Redis 核心教学: 数据结构, 缓存设计, 分布式锁

Redis 核心教学

说明：默认读者已经掌握 MQ、MySQL；重点聚焦工程落地、并发问题、选型边界；通用抽象描述 + 业务举例，穿插 mermaid、Java 代码示例；实验环境：JDK8，Spring‑Boot，spring‑data‑redis / Redisson。

1 Redis｜数据结构

Redis 的业务落地本质就是选对数据结构，不纠结底层实现，聚焦场景匹配。

数据结构	核心特性	典型业务场景
String	字符串，支持增减计数，二进制安全	计数器、库存、Token、普通KV缓存
Hash	key‑field‑value，支持局部更新	对象缓存：用户信息、商品属性
List	双向链表，有序可重复	简单队列、时序列表
Set	无序，元素唯一，集合运算	去重、黑名单、交集计算
ZSet	元素唯一，score分数排序	排行榜、权重排序
Bitmap	按bit位存储，极大节省内存	用户签到、状态标记、布尔批量标记
HyperLogLog	基数统计，低内存，有误差	页面UV、大数据量去重统计
Geo	经纬度存储，距离计算	附近门店、地理位置检索
Stream	持久化消息队列，支持消费组	Redis实现消息队列，消息回溯

1.1 String 示例

// String：库存计数
redisTemplate.opsForValue().set("coupon:stock:1001", "1000");
// 原子递增递减
Long stock = redisTemplate.opsForValue().decrement("coupon:stock:1001");

1.2 Hash 示例，局部更新对象，不要序列化大对象

// Hash 存储用户信息
String userKey = "user:info:10086";
redisTemplate.opsForHash().put(userKey,"name","zhangsan");
redisTemplate.opsForHash().put(userKey,"age","22");
// 只更新age字段，不需要更新整个对象
redisTemplate.opsForHash().put(userKey,"age","23");
Object age = redisTemplate.opsForHash().get(userKey,"age");

1.3 List 示例，简单时序队列

// 左进右出模拟队列
redisTemplate.opsForList().leftPush("msg:queue","msg1");
String msg = redisTemplate.opsForList().rightPop("msg:queue");

1.4 Set 示例，黑名单、集合去重

redisTemplate.opsForSet().add("black:uid","1001","1002","1003");
Boolean isBlack = redisTemplate.opsForSet().isMember("black:uid","1001");

1.5 ZSet 排行榜示例

// score 分数，member 用户ID
redisTemplate.opsForZSet().add("rank:score","uid:1001",950);
redisTemplate.opsForZSet().add("rank:score","uid:1002",880);
// 降序分页 top10
Set<Object> top10 = redisTemplate.opsForZSet().reverseRange("rank:score",0,9);

1.6 Bitmap 位图

原理：把每一个用户映射到一个 bit 位，bit=1代表签到，bit=0未签到；1亿用户仅占用约12MB内存。

// key:sign:20260916  offset 用户id
// 用户1000签到
redisTemplate.opsForValue().setBit("sign:20260916",1000,true);
// 判断用户是否签到
Boolean signFlag = redisTemplate.opsForValue().getBit("sign:20260916",1000);
// 统计当日签到总人数
Long signCount = redisTemplate.execute((RedisCallback<Long>) con -> con.bitCount("sign:20260916".getBytes(StandardCharsets.UTF_8)));

适用：签到、开关状态；局限：用户ID不能离散过大，会造成大量空bit占用。

1.7 HyperLogLog 基数统计

统计不重复元素数量，不存储原始数据，存在0.81%标准误差；内存极小，百万UV只占几KB；不能取出具体元素，只能得到去重数量。

// 访问页面，添加访问用户id
redisTemplate.opsForHyperLogLog().add("uv:page:home","uid:1001","uid:1002","uid:1001");
// 获取UV，自动去重
Long uv = redisTemplate.opsForHyperLogLog().size("uv:page:home");

坑点：只适合统计数量；不能获取明细；允许误差才使用。

1.8 Geo 地理位置

存储经纬度，计算两点距离，查询附近点位。

// 添加门店经纬度：经度,纬度,member
redisTemplate.opsForGeo().add("store:geo",new Point(122.27,30.05),"store_001");
redisTemplate.opsForGeo().add("store:geo",new Point(122.28,30.06),"store_002");
// 查询两店距离
Distance distance = redisTemplate.opsForGeo().distance("store:geo","store_001","store_002",Metrics.KILOMETERS);
// 获取附近5km门店
Circle circle = new Circle(new Point(122.27,30.05),5,Metrics.KILOMETERS);
GeoResults<GeoLocation<Object>> nearList = redisTemplate.opsForGeo().radius("store:geo",circle);

