Redis 缓存高可用监控：实时监控内存碎片率、缓存命中率、Key 驱逐及集群节点状态

小马爱打代码2026-06-17 9:54

Redis 缓存高可用监控：实时监控内存碎片率、缓存命中率、Key 驱逐及集群节点状态

缓存命中率掉了 15%，业务方说"Redis 没挂啊"------确实没挂，但碎片率飙到了 3.2，驱逐线程每秒钟删掉 500 个 Key，集群里还有一个节点在慢性失联。这些事，redis_exporter 全知道。

一、一台"活着但废了"的 Redis

大促当天上午 10 点，缓存命中率从 92% 直线掉到 64%。

运维登录 Redis 执行 INFO stats，evicted_keys 的数字每刷新一次就涨几千。再查 INFO memory，碎片率 3.1。集群共 6 个节点，有 1 个响应延迟比其他节点高了 10 倍，但没彻底断连，监控没报警。
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业务方只知道"缓存不灵了"，数据库被打到 80% CPU。这个故障持续了整整一上午才定位------如果有一个能实时反映 Redis"真实健康状况"的监控面板，30 分钟就能解决。

下面这套基于 redis_exporter 的监控体系，就是用来解决这类问题的。

二、redis_exporter 能干什么？

它连接 Redis 后定期执行 INFO、SLOWLOG、CLUSTER INFO 等命令，把输出转成 Prometheus 指标。

五大监控焦点及对应的指标

监控焦点 核心指标 指标含义 什么算异常
内存健康 redis_memory_fragmentation_ratio 操作系统分配内存 / Redis 实际使用内存 > 1.5 或 < 1.0
缓存效率 redis_keyspace_hits_total / redis_keyspace_misses_total 命中次数 / 未命中次数 命中率 < 85%
内存压力 redis_evicted_keys_total 累计驱逐 Key 数量 驱逐速率持续 > 50/秒
集群状态 redis_cluster_known_nodes / redis_cluster_slots_fail 已知节点数 / 故障槽位数 节点数减少 或 故障槽 > 0
慢操作 redis_slowlog_length 慢查询队列长度 > 5 条未处理

三、整体监控架构


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存储与告警
采集层
Redis 集群
Redis Node 1
Redis Node 2
Redis Node ...
redis_exporter

执行 INFO / SLOWLOG / CLUSTER INFO
Prometheus

指标存储 + 告警规则
Grafana 仪表盘
告警通知

四、指标实战详解

4.1 内存碎片率：Redis 的"骨质疏松"

Redis 在分配和释放内存时会产生碎片。碎片率过高意味着大量内存被浪费，碎片率低于 1.0 意味着 Redis 开始使用 swap------性能会瞬间崩塌。
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警告
正常
碎片率 1.0 - 1.5
有效内存 ≈ 物理内存 ✅
碎片率 1.5 - 2.0
有效内存 = 物理内存的 50%-65% ⚠️
碎片率 > 2.0
有效内存 < 物理内存的 50% ❌
开始使用 swap → 性能崩塌

监控方式：

promql 复制代码 
# 当前碎片率
redis_memory_fragmentation_ratio

# 用告警规则盯住它
redis_memory_fragmentation_ratio > 1.8

某电商大促后发现碎片率长期在 2.0 以上，每天凌晨重启 Redis 临时解决。最终在配置中开启 activedefrag yes，配合设置 active-defrag-cycle-min 5，碎片率稳定在 1.2 以下。

4.2 缓存命中率：判断"缓存有没有白设"的唯一标准

命中率低通常有两种情况：热数据量超过内存容量（需要扩容），或者过期时间设置太短（优化业务代码）。
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是
是
否
是
否
缓存命中率下降
命中率 < 85%?
正常范围

无需处理
10分钟内

下降超过15个百分点?
检查: 大容量刷缓存

或节点故障
热数据量

超过内存容量?
扩容 / 加节点
优化过期时间

调整业务代码

监控方式：

promql 复制代码 
# 命中率计算
sum(rate(redis_keyspace_hits_total[5m])) /
(sum(rate(redis_keyspace_hits_total[5m])) + sum(rate(redis_keyspace_misses_total[5m])))

两条实用告警：

  • 命中率 < 85% 持续 10 分钟 → 需要检查内存和业务
  • 命中率在 10 分钟内下降超过 15 个百分点 → 可能是大容量刷缓存或节点故障

4.3 Key 驱逐：内存不足的直接证据

当 Redis 内存达到 maxmemory 限制时，根据 maxmemory-policy 开始驱逐 Key。
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volatile-lru
allkeys-lfu
noeviction
内存达到 maxmemory
maxmemory-policy?
驱逐最近最少使用的 Key
仅驱逐带过期时间的 Key
驱逐最不经常使用的 Key
不驱逐，返回 OOM 错误
⚠️ 全量扫描场景下

