How to use redis to realize the like function

How to use Redis Realize the like function

This article mainly introduces “ How to use Redis Realize the like function ”, In daily operation , I believe many people are using Redis There are doubts about the implementation of the like function , Xiao Bian consulted all kinds of materials , Sort out simple and easy-to-use operation methods , I hope to answer ” How to use Redis Realize the like function ” Your doubts help ！ Next , Please follow Xiaobian to learn ！

MySQL and Redis Advantages and disadvantages

First, let's talk about the advantages and disadvantages of the two methods ： We use MySQL and Redis For example .

1、 Write directly to the database ：

advantage ： This method is easy to implement , Just complete the addition, deletion, modification and query of the database ;

shortcoming ： Database reading and writing pressure is high , If you encounter a situation where a lot of popular articles are liked in a short time , Directly operating the database will bring great pressure to the database , Affect efficiency .

2、 Use Redis cache ：

advantage ： High performance , Fast reading and writing , Ease the pressure of database reading and writing ;

shortcoming ： Development is complex , Data security cannot be guaranteed, i.e redis When you hang up, you lose data , At the same time, it is not synchronized in time redis Data in , May be in redis Memory replacement is eliminated . But we don't need to be so precise about the likes data , It's not a problem to lose a little data .

Next, we will introduce the like function in detail from the following three aspects

•Redis Cache design

• Database design

• Enable scheduled task persistence to database

1、Redis Cache design and implementation

Redis The integration of has been introduced in the last article , I won't go into details here . We learned that , We need to record the following types of data when making a compliment ： One is the detailed record of a user being liked by other users , One is . Considering the convenience of query and access , I use Hash Structure for storage , The storage structure is as follows ：

（1） A detailed record of a user being liked by other users ： MAP_USER_LIKED  Key value ,  Be liked by users id:: Like user id  by filed, 1 perhaps 0  by value

（2） Statistics on the number of likes of a user ： MAP_USER_LIKED_COUNT  Key value ,  Be liked by users id  by filed, count  by value

Part of the code is as follows

/***  Save the data that users like by other users to redis*/@Overridepublic void saveLiked2Redis(String likedUserId, String likedPostId) {    String key = RedisKeyUtils.getLikedKey(likedUserId, likedPostId);    redisTemplate.opsForHash().put(RedisKeyUtils.MAP_KEY_USER_LIKED,key, LikedStatusEnum.LIKE.getCode());}// Cancel likes @Overridepublic void unlikeFromRedis(String likedUserId, String likedPostId) {    String key = RedisKeyUtils.getLikedKey(likedUserId, likedPostId);    redisTemplate.opsForHash().put(RedisKeyUtils.MAP_KEY_USER_LIKED,key,LikedStatusEnum.UNLIKE.getCode());}/***  Number of users who will be liked +1*/@Overridepublic void incrementLikedCount(String likedUserId) {    redisTemplate.opsForHash().increment(RedisKeyUtils.MAP_KEY_USER_LIKED_COUNT,likedUserId,1);}//[email protected] void decrementLikedCount(String likedUserId) {    redisTemplate.opsForHash().increment(RedisKeyUtils.MAP_KEY_USER_LIKED_COUNT, likedUserId, -1);}/***  obtain Redis User likes details record in */@Overridepublic 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();        // Assemble into  UserLike  object         UserLikeDetail userLikeDetail = new UserLikeDetail(likedUserId, likedPostId, value);        list.add(userLikeDetail);        // Deposit in  list  From  Redis  Delete in         redisTemplate.opsForHash().delete(RedisKeyUtils.MAP_KEY_USER_LIKED, key);    }    return list;}/***  obtain Redis The number of users like in */@Overridepublic 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);        // Deposit in  list  From  Redis  Delete in         redisTemplate.opsForHash().delete(RedisKeyUtils.MAP_KEY_USER_LIKED_COUNT,key);    }    return list;}

Redis The storage structure is shown in the figure

2、 Database design

Here we can save the likes data to the database directly , Design two tables ：

(1) Detailed records of users being liked 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 ' Liked users id',  `liked_post_id` varchar(32) CHARACTER SET utf8 COLLATE utf8_general_ci NOT NULL COMMENT ' Thumb up users id',  `status` tinyint(1) NULL DEFAULT 1 COMMENT ' Like status ,0 Cancel ,1 give the thumbs-up ',  `create_time` timestamp(0) NOT NULL DEFAULT CURRENT_TIMESTAMP(0) COMMENT ' Creation time ',  `update_time` timestamp(0) NOT NULL DEFAULT CURRENT_TIMESTAMP(0) ON UPDATE CURRENT_TIMESTAMP(0) COMMENT ' Modification time ',  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 = ' The user likes the watch ' ROW_FORMAT = Dynamic;SET FOREIGN_KEY_CHECKS = 1;

(2) Statistics on the number of users being liked :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 scheduled task persistence to database

We use Quartz To achieve timing tasks , take Redis Store the data in the database , For demonstration , We can set one or two minutes to store data , This depends on the specific business . In the process of synchronizing data , First of all, we need to Redis The data in is checked in the database , Discard duplicate data , In this way, our data will be more accurate .

Part of the code is as follows

// Sync redis The user likes the data to the database @[email protected] void transLikedFromRedis2DB() {    List<UserLikeDetail> list = redisService.getLikedDataFromRedis();    list.stream().forEach(item->{        // Duplicate check         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);            // There is no record , Deposit directly into             userLikeDetail.setCreateTime(LocalDateTime.now());            userLikeDetailMapper.insert(userLikeDetail);        }else{            // Records , You need to update             userLikeDetail.setStatus(item.getStatus());            userLikeDetail.setUpdateTime(LocalDateTime.now());            userLikeDetailMapper.updateById(item);        }    });}@[email protected] void transLikedCountFromRedis2DB() {    List<UserLikCountDTO> list = redisService.getLikedCountFromRedis();    list.stream().forEach(item->{        UserLikeCount user = userLikeCountMapper.selectById(item.getKey());        // The number of likes is irrelevant , You don't need to throw exceptions if you make mistakes         if (user != null){            Integer likeNum = user.getLikeNum() + item.getValue();            user.setLikeNum(likeNum);            // Update likes             userLikeCountMapper.updateById(user);        }    });}

Here we are , About “ How to use Redis Realize the like function ” That's the end of my study , I hope we can solve your doubts . The combination of theory and practice can better help you learn , Let's try ！ If you want to continue to learn more related knowledge , Please continue to pay attention to Yisu cloud website , Xiaobian will continue to strive to bring you more practical articles ！