redis依赖_请勿过度依赖 Redis 的过期监听

阅读本文大概需要 5 分钟。

来自：http://juejin.im/post/6844904158227595271

Redis 过期监听场景

业务中有类似等待一定时间之后执行某种行为的需求 , 比如 30 分钟之后关闭订单 . 网上有很多使用 Redis 过期监听的 Demo , 但是其实这是个大坑 , 因为 Redis 不能确保 key 在指定时间被删除 , 也就造成了通知的延期 . 不多说 , 跑个测试。

测试情况

先说环境 , redis 运行在 Docker 容器中 , 分配了一个 cpu 以及 512MB 内存, 在 Docker 中执行 redis-benchmark -t set -r 100000 -n 1000000 结果如下:

====== SET ======1000000 requests completed in 171.03 seconds50 parallel clients3 bytes payloadkeep alive: 1host configuration "save": 3600 1 300 100 60 10000host configuration "appendonly": nomulti-thread: no

其实这里有些不严谨 benchmark 线程不应该在 Docker 容器内部运行 . 跑分的时候大概 benchmark 和 redis 主线程各自持有 50%CPU测试代码如下:

@Service
@Slf4j
public class RedisJob {@Autowiredprivate StringRedisTemplate stringRedisTemplate;public DateTimeFormatter dateTimeFormatter = DateTimeFormatter.ofPattern("yyyy-MM-dd HH:mm:ss");public LocalDateTime end = LocalDateTime.of(LocalDate.of(2020, 5, 12), LocalTime.of(8, 0));@Scheduled(cron = "0 56 * * * ?")public void initKeys() {LocalDateTime now = LocalDateTime.now();ValueOperations<String, String> operations = stringRedisTemplate.opsForValue();log.info("开始设置key");LocalDateTime begin = now.withMinute(0).withSecond(0).withNano(0);for (int i = 1; i < 17; i++) {setExpireKey(begin.plusHours(i), 8, operations);}log.info("设置完毕: " + Duration.between(now, LocalDateTime.now()));}private void setExpireKey(LocalDateTime expireTime, int step, ValueOperations<String, String> operations) {LocalDateTime localDateTime = LocalDateTime.now().withNano(0);String nowTime = dateTimeFormatter.format(localDateTime);while (expireTime.getMinute() < 55) {operations.set(nowTime + "@" + dateTimeFormatter.format(expireTime), "A", Duration.between(expireTime, LocalDateTime.now()).abs());expireTime = expireTime.plusSeconds(step);}}
}

大概意思就是每小时 56 分的时候 , 会增加一批在接下来 16 小时过期的 key , 过期时间间隔 8 秒 , 且过期时间都在 55 分之前

@Slf4j
@Component
public class RedisKeyExpirationListener extends KeyExpirationEventMessageListener {public RedisKeyExpirationListener(RedisMessageListenerContainer listenerContainer) {super(listenerContainer);}public DateTimeFormatter dateTimeFormatter = DateTimeFormatter.ofPattern("yyyy-MM-dd HH:mm:ss");@Autowiredprivate StringRedisTemplate stringRedisTemplate;@Overridepublic void onMessage(Message message, byte[] pattern) {String keyName = new String(message.getBody());LocalDateTime parse = LocalDateTime.parse(keyName.split("@")[1], dateTimeFormatter);long seconds = Duration.between(parse, LocalDateTime.now()).getSeconds();stringRedisTemplate.execute((RedisCallback<Object>) connection -> {Long size = connection.dbSize();log.info("过期key:" + keyName + " ,当前size:" + size + " ,滞后时间" + seconds);return null;});}
}

这里是监测到过期之后打印当前的 dbSize 以及滞后时间

@Bean
public RedisMessageListenerContainer configRedisMessageListenerContainer(RedisConnectionFactory connectionFactory) {ThreadPoolTaskExecutor executor = new ThreadPoolTaskExecutor();executor.setCorePoolSize(100);executor.setMaxPoolSize(100);executor.setQueueCapacity(100);executor.setKeepAliveSeconds(3600);executor.setThreadNamePrefix("redis");// rejection-policy：当pool已经达到max size的时候，如何处理新任务// CALLER_RUNS：不在新线程中执行任务，而是由调用者所在的线程来执行executor.setRejectedExecutionHandler(new ThreadPoolExecutor.CallerRunsPolicy());executor.initialize();RedisMessageListenerContainer container = new RedisMessageListenerContainer();// 设置Redis的连接工厂container.setConnectionFactory(connectionFactory);// 设置监听使用的线程池container.setTaskExecutor(executor);// 设置监听的Topicreturn container;
}

