Threadpool

Title:Easy to use object-oriented thread pool framework
Author: Christopher Arndt
Version: 1.2.3
Date: 2006/06/23

Description

A thread pool is an object that maintains a pool of worker threads to perform time consuming operations in parallel. It assigns jobs to the threads by putting them in a work request queue, where they are picked up by the next available thread. This then performs the requested operation in the background and puts the results in a another queue.

The thread pool object can then collect the results from all threads from this queue as soon as they become available or after all threads have finished their work. It's also possible, to define callbacks to handle each result as it comes in.

Basic usage

>>> main = TreadPool(poolsize)
>>> requests = makeRequests(some_callable, list_of_args, callback)
>>> [main.putRequests(req) for req in requests]
>>> main.wait()

See the end of the module source code for a longer, annotated usage example.

Documentation

You can view the API documentation, generated by epydoc, here:

http://chrisarndt.de/en/software/python/threadpool/api/

Download

You can download the latest version of this module here:

http://chrisarndt.de/en/software/python/download/

or see the colorized source code:

http://chrisarndt.de/en/software/python/threadpool/threadpool.py.html

The documentation is also packaged in the distribution.

Discussion

The basic concept and some code was taken from the book "Python in a Nutshell" by Alex Martelli, copyright O'Reilly 2003, ISBN 0-596-00188-6, from section 14.5 "Threaded Program Architecture". I wrapped the main program logic in the ThreadPool class, added the WorkRequest class and the callback system and tweaked the code here and there.

There are some other recipes in the Python Cookbook, that serve a similar purpose. This one distinguishes itself by the following characteristics:

Notes

Due to the parallel nature of threads, you have to keep some things in mind:

  • Do not use simultaneous threads for tasks were they compete for a single, scarce resource (e.g. a harddisk or stdout). This will probably be slower than taking a serialized approach.
  • If you call ThreadPool.wait() the main thread will block until _all_ results have arrived. If you only want to check for results that are available immediately, use ThreadPool.poll().
  • The results of the work requests are not stored anywhere. You should provide an appropriate callback if you want to do so.

References

There are several other recipes similar to this module in the Python Cookbook: