Python中的并发编程实例

import threading
 
class SummingThread(threading.Thread):
  def __init__(self, low, high):
    super(SummingThread, self).__init__()
    self.low = low
    self.high = high
    self.total = 0
 
  def run(self):
    for i in range(self.low, self.high):
      self.total += i
 
thread1 = SummingThread(0, 500000)
thread2 = SummingThread(500000, 1000000)
thread1.start() # This actually causes the thread to run
thread2.start()
thread1.join() # This waits until the thread has completed
thread2.join()
# At this point, both threads have completed
result = thread1.total + thread2.total
print(result)

import threading
import logging
 
def do_threaded_work(work_items, work_func, num_threads=None, per_sync_timeout=1, preserve_result_ordering=True):
  """ Executes work_func on each work_item. Note: Execution order is not preserved, but output ordering is (optionally).
 
    Parameters:
    - num_threads        Default: len(work_items) --- Number of threads to use process items in work_items.
    - per_sync_timeout     Default: 1        --- Each synchronized operation can optionally timeout.
    - preserve_result_ordering Default: True       --- Reorders result_item to match original work_items ordering.
 
    Return: 
    --- list of results from applying work_func to each work_item. Order is optionally preserved.
 
    Example:
 
    def process_url(url):
      # TODO: Do some work with the url
      return url
 
    urls_to_process = ["http://url1.com", "http://url2.com", "http://site1.com", "http://site2.com"]
 
    # process urls in parallel
    result_items = do_threaded_work(urls_to_process, process_url)
 
    # print(results)
    print(repr(result_items))
  """
  global wrapped_work_func
  if not num_threads:
    num_threads = len(work_items)
 
  work_queue = Queue.Queue()
  result_queue = Queue.Queue()
 
  index = 0
  for work_item in work_items:
    if preserve_result_ordering:
      work_queue.put((index, work_item))
    else:
      work_queue.put(work_item)
    index += 1
 
  if preserve_result_ordering:
    wrapped_work_func = lambda work_item: (work_item[0], work_func(work_item[1]))
 
  start_logging_with_thread_info()
 
  #spawn a pool of threads, and pass them queue instance 
  for _ in range(num_threads):
    if preserve_result_ordering:
      t = ThreadedWorker(work_queue, result_queue, work_func=wrapped_work_func, queue_timeout=per_sync_timeout)
    else:
      t = ThreadedWorker(work_queue, result_queue, work_func=work_func, queue_timeout=per_sync_timeout)
    t.setDaemon(True)
    t.start()
 
  work_queue.join()
  stop_logging_with_thread_info()
 
  logging.info('work_queue joined')
 
  result_items = []
  while not result_queue.empty():
    result = result_queue.get(timeout=per_sync_timeout)
    logging.info('found result[:500]: ' + repr(result)[:500])
    if result:
      result_items.append(result)
 
  if preserve_result_ordering:
    result_items = [work_item for index, work_item in result_items]
 
  return result_items
 
class ThreadedWorker(threading.Thread):
  """ Generic Threaded Worker
    Input to work_func: item from work_queue
 
  Example usage:
 
  import Queue
 
  urls_to_process = ["http://url1.com", "http://url2.com", "http://site1.com", "http://site2.com"]
 
  work_queue = Queue.Queue()
  result_queue = Queue.Queue()
 
  def process_url(url):
    # TODO: Do some work with the url
    return url
 
  def main():
    # spawn a pool of threads, and pass them queue instance 
    for i in range(3):
      t = ThreadedWorker(work_queue, result_queue, work_func=process_url)
      t.setDaemon(True)
      t.start()
 
    # populate queue with data  
    for url in urls_to_process:
      work_queue.put(url)
 
    # wait on the queue until everything has been processed   
    work_queue.join()
 
    # print results
    print repr(result_queue)
 
  main()
  """
 
  def __init__(self, work_queue, result_queue, work_func, stop_when_work_queue_empty=True, queue_timeout=1):
    threading.Thread.__init__(self)
    self.work_queue = work_queue
    self.result_queue = result_queue
    self.work_func = work_func
    self.stop_when_work_queue_empty = stop_when_work_queue_empty
    self.queue_timeout = queue_timeout
 
  def should_continue_running(self):
    if self.stop_when_work_queue_empty:
      return not self.work_queue.empty()
    else:
      return True
 
  def run(self):
    while self.should_continue_running():
      try:
        # grabs item from work_queue
        work_item = self.work_queue.get(timeout=self.queue_timeout)
 
        # works on item
        work_result = self.work_func(work_item)
 
        #place work_result into result_queue
        self.result_queue.put(work_result, timeout=self.queue_timeout)
 
      except Queue.Empty:
        logging.warning('ThreadedWorker Queue was empty or Queue.get() timed out')
 
      except Queue.Full:
        logging.warning('ThreadedWorker Queue was full or Queue.put() timed out')
 
      except:
        logging.exception('Error in ThreadedWorker')
 
      finally:
        #signals to work_queue that item is done
        self.work_queue.task_done()
 
def start_logging_with_thread_info():
  try:
    formatter = logging.Formatter('[thread %(thread)-3s] %(message)s')
    logging.getLogger().handlers[0].setFormatter(formatter)
  except:
    logging.exception('Failed to start logging with thread info')
 
def stop_logging_with_thread_info():
  try:
    formatter = logging.Formatter('%(message)s')
    logging.getLogger().handlers[0].setFormatter(formatter)
  except:
    logging.exception('Failed to stop logging with thread info')

from test import ThreadedWorker
from queue import Queue
 
urls_to_process = ["http://facebook.com", "http://pypix.com"]
 
work_queue = Queue()
result_queue = Queue()
 
def process_url(url):
  # TODO: Do some work with the url
  return url
 
def main():
  # spawn a pool of threads, and pass them queue instance 
  for i in range(5):
    t = ThreadedWorker(work_queue, result_queue, work_func=process_url)
    t.setDaemon(True)
    t.start()
 
  # populate queue with data  
  for url in urls_to_process:
    work_queue.put(url)
 
  # wait on the queue until everything has been processed   
  work_queue.join()
 
  # print results
  print(repr(result_queue))
 
main()