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1、Redis实现限流方案的核心原理：

redis实现限流的核心原理在于redis 的key 过期时间，当我们设置一个key到redis中时，会将key设置上过期时间，这里的实现是采用lua脚本来实现原子性的。

2、准备

• 引入相关依赖

<dependency>
<groupId>org.springframework.boot</groupId><artifactId>spring-boot-starter-web</artifactId>
</dependency>
<dependency><groupId>org.springframework.boot</groupId><artifactId>spring-boot-starter-aop</artifactId>
</dependency>
<dependency><groupId>cn.hutool</groupId><artifactId>hutool-all</artifactId><version>5.8.23</version>
</dependency>
<dependency><groupId>org.springframework.boot</groupId><artifactId>spring-boot-starter-data-redis</artifactId><version>3.1.5</version>
</dependency>
<dependency><groupId>org.yaml</groupId><artifactId>snakeyaml</artifactId><version>2.2</version>
</dependency>
<dependency><groupId>org.apache.commons</groupId><artifactId>commons-pool2</artifactId>
</dependency>
<dependency><groupId>com.google.protobuf</groupId><artifactId>protobuf-java</artifactId><version>3.25.1</version>
</dependency>

• 添加redis配置信息

server:port: 6650nosql:redis:host: XXX.XXX.XXX.XXXport: 6379password:database: 0spring:cache:type: redisredis:host: ${nosql.redis.host}port: ${nosql.redis.port}password: ${nosql.redis.password}lettuce:pool:enabled: truemax-active: 8max-idle: 8min-idle: 0max-wait: 1000

• 配置redis Conf

@Configuration
public class RedisConfig {/*** 序列化* jackson2JsonRedisSerializer** @param redisConnectionFactory 复述,连接工厂* @return {@link RedisTemplate}<{@link Object}, {@link Object}>*/@Beanpublic RedisTemplate<Object, Object> redisTemplate(RedisConnectionFactory redisConnectionFactory) {RedisTemplate<Object, Object> template = new RedisTemplate<>();template.setConnectionFactory(redisConnectionFactory);Jackson2JsonRedisSerializer jackson2JsonRedisSerializer = new Jackson2JsonRedisSerializer(Object.class);ObjectMapper om = new ObjectMapper();om.setVisibility(PropertyAccessor.ALL, JsonAutoDetect.Visibility.ANY);om.enableDefaultTyping(ObjectMapper.DefaultTyping.NON_FINAL);jackson2JsonRedisSerializer.setObjectMapper(om);template.setKeySerializer(jackson2JsonRedisSerializer);template.setHashKeySerializer(jackson2JsonRedisSerializer);template.setValueSerializer(jackson2JsonRedisSerializer);template.setHashValueSerializer(jackson2JsonRedisSerializer);template.afterPropertiesSet();return template;}/*** 加载lua脚本* @return {@link DefaultRedisScript}<{@link Long}>*/@Beanpublic DefaultRedisScript<Long> limitScript() {DefaultRedisScript<Long> redisScript = new DefaultRedisScript<>();redisScript.setScriptSource(new ResourceScriptSource(new ClassPathResource("luaFile/rateLimit.lua")));redisScript.setResultType(Long.class);return redisScript;}}

3、限流实现

1. 编写核心lua脚本

local key = KEYS[1]
-- 取出key对应的统计，判断统计是否比限制大，如果比限制大，直接返回当前值
local count = tonumber(ARGV[1])
local time = tonumber(ARGV[2])
local current = redis.call('get', key)
if current and tonumber(current) > count thenreturn tonumber(current)
end
--如果不比限制大，进行++，重新设置时间
current = redis.call('incr', key)
if tonumber(current) == 1 thenredis.call('expire', key, time)
end
return tonumber(current)

2. 编写注解 limiter

@Target(ElementType.METHOD)
@Retention(RetentionPolicy.RUNTIME)
@Documented
public @interface RateLimiter {/*** 限流key*/String key() default "rate_limit:";/*** 限流时间,单位秒*/int time() default 60;/*** 限流次数*/int count() default 100;/*** 限流类型*/LimitType limitType() default LimitType.DEFAULT;
}

3. 增加注解类型

public enum LimitType {/*** 默认策略全局限流*/DEFAULT,/*** 根据请求者IP进行限流*/IP
}

4. 添加IPUtils

@Slf4j
public class IpUtils {/**ip的长度值*/private static final int IP_LEN = 15;/** 使用代理时，多IP分隔符*/private static final String SPLIT_STR = ",";/*** 获取IP地址* <p>* 使用Nginx等反向代理软件， 则不能通过request.getRemoteAddr()获取IP地址* 如果使用了多级反向代理的话，X-Forwarded-For的值并不止一个，而是一串IP地址，X-Forwarded-For中第一个非unknown的有效IP字符串，则为真实IP地址*/public static String getIpAddr(HttpServletRequest request) {String ip = null;try {ip = request.getHeader("x-forwarded-for");if (StrUtil.isBlank(ip)) {ip = request.getHeader("Proxy-Client-IP");}if (StrUtil.isBlank(ip)) {ip = request.getHeader("WL-Proxy-Client-IP");}if (StrUtil.isBlank(ip)) {ip = request.getHeader("HTTP_CLIENT_IP");}if (StrUtil.isBlank(ip)) {ip = request.getHeader("HTTP_X_FORWARDED_FOR");}if (StrUtil.isBlank(ip)) {ip = request.getRemoteAddr();}} catch (Exception e) {log.error("IPUtils ERROR ", e);}//使用代理，则获取第一个IP地址if (!StrUtil.isBlank(ip) && ip.length() > IP_LEN) {if (ip.indexOf(SPLIT_STR) > 0) {ip = ip.substring(0, ip.indexOf(SPLIT_STR));}}return ip;}
}

5. 核心处理类

@Aspect
@Component
@Slf4j
public class RateLimiterAspect {@Resourceprivate RedisTemplate<Object, Object> redisTemplate;@Resourceprivate RedisScript<Long> limitScript;@Before("@annotation(rateLimiter)")public void doBefore(JoinPoint point, RateLimiter rateLimiter) throws Throwable {String key = rateLimiter.key();int time = rateLimiter.time();int count = rateLimiter.count();String combineKey = getCombineKey(rateLimiter, point);List<Object> keys = Collections.singletonList(combineKey);try {Long number = redisTemplate.execute(limitScript, keys, count, time);if (number == null || number.intValue() > count) {throw new ServiceException("访问过于频繁，请稍候再试");}log.info("限制请求'{}',当前请求'{}',缓存key'{}'", count, number.intValue(), key);} catch (ServiceException e) {throw e;} catch (Exception e) {throw new RuntimeException("服务器限流异常，请稍候再试");}}public String getCombineKey(RateLimiter rateLimiter, JoinPoint point) {StringBuilder stringBuilder = new StringBuilder(rateLimiter.key());if (rateLimiter.limitType() == LimitType.IP) {stringBuilder.append(IpUtils.getIpAddr(((ServletRequestAttributes) RequestContextHolder.currentRequestAttributes()).getRequest())).append("-");}MethodSignature signature = (MethodSignature) point.getSignature();Method method = signature.getMethod();Class<?> targetClass = method.getDeclaringClass();stringBuilder.append(targetClass.getName()).append("-").append(method.getName());return stringBuilder.toString();}
}

到此，我们就可以利用注解，对请求方法进行限流了