Async tools for Python
Async Tools for Python.
Table of Contents
Threading is the most simple thing, but because of GIL it’s useless for computation. Only use when you want to parallelize the access to a blocking resource, e.g. network.
Execute functions in parallel and collect results. Each function is executed in its own thread, all threads exit immediately.
- __call__: Add a job. Call the Parallel object so it calls the worker function with the same arguments
- map(jobs): Convenience method to call the worker for every argument
- join(): Wait for all tasks to be finished, and return two lists:
- A list of results
- A list of exceptions
from asynctools.threading import Parallel def request(url): # ... do request return data # Execute pll = Parallel(request) for url in links: pll(url) # Starts a new thread # Wait for the results results, errors = pll.join()
Since the request method takes just one argument, this can be chained:
results, errors = Parallel(request).map(links).join()
Create a pool of threads and execute work in it. Useful if you do want to launch a limited number of long-living threads.
- join(): Wait for all tasks to be finished and return (results, errors) (same as with `Pool <#pool>`__)
- close(): Terminate all threads. The pool is no more usable when closed.
- __enter__, __exit__ context manager to be used with with statement
from asynctools.threading import Pool def request(url): # ... do long request return data # Make pool pool = Pool(request, 5) # Assign some job for url in links: pll(url) # Runs in a pool # Wait for the results results, errors = pll.join()
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|Filename, size & hash SHA256 hash help||File type||Python version||Upload date|
|asynctools-0.1.0-0.linux-x86_64.tar.gz (5.4 kB) Copy SHA256 hash SHA256||Dumb Binary||any||Jul 18, 2014|
|asynctools-0.1.0-0.tar.gz (4.8 kB) Copy SHA256 hash SHA256||Source||None||Jul 18, 2014|