可能误驱逐热数据
✅ 推荐：只驱逐过期 Key

热数据不受影响

promql 复制代码 
# 驱逐速率（每秒）
rate(redis_evicted_keys_total[2m])

某应用 feed 流缓存，maxmemory-policy 设为 allkeys-lru。活动期间访问量翻倍，驱逐速率飙到 200/秒，命中率从 90% 跌到 40%。解决方案是临时调大 maxmemory 并增加集群节点。

4.4 集群节点状态：发现"半死不活"的节点

Redis Cluster 最怕的不是节点彻底挂掉，而是节点"半死"------能响应 ping 但处理极慢，拖垮整个集群。
#mermaid-svg-01Iv0RNeXsuYMZEI{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-01Iv0RNeXsuYMZEI .edge-animation-slow{stroke-dasharray:9,5!important;stroke-dashoffset:900;animation:dash 50s linear infinite;stroke-linecap:round;}#mermaid-svg-01Iv0RNeXsuYMZEI .edge-animation-fast{stroke-dasharray:9,5!important;stroke-dashoffset:900;animation:dash 20s linear infinite;stroke-linecap:round;}#mermaid-svg-01Iv0RNeXsuYMZEI .error-icon{fill:#552222;}#mermaid-svg-01Iv0RNeXsuYMZEI .error-text{fill:#552222;stroke:#552222;}#mermaid-svg-01Iv0RNeXsuYMZEI .edge-thickness-normal{stroke-width:1px;}#mermaid-svg-01Iv0RNeXsuYMZEI .edge-thickness-thick{stroke-width:3.5px;}#mermaid-svg-01Iv0RNeXsuYMZEI .edge-pattern-solid{stroke-dasharray:0;}#mermaid-svg-01Iv0RNeXsuYMZEI .edge-thickness-invisible{stroke-width:0;fill:none;}#mermaid-svg-01Iv0RNeXsuYMZEI .edge-pattern-dashed{stroke-dasharray:3;}#mermaid-svg-01Iv0RNeXsuYMZEI .edge-pattern-dotted{stroke-dasharray:2;}#mermaid-svg-01Iv0RNeXsuYMZEI .marker{fill:#333333;stroke:#333333;}#mermaid-svg-01Iv0RNeXsuYMZEI .marker.cross{stroke:#333333;}#mermaid-svg-01Iv0RNeXsuYMZEI svg{font-family:"trebuchet ms",verdana,arial,sans-serif;font-size:16px;}#mermaid-svg-01Iv0RNeXsuYMZEI p{margin:0;}#mermaid-svg-01Iv0RNeXsuYMZEI defs #statediagram-barbEnd{fill:#333333;stroke:#333333;}#mermaid-svg-01Iv0RNeXsuYMZEI g.stateGroup text{fill:#9370DB;stroke:none;font-size:10px;}#mermaid-svg-01Iv0RNeXsuYMZEI g.stateGroup text{fill:#333;stroke:none;font-size:10px;}#mermaid-svg-01Iv0RNeXsuYMZEI g.stateGroup .state-title{font-weight:bolder;fill:#131300;}#mermaid-svg-01Iv0RNeXsuYMZEI g.stateGroup rect{fill:#ECECFF;stroke:#9370DB;}#mermaid-svg-01Iv0RNeXsuYMZEI g.stateGroup line{stroke:#333333;stroke-width:1;}#mermaid-svg-01Iv0RNeXsuYMZEI 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rect.outer{rx:5px;ry:5px;}#mermaid-svg-01Iv0RNeXsuYMZEI .statediagram-state .divider{stroke:#9370DB;}#mermaid-svg-01Iv0RNeXsuYMZEI .statediagram-state .title-state{rx:5px;ry:5px;}#mermaid-svg-01Iv0RNeXsuYMZEI .statediagram-cluster.statediagram-cluster .inner{fill:white;}#mermaid-svg-01Iv0RNeXsuYMZEI .statediagram-cluster.statediagram-cluster-alt .inner{fill:#f0f0f0;}#mermaid-svg-01Iv0RNeXsuYMZEI .statediagram-cluster .inner{rx:0;ry:0;}#mermaid-svg-01Iv0RNeXsuYMZEI .statediagram-state rect.basic{rx:5px;ry:5px;}#mermaid-svg-01Iv0RNeXsuYMZEI .statediagram-state rect.divider{stroke-dasharray:10,10;fill:#f0f0f0;}#mermaid-svg-01Iv0RNeXsuYMZEI .note-edge{stroke-dasharray:5;}#mermaid-svg-01Iv0RNeXsuYMZEI .statediagram-note rect{fill:#fff5ad;stroke:#aaaa33;stroke-width:1px;rx:0;ry:0;}#mermaid-svg-01Iv0RNeXsuYMZEI .statediagram-note rect{fill:#fff5ad;stroke:#aaaa33;stroke-width:1px;rx:0;ry:0;}#mermaid-svg-01Iv0RNeXsuYMZEI .statediagram-note text{fill:black;}#mermaid-svg-01Iv0RNeXsuYMZEI .statediagram-note .nodeLabel{color:black;}#mermaid-svg-01Iv0RNeXsuYMZEI .statediagram .edgeLabel{color:red;}#mermaid-svg-01Iv0RNeXsuYMZEI #dependencyStart,#mermaid-svg-01Iv0RNeXsuYMZEI #dependencyEnd{fill:#333333;stroke:#333333;stroke-width:1;}#mermaid-svg-01Iv0RNeXsuYMZEI .statediagramTitleText{text-anchor:middle;font-size:18px;fill:#333;}#mermaid-svg-01Iv0RNeXsuYMZEI :root{--mermaid-font-family:"trebuchet ms",verdana,arial,sans-serif;} 节点正常
多数主节点标记 pfail
超时未恢复
节点恢复正常
人工介入修复
Healthy
Pfail
Fail
性能正常，槽位完整
能响应但性能受损(半死不活)
彻底下线，槽位迁移

