caffeine-patterns
Caffeine JVM 本地缓存技能。覆盖本地缓存 vs 远程缓存(Redis)选择决策树、expireAfterWrite vs expireAfterAccess vs refreshAfterWrite 三种过期策略对比与组合规则(refresh expireAfterWrite导致永远不刷新、CacheLoader返回null导致缓存穿透。
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/full-stack-skills/java-skills --skill caffeine-patterns- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads caffeine-patterns automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
Caffeine JVM 本地缓存
来源：https://github.com/ben-manes/caffeine/wiki
GitHub：https://github.com/ben-manes/caffeine
Capability Boundaries
✅ Strong Suits
- 本地 vs 远程选择 — 单服务高频数据用Caffeine，多服务共享用Redis
- 过期策略 — expireAfterWrite(写入后) vs expireAfterAccess(访问后) vs refreshAfterWrite(异步刷新)
- CacheLoader — 自动加载缺失数据 + reload 异步刷新 + 优雅降级(返回旧值)
- Spring Cache 集成 — @Cacheable/@CacheEvict + CaffeineCacheManager
- 驱逐策略 — maximumSize(数量)/maximumWeight(权重)/Window TinyLFU
- 记录统计 — recordStats() + CacheStats 监控命中率
❌ Out of Scope
- 分布式缓存/共享数据 → redis-redisson-patterns
- Redis + Caffeine 两级缓存 → redis-redisson-patterns（已覆盖）
过期策略选择决策
| 策略 | 行为 | 适用场景 |
|---|---|---|
| expireAfterWrite | 写入后固定时间过期 | ✅ 推荐(大多数业务)，可预测的数据刷新 |
| expireAfterAccess | 最后访问后固定时间过期 | 会话缓存/用户登录状态 |
| refreshAfterWrite | 写后固定时间异步刷新(返回旧值) | 热点数据免阻塞刷新 |
| expireAfterWrite + refreshAfterWrite | 刷新在过期前执行，永不阻塞 | ⭐ 推荐组合 |
⚠️
refreshAfterWrite必须 <expireAfterWrite，否则刷新永远不触发
LLM最常犯的错误
| # | 错误 | 正确做法 |
|---|---|---|
| 1 | ConcurrentHashMap<String, Object> + 手动过期 | Caffeine CacheBuilder 窗口 TinyLFU 算法 |
| 2 | 所有缓存都走 Redis | 单服务高频缓存走 Caffeine(纳秒级，Redis 毫秒级) |
| 3 | 不设置 maximumSize | maximumSize(10000) 防止内存溢出 |
| 4 | refreshAfterWrite 设置后不处理加载异常 | 旧值仍可用，加载失败不影响服务 |
| 5 | refreshAfterWrite > expireAfterWrite | 永远不刷新(过期就直接删除了) |
| 6 | CacheLoader.load() 返回 null | 返回 null = 缓存穿透(每次查都走 load) |
| 7 | 只设置 refreshAfterWrite 不设 maximumSize | 无界缓存 → OOM |
| 8 | 修改缓存返回的对象 | 缓存对象被外部修改 → 数据不一致 |
核心模式
模式 1: Caffeine 常用配置(推荐组合)
// ✅ expireAfterWrite + refreshAfterWrite 组合(推荐)
Cache<String, User> cache = Caffeine.newBuilder()
.maximumSize(10_000)
.expireAfterWrite(5, TimeUnit.MINUTES) // 5分钟过期
.refreshAfterWrite(3, TimeUnit.MINUTES) // 3分钟后台刷新(返回旧值)
.recordStats() // 记录命中率
.build(key -> loadFromDB(key)); // CacheLoader
模式 2: CacheLoader + reload 优雅降级
LoadingCache<String, User> cache = Caffeine.newBuilder()
.maximumSize(10_000)
.expireAfterWrite(5, TimeUnit.MINUTES)
.refreshAfterWrite(3, TimeUnit.MINUTES)
.build(new CacheLoader<String, User>() {
@Override
public @Nullable User load(String key) {
// 缓存未命中时加载
User user = userMapper.selectById(Long.parseLong(key));
// ✅ 不要返回 null！使用 Optional 或空对象占位
return user != null ? user : NULL_USER;
}
@Override
public @Nullable User reload(String key, User oldValue) {
// ✅ 刷新时优雅降级：加载失败返回旧值
try {
return load(key);
} catch (Exception e) {
log.error("刷新缓存失败, key={}", key, e);
return oldValue; // ← 返回旧值，不影响服务
}
}
});
// 使用
User user = cache.get("user:1"); // 自动加载
