PostgreSQL表相同数据最后一次相邻发生和第一行

一尘不染

PostgreSQL表相同数据最后一次相邻发生和第一行

sql

我有一个程序，该程序每分钟通过PING检查网络中计算机的状态。每次它将向数据库插入新行，如下所示（我使用的是postgresql）

id_status   status   checking_time(timestamp)   id_device(int)



1           OK       '2017-01-01 00:00:00'      1
2           OK       '2017-01-01 00:00:00'      2
3           OK       '2017-01-01 00:00:00'      3

4           Failed   '2017-01-01 00:01:00'      1
5           OK       '2017-01-01 00:01:00'      2
6           OK       '2017-01-01 00:01:00'      3

7           Failed   '2017-01-01 00:02:00'      1
8           OK       '2017-01-01 00:02:00'      2
9           OK       '2017-01-01 00:02:00'      3

10          Failed   '2017-01-01 00:03:00'      1
11          OK       '2017-01-01 00:03:00'      2
12          OK       '2017-01-01 00:03:00'      3

13          OK       '2017-01-01 00:04:00'      1
14          OK       '2017-01-01 00:04:00'      2
15          OK       '2017-01-01 00:04:00'      3

我希望结果如下

status   from_time(timestamp)    to_time(timestamp)      id_device(int)



OK       '2017-01-01 00:00:00'   '2017-01-01 00:01:00'   1
Failed   '2017-01-01 00:01:00'   '2017-01-01 00:04:00'   1
OK       '2017-01-01 00:04:00'   NOW                     1

OK       '2017-01-01 00:00:00'   NOW                     2
OK       '2017-01-01 00:00:00'   NOW                     3

如何获得此输出？

阅读 167

2021-05-16

共1个答案

一尘不染

这是差距和孤岛的问题。可以解决如下：

select t.status, 
   t.from_time, 
   coalesce(CAST(lead(from_time) over (partition by id_device order by from_time) AS varchar(20)), 'NOW') to_date, 
   t.id_device
from
(
    select t.status, min(checking_time) from_time, t.id_device
    from
    (
        select *, row_number() over (partition by id_device, status order by checking_time) - 
                  row_number() over (partition by id_device order by checking_time) grn
        from data
    ) t
    group by t.id_device, grn, t.status
) t
order by  t.id_device, t.from_time

dbffile演示

关键是最嵌套的子查询，在该子查询中，我使用两个row_number函数来隔离设备上相同状态的连续出现。一旦有了grn价值，剩下的就很容易了。

结果

status  from_time           to_time             id_device
------------------------------------------------------------
OK      2017-01-01 00:00:00 2017-01-01 00:01:00 1
Failed  2017-01-01 00:01:00 2017-01-01 00:04:00 1
OK      2017-01-01 00:04:00 NOW                 1
OK      2017-01-01 00:00:00 NOW                 2
OK      2017-01-01 00:00:00 NOW                 3

2021-05-16