promql 复制代码 
# 集群节点数变化
changes(redis_cluster_known_nodes[5m]) < 0

# 可能故障的节点数（pfail）
redis_cluster_slots_pfail > 0

# 已确认故障的槽位数
redis_cluster_slots_fail > 0

一条被忽视的指标：redis_cluster_slots_pfail。当一个节点被多数主节点标记为 pfail，但还没到 fail 阈值时，集群还能工作但性能已受损。对这个指标设一个 > 0 就告警的规则，能提前发现硬件或网络问题。

五、部署实战：三步搞定

5.1 创建 redis_exporter 部署文件

yaml 复制代码 
apiVersion: apps/v1
kind: Deployment
metadata:
  name: redis-cluster-exporter
spec:
  replicas: 3
  selector:
    matchLabels:
      app: redis-exporter
  template:
    metadata:
      labels:
        app: redis-exporter
    spec:
      containers:
      - name: exporter
        image: oliver006/redis_exporter:latest
        args:
        - --redis.addr=redis://redis-cluster:6379
        - --redis.password=${REDIS_PASSWORD}
        - --check-key-groups=user:*,order:*,cache:*
        - --check-single-keys=config,blacklist
        - --cluster-info           # 开启集群信息采集
        - --include-system-metrics # 开启系统指标
        env:
        - name: REDIS_PASSWORD
          valueFrom:
            secretKeyRef:
              name: redis-secret
              key: password
        ports:
        - containerPort: 9121
          name: metrics

关键参数说明：

参数 说明
--cluster-info 采集集群拓扑和节点状态，集群模式下必须开启
--check-key-groups 按前缀统计 Key 数量和大小的分布
--include-system-metrics 额外采集 Redis 所在机器的 CPU/内存

5.2 Prometheus 采集配置

yaml 复制代码 
apiVersion: monitoring.coreos.com/v1
kind: ServiceMonitor
metadata:
  name: redis-exporter-monitor
spec:
  selector:
    matchLabels:
      app: redis-exporter
  endpoints:
  - port: metrics
    interval: 30s          # Redis 指标变化相对慢，30 秒足够
    scrapeTimeout: 10s
    namespaceSelector:
    matchNames:
    - redis

5.3 验证指标是否到位

在 Prometheus UI 中执行：

promql 复制代码 
{__name__=~"redis_.*"}

看到 redis_memory_fragmentation_ratio、redis_evicted_keys_total、redis_cluster_known_nodes 等指标，说明采集成功。

六、告警规则全清单

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否
是持续5分钟
否
是持续10分钟
否
是持续3分钟
否
是
否
是
否
Prometheus 告警评估
碎片率 > 1.8?
🔔 RedisFragmentationHigh
内存使用率 > 85%?
🔔 RedisMemoryUsageHigh
命中率 < 70%?
🔔 RedisHitRateLow
驱逐速率 > 100/秒?
🔔 RedisEvictionHigh
集群节点数减少?
🔔 RedisClusterNodeDown
存在故障槽位?
🔔 RedisClusterSlotFail
✅ 集群健康

yaml 复制代码 
groups:
- name: redis-high-availability
  rules:
  # 内存碎片率异常
  - alert: RedisFragmentationHigh
    expr: redis_memory_fragmentation_ratio > 1.8
    for: 15m
    annotations:
      summary: "实例 {{ $labels.instance }} 碎片率 {{ $value }}"

  # 内存使用率过高
  - alert: RedisMemoryUsageHigh
    expr: redis_memory_used_bytes / redis_memory_max_bytes > 0.85
    for: 5m
    annotations:
      summary: "内存使用率 {{ $value | humanizePercentage }}"

