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A light-weight task management library

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README.md

TaskIt

TaskIt is a light-weight library for task delegation and control. TaskIt has several aspects, but these are all extensions of the main idea: that dumping off execution, whether to just another thread or to a process running on a distant computer, should be simple, low-latency, and controllable.

TaskIt on Github

Core modules

The basic same-process task module, the logging module, and the multi-purpose resyncronization module fall into this category, as well as the threaded module.

simple.py is just that -- a simple task creator. A task can be run in three ways: waiting, callbacks, or ignored.

log.py provides the refreshingly simple logging mechanism for TaskIt, with splitters, file-like interfaces, and an interface to file-like objects.

resync.py provides a novel way to get the best of both the synchronous and asynchronous worlds, with a simple yet powerful API allowing things such as a basic producer-consumer model, handing off the results of a callback to another function, and more.

threaded.py centralizes the imports (currently three) from thread/_thread.

Distributed modules

These modules are the heart of the distributed task processing model (DTPM). This model allows remote errors, server introspection, and remote control without obfuscating the transport mechanism. By default, the transport mechanism uses standard JSON, but a pickle codec is also available, and writing custom codecs is quite simple.

common.py provides common constants, functions, and classes.

backend.py is the backend of the distributed task processing model. It provides DTPM server writers with the ability to use almost any function without modification, and gives allowances for special cases.

frontend.py is the frontend to the DTPM. The API is similar to that of simple.py, with the allowances of routing all calls through a FrontEnd and using string identifiers. It provides it's own job count as well as access to the backend's job count. It should be noted that a discrepancy between these numbers is generally not a bad sign; client load is not necessarily the same as server load. Generally, the server count is more meaningful than the client count, but the client uses its own count when distributing tasks to avoid the dramatic increase in lag that would result from sorting by the server count.

Daemonizing

The daemonizing folder includes a /etc/init.d/ bash script, a control Python script, and a multiduty port expanding Python module/script. The bash script belongs in /etc/init.d/ and should be customized to fit your setup. The control script and the port expander must be in the same folder. The control script can be used manually, and is also used by the bash script. The bash script may eventually be replaced by a Python version thereof, at which time the port expander script would be changed into a taskit.util module.

Examples

The examples directory contains example scripts for each feature of TaskIt.

  • worker.py is an example for taskit.backend and must be running when main.py is run.
  • main.py is an example for taskit.frontend and requires worker.py to be run first.
  • resync.py demos every feature of taskit.resync.
  • simple.py displays many of the taskit.simple features.

Documentation

More information and documentation can be found in the doc directory.


Copyright (c) 2012 Daniel Foerster/Dsigner Software pydsigner@gmail.com TaskIt is distributed under the LGPLv3 (or greater), see LICENSE for details.

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