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名师互学网 > IT > 软件开发 > 后端开发 > Java

Java使用Springboot+Redis实现点赞功能

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Java使用Springboot+Redis实现点赞功能

1. 流程图 流程图

实现思路 由于点赞属于一种频繁的提交操作,如果直接选用数据库做存储,对于数据库的压力比较大。这里考虑使用缓存作为中间层,然后定时的将数据持久化数据库,降低数据库的读写压力。缓存选用的是redis。 2. 具体实现 2.1 表设计 点赞表
CREATE TABLE `user_likes` (
  `id` varchar(32) CHARACTER SET utf8mb4 COLLATE utf8mb4_general_ci NOT NULL COMMENT '点赞信息ID',
  `info_id` varchar(32) CHARACTER SET utf8mb4 COLLATE utf8mb4_general_ci DEFAULT NULL COMMENT '点赞对象id',
  `create_time` datetime DEFAULT NULL COMMENT '时间',
  `like_user_id` varchar(32) CHARACTER SET utf8mb4 COLLATE utf8mb4_general_ci DEFAULT NULL COMMENT '点赞人ID',
  `update_time` datetime DEFAULT NULL,
  `status` int DEFAULT '0' COMMENT '0 取消 1 点赞',
  PRIMARY KEY (`id`) USING BTREE,
  UNIQUE KEY `agdkey` (`like_user_id`,`info_id`) USING BTREE
) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COLLATE=utf8mb4_general_ci ROW_FORMAT=DYNAMIC COMMENT='点赞记录表';
点赞的内容表
CREATE TABLE `video` (
  `id` varchar(32) CHARACTER SET utf8 COLLATE utf8_general_ci NOT NULL,
  `likes_number` int DEFAULT NULL COMMENT '点赞数',
  `comments_number` int DEFAULT NULL COMMENT '评论数',
  `share_number` int DEFAULT NULL COMMENT '分享数',
  `create_time` datetime DEFAULT NULL COMMENT '创建时间',
  `create_user` varchar(50) CHARACTER SET utf8 COLLATE utf8_general_ci DEFAULT NULL COMMENT '创建者',
  `update_time` datetime DEFAULT NULL COMMENT '更新时间',
  `update_user` varchar(50) CHARACTER SET utf8 COLLATE utf8_general_ci DEFAULT NULL COMMENT '更新者',
  PRIMARY KEY (`id`) USING BTREE
) ENGINE=InnoDB DEFAULT CHARSET=utf8mb3 ROW_FORMAT=DYNAMIC;
2.2 工具类及枚举类 RedisKeyUtils
public class RedisKeyUtils {
    
    public static final String MAP_KEY_USER_LIKED = "MAP_USER_LIKED";
    
    public static final String MAP_KEY_USER_LIKED_COUNT = "MAP_USER_LIKED_COUNT";

    
    public static String getLikedKey(String likedUserId, String likedPostId){
            return likedUserId +
                "::" +
                likedPostId;
    }
RedisConfig
@Configuration
public class RedisConfig {
    @Bean
    @ConditionalOnMissingBean(name = "redisTemplate")
    public RedisTemplate redisTemplate(RedisConnectionFactory redisConnectionFactory)  {

        Jackson2JsonRedisSerializer jackson2JsonRedisSerializer = new Jackson2JsonRedisSerializer(Object.class);
        ObjectMapper om = new ObjectMapper();
        om.setVisibility(PropertyAccessor.ALL, JsonAutoDetect.Visibility.ANY);
        om.enableDefaultTyping(ObjectMapper.DefaultTyping.NON_FINAL);
        jackson2JsonRedisSerializer.setObjectMapper(om);

        RedisTemplate template = new RedisTemplate();
        template.setConnectionFactory(redisConnectionFactory);
        template.setKeySerializer(jackson2JsonRedisSerializer);
        template.setValueSerializer(jackson2JsonRedisSerializer);
        template.setHashKeySerializer(jackson2JsonRedisSerializer);
        template.setHashValueSerializer(jackson2JsonRedisSerializer);
        template.afterPropertiesSet();
        return template;
    }


    @Bean
    @ConditionalOnMissingBean(StringRedisTemplate.class)
    public StringRedisTemplate stringRedisTemplate(RedisConnectionFactory redisConnectionFactory)  {
        StringRedisTemplate template = new StringRedisTemplate();
        template.setConnectionFactory(redisConnectionFactory);
        return template;
    }
}
 
UserLikesDto 
@Data
@AllArgsConstructor
@NoArgsConstructor
public class UserLikesDto {
    private String infoId;
    private String likeUserId;
    private Integer status;

