redis+xxl-job初步设计点赞功能

一般情况下点赞业务涉及以下下几个方面：

1.我们肯定要知道一个题目被多少人点过赞，还要知道，每个人他点赞了哪些题目。

2.点赞的业务特性，频繁。用户一多，时时刻刻都在进行点赞，收藏等等处理，如果说我们采取传统的数据库的模式啊，这个交互量是非常大的，很难去抗住这个并发问题，所以我们采取 redis 的方式来做。（当业务并发量低，以及代码设计的逻辑使得能够应对需求，也可以用mysql做，根据具体业务场景选择最佳的解决方案，本篇主要为学习使用）

3.查询的数据交互，可以通过redis 直接来做，持久化的数据，通过数据库查询即可，这个数据如何去同步到数据库，我们就采取的定时任务 xxl-job 定期来刷数据。

 

 

点赞设计（本文只展示核心代码，数据流的转换便不再展示）

    private static final String SUBJECT_LIKED_KEY = "subject.liked";

    private static final String SUBJECT_LIKED_COUNT_KEY = "subject.liked.count";

    private static final String SUBJECT_LIKED_DETAIL_KEY = "subject.liked.detail";
   




@Override
    public void add(SubjectLikedBO subjectLikedBO) {
        Long subjectId = subjectLikedBO.getSubjectId();
        String likeUserId = subjectLikedBO.getLikeUserId();
        Integer status = subjectLikedBO.getStatus();
        String hashKey = buildSubjectLikedKey(subjectId.toString(), likeUserId);
        redisUtil.putHash(SUBJECT_LIKED_KEY, hashKey, status);
        String detailKey = SUBJECT_LIKED_DETAIL_KEY + "." + subjectId + "." + likeUserId;
        String countKey = SUBJECT_LIKED_COUNT_KEY + "." + subjectId;
        if (SubjectLikedStatusEnum.LIKED.getCode() == status) {
            redisUtil.increment(countKey, 1);
            redisUtil.set(detailKey, "1");
        } else {
            Integer count = redisUtil.getInt(countKey);
            if (Objects.isNull(count) || count <= 0) {
                return;
            }
            redisUtil.increment(countKey, -1);
            redisUtil.del(detailKey);
        }
    }

 private String buildSubjectLikedKey(String subjectId, String userId) {
        return subjectId + ":" + userId;
    }


@Getter
public enum SubjectLikedStatusEnum {

    LIKED(1, "点赞"),
    UN_LIKED(0, "取消点赞");

    public int code;

    public String desc;

    SubjectLikedStatusEnum(int code, String desc) {
        this.code = code;
        this.desc = desc;
    }

    public static SubjectLikedStatusEnum getByCode(int codeVal) {
        for (SubjectLikedStatusEnum resultCodeEnum : SubjectLikedStatusEnum.values()) {
            if (resultCodeEnum.code == codeVal) {
                return resultCodeEnum;
            }
        }
        return null;
    }

}

　　自定义封装redisutil类

@Component
@Slf4j
public class RedisUtil {

    @Resource
    private RedisTemplate redisTemplate;

    private static final String CACHE_KEY_SEPARATOR = ".";

    /**
     * 构建缓存key
     */
    public String buildKey(String... strObjs) {
        return Stream.of(strObjs).collect(Collectors.joining(CACHE_KEY_SEPARATOR));
    }

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

    /**
     * 删除key
     */
    public boolean del(String key) {
        return redisTemplate.delete(key);
    }

    /**
     * set(不带过期)
     */
    public void set(String key, String value) {
        redisTemplate.opsForValue().set(key, value);
    }

    /**
     * set(带过期)
     */
    public boolean setNx(String key, String value, Long time, TimeUnit timeUnit) {
        return redisTemplate.opsForValue().setIfAbsent(key, value, time, timeUnit);
    }

