高并发之商品秒杀系统
一基于redis
利用redis的乐观锁,实现秒杀系统的数据同步(基于watch实现),
用户一:
import redis conn = redis.Redis(host='127.0.0.1',port=6379) # conn.set('count',1000) with conn.pipeline() as pipe: # 先监视,自己的值没有被修改过 conn.watch('count') # 事务开始 pipe.multi() old_count = conn.get('count') count = int(old_count) input('我考虑一下') if count > 0: # 有库存 pipe.set('count', count - 1) # 执行,把所有命令一次性推送过去 pipe.execute() ret = pipe.execute() print(type(ret)) print(ret)
用户二:
import redis conn = redis.Redis(host='127.0.0.1',port=6379) with conn.pipeline() as pipe: # 先监视,自己的值没有被修改过 conn.watch('count') # 事务开始 pipe.multi() old_count = conn.get('count') count = int(old_count) if count > 0: # 有库存 pipe.set('count', count - 1) # 执行,把所有命令一次性推送过去 ret=pipe.execute() print(type(ret))
注:windows下如果数据被修改了,不会抛异常,只是返回结果的列表为空,mac和linux会直接抛异常
秒杀系统核心逻辑测试,创建100个线程并发秒杀
![](https://images.cnblogs.com/OutliningIndicators/ContractedBlock.gif)
import redis from threading import Thread def choose(name, conn): # conn.set('count',10) with conn.pipeline() as pipe: # 先监视,自己的值没有被修改过 conn.watch('count') # 事务开始 pipe.multi() old_count = conn.get('count') count = int(old_count) # input('我考虑一下') # time.sleep(random.randint(1, 2)) if count > 0: # 有库存 pipe.set('count', count - 1) # 执行,把所有命令一次性推送过去 ret = pipe.execute() print(ret) if len(ret) > 0: print('第%s个人抢购成功' % name) else: print('第%s个人抢购失败' % name) if __name__ == '__main__': conn = redis.Redis(host='127.0.0.1', port=6379) for i in range(100): t = Thread(target=choose, args=(i, conn)) t.start()