我有这样的疑问
SELECT Ets_Se_Categorie AS Ets_Se_Categorie,COUNT(Ets_Se_Categorie) AS cpte
FROM TableV_MyTest
GROUP BY Ets_Se_Categorie
它给出了下表/结果
Ets_Se_Categorie cpte
(Seems to be empty string) 5531
Old place
以下代码:
SELECT ets.event_id, ets.event_date, dtn.don_date, dtn.don_date - ets.event_date AS date_diff FROM events ets, donation dtn
GROUP BY ets.event_id, ets.event_date, dtn.don_date
Order BY ets.event_id;
产生以下结果:
我的目标是获得每个event_id的event_date和don_date之间的平均持续时间,但我似乎找不到方法。
假设我有一个ets表,如下所示:
I = ets:new(mytable, [named_table, set]).
ets:insert(I, {10,{10, 4 ,"description"}).
我想使用ets:update_counter更新元素4。
我尝试了不同的方法,但找不到解决方案,例如:
ets:update_counter(I, 10 , {3,1}).
** exception error: bad argument
in function ets:update_counter/3
called as ets:update_c
我导出了一个读取2个配置文件的项目。但是,当我执行jar时,它得到这个错误:
Error: java.io.FileNotFoundException: config/dbConnection.config (No such file or directory)
Error: java.io.FileNotFoundException: config/test.config (No such file or directory)
com.smart.ets.services.ServiceException: com.smart.ets.services.ConfigE
SQL查询:
SELECT
'ETS' AS dataSource,
ES.ShowId,
ES.Title,
EE.EpisodeId,
EE.Title AS EpisodeTitle,
ELG.GenreDescription
AS Genre,
ELST.ShowTypeDescription,
dbo.TVData_UDF_ETS_DirectorString(ES.ShowId) AS DirectorName,
ES.ReleaseYear,
EE.SeasonEpisode
FROM dbo.ETS_Shows AS ES
LEF
我需要一个简单的子选择,如下所示
SELECT ets.agreement_t.id AS ets_agreement_t_id,
...,
(select max(created_date) from ets.agreement_history_t where agreement_group_id = ets.agreement_t.agreement_group_id AS "LastApprovalDate"),
...
在SqlAlchemy中我创建了.subquery()
subqueryLastApprovalDa
在我的查询中,我需要检查对于协议ID (例如45),该表中的3列requester_id、approving_official_id、second_approving_official_id中是否有任何列引用了与特定uniqueidentifier (例如123)相对应的NED_PERSON_T.ID。我只需要返回第一个匹配项。 我比较了以下几种方法的性能: SELECT ets.agreement_t.id AS ets_agreement_t_id
FROM ets.agreement_t
JOIN ned_person_t AS ned_person_t_1
ON e
使用此代码:
#plotting time series from year 1998 to 2008
year.time_series <- ts(t_AMOUNT,start = c(1998) , frequency = 12 ) #Monthly 12
plot(year.time_series)
#splitting the timeseries for further model evaluation
train <- window(year.timeseries, start=1998,end=2005)
test <- wi