Thread vs. Threading

What’s the difference between the threading and thread modules in Python?

Answers:

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Method 1

In Python 3, thread has been renamed to _thread. It is infrastructure code that is used to implement threading, and normal Python code shouldn’t be going anywhere near it.

_thread exposes a fairly raw view of the underlying OS level processes. This is almost never what you want, hence the rename in Py3k to indicate that it is really just an implementation detail.

threading adds some additional automatic accounting, as well as several convenience utilities, all of which makes it the preferred option for standard Python code.

Method 2

threading is just a higher level module that interfaces thread.

See here for the threading docs:

http://docs.python.org/library/threading.html

Method 3

If I’m not mistaken, thread allows you to run a function as a separate thread, whereas with threading you have to create a class, but get more functionality.

EDIT: This is not precisely correct. threading module provides different ways of creating a thread:

• threading.Thread(target=function_name).start()
• Create a child class of threading.Thread with your own run() method, and start it

Method 4

There is another one library in Python which can used for threading and works perfectly.

The library called concurrent.futures. This makes our work easier.

It has for thread pooling and Process pooling.

The following gives an insight:

ThreadPoolExecutor Example

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)))

Another example

import concurrent.futures
import math

PRIMES = [
    112272535095293,
    112582705942171,
    112272535095293,
    115280095190773,
    115797848077099,
    1099726899285419]

def is_prime(n):
    if n % 2 == 0:
        return False

    sqrt_n = int(math.floor(math.sqrt(n)))
    for i in range(3, sqrt_n + 1, 2):
        if n % i == 0:
            return False
    return True

def main():
    with concurrent.futures.ThreadPoolExecutor() as executor:
        for number, prime in zip(PRIMES, executor.map(is_prime, PRIMES)):
            print('%d is prime: %s' % (number, prime))

if __name__ == '__main__':
    main()

Method 5

The module “Thread” treats a thread as a function, while the module “threading” is implemented in an object oriented way, i.e. every thread corresponds to an object.

All methods was sourced from stackoverflow.com or stackexchange.com, is licensed under cc by-sa 2.5, cc by-sa 3.0 and cc by-sa 4.0