asyncoro is a Python framework for asynchronous, concurrent, distributed programming using generator functions, asynchronous completions and message passing. asyncoro API can be used to create coroutines with generator functions, similar to the way threads are created with functions with Python’s threading module. Thus, 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 generators, similar to the way an operating system executes multiple processes.
Unlike threads, creating processes (coroutines) with asyncoro is very efficient. 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 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 (local and remote) resources,
- Remote execution of coroutines for distributed/parallel programming
with Remote Coroutine Invocation
RCIand 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,
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)] time.sleep(5) ru = resource.getrusage(resource.RUSAGE_SELF) print('Max RSS: %.1f MB' % (ru.ru_maxrss / 1024.0)) for coro in coros: coro.resume()
shows that 100,000 coroutines take about 200 MB of resident memory (RSS field).
asyncoro is implemented with standard modules in Python. Under Windows efficient polling notifier I/O Completion Ports is supported only if pywin32 is installed; otherwise, inefficient ‘select’ notifier is used.
asyncoro works with Python 2.7+ and Python 3.1+ and tested on Linux, Mac OS X and Windows; it may work on other platforms too. asyncoro works with PyPy as well.
asyncoro package is available in Python Package Index (PyPI) so it can be installed for Python 2.7+ with:
pip install asyncoro
and/or for Python 3.1+ with:
pip3 install asyncoro
Examples illustrating some of the features of asyncoro are in asyncoro-examples.tar.gz package in asyncoro’s PyPI page. As there is no standard in PyPI for installing such files, they are stored in a separate file that can be downloaded and unpacked manually.
asyncoro can also be downloaded from Sourceforge Files.
- 1. Introduction
- 2. Asynchronous Concurrenct Programming
- 3. Asynchronous Network Programming
- 4. Asynchronous Files and Pipes
- 5. Distributed Programming
- 6. Distributed / Parallel Computing
- 7. Tutorial / Examples