Redis过期删除策略深度剖析：惰性删除与定期删除的协同艺术

Redis过期删除策略深度剖析：惰性删除与定期删除的协同艺术

1. 引言：为什么需要过期删除

Redis作为内存数据库，内存资源是有限且昂贵的。为了高效管理内存，Redis提供了EXPIRE命令为键设置生存时间（TTL）。然而，如何高效地删除这些过期键，是一个经典的计算机科学问题。

如果采用定时删除（每个键设置定时器），CPU开销太大；如果完全不删除，内存会被过期数据撑爆。Redis采用了一种精妙的组合策略：

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过期删除策略

惰性删除

定期删除

CPU友好

内存可能堆积

主动清理

CPU/内存平衡

本文将深入剖析这两种策略的实现原理、执行流程、参数调优以及生产环境的监控实践。

—

2. 策略一：惰性删除（Lazy Expiration）

2.1 核心原理

惰性删除的策略非常直接：不主动删除过期键，而是在访问时检查。

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.state-start{fill:#000000;}#publish-mermaid-1784561275941-1 [data-look=\”neo\”].icon-shape .icon{fill:#9370DB;filter:drop-shadow(1px 2px 2px rgba(185, 185, 185, 1));}#publish-mermaid-1784561275941-1 [data-look=\”neo\”].icon-shape .icon-neo path{stroke:#9370DB;filter:drop-shadow(1px 2px 2px rgba(185, 185, 185, 1));}#publish-mermaid-1784561275941-1 :root{–mermaid-font-family:\”trebuchet ms\”,verdana,arial,sans-serif;}读/写命令否是未过期已过期

客户端请求

操作类型

查找Key

Key是否存在?

返回nil/报错

检查过期时间

正常返回数据

调用expireIfNeeded

从数据库删除Key

传播删除到从库/AOF

返回nil

2.2 触发惰性删除的命令

并非所有命令都会触发惰性删除，只有访问到具体Key的命令才会：

| 触发删除的命令 | 不触发删除的命令 |

|—————|—————–|

| GET, SET, HSET, HGET | EXISTS, TYPE, RENAME |

| LPUSH, RPUSH, SADD | KEYS, SCAN |

| INCR, DECR, APPEND | INFO, DBSIZE |

| DEL（显式删除） | PING, SELECT |

// 惰性删除核心函数（源码简化）
int expireIfNeeded(redisDb *db, robj *key) {
// 获取过期时间戳
long long when = getExpire(db, key);
if (when < 0) return 0; // 永不过期

// 如果服务器正在加载数据，暂不处理
if (server.loading) return 0;

// 当前时间超过过期时间
if (mstime() > when) {
// 删除Key
dbDelete(db, key);
// 传播删除命令到从库和AOF
propagateExpire(db, key);
// 更新统计
server.stat_expiredkeys++;
return 1;
}
return 0;
}

2.3 惰性删除的优缺点

| 维度 | 说明 |

|——|——|

| ✅ 优点 | CPU开销极小，只在访问时检查 |

| ✅ 优点 | 实现简单，不影响正常请求延迟 |

| ❌ 缺点 | 过期但未被访问的Key会一直占用内存（"僵尸Key"） |

| ❌ 缺点 | 在极端情况下可能导致内存泄漏 |

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path{fill:currentColor;stroke:revert;stroke-width:revert;}#publish-mermaid-1784561276183-2 .node .neo-node{stroke:#9370DB;}#publish-mermaid-1784561276183-2 [data-look=\”neo\”].node rect,#publish-mermaid-1784561276183-2 [data-look=\”neo\”].cluster rect,#publish-mermaid-1784561276183-2 [data-look=\”neo\”].node polygon{stroke:#9370DB;filter:drop-shadow(1px 2px 2px rgba(185, 185, 185, 1));}#publish-mermaid-1784561276183-2 [data-look=\”neo\”].swimlane.cluster rect{filter:none;}#publish-mermaid-1784561276183-2 [data-look=\”neo\”].node path{stroke:#9370DB;stroke-width:1px;}#publish-mermaid-1784561276183-2 [data-look=\”neo\”].node .outer-path{filter:drop-shadow(1px 2px 2px rgba(185, 185, 185, 1));}#publish-mermaid-1784561276183-2 [data-look=\”neo\”].node .neo-line path{stroke:#9370DB;filter:none;}#publish-mermaid-1784561276183-2 [data-look=\”neo\”].node circle{stroke:#9370DB;filter:drop-shadow(1px 2px 2px rgba(185, 185, 185, 1));}#publish-mermaid-1784561276183-2 [data-look=\”neo\”].node circle .state-start{fill:#000000;}#publish-mermaid-1784561276183-2 [data-look=\”neo\”].icon-shape .icon{fill:#9370DB;filter:drop-shadow(1px 2px 2px rgba(185, 185, 185, 1));}#publish-mermaid-1784561276183-2 [data-look=\”neo\”].icon-shape .icon-neo path{stroke:#9370DB;filter:drop-shadow(1px 2px 2px rgba(185, 185, 185, 1));}#publish-mermaid-1784561276183-2 :root{–mermaid-font-family:\”trebuchet ms\”,verdana,arial,sans-serif;}惰性删除问题示意无人访问无人访问无人访问

