基于日期的运行计数器

时间:2019-02-15 08:48:46

标签: sql oracle

我有一个包含警报历史记录的表,其中包含开始日期,结束日期和警报原因。

我希望最近30天的每个日期都可以计算当天发生的警报总数,这意味着如果警报从第1天开始并且仍在持续(结束日期为空),则它将计算从该天起的所有天数1倒数。

这是我提出的查询

select cal.trunc_date,assets.group_id,
       alert.*,
       count( alert.asset_id)
             over (PARTITION BY alert.REASON_ID ORDER BY 
                 cal.trunc_date) TOTAL_ASSETS
from g_alert_history alert,
  v_app_calendar cal,V_ACTIVE_ASSETS assets
where REASON_ID in (1,2)
  and assets.asset_id=alert.asset_id
  and assets.group_id=1462
  and cal.trunc_date >= trunc(systimestamp - 30)
  and alert.START_DATE_DEVICE >= trunc(systimestamp - 30)
  and alert.START_DATE_DEVICE >= cal.trunc_date
  and alert.START_DATE_DEVICE  <= cal.trunc_date +1
  and nvl (alert.END_DATE_DEVICE, systimestamp)
  >=cal.trunc_date;

视图v_app_calendar包含日期,而V_ACTIVE_ASSETS包含我要检查的group_id

问题是我得到重复,重复等

这是结果:

TRUNC_DATE  GROUP_ID  REASON_ID  ASSET_ID  GEOFENCE_ID  START_DATE_DEVICE                END_DATE_DEVICE                  TOTAL_ASSETS
---------   --------  ---------  --------  -----------  -------------------------------  -------------------------------  ------------
03-FEB-19       1462          1      1704          134  03-FEB-19 11.50.09.385000000 AM                                             13
03-FEB-19       1462          1      1704          134  03-FEB-19 11.55.09.475000000 AM                                             13
03-FEB-19       1462          1      1704          134  03-FEB-19 12.00.10.073000000 PM                                             13
03-FEB-19       1462          1      1704          134  03-FEB-19 12.05.11.126000000 PM                                             13
03-FEB-19       1462          1      1704          134  03-FEB-19 12.10.12.668000000 PM                                             13
03-FEB-19       1462          1      1704          134  03-FEB-19 12.15.12.858000000 PM                                             13
03-FEB-19       1462          1      1704          134  03-FEB-19 11.45.09.283000000 AM                                             13
03-FEB-19       1462          1      1704          134  03-FEB-19 11.20.03.587000000 AM                                             13
03-FEB-19       1462          1      1704          134  03-FEB-19 11.25.05.434000000 AM                                             13
03-FEB-19       1462          1      1704          134  03-FEB-19 11.30.07.294000000 AM                                             13
03-FEB-19       1462          1      1704          134  03-FEB-19 11.35.09.141000000 AM                                             13
03-FEB-19       1462          1      1704          134  03-FEB-19 11.40.09.251000000 AM                                             13
03-FEB-19       1462          1      1704          134  03-FEB-19 12.20.14.178000000 PM                                             13
05-FEB-19       1462          1      1663          134  05-FEB-19 02.33.02.475000000 PM                                             14
09-FEB-19       1462          1      1663          134  09-FEB-19 09.33.02.475000000 PM  09-FEB-19 11.33.22.475000000 PM            16
09-FEB-19       1462          1      1782          149  09-FEB-19 02.33.02.475000000 PM  09-FEB-19 02.36.02.475000000 PM            16
11-FEB-19       1462          1      2647          134  11-FEB-19 09.56.08.325000000 AM                                            140
11-FEB-19       1462          1      2647          164  11-FEB-19 09.56.08.325000000 AM                                            140
11-FEB-19       1462          1      2646          164  11-FEB-19 10.03.31.611000000 AM                                            140
11-FEB-19       1462          1      2646          134  11-FEB-19 10.03.31.611000000 AM                                            140
11-FEB-19       1462          1      1781          164  11-FEB-19 10.14.09.612000000 AM                                            140
11-FEB-19       1462          1      2647          134  11-FEB-19 11.55.20.281000000 AM                                            140
11-FEB-19       1462          1      1781          134  11-FEB-19 10.14.09.612000000 AM                                            140
11-FEB-19       1462          1      2647          164  11-FEB-19 10.55.32.300000000 AM                                            140
11-FEB-19       1462          1      1781          134  11-FEB-19 02.52.45.104000000 PM                                            140
11-FEB-19       1462          1      1781          164  11-FEB-19 03.20.40.461000000 PM                                            140
11-FEB-19       1462          1      1781          134  11-FEB-19 03.20.40.461000000 PM                                            140
11-FEB-19       1462          1      1781          164  11-FEB-19 08.28.13.331000000 PM                                            140
11-FEB-19       1462          1      1781          134  11-FEB-19 08.28.13.331000000 PM                                            140
11-FEB-19       1462          1      1781          134  11-FEB-19 03.20.42.461000000 PM                                            140
11-FEB-19       1462          1      1781          134  11-FEB-19 08.28.25.939000000 PM                                            140
11-FEB-19       1462          1      1781          164  11-FEB-19 08.28.25.939000000 PM                                            140

