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A simple and fast task queue for executing multiple tasks in parallel.

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__tasks.py__ is a simple and fast task queue for executing multiple tasks in parallel. All you need to do is specify the task as a simple function that takes an argument and you get instant parallelism.

Based on eventlet, multiprocessing and redis.

It is ideal for executing multiple network bound tasks in parallel from a single node, without going through the pain of setting up a map reduce cluster. It uses both processes and green threads to extract the maximum out of a single node setup.


1. Install redis and start the server, **tasks** uses redis for queueing jobs. If you already have a redis server setup, call `tasks.set_redis` and pass a redis connection object with a different database/namespace from what you normally use in your application.

2. Install the redis-py and eventlet libraries.

`pip install redis eventlet`

3. Install tasks or copy this package to your source code.

`pip install tasks-py`

Import `tasks` and call eventlet's monkey patch function in the first line of your module. Call `tasks.set_func` to register your function. This function will be receiving a string as an argument and its return value will be ignored. To indicate failure of the task, raise an error or exception within the function. Call `tasks.main()` to get the interactive command line options.

import eventlet
import tasks

from urllib2 import urlopen

def fetch(url):
f = open('/tmp/download', 'w')
body = urlopen(url).read()


Now to add jobs, create a file with one argument per line and use this command.

`$ python add <list_of_jobs.txt>`

To start (or restart) the job processing (do this in a **screen** session or close the input stream):

`$ python run`

**tasks** has resume support, so it will start where you left off the last time.

To view the current status while it is running:

`$ python status`

Once you are done, you can clear the logs and the completed tasks by calling reset.

`$ python reset`

See the code or the file for more information. Feel free to fork and modify this.


**Author** : Vivek Narayanan <<>>

**License** : BSD

(C) Vivek Narayanan (2014)

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