模式 3: Spring Cache 集成
@Configuration
@EnableCaching // ← 必须！
public class CacheConfig {
@Bean
public CacheManager cacheManager() {
CaffeineCacheManager mgr = new CaffeineCacheManager();
mgr.setCaffeine(Caffeine.newBuilder()
.expireAfterWrite(10, TimeUnit.MINUTES)
.maximumSize(10_000)
.recordStats());
return mgr;
}
}
@Service
public class UserService {
@Cacheable(value = "users", key = "#id", unless = "#result == null")
public User getUser(Long id) {
return userMapper.selectById(id);
}
@CacheEvict(value = "users", key = "#id")
public void updateUser(Long id, UserDTO dto) {
userMapper.updateById(dto.toEntity(id));
}
@CacheEvict(value = "users", allEntries = true) // 清空整个缓存
public void refreshAll() { }
}
模式 4: 异步加载(AsyncCache)
AsyncCache<String, User> asyncCache = Caffeine.newBuilder()
.maximumSize(10_000)
.expireAfterWrite(5, TimeUnit.MINUTES)
.refreshAfterWrite(1, TimeUnit.MINUTES) // 异步刷新
.buildAsync(key -> loadFromDB(key));
// 异步获取
CompletableFuture<User> future = asyncCache.get("user:1");
future.thenAccept(user -> System.out.println(user));
模式 5: 手动缓存操作 + 统计
Cache<String, User> cache = Caffeine.newBuilder()
.maximumSize(10_000)
.expireAfterWrite(10, TimeUnit.MINUTES)
.recordStats() // 启用统计
.build();
// 手动写入
cache.put("user:1", user);
// 手动失效
cache.invalidate("user:1");
// 统计数据
CacheStats stats = cache.stats();
log.info("命中率: {}, 加载次数: {}, 驱逐次数: {}",
stats.hitRate(), stats.loadCount(), stats.evictionCount());
Gotchas
- Caffeine 是进程内缓存 — 多实例部署每个实例有独立缓存，数据不一致
- expireAfterAccess 适合会话缓存 — 写入后频率很高就设置 access
- refreshAfterWrite 必须 < expireAfterWrite — 否则刷新没意义
- CacheLoader.load() 不能返回 null — 返回空占位对象或抛异常
- Spring Cache 的 @Cacheable 返回值必须可序列化 — 如果用 Redis 作为后端
- maximumSize 驱逐可能丢失数据 — 重要数据用数据库作为最终保障
- 与 Redisson 配合 — Caffeine(L1) + Redis(L2) 是经典的两级缓存组合
- 缓存对象可变时注意防御性拷贝 — 外部修改缓存对象导致数据不一致
- recordStats() 不影响性能 — 生产环境建议开启
- expireAfter(Expiry) 支持自定义过期时间 — 根据 value 动态决定 TTL
- Caffeine 3.x 与 2.x 兼容性 — Spring Boot 2.x 使用 Caffeine 2.x，Spring Boot 3.x 使用 3.x
- weakKeys/softValues 慎用 — GC 触发的驱逐不可预测
Data Privacy
本技能不收集、存储或传输任何用户数据。
FAQ
What does the caffeine-patterns skill do?
Caffeine JVM 本地缓存技能。覆盖本地缓存 vs 远程缓存(Redis)选择决策树、expireAfterWrite vs expireAfterAccess vs refreshAfterWrite 三种过期策略对比与组合规则(refresh expireAfterWrite导致永远不刷新、CacheLoader返回null导致缓存穿透。
How do I install caffeine-patterns?
Run npx skills add https://github.com/full-stack-skills/java-skills --skill caffeine-patterns in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/caffeine-patterns/ (personal) or .claude/skills/caffeine-patterns/ (project).
Which AI agents can use caffeine-patterns?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is caffeine-patterns safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains caffeine-patterns?
It is published by full-stack-skills in full-stack-skills/java-skills. It has 79 recorded installs.
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