Memcache 深度解析

Memcache 深度解析 ------ 那个被 Redis 抢了风头但还在默默搬砖的老伙计

有人说 Memcache 已经死了，我说：你才死了，你全家都死了！ 它只是低调得像你那个从不发朋友圈但工资最高的同事。

一、一句话介绍

Memcache 是一个高性能、分布式内存对象缓存系统。说人话就是：把数据库里查出来的数据往内存里一塞，下次再查就直接从内存拿，快到飞起。

二、整体架构

2.1 架构全景图（Mermaid 版）

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🐘 Memcache 集群
👤 客户端层
缓存未命中
缓存未命中
缓存未命中
写入缓存
写入缓存
写入缓存
SpringBoot App #1
SpringBoot App #2
SpringBoot App #3
Memcache Node-1

127.0.0.1:11211
Memcache Node-2

127.0.0.1:11212
Memcache Node-3

127.0.0.1:11213
MySQL / PostgreSQL

慢如蜗牛但靠谱

2.2 内部架构

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网络层

libevent 事件驱动

单线程但贼快
Slab Allocator

内存管理器

专治内存碎片
LRU 淘汰机制

不用的数据？

踢出去！
Hash 表

O(1) 查找

比找对象快多了
请求来了!
返回结果 / 踢老数据

2.3 Slab Allocation 内存管理（Memcache 的灵魂）

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💾 总内存 1GB
Page

1MB
Page

1MB
...
Slab Class 1

Chunk: 96 bytes
Slab Class 2

Chunk: 120 bytes
Slab Class 3

Chunk: 152 bytes
Slab Class N

Chunk: 更大...

⚠️ 冷知识 ：Memcache 把内存切成固定大小的 Chunk，就像大学宿舍------不管你是 1 个行李箱还是 10 个，每个床位面积都一样。这虽然有点浪费，但分配回收快啊！

2.4 分布式------一致性哈希

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Node A

哈希值: 1000
Node B

哈希值: 1亿
Node C

哈希值: 30亿
Key: user_100

哈希: 500 → 路由到 Node A
Key: user_200

哈希: 2亿 → 路由到 Node B
Key: user_300

哈希: 35亿 → 路由到 Node C

💡 关键理解 ：Memcache 自身不搞分布式 ，节点之间老死不相往来，各管各的。分布式全靠客户端 用一致性哈希算出来 Key 该扔给哪个节点。这就是传说中的------各人自扫门前雪 架构。

三、使用场景

3.1 典型场景全景图

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path,#mermaid-svg-AxomwgbW6H53hXvG .section-root circle,#mermaid-svg-AxomwgbW6H53hXvG .section-root polygon{fill:hsl(240, 100%, 46.2745098039%);}#mermaid-svg-AxomwgbW6H53hXvG .section-root text{fill:#ffffff;}#mermaid-svg-AxomwgbW6H53hXvG .section-root span{color:#ffffff;}#mermaid-svg-AxomwgbW6H53hXvG .section-2 span{color:#ffffff;}#mermaid-svg-AxomwgbW6H53hXvG .icon-container{height:100%;display:flex;justify-content:center;align-items:center;}#mermaid-svg-AxomwgbW6H53hXvG .edge{fill:none;}#mermaid-svg-AxomwgbW6H53hXvG .mindmap-node-label{dy:1em;alignment-baseline:middle;text-anchor:middle;dominant-baseline:middle;text-align:center;}#mermaid-svg-AxomwgbW6H53hXvG :root{--mermaid-font-family:"trebuchet ms",verdana,arial,sans-serif;} Memcache

使用场景
页面缓存
HTML片段缓存
商品详情页
新闻列表页
Session共享
多Tomcat集群
Spring Session
登录状态保持
数据库查询缓存
热门商品列表
用户基本信息
配置项缓存
临时数据
验证码
接口限流计数器
CSRF Token
排行榜
简单的计数排行
PV/UV统计