1.9 Stream Redis内置消息队列

持久化消息，支持消费组、消息ACK、消息回溯；弥补List丢失消息问题；替代部分MQ轻量场景。

// 发送消息
redis.xadd("stream:order", Map.of("orderId", "O10001"));

// 创建消费组（只需一次）
redis.xgroupCreate("stream:order", "g1", "0");

// 消费组读取消息
List<Map<String, String>> msgs = redis.xreadGroup(
    "g1", "consumer-1",
    Map.of("stream:order", ">")  // ">" 表示只读新消息
);

for (Map<String, String> msg : msgs) {
    handle(msg);  // 业务处理

    // 处理成功才 ACK，消息不会丢
    redis.xack("stream:order", "g1", msg.get("id"));
}

// 不 ACK：留在 pending，可重试
// xreadGroup 传 "0" 而非 ">"：从头读，可回溯

选型边界：简单业务可用；可靠性、事务、死信队列不如专业MQ(RocketMQ/Kafka)。
落地小提示

1. 对象缓存优先 Hash，不要把整个对象序列化塞进 String，局部更新代价很高。
2. 排行榜一定用 ZSet，不要在业务代码内存排序。
3. 计数、库存优先 String。
4. Bitmap/HyperLogLog 是空间换性能，接受业务本身限制再使用。

---

2 Redis｜缓存架构：读缓存、写缓存、读写缓存（搭配 MQ + MySQL）

整体架构思想：Redis承接高并发，MySQL持久化落数据，MQ做异步削峰解耦。

2.1 读缓存模式（读多写少场景）

抽象流程：请求先访问缓存；命中直接返回；未命中查数据库，回写缓存再返回。目的减轻数据库读压力。
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未命中
业务请求
Redis缓存命中?
直接返回
查询MySQL
结果回写Redis
返回数据

Java伪代码

public ProductDTO getProduct(Long productId) {
    String key = "product:" + productId;
    String cache = redisTemplate.opsForValue().get(key);

    if (NULL_PLACEHOLDER.equals(cache)) return null;
    if (cache != null) return JSON.parseObject(cache, ProductDTO.class);

    // 只让一个线程去查DB
    String lockKey = "lock:product:" + productId;
    Boolean locked = redisTemplate.opsForValue()
            .setIfAbsent(lockKey, "1", Duration.ofSeconds(10));

    if (Boolean.TRUE.equals(locked)) {
        try {
            // 再查一次缓存，防止重复查（双重检查）
            cache = redisTemplate.opsForValue().get(key);
            if (cache != null) {
                return NULL_PLACEHOLDER.equals(cache) ? null
                        : JSON.parseObject(cache, ProductDTO.class);
            }

            ProductDTO dbData = productMapper.selectById(productId);
            if (dbData == null) {
                redisTemplate.opsForValue().set(key, NULL_PLACEHOLDER, Duration.ofMinutes(2));
                return null;
            }
            redisTemplate.opsForValue().set(key, JSON.toJSONString(dbData), Duration.ofMinutes(30));
            return dbData;
        } finally {
            redisTemplate.delete(lockKey);
        }
    } else {
        // 没抢到锁，等一会再读缓存（或自旋）
        try { Thread.sleep(50); } catch (InterruptedException e) { }
        return getProduct(productId);  // 重试
    }
}

业务举例：商品详情、活动配置查询。

2.2 写缓存模式（高并发写，库存扣减类）

抽象流程：Redis做预扣减与流量拦截；库存充足发送消息到 MQ；MQ消费者真正操作 MySQL；库存不足直接拒绝请求。
核心：高并发写请求不直接打 MySQL，Redis拦截大部分流量。
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库存不足
并发写请求
Redis校验+预扣库存
发送消息至MQ
返回失败
MQ消费
MySQL执行真实扣减落库

Java伪代码

private static final DefaultRedisScript<Long> RECEIVE_SCRIPT =
    new DefaultRedisScript<>(
        "local stock = tonumber(redis.call('get', KEYS[1]))\n" +
        "if not stock or stock <= 0 then return 0 end\n" +
        "if redis.call('sismember', KEYS[2], ARGV[1]) == 1 then return -1 end\n" + // 已领过
        "redis.call('decr', KEYS[1])\n" +
        "redis.call('sadd', KEYS[2], ARGV[1])\n" +
        "return 1",
        Long.class);

public boolean receiveCoupon(Long couponId, Long userId) {
    String stockKey = "coupon:stock:" + couponId;
    String userKey  = "coupon:users:" + couponId;

    Long r = redisTemplate.execute(RECEIVE_SCRIPT,
            Arrays.asList(stockKey, userKey), userId.toString());

    if (r == null || r == 0) return false;   // 无库存
    if (r == -1) return false;               // 已领过

