Metadata-Version: 2.1
Name: polymer
Version: 0.0.31
Summary: Polymer
Home-page: http://github.com/mpenning/polymer
Author: David Michael Pennington
Author-email: mike@pennington.net
License: GPL
Platform: any
Classifier: Development Status :: 4 - Beta
Classifier: Environment :: Plugins
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: System Administrators
Classifier: Intended Audience :: Information Technology
Classifier: License :: OSI Approved :: GNU General Public License (GPL)
Classifier: Natural Language :: English
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Dist: colorama

Summary
-------

A simple framework to run tasks in parallel.  It's similar to 
multiprocessing.Pool, but has a few enhancements over that.  For example,
mp.Pool is only useful for multiprocessing functions (not objects).  You can
wrap a function around the object, but it's nicer just to deal with task
objects themselves.

Polymer is mostly useful for its Worker error logging and run-time statistics.
It also restarts crashed multiprocessing workers automatically (not true with
multiprocessing.Pool).  When a worker crashes, Polymer knows what the worker 
was doing and resubmits that task as well.  This definitely is not fool-proof;
however, it's a helpful feature.

Once TaskMgr().supervise() finishes, a list of object instances is returned. 
You can store per-task results as an attribute of each object instance.

Usage
-----

.. code:: python

   import time

   from polymer.Polymer import ControllerQueue, TaskMgr
   from polymer.abc_task import BaseTask

   class SimpleTask(BaseTask):
     def __init__(self, text="", wait=0.0):
         super(SimpleTask, self).__init__()
         self.text = text
         self.wait = wait

     def run(self):
         """run() is where all the work is done; this is called by TaskMgr()"""
         ## WARNING... using try / except in run() could squash Polymer's
         ##      internal error logging...
         #time.sleep(float(self.wait/10))
         print(self.text, self.wait/10.0)

     def __eq__(self, other):
         """Define how tasks are uniquely identified"""
         if isinstance(other, SimpleTask) and (other.text==self.text):
             return True
         return False

     def __repr__(self):
         return """<{0}, wait: {1}>""".format(self.text, self.wait)

     def __hash__(self):
         return id(self)

   def Controller():
      """Controller() builds a list of tasks, and queues them to the TaskMgr
      There is nothing special about the name Controller()... it's just some
      code to build a list of SimpleTask() instances."""

      tasks = list()

      ## Build ten tasks... do *not* depend on execution order...
      num_tasks = 10
      for ii in range(0, num_tasks):
          tasks.append(SimpleTask(text="Task {0}".format(ii), wait=ii))

      targs = {
          'work_todo': tasks,  # a list of SimpleTask() instances
          'hot_loop': False,   # If True, continuously loop over the tasks
          'worker_count': 3,           # Number of workers (default: 5)
          'resubmit_on_error': False,  # Do not retry errored jobs...
          'queue': ControllerQueue(),
          'worker_cycle_sleep': 0.001, # Worker sleep time after a task
          'log_stdout': False,         # Don't log to stdout (default: True)
          'log_path':  "taskmgr.log",  # Log file name
          'log_level': 0,              # Logging off is 0 (debugging=3)
          'log_interval': 10,          # Statistics logging interval
      }

      ## task_mgr reads and executes the queued tasks
      task_mgr = TaskMgr(**targs)

      ## a set() of completed task objects are returned after supervise()
      results = task_mgr.supervise()
      return results

   if __name__=='__main__':
      Controller()



License
-------

GPLv3


