Sql 按队列每周动态比较_Sql_Sql Server_Pivot_Partitioning

Sql 按队列每周动态比较

sql sql-server

Sql 按队列每周动态比较,sql,sql-server,pivot,partitioning,Sql,Sql Server,Pivot,Partitioning,目标： SELECT CONCAT(DATEPART(YEAR,m.date_created),'-',DATEPART(MONTH,m.date_created)) AS Cohort ,CONCAT(subquery.[YYYY],'-',subquery.[ISO]) AS YYYY_ISO ,m.email FROM member as m INNER JOIN (SELECT DATEPART(YEAR,log.login_tim

目标：

SELECT CONCAT(DATEPART(YEAR,m.date_created),'-',DATEPART(MONTH,m.date_created)) AS Cohort
    ,CONCAT(subquery.[YYYY],'-',subquery.[ISO])                     AS YYYY_ISO
    ,m.email
FROM member as m
INNER JOIN (SELECT DATEPART(YEAR,log.login_time)    AS [YYYY]
        ,DATEPART(ISO_WEEK,log.login_time)  AS [ISO]
        ,log.email
        ,ROW_NUMBER()
            OVER(PARTITION BY 
            DATEPART(YEAR,log.login_time), 
            DATEPART(ISO_WEEK,log.login_time),
            log.email
        ORDER BY log.login_time ASC) AS Log_Rank
        FROM login AS log
        WHERE CAST(log.login_time AS DATE) >= '2019-01-01'
        ) AS subquery ON m.email=subquery.email AND Log_Rank = 1
ORDER BY cohort

CREATE TABLE member
    ([email] varchar(50), [date_created] Datetime)

CREATE TABLE login
    ([email] varchar(50), [login_time] Datetime)

INSERT INTO member
VALUES
    ('player123@google.com', '2018-03-01 05:00:00'),
    ('player999@google.com', '2018-04-12 12:00:00'),
    ('player555@google.com', '2018-04-25 20:15:00')

INSERT INTO login
VALUES
    ('player123@google.com', '2019-01-07 05:30:00'),
    ('player123@google.com', '2019-01-08 08:30:00'),
    ('player123@google.com', '2019-01-15 06:30:00'),
    ('player999@google.com', '2019-01-08 11:30:00'),
    ('player999@google.com', '2019-01-10 07:30:00'),
    ('player555@google.com', '2019-01-08 04:30:00')

在第1周获得Id登录。那么在第2周有多少ID登录。在第2周到第3周重新启动相同的逻辑。然后是第3周和第4周等等。。。这个练习需要每周做一次。 ID需要按照队列进行细分，队列是他们订阅的月份和年份

故事：

SELECT CONCAT(DATEPART(YEAR,m.date_created),'-',DATEPART(MONTH,m.date_created)) AS Cohort
    ,CONCAT(subquery.[YYYY],'-',subquery.[ISO])                     AS YYYY_ISO
    ,m.email
FROM member as m
INNER JOIN (SELECT DATEPART(YEAR,log.login_time)    AS [YYYY]
        ,DATEPART(ISO_WEEK,log.login_time)  AS [ISO]
        ,log.email
        ,ROW_NUMBER()
            OVER(PARTITION BY 
            DATEPART(YEAR,log.login_time), 
            DATEPART(ISO_WEEK,log.login_time),
            log.email
        ORDER BY log.login_time ASC) AS Log_Rank
        FROM login AS log
        WHERE CAST(log.login_time AS DATE) >= '2019-01-01'
        ) AS subquery ON m.email=subquery.email AND Log_Rank = 1
ORDER BY cohort

CREATE TABLE member
    ([email] varchar(50), [date_created] Datetime)

CREATE TABLE login
    ([email] varchar(50), [login_time] Datetime)

INSERT INTO member
VALUES
    ('player123@google.com', '2018-03-01 05:00:00'),
    ('player999@google.com', '2018-04-12 12:00:00'),
    ('player555@google.com', '2018-04-25 20:15:00')

INSERT INTO login
VALUES
    ('player123@google.com', '2019-01-07 05:30:00'),
    ('player123@google.com', '2019-01-08 08:30:00'),
    ('player123@google.com', '2019-01-15 06:30:00'),
    ('player999@google.com', '2019-01-08 11:30:00'),
    ('player999@google.com', '2019-01-10 07:30:00'),
    ('player555@google.com', '2019-01-08 04:30:00')

第一个表（成员）包含电子邮件及其创建日期。第二个表（登录表）是登录活动。首先，我需要按创建日期（月-年）对电子邮件进行分组，以创建群组。然后，对每个队列每周的登录活动进行比较。可能吗此查询是否每周都是动态的