    // 先落本地消息表（状态=待发送），再发MQ
    try {
        mqProducer.send("coupon_consume_topic", couponId, userId);
        // 标记消息已发送
    } catch (Exception e) {
        redisTemplate.opsForValue().increment(stockKey);        // 补库存
        redisTemplate.opsForSet().remove(userKey, userId);      // 补去重
        throw e;
    }
    return true;
}

业务举例：大促领券、秒杀库存扣减。

2.3 读写缓存模式（排行榜类）

抽象流程：业务产生分数变更，通过 MQ 异步更新 Redis ZSet；查询直接读 ZSet；定时任务把排行榜数据同步 MySQL 持久化备份。
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MQ异步更新ZSet分值
用户查询排行榜
读取Redis ZSet分页返回
定时任务同步至MySQL备份

Java伪代码

// 发送：传增量分数
public void scoreChange(Long userId, Integer delta) {
    Map<String, Object> msg = Map.of(
        "msgId", UUID.randomUUID().toString(),   // 幂等用
        "userId", userId,
        "delta", delta
    );
    mqProducer.send("score_update_topic", JSON.toJSONString(msg));
}

// 消费端
public void handleScoreMsg(String msg) {
    if (msg == null) return;
    ScoreDTO dto = JSON.parseObject(msg, ScoreDTO.class);
    if (dto == null || dto.getUserId() == null || dto.getDelta() == null) return;

    // 1. 幂等：已处理过直接返回
    if (!markProcessed(dto.getMsgId())) return;

    // 2. 落库（真实来源）
    userScoreMapper.addScore(dto.getUserId(), dto.getDelta());

    // 3. 更新 Redis 排行榜（累加，不是覆盖）
    redisTemplate.opsForZSet()
        .incrementScore("user:rank", dto.getUserId().toString(), dto.getDelta());
}

// 查询：带分数
public Set<TypedTuple<Object>> getTopRank(int topN) {
    if (topN <= 0) return Collections.emptySet();
    return redisTemplate.opsForZSet()
        .reverseRangeWithScores("user:rank", 0, topN - 1);
}

模式选型小结

模式	适用场景	核心收益
读缓存	读多写少静态数据	分担数据库读压力
写缓存+MQ异步落库	高并发写：库存、秒杀	削峰，保护数据库
读写缓存(ZSet)	排行榜、积分排名	高并发有序分页查询

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3 Redis｜Lua脚本：保证原子性

Redis 单条命令原子；多条命令组合不是原子。Java代码做"判断+执行"两步操作，并发下会出现超卖、数据错乱。

Lua脚本：把多条Redis命令封装，Redis单线程完整执行脚本，脚本内命令原子执行，中间不会插入其他客户端命令；同时减少网络往返。
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一次网络请求提交Lua脚本
执行期间不处理其他请求，原子完成
Redis服务端：读取‑判断‑扣减完整执行
Java多命令（非原子）
Java读取stock
中间可被其他线程插队，并发超卖风险
Java执行decr
stock>0?

Lua脚本 stock.lua

-- KEYS[1]:库存key ARGV[1]:扣减数量
local stock = tonumber(redis.call('get',KEYS[1]))
local deduct = tonumber(ARGV[1])
if not stock or not deduct or stock < deduct then
    return 0
end
redis.call('decrby',KEYS[1],deduct)
return 1

Java调用Lua脚本

public boolean deductStockLua(String stockKey,int deductNum){
    String script = "local stock = tonumber(redis.call('get',KEYS[1]))\n" +
            "local deduct = tonumber(ARGV[1])\n" +
            "if not stock or not deduct or stock < deduct then\n" +
            "    return 0\n" +
            "end\n" +
            "redis.call('decrby',KEYS[1],deduct)\n" +
            "return 1";
    DefaultRedisScript<Long> redisScript = new DefaultRedisScript<>(script, Long.class);
    Long res = redisTemplate.execute(redisScript, Collections.singletonList(stockKey), deductNum);
    return res != null && res == 1L;
}

选型边界

• 只操作Redis，无MySQL参与：优先Lua，性能高于分布式锁；
• 如果逻辑涉及 Redis + MySQL 跨中间件，Lua无法保证MySQL原子性，需要分布式锁。

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4 Redis｜缓存问题集：穿透、击穿、雪崩；缓存一致性、延迟双删、缓存预热

