根据日期运行计数器



我有一个带有警报历史记录的表,包含开始日期,end_date和警报的原因。

我想在最后30天的每个日期计算当天发生的总警报,这意味着如果警报开始了第1天,并且仍在进行(结束日期为null),那么它将计算在一天之内的所有天数1到持续。

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;

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

问题在于我获得了重复,一式三份等。

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

如果您需要在一天级别的数据,则必须在将时间戳列后应用不同的子句。

像以下类似的东西 -

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;

您提供的数据并不重复,因为它包含每个记录的唯一时间戳。

希望这有帮助

尝试以下代码。

日期表包含过去30天的所有日期,包括今天。

我还将您的JOIN语法更改为较新的表格。

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

我希望我有帮助!

,因为您想每天发生所有警报(警报本来可以在前一天开始,或者在以后的一天结束),然后您想使用聚合按日分组(可能是其他条件),而不是您在查询中显示的分析数。要执行骨料,在给定的一天中具有多余的重复物,您需要消除提供非不同值的列。通常,这是您的警报开始和结束日期,以及asset_idgeofence_id

以下查询将为您提供对过去30天中每一个发生的请求的group_idreason_id S发生的变化计数。

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;

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