simudream / tasks

Parallelize network dependent tasks in python.

tasks

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.

Installation

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

Usage

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
eventlet.monkey_patch()
import tasks

from urllib2 import urlopen

def fetch(url):
	f = open('/tmp/download', 'w')
	body = urlopen(url).read()
	f.write(body)
	f.close()
	
tasks.set_func(fetch)
tasks.main()

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

$ python yourfile.py add <list_of_jobs.txt>

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

$ python yourfile.py 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 yourfile.py status

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

$ python yourfile.py reset

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

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Author : Vivek Narayanan <[email protected]>

License : BSD

(C) Vivek Narayanan (2014)