RedisLua限流实现
...大约 2 分钟
RedisLua限流实现
1. Redis 配置
其中配置了默认的限流脚本就是采用Lua 的形式
/**
* redis配置
*
*/
@Configuration
@EnableCaching
public class RedisConfig extends CachingConfigurerSupport
{
@Bean
@SuppressWarnings(value = { "unchecked", "rawtypes" })
public RedisTemplate<Object, Object> redisTemplate(RedisConnectionFactory connectionFactory)
{
RedisTemplate<Object, Object> template = new RedisTemplate<>();
template.setConnectionFactory(connectionFactory);
FastJson2JsonRedisSerializer serializer = new FastJson2JsonRedisSerializer(Object.class);
ObjectMapper mapper = new ObjectMapper();
mapper.setVisibility(PropertyAccessor.ALL, JsonAutoDetect.Visibility.ANY);
mapper.activateDefaultTyping(LaissezFaireSubTypeValidator.instance, ObjectMapper.DefaultTyping.NON_FINAL, JsonTypeInfo.As.PROPERTY);
serializer.setObjectMapper(mapper);
// 使用StringRedisSerializer来序列化和反序列化redis的key值
template.setKeySerializer(new StringRedisSerializer());
template.setValueSerializer(serializer);
// Hash的key也采用StringRedisSerializer的序列化方式
template.setHashKeySerializer(new StringRedisSerializer());
template.setHashValueSerializer(serializer);
template.afterPropertiesSet();
return template;
}
@Bean
public DefaultRedisScript<Long> limitScript()
{
DefaultRedisScript<Long> redisScript = new DefaultRedisScript<>();
redisScript.setScriptText(limitScriptText());
redisScript.setResultType(Long.class);
return redisScript;
}
/**
* 限流脚本
*/
private String limitScriptText()
{
return "local key = KEYS[1]\n" +
"local count = tonumber(ARGV[1])\n" +
"local time = tonumber(ARGV[2])\n" +
"local current = redis.call('get', key);\n" +
"if current and tonumber(current) > count then\n" +
" return tonumber(current);\n" +
"end\n" +
"current = redis.call('incr', key)\n" +
"if tonumber(current) == 1 then\n" +
" redis.call('expire', key, time)\n" +
"end\n" +
"return tonumber(current);";
}
}
2. 定义限流注解
/**
* 限流注解
*/
@Target(ElementType.METHOD)
@Retention(RetentionPolicy.RUNTIME)
@Documented
public @interface RateLimiter
{
/**
* 限流key
*/
public String key() default Constants.RATE_LIMIT_KEY;
/**
* 限流时间,单位秒
*/
public int time() default 60;
/**
* 限流次数
*/
public int count() default 100;
/**
* 限流类型
*/
public LimitType limitType() default LimitType.DEFAULT;
}
3. 定义限流类型
/**
* 限流类型
*/
public enum LimitType
{
/**
* 默认策略全局限流
*/
DEFAULT,
/**
* 根据请求者IP进行限流
*/
IP
}
4. 限流切面
/**
* 限流处理
*
* @author fd
*/
@Aspect
@Component
public class RateLimiterAspect
{
private static final Logger log = LoggerFactory.getLogger(RateLimiterAspect.class);
private RedisTemplate<Object, Object> redisTemplate;
private RedisScript<Long> limitScript;
@Autowired
public void setRedisTemplate1(RedisTemplate<Object, Object> redisTemplate)
{
this.redisTemplate = redisTemplate;
}
@Autowired
public void setLimitScript(RedisScript<Long> limitScript)
{
this.limitScript = 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 (StringUtils.isNull(number) || 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)
{
StringBuffer stringBuffer = new StringBuffer(rateLimiter.key());
if (rateLimiter.limitType() == LimitType.IP)
{
stringBuffer.append(IpUtils.getIpAddr(ServletUtils.getRequest())).append("-");
}
MethodSignature signature = (MethodSignature) point.getSignature();
Method method = signature.getMethod();
Class<?> targetClass = method.getDeclaringClass();
stringBuffer.append(targetClass.getName()).append("-").append(method.getName());
return stringBuffer.toString();
}
}
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