缓存更新策略

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缓存穿透

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缓存穿透是指客户端请求的数据在缓存中和数据库中都不存在,这样缓存永远不会生效,这些请求都会打到数据库.

• 常见的解决方案:
  • 缓存空对象
    • 优点: 实现简单, 维护方便
    • 缺点: 额外的内存消耗, 可能造成短期的不一致
  • 布隆过滤
    • 优点: 内存占用较少(保存的是数据的二进制位)
    • 缺点: 实现复杂, 存在误判可能

• 缓存空对象业务逻辑
  
• 写入空值

redisTemplate.opsForValue().set("item:1001", "", 30L, TimeUnit.SECONDS);

缓存雪崩

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缓存雪崩是指在同一时段大量的缓存key同时失效或者Redis服务宕机, 导致大量请求到达数据库, 带来巨大压力.

解决方案:

• 给不同的key的TTL添加随机值
• 利用Redis集群提高服务的可用性
• 给缓存业务添加降级限流策略
• 给业务添加多级缓存

缓存击穿

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缓存击穿问题也叫热点Key问题, 就是一个高并发访问并且缓存重建业务较复杂的key突然失效了, 无数的请求访问会瞬间给数据库带来巨大的冲击.

常见的解决方案:

• 互斥锁
• 逻辑过期

互斥锁

• 定义获取锁和释放锁的方法

	private boolean tryLock(String key){Boolean flag = redisTemplate.opsForValue().setIfAbsent(key, "1", 10L, TimeUnit.SECONDS);return BooleanUtils.isTrue(flag); }private void unlock(String key){redisTemplate.delete(key);}

逻辑过期

• 逻辑过期业务逻辑

• 缓存预热

• RedisData.java

@Data
public class RedisData {private LocalDateTime expireTime;private Object data;}

• 判断是否逻辑过期

• 开启独立线程重建缓存

封装Redis工具类

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缓存工具类

import cn.hutool.json.JSONUtil;
import lombok.Data;
import org.apache.commons.lang3.StringUtils;
import org.springframework.data.redis.core.StringRedisTemplate;
import org.springframework.stereotype.Component;import java.time.LocalDateTime;
import java.util.concurrent.TimeUnit;
import java.util.function.Function;@Component
public class CacheClient {private final StringRedisTemplate stringRedisTemplate;private static final long NULL_TTL = 1L;public CacheClient(StringRedisTemplate stringRedisTemplate) {this.stringRedisTemplate = stringRedisTemplate;}public void set(String key, Object value, Long time, TimeUnit unit) {stringRedisTemplate.opsForValue().set(key, JSONUtil.toJsonStr(value), time, unit);}public String get(String key) {return stringRedisTemplate.opsForValue().get(key);}public void delete(String key) {stringRedisTemplate.delete(key);}public void setWithLogicalExpire(String key, Object value, Long time, TimeUnit unit) {// 设置逻辑过期RedisData redisData = new RedisData();redisData.setData(value);redisData.setExpireTime(LocalDateTime.now().plusSeconds(unit.toSeconds(time)));// 写入redisstringRedisTemplate.opsForValue().set(key, JSONUtil.toJsonStr(redisData));}/*** 查询--缓存穿透处理*/public <R, ID> R queryWithPassThrough(String keyPrefix, ID id, Class<R> type, Long time, TimeUnit unit, Function<ID, R> dbFallBack) {String key = keyPrefix + id;String json = stringRedisTemplate.opsForValue().get(key);if (StringUtils.isNotBlank(json)) {// 存在,直接返回return JSONUtil.toBean(json, type);}// 判断命中的是否为空值,缓存穿透处理会存""if (json != null) {// 返回一个错误信息return null;}R r = dbFallBack.apply(id);if (r == null) {// 将空值写入redis, 缓存穿透处理stringRedisTemplate.opsForValue().set(key, "", NULL_TTL, TimeUnit.SECONDS);return null;}// 存在, 写入redisthis.set(key, JSONUtil.toJsonStr(r), time, unit);return r;}}@Data
class RedisData {// 逻辑过期时间private LocalDateTime expireTime;private Object data;
}

分布式锁类

• ILock.java

public interface ILock {/*** 尝试获取锁* @param timeoutSec* @return*/boolean tryLock(long timeoutSec);/*** 释放锁*/void unlock();
}

• SimpleRedisLock.java

import cn.hutool.core.lang.UUID;
import org.springframework.data.redis.core.StringRedisTemplate;import java.util.concurrent.TimeUnit;public class SimpleRedisLock implements ILock {private StringRedisTemplate stringRedisTemplate;private String name;private static final String KEY_PREFIX = "lock:";private static final String ID_PREFIX = UUID.randomUUID().toString(true) + "-";public SimpleRedisLock(StringRedisTemplate stringRedisTemplate, String name) {this.stringRedisTemplate = stringRedisTemplate;this.name = name;}@Overridepublic boolean tryLock(long timeoutSec) {String threadId = ID_PREFIX + Thread.currentThread().getId();Boolean success = stringRedisTemplate.opsForValue().setIfAbsent(KEY_PREFIX + name, threadId, timeoutSec, TimeUnit.SECONDS);return Boolean.TRUE.equals(success);}@Overridepublic void unlock() {String threadId = ID_PREFIX + Thread.currentThread().getId();String id = stringRedisTemplate.opsForValue().get(KEY_PREFIX + name);if (threadId.equals(id)) {// 释放锁stringRedisTemplate.delete(KEY_PREFIX + name);}}
}