SQL Thinking
s2下半年我在内部有一次部门级别的技术分享会，以本文内容分享为主。
其实有很多人问过我相同的问题，遇到需要改写的慢sql，不知道怎么改，改好了以后也不知道等不等价？不等价了也不知道错在哪？这个要怎么破？
其实都是因为绝大多数人没有做过开发，看不懂sql，不会写sql，没有sql思维，下面通过几个案例将sql思维给引出来。
1、row_number() over() 逻辑实现
这种排名的窗口函数（row_number()、rank()、dense_rank()）据我所知，除了在oracle上大数据量执行性能表现得不错，其他各种数据库（包括列式存储的ap库），性能都表现都很一般。
所以如果在项目上遇到row_number() over() 性能不理想的情况下，可以尝试进行改写优化。
PS：当然可以通过加并行hint来提升效率，但是我们这篇文章主要是讲如何将数据库提供的逻辑通过另外一种方式来实现，提升自己sql代码思维。
-- student 数据量
obclient [YZJ]> select count(1) from student;
+----------+
| COUNT(1) |
+----------+
| 500001 |
+----------+
1 row in set (0.002 sec)
-- 案例sql
-- S.CLASS : 学科、包含null值
-- S.S_DATE: 入学时间
SELECT * FROM (
SELECT
S.*,
row_number() OVER(PARTITION BY S.CLASS ORDER BY S.S_DATE DESC) RN
FROM student S
) WHERE RN = 1;
27 rows in set (0.639 sec)
+---------------------------------------------------------------------------------------------------------------------------------------------------------------+
| Query Plan |
+---------------------------------------------------------------------------------------------------------------------------------------------------------------+
| ================================================================ |
| |ID|OPERATOR |NAME |EST.ROWS|EST.TIME(us)| |
| ---------------------------------------------------------------- |
| |0 |SUBPLAN SCAN |ANONYMOUS_VIEW1|4 |691395 | |
| |1 |└─WINDOW FUNCTION | |500001 |682867 | |
| |2 | └─PARTITION SORT | |500001 |249986 | |
| |3 | └─TABLE FULL SCAN|S |500001 |46352 | |
| ================================================================ |
| Outputs & filters: |
| ------------------------------------- |
| 0 - output([.ID], [.NAME], [.AGE], [.SEX], [.S_DATE], [.CLASS], [.RN]), filter([.RN = 1]), rowset=256 |
| access([.RN], [.ID], [.NAME], [.AGE], [.SEX], [.S_DATE], [.CLASS]) |
| 1 - output([S.ID], [S.NAME], [S.AGE], [S.SEX], [S.S_DATE], [S.CLASS], [T_WIN_FUN_ROW_NUMBER()]), filter(nil), rowset=256 |
| win_expr(T_WIN_FUN_ROW_NUMBER()), partition_by([S.CLASS]), order_by([S.S_DATE, DESC]), window_type(RANGE), upper(UNBOUNDED PRECEDING), lower(UNBOUNDED |
| FOLLOWING) |
| 2 - output([S.ID], [S.NAME], [S.AGE], [S.SEX], [S.S_DATE], [S.CLASS]), filter(nil), rowset=256 |
| sort_keys([HASH(S.CLASS), ASC], [S.CLASS, ASC], [S.S_DATE, DESC]) |
| 3 - output([S.ID], [S.NAME], [S.AGE], [S.SEX], [S.S_DATE], [S.CLASS]), filter(nil), rowset=256 |
| access([S.ID], [S.NAME], [S.AGE], [S.SEX], [S.S_DATE], [S.CLASS]), partitions(p0) |
| is_index_back=false, is_global_index=false, |
| range_key([S.ID]), range(MIN ; MAX)always true |
+---------------------------------------------------------------------------------------------------------------------------------------------------------------+
21 rows in set (0.004 sec)
