Python多线程多进程实例对比解析

# 多进程编程
# 耗cpu的操作,用多进程编程, 对于io操作来说,使用多线程编程
import time
from concurrent.futures import ThreadPoolExecutor, as_completed
from concurrent.futures import ProcessPoolExecutor


def fib(n):
  if n <= 2:
    return 1
  return fib(n - 2) + fib(n - 1)

if __name__ == '__main__':

  # 1. 对于耗cpu操作,多进程优于多线程

  # with ThreadPoolExecutor(3) as executor:
  #   all_task = [executor.submit(fib, num) for num in range(25, 35)]
  #   start_time = time.time()
  #   for future in as_completed(all_task):
  #     data = future.result()
  #     print(data)
  #   print("last time :{}".format(time.time() - start_time)) # 3.905290126800537

  # 多进程 ,在window环境 下必须放在main方法中执行,否则抛异常
  with ProcessPoolExecutor(3) as executor:
    all_task = [executor.submit(fib, num) for num in range(25, 35)]
    start_time = time.time()
    for future in as_completed(all_task):
      data = future.result()
      print(data)
    print("last time :{}".format(time.time() - start_time)) # 2.6130592823028564

# 多进程编程
# 耗cpu的操作,用多进程编程, 对于io操作来说,使用多线程编程
import time
from concurrent.futures import ThreadPoolExecutor, as_completed
from concurrent.futures import ProcessPoolExecutor

def io_operation(n):
  time.sleep(2)
  return n


if __name__ == '__main__':

  # 1. 对于耗cpu操作,多进程优于多线程

  # with ThreadPoolExecutor(3) as executor:
  #   all_task = [executor.submit(io_operation, num) for num in range(25, 35)]
  #   start_time = time.time()
  #   for future in as_completed(all_task):
  #     data = future.result()
  #     print(data)
  #   print("last time :{}".format(time.time() - start_time)) # 8.00358772277832



  # 多进程 ,在window环境 下必须放在main方法中执行,否则抛异常
  with ProcessPoolExecutor(3) as executor:
    all_task = [executor.submit(io_operation, num) for num in range(25, 35)]
    start_time = time.time()
    for future in as_completed(all_task):
      data = future.result()
      print(data)
    print("last time :{}".format(time.time() - start_time)) # 8.12435245513916