如何计算postgresql中每月重复事件的间隔

使用postgresql 9.6

问题如下，我们有一个数据库跟踪订阅业务中的订单。一旦一个人订阅了订单，每月都会为他生成一份订单。每个人都有能力跳过一个月（或跳过一个月的周期，或3个月等，有效地跳过x个月）。我试图确定在一个月内“跳过”的人数

我们有一个订单表，如下所示（简化）：

我可以使用窗口函数对每个人的订单进行排序，如下所示：

    select timestamp_, person_id, row_number() 
           over (partition by person_id order by timestamp_)
    from orders

    select timestamp_, person_id,
      (date_trunc('month', timestamp_) - date_trunc('month',timestamp_)) 
      over (partition by person_id order by timestamp_))
    from orders;

timestamp_              person_id                           lag
2017-03-14 12:38:38 00050c43-08c5-11e7-b433-01007e15dd78    
2017-04-14 10:04:13 00050c43-08c5-11e7-b433-01007e15dd78    0 years 0 mons 31 days 0 hours 0 mins 0.00 secs
2017-07-14 10:05:17 00050c43-08c5-11e7-b433-01007e15dd78    0 years 0 mons 91 days 0 hours 0 mins 0.00 secs
2017-08-14 10:02:37 00050c43-08c5-11e7-b433-01007e15dd78    0 years 0 mons 31 days 0 hours 0 mins 0.00 secs
2017-09-14 10:04:37 00050c43-08c5-11e7-b433-01007e15dd78    0 years 0 mons 31 days 0 hours 0 mins 0.00 secs
2017-10-14 10:02:08 00050c43-08c5-11e7-b433-01007e15dd78    0 years 0 mons 30 days 0 hours 0 mins 0.00 secs
2017-11-14 10:05:35 00050c43-08c5-11e7-b433-01007e15dd78    0 years 0 mons 31 days 0 hours 0 mins 0.00 secs
2017-12-14 10:02:52 00050c43-08c5-11e7-b433-01007e15dd78    0 years 0 mons 30 days 0 hours 0 mins 0.00 secs
2018-01-14 10:05:38 00050c43-08c5-11e7-b433-01007e15dd78    0 years 0 mons 31 days 0 hours 0 mins 0.00 secs
2017-11-15 03:54:57 000b5c80-c9b8-11e7-a1c1-0242ac110003    
2017-12-14 10:00:34 000b5c80-c9b8-11e7-a1c1-0242ac110003    0 years 0 mons 30 days 0 hours 0 mins 0.00 secs
2018-01-14 10:07:17 000b5c80-c9b8-11e7-a1c1-0242ac110003    0 years 0 mons 31 days 0 hours 0 mins 0.00 secs
2016-12-24 10:15:58 0017c8ad-b252-11e6-b4db-0100ab184d8f    
2017-01-24 10:54:49 0017c8ad-b252-11e6-b4db-0100ab184d8f    0 years 0 mons 31 days 0 hours 0 mins 0.00 secs

Month       Number of people who skipped subscription 
2017-03-1    14
2017-04-1    8
2017-05-1    4

输出：

 timestamp_             person_id                      row_number
2017-03-14 12:38:38 00050c43-08c5-11e7-b433-01007e15dd78    1
2017-04-14 10:04:13 00050c43-08c5-11e7-b433-01007e15dd78    2
2017-07-14 10:05:17 00050c43-08c5-11e7-b433-01007e15dd78    3
2017-08-14 10:02:37 00050c43-08c5-11e7-b433-01007e15dd78    4
2017-09-14 10:04:37 00050c43-08c5-11e7-b433-01007e15dd78    5
2017-10-14 10:02:08 00050c43-08c5-11e7-b433-01007e15dd78    6
2017-11-14 10:05:35 00050c43-08c5-11e7-b433-01007e15dd78    7
2017-12-14 10:02:52 00050c43-08c5-11e7-b433-01007e15dd78    8
2018-01-14 10:05:38 00050c43-08c5-11e7-b433-01007e15dd78    9
2017-11-15 03:54:57 000b5c80-c9b8-11e7-a1c1-0242ac110003    1
2017-12-14 10:00:34 000b5c80-c9b8-11e7-a1c1-0242ac110003    2
2018-01-14 10:07:17 000b5c80-c9b8-11e7-a1c1-0242ac110003    3
2016-12-24 10:15:58 0017c8ad-b252-11e6-b4db-0100ab184d8f    1
2017-01-24 10:54:49 0017c8ad-b252-11e6-b4db-0100ab184d8f    2

