AccountID, 'DEF Account' AS AccountName, 1 AS CompanyID, 'Stark Enterprise' AS CompanyName
SELECT DENSE_RANK根据我对SELECT DENSE_RANK() OVER (PARTITION BY AccountID ORDER BY CompanyName) as AccountRANK, * FROM #tmpAccountsTable的理解,应该为所有相同的AccountId创建一个分区,并
我试图使用动态sql构建一个查询,如下所示, dense_rank() over(partition by column1,column2 order by column2),
dense_rank () over(partition by column1,column2,column3 order column3) from tablename
是否有人必须在kdb+中模拟SQL的rank()、dense_rank()和row_number()的结果?下面是一些用来演示这些特性的SQL。如果有人在下面有一个具体的解决方案,也许我可以将其推广到支持多分区和按列排序--并在这个站点上发布。, RANK() OVER (PARTITION BY course ORDER BY mark DESC) AS rank,
我目前的工作是预测从仓库到相应商店的商品需求。但为了预测,我需要至少有2个时间序列的每个产品从仓库到他们各自的商店。SELECT t.date,t.Qty,t.ItemID,t.Warehouse,t.Store Warehouse,ItemID,Count (*) OVER (PARTITION BY ItemID,Warehouse,Store ) as cnt GROUP BY date,Warehouse,ItemID,Store)t
WHERE cnt &