Oracle ---- 窗口函数（一）

窗口函数的名字是over()函数，常用的有两个属性partition by和order by，partition by类似于group by，我们通常将group by叫做分组，而partition by称作分区。

一般结构为：

Function(arg1 , arg2 ……) over(partition by clause order by clause windowing clause )

Windowing clause : rows | range between start_expr and end_expr
Start_expr is unbounded preceding | current row | n preceding | n following
End_expr is unbounded following | current row | n preceding | n following

Function可以是下面函数，后面有星号 （*） 的函数允许完整的语法，包括windowing_clause。

AVG *
CLUSTER_DETAILS
CLUSTER_DISTANCE
CLUSTER_ID
CLUSTER_PROBABILITY
CLUSTER_SET
CORR *
COUNT *
COVAR_POP *
COVAR_SAMP *
CUME_DIST
DENSE_RANK
FEATURE_DETAILS
FEATURE_ID
FEATURE_SET
FEATURE_VALUE
FIRST
FIRST_VALUE *
LAG
LAST
LAST_VALUE *
LEAD
LISTAGG
MAX *
MIN *
NTH_VALUE *
NTILE
PERCENT_RANK
PERCENTILE_CONT
PERCENTILE_DISC
PREDICTION
PREDICTION_COST
PREDICTION_DETAILS
PREDICTION_PROBABILITY
PREDICTION_SET
RANK
RATIO_TO_REPORT
REGR_ (Linear Regression) Functions *
ROW_NUMBER
STDDEV *
STDDEV_POP *
STDDEV_SAMP *
SUM *
VAR_POP *
VAR_SAMP *
VARIANCE *

Windowing clause 指定分析函数的对象物理或逻辑行集（ ROWS | RANGE ）。

举个例子。

conn test/test@localhost:1521/pdb

create table test_tab (student_id number, subject_id number, score number);

insert into test_tab values(1,1,90);
insert into test_tab values(1,2,98);
insert into test_tab values(1,3,99);
insert into test_tab values(1,4,95);
insert into test_tab values(2,1,98);
insert into test_tab values(2,2,95);
insert into test_tab values(2,3,98);
insert into test_tab values(2,4,97);
insert into test_tab values(3,1,93);
insert into test_tab values(3,2,94);
insert into test_tab values(3,3,94);
insert into test_tab values(3,4,91);
commit;

--以“subject_id”分区，找出每个人和相同“subject_id”的平均“score”的偏离值。

SQL> set autot on
SQL> select t.*,(t.score-avg(t.score) over( partition by t.subject_id)) as gaps
from test_tab t
order by student_id,subject_id;  2    3

STUDENT_ID SUBJECT_ID      SCORE       GAPS
---------- ---------- ---------- ----------
         1          1         90 -3.6666667
         1          2         98 2.33333333
         1          3         99          2
         1          4         95 .666666667
         2          1         98 4.33333333
         2          2         95 -.66666667
         2          3         98          1
         2          4         97 2.66666667
         3          1         93 -.66666667
         3          2         94 -1.6666667
         3          3         94         -3
         3          4         91 -3.3333333

12行が選択されました。


実行計画
----------------------------------------------------------
Plan hash value: 2491645504

--------------------------------------------------------------------------------
| Id  | Operation           | Name     | Rows  | Bytes | Cost (%CPU)| Time     |
--------------------------------------------------------------------------------
|   0 | SELECT STATEMENT    |          |    12 |   108 |     5  (40)| 00:00:01 |
|   1 |  SORT ORDER BY      |          |    12 |   108 |     5  (40)| 00:00:01 |
|   2 |   WINDOW SORT       |          |    12 |   108 |     5  (40)| 00:00:01 |
|   3 |    TABLE ACCESS FULL| TEST_TAB |    12 |   108 |     3   (0)| 00:00:01 |
--------------------------------------------------------------------------------


統計
----------------------------------------------------------
         59  recursive calls
         23  db block gets
         99  consistent gets
          1  physical reads
       4080  redo size
       1158  bytes sent via SQL*Net to client
        608  bytes received via SQL*Net from client
          2  SQL*Net roundtrips to/from client
          7  sorts (memory)
          0  sorts (disk)
         12  rows processed

--不使用窗口函数取得上面的结果。

select t1.*, (t1.score - t3.avgs) as gaps
  from test_student_score t1,
       (select t2.subject_id, avg(t2.score) as avgs
          from test_student_score t2
         group by t2.subject_id) t3
where t1.subject_id = t3.subject_id
order by t1.student_id,t1.subject_id;

STUDENT_ID SUBJECT_ID      SCORE       GAPS
---------- ---------- ---------- ----------
         1          1         90 -3.6666667
         1          2         98 2.33333333
         1          3         99          2
         1          4         95 .666666667
         2          1         98 4.33333333
         2          2         95 -.66666667
         2          3         98          1
         2          4         97 2.66666667
         3          1         93 -.66666667
         3          2         94 -1.6666667
         3          3         94         -3
         3          4         91 -3.3333333

12行が選択されました。


実行計画
----------------------------------------------------------
Plan hash value: 1945508744

------------------------------------------------------------------------------------------
| Id  | Operation           | Name               | Rows  | Bytes | Cost (%CPU)| Time     |
------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT    |                    |    36 |  2772 |     7  (15)| 00:00:01 |
|   1 |  SORT GROUP BY      |                    |    36 |  2772 |     7  (15)| 00:00:01 |
|*  2 |   HASH JOIN         |                    |    36 |  2772 |     6   (0)| 00:00:01 |
|   3 |    TABLE ACCESS FULL| TEST_STUDENT_SCORE |    12 |   612 |     3   (0)| 00:00:01 |
|   4 |    TABLE ACCESS FULL| TEST_STUDENT_SCORE |    12 |   312 |     3   (0)| 00:00:01 |
------------------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------

   2 - access("T1"."SUBJECT_ID"="T2"."SUBJECT_ID")

Note
-----
   - dynamic statistics used: dynamic sampling (level=2)


統計
----------------------------------------------------------
        374  recursive calls
          0  db block gets
        363  consistent gets
          0  physical reads
          0  redo size
       1158  bytes sent via SQL*Net to client
        608  bytes received via SQL*Net from client
          2  SQL*Net roundtrips to/from client
         28  sorts (memory)
          0  sorts (disk)
         12  rows processed

简单比较一下，似乎使用窗口函数时的COST更小。
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