我也一直在玩这样的滞后函数：

    select timestamp_, person_id, row_number() 
           over (partition by person_id order by timestamp_)
    from orders

    select timestamp_, person_id,
      (date_trunc('month', timestamp_) - date_trunc('month',timestamp_)) 
      over (partition by person_id order by timestamp_))
    from orders;

timestamp_              person_id                           lag
2017-03-14 12:38:38 00050c43-08c5-11e7-b433-01007e15dd78    
2017-04-14 10:04:13 00050c43-08c5-11e7-b433-01007e15dd78    0 years 0 mons 31 days 0 hours 0 mins 0.00 secs
2017-07-14 10:05:17 00050c43-08c5-11e7-b433-01007e15dd78    0 years 0 mons 91 days 0 hours 0 mins 0.00 secs
2017-08-14 10:02:37 00050c43-08c5-11e7-b433-01007e15dd78    0 years 0 mons 31 days 0 hours 0 mins 0.00 secs
2017-09-14 10:04:37 00050c43-08c5-11e7-b433-01007e15dd78    0 years 0 mons 31 days 0 hours 0 mins 0.00 secs
2017-10-14 10:02:08 00050c43-08c5-11e7-b433-01007e15dd78    0 years 0 mons 30 days 0 hours 0 mins 0.00 secs
2017-11-14 10:05:35 00050c43-08c5-11e7-b433-01007e15dd78    0 years 0 mons 31 days 0 hours 0 mins 0.00 secs
2017-12-14 10:02:52 00050c43-08c5-11e7-b433-01007e15dd78    0 years 0 mons 30 days 0 hours 0 mins 0.00 secs
2018-01-14 10:05:38 00050c43-08c5-11e7-b433-01007e15dd78    0 years 0 mons 31 days 0 hours 0 mins 0.00 secs
2017-11-15 03:54:57 000b5c80-c9b8-11e7-a1c1-0242ac110003    
2017-12-14 10:00:34 000b5c80-c9b8-11e7-a1c1-0242ac110003    0 years 0 mons 30 days 0 hours 0 mins 0.00 secs
2018-01-14 10:07:17 000b5c80-c9b8-11e7-a1c1-0242ac110003    0 years 0 mons 31 days 0 hours 0 mins 0.00 secs
2016-12-24 10:15:58 0017c8ad-b252-11e6-b4db-0100ab184d8f    
2017-01-24 10:54:49 0017c8ad-b252-11e6-b4db-0100ab184d8f    0 years 0 mons 31 days 0 hours 0 mins 0.00 secs

Month       Number of people who skipped subscription 
2017-03-1    14
2017-04-1    8
2017-05-1    4

给我一个这样的结果：

    select timestamp_, person_id, row_number() 
           over (partition by person_id order by timestamp_)
    from orders

    select timestamp_, person_id,
      (date_trunc('month', timestamp_) - date_trunc('month',timestamp_)) 
      over (partition by person_id order by timestamp_))
    from orders;

timestamp_              person_id                           lag
2017-03-14 12:38:38 00050c43-08c5-11e7-b433-01007e15dd78    
2017-04-14 10:04:13 00050c43-08c5-11e7-b433-01007e15dd78    0 years 0 mons 31 days 0 hours 0 mins 0.00 secs
2017-07-14 10:05:17 00050c43-08c5-11e7-b433-01007e15dd78    0 years 0 mons 91 days 0 hours 0 mins 0.00 secs
2017-08-14 10:02:37 00050c43-08c5-11e7-b433-01007e15dd78    0 years 0 mons 31 days 0 hours 0 mins 0.00 secs
2017-09-14 10:04:37 00050c43-08c5-11e7-b433-01007e15dd78    0 years 0 mons 31 days 0 hours 0 mins 0.00 secs
2017-10-14 10:02:08 00050c43-08c5-11e7-b433-01007e15dd78    0 years 0 mons 30 days 0 hours 0 mins 0.00 secs
2017-11-14 10:05:35 00050c43-08c5-11e7-b433-01007e15dd78    0 years 0 mons 31 days 0 hours 0 mins 0.00 secs
2017-12-14 10:02:52 00050c43-08c5-11e7-b433-01007e15dd78    0 years 0 mons 30 days 0 hours 0 mins 0.00 secs
2018-01-14 10:05:38 00050c43-08c5-11e7-b433-01007e15dd78    0 years 0 mons 31 days 0 hours 0 mins 0.00 secs
2017-11-15 03:54:57 000b5c80-c9b8-11e7-a1c1-0242ac110003    
2017-12-14 10:00:34 000b5c80-c9b8-11e7-a1c1-0242ac110003    0 years 0 mons 30 days 0 hours 0 mins 0.00 secs
2018-01-14 10:07:17 000b5c80-c9b8-11e7-a1c1-0242ac110003    0 years 0 mons 31 days 0 hours 0 mins 0.00 secs
2016-12-24 10:15:58 0017c8ad-b252-11e6-b4db-0100ab184d8f    
2017-01-24 10:54:49 0017c8ad-b252-11e6-b4db-0100ab184d8f    0 years 0 mons 31 days 0 hours 0 mins 0.00 secs

Month       Number of people who skipped subscription 
2017-03-1    14
2017-04-1    8
2017-05-1    4

