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Easy to use object-oriented thread pool framework

Project description


Easy to use object-oriented thread pool framework


Christopher Arndt


MIT License


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 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 then possible to define callbacks to handle each result as it comes in.

Basic usage

>>> pool = ThreadPool(poolsize)
>>> requests = makeRequests(some_callable, list_of_args, callback)
>>> [pool.putRequest(req) for req in requests]
>>> pool.wait()

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


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

API documentation

The documentation is also packaged in the distribution.


You can download the latest version of this module here:

Download directory

or see the colorized source code:

You can also install it from the Cheeseshop via easy_install:

[sudo] easy:install threadpool

Or you can check out the latest development version from the Subversion repository:

svn co svn:// threadpool


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:

  • Object-oriented, reusable design

  • Provides callback mechanism to process results as they are returned from the worker threads.

  • WorkRequest objects wrap the tasks assigned to the worker threads and allow for easy passing of arbitrary data to the callbacks.

  • The use of the Queue class solves most locking issues.

  • All worker threads are daemonic, so they exit when the main programm exits, no need for joining.

  • Threads start running as soon as you create them. No need to start or stop them. You can increase or decrease the pool size at any time, superfluous threads will just exit when they finish their current task.

  • You don’t need to keep a reference to a thread after you have assigned the last task to it. You just tell it: “don’t come back looking for work, when you’re done!”

  • Threads don’t eat up cycles while waiting to be assigned a task, they just block when the task queue is empty (though they wake up every few seconds to check whether they are dismissd).


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.


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

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