红移:查找满足条件的前一行构成序列。

Question

一个星期以来，我一直在试图找到一个解决以下红移谜语的方法(我觉得我对它越来越着迷了)：

Redshift ("event_user_item")中有一个事件表，用户通过输入出现在event_value列中的项的代码来触发特定项的事件。

失败提交由event_type序列组成，但此类事件类型不一定是连续的，这意味着每个user_id之间都可以有许多其他事件类型。

我张贴了一个基于3种不同user_ids的小片段，它应该说明相关的场景，重点放在失败的提交上。

ord_num event_type          event_value     user_id     event_datetime
1       PageLoad                            124         03/09/2018 21:48:39
2       ItemCode            LG56731         124         03/09/2018 21:48:53
4       Details1PageLoad                    124         03/09/2018 21:48:56
8       PageLoad                            124         03/09/2018 22:02:23
9       ItemCode            GU07019         124         03/09/2018 22:02:32
10      ErrorResponse       Some message    124         03/09/2018 22:02:32
51      PageLoad                            228         04/09/2018 12:38:30
52      ItemCode            EQ23487         228         04/09/2018 12:38:33
53      ErrorResponse       Some message    228         04/09/2018 12:38:34
54      PageLoad                            304         04/09/2018 15:43:14
55      ItemCode            OB68102         304         04/09/2018 15:43:57
56      ErrorResponse       Some message    304         04/09/2018 15:43:58
57      ItemCode            PB68102         304         04/09/2018 15:44:21
58      ErrorResponse       Some message    304         04/09/2018 15:44:22
59      PageLoad                            304         05/09/2018 11:19:37
60      ItemCode            OB68102         304         05/09/2018 11:20:17
62      Details1PageLoad                    304         05/09/2018 11:20:20

目标:找到每个user_id每个ItemCode的失败提交数。重要的是不要混淆失败提交和成功提交的项目代码。此外，同一项代码也可能有多个失败项。

我不是Redshift的专家，尤其是它的窗口功能，但我尝试坚持的第一个想法是滞后函数。为了做到这一点，我打算识别符合计数条件的ord_nums序列，例如

ord_num event_type          event_value     user_id event_datetime           error?     sequence
1       PageLoad                            124     03/09/2018 21:48:39     
2       ItemCode            LG56731         124     03/09/2018 21:48:53     
4       Details1PageLoad                    124     03/09/2018 21:48:56     
8       PageLoad                            124     03/09/2018 22:02:23     
9       ItemCode            GU07019         124     03/09/2018 22:02:32     
10      ErrorResponse       Some message    124     03/09/2018 22:02:32     1       8-9-10
51      PageLoad                            228     04/09/2018 12:38:30     
52      ItemCode            EQ23487         228     04/09/2018 12:38:33     
53      ErrorResponse       Some message    228     04/09/2018 12:38:34     1       51-52-53
54      PageLoad                            304     04/09/2018 15:43:14     
55      ItemCode            OB68102         304     04/09/2018 15:43:57     
56      ErrorResponse       Some message    304     04/09/2018 15:43:58     1       54-55-56
57      ItemCode            PB68102         304     04/09/2018 15:44:21     
58      ErrorResponse       Some message    304     04/09/2018 15:44:22     1       54-57-58
59      PageLoad                            304     05/09/2018 11:19:37     
60      ItemCode            OB68102         304     05/09/2018 11:20:17     
62      Details1PageLoad                    304     05/09/2018 11:20:20     

因此，对user_id来说，应该有以下几点：

user_id     nr_failed_submissions   
124         1   
228         1   
304         2

但是，由于从上面的数据集和预期的结果中可以看到，无法预测有多少记录要向后移动，我需要一个不能放在滞后内的附加条件。

我试过很多选择，但都不合适。

非常有用和有洞察力的职位

• lag function to get the last different value(redshift)，
• Last Non-Null Value in Redshift by Group
• How can I get the Redshift/Postgresql LAG window function to selectively exclude records?
• Assign a Sequence (session ID) to my table based on A value in field

但到目前为止，我还没有成功地将它们融合到解决方案中。在红班一定有办法做到这一点吗？

Answer 1

以下方法和查询基于的答案1，适合我，因为它们应该：

create temporary table items_per_pageload as 
with timeranges as (
  select A.user_id
    ,A.event_datetime as time1
    ,nvl(max(B.event_datetime), '2099-01-01') as time2
    ,LEAD(A.event_datetime,1) over (partition by A.user_id order by A.event_datetime) as next_load_time 
  from event_user_item as A 
  left join event_user_item as B on A.user_id=B.user_id and A.event_datetime < B.event_datetime and A.event_type=B.event_type
  where A.event_type='PageLoad' 
  group by A.user_id, A.event_datetime
  )
select timeranges.time1 as pageloadtime, event_user_item.* 
from event_user_item left join timeranges on event_user_item.event_datetime>=timeranges.time1 and event_user_item.event_datetime<nvl(timeranges.next_load_time,timeranges.time2) 
where event_user_item.event_type='ItemCode';

