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滑动窗口限流算法：基于Redis有序集合的实现与优化

news 2025/7/15 6:22:42

滑动窗口限流算法是一种基于时间窗口的流量控制策略，它将时间划分为固定大小的窗口，并在每个窗口内记录请求次数。通过动态滑动窗口，算法能够灵活调整限流速率，以应对流量的波动。

算法核心步骤

统计窗口内的请求数量：记录当前时间窗口内的请求次数。
应用限流规则：根据预设的阈值判断是否允许当前请求通过。

Redis有序集合的应用

Redis的有序集合（Sorted Set）为滑动窗口限流提供了理想的实现方式。每个有序集合的成员都有一个分数（score），我们可以利用分数来定义时间窗口。每当有请求进入时，将当前时间戳作为分数，并将请求的唯一标识作为成员添加到集合中。这样，通过统计窗口内的成员数量，即可实现限流。

实现细节

Redis命令简化

通过Redis的ZADD命令，我们可以将请求的时间戳和唯一标识添加到有序集合中：

ZADD 资源标识 时间戳 请求标识

Java代码实现

以下是基于Java和Redis的滑动窗口限流实现：

public boolean isAllow(String key) {ZSetOperations<String, String> zSetOperations = stringRedisTemplate.opsForZSet();long currentTime = System.currentTimeMillis();long windowStart = currentTime - period;zSetOperations.removeRangeByScore(key, 0, windowStart);Long count = zSetOperations.zCard(key);if (count >= threshold) {return false;}String value = "请求唯一标识（如：请求流水号、哈希值、MD5值等）";zSetOperations.add(key, value, currentTime);stringRedisTemplate.expire(key, period, TimeUnit.MILLISECONDS);return true;
}

Lua脚本优化

为了确保在高并发场景下的原子性操作，我们可以将上述逻辑封装为Lua脚本：

local key = KEYS[1]
local current_time = tonumber(ARGV[1])
local window_size = tonumber(ARGV[2])
local threshold = tonumber(ARGV[3])
redis.call('ZREMRANGEBYSCORE', key, 0, current_time - window_size)
local count = redis.call('ZCARD', key)
if count >= threshold thenreturn tostring(0)
elseredis.call('ZADD', key, tostring(current_time), current_time)return tostring(1)
end

完整Java代码

package com.example.demo.controller;import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.data.redis.core.StringRedisTemplate;
import org.springframework.data.redis.core.ZSetOperations;
import org.springframework.data.redis.core.script.DefaultRedisScript;
import org.springframework.stereotype.Service;import java.util.Collections;
import java.util.concurrent.TimeUnit;@Service
public class SlidingWindowRatelimiter {private long period = 60 * 1000; // 1分钟private int threshold = 3; // 3次@Autowiredprivate StringRedisTemplate stringRedisTemplate;public boolean isAllow(String key) {ZSetOperations<String, String> zSetOperations = stringRedisTemplate.opsForZSet();long currentTime = System.currentTimeMillis();long windowStart = currentTime - period;zSetOperations.removeRangeByScore(key, 0, windowStart);Long count = zSetOperations.zCard(key);if (count >= threshold) {return false;}String value = "请求唯一标识（如：请求流水号、哈希值、MD5值等）";zSetOperations.add(key, value, currentTime);stringRedisTemplate.expire(key, period, TimeUnit.MILLISECONDS);return true;}public boolean isAllow2(String key) {String luaScript = "local key = KEYS[1]\n" +"local current_time = tonumber(ARGV[1])\n" +"local window_size = tonumber(ARGV[2])\n" +"local threshold = tonumber(ARGV[3])\n" +"redis.call('ZREMRANGEBYSCORE', key, 0, current_time - window_size)\n" +"local count = redis.call('ZCARD', key)\n" +"if count >= threshold then\n" +" return tostring(0)\n" +"else\n" +" redis.call('ZADD', key, tostring(current_time), current_time)\n" +" return tostring(1)\n" +"end";long currentTime = System.currentTimeMillis();DefaultRedisScript<String> redisScript = new DefaultRedisScript<>(luaScript, String.class);String result = stringRedisTemplate.execute(redisScript, Collections.singletonList(key), String.valueOf(currentTime), String.valueOf(period), String.valueOf(threshold));return "1".equals(result);}
}

AOP实现限流

为了更方便地应用限流策略，我们可以通过AOP（面向切面编程）来拦截请求并应用限流规则。

自定义注解

首先，定义一个限流注解：

package com.example.demo.controller;import java.lang.annotation.*;@Documented
@Target(ElementType.METHOD)
@Retention(RetentionPolicy.RUNTIME)
public @interface RateLimit {long period() default 60; // 窗口大小（默认：60秒）long threshold() default 3; // 阈值（默认：3次）
}

切面实现

然后，实现一个切面来拦截带有@RateLimit注解的方法：

package com.example.demo.controller;import jakarta.servlet.http.HttpServletRequest;
import lombok.extern.slf4j.Slf4j;
import org.aspectj.lang.JoinPoint;
import org.aspectj.lang.annotation.Aspect;
import org.aspectj.lang.annotation.Before;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.data.redis.core.StringRedisTemplate;
import org.springframework.data.redis.core.ZSetOperations;
import org.springframework.stereotype.Component;import java.util.concurrent.TimeUnit;@Slf4j
@Aspect
@Component
public class RateLimitAspect {@Autowiredprivate StringRedisTemplate stringRedisTemplate;@Before("@annotation(rateLimit)")public void doBefore(JoinPoint joinPoint, RateLimit rateLimit) {long period = rateLimit.period();long threshold = rateLimit.threshold();HttpServletRequest httpServletRequest = ((ServletRequestAttributes) RequestContextHolder.getRequestAttributes()).getRequest();String uri = httpServletRequest.getRequestURI();Long userId = 123L; // 模拟获取用户IDString key = "limit:" + userId + ":" + uri;ZSetOperations<String, String> zSetOperations = stringRedisTemplate.opsForZSet();long currentTime = System.currentTimeMillis();long windowStart = currentTime - period * 1000;zSetOperations.removeRangeByScore(key, 0, windowStart);Long count = zSetOperations.zCard(key);if (count >= threshold) {throw new RuntimeException("请求过于频繁！");} else {zSetOperations.add(key, String.valueOf(currentTime), currentTime);stringRedisTemplate.expire(key, period, TimeUnit.SECONDS);}}
}

使用注解

最后，在需要限流的方法上添加@RateLimit注解：

@RestController
@RequestMapping("/hello")
public class HelloController {@RateLimit(period = 30, threshold = 2)@GetMapping("/sayHi")public void sayHi() {}
}