我需要帮助将这两个查询结合起来，并按月应用

分组来计算跳过该月的人数：

select month, count(person_id) as skips
from ( some inner query)
group by month

要得到这样的东西：

    select timestamp_, person_id, row_number() 
           over (partition by person_id order by timestamp_)
    from orders

    select timestamp_, person_id,
      (date_trunc('month', timestamp_) - date_trunc('month',timestamp_)) 
      over (partition by person_id order by timestamp_))
    from orders;

timestamp_              person_id                           lag
2017-03-14 12:38:38 00050c43-08c5-11e7-b433-01007e15dd78    
2017-04-14 10:04:13 00050c43-08c5-11e7-b433-01007e15dd78    0 years 0 mons 31 days 0 hours 0 mins 0.00 secs
2017-07-14 10:05:17 00050c43-08c5-11e7-b433-01007e15dd78    0 years 0 mons 91 days 0 hours 0 mins 0.00 secs
2017-08-14 10:02:37 00050c43-08c5-11e7-b433-01007e15dd78    0 years 0 mons 31 days 0 hours 0 mins 0.00 secs
2017-09-14 10:04:37 00050c43-08c5-11e7-b433-01007e15dd78    0 years 0 mons 31 days 0 hours 0 mins 0.00 secs
2017-10-14 10:02:08 00050c43-08c5-11e7-b433-01007e15dd78    0 years 0 mons 30 days 0 hours 0 mins 0.00 secs
2017-11-14 10:05:35 00050c43-08c5-11e7-b433-01007e15dd78    0 years 0 mons 31 days 0 hours 0 mins 0.00 secs
2017-12-14 10:02:52 00050c43-08c5-11e7-b433-01007e15dd78    0 years 0 mons 30 days 0 hours 0 mins 0.00 secs
2018-01-14 10:05:38 00050c43-08c5-11e7-b433-01007e15dd78    0 years 0 mons 31 days 0 hours 0 mins 0.00 secs
2017-11-15 03:54:57 000b5c80-c9b8-11e7-a1c1-0242ac110003    
2017-12-14 10:00:34 000b5c80-c9b8-11e7-a1c1-0242ac110003    0 years 0 mons 30 days 0 hours 0 mins 0.00 secs
2018-01-14 10:07:17 000b5c80-c9b8-11e7-a1c1-0242ac110003    0 years 0 mons 31 days 0 hours 0 mins 0.00 secs
2016-12-24 10:15:58 0017c8ad-b252-11e6-b4db-0100ab184d8f    
2017-01-24 10:54:49 0017c8ad-b252-11e6-b4db-0100ab184d8f    0 years 0 mons 31 days 0 hours 0 mins 0.00 secs

Month       Number of people who skipped subscription 
2017-03-1    14
2017-04-1    8
2017-05-1    4

想到的方法是在每个人的第一个时间戳和最后一个时间戳之间为每个人每月生成一行（您可能希望使用固定日期）

然后，检查哪些有订单：

select m.mon, count(*) as num_missing
from generate_series('2017-03-01'::timestamp, '2017-05-01'::timestamp, interval '1 month') m(mon) join
     (select person_id, min(timestamp_) as mints, max(timestamp_) as maxts
      from orders
      group by person_id
     ) p
     on m.mon between date_trunc('month', mints) and date_trunc('month', maxtx) left join
     orders o
     on p.person_id = o.personid and m.mon = date_trunc('month', o.timestamp_)
where o.person_id is null
group by m.mon
order by m.mon;

想到的方法是在每个人的第一个时间戳和最后一个时间戳之间为每个人每月生成一行（您可能希望使用固定日期）

然后，检查哪些有订单：

select m.mon, count(*) as num_missing
from generate_series('2017-03-01'::timestamp, '2017-05-01'::timestamp, interval '1 month') m(mon) join
     (select person_id, min(timestamp_) as mints, max(timestamp_) as maxts
      from orders
      group by person_id
     ) p
     on m.mon between date_trunc('month', mints) and date_trunc('month', maxtx) left join
     orders o
     on p.person_id = o.personid and m.mon = date_trunc('month', o.timestamp_)
where o.person_id is null
group by m.mon
order by m.mon;

如果一个人有2017年1月至2017年12月的记录，即缺少2018年1月的条目，我如何知道该人当时是否已结束订阅，或者只是略过几个月？我是否应将2018年1月计算为此人的跳过日期？（如果您回答：“跳过”，那么：如果最后一个月是1997年12月，那么此人是否仅仅跳过了20年？；-）好问题，假设我们有一个终止订阅的事件，因此我们可以划分skipsIf某人从2017年1月到2017年12月的记录，即缺少2018年1月的条目，我怎么知道那个人当时是结束了订阅，还是只是略过了几个月？我是否应将2018年1月计算为此人的跳过日期？（如果你回答：“跳过”，那么：如果最后一个月是1997年12月，那么这个人仅仅跳过了20年吗？；-）好问题，假设我们有一个终止订阅的事件，这样我们就可以在where子句中为SKIPPSIN划分这些。person_id

，看起来像一个打字错误，因为没有t别名。@maxTrialfire。是的，这是一个输入错误。在where子句中，您有

t.person\u id

，看起来像是一个输入错误，因为没有t别名。@maxTrialfire。是的，那是个打字错误。