设置 Redis 的过期监听以及线程池信息 ,最后的测试结果是当 key 数量小于 1 万的时候 , 基本上都可以在 10s 内完成过期通知 , 但是如果数量到 3 万 , 就有部分 key 会延迟 120s . 顺便贴一下我最新的日志。

2020-05-13 22:16:48.383  : 过期key:2020-05-13 11:56:02@2020-05-13 22:14:08 ,当前size:57405 ,滞后时间160
2020-05-13 22:16:49.389  : 过期key:2020-05-13 11:56:02@2020-05-13 22:14:32 ,当前size:57404 ,滞后时间137
2020-05-13 22:16:49.591  : 过期key:2020-05-13 10:56:02@2020-05-13 22:13:20 ,当前size:57403 ,滞后时间209
2020-05-13 22:16:50.093  : 过期key:2020-05-13 20:56:00@2020-05-13 22:12:32 ,当前size:57402 ,滞后时间258
2020-05-13 22:16:50.596  : 过期key:2020-05-13 07:56:03@2020-05-13 22:13:28 ,当前size:57401 ,滞后时间202
2020-05-13 22:16:50.697  : 过期key:2020-05-13 20:56:00@2020-05-13 22:14:32 ,当前size:57400 ,滞后时间138
2020-05-13 22:16:50.999  : 过期key:2020-05-13 19:56:00@2020-05-13 22:13:44 ,当前size:57399 ,滞后时间186
2020-05-13 22:16:51.199  : 过期key:2020-05-13 20:56:00@2020-05-13 22:14:40 ,当前size:57398 ,滞后时间131
2020-05-13 22:16:52.205  : 过期key:2020-05-13 15:56:01@2020-05-13 22:16:24 ,当前size:57397 ,滞后时间28
2020-05-13 22:16:52.808  : 过期key:2020-05-13 06:56:03@2020-05-13 22:15:04 ,当前size:57396 ,滞后时间108
2020-05-13 22:16:53.009  : 过期key:2020-05-13 06:56:03@2020-05-13 22:16:40 ,当前size:57395 ,滞后时间13
2020-05-13 22:16:53.110  : 过期key:2020-05-13 20:56:00@2020-05-13 22:14:56 ,当前size:57394 ,滞后时间117
2020-05-13 22:16:53.211  : 过期key:2020-05-13 06:56:03@2020-05-13 22:13:44 ,当前size:57393 ,滞后时间189
2020-05-13 22:16:53.613  : 过期key:2020-05-13 15:56:01@2020-05-13 22:12:24 ,当前size:57392 ,滞后时间269
2020-05-13 22:16:54.317  : 过期key:2020-05-13 15:56:01@2020-05-13 22:16:00 ,当前size:57391 ,滞后时间54
2020-05-13 22:16:54.517  : 过期key:2020-05-13 18:56:00@2020-05-13 22:15:44 ,当前size:57390 ,滞后时间70
2020-05-13 22:16:54.618  : 过期key:2020-05-13 21:56:00@2020-05-13 22:14:24 ,当前size:57389 ,滞后时间150
2020-05-13 22:16:54.819  : 过期key:2020-05-13 17:56:00@2020-05-13 22:14:40 ,当前size:57388 ,滞后时间134
2020-05-13 22:16:55.322  : 过期key:2020-05-13 10:56:02@2020-05-13 22:13:52 ,当前size:57387 ,滞后时间183
2020-05-13 22:16:55.423  : 过期key:2020-05-13 07:56:03@2020-05-13 22:14:16 ,当前size:57386 ,滞后时间159

可以看到 , 当数量到达 5 万的时候 , 大部分都已经滞后了两分钟 , 对于业务方来说已经完全无法忍受了。

总结

可能到这里 , 你会说 Redis 给你挖了一个大坑 , 但其实这些都在文档上写的明明白白。

How Redis expires keys：https://redis.io/commands/expire#how-redis-expires-keys
Timing of expired events：https://redis.io/topics/notifications#timing-of-expired-events

尤其是在 Timing of expired events 中 , 明确的说明了 "Basically expired events are generated when the Redis server deletes the key and not when the time to live theoretically reaches the value of zero.", 这两个文章读下来你会感觉 , 卧槽 Redis 的过期策略其实也挺'Low'的。