Doris 游戏数据分析实战，计算留存

基于Doris BitMap函数计算留存率，Doris拥有丰富的BitMap函数，计算留存使用到的有：bitmap_union、intersect_count、bitmap_intersect等；

其中核心思路就是：第一天与第二天的交集就是第二天的留存；第一天与第三天的交集就是第三天留存；以此类推，采用BitMap函数交集可以快速计算留存

例如：

1、已有埋点数据表

CREATE TABLE rt_dwd_app_event (
    user_uid varchar(40) NOT NULL COMMENT '用户id',
    create_time DATETIMEV2 NOT NULL COMMENT '时间',
    event varchar(40) NOT NULL COMMENT '事件名称'
)

2、定义用户埋点BitMap表

CREATE TABLE `rt_ads_app_event_1105_union` (
   event VARCHAR(50),
    create_time DATETIME,
    user_id BITMAP BITMAP_UNION
) ENGINE=OLAP
AGGREGATE KEY(`event`, `create_time`)
COMMENT "OLAP"
DISTRIBUTED BY HASH(`create_time`) BUCKETS 2;

3、将埋点数据表导入BitMap表

INSERT INTO rt_ads_app_event_bigmap (event, create_time, user_id)
SELECT
    event,
    create_time,
    bitmap_union(BITMAP_HASH(user_uid))
FROM
    rt_dwd_app_event
group by event, create_time

4、假如初始事件是A，回访事件是A, 计算目标留存

select
'2024-11-01' actday,
intersect_count(user_id,DATE_FORMAT(create_time,'%Y-%m-%d'),'2024-11-01') as login_num,
intersect_count(user_id,DATE_FORMAT(create_time,'%Y-%m-%d'),'2024-11-01','2024-11-02') as retention_1101_1,
intersect_count(user_id,DATE_FORMAT(create_time,'%Y-%m-%d'),'2024-11-01','2024-11-03') as retention_1101_2,
intersect_count(user_id,DATE_FORMAT(create_time,'%Y-%m-%d'),'2024-11-01','2024-11-04') as retention_1101_3
from rt_ads_app_event_bigmap where event='login'

5、假如初始事件是A，回访事件是B, 计算目标留存

SELECT
    a.event AS a_start_event,
    a.create_time AS a_start_time,
    b.event AS b_start_event,
    b.create_time AS b_start_time,
    bitmap_count(bitmap_and(a.user_id, b.user_id)) AS retention
FROM 
    (SELECT *
     FROM rt_ads_app_event_1105_union
     WHERE event = 'login'
       AND DATE_FORMAT(create_time, '%Y-%m-%d') = '2024-11-01') AS a
CROSS JOIN 
    (SELECT *
     FROM rt_ads_app_event_1105_union
     WHERE event = 'logout') AS b
ORDER BY
    a.create_time, 
    b.create_time;

小结：自此完成哈！3000w的埋点数据，形成68个bigmap列；留存计算耗时在70~100ms左右

原文地址：https://blog.csdn.net/qq_25954159/article/details/143585710

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