The event loop is the central execution device provided by asyncio. It provides multiple facilities, including:

I’ll focus especially on “Delegating costly function calls”. Most of the work scheduled on the event loop should be fairly quick and non-blocking. If anything starts to use a lot of CPU it should be delegated to something else. Otherwise the health of your app will start to degrade.

The “health” of the event loop isn’t a binary healthy/unhealthy thing so instead I want to see a value across time showing me how much work I’m putting on the loop.

One of the simplest ways to do this is to time how long it takes to resume a piece of code that’s performed an asyncio.sleep. In ideal conditions the code after the call to sleep will be executed immediately after the requested duration. In reality it’ll take time for other work in the loop to be dealt with. Measuring this difference will give us a number for how busy the event loop is.

The code samples below assume Python 3.7 (but the approach will make sense for 3.6 or 3.8)

from asyncio import get_running_loop, sleep, AbstractEventLoop

class Monitor:
    lag: float = 0

    def __init__(self, interval: float = 0.25):
        self._interval = interval

    def start(self):
        loop = get_running_loop()

    async def _monitor_loop(self, loop: AbstractEventLoop):
        while loop.is_running():
            start = loop.time()
            await sleep(self._interval)
            time_slept = loop.time() - start
            # TODO: push this lag into a monitoring system
            self.lag = time_slept - self._interval  

calling Monitor().start() creates a new task on the event loop. This task records a time, sleeps for 0.25 seconds then measures the new time. This lag can then be pushed to some monitoring system and graphed.

If after a change is made to production this value jumps up then I know those changes have introduced more blocking work onto the loop. This can act as an early warning sign before things get too bad.

Another aspect I want to monitor is the number of active tasks on the event loop. For a system in a steady healthy state this number should stay roughly constant.

This can be tracked by adding some code to the _monitor_loop task from earlier:

from asyncio import AbstractEventLoop, Task

class Monitor:
    active_tasks: float = 0

    async def _monitor_loop(self, loop: AbstractEventLoop):
        while loop.is_running():
            #...All of the lag timing code from before...
            tasks = [t for t in Task.all_tasks(loop) if not t.done()]
            self.active_tasks = len(tasks)

Again this active_tasks value should be pushed into whatever monitoring system is being used.

Now the only thing I need to do is start this monitor when the app loads. For example in an app I run built using FastAPI:

from fastapi import FastAPI
from british_food_generator.monitoring.asyncio import Monitor

monitor = Monitor(0.25)
app = FastAPI(title="British Food Generator")

def start_monitoring():

The real benefit with this approach is that I can spot warning signs of a problem before any users notice a performance impact. If either measure indicates some problems I can start investigating straight away. At this stage the code could be run locally with the event loop in debug mode. This is explained a little better in the asyncio dev docs and gives some tooling around spotting blocking tasks and other issues.

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