HiveSQL——设计一张最近180天的注册、活跃留存表

0 问题描述

现有一个用户活跃表user_active(user_id,active_date)、 用户注册表user_regist(user_id,regist_date),表中分区字段都为dt(yyyy-MM-dd),用户字段均为user_id; 设计一张 1-180天的注册活跃留存表;表结构如下:

1 数据分析

完整的代码如下:

sql 复制代码
select
    regist_date,
    diff,
    active_user_cnt,
    case
        when nvl(regis_cnt, 0) != 0 then active_user_cnt / regis_cnt
        end as retention_rate
from (
         select
             t1.regist_date,
             max(t1.regist_count)                     as regis_cnt,
             datediff(t2.active_date, t1.regist_date) as diff,
             count(t2.user_id)                        as active_user_cnt
         from (select
                   user_id,
                   to_date(regist_date)                                    as regist_date,
                   count(user_id) over (partition by to_date(regist_date)) as regist_count
               from user_regist
               where dt >= date_sub(current_date(), 180)) t1
                  left join
              (select
                   user_id,
                   to_date(active_date) as active_date
               from user_active
               where dt >= date_sub(current_date(), 180)
               group by user_id, to_date(active_date)) t2
              on t1.user_id = t2.user_id
         where datediff(active_date, regist_date) >= 1
           and datediff(active_date, regist_date) <= 180
         group by t1.regist_date, datediff(t2.active_date, t1.regist_date)
     ) t3
order by regist_date,
         diff;

上述代码解析:

步骤一:基于注册表,求出用户的注册日期regist_date、每日的用户注册数量regist_count

sql 复制代码
select
    user_id,
    to_date(regist_date)                                    as regist_date,
    count(user_id) over (partition by to_date(regist_date)) as regist_count
from user_regist
where dt >= date_sub(current_date(), 180);

步骤二:将用户注册表作为主表 ,关联活跃表,关联键为user_id,**一对多的关系,形成笛卡尔积。**需要注意:活跃用户表,每个用户每天可能会有多次活跃的情况,因此需要去重。

sql 复制代码
select
    t1.regist_date,
    t1.user_id,
    t1.regist_count,
    t2.user_id,
    t2.active_date,
    datediff(t2.active_date, t1.regist_date) as diff
from (select
          user_id,
          to_date(regist_date)                                    as regist_date,
          count(user_id) over (partition by to_date(regist_date)) as regist_count
      from user_regist
      where dt >= date_sub(current_date(), 180)) t1
  left join
     (select
          user_id,
          to_date(active_date) as active_date
      from user_active
      where dt >= date_sub(current_date(), 180)
      group by user_id, to_date(active_date)) t2
  on t1.user_id = t2.user_id;

步骤三:基于注册日期,留存周期分组(以"天"为单位),计算该留存周期下的活跃用户数

sql 复制代码
select
    t1.regist_date,
    max(t1.regist_count)                     as regis_cnt,
    datediff(t2.active_date, t1.regist_date) as diff,
    count(t2.user_id)                        as active_user_cnt

from (select
          user_id,
          to_date(regist_date)                                    as regist_date,
          count(user_id) over (partition by to_date(regist_date)) as regist_count
      from user_regist
      where dt >= date_sub(current_date(), 180)) t1
         left join
     (select
          user_id,
          to_date(active_date) as active_date
      from user_active
      where dt >= date_sub(current_date(), 180)
      group by user_id, to_date(active_date)) t2
     on t1.user_id = t2.user_id
where datediff(active_date, regist_date) >= 1
  and datediff(active_date, regist_date) <= 180
group by t1.regist_date, datediff(t2.active_date, t1.regist_date);

步骤四:计算留存率retention_rate

sql 复制代码
select
    regist_date,
    diff,
    active_user_cnt,
    case
        when nvl(regis_cnt, 0) != 0 then active_user_cnt / regis_cnt
        end as retention_rate
from (
         select
             t1.regist_date,
             max(t1.regist_count)                     as regis_cnt,
             datediff(t2.active_date, t1.regist_date) as diff,
             count(t2.user_id)                        as active_user_cnt
         from (select
                   user_id,
                   to_date(regist_date)                                    as regist_date,
                   count(user_id) over (partition by to_date(regist_date)) as regist_count
               from user_regist
               where dt >= date_sub(current_date(), 180)) t1
                  left join
              (select
                   user_id,
                   to_date(active_date) as active_date
               from user_active
               where dt >= date_sub(current_date(), 180)
               group by user_id, to_date(active_date)) t2
              on t1.user_id = t2.user_id
         where datediff(active_date, regist_date) >= 1
           and datediff(active_date, regist_date) <= 180
         group by t1.regist_date, datediff(t2.active_date, t1.regist_date)
     ) t3
order by regist_date,
         diff;

3 总结

利用left join左表关联,笛卡尔积的形式设计最近180天的注册活跃留存表。

相关推荐
冯RI375II694871 分钟前
儿童牙胶CPSC和FDA认证的测试费用大概是多少?
大数据
企业架构师老王5 分钟前
药品生产环节:用实在Agent自动生成批记录与打印领料单的合规设计与架构落地
大数据·人工智能·ai·架构
黎阳之光9 分钟前
视频孪生重构轨交数字孪生新范式|黎阳之光以自主核心技术破解落地难题
大数据·人工智能·算法·安全·数字孪生
大大大大晴天️20 分钟前
Flink技术实践-监控指标异常诊断与运维
大数据·运维·flink
Tartly26 分钟前
双旗舰赋能广电数智化转型 华硕NUC参展第三十二届CCBN2026
大数据·人工智能
QEasyCloud20221 小时前
企业数据仓库建设实践与价值分析
数据仓库
七颗糖很甜1 小时前
开源雷达NEXRAD Level 3 数据完整获取与 Python 处理教程
大数据·python·算法
cui17875681 小时前
排队免单模式:从爆火到优化,探寻实体商业新出路
大数据·人工智能·设计模式·个人开发·设计规范
郑寿昌1 小时前
2026 SPARQL流式子图匹配技术前瞻
大数据
csgo打的菜又爱玩1 小时前
8.WebMonitorEndpoint解析.md
大数据·架构·flink