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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);
}

通过使用策略模式,我们将限流算法与具体的限流策略解耦,提高了代码的可扩展性和可维护性。未来如果需要新的限流策略,只需要实现 RateLimitStrategy 接口并配置即可,无需修改核心的限流逻辑。