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Magic decorator syntax for asynchronous code.

Project description

Magic decorator syntax for asynchronous code in Python

Installation

Tomorrow is conveniently available via pip:

sudo pip install tomorrow

or installable via git clone and setup.py

git clone git@github.com:madisonmay/Tomorrow.git
sudo python setup.py install

Usage

The tomorrow library enables you to utilize the benefits of multi-threading with minimal concern about the implementation details.

Let’s take a look at how simple it is to speed up an inefficient chunk of blocking code with minimal effort.

Naive Web Scraper

You’ve collected a list of urls and are looking to download the HTML of the lot. The following is a perfectly reasonable first stab at solving the task.

For the following examples, we’ll be using the top sites from the Alexa rankings.

urls = [
    'http://google.com',
    'http://facebook.com',
    'http://youtube.com',
    'http://baidu.com',
    'http://yahoo.com',
]

Right then, let’s get on to the code.

import time
import requests

from tomorrow import threads

def download(url):
    return requests.get(url)

if __name__ == "__main__":

    start = time.time()
    responses = [download(url) for url in urls]
    html = [response.text for response in responses]
    end = time.time()
    print "Time: %f seconds" % (end - start)

More Efficient Web Scraper

Using tomorrow’s decorator syntax, we can define a function that executes in multiple threads. Individual calls to download are non-blocking, but we can largely ignore this fact and write code identically to how we would in a synchronous paradigm.

import time
import requests

@threads(10)
def download(url):
    return requests.get(url)

if __name__ == "__main__":
    import time

    start = time.time()
    responses = [download(url) for url in urls]
    html = [response.text for response in responses]
    end = time.time()
    print "Time: %f seconds" % (end - start)

Awesome! With a single line of additional code (and no explicit threading logic) we can now download websites ~10x as efficiently.

How Does it Work?

Feel free to read the source for a peek behind the scenes – it’s less that 50 lines of code.

Project details


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