Redis学习手册:掌握限流技术

发表时间: 2024-03-07 10:31

1. 引言

Redis作为一个内存数据库其读写速度非常快,并且支持原子操作,这使得它非常适合处理频繁的请求,一般情况下,我们会使用Redis作为缓存数据库,但处理做缓存数据库之外,Redis的应用还十分广泛,比如这一节,我们将讲解Redis在限流方面的应用。

2. 通过setnx实现限流

我们通过切面,来获取某给接口在一段时间内的请求次数,当请求次数超过某个值时,抛出限流异常,直接返回,不执行业务逻辑。思路大致如下:

2.1. 初步实现

我们参照上面的流程,对Redis限流进行实现。首先引入aop切面相关的依赖

  <dependency>            <groupId>org.springframework.boot</groupId>            <artifactId>spring-boot-starter-aop</artifactId>        </dependency>

然后添加一个限流注解类,这个注解有三个属性,maxTimes表示最大访问次数,interval表示限流间隙,unit表示时间的单位,假设配置的值为maxTimes=10, interval=1, unit= TimeUnit.SECONDS,那么表示在1秒内,限制访问次数为10次。

package org.example.annotations;import java.lang.annotation.ElementType;import java.lang.annotation.Retention;import java.lang.annotation.RetentionPolicy;import java.lang.annotation.Target;import java.util.concurrent.TimeUnit;@Target(value = ElementType.METHOD)@Retention(RetentionPolicy.RUNTIME)public @interface Limit {    // 访问次数    public int maxTimes() default 1;    // 间隔时间    public int interval() default 1;    // 时间单位    public TimeUnit unit() default TimeUnit.SECONDS;}

返回结果类:

package org.example.common;import lombok.Getter;import java.io.Serializable;public class Response <T>  implements Serializable {    @Getter    private int code;    @Getter    private String msg;    @Getter    private T data;    private Response(int code, String msg) {        this.code = code;        this.msg = msg;    }    private Response(int code, String msg, T data) {        this.code = code;        this.msg = msg;        this.data = data;    }    private Response(ResultCode resultCode) {        this.code = resultCode.getCode();        this.msg = resultCode.getMsg();    }    private Response(ResultCode resultCode, T data) {        this.code = resultCode.getCode();        this.msg = resultCode.getMsg();        this.data = data;    }    public static <T> Response success() {        return new Response(ResultCode.SUCCESS);    }    public static <T> Response success(T data) {        return new Response(ResultCode.SUCCESS, data);    }    public static <T> Response fail() {        return new Response(ResultCode.FAIL);    }    public static <T> Response fail(ResultCode resultCode) {        return new Response(resultCode);    }    public static <T> Response error() {        return new Response(ResultCode.SERVER_ERROR);    }    public static <T> Response error(String msg) {        return new Response(ResultCode.SERVER_ERROR.getCode(), msg);    }}

错误码类,在错误码中,我们添加一个LIMIT_ERROR,表示该接口被限流。

package org.example.common;public enum ResultCode {    SUCCESS(200, "操作成功"),    FAIL(400, "操作失败"),    SERVER_ERROR(500, "服务器错误"),    LIMIT_ERROR(400, "限流");    int code;    String msg;    ResultCode(int code, String msg) {        this.code = code;        this.msg = msg;    }    public int getCode() {        return this.code;    }    public String getMsg() {        return this.msg;    }}

业务异常类

public class BusinessException extends RuntimeException {    private ResultCode resultCode;    public BusinessException(ResultCode resultCode) {        super(resultCode.getMsg());        this.resultCode = resultCode;    }    public ResultCode getResultCode() {        return this.resultCode;    }}

全局异常处理类,在我们的切面中,如果发现访问次数大于最大访问次数,那么抛出限流异常,由全局异常处理类进行处理,返回对应的结果

package org.example.exception;import org.example.common.Response;import org.springframework.web.bind.annotation.ExceptionHandler;import org.springframework.web.bind.annotation.RestControllerAdvice;@RestControllerAdvicepublic class GlobalExceptionHandler {    @ExceptionHandler(value = BusinessException.class)    public Response handleBusinessException(BusinessException e) {        return Response.fail(e.getResultCode());    }    @ExceptionHandler(value = Exception.class)    public Response handleException(Exception e) {        return Response.error(e.getMessage());    }}