1.1、改写方式一
改写窗口函数的逻辑，必须要将表关联2次或者2次+，需要将另外一张关联的表理解成一个“滑动”的窗口。
一般有两种改法：
- 方法1、相同表 join 两次。
- 方法2、表量子查询实现，将子查询的那张表理解成一个“滑动”的窗口。
SELECT s.* FROM student s -- 原表
INNER JOIN
(
SELECT
class,
max(s_date) max_date
FROM student
GROUP BY class
) s1 -- 滑动的窗口表
ON decode(s.class,NULL,'aab',s.class) = decode(s1.class,NULL,'aab',s1.class)
-- decode 处理 s表和s1窗口表的分组列class
AND s.s_date = s1.max_date
-- s表的s_date和窗口表s1的最大的日期去比较
WHERE exists(
SELECT 1 FROM student s2 where s1.max_date = s2.s_date GROUP BY s2.class having MAX(s2.ID) = S.ID
/* exists 这段比较抽象，我也是想了一会才实现逻辑。
实现 row_number() 唯一排名的数据
*/
);
27 rows in set (0.038 sec)
+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| Query Plan |
+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| ============================================================================================== |
| |ID|OPERATOR |NAME |EST.ROWS|EST.TIME(us)| |
| ---------------------------------------------------------------------------------------------- |
| |0 |SUBPLAN FILTER | |4 |28745 | |
| |1 |├─NESTED-LOOP JOIN | |8 |28555 | |
| |2 |│ ├─SUBPLAN SCAN |S1 |26 |27712 | |
| |3 |│ │ └─MERGE GROUP BY | |26 |27712 | |
| |4 |│ │ └─TABLE FULL SCAN |STUDENT(IDX_CLASS_SDATE_STUDENT)|500001 |14409 | |
| |5 |│ └─DISTRIBUTED TABLE RANGE SCAN |S(IDX_S_DATE_CALSS_ID) |1 |32 | |
| |6 |└─LIMIT | |1 |27 | |
| |7 | └─MERGE GROUP BY | |1 |27 | |
| |8 | └─DISTRIBUTED TABLE RANGE SCAN|S2(IDX_S_DATE_CALSS_ID) |56 |23 | |
| ============================================================================================== |
| Outputs & filters: |
| ------------------------------------- |
| 0 - output([S.ID], [S.NAME], [S.AGE], [S.SEX], [S.S_DATE], [S.CLASS]), filter([(T_OP_EXISTS, subquery(1))]), rowset=256 |
| exec_params_([S1.MAX_DATE(:0)], [S.ID(:1)]), onetime_exprs_(nil), init_plan_idxs_(nil), use_batch=false |
| 1 - output([S1.MAX_DATE], [S.ID], [S.NAME], [S.AGE], [S.SEX], [S.S_DATE], [S.CLASS]), filter(nil), rowset=256 |
| conds(nil), nl_params_([S1.CLASS(:4)], [S1.MAX_DATE(:5)]), use_batch=false |
| 2 - output([S1.CLASS], [S1.MAX_DATE]), filter(nil), rowset=256 |
| access([S1.CLASS], [S1.MAX_DATE]) |
| 3 - output([STUDENT.CLASS], [T_FUN_MAX(STUDENT.S_DATE)]), filter(nil), rowset=256 |
| group([STUDENT.CLASS]), agg_func([T_FUN_MAX(STUDENT.S_DATE)]) |
| 4 - output([STUDENT.CLASS], [STUDENT.S_DATE]), filter(nil), rowset=256 |
| access([STUDENT.CLASS], [STUDENT.S_DATE]), partitions(p0) |
| is_index_back=false, is_global_index=false, |
| range_key([STUDENT.CLASS], [STUDENT.S_DATE], [STUDENT.ID]), range(MIN,MIN,MIN ; MAX,MAX,MAX)always true |
| 5 - output([S.ID], [S.CLASS], [S.S_DATE], [S.NAME], [S.AGE], [S.SEX]), filter([ora_decode(cast(S.CLASS, VARCHAR2(1 BYTE)), cast(NULL, VARCHAR2(0 )), cast('aab', |