3.2 什么时候用 Memcache，而不是 Redis？

场景	选 Memcache ✅	选 Redis 🚀	原因
简单 KV 缓存	✅	✅	都能干
只需 GET/SET	✅	✅	Memcache 更轻
需要持久化	❌	✅	Memcache 重启数据就没了
需要 List/Hash/Set/ZSet	❌	✅	Memcache 只支持 String
需要发布订阅	❌	✅	Memcache 没有
需要事务/Lua 脚本	❌	✅	Memcache 就是个傻快的 KV
多核 CPU 场景	✅	⚠️	Memcache 天然多线程
内存碎片敏感	✅	⚠️	Slab 分配器更可控

四、市场份额

#mermaid-svg-0Oq1EmmeRdoIk3Ff{font-family:"trebuchet ms",verdana,arial,sans-serif;font-size:16px;fill:#333;}@keyframes edge-animation-frame{from{stroke-dashoffset:0;}}@keyframes dash{to{stroke-dashoffset:0;}}#mermaid-svg-0Oq1EmmeRdoIk3Ff .edge-animation-slow{stroke-dasharray:9,5!important;stroke-dashoffset:900;animation:dash 50s linear infinite;stroke-linecap:round;}#mermaid-svg-0Oq1EmmeRdoIk3Ff .edge-animation-fast{stroke-dasharray:9,5!important;stroke-dashoffset:900;animation:dash 20s linear infinite;stroke-linecap:round;}#mermaid-svg-0Oq1EmmeRdoIk3Ff .error-icon{fill:#552222;}#mermaid-svg-0Oq1EmmeRdoIk3Ff .error-text{fill:#552222;stroke:#552222;}#mermaid-svg-0Oq1EmmeRdoIk3Ff .edge-thickness-normal{stroke-width:1px;}#mermaid-svg-0Oq1EmmeRdoIk3Ff .edge-thickness-thick{stroke-width:3.5px;}#mermaid-svg-0Oq1EmmeRdoIk3Ff .edge-pattern-solid{stroke-dasharray:0;}#mermaid-svg-0Oq1EmmeRdoIk3Ff .edge-thickness-invisible{stroke-width:0;fill:none;}#mermaid-svg-0Oq1EmmeRdoIk3Ff .edge-pattern-dashed{stroke-dasharray:3;}#mermaid-svg-0Oq1EmmeRdoIk3Ff .edge-pattern-dotted{stroke-dasharray:2;}#mermaid-svg-0Oq1EmmeRdoIk3Ff .marker{fill:#333333;stroke:#333333;}#mermaid-svg-0Oq1EmmeRdoIk3Ff .marker.cross{stroke:#333333;}#mermaid-svg-0Oq1EmmeRdoIk3Ff svg{font-family:"trebuchet ms",verdana,arial,sans-serif;font-size:16px;}#mermaid-svg-0Oq1EmmeRdoIk3Ff p{margin:0;}#mermaid-svg-0Oq1EmmeRdoIk3Ff .pieCircle{stroke:#000000;stroke-width:2px;opacity:0.7;}#mermaid-svg-0Oq1EmmeRdoIk3Ff .pieOuterCircle{stroke:#000000;stroke-width:1px;fill:none;}#mermaid-svg-0Oq1EmmeRdoIk3Ff .pieTitleText{text-anchor:middle;font-size:25px;fill:#000000;font-family:"trebuchet ms",verdana,arial,sans-serif;}#mermaid-svg-0Oq1EmmeRdoIk3Ff .slice{font-family:"trebuchet ms",verdana,arial,sans-serif;fill:#000000;font-size:17px;}#mermaid-svg-0Oq1EmmeRdoIk3Ff .legend text{fill:#000000;font-family:"trebuchet ms",verdana,arial,sans-serif;font-size:17px;}#mermaid-svg-0Oq1EmmeRdoIk3Ff :root{--mermaid-font-family:"trebuchet ms",verdana,arial,sans-serif;} 65% 18% 5% 5% 4% 3% 缓存中间件市场份额（） Redis Memcache Hazelcast Couchbase Ehcache 其他

