How to use Redis to implement the like function

MySQL and Redis Advantages and Disadvantages

First of all, let’s talk about the advantages and disadvantages of the two methods: Let’s take MySQL and Redis as examples.

1. Directly write to the database:

Advantages: This method is simple to implement, and only needs to complete the addition, deletion, modification and query of the database;

Disadvantages: The database is under great pressure to read and write , if a popular article receives a large number of likes in a short period of time, directly operating the database will put great pressure on the database and affect efficiency.

2. Use Redis cache:

Advantages: high performance, fast reading and writing speed, alleviating the pressure of database reading and writing;

Disadvantages: complex development, data security cannot be guaranteed The problem is that data will be lost when redis hangs. At the same time, if the data in redis is not synchronized in time, it may be eliminated when redis memory is replaced. However, we don’t need to be so precise about the like data, and losing a little data is not a big problem.

Next, we will give a detailed introduction to the like function from the following three aspects

•Redis cache design

•Database design

•Enable persistent storage of scheduled tasks to the database

1. Redis cache design and implementation

We have introduced how to integrate Redis in the previous article, here I won’t repeat the explanation again. We understand that when performing a like operation, the following data need to be recorded: detailed records of users being liked by other users and records of like operations. In order to facilitate query and access, I used a Hash structure for storage. The storage structure is as follows:

(1) Detailed records of a user being liked by other users: MAP_USER_LIKED is the key value , Liked user id:: Liked user id is filed, 1 or 0 is value

(2) Statistics of the number of likes for a user: MAP_USER_LIKED_COUNT is the key value, and is Like user id is filed, count is value

Part of the code is as follows

/**
* 将用户被其他用户点赞的数据存到redis
*/
@Override
public void saveLiked2Redis(String likedUserId, String likedPostId) {
    String key = RedisKeyUtils.getLikedKey(likedUserId, likedPostId);
    redisTemplate.opsForHash().put(RedisKeyUtils.MAP_KEY_USER_LIKED,key, LikedStatusEnum.LIKE.getCode());
}

//取消点赞
@Override
public void unlikeFromRedis(String likedUserId, String likedPostId) {
    String key = RedisKeyUtils.getLikedKey(likedUserId, likedPostId);
    redisTemplate.opsForHash().put(RedisKeyUtils.MAP_KEY_USER_LIKED,key,LikedStatusEnum.UNLIKE.getCode());
}

/**
* 将被点赞用户的数量+1
*/
@Override
public void incrementLikedCount(String likedUserId) {
    redisTemplate.opsForHash().increment(RedisKeyUtils.MAP_KEY_USER_LIKED_COUNT,likedUserId,1);
}

//-1
@Override
public void decrementLikedCount(String likedUserId) {
    redisTemplate.opsForHash().increment(RedisKeyUtils.MAP_KEY_USER_LIKED_COUNT, likedUserId, -1);
}

/**
* 获取Redis中的用户点赞详情记录
*/
@Override
public List<UserLikeDetail> getLikedDataFromRedis() {
    Cursor<Map.Entry<Object,Object>> scan = redisTemplate.opsForHash().scan(RedisKeyUtils.MAP_KEY_USER_LIKED, ScanOptions.NONE);
    List<UserLikeDetail> list = new ArrayList<>();
    while (scan.hasNext()){
        Map.Entry<Object, Object> entry = scan.next();
        String key = (String) entry.getKey();
        String[] split = key.split("::");
        String likedUserId = split[0];
        String likedPostId = split[1];
        Integer value = (Integer) entry.getValue();
        //组装成 UserLike 对象
        UserLikeDetail userLikeDetail = new UserLikeDetail(likedUserId, likedPostId, value);
        list.add(userLikeDetail);
        //存到 list 后从 Redis 中删除
        redisTemplate.opsForHash().delete(RedisKeyUtils.MAP_KEY_USER_LIKED, key);
    }
    return list;
}

/**
* 获取Redis中的用户被点赞数量
*/
@Override
public List<UserLikCountDTO> getLikedCountFromRedis() {
    Cursor<Map.Entry<Object,Object>> cursor = redisTemplate.opsForHash().scan(RedisKeyUtils.MAP_KEY_USER_LIKED_COUNT, ScanOptions.NONE);
    List<UserLikCountDTO> list = new ArrayList<>();
    while(cursor.hasNext()){
        Map.Entry<Object, Object> map = cursor.next();
        String key = (String) map.getKey();
        Integer value = (Integer) map.getValue();
        UserLikCountDTO userLikCountDTO = new UserLikCountDTO(key,value);
        list.add(userLikCountDTO);
        //存到 list 后从 Redis 中删除
        redisTemplate.opsForHash().delete(RedisKeyUtils.MAP_KEY_USER_LIKED_COUNT,key);
    }
    return list;
}