3 个答案:

答案 0 :(得分:0)

如果您需要日间的数据,则必须在将时间戳列转换为日期后再应用与之不同的子句。

类似下面的东西-

select cal.trunc_date,assets.group_id,
       alert.req_col,
       cast(alert.start_date_device as date),
       cast(alert.end_date_device as date)
       count( alert.asset_id)
             over (PARTITION BY alert.REASON_ID ORDER BY 
                 cal.trunc_date) TOTAL_ASSETS
from g_alert_history alert,
  v_app_calendar cal,V_ACTIVE_ASSETS assets
where REASON_ID in (1,2)
  and assets.asset_id=alert.asset_id
  and assets.group_id=1462
  and cal.trunc_date >= trunc(systimestamp - 30)
  and alert.START_DATE_DEVICE >= trunc(systimestamp - 30)
  and alert.START_DATE_DEVICE >= cal.trunc_date
  and alert.START_DATE_DEVICE  <= cal.trunc_date +1
  and nvl (alert.END_DATE_DEVICE, systimestamp)
  >=cal.trunc_date;

您提供的数据不是重复的,因为它包含每个记录的唯一时间戳。

希望这会有所帮助

答案 1 :(得分:0)

尝试以下代码。

“日期”表包含过去30天(包括今天)的所有日期。

我还将您的JOIN语法更改为一种新的形式。

with dates as (
    select trunc(sysdate) - (level - 1) trunc_date from dual connect by level<=30
)
select dates.trunc_date
     , count(alert.asset_id)
  from g_alert_history alert
  join v_app_calendar cal
    on (alert.START_DATE_DEVICE between cal.trunc_date and (cal.trunc_date +1)
        and nvl (alert.END_DATE_DEVICE, systimestamp) >= cal.trunc_date )
  join V_ACTIVE_ASSETS assets
    on (assets.asset_id=alert.asset_id)
 where REASON_ID in (1,2)
   and dates.trunc_date between trunc(alert.START_DATE_DEVICE) and nvl(alert.END_DATE_DEVICE, trunc(sysdate))
   and cal.trunc_date >= trunc(systimestamp - 30)
   and assets.group_id=1462
 group by dates.trunc_date

希望我能帮上忙!

答案 2 :(得分:0)

由于您希望对当天发生的所有警报进行每日计数(警报可能在前一天开始,或在以后的一天结束),因此您要使用按天分组的汇总计数(可能还有其他条件),而不是查询中显示的分析计数。要在给定的一天中没有过多重复执行汇总,您需要消除提供非唯一值的列。主要是您的警报开始和结束日期,以及asset_idgeofence_id

下面的查询将为您提供请求的group_idreason_id在最近30天内发生的变更次数。

select cal.trunc_date
     , assets.group_id
     , alert.reason_id
     , count( alert.asset_id) TOTAL_ASSETS
  from g_alert_history alert
  join V_ACTIVE_ASSETS assets
    on assets.asset_id=alert.asset_id
  join v_app_calendar cal
    on alert.START_DATE_DEVICE < cal.trunc_date + 1
   and (alert.END_DATE_DEVICE is null or cal.trunc_date <= alert.END_DATE_DEVICE)
 where alert.REASON_ID in (1,2)
   and assets.group_id=1462
   and cal.trunc_date between trunc(sysdate - 30) and sysdate
 group by cal.trunc_date
     , assets.group_id
     , alert.reason_id
 order by cal.trunc_date
     , assets.group_id
     , alert.reason_id;