    /**
     * 获取string类型缓存
     */
    public String get(String key) {
        return (String) redisTemplate.opsForValue().get(key);
    }

    public Boolean zAdd(String key, String value, Long score) {
        return redisTemplate.opsForZSet().add(key, value, Double.valueOf(String.valueOf(score)));
    }

    public Long countZset(String key) {
        return redisTemplate.opsForZSet().size(key);
    }

    public Set<String> rangeZset(String key, long start, long end) {
        return redisTemplate.opsForZSet().range(key, start, end);
    }

    public Long removeZset(String key, Object value) {
        return redisTemplate.opsForZSet().remove(key, value);
    }

    public void removeZsetList(String key, Set<String> value) {
        value.stream().forEach((val) -> redisTemplate.opsForZSet().remove(key, val));
    }

    public Double score(String key, Object value) {
        return redisTemplate.opsForZSet().score(key, value);
    }

    public Set<String> rangeByScore(String key, long start, long end) {
        return redisTemplate.opsForZSet().rangeByScore(key, Double.valueOf(String.valueOf(start)), Double.valueOf(String.valueOf(end)));
    }

    public Object addScore(String key, Object obj, double score) {
        return redisTemplate.opsForZSet().incrementScore(key, obj, score);
    }

    public Object rank(String key, Object obj) {
        return redisTemplate.opsForZSet().rank(key, obj);
    }

    public Set<ZSetOperations.TypedTuple<String>> rankWithScore(String key, long start, long end) {
        Set<ZSetOperations.TypedTuple<String>> set = redisTemplate.opsForZSet().reverseRangeWithScores(key, start, end);
        return set;
    }

    public void putHash(String key, String hashKey, Object hashVal) {
        redisTemplate.opsForHash().put(key, hashKey, hashVal);
    }

    public Integer getInt(String key) {
        return (Integer) redisTemplate.opsForValue().get(key);
    }

    public void increment(String key, Integer count) {
        redisTemplate.opsForValue().increment(key, count);
    }

    public Map<Object, Object> getHashAndDelete(String key) {
        Map<Object, Object> map = new HashMap<>();
        Cursor<Map.Entry<Object, Object>> cursor = redisTemplate.opsForHash().scan(key, ScanOptions.NONE);
        while (cursor.hasNext()) {
            Map.Entry<Object, Object> entry = cursor.next();
            Object hashKey = entry.getKey();
            Object value = entry.getValue();
            map.put(hashKey, value);
            redisTemplate.opsForHash().delete(key, hashKey);
        }
        return map;
    }
}

　　

 

 

subject.liked.detail用户点赞状态

subject.liked.count 某题目被点赞的数量

subject.liked点赞人的数据

 点赞设计已完成，那么我们如何通过xxl-job统一调度任务？

官方文档：https://www.xuxueli.com/xxl-job/#%E3%80%8A%E5%88%86%E5%B8%83%E5%BC%8F%E4%BB%BB%E5%8A%A1%E8%B0%83%E5%BA%A6%E5%B9%B3%E5%8F%B0XXL-JOB%E3%80%8B

 本文演示本地使用xxl-job，文末附带docker部署xxl-job

修改admin下本地数据库连接，

 doc/db下执行sql

 

#
# XXL-JOB v2.4.2-SNAPSHOT
# Copyright (c) 2015-present, xuxueli.

CREATE database if NOT EXISTS `xxl_job` default character set utf8mb4 collate utf8mb4_unicode_ci;
use `xxl_job`;

SET NAMES utf8mb4;