Key1 过期

一直占用内存

Key2 过期

一直占用内存

Key3 过期

一直占用内存

内存持续增长

—

3. 策略二：定期删除（Active Expiration）

3.1 核心原理

定期删除通过定时任务主动扫描过期Key，弥补惰性删除的不足。Redis默认每秒执行10次（由hz参数控制）。

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path{fill:currentColor;stroke:revert;stroke-width:revert;}#publish-mermaid-1784561276290-3 .node .neo-node{stroke:#9370DB;}#publish-mermaid-1784561276290-3 [data-look=\”neo\”].node rect,#publish-mermaid-1784561276290-3 [data-look=\”neo\”].cluster rect,#publish-mermaid-1784561276290-3 [data-look=\”neo\”].node polygon{stroke:#9370DB;filter:drop-shadow(1px 2px 2px rgba(185, 185, 185, 1));}#publish-mermaid-1784561276290-3 [data-look=\”neo\”].swimlane.cluster rect{filter:none;}#publish-mermaid-1784561276290-3 [data-look=\”neo\”].node path{stroke:#9370DB;stroke-width:1px;}#publish-mermaid-1784561276290-3 [data-look=\”neo\”].node .outer-path{filter:drop-shadow(1px 2px 2px rgba(185, 185, 185, 1));}#publish-mermaid-1784561276290-3 [data-look=\”neo\”].node .neo-line path{stroke:#9370DB;filter:none;}#publish-mermaid-1784561276290-3 [data-look=\”neo\”].node circle{stroke:#9370DB;filter:drop-shadow(1px 2px 2px rgba(185, 185, 185, 1));}#publish-mermaid-1784561276290-3 [data-look=\”neo\”].node circle .state-start{fill:#000000;}#publish-mermaid-1784561276290-3 [data-look=\”neo\”].icon-shape .icon{fill:#9370DB;filter:drop-shadow(1px 2px 2px rgba(185, 185, 185, 1));}#publish-mermaid-1784561276290-3 [data-look=\”neo\”].icon-shape .icon-neo path{stroke:#9370DB;filter:drop-shadow(1px 2px 2px rgba(185, 185, 185, 1));}#publish-mermaid-1784561276290-3 :root{–mermaid-font-family:\”trebuchet ms\”,verdana,arial,sans-serif;}是否是否否是

serverCron定时任务每秒10次

执行activeExpireCycle

遍历所有数据库

从当前DB随机抽取20个Key（仅限设置了过期时间的）

统计其中过期Key数量

删除所有过期Key

过期比例 > 25%?

继续抽取下一批20个

进入下一个DB

总执行时间 > 25ms?

立即结束，下轮继续

所有DB遍历完?

本次定期删除结束

3.2 核心参数详解

| 参数 | 默认值 | 说明 |

|——|——–|——|

| hz | 10 | 每秒执行serverCron任务的次数 |

| ACTIVE_EXPIRE_CYCLE_KEYS_PER_LOOP | 20 | 每次抽样检查的Key数量 |

| ACTIVE_EXPIRE_CYCLE_SLOW_TIME_PERC | 25 | 最大CPU使用时间占比（25ms/100ms） |

| active-expire-effort | 1（1-10） | 努力程度，值越大清理越积极 |

3.3 定期删除的两种模式

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.state-start{fill:#000000;}#publish-mermaid-1784561276438-4 [data-look=\”neo\”].icon-shape .icon{fill:#9370DB;filter:drop-shadow(1px 2px 2px rgba(185, 185, 185, 1));}#publish-mermaid-1784561276438-4 [data-look=\”neo\”].icon-shape .icon-neo path{stroke:#9370DB;filter:drop-shadow(1px 2px 2px rgba(185, 185, 185, 1));}#publish-mermaid-1784561276438-4 :root{–mermaid-font-family:\”trebuchet ms\”,verdana,arial,sans-serif;}hz=30

