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做鱫视频网站,申请网站怎样申请,wordpress什么删留言,近期莱芜命案我们可以使用策略模式来统一单机限流和分布式限流的实现,提高代码的可扩展性和可维护性。思路是定义一个 RateLimitStrategy 接口,并分别实现单机限流策略 LocalRateLimitStrategy 和分布式限流策略 DistributedRateLimitStrategy。在 AOP 切面中,根据配置决定使用哪种限流策…

我们可以使用策略模式来统一单机限流和分布式限流的实现,提高代码的可扩展性和可维护性。

思路是定义一个 RateLimitStrategy 接口,并分别实现单机限流策略 LocalRateLimitStrategy 和分布式限流策略 DistributedRateLimitStrategy。在 AOP 切面中,根据配置决定使用哪种限流策略。

定义策略接口

public interface RateLimitStrategy {boolean tryAcquire(String key, double qps, long timeout, TimeUnit timeUnit);
}

实现单机限流策略

import com.google.common.util.concurrent.RateLimiter;import java.util.Map;
import java.util.concurrent.ConcurrentHashMap;
import java.util.concurrent.TimeUnit;public class LocalRateLimitStrategy implements RateLimitStrategy {private final Map<String, RateLimiter> rateLimiters = new ConcurrentHashMap<>();@Overridepublic boolean tryAcquire(String key, double qps, long timeout, TimeUnit timeUnit) {RateLimiter limiter = rateLimiters.computeIfAbsent(key, k -> RateLimiter.create(qps));if (timeout > 0) {return limiter.tryAcquire(timeout, timeUnit);} else {return limiter.tryAcquire();}}
}

实现分布式限流策略

import org.springframework.data.redis.core.RedisTemplate;
import org.springframework.data.redis.core.script.RedisScript;import java.util.Collections;
import java.util.List;
import java.util.concurrent.TimeUnit;public class DistributedRateLimitStrategy implements RateLimitStrategy {private final RedisTemplate<String, Object> redisTemplate;public DistributedRateLimitStrategy(RedisTemplate<String, Object> redisTemplate) {this.redisTemplate = redisTemplate;}@Overridepublic boolean tryAcquire(String key, double qps, long timeout, TimeUnit timeUnit) {long window = timeUnit.toSeconds(timeout);List<String> keys = Collections.singletonList(key);String luaScript = buildLuaScript();RedisScript<Long> redisScript = new DefaultRedisScript<>(luaScript, Long.class);Long currentCount = redisTemplate.execute(redisScript, keys, Collections.singletonList(window), Collections.singletonList(qps));return currentCount <= qps;}private String buildLuaScript() {return "local key = KEYS[1]\n" +"local window = tonumber(ARGV[1])\n" +"local qps = tonumber(ARGV[2])\n" +"local current = redis.call('incrBy', key, 1)\n" +"if current == 1 then\n" +"    redis.call('expire', key, window)\n" +"end\n" +"if current > qps then\n" +"    return redis.call('decrBy', key, 1)\n" +"else\n" +"    return current\n" +"end";}
}

修改切面逻辑

import org.aspectj.lang.ProceedingJoinPoint;
import org.aspectj.lang.annotation.Around;
import org.aspectj.lang.annotation.Aspect;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.stereotype.Component;import java.util.concurrent.TimeUnit;@Aspect
@Component
public class RateLimitAspect {@Autowiredprivate RateLimitStrategy rateLimitStrategy;@Around("@annotation(rateLimitAnnotation)")public Object around(ProceedingJoinPoint joinPoint, RateLimit rateLimitAnnotation) throws Throwable {String key = joinPoint.getSignature().toLongString();double qps = rateLimitAnnotation.qps();long timeout = rateLimitAnnotation.timeout();TimeUnit timeUnit = rateLimitAnnotation.timeUnit();boolean acquired = rateLimitStrategy.tryAcquire(key, qps, timeout, timeUnit);if (!acquired) {throw new RuntimeException("Rate limit exceeded");}return joinPoint.proceed();}
}

在切面逻辑中,我们注入了 RateLimitStrategy 的实现类。根据配置决定使用单机限流还是分布式限流策略。

使用示例

@RestController
public class DemoController {@Autowiredprivate RateLimitStrategy rateLimitStrategy;@GetMapping("/test")@ApiRateLimit(qps = 10, timeout = 60, timeUnit = TimeUnit.SECONDS)public String test() {return "hello world";}
}

在使用时,我们只需要在方法上标注 @RateLimit 注解即可,而不需要关心底层使用的是单机限流还是分布式限流。

配置限流策略

在 Spring 配置中,我们可以根据需求注入不同的 RateLimitStrategy 实现类:

// 单机限流配置
@Bean
public RateLimitStrategy localRateLimitStrategy() {return new LocalRateLimitStrategy();
}// 分布式限流配置
@Bean
public RateLimitStrategy distributedRateLimitStrategy(RedisTemplate<String, Object> redisTemplate) {return new DistributedRateLimitStrategy(redisTemplate);
}