一、窗口函數

OVER()指定分析函數工作的數據窗口大小，這個數據窗口大小可能會隨著行的變而變化

• CURRENT ROW：當前行

• n PRECEDING：往前n行數據

• n FOLLOWING：往后n行數據

• UNBOUNDED：起點，UNBOUNDED PRECEDING 表示從前面的起點， UNBOUNDED FOLLOWING表示到后面的終點

• LAG(col,n)：往前第n行數據

• LEAD(col,n)：往后第n行數據

• NTILE(n)：把有序分區中的行分發到指定數據的組中，各個組有編號，編號從1開始，對于每一行，NTILE返回此行所屬的組的編號。注意：n必須為int類型。

select name,orderdate,cost, sum(cost) over() as sample1,--所有行相加 sum(cost) over(partition by name) as sample2,--按name分組，組內數據相加 sum(cost) over(partition by name order by orderdate) as sample3,--按name分組，組內數據累加 sum(cost) over(partition by name order by orderdate rows between UNBOUNDED PRECEDING and current row ) as sample4 ,--和sample3一樣,由起點到當前行的聚合 sum(cost) over(partition by name order by orderdate rows between 1 PRECEDING and current row) as sample5, --當前行和前面一行做聚合 sum(cost) over(partition by name order by orderdate rows between 1 PRECEDING AND 1 FOLLOWING ) as sample6,--當前行和前邊一行及后面一行 sum(cost) over(partition by name order by orderdate rows between current row and UNBOUNDED FOLLOWING ) as sample7 --當前行及后面所有行 from business;select name,orderdate,cost, lag(orderdate,1,'1900-01-01') over(partition by name order by orderdate ) as time1, lag(orderdate,2) over (partition by name order by orderdate) as time2 from business;查詢前20%時間的訂單信息 select * from (select name,orderdate,cost, ntile(5) over(order by orderdate) sortedfrom business ) t where sorted = 1;

二、案例

• 統計截止到當前時間段的店鋪累計銷售金額

select shop_id, stat_date, ordamt, sum(ordamt) over(partition by shop_id order by stat_date) from rt_data where dt = '2015-01-11' and shop_id = 10026;10026 201501120030 5170 5170 10026 201501120100 5669 10839 10026 201501120130 2396 13235 10026 201501120200 1498 14733 10026 201501120230 1997 16730 10026 201501120300 1188 17918 10026 201501120330 598 18516 10026 201501120400 479 18995 10026 201501120430 1587 20582 10026 201501120530 799 21381

• 統計每個時間段的銷售占比

select shop_id, stat_date, ordamt, ordamt / sum(ordamt) over(partition by shop_id) from rt_data where dt = '2015-01-11' and shop_id = 10026 order by stat_date;10026 201501120900 5406 0.014227 10026 201501120930 13098 0.034471 10026 201501121000 15679 0.041263 10026 201501121030 12134 0.031934 10026 201501121100 26259 0.069107 10026 201501121130 16516 0.043466 10026 201501121200 20127 0.052969

• 找出2點的銷售金額及前半小時的銷售金額和后1個小時的銷售金額

  lag(field, N)是取前N行的值，lead(field, N)是取后N行的值。

select * from ( select shop_id, stat_date,ordamt, lag(stat_date,1) over(partition by shop_id order by stat_date), lag(ordamt,1) over(partition by shop_id order by stat_date), lead(stat_date,2) over(partition by shop_id order by stat_date),lead(ordamt,2) over(partition by shop_id order by stat_date) from rt_data where dt = '2015-01-11' and shop_id = 10026 ) t where stat_date = 201501120200;10026 201501120200 1498 201501120130 2396 201501120300 1188

• 按銷售金額排序，取出前20%的時間段和相應金額

select * from (select shop_id, stat_date, ordamt, ntile(5) over(partition by shop_id order by ordamt desc) sorted from rt_data where dt = '2015-01-11' and shop_id = 10026 ) t where sorted = 1;10026 201501121100 26259.0 1 10026 201501121200 20127.0 1 10026 201501121300 18138.0 1 10026 201501121130 16516.0 1 10026 201501121400 15956.0 1 10026 201501121000 15679.0 1 10026 201501122200 14290.0 1 10026 201501121430 14203.0 1 10026 201501121330 13290.0 1

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