| VARCHAR2(3 BYTE)), cast(S.CLASS, VARCHAR2(3 BYTE))) = ora_decode(cast(:4, VARCHAR2(1 BYTE)), cast(NULL, VARCHAR2(0 )), cast('aab', VARCHAR2(3 BYTE)), cast(:4, |
| VARCHAR2(3 BYTE)))]), rowset=256 |
| access([S.ID], [S.CLASS], [S.S_DATE], [S.NAME], [S.AGE], [S.SEX]), partitions(p0) |
| is_index_back=true, is_global_index=false, filter_before_indexback[true], |
| range_key([S.S_DATE], [S.CLASS], [S.ID]), range(MIN ; MAX), |
| range_cond([S.S_DATE = :5]) |
| 6 - output([1]), filter(nil), rowset=256 |
| limit(1), offset(nil) |
| 7 - output(nil), filter([T_FUN_MAX(S2.ID) = :1]), rowset=256 |
| group([S2.CLASS]), agg_func([T_FUN_MAX(S2.ID)]) |
| 8 - output([S2.ID], [S2.CLASS]), filter(nil), rowset=256 |
| access([S2.ID], [S2.CLASS]), partitions(p0) |
| is_index_back=false, is_global_index=false, |
| range_key([S2.S_DATE], [S2.CLASS], [S2.ID]), range(MIN,MIN,MIN ; MAX,MAX,MAX)always true, |
| range_cond([:0 = S2.S_DATE]) |
+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------+
43 rows in set (0.008 sec)
1.2、改写方式二
SELECT S1.*
FROM student S1
INNER JOIN
(
SELECT
decode(class,null,'aab',class) class,
MAX(S2.S_DATE) MAX_S_DATE,
MAX(ID) KEEP (DENSE_RANK LAST ORDER BY S_DATE) MAX_ID
FROM student S2
GROUP BY S2.CLASS
/*
改写逻辑：
1、decode(class,null,'aab',class) class, 提前处理null值。如果有其他案例谓词过滤条件要转换列名，
例如：upper(列名) = upper(:1) ,可以提前在先写个select括起来内表处理字段，弄个别名，然后外面再过滤。
select * from (
select upper(col) col1 from tb
) where col1 = upper(:1);
2、MAX(S2.S_DATE) MAX_S_DATE 不多说，找最大日期，等价 desc
3、MAX(ID) KEEP (DENSE_RANK LAST ORDER BY S_DATE) MAX_ID 等价于上个sql 的 exists逻辑，
利用keep 唯一的特性，取出不同组里面最大的 id + ORDER BY S_DATE LAST 最后的唯一数据。
相当于 MAX_S_DATE，和 MAX(id) keep 两个列的数据都是一条 ，相当于rn=1。
*/
) S2
ON decode(S1.CLASS,null,'aab',S1.CLASS) = S2.CLASS /* 处理关联列 */
AND S1.ID = S2.MAX_ID ;
-- AND S1.S_DATE = S2.MAX_S_DATE;
/* 其实用S1.ID = S2.MAX_ID 关联就行，
S1.S_DATE = S2.MAX_S_DATE 这个写不写都行,如果想逻辑更严谨点可以写上。
*/
27 rows in set (0.114 sec)
+------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| Query Plan |
+------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| ============================================================================== |
| |ID|OPERATOR |NAME |EST.ROWS|EST.TIME(us)| |
| ------------------------------------------------------------------------------ |
| |0 |NESTED-LOOP JOIN | |2 |44008 | |
| |1 |├─SUBPLAN SCAN |S2 |26 |43466 | |
| |2 |│ └─MERGE GROUP BY | |26 |43466 | |
| |3 |│ └─TABLE FULL SCAN |S2(IDX_CLASS_SDATE_STUDENT)|500001 |22423 | |
| |4 |└─DISTRIBUTED TABLE GET|S1 |1 |21 | |
| ============================================================================== |
| Outputs & filters: |
| ------------------------------------- |