CREATE TABLE `xxl_job_info` (
  `id` int(11) NOT NULL AUTO_INCREMENT,
  `job_group` int(11) NOT NULL COMMENT '执行器主键ID',
  `job_desc` varchar(255) NOT NULL,
  `add_time` datetime DEFAULT NULL,
  `update_time` datetime DEFAULT NULL,
  `author` varchar(64) DEFAULT NULL COMMENT '作者',
  `alarm_email` varchar(255) DEFAULT NULL COMMENT '报警邮件',
  `schedule_type` varchar(50) NOT NULL DEFAULT 'NONE' COMMENT '调度类型',
  `schedule_conf` varchar(128) DEFAULT NULL COMMENT '调度配置，值含义取决于调度类型',
  `misfire_strategy` varchar(50) NOT NULL DEFAULT 'DO_NOTHING' COMMENT '调度过期策略',
  `executor_route_strategy` varchar(50) DEFAULT NULL COMMENT '执行器路由策略',
  `executor_handler` varchar(255) DEFAULT NULL COMMENT '执行器任务handler',
  `executor_param` varchar(512) DEFAULT NULL COMMENT '执行器任务参数',
  `executor_block_strategy` varchar(50) DEFAULT NULL COMMENT '阻塞处理策略',
  `executor_timeout` int(11) NOT NULL DEFAULT '0' COMMENT '任务执行超时时间，单位秒',
  `executor_fail_retry_count` int(11) NOT NULL DEFAULT '0' COMMENT '失败重试次数',
  `glue_type` varchar(50) NOT NULL COMMENT 'GLUE类型',
  `glue_source` mediumtext COMMENT 'GLUE源代码',
  `glue_remark` varchar(128) DEFAULT NULL COMMENT 'GLUE备注',
  `glue_updatetime` datetime DEFAULT NULL COMMENT 'GLUE更新时间',
  `child_jobid` varchar(255) DEFAULT NULL COMMENT '子任务ID，多个逗号分隔',
  `trigger_status` tinyint(4) NOT NULL DEFAULT '0' COMMENT '调度状态：0-停止，1-运行',
  `trigger_last_time` bigint(13) NOT NULL DEFAULT '0' COMMENT '上次调度时间',
  `trigger_next_time` bigint(13) NOT NULL DEFAULT '0' COMMENT '下次调度时间',
  PRIMARY KEY (`id`)
) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4;

CREATE TABLE `xxl_job_log` (
  `id` bigint(20) NOT NULL AUTO_INCREMENT,
  `job_group` int(11) NOT NULL COMMENT '执行器主键ID',
  `job_id` int(11) NOT NULL COMMENT '任务，主键ID',
  `executor_address` varchar(255) DEFAULT NULL COMMENT '执行器地址，本次执行的地址',
  `executor_handler` varchar(255) DEFAULT NULL COMMENT '执行器任务handler',
  `executor_param` varchar(512) DEFAULT NULL COMMENT '执行器任务参数',
  `executor_sharding_param` varchar(20) DEFAULT NULL COMMENT '执行器任务分片参数，格式如 1/2',
  `executor_fail_retry_count` int(11) NOT NULL DEFAULT '0' COMMENT '失败重试次数',
  `trigger_time` datetime DEFAULT NULL COMMENT '调度-时间',
  `trigger_code` int(11) NOT NULL COMMENT '调度-结果',
  `trigger_msg` text COMMENT '调度-日志',
  `handle_time` datetime DEFAULT NULL COMMENT '执行-时间',
  `handle_code` int(11) NOT NULL COMMENT '执行-状态',
  `handle_msg` text COMMENT '执行-日志',
  `alarm_status` tinyint(4) NOT NULL DEFAULT '0' COMMENT '告警状态：0-默认、1-无需告警、2-告警成功、3-告警失败',
  PRIMARY KEY (`id`),
  KEY `I_trigger_time` (`trigger_time`),
  KEY `I_handle_code` (`handle_code`)
) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4;

CREATE TABLE `xxl_job_log_report` (
  `id` int(11) NOT NULL AUTO_INCREMENT,
  `trigger_day` datetime DEFAULT NULL COMMENT '调度-时间',
  `running_count` int(11) NOT NULL DEFAULT '0' COMMENT '运行中-日志数量',
  `suc_count` int(11) NOT NULL DEFAULT '0' COMMENT '执行成功-日志数量',
  `fail_count` int(11) NOT NULL DEFAULT '0' COMMENT '执行失败-日志数量',
  `update_time` datetime DEFAULT NULL,
  PRIMARY KEY (`id`),
  UNIQUE KEY `i_trigger_day` (`trigger_day`) USING BTREE
) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4;

