A simple distributed queue designed for handling one-off tasks with large sets of tasks
Latest commit 79a312c Sep 9, 2014 @dcramer Merge pull request #7 from lowks/master
Adding a description line and some pep8 cleanup



Taskmaster is a simple distributed queue designed for handling large numbers of one-off tasks.

We built this at DISQUS to handle frequent, but uncommon tasks like "migrate this data to a new schema".


You might ask, "Why not use Celery?". Well the answer is simply that normal queueing requires (not literally, but it'd be painful without) you to buffer all tasks into a central location. This becomes a problem when you have a large amount of tasks, especially when they contain a large amount of data.

Imagine you have 1 billion tasks, each weighing in at 5k. Thats, uncompressed, at minimum 4 terabytes of storage required just to keep that around, and gains you very little.

Taskmaster on the other hand is designed to take a resumable iterator, and only pull in a maximum number of jobs at a time (using standard Python Queue's). This ensures a consistent memory pattern that can scale linearly.


Requirements should be handled by setuptools, but if they are not, you will need the following Python packages:

  • progressbar
  • pyzmq (zeromq)
  • gevent
  • gevent_zeromq

A note on Gevent

Being that Taskmaster uses gevent for both its iterator task (master) and its consumers, your application will need to correctly implement non-blocking gevent compatible callers. In most cases this won't be a problem, but if you're using the network you'll need to look for a compatible library for your adapter. For example, there is an alternative version of psycopg2 designed for gevent called gevent-psycopg2.


Create an iterator, and callback:

# taskmaster/example.py
def get_jobs(last=0):
    # last would be sent if state was resumed
    # from a previous run
    for i in xrange(last, 100000000):
        # jobs yielded must be serializeable with pickle
        yield i

def handle_job(i):
    # this **must** be idempotent, as resuming the process may execute a job
    # that had already been run
    print "Got %r!" % i

Spawn a master:

$ tm-master taskmaster.example

You can also pass keyword arguments for the master:

$ tm-master taskmaster.example argument=value

Spawn a slave:

$ tm-slave taskmaster.example

Or spawn 8 slaves (each containing a threadpool):

$ tm-spawn taskmaster.example 8

Dont like the magical function discover for master/slave? Specify your own targets:

$ tm-master taskmaster.example:get_jobs
$ tm-slave taskmaster.example:handle_job

Maybe you simply need to run things on the same server?

$ tm-run taskmaster/example.py 8


All arguments are optional, and the address will default to tcp://