Java后端防止频繁请求、重复提交

在客户端网络慢或者服务器响应慢时，用户有时是会频繁刷新页面或重复提交表单的，这样是会给服务器造成不小的负担的，同时在添加数据时有可能造成不必要的麻烦。所以我们在后端也有必要进行防抖操作。

1. 自定义注解

/**
 * @author Tzeao
 */
@Retention(RetentionPolicy.RUNTIME)
@Target(ElementType.METHOD)
public @interface NoRepeatSubmit {

    // 名称，如果不给就是要默认的
    String name() default "name";
}

2. 使用AOP实现该注解

/**
 * @author Tzeao
 */
@Aspect
@Component
public class NoRepeatSubmitAop {

    @Autowired
    private RedisService redisService;

    private Logger logger = LoggerFactory.getLogger(NoRepeatSubmitAop.class);

    /**
     * 切入点
     */
    @Pointcut("@annotation(com.qwt.part_time_admin_api.common.validation.NoRepeatSubmit)")
    public void pt() {
    }

    @Around("pt()")
    public Object around(ProceedingJoinPoint joinPoint) throws Throwable {

        ServletRequestAttributes attributes = (ServletRequestAttributes) RequestContextHolder.getRequestAttributes();
        assert attributes != null;
        HttpServletRequest request = attributes.getRequest();
        // 这里是唯一标识，根据情况而定
        String key = "1" + "-" + request.getServletPath();
        // 如果缓存中有这个url视为重复提交
        if (!redisService.hasKey(key)) {
            // 通过，执行下一步
            Object result = joinPoint.proceed();
            // 然后存入redis，并且设置15s倒计时
            redisService.setCacheObject(key, 0, 15, TimeUnit.SECONDS);
            // 返回结果
            return result;
        } else {
            logger.error("请勿重复提交或者操作过于频繁！");
            return Result.fail(400, "请勿重复提交或者操作过于频繁！");
        }
    }
}

3. serice，也可以放在工具包里面，这里我们使用到了Redis来对key和标识码进行存储和倒计时,所以在使用时还需要连接一下Redis

package com.qwt.part_time_admin_api.service;

import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.data.redis.core.*;
import org.springframework.stereotype.Component;

import java.util.*;
import java.util.concurrent.TimeUnit;

/**
 * Redis操作服务类
 */
@Component
public class RedisService {

    @Autowired
    public RedisTemplate<String, Object> redisTemplate;

    /**
     * 缓存基本的对象，Integer、String、实体类等
     *
     * @param key   缓存的键值
     * @param value 缓存的值
     * @return 缓存的对象
     */
    public <T> ValueOperations<String, T> setCacheObject(String key, T value) {
        ValueOperations<String, T> operation = redisTemplate.opsForValue();
        operation.set(key, value);
        return operation;
    }

    /**
     * 缓存基本的对象，Integer、String、实体类等
     *
     * @param key      缓存的键值
     * @param value    缓存的值
     * @param timeout  时间
     * @param timeUnit 时间颗粒度
     * @return 缓存的对象
     */
    public <T> ValueOperations<String, T> setCacheObject(String key, T value, Integer timeout, TimeUnit timeUnit) {
        ValueOperations<String, T> operation = redisTemplate.opsForValue();
        operation.set(key, value, timeout, timeUnit);
        return operation;
    }

    /**
     * 获得缓存的基本对象。
     *
     * @param key 缓存键值
     * @return 缓存键值对应的数据
     */
    public <T> T getCacheObject(String key) {
        ValueOperations<String, T> operation = redisTemplate.opsForValue();
        return operation.get(key);
    }

    /**
     * 删除单个对象
     *
     * @param key
     */
    public void deleteObject(String key) {
        redisTemplate.delete(key);
    }

    /**
     * 删除集合对象
     *
     * @param collection
     */
    public void deleteObject(Collection<String> collection) {
        redisTemplate.delete(collection);
    }

    /**
     * 缓存List数据
     *
     * @param key      缓存的键值
     * @param dataList 待缓存的List数据
     * @return 缓存的对象
     */
    public <T> ListOperations<String, T> setCacheList(String key, List<T> dataList) {
        ListOperations<String, T> listOperation = redisTemplate.opsForList();
        if (null != dataList) {
            int size = dataList.size();
            for (int i = 0; i < size; i++) {
                listOperation.leftPush(key, dataList.get(i));
            }
        }
        return listOperation;
    }