CREATE TABLE `xxl_job_logglue` (
  `id` int(11) NOT NULL AUTO_INCREMENT,
  `job_id` int(11) NOT NULL COMMENT '任务，主键ID',
  `glue_type` varchar(50) DEFAULT NULL COMMENT 'GLUE类型',
  `glue_source` mediumtext COMMENT 'GLUE源代码',
  `glue_remark` varchar(128) NOT NULL COMMENT 'GLUE备注',
  `add_time` datetime DEFAULT NULL,
  `update_time` datetime DEFAULT NULL,
  PRIMARY KEY (`id`)
) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4;

CREATE TABLE `xxl_job_registry` (
  `id` int(11) NOT NULL AUTO_INCREMENT,
  `registry_group` varchar(50) NOT NULL,
  `registry_key` varchar(255) NOT NULL,
  `registry_value` varchar(255) NOT NULL,
  `update_time` datetime DEFAULT NULL,
  PRIMARY KEY (`id`),
  KEY `i_g_k_v` (`registry_group`,`registry_key`,`registry_value`)
) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4;

CREATE TABLE `xxl_job_group` (
  `id` int(11) NOT NULL AUTO_INCREMENT,
  `app_name` varchar(64) NOT NULL COMMENT '执行器AppName',
  `title` varchar(12) NOT NULL COMMENT '执行器名称',
  `address_type` tinyint(4) NOT NULL DEFAULT '0' COMMENT '执行器地址类型：0=自动注册、1=手动录入',
  `address_list` text COMMENT '执行器地址列表，多地址逗号分隔',
  `update_time` datetime DEFAULT NULL,
  PRIMARY KEY (`id`)
) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4;

CREATE TABLE `xxl_job_user` (
  `id` int(11) NOT NULL AUTO_INCREMENT,
  `username` varchar(50) NOT NULL COMMENT '账号',
  `password` varchar(50) NOT NULL COMMENT '密码',
  `role` tinyint(4) NOT NULL COMMENT '角色：0-普通用户、1-管理员',
  `permission` varchar(255) DEFAULT NULL COMMENT '权限：执行器ID列表，多个逗号分割',
  PRIMARY KEY (`id`),
  UNIQUE KEY `i_username` (`username`) USING BTREE
) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4;

CREATE TABLE `xxl_job_lock` (
  `lock_name` varchar(50) NOT NULL COMMENT '锁名称',
  PRIMARY KEY (`lock_name`)
) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4;

INSERT INTO `xxl_job_group`(`id`, `app_name`, `title`, `address_type`, `address_list`, `update_time`) VALUES (1, 'xxl-job-executor-sample', '示例执行器', 0, NULL, '2018-11-03 22:21:31' );
INSERT INTO `xxl_job_info`(`id`, `job_group`, `job_desc`, `add_time`, `update_time`, `author`, `alarm_email`, `schedule_type`, `schedule_conf`, `misfire_strategy`, `executor_route_strategy`, `executor_handler`, `executor_param`, `executor_block_strategy`, `executor_timeout`, `executor_fail_retry_count`, `glue_type`, `glue_source`, `glue_remark`, `glue_updatetime`, `child_jobid`) VALUES (1, 1, '测试任务1', '2018-11-03 22:21:31', '2018-11-03 22:21:31', 'XXL', '', 'CRON', '0 0 0 * * ? *', 'DO_NOTHING', 'FIRST', 'demoJobHandler', '', 'SERIAL_EXECUTION', 0, 0, 'BEAN', '', 'GLUE代码初始化', '2018-11-03 22:21:31', '');
INSERT INTO `xxl_job_user`(`id`, `username`, `password`, `role`, `permission`) VALUES (1, 'admin', 'e10adc3949ba59abbe56e057f20f883e', 1, NULL);
INSERT INTO `xxl_job_lock` ( `lock_name`) VALUES ( 'schedule_lock');

commit;