限流切面类

package org.example.aspect;import org.aspectj.lang.JoinPoint;import org.aspectj.lang.annotation.Aspect;import org.aspectj.lang.annotation.Before;import org.aspectj.lang.annotation.Pointcut;import org.aspectj.lang.reflect.MethodSignature;import org.example.annotations.Limit;import org.example.common.ResultCode;import org.example.exception.BusinessException;import org.example.util.RedisUtils;import org.springframework.beans.factory.annotation.Autowired;import org.springframework.stereotype.Component;@Component@Aspectpublic class LimitAspect {    @Autowired    private RedisUtils redisUtils;    @Pointcut("@annotation(org.example.annotations.Limit)")    public void pointCut() {    }    @Before("pointCut()")    public void beforeAdvice(JoinPoint joinPoint) {        // 获取方法名        String methodName = joinPoint.getSignature().getName();        String prefixMethod = joinPoint.getSignature().getDeclaringTypeName();        String fullMethodName = prefixMethod + "." + methodName;        System.out.println("methodName:" + fullMethodName);        Object[] args = joinPoint.getArgs();        for (Object arg : args) {            System.out.println("method argument:" + arg);        }        // 获取注解参数        MethodSignature methodSignature = (MethodSignature) joinPoint.getSignature();        Limit annotation = methodSignature.getMethod().getAnnotation(Limit.class);        System.out.println(annotation.unit());        System.out.println(annotation.maxTimes());        System.out.println(annotation.interval());        // 获取redis值        Object key = redisUtils.getKey(fullMethodName);        if (key != null) {            Integer redisValue = (Integer) key;            // 小于限流值            if (redisValue.compareTo(annotation.maxTimes()) < 0) {                redisUtils.increment(fullMethodName);                return;            }            // 大于限流值            throw new BusinessException(ResultCode.LIMIT_ERROR);        }        // 获取的值为null, 设置数据到redis中        redisUtils.addKey(fullMethodName, 1, annotation.interval(), annotation.unit());    }}

最后添加一个TestController类,用于进行接口的测试:

package org.example.controller;import org.example.annotations.Limit;import org.example.common.Response;import org.example.common.ResultCode;import org.example.exception.BusinessException;import org.springframework.web.bind.annotation.GetMapping;import org.springframework.web.bind.annotation.RequestMapping;import org.springframework.web.bind.annotation.RequestParam;import org.springframework.web.bind.annotation.RestController;import java.util.concurrent.TimeUnit;@RestController@RequestMapping(value = "/test")public class TestController {    @GetMapping(value = "/hello1")    @Limit(maxTimes = 10, interval = 100, unit = TimeUnit.SECONDS)    public Response hello1(@RequestParam(name = "name", defaultValue = "cxy") String name) {        return Response.success("hello1 success " + name);    }}

从上面的接口注解配置中,可以看出,这个接口在100秒内最多访问10次,我们启动项目,访问/test/hello1,前10次的访问结果为:

第11次时,开始限流了

这里看起来不是很直观,我们将时间间隙改为2,表示2秒最多由10个请求能执行

@GetMapping(value = "/hello1")    @Limit(maxTimes = 10, interval = 2, unit = TimeUnit.SECONDS)    public Response hello1(@RequestParam(name = "name", defaultValue = "cxy") String name) {        return Response.success("hello1 success " + name);    }

使用postman进行并发请求,下面的redis限流测试,就是刚才提到的
http://localhost:8080/test/hello1?name=cxy这个请求

执行该并发测试,结果如下:

这里20个请求中,有10个成功,10个被限流。不过这个postman结果展示不太好,只能一个一个查看结果,这里就不一一展示了。

2.2. 职责分离

上面的代码,虽然能成功限流,但是有一个问题,就是切面类的beforeAdvice方法中,做的事情太多了,又是解析请求参数、解析注解参数,又是使用查询Redis,进行限流判断,我们应该将限流逻辑的判断,此外,这里使用的是Redis,如果后续我们不使用Redis,换成其他方式进行限流判断的话,需要改很多处代码,因此,这里要做一些优化,包括:

1)定义限流请求类,用于封装访问的方法名、注解信息等内容

2)定义限流处理接口

3)定义Redis限流处理类,通过Redis实现限流处理接口

我们首先定义一个限流请求类,封装限流处理所需要的参数:

package org.example.request;import lombok.Data;import java.io.Serializable;import java.util.HashMap;import java.util.Map;import java.util.concurrent.TimeUnit;@Datapublic class LimitRequest implements Serializable {    private String methodName;    private Integer interval;    private Integer maxTimes;    private TimeUnit timeUnit;    private Map<String, Object> extendMap = new HashMap<>();}

定义限流处理接口

package org.example.limit;import org.example.request.limit.LimitRequest;public interface LimitHandler {    void handleLimit(LimitRequest limitRequest);}

定义Redis的限流处理类

package org.example.limit;import org.example.common.ResultCode;import org.example.exception.BusinessException;import org.example.request.limit.LimitRequest;import org.example.util.RedisUtils;import org.springframework.beans.factory.annotation.Autowired;import org.springframework.stereotype.Component;@Componentpublic class RedisLimitHandler implements LimitHandler{    @Autowired    private RedisUtils redisUtils;    @Override    public void handleLimit(LimitRequest limitRequest) {        String methodName = limitRequest.getMethodName();        // 获取redis值        Object key = redisUtils.getKey(methodName);        if (key != null) {            Integer redisValue = (Integer) key;            // 小于限流值            if (redisValue.compareTo(limitRequest.getMaxTimes()) <= 0) {                redisUtils.increment(methodName);                return;            }            // 大于限流值            throw new BusinessException(ResultCode.LIMIT_ERROR);        }        // 获取的值为null, 设置数据到redis中        redisUtils.addKey(methodName, 1, limitRequest.getInterval(), limitRequest.getTimeUnit());    }}

