aiomultiprocess | Take a modern Python codebase to the next level of performance | Reactive Programming library
kandi X-RAY | aiomultiprocess Summary
kandi X-RAY | aiomultiprocess Summary
Take a modern Python codebase to the next level of performance. [code style] On their own, AsyncIO and multiprocessing are useful, but limited: AsyncIO still can’t exceed the speed of GIL, and multiprocessing only works on one task at a time. But together, they can fully realize their true potential. aiomultiprocess presents a simple interface, while running a full AsyncIO event loop on each child process, enabling levels of concurrency never before seen in a Python application. Each child process can execute multiple coroutines at once, limited only by the workload and number of cores available.
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Top functions reviewed by kandi - BETA
- Run the pool .
- The main loop .
- Sets the start method .
- Schedule a task .
- Wait for the process to finish .
- Runs the process asynchronously .
- Set the start method for the current context .
- Get the multiprocessing manager .
- Initialize the queue .
- Get the global context .
aiomultiprocess Key Features
aiomultiprocess Examples and Code Snippets
Community Discussions
Trending Discussions on aiomultiprocess
QUESTION
I have url checker code to check its response 200 or not but I want make it asynchronous along with multiprocessing but i stuck in the code plz help me build the code.
...ANSWER
Answered 2021-Jun-02 at 19:38The easiest way would be to use Pool.starmap()
:
QUESTION
I want to download and extract 100 tar.gz files that are each 1GB in size. Currently, I've sped it up with multithreading and by avoiding disk IO via in-memory byte streams, but can anyone show me how to make this faster (just for curiosity's sake)?
...ANSWER
Answered 2021-May-11 at 07:53Your computation is likely IO bound. Compression is generally a slow task, especially the gzip algorithm (new algorithms can be much faster). From the provided information, the average reading speed is about 70 Mo/s. This means that the storage throughput is at least roughly 140 Mo/s. It looks like totally normal and expected. This is especially true if you use a HDD or a slow SSD.
Besides this, it seems you iterate over the files twice due to the selection of members
. Keep in mind that tar gz files are a big block of files packed together and then compressed with gzip. To iterate over the filenames the tar file need to be already partially decompressed. This may not be a problem regarding the implementation of tarfile
(possible caching). If the size of all the discarded files is small, it may be better to simply decompress the whole archive in a raw and then remove the files to discard. Moreover, if you have a lot a memory and the size of all discarded files is not small, you can decompress the files in an in-memory virtual storage device first in order to write the discarded files. This can be natively done on Linux systems.
QUESTION
I found this package aiomultiprocess
that seems like it can do both multiprocessing and asyncio.
ANSWER
Answered 2020-Oct-19 at 16:30The aiomultiprocess
documentation example does not cover how to call the loop. The function needs to be called via asyncio.
QUESTION
I have a problem with the multiprocessing in Python. I need to create async processes, which run a undefined time and the number of processes is also undefined. As soon as a new request arrives, a new process must be created with the specifications from the request. We use ZeroMQ for messaging. There is also a Process which is started at the beginning and only ends if the whole script terminates.
Now I am searching for a solution how I can await all processes, while being able to add additional processes.
...ANSWER
Answered 2020-Jul-10 at 13:41You need to create a list of tasks or a future object for the processes. Also you cannot add process to the event loop while awaiting other tasks
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