This is a pre-production deployment of Warehouse, however changes made here WILL affect the production instance of PyPI.
Latest Version Dependencies status unknown Test status unknown Test coverage unknown
Project Description

What is

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. was built using python’s multiprocessing library and a liberal dose of decorator syntax.


Install via pip

sudo pip install semisync

or via

sudo python install

Let’s See Some Code

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
def revenue():
  # simulated api call
  shared.revenue = randint(1, 1000)
  return "Revenue", shared.revenue

def expenses():
  # simulated api call
  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

# executes queued calls semi-synchronously

To repeat the process, simply clear the cache of function calls by using semisync.clear() after each iteration

for i in range(10):

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.

Additional Notes

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.

Release History

Release History


This version

History Node

TODO: Figure out how to actually get changelog content.

Changelog content for this version goes here.

Donec et mollis dolor. Praesent et diam eget libero egestas mattis sit amet vitae augue. Nam tincidunt congue enim, ut porta lorem lacinia consectetur. Donec ut libero sed arcu vehicula ultricies a non tortor. Lorem ipsum dolor sit amet, consectetur adipiscing elit.

Show More

Download Files

Download Files

TODO: Brief introduction on what you do with files - including link to relevant help section.

File Name & Checksum SHA256 Checksum Help Version File Type Upload Date
semisync-0.1.tar.gz (1.8 kB) Copy SHA256 Checksum SHA256 Source Dec 29, 2013

Supported By

WebFaction WebFaction Technical Writing Elastic Elastic Search Pingdom Pingdom Monitoring Dyn Dyn DNS Sentry Sentry Error Logging CloudAMQP CloudAMQP RabbitMQ Heroku Heroku PaaS Kabu Creative Kabu Creative UX & Design Fastly Fastly CDN DigiCert DigiCert EV Certificate Rackspace Rackspace Cloud Servers DreamHost DreamHost Log Hosting