趋势解读：

🔴 Redis 确实一家独大，65%+ 的份额，独孤求败
🟡 Memcache 守住 ~18% 阵地，主要靠老牌互联网公司和简单场景
🟢 但注意：Facebook/Twitter/YouTube 等大厂还在重度使用 Memcache，毕竟人家祖传代码跑得好好的，凭啥换？

有个段子说得好："Redis 是缓存界的瑞士军刀，Memcache 是缓存界的一把大铁锤。你只需要砸钉子的时候，锤子比军刀好用。"

五、常用命令

Memcache 的命令简单到令人发指，一共就十几个，跟 Redis 的上百个命令比简直小清新。

5.1 基本 CRUD

命令	语法	说明	人类翻译
`set`	`set <key> <flags> <exptime> <bytes>\r\n<value>`	存数据	给我存，有就覆盖
`add`	`add <key> <flags> <exptime> <bytes>\r\n<value>`	存数据（不存在才存）	如果没人抢，我再存
`replace`	`replace <key> <flags> <exptime> <bytes>\r\n<value>`	替换已有数据	没有就别瞎存
`get`	`get <key>`	取数据	给我拿出来！
`gets`	`gets <key>`	取数据 + CAS token	拿出来，顺便给个版本号
`delete`	`delete <key>`	删数据	滚蛋！
`cas`	`cas <key> <flags> <exptime> <bytes> <cas_token>\r\n<value>`	乐观锁更新	版本号对上了才给你改

5.2 进阶命令

命令	语法	说明
`append`	`append <key> <flags> <exptime> <bytes>\r\n<追加内容>`	往后面加东西
`prepend`	`prepend <key> <flags> <exptime> <bytes>\r\n<前置内容>`	往前面加东西
`incr`	`incr <key> <value>`	数值 +N
`decr`	`decr <key> <value>`	数值 -N
`stats`	`stats`	看运行状态
`flush_all`	`flush_all`	清空所有数据（慎用！堪比 rm -rf）

5.3 Telnet 实战演示

bash 复制代码

# 连上 Memcache（用 telnet，这很复古）
telnet 127.0.0.1 11211

# 存一个值
set username 0 3600 5
hello
STORED

# 取出来
get username
VALUE username 0 5
hello
END

# 让计数器 +1
set counter 0 0 1
1
STORED
incr counter 1
2

# 看看服务器状态
stats
STAT pid 12345
STAT uptime 86400
STAT time 1718265600
STAT version 1.6.21
STAT curr_items 1000
STAT total_items 50000
STAT bytes 104857600
STAT get_hits 999999
STAT get_misses 42
...
END

# 退出
quit

🎯 注意 exptime 参数：0 表示永不过期（直到被 LRU 踢掉），秒级 Unix 时间戳（大于 30 天自动转换）。超过 30 天的时间戳处理是 Memcache 的特色魔法。

六、Java / SpringBoot 使用案例

6.1 添加依赖

xml 复制代码

<!-- pom.xml -->
<dependency>
    <groupId>net.spy</groupId>
    <artifactId>spymemcached</artifactId>
    <version>2.12.3</version>
</dependency>

<!-- Spring Cache 也需要缓存抽象支持 -->
<dependency>
    <groupId>org.springframework.boot</groupId>
    <artifactId>spring-boot-starter-cache</artifactId>
</dependency>

6.2 application.yml 配置

yaml 复制代码

spring:
  cache:
    type: memcached
    memcached:
      servers: 127.0.0.1:11211,127.0.0.1:11212
      protocol: BINARY
      expiration: 300  # 默认过期时间（秒）

⚠️ 实际上 Spring Boot 官方没有内置 Memcache 的 CacheManager 实现，需要自己封装。下面就是。

6.3 MemcacheConfig 配置类

java 复制代码

package com.example.demo.config;

import net.spy.memcached.AddrUtil;
import net.spy.memcached.ConnectionFactoryBuilder;
import net.spy.memcached.MemcachedClient;
import net.spy.memcached.FailureMode;
import org.springframework.beans.factory.annotation.Value;
import org.springframework.cache.CacheManager;
import org.springframework.cache.annotation.EnableCaching;
import org.springframework.cache.support.SimpleCacheManager;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;

import java.io.IOException;
import java.util.Arrays;

