It started with a silent failure. Our microservice, handling roughly 800 requests per second (RPS), didn't crash; it just stopped responding. The health checks were timing out, but the CPU usage on the AWS Fargate container wasn't maxed out—it was hovering weirdly at 100% of a single vCPU while the others sat idle.
If you are reading this, you are likely staring at a similar situation: your Python Asyncio application is suffering from "Event Loop Starvation." You might see intermittent "Task was destroyed but it is pending!" warnings or the dreaded RuntimeError: Event loop is closed during graceful shutdowns. This isn't just a coding error; it's an architectural bottleneck inherent to Concurrency Programming in Python.
Analysis: The Single-Threaded Trap
To understand why your application hangs, we need to revisit the core of Python's async model. Unlike Java or Go, which utilize OS threads or green threads that are preemptively scheduled, Python's asyncio relies on cooperative multitasking.
The Event Loop is a single thread loop that runs tasks sequentially. It relies on tasks effectively saying, "I'm waiting for I/O, you can run someone else now" (via `await`). If a piece of code executes a synchronous, CPU-intensive operation or a blocking I/O call (like standard `requests.get` or a heavy cryptographic hash) without `await`, it holds the entire loop hostage.
In our scenario (Python 3.11 running on Linux Kernel 5.10), a specific third-party library for PDF generation was synchronous. Even though it only took 200ms to run, under a load of 50 concurrent requests, those 200ms stacked up. The cumulative latency caused the heartbeat pings to fail, leading the load balancer to drain the node.
BlockingIOError: [Errno 11] Resource temporarily unavailable
...
Executing <Task finished name='Task-123' ...> took 2.045 seconds
If you've checked my previous guide on Gunicorn workers, you know that mixing sync and async workers is a recipe for disaster. But often, the blocking call is hidden deep inside a dependency.
Why Standard Profiling Failed
My first instinct was to attach `py-spy` or standard cProfile. The problem is that standard profilers show you where time is spent, but they often struggle to correlate that with "blocking the event loop" specifically. I saw high time in `lib_pdf.generate()`, but I assumed it was running concurrently because I had wrapped it in an `async def`.
This is a critical misconception: Just putting `async` in front of a function definition does not make the synchronous code inside it non-blocking. It just allows that function to be awaited. The code inside still executes on the main thread unless explicitly offloaded.
The Solution: Detection & Offloading
We need a two-step approach for Async Debugging: strictly enforcing detection in development and offloading blocking calls in production.
Step 1: Enabling Native Debug Mode
Python has a built-in debug mode that is often overlooked. It monitors the duration of event loop "ticks." If a tick takes longer than a threshold (default 100ms), it logs a warning with the traceback of the offender.
Set the environment variable PYTHONASYNCIODEBUG=1 or configure it in code:
import asyncio
import logging
import time
# Configure logging to catch asyncio warnings
logging.basicConfig(level=logging.DEBUG)
logger = logging.getLogger(__name__)
async def hidden_blocking_task():
# This looks innocent, but time.sleep is SYNCHRONOUS
# It freezes the entire application for 1 second.
time.sleep(1)
return "Done"
async def main():
loop = asyncio.get_running_loop()
# CRITICAL: Set debug mode to True to detect "slow callbacks"
loop.set_debug(True)
# Lower the threshold to catch smaller blocks (e.g., 50ms)
loop.slow_callback_duration = 0.05
logger.info("Starting task...")
await hidden_blocking_task()
logger.info("Task finished")
if __name__ == "__main__":
asyncio.run(main())
When you run this, you will immediately see a log output: Executing <Task...> took 1.002 seconds. This pinpoints the exact line causing the Event Loop Blocking.
Step 2: The Non-Blocking Fix
Once identified, you must move these blocking calls off the main thread. The standard way to handle Python Performance issues involving CPU-bound or legacy blocking I/O is `loop.run_in_executor`. This delegates the task to a ThreadPoolExecutor.
import asyncio
import time
from concurrent.futures import ThreadPoolExecutor
# A simulation of a heavy library function (e.g., image processing, encryption)
def blocking_cpu_bound_operation(data):
time.sleep(1) # Simulating work
return f"Processed {data}"
async def optimized_handler():
loop = asyncio.get_running_loop()
# We use a custom executor to control the thread pool size
# If None is passed as the first arg, it uses the default internal executor
with ThreadPoolExecutor(max_workers=4) as pool:
# run_in_executor returns a Future, so we must AWAIT it
result = await loop.run_in_executor(
pool,
blocking_cpu_bound_operation,
"User Data"
)
print(result)
# Result: The event loop remains free to handle other requests
# while the thread sleeps/works.In the code above, `run_in_executor` wraps the synchronous function call. The `await` keyword here yields control back to the event loop immediately, allowing other incoming HTTP requests to be processed while a separate OS thread handles the `blocking_cpu_bound_operation`.
Performance Benchmark
We simulated a load of 50 concurrent users hitting an endpoint that performs a cryptographic signature verification (CPU bound). The "Legacy" version ran the signature verification directly in the async view. The "Optimized" version offloaded it to a thread pool.
| Metric | Legacy (Blocking) | Optimized (ThreadPool) |
|---|---|---|
| Throughput (RPS) | 45 req/sec | 720 req/sec |
| Avg Latency | 1.2s | 0.08s |
| Error Rate (Timeouts) | 12% | 0% |
The difference is staggering. In the blocking version, the event loop could not accept new connections while verifying a signature. In the optimized version, the throughput scales with the number of threads available, and the event loop remains responsive.
Official Docs: Debug ModeEdge Cases & Context Switching
While offloading to threads solves the blocking issue, it introduces new challenges in Concurrency Programming.
When you move execution to a thread, `contextvars` (introduced in Python 3.7) are not automatically copied unless you use `contextvars.copy_context().run()`. Libraries like Starlette or FastAPI handle some of this, but if you are manually managing database sessions, you might find your thread has lost access to the current transaction.
Furthermore, threads are not free. There is a memory overhead per thread, and the Global Interpreter Lock (GIL) still prevents true parallelism for CPU-bound tasks in standard Python. For extremely CPU-heavy tasks (like video encoding), consider `ProcessPoolExecutor` instead of `ThreadPoolExecutor` to bypass the GIL entirely.
Conclusion
Fixing "Event loop is closed" errors and random hangs usually boils down to one rule: Never block the Loop. By enabling `PYTHONASYNCIODEBUG` during development and rigorously wrapping synchronous dependencies in `run_in_executor`, you ensure your Python Asyncio services remain performant and resilient under load.
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