每秒30次

每次8.3ms

CPU占用约25%

过期删除更及时

hz=10 默认

每秒10次

每次25ms

CPU占用约25%

过期删除较温和

快模式触发条件：当慢模式中过期Key比例超过25%时，会进入快模式加速清理。

3.4 源码核心逻辑

void activeExpireCycle(int type) {
static unsigned int current_db = 0;
int dbs_per_call = 16; // 每次最多检查16个DB
long long start = ustime(), timelimit;

// 计算时间限制（慢模式25ms，快模式1ms）
if (type == ACTIVE_EXPIRE_CYCLE_FAST)
timelimit = 1000; // 1ms
else
timelimit = 25000; // 25ms

for (int j = 0; j < dbs_per_call; j++) {
redisDb *db = server.db + current_db;
current_db++;

do {
// 1. 随机抽取20个设置了过期时间的Key
expired = 0;
for (i = 0; i < 20; i++) {
de = dbFindExpires(db);
if (de && isExpired(db, de)) {
dbDelete(db, de);
expired++;
}
}

// 2. 如果过期比例 > 25%，继续抽取
} while (expired > 20 * 0.25 &&
(ustime() – start) < timelimit);

// 3. 如果超时，跳出循环
if ((ustime() – start) > timelimit) break;
}
}

3.5 active-expire-effort 参数详解

Redis 6.0+ 引入了active-expire-effort参数（1-10），控制定期删除的激进程度：

| 值 | 每次抽样数 | 执行时间上限 | 适用场景 |

|—-|———–|————-|———|

| 1（默认） | 20 | 25ms | 通用场景 |

| 2-3 | 20-30 | 30-40ms | 有较多过期Key |

| 4-6 | 30-40 | 40-50ms | 内存紧张，需积极清理 |

| 7-10 | 40-50 | 50-60ms | 极端场景，CPU换内存 |

# 调高active-expire-effort加速过期清理
active-expire-effort 3

—

4. 两种策略的协同工作流

4.1 完整执行时序图

C2内存定时任务(serverCron)Redis主线程客户端C2内存定时任务(serverCron)Redis主线程客户端#publish-mermaid-1784561276638-5{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;}}#publish-mermaid-1784561276638-5 .edge-animation-slow{stroke-dasharray:9,5!important;stroke-dashoffset:900;animation:dash 50s linear infinite;stroke-linecap:round;}#publish-mermaid-1784561276638-5 .edge-animation-fast{stroke-dasharray:9,5!important;stroke-dashoffset:900;animation:dash 20s linear infinite;stroke-linecap:round;}#publish-mermaid-1784561276638-5 .error-icon{fill:#552222;}#publish-mermaid-1784561276638-5 .error-text{fill:#552222;stroke:#552222;}#publish-mermaid-1784561276638-5 .edge-thickness-normal{stroke-width:1px;}#publish-mermaid-1784561276638-5 .edge-thickness-thick{stroke-width:3.5px;}#publish-mermaid-1784561276638-5 .edge-pattern-solid{stroke-dasharray:0;}#publish-mermaid-1784561276638-5 .edge-thickness-invisible{stroke-width:0;fill:none;}#publish-mermaid-1784561276638-5 .edge-pattern-dashed{stroke-dasharray:3;}#publish-mermaid-1784561276638-5 .edge-pattern-dotted{stroke-dasharray:2;}#publish-mermaid-1784561276638-5 .marker{fill:#333333;stroke:#333333;}#publish-mermaid-1784561276638-5 .marker.cross{stroke:#333333;}#publish-mermaid-1784561276638-5 svg{font-family:\”trebuchet ms\”,verdana,arial,sans-serif;font-size:16px;}#publish-mermaid-1784561276638-5 p{margin:0;}#publish-mermaid-1784561276638-5 .actor{stroke:#9370DB;fill:#ECECFF;stroke-width:1;}#publish-mermaid-1784561276638-5 rect.actor.outer-path[data-look=\”neo\”]{filter:drop-shadow(1px 2px 2px rgba(185, 185, 185, 1));}#publish-mermaid-1784561276638-5 rect.note[data-look=\”neo\”]{stroke:#aaaa33;fill:#fff5ad;filter:drop-shadow(1px 2px 2px rgba(185, 185, 185, 1));}#publish-mermaid-1784561276638-5 text.actor>tspan{fill:black;stroke:none;}#publish-mermaid-1784561276638-5 .actor-line{stroke:#9370DB;}#publish-mermaid-1784561276638-5 .innerArc{stroke-width:1.5;stroke-dasharray:none;}#publish-mermaid-1784561276638-5 .messageLine0{stroke-width:1.5;stroke-dasharray:none;stroke:#333;}#publish-mermaid-1784561276638-5 .messageLine1{stroke-width:1.5;stroke-dasharray:2,2;stroke:#333;}#publish-mermaid-1784561276638-5 [id$=\”-arrowhead\”] path{fill:#333;stroke:#333;}#publish-mermaid-1784561276638-5 .sequenceNumber{fill:white;}#publish-mermaid-1784561276638-5 [id$=\”-sequencenumber\”]{fill:#333;}#publish-mermaid-1784561276638-5 [id$=\”-crosshead\”] path{fill:#333;stroke:#333;}#publish-mermaid-1784561276638-5 .messageText{fill:#333;stroke:none;}#publish-mermaid-1784561276638-5 .labelBox{stroke:#9370DB;fill:#ECECFF;filter:none;}#publish-mermaid-1784561276638-5 .labelText,#publish-mermaid-1784561276638-5 .labelText>tspan{fill:black;stroke:none;}#publish-mermaid-1784561276638-5 .loopText,#publish-mermaid-1784561276638-5 .loopText>tspan{fill:black;stroke:none;}#publish-mermaid-1784561276638-5 .sectionTitle,#publish-mermaid-1784561276638-5 .sectionTitle>tspan{fill:black;stroke:none;}#publish-mermaid-1784561276638-5 .loopLine{stroke-width:2px;stroke-dasharray:2,2;stroke:#9370DB;fill:#9370DB;}#publish-mermaid-1784561276638-5 .note{stroke:#aaaa33;fill:#fff5ad;}#publish-mermaid-1784561276638-5 .noteText,#publish-mermaid-1784561276638-5 .noteText>tspan{fill:black;stroke:none;font-weight:normal;}#publish-mermaid-1784561276638-5 .activation0{fill:#f4f4f4;stroke:#666;}#publish-mermaid-1784561276638-5 .activation1{fill:#f4f4f4;stroke:#666;}#publish-mermaid-1784561276638-5 .activation2{fill:#f4f4f4;stroke:#666;}#publish-mermaid-1784561276638-5 .actorPopupMenu{position:absolute;}#publish-mermaid-1784561276638-5 .actorPopupMenuPanel{position:absolute;fill:#ECECFF;box-shadow:0px 8px 16px 0px rgba(0,0,0,0.2);filter:drop-shadow(3px 5px 2px rgb(0 0 0 / 0.4));}#publish-mermaid-1784561276638-5 .actor-man circle,#publish-mermaid-1784561276638-5 line{fill:#ECECFF;stroke-width:2px;}#publish-mermaid-1784561276638-5 g rect.rect{filter:drop-shadow(1px 2px 2px rgba(185, 185, 185, 1));stroke:#9370DB;}#publish-mermaid-1784561276638-5 .node .neo-node{stroke:#9370DB;}#publish-mermaid-1784561276638-5 [data-look=\”neo\”].node rect,#publish-mermaid-1784561276638-5 [data-look=\”neo\”].cluster rect,#publish-mermaid-1784561276638-5 [data-look=\”neo\”].node polygon{stroke:#9370DB;filter:drop-shadow(1px 2px 2px rgba(185, 185, 185, 1));}#publish-mermaid-1784561276638-5 [data-look=\”neo\”].swimlane.cluster rect{filter:none;}#publish-mermaid-1784561276638-5 [data-look=\”neo\”].node path{stroke:#9370DB;stroke-width:1px;}#publish-mermaid-1784561276638-5 [data-look=\”neo\”].node .outer-path{filter:drop-shadow(1px 2px 2px rgba(185, 185, 185, 1));}#publish-mermaid-1784561276638-5 [data-look=\”neo\”].node .neo-line path{stroke:#9370DB;filter:none;}#publish-mermaid-1784561276638-5 [data-look=\”neo\”].node circle{stroke:#9370DB;filter:drop-shadow(1px 2px 2px rgba(185, 185, 185, 1));}#publish-mermaid-1784561276638-5 [data-look=\”neo\”].node circle .state-start{fill:#000000;}#publish-mermaid-1784561276638-5 [data-look=\”neo\”].icon-shape .icon{fill:#9370DB;filter:drop-shadow(1px 2px 2px rgba(185, 185, 185, 1));}#publish-mermaid-1784561276638-5 [data-look=\”neo\”].icon-shape .icon-neo path{stroke:#9370DB;filter:drop-shadow(1px 2px 2px rgba(185, 185, 185, 1));}#publish-mermaid-1784561276638-5 :root{–mermaid-font-family:\”trebuchet ms\”,verdana,arial,sans-serif;}时间线10秒后，Key已过期但仍在内存或者：惰性删除先行同时触发定期删除的抽样SET key EX 10 "value"存储Key，设置过期时间10秒OK每秒10次定期删除任务随机抽取20个Key检查发现Key已过期删除Key，释放内存GET key（延迟访问）检查Key是否存在不存在（已被定期删除）nilGET another_key（未过期）检查发现已过期删除Keynil

