针对有人频繁重复调用接口，进行恶意访问，例如使用爬虫一类，对服务造成巨大负担，所以需要在接口上进行拦截。

这里使用自定义注解的方式进行拦截，方便重复调用使用。

我采用的是使用单机SpringBoot + Redis的方式进行处理，没有用分布式锁

Redis的配置需要自行配置

一、后端代码

1. 依赖引入 pom.xml和 yml配置文件中配置Redis

    org.springframework.boot
    spring-boot-starter-data-redis



    org.apache.commons
    commons-pool2

spring:
  # redis 配置
  redis:
    # 地址
    host: 127.0.0.1
    # 端口，默认为6379
    port: 6379
    # 数据库索引
    database: 0
    # 密码
    password: 123456
    # 连接超时时间
    timeout: 10s
    lettuce:
      pool:
        # 连接池中的最小空闲连接
        min-idle: 0
        # 连接池中的最大空闲连接
        max-idle: 8
        # 连接池的最大数据库连接数
        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: -1ms

 

2. redis部分

Redis工具类

Redis工具类，方便Redis的基础操作

import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.data.redis.core.BoundSetOperations;
import org.springframework.data.redis.core.HashOperations;
import org.springframework.data.redis.core.RedisTemplate;
import org.springframework.data.redis.core.ValueOperations;
import org.springframework.stereotype.Component;
import java.util.*;
import java.util.concurrent.TimeUnit;


@SuppressWarnings(value = {"unchecked", "rawtypes"})
@Component
public class Redisutil {

    @Autowired
    public RedisTemplate redisTemplate;

    
    public  void setCacheObject(final String key, final T value) {
        redisTemplate.opsForValue().set(key, value);
    }

    
    public  void setCacheObject(final String key, final T value, final Integer timeout, final TimeUnit timeUnit) {
        redisTemplate.opsForValue().set(key, value, timeout, timeUnit);
    }

    
    public boolean expire(final String key, final long timeout) {
        return expire(key, timeout, TimeUnit.SECONDS);
    }

    
    public boolean expire(final String key, final long timeout, final TimeUnit unit) {
        return redisTemplate.expire(key, timeout, unit);
    }

    
    public  T getCacheObject(final String key) {
        ValueOperations operation = redisTemplate.opsForValue();
        return operation.get(key);
    }

    
    public boolean deleteObject(final String key) {
        return redisTemplate.delete(key);
    }

    
    public long deleteObject(final Collection collection) {
        return redisTemplate.delete(collection);
    }

    
    public  long setCacheList(final String key, final List dataList) {
        Long count = redisTemplate.opsForList().rightPushAll(key, dataList);
        return count == null ? 0 : count;
    }

    
    public  List getCacheList(final String key) {
        return redisTemplate.opsForList().range(key, 0, -1);
    }

    
    public  BoundSetOperations setCacheSet(final String key, final Set dataSet) {
        BoundSetOperations setOperation = redisTemplate.boundSetOps(key);
        Iterator it = dataSet.iterator();
        while (it.hasNext()) {
            setOperation.add(it.next());
        }
        return setOperation;
    }

    
    public  Set getCacheSet(final String key) {
        return redisTemplate.opsForSet().members(key);
    }

    
    public  void setCacheMap(final String key, final Map dataMap) {
        if (dataMap != null) {
            redisTemplate.opsForHash().putAll(key, dataMap);
        }
    }

    
    public  Map getCacheMap(final String key) {
        return redisTemplate.opsForHash().entries(key);
    }

    
    public  void setCacheMapValue(final String key, final String hKey, final T value) {
        redisTemplate.opsForHash().put(key, hKey, value);
    }

    
    public  T getCacheMapValue(final String key, final String hKey) {
        HashOperations opsForHash = redisTemplate.opsForHash();
        return opsForHash.get(key, hKey);
    }

    
    public  List getMultiCacheMapValue(final String key, final Collection