@Configuration
@EnableCaching  // 这个注解你不加，@Cacheable 就是个摆设
public class MemcacheConfig {

    @Value("${memcached.servers:127.0.0.1:11211}")
    private String servers;

    @Bean
    public MemcachedClient memcachedClient() throws IOException {
        return new MemcachedClient(
            new ConnectionFactoryBuilder()
                .setProtocol(ConnectionFactoryBuilder.Protocol.BINARY)
                .setFailureMode(FailureMode.Redistribute)
                .setOpTimeout(3000)
                .build(),
            AddrUtil.getAddresses(servers)  // 支持逗号分隔的多节点
        );
    }

    @Bean
    public CacheManager cacheManager(MemcachedClient memcachedClient) {
        SimpleCacheManager manager = new SimpleCacheManager();
        manager.setCaches(Arrays.asList(
            new MemcachedCache("users", memcachedClient, 300),
            new MemcachedCache("products", memcachedClient, 600)
        ));
        return manager;
    }
}

6.4 自定义 MemcachedCache

java 复制代码

package com.example.demo.config;

import net.spy.memcached.MemcachedClient;
import org.springframework.cache.Cache;
import org.springframework.cache.support.AbstractValueAdaptingCache;

import java.util.concurrent.Callable;

public class MemcachedCache extends AbstractValueAdaptingCache {

    private final String name;
    private final MemcachedClient client;
    private final int expiration;

    public MemcachedCache(String name, MemcachedClient client, int expiration) {
        super(true);  // 允许存 null（但你最好别存 null）
        this.name = name;
        this.client = client;
        this.expiration = expiration;
    }

    @Override
    public String getName() {
        return this.name;
    }

    @Override
    public Object getNativeCache() {
        return this.client;
    }

    // 核心方法：构建带命名空间的 key
    private String buildKey(Object key) {
        return this.name + ":" + key.toString();
    }

    @Override
    protected Object lookup(Object key) {
        return client.get(buildKey(key));
    }

    @Override
    public void put(Object key, Object value) {
        client.set(buildKey(key), expiration, value);
    }

    @Override
    public void evict(Object key) {
        client.delete(buildKey(key));
    }

    @Override
    public void clear() {
        // 注意：Memcache 不支持按命名空间清除，flush_all 会清掉所有节点！
        // 所以这里我们...什么也不做，等它 LRU 自己过期吧
        // 这就是 Memcache 不如 Redis 的地方之一 😅
    }

    @Override
    public <T> T get(Object key, Callable<T> valueLoader) {
        T value = (T) lookup(key);
        if (value == null) {
            synchronized (buildKey(key)) {  // 防缓存击穿的简单互斥锁
                value = (T) lookup(key);
                if (value == null) {
                    try {
                        value = valueLoader.call();
                        put(key, value);
                    } catch (Exception e) {
                        throw new ValueRetrievalException(key, valueLoader, e);
                    }
                }
            }
        }
        return value;
    }
}

6.5 Service 层使用缓存

java 复制代码

package com.example.demo.service;

import com.example.demo.entity.User;
import org.springframework.cache.annotation.CacheEvict;
import org.springframework.cache.annotation.CachePut;
import org.springframework.cache.annotation.Cacheable;
import org.springframework.stereotype.Service;

@Service
public class UserService {

    /**
     * 查用户 ------ 先翻 Memcache 兜，没有再查库
     * 结果会自动缓存，下次直接命中
     */
    @Cacheable(value = "users", key = "#userId", unless = "#result == null")
    public User getUserById(Long userId) {
        // 假装这是个很慢的数据库查询
        simulateSlowQuery();
        return new User(userId, "张三_" + userId, "zhangsan@qq.com");
    }

    /**
     * 更新用户 ------ 同时更新缓存（CachePut 每次都会执行方法并写入缓存）
     */
    @CachePut(value = "users", key = "#user.id")
    public User updateUser(User user) {
        // 假装更新了数据库
        return user;
    }