    /**
     * 获得缓存的list对象
     *
     * @param key 缓存的键值
     * @return 缓存键值对应的数据
     */
    public <T> List<T> getCacheList(String key) {
        List<T> dataList = new ArrayList<>();
        ListOperations<String, T> listOperation = redisTemplate.opsForList();
        Long size = listOperation.size(key);

        for (int i = 0; i < size; i++) {
            dataList.add(listOperation.index(key, i));
        }
        return dataList;
    }

    /**
     * 缓存Set
     *
     * @param key     缓存键值
     * @param dataSet 缓存的数据
     * @return 缓存数据的对象
     */
    public <T> BoundSetOperations<String, T> setCacheSet(String key, Set<T> dataSet) {
        BoundSetOperations<String, T> setOperation = redisTemplate.boundSetOps(key);
        Iterator<T> it = dataSet.iterator();
        while (it.hasNext()) {
            setOperation.add(it.next());
        }
        return setOperation;
    }

    /**
     * 获得缓存的set
     *
     * @param key
     * @return
     */
    public <T> Set<T> getCacheSet(String key) {
        Set<T> dataSet = new HashSet<>();
        BoundSetOperations<String, T> operation = redisTemplate.boundSetOps(key);
        dataSet = operation.members();
        return dataSet;
    }

    /**
     * 缓存Map
     *
     * @param key
     * @param dataMap
     * @return
     */
    public <T> HashOperations<String, String, T> setCacheMap(String key, Map<String, T> dataMap) {
        HashOperations<String, String, T> hashOperations = redisTemplate.opsForHash();
        if (null != dataMap) {
            for (Map.Entry<String, T> entry : dataMap.entrySet()) {
                hashOperations.put(key, entry.getKey(), entry.getValue());
            }
        }
        return hashOperations;
    }

    /**
     * 获得缓存的Map
     *
     * @param key
     * @return
     */
    public <T> Map<String, T> getCacheMap(String key) {
        Map<String, T> map = redisTemplate.opsForHash().entries(key);
        return map;
    }

    /**
     * 获得缓存的基本对象列表
     *
     * @param pattern 字符串前缀
     * @return 对象列表
     */
    public Collection<String> keys(String pattern) {
        return redisTemplate.keys(pattern);
    }

    /**
     * 判断key是否存在
     *
     * @param key
     * @return
     */
    public boolean hasKey(String key) {
        return redisTemplate.hasKey(key);
    }

    /**
     * 获取key的过期时间
     *
     * @param key
     * @return
     */
    public Long getExpire(String key) {
        return redisTemplate.getExpire(key);
    }

    /**
     * 缓存list<Map<String, Object>>
     *
     * @param key      缓存的键值
     * @param value    缓存的值
     * @param timeout  时间
     * @param timeUnit 时间颗粒度
     * @return 缓存的对象
     */
    public <T> ValueOperations<String, T> setBillObject(String key, List<Map<String, Object>> value, Integer timeout, TimeUnit timeUnit) {
        ValueOperations<String, T> operation = redisTemplate.opsForValue();
        operation.set(key, (T) value, timeout, timeUnit);
        return operation;
    }

    /**
     * 缓存Map
     *
     * @param key
     * @param dataMap
     * @return
     */
    public <T> HashOperations<String, String, T> setCKdBillMap(String key, Map<String, T> dataMap) {
        HashOperations<String, String, T> hashOperations = redisTemplate.opsForHash();
        if (null != dataMap) {
            for (Map.Entry<String, T> entry : dataMap.entrySet()) {
                hashOperations.put(key, entry.getKey(), entry.getValue());
            }
        }
        return hashOperations;
    }
}

4. 测试

@NoRepeatSubmit(name = "test") // 也可以不给名字，这样就会走默认名字
@GetMapping("test")
public Result test() {
    // TODO: 添加测试逻辑代码 可忽略不计
    return Result.success("测试阶段！");
}

15秒内重复点击就会给提示

这样一个简单的防止重复提交的功能就完成了，其中肯定还有瑕疵，也希望指出来