}
UserLikCountDTO
@Data
public class UserLikCountDTO implements Serializable {

    private String infoId;
    private Integer value;

    public UserLikCountDTO(String infoId, Integer value) {
        this.infoId = infoId;
        this.value = value;
    }
}
2.3 代码实现 使用redisTemplate.opsForHash()方法,创建2个hash对象,一个存储点赞信息,一个存储点赞数。点赞信息的key是通过内容id拼接点赞者id拼接而成,value则为点赞状态。例如(1::2,0) likeStatus方法
public Object likeStatus(String infoId, String likeUserId) {
    if (redisTemplate.opsForHash().hasKey(RedisKeyUtils.MAP_KEY_USER_LIKED, RedisKeyUtils.getLikedKey(infoId, likeUserId))) {
        String o = redisTemplate.opsForHash().get(RedisKeyUtils.MAP_KEY_USER_LIKED, RedisKeyUtils.getLikedKey(infoId, likeUserId)).toString();
        if ("1".equals(o)) {
            unLikes(infoId, likeUserId);
            return LikedStatusEum.UNLIKE;
        }
        if ("0".equals(o)) {
            likes(infoId, likeUserId);
            return LikedStatusEum.LIKE;
        }
    }
    UserLikes userLikes = userLikesDao.selectOne(new QueryWrapper().eq("info_id", infoId).eq("like_user_id", likeUserId));
    if (userLikes == null) {
        UserLikes userLikes1 = new UserLikes();
        userLikes1.setInfoId(infoId);
        userLikes1.setLikeUserId(likeUserId);
        userLikesDao.insert(userLikes1);
        likes(infoId, likeUserId);
        return LikedStatusEum.LIKE;
    }
    if (userLikes.getStatus() == 1) {
        unLikes(infoId, likeUserId);
        return LikedStatusEum.UNLIKE;
    }

    if (userLikes.getStatus() == 0) {
        likes(infoId, likeUserId);
        return LikedStatusEum.LIKE;
    }
    return "";
}
like方法
    public void likes(String infoId, String likeUserId) {
        String likedKey = RedisKeyUtils.getLikedKey(infoId, likeUserId);
        redisTemplate.opsForHash().increment(RedisKeyUtils.MAP_KEY_USER_LIKED_COUNT, infoId, 1);
        redisTemplate.opsForHash().put(RedisKeyUtils.MAP_KEY_USER_LIKED, likedKey, LikedStatusEum.LIKE.getCode());
    }
unlike方法
    public void unLikes(String infoId, String likeUserId) {
        String likedKey = RedisKeyUtils.getLikedKey(infoId, likeUserId);
        redisTemplate.opsForHash().increment(RedisKeyUtils.MAP_KEY_USER_LIKED_COUNT, infoId, -1);
            redisTemplate.opsForHash().delete(RedisKeyUtils.MAP_KEY_USER_LIKED, likedKey);
    }
统计点赞变化情况的getLikedDataFromRedis方法
 public List getLikedDataFromRedis() {
        Cursor> scan = redisTemplate.opsForHash().scan(RedisKeyUtils.MAP_KEY_USER_LIKED, ScanOptions.NONE);
        List list = new ArrayList<>();
        while (scan.hasNext()) {
            Map.Entry entry = scan.next();
            String key = (String) entry.getKey();
            String[] split = key.split("::");
            String infoId = split[0];
            String likeUserId = split[1];
            Integer value = (Integer) entry.getValue();
            //组装成 UserLike 对象
            UserLikesDto userLikeDetail = new UserLikesDto(infoId, likeUserId, value);
            list.add(userLikeDetail);
            //存到 list 后从 Redis 中删除
            redisTemplate.opsForHash().delete(RedisKeyUtils.MAP_KEY_USER_LIKED, key);
        }
        return list;
    }
统计点赞数量的getLikedCountFromRedis方法
public List getLikedCountFromRedis() {
        Cursor> cursor = redisTemplate.opsForHash().scan(RedisKeyUtils.MAP_KEY_USER_LIKED_COUNT, ScanOptions.NONE);
        List list = new ArrayList<>();
        while (cursor.hasNext()) {
            Map.Entry map = cursor.next();
            String key = (String) map.getKey();
            Integer value = (Integer) map.getValue();
            UserLikCountDTO userLikCountDTO = new UserLikCountDTO(key, value);
            list.add(userLikCountDTO);
            redisTemplate.opsForHash().delete(RedisKeyUtils.MAP_KEY_USER_LIKED_COUNT, key);
        }
        return list;
    }
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