> So the best practice is to use `asyncio.gather()` to delimitate the pyramid of the task life cycle. Unfortunatly few people kow this, and hence do it.
And even then, any async function might run `loop = asyncio.get_event_loop()`, run some background processes, and return before they stopped! I actually had this exact problem with my realtime sensor data server: the background tasks were never properly closed, and the sockets remained open.
On a related note, I found that asyncio (cooperative multitasking in Python) has made huge strides in usability in Python 3.7. The code feels sequential, and it’s quite easy to understand the flow of events ; it’s very explicit yet you never have to run the callbacks yourself, the exceptions do show up, and you don’t even need to manipulate a `loop` object anymore! If you're trying to learn how to use it, my advice would be:
* "Stick to the official documentation[1]!". Many resources found online are outdated, and give convoluted or plain wrong examples, and fail to mention the recent additions.
* Use `asyncio.run`. The alternative way (using get_event_loop() and loop.run_until_complete() is cumbersome and hard to get right. Even the documentation wasn't correct[2].). The part on Coroutines and Tasks is well-written, and the best part is the examples. Simply reading all the examples on after another gives a a good insight on how asyncio should be used.
* If you want to work with sockets, use the high-level "Streams"[3] if you want to stay in the standard library. `asyncio.start_server` is a powerful abstraction. If you’re willing to use a third-party library, I found that `pynng`[4] was a breeze to work with. It is compatible with asyncio and other async frameworks, and it found it more straightforward than pyzmq, which is also compatible with asyncio[5].
* If you want to run async functions in the main event loop and blocking functions in threads (with `loop.run_in_executor), Janus[7] seems to be a great way to share data. I have not used it yet though.
My use case was that I wanted to read sensor data on one computer (server) and broadcast it to other computers (clients) which would in turn graph it live, or write it to disk. The server used `pyserial_asyncio`[6] to asynchronously read serial data and published it via TCP using a the pub/sub scheme from pynng (pynng.Pub0).
The clients could then either synchronously or asynchronously receive the data by subscribing to the server (pynng.Sub0), and make plots in realtime.
> Starting from an array A that has n distinct integers
I don't know in what ways they are differents, but these programs were not designed to work with duplicates in the input. This probably explains the results.
And this issue could be easily overcome if more projects implemented the "socks5h://" protocol (note the 'h'), initially introduced by curl[1] to mandate the SOCKS proxy to also tunnel DNS requests. Sadly it seems that very few programs recognize this (non-standard) protocol: git[2] (using curl),
python's request (and urllib) module[3] ... And not much else.
For your information you can get the Firefox for Android NoScript porting with the cheeky (but somewhat relevant) name NoScript Anywhere++ (NSA++) here: https://noscript.net/nsa/
It's quite experimental, the UI isn't great if you have a small phone but it works.
If you feel brave, you could try the C++ implementation of the i2p router, purplei2p[1] (aka i2pd[2]). Last time I tried, there were a few rough edges, but it is now over two years old, so it has probably improved (or you may even improve it yourself!).
And even then, any async function might run `loop = asyncio.get_event_loop()`, run some background processes, and return before they stopped! I actually had this exact problem with my realtime sensor data server: the background tasks were never properly closed, and the sockets remained open.
The article you linked to (https://vorpus.org/blog/notes-on-structured-concurrency-or-g...) is super interesting. I didn't get the appeal of `trio` before, but now it does seem really useful.