Async tools for Python
Project description
AsyncTools
Async Tools for Python.
Table of Contents
Threading
Parallel
Pool
Threading
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.
Parallel
Source: asynctools/threading/Parallel.py
Execute functions in parallel and collect results. Each function is executed in its own thread, all threads exit immediately.
Methods:
__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
Example:
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()
Pool
Source: asynctools/threading/Pool.py
Create a pool of threads and execute work in it. Useful if you do want to launch a limited number of long-living threads.
Methods:
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
Example:
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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