修改LimitAspect代码,但后续更换限流策略是,只需要修改LimitHandler的bean即可。

package org.example.aspect;import org.aspectj.lang.JoinPoint;import org.aspectj.lang.annotation.Aspect;import org.aspectj.lang.annotation.Before;import org.aspectj.lang.annotation.Pointcut;import org.aspectj.lang.reflect.MethodSignature;import org.example.annotations.Limit;import org.example.limit.LimitHandler;import org.example.request.limit.LimitRequest;import org.springframework.stereotype.Component;import javax.annotation.Resource;@Component@Aspectpublic class LimitAspect {    @Resource    private LimitHandler redisLimitHandler;    @Pointcut("@annotation(org.example.annotations.Limit)")    public void pointCut() {    }    @Before("pointCut()")    public void beforeAdvice(JoinPoint joinPoint) {        LimitRequest limitRequest = convert2LimitRequest(joinPoint);        redisLimitHandler.handleLimit(limitRequest);    }    private LimitRequest convert2LimitRequest(JoinPoint joinPoint) {        LimitRequest limitRequest = new LimitRequest();        String methodName = joinPoint.getSignature().getName();        String prefixMethod = joinPoint.getSignature().getDeclaringTypeName();        limitRequest.setMethodName(prefixMethod + "." + methodName);        Object[] args = joinPoint.getArgs();        limitRequest.getExtendMap().put("args", args);        MethodSignature methodSignature = (MethodSignature) joinPoint.getSignature();        Limit annotation = methodSignature.getMethod().getAnnotation(Limit.class);        limitRequest.setInterval(annotation.interval());        limitRequest.setMaxTimes(annotation.maxTimes());        limitRequest.setTimeUnit(annotation.unit());        return limitRequest;    }}


3. 通过Zset实现限流

我们可以将请求打造成一个zset数组,每一次请求进来时,value保持一致,可以用UUID生成,然后score用当前时间戳表示,通过range方法,来获取某个时间范围内,请求的个数,然后根据这个个数与限流值对比,当大于限流值时,进行限流操作。

我们修改RedisLimitHandler代码如下:

 @Override    public void handleLimit(LimitRequest limitRequest) {       handleLimitByZSet(limitRequest);    }    private void handleLimitByZSet(LimitRequest limitRequest) {        String methodName = limitRequest.getMethodName();        long currentTime = System.currentTimeMillis();        long interval = TimeUnit.MILLISECONDS.convert(limitRequest.getInterval(), limitRequest.getTimeUnit());        if (redisUtils.hasKey(methodName)) {            int count = redisUtils.rangeByScore(methodName, Double.valueOf(currentTime - interval), Double.valueOf(currentTime)).size();            if (count > limitRequest.getMaxTimes()) {                throw new BusinessException(ResultCode.LIMIT_ERROR);            }        }        redisUtils.addZSet(methodName, UUID.randomUUID().toString(), Double.valueOf(currentTime));    }

然后添加一个测试类,用于模拟并发场景下的多个请求

package org.example;import com.alibaba.fastjson.JSONObject;import org.example.common.Response;import org.example.common.ResultCode;import org.example.controller.TestController;import org.example.exception.BusinessException;import org.junit.jupiter.api.Test;import org.springframework.beans.factory.annotation.Autowired;import org.springframework.boot.test.context.SpringBootTest;import java.util.ArrayList;import java.util.List;import java.util.concurrent.*;@SpringBootTestpublic class RedisLimitTest {    @Autowired    private TestController testController;    @Test    public void testLimit() throws ExecutionException, InterruptedException {        ExecutorService executorService = Executors.newFixedThreadPool(5);        Callable<Response> callable = () -> {            try {                String name = "cxy";                return testController.hello1(name);            } catch (BusinessException e) {                return Response.fail(e.getResultCode());            }        };        List<Future<Response>> futureList = new ArrayList<>();        for (int i = 0; i < 20; i++) {            Future<Response> submit = executorService.submit(callable);            futureList.add(submit);        }        for (Future<Response> future : futureList) {            System.out.println(JSONObject.toJSONString(future.get()));        }    }}

运行结果如下:

我们可以看到,这里确实进行限流了,但是,这个限流个数不太对,这是因为可能多个请求都执行到这条代码,获取到同一个值,然后才进行更新。

int count = redisUtils.rangeByScore(methodName, Double.valueOf(currentTime - interval), Double.valueOf(currentTime)).size();

比如有5个请求同时打过来,此时的执行到上面这条代码时,redis中符合范围的刚好有9条,那么这5个请求在进行判断时,都小于限流值,因此都会执行,然后才是更新zset,这个就是并发场景下的问题了。

另外,使用zset还有一个问题,它虽然能达到滑动窗口的效果,但是zset的数据结构会越来越大。