最近被多线程给坑了下,没意识到类变量在多线程下是共享的,还有一个就是没意识到 内存释放问题,导致越累越大
1.python 类变量 在多线程情况 下的 是共享的
2.python 类变量 在多线程情况 下的 释放是不完全的
3.python 类变量 在多线程情况 下没释放的那部分 内存 是可以重复利用的
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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 ) |
总结
公用的数据,除非是只读的,不然不要当类成员变量,一是会共享,二是不好释放。
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