    /**
     * 删除用户 ------ 顺带把缓存也清了
     */
    @CacheEvict(value = "users", key = "#userId")
    public void deleteUser(Long userId) {
        // 假装删了数据库记录
    }

    private void simulateSlowQuery() {
        try { Thread.sleep(50); } catch (InterruptedException e) { /* zzz */ }
    }
}

6.6 直接用 MemcachedClient（不玩 Spring Cache 那套）

java 复制代码

@Component
public class QuickCacheHelper {

    @Autowired
    private MemcachedClient client;

    // 存
    public void put(String key, Object value, int expireSeconds) {
        client.set(key, expireSeconds, value);
    }

    // 取（带降级）
    public <T> T getOrLoad(String key, int expireSeconds, Supplier<T> dbLoader) {
        T value = (T) client.get(key);
        if (value == null) {
            value = dbLoader.get();
            if (value != null) {
                client.set(key, expireSeconds, value);
            }
        }
        return value;
    }

    // 计数器：接口限流就用它
    public long incr(String key, long delta) {
        return client.incr(key, delta);
    }

    // 删
    public void delete(String key) {
        client.delete(key);
    }
}

七、同类产品对比

#mermaid-svg-9jqLsSNOaqUU6uA7{font-family:"trebuchet ms",verdana,arial,sans-serif;font-size:16px;fill:#333;}@keyframes edge-animation-frame{from{stroke-dashoffset:0;}}@keyframes dash{to{stroke-dashoffset:0;}}#mermaid-svg-9jqLsSNOaqUU6uA7 .edge-animation-slow{stroke-dasharray:9,5!important;stroke-dashoffset:900;animation:dash 50s linear infinite;stroke-linecap:round;}#mermaid-svg-9jqLsSNOaqUU6uA7 .edge-animation-fast{stroke-dasharray:9,5!important;stroke-dashoffset:900;animation:dash 20s linear infinite;stroke-linecap:round;}#mermaid-svg-9jqLsSNOaqUU6uA7 .error-icon{fill:#552222;}#mermaid-svg-9jqLsSNOaqUU6uA7 .error-text{fill:#552222;stroke:#552222;}#mermaid-svg-9jqLsSNOaqUU6uA7 .edge-thickness-normal{stroke-width:1px;}#mermaid-svg-9jqLsSNOaqUU6uA7 .edge-thickness-thick{stroke-width:3.5px;}#mermaid-svg-9jqLsSNOaqUU6uA7 .edge-pattern-solid{stroke-dasharray:0;}#mermaid-svg-9jqLsSNOaqUU6uA7 .edge-thickness-invisible{stroke-width:0;fill:none;}#mermaid-svg-9jqLsSNOaqUU6uA7 .edge-pattern-dashed{stroke-dasharray:3;}#mermaid-svg-9jqLsSNOaqUU6uA7 .edge-pattern-dotted{stroke-dasharray:2;}#mermaid-svg-9jqLsSNOaqUU6uA7 .marker{fill:#333333;stroke:#333333;}#mermaid-svg-9jqLsSNOaqUU6uA7 .marker.cross{stroke:#333333;}#mermaid-svg-9jqLsSNOaqUU6uA7 svg{font-family:"trebuchet ms",verdana,arial,sans-serif;font-size:16px;}#mermaid-svg-9jqLsSNOaqUU6uA7 p{margin:0;}#mermaid-svg-9jqLsSNOaqUU6uA7 :root{--mermaid-font-family:"trebuchet ms",verdana,arial,sans-serif;} 全能战神性能王者简易轻量功能怪兽 Aerospike Ehcache Hazelcast Couchbase Memcache Redis 功能简单功能丰富性能低性能高 "缓存中间件象限图"