4.2 组合策略的优势互补

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path{fill:currentColor;stroke:revert;stroke-width:revert;}#publish-mermaid-1784561276707-6 .node .neo-node{stroke:#9370DB;}#publish-mermaid-1784561276707-6 [data-look=\”neo\”].node rect,#publish-mermaid-1784561276707-6 [data-look=\”neo\”].cluster rect,#publish-mermaid-1784561276707-6 [data-look=\”neo\”].node polygon{stroke:#9370DB;filter:drop-shadow(1px 2px 2px rgba(185, 185, 185, 1));}#publish-mermaid-1784561276707-6 [data-look=\”neo\”].swimlane.cluster rect{filter:none;}#publish-mermaid-1784561276707-6 [data-look=\”neo\”].node path{stroke:#9370DB;stroke-width:1px;}#publish-mermaid-1784561276707-6 [data-look=\”neo\”].node .outer-path{filter:drop-shadow(1px 2px 2px rgba(185, 185, 185, 1));}#publish-mermaid-1784561276707-6 [data-look=\”neo\”].node .neo-line path{stroke:#9370DB;filter:none;}#publish-mermaid-1784561276707-6 [data-look=\”neo\”].node circle{stroke:#9370DB;filter:drop-shadow(1px 2px 2px rgba(185, 185, 185, 1));}#publish-mermaid-1784561276707-6 [data-look=\”neo\”].node circle .state-start{fill:#000000;}#publish-mermaid-1784561276707-6 [data-look=\”neo\”].icon-shape .icon{fill:#9370DB;filter:drop-shadow(1px 2px 2px rgba(185, 185, 185, 1));}#publish-mermaid-1784561276707-6 [data-look=\”neo\”].icon-shape .icon-neo path{stroke:#9370DB;filter:drop-shadow(1px 2px 2px rgba(185, 185, 185, 1));}#publish-mermaid-1784561276707-6 :root{–mermaid-font-family:\”trebuchet ms\”,verdana,arial,sans-serif;}定期删除惰性删除弥补控制