4.1 缓存穿透

问题：查询不存在的数据，缓存查不到、数据库也查不到；请求直接打到DB；恶意请求可以打垮数据库。

解决方案：

1. 空值缓存：不存在的数据缓存短期空key；
2. 布隆过滤器前置过滤不存在key；
3. 接口参数校验、黑名单。

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Redis无数据
全部请求打到MySQL

4.2 缓存击穿

问题：热点key过期瞬间，大量并发同时打到数据库。

解决方案：

1. 热点key设置永不过期；
2. 互斥锁：仅一个线程更新缓存，其他等待；
3. 定时任务主动刷新热点key。

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大量并发同时涌入
数据库压力突增

4.3 缓存雪崩

问题：大量key同一时刻过期，或者Redis整体宕机；大量流量全部压向数据库。

解决方案：

1. TTL增加随机偏移，避免集中过期；
2. Redis高可用：主从、哨兵、集群；
3. 服务侧限流熔断降级；
4. 多级缓存：本地缓存 + Redis。

4.4 缓存与MySQL一致性

主流策略对比

策略	流程	缺点
先更新库，再更新缓存	update mysql → set redis	很多缓存数据没人读，浪费写性能
先更新库，再删除缓存	update mysql → del redis	极端并发会短暂脏数据

延迟双删（最终一致性方案）

抽象流程：

1. 更新 MySQL；
2. 立刻删除缓存；
3. 通过延时MQ消息，延迟一段时间再次删除缓存；解决并发读写造成的脏缓存残留。

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立即删除缓存
发送延时MQ消息
延迟到达，再次删除缓存

Java伪代码

// 更新商品，延迟双删保证最终一致性
public void updateProduct(ProductDTO dto){
    productMapper.updateById(dto);
    String cacheKey = "product:" + dto.getId();
    // 立即删除缓存
    redisTemplate.delete(cacheKey);
    // 发送延时MQ，延迟1s再次删除缓存
    mqProducer.sendDelayMsg("cache_delay_delete_topic",cacheKey,1000);
}

缓存预热

活动上线/系统启动前，提前把热点数据加载进Redis；避免上线瞬间大量请求穿透数据库。适用大促、首页热点。

坑点小结

• 穿透：查询不存在数据 ；击穿：单个热点key过期 ；雪崩：批量key失效/Redis宕机。
• 优先删缓存，不要频繁更新缓存；普通业务接受最终一致性，用延迟双删。

---

5 Redis｜分布式锁 Redisson

原生 SETNX 锁存在：不可重入、锁被其他线程误删、无自动续期；生产环境直接使用 Redisson。

依赖

<dependency>
    <groupId>org.redisson</groupId>
    <artifactId>redisson-spring-boot-starter</artifactId>
    <version>3.23.5</version>
</dependency>

Redisson核心能力

1. 可重入锁：同一个线程多次加锁不会死锁；底层Hash结构记录锁计数。
2. 看门狗自动续期：默认锁30s，业务未执行完后台自动续期；防止业务没跑完锁过期释放。
3. 超时防死锁：实例宕机锁会自动过期释放，杜绝死锁。

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执行业务逻辑
看门狗后台线程自动续期
finally块释放锁
服务宕机，锁到期自动释放

Java示例

public void createOrder(Long orderId){
    String lockKey = "lock:order:" + orderId;
    RLock rLock = redissonClient.getLock(lockKey);
    try {
        // 获取锁，不设置leaseTime，开启看门狗续期
        boolean getLock = rLock.tryLock(10, TimeUnit.SECONDS);
        if(getLock){
            // 业务逻辑：操作MySQL+Redis跨中间件
        } else {
            throw new RuntimeException("获取锁失败");
        }
    } finally {
        // 判断当前线程持有锁才释放
        if(rLock.isHeldByCurrentThread()){
            rLock.unlock();
        }
    }
}

⚠️ Lua脚本 vs Redisson锁 选型边界（重点）

1. 逻辑只操作Redis，不操作MySQL：优先Lua脚本，性能更高。>

示例：Redis内库存预扣、计数校验、防重。

2. 跨中间件：Redis + MySQL，复杂业务、长事务：必须用Redisson分布式锁。>

示例：订单创建、资金、状态流转。

使用规范

• 锁释放写在finally块，防止异常不释放；
• 尽量不手动指定锁过期时间，交给看门狗；
• 高并发场景按需使用读写锁、公平锁。

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整体总结

1. Redis业务落地第一步：选对数据结构；String计数、Hash存对象、ZSet排行；Bitmap签到、HyperLogLog UV统计、Geo地理位置、Stream轻量消息队列。
2. 缓存三大范式：读缓存分担读压力；写缓存配合MQ异步落库扛高并发写；ZSet实现排行榜。
3. Lua脚本保证Redis多命令原子，减少网络IO；纯Redis逻辑优先Lua。
4. 分清穿透/击穿/雪崩；普通业务用延迟双删做到最终一致性；热点场景做缓存预热。
5. 分布式锁严格选型：纯Redis逻辑用Lua；跨Redis+MySQL复杂业务用Redisson；利用可重入、看门狗解决死锁与锁过期问题。