Some problems are best solved synchronously, while others are a better fit for the asynchronous paradigm. Most problems fall somewhere in between – they could benefit from asynchronous execution, but require some events to happen in a certain order. This module seeks to make blending the two paradigms a bit easier by introducing a concept of dependencies. If one process must not run until another process has completed, that process is said to be “dependent” on the second process. Semisync.py was built using python’s multiprocessing library and a liberal dose of decorator syntax.
Install via pip
sudo pip install semisync
or via setup.py
sudo python setup.py install
from semisync import semisync from multiprocessing import Manager from random import random, randint from time import sleep # shared data between processes shared = Manager().Namespace() # a demo callback function def output(field, value): print field + ": $" + str(value) # simple callback syntax @semisync(callback=output) def revenue(): # simulated api call sleep(random()) shared.revenue = randint(1, 1000) return "Revenue", shared.revenue @semisync(callback=output) def expenses(): # simulated api call sleep(random()) shared.expenses = randint(1, 500) return "Expenses", shared.expenses # will run only when revenue() and expenses() have completed @semisync(callback=output, dependencies=[revenue, expenses]) def profit(): shared.profit = shared.revenue - shared.expenses return "Profit", shared.profit # queue function calls revenue() expenses() profit() # executes queued calls semi-synchronously semisync.begin()
To repeat the process, simply clear the cache of function calls by using semisync.clear() after each iteration
for i in range(10): revenue() expenses() profit() semisync.begin() semisync.clear()
In this simple example, moving from synchronous to semi-synchronous execution cuts the average execution time from 1.00 seconds to .700 seconds. And although the example used is trivial, dependency trees can be arbitrarily complex.
In order to make the module more flexible, few assumptions are made about how you choose to deal with shared data. Although Manager() from the multiprocessing library is used in the example, you’re free to use whatever format you desire. You’re also in charge of locking shared data if multiple processes access the same variable. With great flexibility comes great responsibility.
TODO: Figure out how to actually get changelog content.
Changelog content for this version goes here.