  # 命中率过低
  - alert: RedisHitRateLow
    expr: |
      sum(rate(redis_keyspace_hits_total[10m])) /
      (sum(rate(redis_keyspace_hits_total[10m])) + sum(rate(redis_keyspace_misses_total[10m]))) < 0.7
    for: 10m
    annotations:
      summary: "缓存命中率仅 {{ $value | humanizePercentage }}"

  # Key 驱逐太快
  - alert: RedisEvictionHigh
    expr: rate(redis_evicted_keys_total[5m]) > 100
    for: 3m
    annotations:
      summary: "驱逐速率 {{ $value }}/秒，内存严重不足"

  # 集群节点掉线
  - alert: RedisClusterNodeDown
    expr: redis_cluster_known_nodes < 3
    for: 1m
    annotations:
      summary: "集群节点数不足，仅剩 {{ $value }} 个"

  # 集群有故障槽位
  - alert: RedisClusterSlotFail
    expr: redis_cluster_slots_fail > 0
    for: 0m
    annotations:
      summary: "集群有 {{ $value }} 个槽位故障"

  # 慢查询堆积
  - alert: RedisSlowLog堆积
    expr: redis_slowlog_length > 10
    for: 5m
    annotations:
      summary: "慢查询队列有 {{ $value }} 条未处理"

七、一次真实故障复盘

7.1 背景

电商平台 Redis Cluster，6 节点，每个节点 8GB 内存。

7.2 故障现象

  • 业务反馈接口响应从 20ms 涨到 200ms
  • 缓存命中率从 92% 降到 67%
  • MySQL CPU 飙到 75%（因为缓存失效）

7.3 根因分析流程

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节点存储大量频繁更新的 JSON
内存分配回收产生大量碎片
有效内存 8GB → 4GB
触发 Key 驱逐
驱逐消耗 CPU，形成恶性循环
接口响应 20ms → 200ms
查看缓存命中率

92% → 67%
查看碎片率趋势

一个节点 1.1 → 2.4（持续两周）
查看驱逐速率

达到 180/秒
查看集群状态

该节点 slots_pfail = 1

指标 数据
碎片率趋势图 一个节点碎片率从 1.1 爬升到 2.4，持续了两周
驱逐速率 故障期间达到 180/秒
集群状态 该节点的 redis_cluster_slots_pfail 为 1，其他节点认为它"可能挂了"

根因：该节点存储了大量频繁更新的 JSON 对象，内存分配回收产生大量碎片。碎片导致有效内存缩水到 4GB，触发驱逐，驱逐又消耗 CPU，形成恶性循环。

7.4 解决方案

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临时措施
立即重启该节点

恢复内存布局
开启 activedefrag

自动整理碎片
大 JSON 拆散为 Hash

减少内存分配波动
碎片率稳定在 1.2 以下 ✅

八、三个最容易踩的坑

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allkeys-lru + 全量扫描
热数据被驱逐
坑2: 忽略复制缓冲区
只看 seconds_behind_master = 0

以为同步正常
忽略 repl_backlog_size

被写满导致全量复制
坑1: used_memory_rss 误判
只看 used_memory_rss = 8GB

以为内存充足
忽略碎片率 2.0

有效内存仅 4GB

8.1 坑 1：把 used_memory_rss 当成真实内存使用

used_memory_rss 包含碎片，一个碎片率 2.0 的节点，8GB 物理内存中只有 4GB 存了有效数据。监控内存应该同时看使用量和碎片率。

8.2 坑 2：只看 seconds_behind_master 忽略复制缓冲区

主从架构中，从库延迟为 0 不代表同步健康。还要看 redis_master_repl_offset 和 redis_slave_repl_offset 的差值，以及 redis_repl_backlog_size 是否被写满。

8.3 坑 3：对 maxmemory-policy 配置盲目信任

allkeys-lru 在内存满时会驱逐最近最少使用的 Key。但如果访问模式是"全量扫描"，LRU 会驱逐真正的热数据。建议用 volatile-lru 只驱逐设置了过期时间的 Key。

九、总结

Redis"活着"不等于"健康地活着"。
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① 碎片率 > 1.5 就要介入

早期信号，别等到 3.0
② 命中率下降 + 驱逐上升 = 内存不够

加内存或扩容集群
③ 节点数不变但性能下降

去看 pfail 指标
主动重启 / 开启 activedefrag
扩容 / 增加节点
定位半死不活的节点

监控不是目的，提前发现问题才是。

方案落地后，我们团队再也没有出现过"Redis 没挂但缓存炸了"的故障。碎片率告警让他们在大促前主动重启了问题节点，命中率趋势图帮他们发现了业务代码中的一次全表缓存刷新操作。

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