I created Asynckit.py over 5 years ago. There is plenty of other ways to do this by now. If you use Python 3, take a look at the asyncio module instead.

A few weeks ago i needed a python script to do a bunch of similar requests in parallel and return the results. I turned to PyPI for a simple little library to help me out. To my surprise i couldn’t find what i was looking for (maybe due to PyPI’s search being horrible, or my search terms, who knows).

Two days later i published the first version of asynckit to PyPI.

So, what is asynckit? §

Asynckit is a tiny library that enables you to run your existing functions in parallel and retrieve the return values when the work completes.

What to use it for? §

You could use asynckit to download a bunch of websites in parallel, like this:

from asynckit import Pool
from urllib2 import urlopen

pool = Pool(worker_count=3)

urls = (

results = [pool.do(lambda x: urlopen(x).read(), url) for url in urls]

print [len(result.get(True)) for result in results]

(the .get(True) call on the result blocks until result is ready, then returns the stored value)

If one of the calls raised an exception, a call to .get() will re-raise it. You can check if a call raised an exception by calling .is_error() on the result object.

When should you use it? §

For those tiny python utilities in your ~/src/misc or wherever you place your tiny hacks.

Asynckit has no dependencies outside the standard library, so it is great if you try to keep dependencies to a minimum.

It also installs in a few seconds ( pip install asynckit ) and requires no configuration.

Personally i keep it installed globally and use it in most of my single-script python tools that i have build over the last month.

I find it to be a really great companion for anything involving urllib2 work (like scraping a bunch of websites).

When shouldn’t you use it? §

When you could use Celery instead. Seriously, it is super awesome!

Celery is better in almost every way, but requires an external message queue, and quite a bit of configuration.

In most larger projects you will want to look at Celery or equivalent instead.

Head over to github.com/tbug/asynckit.py to see installation instructions and usage, or hang around for a few more examples.

Some Asynckit examples §

Download websites in parallel, wait for all downloads to complete, then print total bytes downloaded: §

from asynckit import Pool, AsyncList
from urllib2 import urlopen

pool = Pool(worker_count=4)

urls = (
    # more urls here

result = AsyncList([pool.do(lambda x: len(urlopen(x).read()), url) for url in urls])

print sum(result.get(True))

AsyncList accepts a list of AsyncValue objects as first argument and returns a list of “real” values when calling .get().

Nested and chained results §

Using an AsyncValue as an argument to the .do() or .chain() methods will wait for that value to be ready before running the work that requires it.

Chained work is a way of waiting for a result, without using it as an argument. Like a cleanup job, chained after some big work.

(note that there is currently no way of chaining something to an AsyncList)

from asynckit import Pool, AsyncList
from urllib2 import urlopen

pool = Pool(worker_count=1)

def heavy_work(a,b):
    return a+b

def proudly_display(result):
    print result.get(True)

def say_bye():
    print "bye"

# nested example. Use an AsyncValue object as argument to .do()
# ( .do() itself returns an AsyncValue )
nested_call = pool.do(heavy_work, 1, 
                pool.do(heavy_work, 2, 
                    pool.do(heavy_work, 1, 3)))

#chain example, say bye after proudly displaying the result, and wait for it all to happen
pool.do(proudly_display, nested_call).chain(say_bye).wait()

See more examples and report any issues on GitHub.