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
xml
<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
yaml
server:
port: 6650
nosql:
redis:
host: XXX.XXX.XXX.XXX
port: 6379
password:
database: 0
spring:
cache:
type: redis
redis:
host: ${nosql.redis.host}
port: ${nosql.redis.port}
password: ${nosql.redis.password}
lettuce:
pool:
enabled: true
max-active: 8
max-idle: 8
min-idle: 0
max-wait: 1000- 配置redis Conf
java
@Configuration
public class RedisConfig {
/**
* 序列化
* jackson2JsonRedisSerializer
*
* @param redisConnectionFactory 复述,连接工厂
* @return {@link RedisTemplate}<{@link Object}, {@link Object}>
*/
@Bean
public 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}>
*/
@Bean
public 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
- 编写核心lua脚本
lua
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 then
return 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 then
redis.call('expire', key, time)
end
return tonumber(current)- 编写注解 limiter
java
@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;
}- 增加注解类型
java
public enum LimitType {
/**
* 默认策略全局限流
*/
DEFAULT,
/**
* 根据请求者IP进行限流
*/
IP
}- 添加IPUtils
java
@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;
}
}- 核心处理类
java
@Aspect
@Component
@Slf4j
public class RateLimiterAspect {
@Resource
private RedisTemplate<Object, Object> redisTemplate;
@Resource
private 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