最近被多線程給坑了下，沒意識到類變量在多線程下是共享的，還有一個就是沒意識到內存釋放問題，導致越累越大
1.python 類變量在多線程情況下的是共享的
2.python 類別變數在多執行緒情況下的釋放是不完全的
3.python 類別變數在多執行緒情況下沒釋放的那部分記憶體是可以重複使用的
import threading
import time
class Test:
cache = {}
@classmethod
def get_value(self, key):
value = Test.cache.get(key, [])
return len(value)
@classmethod
def store_value(self, key, value):
if not Test.cache.has_key(key):
Test.cache[key] = range(value)
else:
Test.cache[key].extend(range(value))
return len(Test.cache[key])
@classmethod
def release_value(self, key):
if Test.cache.has_key(key):
Test.cache.pop(key)
return True
@classmethod
def print_cache(self):
print 'print_cache:'
for key in Test.cache:
print 'key: %d, value:%d' % (key, len(Test.cache[key]))
def worker(number, value):
key = number % 5
print 'threading: %d, store_value: %d' % (number, Test.store_value(key, value))
time.sleep(10)
print 'threading: %d, release_value: %s' % (number, Test.release_value(key))
if __name__ == '__main__':
thread_num = 10
thread_pool = []
for i in range(thread_num):
th = threading.Thread(target=worker,args=[i, 1000000])
thread_pool.append(th)
thread_pool[i].start()
for thread in thread_pool:
threading.Thread.join(thread)
Test.print_cache()
time.sleep(10)
thread_pool = []
for i in range(thread_num):
th = threading.Thread(target=worker,args=[i, 100000])
thread_pool.append(th)
thread_pool[i].start()
for thread in thread_pool:
threading.Thread.join(thread)
Test.print_cache()
time.sleep(10)公用的數據，除非是唯讀的，不然不要當類別成員變量，一是會共享，二是不好釋放。
