Python_Multiprocessing_ThreadPoolExecutor.py

# Python multiprocessing - Process-based parallelism
# The following scripts are written to demonstrate multiprocessing (Process-based parallelism) 
# using Python.
# Multiprocessing is a Python package that supports spawning processes using an API similar to 
# the threading module. The multiprocessing package offers both local and remote concurrency, 
# effectively side-stepping the Global Interpreter Lock by using subprocesses instead of threads. 
# Due to this, the multiprocessing module allows the programmer to fully leverage multiple 
# processors on a given machine. It runs on both Unix and Windows.
# The multiprocessing module also introduces APIs which do not have analogs in the threading module. 
# A prime example of this is the Pool object which offers a convenient means of parallelizing the 
# execution of a function across multiple input values, distributing the input data across processes 
# (data parallelism). 
# Using 'ThreadPoolExecutor'
 
import concurrent.futures
import urllib.request

URLS = ['http://www.foxnews.com/',
        'http://www.cnn.com/',
        'http://europe.wsj.com/',
        'http://www.bbc.co.uk/',
        'http://some-made-up-domain.com/']

# Retrieve a single page and report the URL and contents

def load_url(url, timeout):

    with urllib.request.urlopen(url, timeout=timeout) as conn:
        return conn.read()

# We can use a with statement to ensure threads are cleaned up promptly

with concurrent.futures.ThreadPoolExecutor(max_workers=5) as executor:

    # Start the load operations and mark each future with its URL

    future_to_url = {executor.submit(load_url, url, 60): url for url in URLS}

    for future in concurrent.futures.as_completed(future_to_url):
        url = future_to_url[future]

        try:
            data = future.result()

        except Exception as exc:
            print('%r generated an exception: %s' % (url, exc))

        else:
            print('%r page is %d bytes' % (url, len(data)))