优点: CPU开销低

缺点: 内存可能堆积

优点: 主动清理过期Key

缺点: CPU消耗可控

最终效果: CPU和内存平衡

—

5. 过期删除与内存淘汰的关系

5.1 两者的区别

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内存管理

过期删除

内存淘汰

针对设置了EXPIRE的Key

主动/被动清理过期数据

目的是释放过期数据

针对所有Key（取决于策略）

内存达到maxmemory时触发

目的是防止OOM

| 对比维度 | 过期删除 | 内存淘汰 |

|———|———|———|

| 触发条件 | 基于TTL时间 | 内存达到maxmemory |

| 作用对象 | 仅设置了EXPIRE的Key | 取决于淘汰策略 |

| 执行时机 | 持续运行 | 内存超限时 |

| 目的 | 释放过期数据 | 腾出内存空间 |

5.2 协同工作流程

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circle,#publish-mermaid-1784561276873-8 .node ellipse,#publish-mermaid-1784561276873-8 .node polygon,#publish-mermaid-1784561276873-8 .node path{fill:#ECECFF;stroke:#9370DB;stroke-width:1px;}#publish-mermaid-1784561276873-8 .rough-node .label text,#publish-mermaid-1784561276873-8 .node .label text,#publish-mermaid-1784561276873-8 .image-shape .label,#publish-mermaid-1784561276873-8 .icon-shape .label{text-anchor:middle;}#publish-mermaid-1784561276873-8 .node .katex path{fill:#000;stroke:#000;stroke-width:1px;}#publish-mermaid-1784561276873-8 .rough-node .label,#publish-mermaid-1784561276873-8 .node .label,#publish-mermaid-1784561276873-8 .image-shape .label,#publish-mermaid-1784561276873-8 .icon-shape .label{text-align:center;}#publish-mermaid-1784561276873-8 .node.clickable{cursor:pointer;}#publish-mermaid-1784561276873-8 .root .anchor path{fill:#333333!important;stroke-width:0;stroke:#333333;}#publish-mermaid-1784561276873-8 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path{fill:currentColor;stroke:revert;stroke-width:revert;}#publish-mermaid-1784561276873-8 .node .neo-node{stroke:#9370DB;}#publish-mermaid-1784561276873-8 [data-look=\”neo\”].node rect,#publish-mermaid-1784561276873-8 [data-look=\”neo\”].cluster rect,#publish-mermaid-1784561276873-8 [data-look=\”neo\”].node polygon{stroke:#9370DB;filter:drop-shadow(1px 2px 2px rgba(185, 185, 185, 1));}#publish-mermaid-1784561276873-8 [data-look=\”neo\”].swimlane.cluster rect{filter:none;}#publish-mermaid-1784561276873-8 [data-look=\”neo\”].node path{stroke:#9370DB;stroke-width:1px;}#publish-mermaid-1784561276873-8 [data-look=\”neo\”].node .outer-path{filter:drop-shadow(1px 2px 2px rgba(185, 185, 185, 1));}#publish-mermaid-1784561276873-8 [data-look=\”neo\”].node .neo-line path{stroke:#9370DB;filter:none;}#publish-mermaid-1784561276873-8 [data-look=\”neo\”].node circle{stroke:#9370DB;filter:drop-shadow(1px 2px 2px rgba(185, 185, 185, 1));}#publish-mermaid-1784561276873-8 [data-look=\”neo\”].node circle .state-start{fill:#000000;}#publish-mermaid-1784561276873-8 [data-look=\”neo\”].icon-shape .icon{fill:#9370DB;filter:drop-shadow(1px 2px 2px rgba(185, 185, 185, 1));}#publish-mermaid-1784561276873-8 [data-look=\”neo\”].icon-shape .icon-neo path{stroke:#9370DB;filter:drop-shadow(1px 2px 2px rgba(185, 185, 185, 1));}#publish-mermaid-1784561276873-8 :root{–mermaid-font-family:\”trebuchet ms\”,verdana,arial,sans-serif;}是否

写请求

分配内存

used_memory > maxmemory?

