asyncio.Semaphore limits how many coroutines may enter a protected section at once. It is a concurrency limit, not a rate limit measured per second.
What You Will Learn
This lesson focuses on Control Concurrent Requests with Semaphore. You will connect the operation to a concrete input, inspect the result and identify the situation in which a different approach would be safer or clearer.
Asynchronous programming is useful when a program spends time waiting for other work, such as a socket or a remote service. One event loop can keep several operations in progress, provided each operation yields at an appropriate await point.
How It Works
Keep the semaphore section as short as is correct; always release the permit via async with or a finally block.
Worked Example
The following example isolates the main behavior so you can run it from a local Python installation. Read the inputs first, then follow the operation and the final printed result.
import asyncio
async def job(number, semaphore, active):
async with semaphore:
active.add(number)
assert len(active) <= 2
await asyncio.sleep(0.01)
active.remove(number)
return number
async def main():
active = set()
gate = asyncio.Semaphore(2)
print(await asyncio.gather(*(job(i, gate, active) for i in range(5))))
asyncio.run(main())Understanding the Example
Start with the setup and import statements; these define the objects that the operation needs. Follow each assignment or function call in order, noting which arguments are provided and whether the operation creates a new value, changes existing state or returns a result. Finally, inspect the printed value or assertion rather than assuming that the operation worked.
The important behavior demonstrated here is: asyncio.Semaphore limits how many coroutines may enter a protected section at once. It is a concurrency limit, not a rate limit measured per second. The example uses deliberately small values so the intermediate result is easy to reason about. A larger application must still handle validation, errors and resource cleanup.
Environment note: this example may require a separately installed package, local service, platform permission or network connection. Set up the indicated environment before running it; it has not been executed automatically for this guide.
When to Use This Approach
Use the design when the underlying operation is awaitable or when integrating with a well-defined async interface. For a single straightforward blocking task, regular synchronous code can be easier to maintain.
Important Behavior and Edge Cases
Using Semaphore(5) and assuming that permits only five requests per minute.
Own the lifetime of tasks. Bound concurrency, observe exceptions, set appropriate deadlines and close resources after cancellation. A synchronous blocking function can still freeze an event loop.
Practice Exercise
Reduce the limit to one and explain how task overlap changes.
Before running your modified code, predict the output or expected failure. Confirm the result using at least one ordinary case and one invalid, missing or boundary input. If the operation accesses external resources, first exercise it against a controlled local or test environment.
Common Beginner Mistakes
- Using Semaphore(5) and assuming that permits only five requests per minute.
- Own the lifetime of tasks. Bound concurrency, observe exceptions, set appropriate deadlines and close resources after cancellation. A synchronous blocking function can still freeze an event loop.
Key Takeaways
- asyncio.Semaphore limits how many coroutines may enter a protected section at once. It is a concurrency limit, not a rate limit measured per second.
- Choose this technique for the problem it solves, but validate inputs and follow the cleanup and failure behavior shown above.
- Next practice step: Reduce the limit to one and explain how task overlap changes.