Redis分布式限流技术原理与实战

文章目录

• • 一、限流的本质与算法演进
  • • 四种经典算法解析
    • • 1️⃣ 固定窗口计数器法（Fixed Window Counter）
      • 2️⃣ 滑动窗口法（Sliding Window Log/Counter）
      • 3️⃣ 漏桶算法（Leaky Bucket）
      • 4️⃣ 令牌桶算法（Token Bucket）
  • 二、分层限流架构
  • • 三层典型架构
  • 三、工程级方案详解
  • • 方案一：Guava 本地令牌桶（单机限流）
    • 方案二：Redis + Lua Script 分布式滑动窗口限流
    • • Lua 脚本示例（`sliding_window.lua`）
      • Java 接入示例
      • 时序图说明
    • 方案三：Alibaba Sentinel 微服务限流
  • 四、方案对比与选型建议
  • 五、实战优化与注意事项
  • • 限流维度建议
    • 限流之后的应对策略
    • 多级限流策略（热点Key优化）
  • 六、总结

一、限流的本质与算法演进

在高并发系统中，限流（Rate Limiting） 是应对突发流量、保护下游系统资源的核心手段之一。 限流的目的是在保证系统可用性的前提下，平滑地削峰填谷。

• 核心算法思维导图：

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限流核心算法

Fixed Window Counter

简单实现

存在毛刺问题

Sliding Window

平滑控制

Redis ZSet实现

Leaky Bucket

输出速率恒定

平滑突发流量

Token Bucket

定期发放令牌

支持突发流量

四种经典算法解析

1️⃣ 固定窗口计数器法（Fixed Window Counter）

原理： 在固定时间窗口内统计请求总数，当超过阈值后拒绝请求。

优点： 简单易实现 缺点： 临界区间可能出现突发流量 (“毛刺”)

2️⃣ 滑动窗口法（Sliding Window Log/Counter）

原理： 将时间等分为若干小窗口，随着时间推进不断滑动删除过期记录。

优点： 精度高、流量分布更均匀 缺点： 统计基于历史记录，略有性能消耗

3️⃣ 漏桶算法（Leaky Bucket）

原理： 请求流如同水流进桶，桶底以固定速率漏水（处理请求），超出部分直接溢出。

特点： 输出速率稳定，强制限速

4️⃣ 令牌桶算法（Token Bucket）

原理： 系统按固定速率生成令牌放入桶中，请求需消耗令牌才能处理。

特点： 支持一定程度的突发流量，互联网高并发限流首选。

二、分层限流架构

限流既有“地面防线”（应用层），也有“网络关卡”（网关层），多层协同构成系统保护伞。

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网络入口

网关层限流 Nginx/OpenResty

分布式限流 Redis + Lua

应用层限流 Sentinel/Guava

业务处理与降级

三层典型架构

三、工程级方案详解

方案一：Guava 本地令牌桶（单机限流）

import com.google.common.util.concurrent.RateLimiter;

public class LocalLimitService {

// 每秒生成 5 个令牌
private final RateLimiter rateLimiter = RateLimiter.create(5.0);

public void accessApi() {
if (rateLimiter.tryAcquire()) {
System.out.println("请求成功，执行业务逻辑");
} else {
System.out.println("限流中，请稍后再试");
throw new RuntimeException("Too Many Requests");
}
}
}

适用场景： 非分布式系统、小型服务限流。

方案二：Redis + Lua Script 分布式滑动窗口限流

思路： 利用 Redis 的 ZSet 数据结构，将请求时间戳记录为 score，实现可滑动的时间窗口统计。

Lua 脚本示例（sliding_window.lua）

— KEYS[1]: 限流Key
— ARGV[1]: 窗口时间 (毫秒)
— ARGV[2]: 当前时间 (毫秒)
— ARGV[3]: 阈值(最大请求数)

local key = KEYS[1]
local window_size = tonumber(ARGV[1])
local now = tonumber(ARGV[2])
local limit = tonumber(ARGV[3])

— 1. 移除过期数据
redis.call('ZREMRANGEBYSCORE', key, 0, now – window_size)

— 2. 查看当前计数
local current_count = redis.call('ZCARD', key)

— 3. 判断是否超限
if current_count < limit then
redis.call('ZADD', key, now, now .. "-" .. math.random(10000))
redis.call('PEXPIRE', key, window_size + 1000)
return 1
else
return 0
end

Java 接入示例

public boolean allowRequest(String key, int limit, long windowMillis) {
String script = loadLuaScript("sliding_window.lua");
long now = System.currentTimeMillis();

Object result = redisTemplate.execute(
new DefaultRedisScript<>(script, Long.class),
Collections.singletonList(key),
windowMillis, now, limit
);
return (Long) result == 1;
}

时序图说明

Redis

Server

Client

Redis

Server

Client

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alt

[允许]

[超限]

请求API

执行Lua限流脚本

返回是否允许(1/0)

业务正常响应

返回 HTTP 429

优点：

• 原子操作（Lua脚本执行不可分割）
• 精确控制时间窗口
• 支持分布式水平扩展

方案三：Alibaba Sentinel 微服务限流

优势：

• 提供完善的控制台
• 支持热点参数限流、链路限流
• 可与 Nacos/Apollo 动态配置联动

@SentinelResource(value = "createOrder", blockHandler = "handleBlock")
public String createOrder(String orderId) {
return "订单创建成功: " + orderId;
}

public String handleBlock(String orderId, BlockException ex) {
return "系统繁忙，请稍后再试 (限流)";
}

规则配置：

• QPS 阈值：10
• 流控模式：直接
• 控制效果：快速失败

四、方案对比与选型建议

五、实战优化与注意事项

限流维度建议

限流之后的应对策略

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否

是

可降级

异步削峰

请求流入

是否限流?

正常处理

返回429

返回缓存/默认响应

进入消息队列排队

多级限流策略（热点Key优化）

分布式限流在极端高并发场景下可能导致 Redis 热点。 ✅ 建议方案：本地缓存 + Redis 双层限流。

– 本地限流 80% 请求
– Redis 限流校验剩余 20%

六、总结

🔹 限流是高可用系统的“安全阀”，能有效削峰、保护服务稳定。 🔹 Redis 限流凭借其高性能、原子操作特性，是业界最常用的分布式限流手段。 🔹 最佳实践：

网关限流兜底 + Redis 全局限流 + 应用层防御 打造“外防恶流、内稳业务”的坚固防线。