触发内存淘汰

继续执行

定时任务

定期删除过期Key

释放部分内存

读请求

惰性删除检查

删除过期Key释放内存

—

6. 生产环境配置与调优

6.1 核心配置参数

# ====== 过期删除相关配置 ======

# serverCron频率（每秒执行次数）
# 默认10，取值范围1-500
hz 10

# 主动过期清理的努力程度（1-10）
# 默认1，值越大清理越积极
active-expire-effort 1

# 最大内存及淘汰策略
maxmemory 4gb
maxmemory-policy allkeys-lru

6.2 hz参数的影响

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path{fill:currentColor;stroke:revert;stroke-width:revert;}#publish-mermaid-1784561277003-9 .node .neo-node{stroke:#9370DB;}#publish-mermaid-1784561277003-9 [data-look=\”neo\”].node rect,#publish-mermaid-1784561277003-9 [data-look=\”neo\”].cluster rect,#publish-mermaid-1784561277003-9 [data-look=\”neo\”].node polygon{stroke:#9370DB;filter:drop-shadow(1px 2px 2px rgba(185, 185, 185, 1));}#publish-mermaid-1784561277003-9 [data-look=\”neo\”].swimlane.cluster rect{filter:none;}#publish-mermaid-1784561277003-9 [data-look=\”neo\”].node path{stroke:#9370DB;stroke-width:1px;}#publish-mermaid-1784561277003-9 [data-look=\”neo\”].node .outer-path{filter:drop-shadow(1px 2px 2px rgba(185, 185, 185, 1));}#publish-mermaid-1784561277003-9 [data-look=\”neo\”].node .neo-line path{stroke:#9370DB;filter:none;}#publish-mermaid-1784561277003-9 [data-look=\”neo\”].node circle{stroke:#9370DB;filter:drop-shadow(1px 2px 2px rgba(185, 185, 185, 1));}#publish-mermaid-1784561277003-9 [data-look=\”neo\”].node circle .state-start{fill:#000000;}#publish-mermaid-1784561277003-9 [data-look=\”neo\”].icon-shape .icon{fill:#9370DB;filter:drop-shadow(1px 2px 2px rgba(185, 185, 185, 1));}#publish-mermaid-1784561277003-9 [data-look=\”neo\”].icon-shape .icon-neo path{stroke:#9370DB;filter:drop-shadow(1px 2px 2px rgba(185, 185, 185, 1));}#publish-mermaid-1784561277003-9 :root{–mermaid-font-family:\”trebuchet ms\”,verdana,arial,sans-serif;}hz=30

每秒30次

每次8.3ms

CPU占用约25%

过期删除更及时

hz=10（默认）

每秒10次

每次25ms

CPU占用约25%

过期删除较温和

| hz值 | CPU占用 | 过期删除及时性 | 适用场景 |

|——|———|—————|———|

| 5 | 低 | 较低 | 低负载环境 |

| 10（默认） | 中等 | 中等 | 通用场景 |

| 20 | 较高 | 较高 | 高并发写入 |

| 50+ | 高 | 很高 | 谨慎使用（CPU敏感） |

6.3 监控指标

# 1. 查看过期Key统计
INFO stats
# expired_keys: 1234567 # 累计过期删除数量
# evicted_keys: 0 # 内存淘汰数量

# 2. 查看数据库Key数量
INFO keyspace
# db0: keys=1000000, expires=200000, avg_ttl=3600

# 3. 查看当前时间与过期Key情况
DBSIZE
# 对比expires字段，评估过期Key比例

6.4 监控告警规则

# Prometheus告警规则
groups:
– name: redis_expire
rules:
# 过期Key堆积告警
– alert: RedisExpiredKeysHigh
expr: redis_db_keys{expires="true"} / redis_db_keys > 0.3
annotations:
summary: "过期Key占比超过30%"

# 内存淘汰告警
– alert: RedisEviction
expr: rate(redis_evicted_keys_total[5m]) > 0
annotations:
summary: "发生内存淘汰，可能需要扩容"

—

7. 常见问题与解决方案

7.1 大量Key同时过期导致CPU飙升

问题现象：

• 业务高峰，大量Key在同一秒过期
• CPU突然飙升至100%
• 请求延迟增加

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凌晨0点

100万个Key同时过期

定期删除集中扫描

CPU飙升

服务抖动

解决方案：

// 解决方案1: 过期时间加随机偏移
long ttl = 3600 + ThreadLocalRandom.current().nextInt(300); // 3600±150秒
redisTemplate.expire("key:" + id, ttl, TimeUnit.SECONDS);

// 解决方案2: 分散过期时间
// 按业务ID取模，分散到不同时间点
long baseTTL = 3600;
long randomOffset = (id % 100) * 30; // 0-2970秒随机偏移
redisTemplate.expire("key:" + id, baseTTL + randomOffset, TimeUnit.SECONDS);