| 0 - output([S1.ID], [S1.NAME], [S1.AGE], [S1.SEX], [S1.S_DATE], [S1.CLASS]), filter(nil), rowset=256 |
| conds(nil), nl_params_([S2.CLASS(:3)], [S2.MAX_S_DATE(:4)], [S2.MAX_ID(:5)]), use_batch=false |
| 1 - output([S2.CLASS], [S2.MAX_S_DATE], [S2.MAX_ID]), filter(nil), rowset=256 |
| access([S2.CLASS], [S2.MAX_S_DATE], [S2.MAX_ID]) |
| 2 - output([ora_decode(cast(S2.CLASS, VARCHAR2(1 BYTE)), cast(NULL, VARCHAR2(0 )), cast('aab', VARCHAR2(3 BYTE)), cast(S2.CLASS, VARCHAR2(3 BYTE)))], |
| [T_FUN_MAX(S2.S_DATE)], [T_FUN_KEEP_MAX(S2.ID) order_items(S2.S_DATE)]), filter(nil), rowset=256 |
| group([S2.CLASS]), agg_func([T_FUN_MAX(S2.S_DATE)], [T_FUN_KEEP_MAX(S2.ID) order_items(S2.S_DATE)]) |
| 3 - output([S2.ID], [S2.CLASS], [S2.S_DATE]), filter(nil), rowset=256 |
| access([S2.ID], [S2.CLASS], [S2.S_DATE]), partitions(p0) |
| is_index_back=false, is_global_index=false, |
| range_key([S2.CLASS], [S2.S_DATE], [S2.ID]), range(MIN,MIN,MIN ; MAX,MAX,MAX)always true |
| 4 - output([S1.ID], [S1.CLASS], [S1.S_DATE], [S1.NAME], [S1.AGE], [S1.SEX]), filter([S1.S_DATE = :4], [ora_decode(cast(S1.CLASS, VARCHAR2(1 BYTE)), cast(NULL, |
| VARCHAR2(0 )), cast('aab', VARCHAR2(3 BYTE)), cast(S1.CLASS, VARCHAR2(3 BYTE))) = :3]), rowset=256 |
| access([S1.ID], [S1.CLASS], [S1.S_DATE], [S1.NAME], [S1.AGE], [S1.SEX]), partitions(p0) |
| is_index_back=false, is_global_index=false, filter_before_indexback[false,false], |
| range_key([S1.ID]), range(MIN ; MAX), |
| range_cond([S1.ID = :5]) |
+------------------------------------------------------------------------------------------------------------------------------------------------------------------+
28 rows in set (0.011 sec)
row_number() over() 的逻辑还可以用表量子查询来实现，不过性能会更慢，实现起来很麻烦，就不在这里演示。
2、keep() 函数逻辑实现
某些数据库可能不支持keep函数，或者keep函数的性能并不理想，我们也可以尝试通过另外一种方式来实现keep函数的逻辑。
2.1、keep 形式一
SELECT
s1.class,
max(s1.s_date) MAX_S_DATE,
MAX(name) KEEP (DENSE_RANK LAST ORDER BY s1.s_date) max_name,
min(s1.s_date) MIN_S_DATE,
MIN(name) KEEP (DENSE_RANK FIRST ORDER BY s1.s_date) min_name
FROM student s1
GROUP BY
s1.class;
27 rows in set (0.495 sec)
+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| Query Plan |
+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| =================================================== |
| |ID|OPERATOR |NAME|EST.ROWS|EST.TIME(us)| |
| --------------------------------------------------- |
| |0 |MERGE GROUP BY | |26 |144304 | |
| |1 |└─PARTITION SORT | |500001 |107784 | |
| |2 | └─TABLE FULL SCAN|S1 |500001 |22309 | |
| =================================================== |
| Outputs & filters: |
| ------------------------------------- |
| 0 - output([S1.CLASS], [T_FUN_MAX(S1.S_DATE)], [T_FUN_KEEP_MAX(S1.NAME) order_items(S1.S_DATE)], [T_FUN_MIN(S1.S_DATE)], [T_FUN_KEEP_MIN(S1.NAME) order_items(S1.S_DATE)]), filter(nil), rowset=256 |
