对比python的进程和线程:多线程是假的
import multiprocessing
import os
import time def add2():
start_time = time.time()
for i in range(100000000):
pass
end_time = time.time()
use_time = end_time - start_time
print("进程id: %s use_time: %s" % (os.getpid(), use_time)) if __name__ == '__main__':
print("【进程测试】")
p1 = multiprocessing.Process(target=add2, args=(), name="p1-进程")
print("p1.name :%s" % p1.name)
p2 = multiprocessing.Process(target=add2, args=(), name="p2-进程")
start_time = time.time()
p1.start()
p2.start()
p1.join()
p2.join()
end_time = time.time()
use_time = end_time - start_time
print("主进程id:%s use_time: %s" % (os.getpid(),use_time)) print("====主进程单独运行一次循环耗时:=====")
add2()
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" alt="" /> .png)
import threading
import time def add2():
start_time = time.time()
for i in range(100000000):
pass
end_time = time.time()
use_time = end_time - start_time
print("线程id:%s 耗时:%s" % (threading.current_thread().ident, use_time)) if __name__ == '__main__':
print("【线程测试】")
print("主线程:%s 主线程id:%s" % (threading.current_thread(), threading.current_thread().ident))
t1 = threading.Thread(target=add2, args=(), name="t1-线程")
t2 = threading.Thread(target=add2, args=(), name="t2-线程")
start_time = time.time()
t1.start()
t2.start()
t1.join()
t2.join()
end_time = time.time()
use_time = end_time - start_time
print("线程id:%s 耗时:%s (主线程)" % (threading.current_thread().ident, use_time)) print("====主线程单独运行一次循环耗时:=====")
add2()
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