配置优化：

# 适当调低hz值，减少CPU占用
hz 8

# 降低active-expire-effort
active-expire-effort 1

7.2 过期Key堆积导致内存不足

问题现象：

• 内存使用持续增长
• expired_keys增长缓慢
• 频繁触发内存淘汰

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过期Key堆积

内存持续增长

maxmemory上限

触发淘汰策略

可能淘汰未过期Key

缓存命中率下降

解决方案：

| 方案 | 操作 | 适用场景 |

|——|——|———|

| 提高hz | CONFIG SET hz 20 | CPU有余量 |

| 提高active-expire-effort | CONFIG SET active-expire-effort 3 | 积极清理 |

| 手动扫描 | SCAN + TTL + DEL | 一次性清理 |

| 内存扩容 | 增加maxmemory | 根本解决 |

7.3 主从复制中的过期删除

重要原则：从库不主动执行过期删除，全部依赖主库同步。

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circle,#publish-mermaid-1784561277279-12 line{fill:#ECECFF;stroke-width:2px;}#publish-mermaid-1784561277279-12 g rect.rect{filter:drop-shadow(1px 2px 2px rgba(185, 185, 185, 1));stroke:#9370DB;}#publish-mermaid-1784561277279-12 .node .neo-node{stroke:#9370DB;}#publish-mermaid-1784561277279-12 [data-look=\”neo\”].node rect,#publish-mermaid-1784561277279-12 [data-look=\”neo\”].cluster rect,#publish-mermaid-1784561277279-12 [data-look=\”neo\”].node polygon{stroke:#9370DB;filter:drop-shadow(1px 2px 2px rgba(185, 185, 185, 1));}#publish-mermaid-1784561277279-12 [data-look=\”neo\”].swimlane.cluster rect{filter:none;}#publish-mermaid-1784561277279-12 [data-look=\”neo\”].node path{stroke:#9370DB;stroke-width:1px;}#publish-mermaid-1784561277279-12 [data-look=\”neo\”].node .outer-path{filter:drop-shadow(1px 2px 2px rgba(185, 185, 185, 1));}#publish-mermaid-1784561277279-12 [data-look=\”neo\”].node .neo-line path{stroke:#9370DB;filter:none;}#publish-mermaid-1784561277279-12 [data-look=\”neo\”].node circle{stroke:#9370DB;filter:drop-shadow(1px 2px 2px rgba(185, 185, 185, 1));}#publish-mermaid-1784561277279-12 [data-look=\”neo\”].node circle .state-start{fill:#000000;}#publish-mermaid-1784561277279-12 [data-look=\”neo\”].icon-shape .icon{fill:#9370DB;filter:drop-shadow(1px 2px 2px rgba(185, 185, 185, 1));}#publish-mermaid-1784561277279-12 [data-look=\”neo\”].icon-shape .icon-neo path{stroke:#9370DB;filter:drop-shadow(1px 2px 2px rgba(185, 185, 185, 1));}#publish-mermaid-1784561277279-12 :root{–mermaid-font-family:\”trebuchet ms\”,verdana,arial,sans-serif;}Key过期从库不检查过期时间惰性/定期删除DEL命令（通过复制流）执行DEL删除KeyGET key（从库读取）可能返回过期数据！

注意事项：

• 从库读请求可能返回过期数据（主库未及时同步DEL）
• 从库的replica-read-only应保持yes
• 监控主从延迟，避免过期数据长时间滞留从库

—

8. 性能测试与经验数据

8.1 不同策略下的CPU/内存对比

| 场景 | 惰性删除CPU | 定期删除CPU | 内存占用 |

|——|————|————|———|

| 100%访问活跃Key | 低（5%） | 中（10%） | 低 |

| 50%访问过期Key | 中（15%） | 中（12%） | 中 |

| 无访问过期Key | 低（2%） | 中（15%） | 高（堆积） |

| 大量同时过期 | 高（30%） | 高（40%） | 逐步释放 |

8.2 经验调优建议

# 根据业务特征调整
业务特征:
读多写少:
hz: 10
active-expire-effort: 1
写多读少:
hz: 15-20
active-expire-effort: 2-3
大量短TTL Key:
hz: 20-30
active-expire-effort: 3-5
内存紧张:
hz: 15
active-expire-effort: 3-5
maxmemory-policy: volatile-lru

—

9. 总结：平衡的艺术

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CPU资源

惰性删除

定期删除

内存资源

定期删除

CPU友好,内存可能堆积

CPU/内存平衡

两者互补

生产环境稳定运行

核心要点速记

┌─────────────────────────────────────────────────────────────────┐
│ 过期删除策略核心口诀 │
├─────────────────────────────────────────────────────────────────┤
│ 惰性删除不主动，访问检查即清除； │
│ 定期任务每秒跑，抽样检查二十个； │
│ 过期比例超25%，继续抽取不停歇； │
│ 时间限制25ms，CPU内存两不误； │
│ 两者配合补短板，生产环境稳如山。 │
└─────────────────────────────────────────────────────────────────┘

最终建议

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