输出： 结果应该如下所示：

+--------+--------+--------+--------+---------+
| Cohort | 2019-1 | 2019-2 | 2019-3 | 2019-4  |...
+--------+--------+--------+--------+---------+
| 2018-3 |   7000 |   6800 |    7400|    7100 |...
| 2018-4 |   6800 |   6500 |    8400|    8000 |...
| 2018-5 |   9500 |   8000 |    6400|    6200 |...
| 2018-6 |   9100 |   8500 |    8000|    7800 |...
| 2018-7 |  10000 |   8000 |    7000|    6800 |...
+--------+--------+--------+--------+---------+

我的尝试：

SELECT CONCAT(DATEPART(YEAR,m.date_created),'-',DATEPART(MONTH,m.date_created)) AS Cohort
    ,CONCAT(subquery.[YYYY],'-',subquery.[ISO])                     AS YYYY_ISO
    ,m.email
FROM member as m
INNER JOIN (SELECT DATEPART(YEAR,log.login_time)    AS [YYYY]
        ,DATEPART(ISO_WEEK,log.login_time)  AS [ISO]
        ,log.email
        ,ROW_NUMBER()
            OVER(PARTITION BY 
            DATEPART(YEAR,log.login_time), 
            DATEPART(ISO_WEEK,log.login_time),
            log.email
        ORDER BY log.login_time ASC) AS Log_Rank
        FROM login AS log
        WHERE CAST(log.login_time AS DATE) >= '2019-01-01'
        ) AS subquery ON m.email=subquery.email AND Log_Rank = 1
ORDER BY cohort

CREATE TABLE member
    ([email] varchar(50), [date_created] Datetime)

CREATE TABLE login
    ([email] varchar(50), [login_time] Datetime)

INSERT INTO member
VALUES
    ('player123@google.com', '2018-03-01 05:00:00'),
    ('player999@google.com', '2018-04-12 12:00:00'),
    ('player555@google.com', '2018-04-25 20:15:00')

INSERT INTO login
VALUES
    ('player123@google.com', '2019-01-07 05:30:00'),
    ('player123@google.com', '2019-01-08 08:30:00'),
    ('player123@google.com', '2019-01-15 06:30:00'),
    ('player999@google.com', '2019-01-08 11:30:00'),
    ('player999@google.com', '2019-01-10 07:30:00'),
    ('player555@google.com', '2019-01-08 04:30:00')

样本数据：

SELECT CONCAT(DATEPART(YEAR,m.date_created),'-',DATEPART(MONTH,m.date_created)) AS Cohort
    ,CONCAT(subquery.[YYYY],'-',subquery.[ISO])                     AS YYYY_ISO
    ,m.email
FROM member as m
INNER JOIN (SELECT DATEPART(YEAR,log.login_time)    AS [YYYY]
        ,DATEPART(ISO_WEEK,log.login_time)  AS [ISO]
        ,log.email
        ,ROW_NUMBER()
            OVER(PARTITION BY 
            DATEPART(YEAR,log.login_time), 
            DATEPART(ISO_WEEK,log.login_time),
            log.email
        ORDER BY log.login_time ASC) AS Log_Rank
        FROM login AS log
        WHERE CAST(log.login_time AS DATE) >= '2019-01-01'
        ) AS subquery ON m.email=subquery.email AND Log_Rank = 1
ORDER BY cohort

CREATE TABLE member
    ([email] varchar(50), [date_created] Datetime)

CREATE TABLE login
    ([email] varchar(50), [login_time] Datetime)

INSERT INTO member
VALUES
    ('player123@google.com', '2018-03-01 05:00:00'),
    ('player999@google.com', '2018-04-12 12:00:00'),
    ('player555@google.com', '2018-04-25 20:15:00')

INSERT INTO login
VALUES
    ('player123@google.com', '2019-01-07 05:30:00'),
    ('player123@google.com', '2019-01-08 08:30:00'),
    ('player123@google.com', '2019-01-15 06:30:00'),
    ('player999@google.com', '2019-01-08 11:30:00'),
    ('player999@google.com', '2019-01-10 07:30:00'),
    ('player555@google.com', '2019-01-08 04:30:00')

像

这样的值来自哪里？我对那件事有点困惑。我们是否丢失（大量）数据？随机生成。基本上，如果将第1周与第2周进行比较。第2周不能超过第1周。当第2周与第3周进行比较时，第3周不能超过第2周，以此类推@但是

小于

？虽然我们有一些非常好的样本数据，但我不知道你的逻辑是什么。@Larnu是的，很好。逻辑如下：如果1000人（2018年3月订阅）在第1周登录，其中700人在第2周登录。这是70%的保留金。我需要了解如何为其他群组（即2018年4月、2018年5月等的订户）做到这一点；一周又一周。输出用作最终产品的视觉表示。看起来应该用支点，但我unsure@RogerSteinberg您好，如果我了解您想要什么，我想您必须使用（pivot、groupby、dynamic exec query和一些agregate函数，如count、）