详细对比表

特性	Memcache	Redis	Ehcache	Couchbase	Hazelcast
定位	纯 KV 缓存	数据结构服务器	JVM 内缓存	分布式 NoSQL	内存数据网格
数据类型	仅 String	String/Hash/List/Set/ZSet/Stream...	POJO	JSON/Document	POJO
持久化	❌	✅ RDB + AOF	✅ 磁盘持久化	✅ 内置	✅
集群	客户端分片	Redis Cluster / Sentinel	Terracotta	自带	自带
事务	❌	✅ (Lua)	✅ (JTA)	✅ (ACID)	✅
发布订阅	❌	✅	✅ (JCache)	❌	✅
多线程	✅ 天然多线程	⚠️ 6.0+ 引入多线程 IO	✅	✅	✅
Lua 脚本	❌	✅	❌	❌	❌
内存效率	⭐⭐⭐⭐⭐	⭐⭐⭐	⭐⭐⭐	⭐⭐⭐	⭐⭐
学习曲线	平缓（20分钟上手）	适中	平缓	陡峭	适中
社区生态	⭐⭐⭐	⭐⭐⭐⭐⭐	⭐⭐⭐	⭐⭐⭐	⭐⭐⭐

八、Redis 是不是完全替代了 Memcache？

结论先行：没有，也不可能完全替代。

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只是简单 KV 缓存

要求极致性能
需要 List/Hash/Set

等复杂数据结构
需要持久化

数据不能丢
需要 Pub/Sub

消息通知
👉 Memcache

内存效率更高

多线程天然优势
👉 Redis

数据结构丰富

一个顶八个
👉 Redis

RDB + AOF

Memcache 重启即失忆
👉 Redis

Memcache 是哑巴

为什么 Memcache 没有被完全替代？

1. 内存效率更高

Memcache 的 Slab Allocator 专为缓存量身定做，内存碎片更少
Redis 的 jemalloc 虽然也厉害，但数据结构复杂导致额外开销更大
同样 1GB 内存，Memcache 能存更多数据

2. 多线程优势

Memcache 从出生就是多线程的，在多核 CPU 上能榨干性能
Redis 长期单线程（6.0 才引入多线程 IO，主流程仍是单线程）
在高并发纯 KV 场景下，Memcache 有时反而更快

3. 简单就是王道

代码量小（Core 不到 2 万行），出问题好排查
没有复杂的数据结构，没有持久化焦虑
"我就想存个字符串，你给我一把瑞士军刀干嘛？"

4. 大厂的惯性

Facebook 的 Memcache 集群规模大到有自己的论文（Scaling Memcache at Facebook）
很多大厂的缓存体系早就围绕 Memcache 建好了，迁移成本太高
"能用就别动"------运维第一定律

5. 场景分离

Redis 现在更像是一个内存数据库，而不只是缓存
Memcache 专注做纯缓存，职责单一不会乱来

架构演进中 Memcache 的定位

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miss
miss
CDN

边缘缓存
Nginx

本地缓存
Memcache

分布式热点缓存

扛住 80% 流量
Redis

业务缓存 + 数据结构
MySQL

持久存储
客户端

🎯 最佳实践 ：Memcache 做一级缓存扛流量，Redis 做二级缓存 + 业务数据，MySQL 兜底。三个人的电影，每个人都有名字。

九、幽默总结

复制代码

┌─────────────────────────────────────────────────────────────┐
│                                                             │
│   Memcache 就像是编程界的老牌螺丝刀：                         │
│                                                             │
│   🔧 功能不多，但每个都好用                                  │
│   🔧 长得不帅，但干活实在                                    │
│   🔧 有人嫌它过时，但它还在大厂核心系统里扛活                  │
│   🔧 Redis 是瑞士军刀，但你不能用军刀去拧所有的螺丝            │
│                                                             │
│   选型口诀：                                                 │
│   ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━          │
│   要存字符串 → 两个都行                                      │
│   要存复杂数据 → Redis（Memcache 不伺候）                    │
│   要持久化 → Redis（Memcache 说我记性不好）                   │
│   要高并发简单 KV → Memcache（Redis 说你还嫩了点）            │
│   孩子才做选择，成年人两个都上 → 双缓存架构 👍                │
│   ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━          │
│                                                             │
└─────────────────────────────────────────────────────────────┘

📚 参考资料

Memcached 官方 Wiki

Scaling Memcache at Facebook（论文）

spymemcached 客户端：GitHub