Introduction to Billiard: Advanced Task Management in Python

Billiard is a Python library for spawning and managing parallel tasks, leveraging the multiprocessing capabilities of Python. It is a fork of the Python standard multiprocessing module and offers enhancements for better performance and additional functionality.

Useful Billiard API Explanations and Code Snippets

Creating a Simple Process

  from billiard import Process

  def hello(name):
      print(f'Hello, {name}!')

  if __name__ == '__main__':
      p = Process(target=hello, args=('World',))
      p.start()
      p.join()

Using a Pool for Multiprocessing

  from billiard import Pool

  def square(x):
      return x * x

  if __name__ == '__main__':
      with Pool(4) as pool:
          results = pool.map(square, range(10))
      print(results)

Managing Shared Data with Value

  from billiard import Process, Value
  from time import sleep

  def increment(counter):
      for _ in range(100):
          with counter.get_lock():
              counter.value += 1
          sleep(0.01)

  if __name__ == '__main__':
      counter = Value('i', 0)
      processes = [Process(target=increment, args=(counter,)) for _ in range(4)]

      [p.start() for p in processes]
      [p.join() for p in processes]

      print(f'Counter: {counter.value}')

Using Queue for Interprocess Communication

  from billiard import Process, Queue

  def worker(q):
      q.put('Hello from worker!')

  if __name__ == '__main__':
      q = Queue()
      p = Process(target=worker, args=(q,))
      p.start()
      p.join()
      print(q.get())

Example App with Billiard Library

  from billiard import Process, Queue
  import requests

  def fetch_url(q, url):
      response = requests.get(url)
      q.put((url, response.status_code))

  if __name__ == '__main__':
      urls = [
          'https://www.google.com',
          'https://www.github.com',
          'https://www.python.org'
      ]
      
      q = Queue()
      processes = [Process(target=fetch_url, args=(q, url)) for url in urls]

      [p.start() for p in processes]
      [p.join() for p in processes]

      while not q.empty():
          url, status = q.get()
          print(f'{url} returned status {status}')

By leveraging the power of the Billiard library, developers can easily implement parallel processing in their Python applications, enhancing performance and efficiency for intensive tasks.

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