TL;DR Making the Right Choice: We have walked through the most popular forms of concurrency. But the question remains – when should choose which one? It really depends on the use cases. From my experience (and reading), I tend to follow this pseudo code: if io_bound: if io_very_slow: print(“Use Asyncio”) else: print(“Use Threads”) else: print(“Multi … Read more
async def endpoint You could use loop.run_in_executor with ProcessPoolExecutor to start function at a separate process. @app.post(“/async-endpoint”) async def test_endpoint(): loop = asyncio.get_event_loop() with concurrent.futures.ProcessPoolExecutor() as pool: result = await loop.run_in_executor(pool, cpu_bound_func) # wait result def endpoint Since def endpoints are run implicitly in a separate thread, you can use the full power of modules … Read more
I believe you should follow the approach outlined here: https://uwsgi-docs.readthedocs.io/en/latest/WSGIquickstart.html#bonus-multiple-python-versions-for-the-same-uwsgi-binary Basically: Build a core uwsgi binary that does not have the Python plugin (to be used by the emperor): make PROFILE=nolang And then build plugins for each python version that you use (for the vassals), and configure each vassal to use the correct plugin.
Use direct __await__() call: async def new_sleep(): await asyncio.sleep(2) class Waiting: def __await__(self): return new_sleep().__await__() The solution was recommended by Yury Selivanov (the author of PEP 492) for aioodbc library
Although it’s not exactly a limit on the number of requests per second, note that since v2.0, when using a ClientSession, aiohttp automatically limits the number of simultaneous connections to 100. You can modify the limit by creating your own TCPConnector and passing it into the ClientSession. For instance, to create a client limited to … Read more
It works for me os.environ[“DJANGO_ALLOW_ASYNC_UNSAFE”] = “true” BTW, I start my notebook using the command ./manage.py shell_plus –notebook
There is no one-size-fits-all road to making an asyncio coroutine-based codebase useable from traditional synchronous codebases. You have to make choices per codepath. Pick and choose from a series of tools: Synchronous versions using asyncio.run() Provide synchronous wrappers around coroutines, which block until the coroutine completes. Even an async generator function such as ticker() can … Read more
This article answers your questions. TL;DR? Threading in Python is inefficient because of the GIL (Global Interpreter Lock) which means that multiple threads cannot be run in parallel as you would expect on a multi-processor system. Plus you have to rely on the interpreter to switch between threads, this adds to the inefficiency. asyc/asyncio allows … Read more
Your queues must be created inside the loop. You created them outside the loop created for asyncio.run(), so they use events.get_event_loop(). asyncio.run() creates a new loop, and futures created for the queue in one loop can’t then be used in the other. Create your queues in your top-level run() coroutine, and either pass them to … 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