redis+lua实现限流的项目实践
作者:Best_Liu~
redis有很多限流的算法(比如:令牌桶,计数器,时间窗口)等,在分布式里面进行限流的话,我们则可以使用redis+lua脚本进行限流,下面就来介绍一下redis+lua实现限流
1、需要引入Redis的maven坐标
<!--redis和 springboot集成的包 --> <dependency> <groupId>org.springframework.boot</groupId> <artifactId>spring-boot-starter-data-redis</artifactId> <version>2.3.0.RELEASE</version> </dependency>
2、redis配置
spring: # Redis数据库索引 redis: database: 0 # Redis服务器地址 host: 127.0.0.1 # Redis服务器连接端口 port: 6379 # Redis服务器连接密码(默认为空) password: # 连接池最大连接数(使用负值表示没有限制) jedis: pool: max-active: 8 # 连接池最大阻塞等待时间(使用负值表示没有限制) max-wait: -1 # 连接池中的最大空闲连接 max-idle: 8 # 连接池中的最小空闲连接 min-idle: 0 # 连接超时时间(毫秒) timeout: 10000
3、新建脚本放在该项目的 resources 目录下,新建 limit.lua
local key = KEYS[1] --限流KEY local limit = tonumber(ARGV[1]) --限流大小 local current = tonumber(redis.call('get', key) or "0") if current + 1 > limit then return 0 else redis.call("INCRBY", key,"1") redis.call("expire", key,"2") return current + 1 end
4、自定义限流注解
import java.lang.annotation.*; @Target(value = ElementType.METHOD) @Retention(RetentionPolicy.RUNTIME) @Documented public @interface RedisRateLimiter { //往令牌桶放入令牌的速率 double value() default Double.MAX_VALUE; //获取令牌的超时时间 double limit() default Double.MAX_VALUE; }
5、自定义切面类 RedisLimiterAspect 类 ,修改扫描自己controller类
import com.imooc.annotation.RedisRateLimiter; import org.apache.commons.lang3.StringUtils; import org.aspectj.lang.ProceedingJoinPoint; import org.aspectj.lang.annotation.Around; import org.aspectj.lang.annotation.Aspect; import org.aspectj.lang.annotation.Pointcut; import org.aspectj.lang.reflect.MethodSignature; import org.assertj.core.util.Lists; import org.json.JSONObject; import org.slf4j.Logger; import org.slf4j.LoggerFactory; import org.springframework.beans.factory.annotation.Autowired; import org.springframework.core.io.ClassPathResource; import org.springframework.data.redis.core.StringRedisTemplate; import org.springframework.data.redis.core.script.DefaultRedisScript; import org.springframework.scripting.support.ResourceScriptSource; import org.springframework.stereotype.Component; import javax.annotation.PostConstruct; import javax.servlet.http.HttpServletResponse; import java.io.PrintWriter; import java.util.List; @Aspect @Component public class RedisLimiterAspect { @Autowired private HttpServletResponse response; /** * 注入redis操作类 */ @Autowired private StringRedisTemplate stringRedisTemplate; private DefaultRedisScript<List> redisScript; /** * 初始化 redisScript 类 * 返回值为 List */ @PostConstruct public void init(){ redisScript = new DefaultRedisScript<List>(); redisScript.setResultType(List.class); redisScript.setScriptSource(new ResourceScriptSource(new ClassPathResource("limit.lua"))); } public final static Logger log = LoggerFactory.getLogger(RedisLimiterAspect.class); @Pointcut("execution( public * com.zz.controller.*.*(..))") public void pointcut(){ } @Around("pointcut()") public Object process(ProceedingJoinPoint proceedingJoinPoint) throws Throwable { MethodSignature signature = (MethodSignature)proceedingJoinPoint.getSignature(); //使用Java 反射技术获取方法上是否有@RedisRateLimiter 注解类 RedisRateLimiter redisRateLimiter = signature.getMethod().getDeclaredAnnotation(RedisRateLimiter.class); if(redisRateLimiter == null){ //正常执行方法,执行正常业务逻辑 return proceedingJoinPoint.proceed(); } //获取注解上的参数,获取配置的速率 double value = redisRateLimiter.value(); double time = redisRateLimiter.limit(); //list设置lua的keys[1] //取当前时间戳到单位秒 String key = "ip:"+ System.currentTimeMillis() / 1000; List<String> keyList = Lists.newArrayList(key); //用户Mpa设置Lua 的ARGV[1] //List<String> argList = Lists.newArrayList(String.valueOf(value)); //调用脚本并执行 List result = stringRedisTemplate.execute(redisScript, keyList, String.valueOf(value),String.valueOf(time)); log.info("限流时间段内访问第:{} 次", result.toString()); //lua 脚本返回 "0" 表示超出流量大小,返回1表示没有超出流量大小 if(StringUtils.equals(result.get(0).toString(),"0")){ //服务降级 fullback(); return null; } // 没有限流,直接放行 return proceedingJoinPoint.proceed(); } /** * 服务降级方法 */ private void fullback(){ response.setCharacterEncoding("UTF-8"); response.setContentType("application/json; charset=utf-8"); PrintWriter writer = null; try { writer= response.getWriter(); JSONObject o = new JSONObject(); o.put("status",500); o.put("msg","Redis限流:请求太频繁,请稍后重试!"); o.put("data",null); writer.printf(o.toString() ); }catch (Exception e){ e.printStackTrace(); }finally { if(writer != null){ writer.close(); } } } }
6、在需要限流的类添加注解
import com.imooc.annotation.RedisRateLimiter; import io.swagger.annotations.Api; import io.swagger.annotations.ApiOperation; import org.springframework.web.bind.annotation.GetMapping; import org.springframework.web.bind.annotation.RequestMapping; import org.springframework.web.bind.annotation.RestController; import java.util.concurrent.TimeUnit; @RestController @Api(value = "限流", tags = {"限流测试接口"}) @RequestMapping("limiter") public class LimiterController { @ApiOperation(value = "Redis限流注解测试接口",notes = "Redis限流注解测试接口", httpMethod = "GET") @RedisRateLimiter(value = 10, limit = 1) @GetMapping("/redislimit") public IMOOCJSONResult redislimit(){ System.out.println("Redis限流注解测试接口"); return IMOOCJSONResult.ok(); } }
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