The equivalent would be using asyncio.gather: import asyncio async def bar(i): print(‘started’, i) await asyncio.sleep(1) print(‘finished’, i) async def main(): await asyncio.gather(*[bar(i) for i in range(10)]) loop = asyncio.get_event_loop() loop.run_until_complete(main()) loop.close() Why doesn’t my approach work? Because when you await each item in seq, you block that coroutine. So in essence, you have synchronous code … Read more
EDIT FEB 21st, 2019: Problem Fixed This is no longer an issue on the latest version of Jupyter Notebook. Authors of Jupyter Notebook detailed the case here. Answer below was the original response that was marked correct by the op. This was posted quite a bit ago, but in case other people are looking for … Read more
Edit: If using Python 3.8 or above, you should use the asyncio repl, as explained in zeronone’s answer. If using 3.7 or lower, you can use this answer. You can run the event loop inside a background thread: >>> import asyncio >>> >>> @asyncio.coroutine … def greet_every_two_seconds(): … while True: … print(‘Hello World’) … yield … Read more
On Windows seems to be a problem with EventLoopPolicy, use this snippet to work around it: asyncio.set_event_loop_policy(asyncio.WindowsSelectorEventLoopPolicy()) asyncio.run(main())
To answer your questions, I will provide 3 different solutions to the same problem. Case 1: just normal Python import time def sleep(): print(f’Time: {time.time() – start:.2f}’) time.sleep(1) def sum(name, numbers): total = 0 for number in numbers: print(f’Task {name}: Computing {total}+{number}’) sleep() total += number print(f’Task {name}: Sum = {total}\n’) start = time.time() tasks … Read more
When the server closes the connection, sock_recv returns an empty bytearray (b”), indicating end of file. Since you don’t handle that condition, your code ends up stuck in an infinite loop processing the same buffer. To correct it, add something like: if data == b”: break …after the data = await loop.sock_recv(…) line. But the … Read more
It is not possible to share a python object between different processes straightforwardly. The facilities included in the multiprocessing module (like managers or shared memory) are not suitable for sharing resources between workers, since they require a master process creating the resources and do not have the durability property. Also server processes can be run … Read more
You can retrieve unfinished tasks and run the loop again until they finished, then close the loop or exit your program. pending = asyncio.all_tasks() loop.run_until_complete(asyncio.gather(*pending)) pending is a list of pending tasks. asyncio.gather() allows to wait on several tasks at once. If you want to ensure all the tasks are completed inside a coroutine (maybe … Read more
The closest literal translation of the threading code would create the socket as before, make it non-blocking, and use asyncio low-level socket operations to implement the server. Here is an example, sticking to the more relevant server part (the client is single-threaded and likely fine as-is): import asyncio, socket async def handle_client(client): loop = asyncio.get_event_loop() … Read more
requests is a synchronous library. You need to use an asyncio-based library to make requests asynchronously. httpx httpx is typically used in FastAPI applications to request external services. It provides synchronous and asynchronous clients which can be used in def and async def path operations appropriately. It is also recommended for asynchronous tests of application. … Read more