asyncoro is a Python framework for asynchronous, concurrent, distributed programming using coroutines. asyncoro features include:

  • No callbacks or event loops! No need to lock critical sections either,
  • Efficient polling mechanisms epoll, kqueue, /dev/poll, Windows I/O Completion Ports (IOCP) for high performance and scalability,
  • Asynchronous (non-blocking) sockets and pipes, for concurrent processing of I/O,
  • SSL for security,
  • Asynchronous timers, including non-blocking sleep,
  • Asynchronous locking primitives similar to Python threading module,
  • Message passing for (local and remote) coroutines to exchange messages one-to-one with Message Queue Pattern or through broadcasting channels with Publish-subscribe Pattern,
  • Location transparency with naming and locating resources,
  • Remote execution of coroutines for distributed/parallel programming with Remote Coroutine Invocation RCI and message passing,
  • Monitoring and restarting of (local or remote) coroutines, for fault detection and fault-tolerance,
  • Hot-swapping of coroutine functions, for dynamic system reconfiguration,
  • Distributing computation fragments for remote execution of coroutines with Distributed / Parallel Computing,
  • Thread pools with asynchronous task completions, for executing time consuming synchronous tasks,

Programs developed with asyncoro have same logic and structure as programs with threads, except for a few syntactic changes - mostly using yield with asynchronous completions that give control to asyncoro’s scheduler, which interleaves executions of coroutines, similar to the way an operating system executes multiple processes.

Unlike threads, creating processes (coroutines) with asyncoro is very efficient. For reference purposes, asyncoro with Python 2.7 on Ubuntu Linux 12.04 running the concurrent program:

import asyncoro, resource, time
def coro_proc(coro=None):
    yield coro.suspend()

coros = [asyncoro.Coro(coro_proc) for i in xrange(100000)]
ru = resource.getrusage(resource.RUSAGE_SELF)
print('Max RSS: %.1f MB' % (ru.ru_maxrss / 1024.0))
for coro in coros:

shows that 100,000 coroutines take about 200 MB of resident memory (RSS field). Moreover, with asyncoro context switch occurs only when coroutines use yield (typically with an asychronous call), so there is no need for locking and there is no overhead of unnecessary context switches.


asyncoro can be downloaded from Sourceforge Files. It is released under MIT license, a common license used for Python modules. Files, etc. in asyncoro-* are to be used with Python 2.7+ and files, etc. in asyncoro3-* are to be used with Python 3.1+. If using Python 3.1+ exclusively, may be renamed to etc.


Indices and tables

Table Of Contents

Next topic

1. Introduction

This Page