Redis storage structure is as shown

2. Database design

Here we can design two tables just like directly saving the like data to the database:

(1) User Detailed records of likes by other users: user_like_detail

DROP TABLE IF EXISTS `user_like_detail`;
CREATE TABLE `user_like_detail`  (
  `id` int(11) NOT NULL AUTO_INCREMENT,
  `liked_user_id` varchar(32) CHARACTER SET utf8 COLLATE utf8_general_ci NOT NULL COMMENT '被点赞的用户id',
  `liked_post_id` varchar(32) CHARACTER SET utf8 COLLATE utf8_general_ci NOT NULL COMMENT '点赞的用户id',
  `status` tinyint(1) NULL DEFAULT 1 COMMENT '点赞状态，0取消，1点赞',
  `create_time` timestamp(0) NOT NULL DEFAULT CURRENT_TIMESTAMP(0) COMMENT '创建时间',
  `update_time` timestamp(0) NOT NULL DEFAULT CURRENT_TIMESTAMP(0) ON UPDATE CURRENT_TIMESTAMP(0) COMMENT '修改时间',
  PRIMARY KEY (`id`) USING BTREE,
  INDEX `liked_user_id`(`liked_user_id`) USING BTREE,
  INDEX `liked_post_id`(`liked_post_id`) USING BTREE
) ENGINE = InnoDB AUTO_INCREMENT = 7 CHARACTER SET = utf8 COLLATE = utf8_general_ci COMMENT = '用户点赞表' ROW_FORMAT = Dynamic;

SET FOREIGN_KEY_CHECKS = 1;

(2) Statistics of the number of likes by users: user_like_count

DROP TABLE IF EXISTS `user_like_count`;
CREATE TABLE `user_like_count`  (
  `id` int(11) NOT NULL AUTO_INCREMENT,
  `like_num` int(11) NULL DEFAULT 0,
  PRIMARY KEY (`id`) USING BTREE
) ENGINE = InnoDB AUTO_INCREMENT = 7 CHARACTER SET = utf8 COLLATE = utf8_general_ci ROW_FORMAT = Dynamic;

SET FOREIGN_KEY_CHECKS = 1;

3. Enable persistent storage of scheduled tasks to the database

We use Quartz to implement scheduled tasks and store data in Redis into the database. In order to demonstrate the effect, we can set the data to be stored once in one minute or two minutes, depending on the specific business. In the process of synchronizing data, we must first check the data in Redis in the database and discard duplicate data, so that our data will be more accurate.

Part of the code is as follows

//同步redis的用户点赞数据到数据库
@Override
@Transactional
public void transLikedFromRedis2DB() {
    List<UserLikeDetail> list = redisService.getLikedDataFromRedis();
    list.stream().forEach(item->{
        //查重
        UserLikeDetail userLikeDetail = userLikeDetailMapper.selectOne(new LambdaQueryWrapper<UserLikeDetail>()
           .eq(UserLikeDetail::getLikedUserId, item.getLikedUserId())
           .eq(UserLikeDetail::getLikedPostId, item.getLikedPostId()));
        if (userLikeDetail == null){
            userLikeDetail = new UserLikeDetail();
            BeanUtils.copyProperties(item, userLikeDetail);
            //没有记录，直接存入
            userLikeDetail.setCreateTime(LocalDateTime.now());
            userLikeDetailMapper.insert(userLikeDetail);
        }else{
            //有记录，需要更新
            userLikeDetail.setStatus(item.getStatus());
            userLikeDetail.setUpdateTime(LocalDateTime.now());
            userLikeDetailMapper.updateById(item);
        }
    });
}

@Override
@Transactional
public void transLikedCountFromRedis2DB() {
    List<UserLikCountDTO> list = redisService.getLikedCountFromRedis();
    list.stream().forEach(item->{
        UserLikeCount user = userLikeCountMapper.selectById(item.getKey());
        //点赞数量属于无关紧要的操作，出错无需抛异常
        if (user != null){
            Integer likeNum = user.getLikeNum() + item.getValue();
            user.setLikeNum(likeNum);
            //更新点赞数量
            userLikeCountMapper.updateById(user);
        }
    });
}

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