create temporary table pageloads_with_errors as 
with timeranges as (
  select A.user_id
    ,A.event_datetime as time1
    ,nvl(max(B.event_datetime), '2099-01-01') as time2
    ,LEAD(A.event_datetime,1) over (partition by A.user_id order by A.event_datetime) as next_load_time 
  from event_user_item as A left join event_user_item as B on A.user_id=B.user_id and A.event_datetime < B.event_datetime and A.event_type=B.event_type
  where A.event_type='PageLoad' 
  group by A.user_id, A.event_datetime
  )
select timeranges.time1 as pageloadtime,timeranges.user_id,bool_or(event_user_item.event_type='ErrorResponse') as has_error 
from timeranges 
left join event_user_item on event_datetime > time1 and event_datetime < nvl(next_load_time,time2)
group by timeranges.time1,timeranges.user_id 
having has_error;

/* final counts */
select count(1), user_id, event_value from (
    select items_per_pageload.* 
    from items_per_pageload 
    join pageloads_with_errors on items_per_pageload.user_id = pageloads_with_errors.user_id and items_per_pageload.pageloadtime = pageloads_with_errors.pageloadtime 
) 
group by user_id, event_value;

Answer 2

此查询将创建“时间范围”，其中time1表示PageLoad事件的时间戳，time2表示该用户下一个PageLoad事件的时间戳：

WITH timeranges AS
(
  SELECT A.user_id,
         A.event_datetime AS time1,
         nvl(MAX(B.event_datetime),'2099-01-01') AS time2
  FROM foo AS A
    LEFT JOIN foo AS B
           ON A.user_id = B.user_id
          AND A.event_datetime < B.event_datetime
          AND A.event_type = B.event_type
  WHERE A.event_type = 'PageLoad'
  GROUP BY A.user_id,
           A.event_datetime
)

此查询的基础是将每个“ItemCode”事件与其相应的“PageLoad”的时间戳关联起来：

SELECT timeranges.time1 AS pageloadtime,
       foo.*
FROM foo
  LEFT JOIN timeranges
         ON foo.event_datetime >= timeranges.time1
        AND foo.event_datetime < timeranges.time2
WHERE foo.event_type = 'ItemCode'

此查询确定在每个“ErrorResponse”事件范围内是否有“”事件：

SELECT timeranges.time1 AS pageloadtime,
       timeranges.user_id,
       BOOL_OR(foo.event_type = 'ErrorResponse') AS has_error
FROM timeranges
  LEFT JOIN foo
         ON event_datetime > time1
        AND event_datetime < time2
GROUP BY timeranges.time1,
         timeranges.user_id
HAVING has_error;

这应该给我们所有我们需要的部分--对于每个页面事件，我们知道(1)页面是否有错误，(2)我们知道所有与有效负载相关的ItemCode事件。在这两个结果集之间加入应该会给我们我们正在寻找的东西。

redshift的一个特性给我尝试直接连接这两个数据集带来了一些麻烦，所以我不得不创建两个临时表。这个可怕的格式查询给了我预期的结果：

create temporary table items_per_pageload as 
with timeranges as (select A.user_id, A.event_datetime as time1, nvl(max(B.event_datetime), '2099-01-01') as time2 from event_user_item as A left join event_user_item as B on A.user_id=B.user_id and A.event_datetime < B.event_datetime and A.event_type=B.event_type
where A.event_type='PageLoad' group by A.user_id, A.event_datetime)
select timeranges.time1 as pageloadtime, event_user_item.* from event_user_item left join timeranges on event_user_item.event_datetime>=timeranges.time1 and event_user_item.event_datetime<timeranges.time2 where event_user_item.event_type='ItemCode'

create temporary table pageloads_with_errors as 
with timeranges as (select A.user_id, A.event_datetime as time1, nvl(max(B.event_datetime), '2099-01-01') as time2 from event_user_item as A left join event_user_item as B on A.user_id=B.user_id and A.event_datetime < B.event_datetime and A.event_type=B.event_type
where A.event_type='PageLoad' group by A.user_id, A.event_datetime)
select timeranges.time1 as pageloadtime, timeranges.user_id, bool_or(event_user_item.event_type='ErrorResponse') as has_error from timeranges left join event_user_item on event_datetime > time1 and event_datetime < time2
group by timeranges.time1, timeranges.user_id having has_error;

select count(1), user_id, event_value from (
select items_per_pageload.* from items_per_pageload join pageloads_with_errors on items_per_pageload.user_id = pageloads_with_errors.user_id and items_per_pageload.pageloadtime = pageloads_with_errors.pageloadtime 
) group by user_id, event_value