　　

 

 

 

启动服务

 

http://127.0.0.1:8080/xxl-job-admin访问

账户admin

密码123456

初始信息

 

新增执行器

 

 新增任务

 

 

 复制过来，原路径

 

 

   @Override
    public void syncLiked() {
        Map<Object, Object> subjectLikedMap = redisUtil.getHashAndDelete(SUBJECT_LIKED_KEY);
        if (log.isInfoEnabled()) {
            log.info("syncLiked.subjectLikedMap:{}", JSON.toJSONString(subjectLikedMap));
        }
        if (MapUtils.isEmpty(subjectLikedMap)) {
            return;
        }
        //批量同步到数据库
        List<SubjectLiked> subjectLikedList = new LinkedList<>();
        subjectLikedMap.forEach((key, val) -> {
            SubjectLiked subjectLiked = new SubjectLiked();
            String[] keyArr = key.toString().split(":");
            String subjectId = keyArr[0];
            String likedUser = keyArr[1];
            subjectLiked.setSubjectId(Long.valueOf(subjectId));
            subjectLiked.setLikeUserId(likedUser);
            subjectLiked.setStatus(Integer.valueOf(val.toString()));
            subjectLiked.setIsDeleted(IsDeletedFlagEnum.UN_DELETED.getCode());
            subjectLikedList.add(subjectLiked);
        });
        if (log.isInfoEnabled()) {
            log.info("syncLiked.subjectLikedList:{}", JSON.toJSONString(subjectLikedList));
        }
        subjectLikedService.batchInsertOrUpdate(subjectLikedList);
    }

　　

 <insert id="batchInsertOrUpdate">
        INSERT INTO subject_liked
        (subject_id, like_user_id, status, created_by, created_time, update_by, update_time, is_deleted)
        VALUES
        <foreach collection="entities" item="item" separator=",">
            (#{item.subjectId}, #{item.likeUserId}, #{item.status}, #{item.createdBy}, #{item.createdTime}, #{item.updateBy}, #{item.updateTime}, #{item.isDeleted})
        </foreach>
        ON DUPLICATE KEY UPDATE
        status = VALUES(status),
        created_by = VALUES(created_by),
        created_time = VALUES(created_time),
        update_by = VALUES(update_by),
        update_time = VALUES(update_time),
        is_deleted = VALUES(is_deleted)
    </insert>

　　主要通过下面方法获取subject.like键然后传入

subjectLikedMap，最后传入

subjectLikedList完成数据传输问题

这里有个个人问题便是，我们使用了mybatis拦截器填充默认值，在获取loginid时由于线程问题会抛出一个异常，所有我们需要trycatch一下

 //String loginId = LoginUtil.getLoginId();
        String loginId;
        try {
            loginId = Optional.ofNullable(LoginUtil.getLoginId()).orElse("账户匿名");
        } catch (Exception e) {
            log.error("Failed to get login ID from LoginUtil", e);
            return invocation.proceed();
        }

　　这样，我们的调度便完成了

docker部署xxl-job

docker search xxl-job
docker pull xuxueli/xxl-job-admin:2.4.0

docker run  -d \
        -p 8088:8088\
        -v /tool/xxl-job/logs:/data/applogs \
        -v /tool/xxl-job/application.properties:/xxl-job/xxl-job-admin/src/main/resources/application.properties \
        -e PARAMS="--server.port=8088\
        --spring.datasource.url=jdbc:mysql://117.72.14.166:3306/xxl_job?useUnicode=true&characterEncoding=UTF-8&autoReconnect=true&serverTimezone=Asia/Shanghai \
        --spring.datasource.username=root \
        --spring.datasource.password=Wing1Q2W#E" \
        --name xxl-job-admin  \
xuxueli/xxl-job-admin:2.4.0