Намери подобно съдържание

https://docs.python.org/3/library/asyncio-task.html#asyncio.run_coroutine_threadsafe #asyncio.run_coroutine_threadsafe(coro, loop) Submit a coroutine object to a given event loop. Return a concurrent.futures.Future to access the result.

https://docs.python.org/3/library/asyncio-eventloop.html The event loop is the central execution device provided by #asyncio. It provides multiple facilities, including: Registering, executing and cancelling delayed calls (timeouts). Creating client and server transports for various kinds of communication. Launching subprocesses and the associated transports for communication with an external program. Delegating costly function calls to a pool of threads.

http://blog.povilasb.com/posts/python-asyncio-vs-nginx-performance/ While I was playing with Python #asyncio I got interested in how well it performs serving data over TLS compared to #Nginx. So I implemented a small HTTPS server with asyncio:

http://www.aparat.com/v/mKvRl Thinking In #Coroutines - PyCon 2016 ‏ #Asyncio

http://www.aparat.com/v/FCaeJ #Asyncio - #coroutines

https://glyph.twistedmatrix.com/2014/02/unyielding.html As we know, #threads are a bad idea, (for most purposes). Threads make local reasoning difficult, and local reasoning is perhaps the most important thing in software development. With the word “threads”, I am referring to shared-state multithreading, despite the fact that there are languages, like Erlang and Haskell which refer to concurrent processes – those which do not implicitly share state, and require explicit coordination – as “threads”. #asyncio

http://programtalk.com/python-examples/aiohttp.web.Application/?ipage=1 Here are the examples of the python api #aiohttp.web.Application taken from open source projects. By voting up you can indicate which examples are most useful and appropriate. #asyncio#learn

https://pawelmhm.github.io/asyncio/python/aiohttp/2016/04/22/asyncio-aiohttp.html 👌Making 1 million requests with python -#aiohttp Apr 22, 2016 - by Paweł Miech - about: #asyncio, aiohttp, #python In this post I’d like to test limits of python aiohttp and check its performance in terms of requests per minute. Everyone knows that asynchronous code performs better when applied to network operations, but it’s still interesting to check this assumption and understand how exactly it is better and why it’s is better. I’m going to check it by trying to make 1 million #requests with aiohttp client. How many requests per minute will aiohttp make? What kind of exceptions and crashes can you expect when you try to make such volume of requests with very primitive scripts? What are main gotchas that you need to think about when trying to make such volume of requests?

https://github.com/aio-libs/aiobotocore Async client for amazon services using #botocore and #aiohttp/#asyncio. Main purpose of this library to support amazon s3 api, but other services should work (may be with minor fixes). For now we have tested only upload/download api for s3, other users report that SQS and Dynamo services work also. More tests coming soon.

https://docs.python.org/3/library/asyncio.html #asyncio #Asynchronous programming is more complex than classical “#sequential” programming: see the Develop with asyncio page which lists common traps and explains how to avoid them. Enable the debug mode during development to detect common issues.

http://stackoverflow.com/questions/32054066/python-how-to-run-multiple-coroutines-concurrently-using-asyncio how to run multiple #coroutines#concurrently using #asyncio? You can use #asyncio.async() to run as many #coroutines as you want, before executing blocking call for starting event loop.

http://programtalk.com/vs2/python/9287/sempervirens/sempervirens/server.py/ This file is part of sempervirens #aiohttp#asyncio#learn#request