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
Sammyyyyy24 分钟前
如何在不停项目不停机的情况下切换AI大模型
大数据·人工智能
circuitsosk1 小时前
跨境电商智能化实战:AI如何赋能客服自动回复、广告智能投放与供应链预测
大数据·人工智能·python·langchain·智能客服
数智化管理手记2 小时前
海量数据如何沉淀有效数据资产?数据标准化治理方案如何搭建?
java·大数据·人工智能
刀客Doc3 小时前
即时零售不只是多一个渠道,品牌开始重做终端生意
大数据·数据库
湘美书院--湘美谈教育3 小时前
湘美书院主理人谈AI文学:提示词与Skill的与时俱进
大数据·人工智能·安全·自动化·生活
weixin_549808363 小时前
人力资源数字化转型:从工具堆叠到AI原生架构的组织级跃迁路径
大数据·人工智能
l0001093 小时前
图书馆静谧环境构建:主流声学品牌产品与服务梳理
大数据·人工智能·声音
ACP广源盛139246256734 小时前
蚂蚁百灵 Ling‑3.0‑flash 开源 + 昇腾 0‑Day 原生适配@ACP#GSV9001E 在国产算力矩阵中的机会与落地场景
大数据·人工智能·分布式·单片机·嵌入式硬件
微三云-张梅6 小时前
东莞企业做GEO:AI信任体系的三个建设层级
大数据·人工智能·微三云geo·东莞系统开发·东莞geo
starzy19906 小时前
Spark 核心之 Spark-SortShuffle 原理深度剖析
大数据·spark