| group([S1.CLASS]), agg_func([T_FUN_MAX(S1.S_DATE)], [T_FUN_KEEP_MAX(S1.NAME) order_items(S1.S_DATE)], [T_FUN_MIN(S1.S_DATE)], [T_FUN_KEEP_MIN(S1.NAME) |
| order_items(S1.S_DATE)]) |
| 1 - output([S1.CLASS], [S1.S_DATE], [S1.NAME]), filter(nil), rowset=256 |
| sort_keys([HASH(S1.CLASS), ASC], [S1.CLASS, ASC]) |
| 2 - output([S1.CLASS], [S1.S_DATE], [S1.NAME]), filter(nil), rowset=256 |
| access([S1.CLASS], [S1.S_DATE], [S1.NAME]), partitions(p0) |
| is_index_back=false, is_global_index=false, |
| range_key([S1.ID]), range(MIN ; MAX)always true |
+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
18 rows in set (0.004 sec)
2.2、keep 形式一改写标量子查询
改写逻辑很简单，先分组找出最大、最小的 日期，然后通过日期 + class 外部标量子查询找出最大、最小的name。
SELECT
CLASS,
MAX_S_DATE,
(SELECT max(NAME) FROM student s2 WHERE
decode(s1.CLASS,null,'bb',s1.CLASS) = decode(s2.CLASS,null,'bb',s2.CLASS) AND s2.S_DATE = s1.MAX_S_DATE) max_name,
MIN_S_DATE,
(SELECT min(NAME) FROM student s2 WHERE
decode(s1.CLASS,null,'bb',s1.CLASS) = decode(s2.CLASS,null,'bb',s2.CLASS) AND s2.S_DATE = s1.MIN_S_DATE) min_name
FROM (
SELECT
CLASS,
max(S_DATE) MAX_S_DATE,
min(S_DATE) MIN_S_DATE
FROM student
GROUP BY
CLASS
) s1 ；
27 rows in set (0.040 sec)
+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| Query Plan |
+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| ============================================================================================ |
| |ID|OPERATOR |NAME |EST.ROWS|EST.TIME(us)| |
| -------------------------------------------------------------------------------------------- |
| |0 |SUBPLAN FILTER | |26 |36802 | |
| |1 |├─MERGE GROUP BY | |26 |35450 | |
| |2 |│ └─TABLE FULL SCAN |STUDENT(IDX_CLASS_SDATE_STUDENT)|500001 |14409 | |
| |3 |├─SCALAR GROUP BY | |1 |26 | |
| |4 |│ └─DISTRIBUTED TABLE RANGE SCAN|S2(IDX_S_DATE_CALSS_ID) |1 |26 | |
| |5 |└─SCALAR GROUP BY | |1 |26 | |
| |6 | └─DISTRIBUTED TABLE RANGE SCAN|S2(IDX_S_DATE_CALSS_ID) |1 |26 | |
| ============================================================================================ |
| Outputs & filters: |
| ------------------------------------- |
| 0 - output([STUDENT.CLASS], [T_FUN_MAX(STUDENT.S_DATE)], [subquery(1)], [T_FUN_MIN(STUDENT.S_DATE)], [subquery(2)]), filter(nil), rowset=256 |
| exec_params_([STUDENT.CLASS(:0)], [T_FUN_MAX(STUDENT.S_DATE)(:1)], [STUDENT.CLASS(:2)], [T_FUN_MIN(STUDENT.S_DATE)(:3)]), onetime_exprs_(nil), init_plan_idxs_(nil), |
| use_batch=false |
| 1 - output([STUDENT.CLASS], [T_FUN_MAX(STUDENT.S_DATE)], [T_FUN_MIN(STUDENT.S_DATE)]), filter(nil), rowset=256 |
| group([STUDENT.CLASS]), agg_func([T_FUN_MAX(STUDENT.S_DATE)], [T_FUN_MIN(STUDENT.S_DATE)]) |
| 2 - output([STUDENT.CLASS], [STUDENT.S_DATE]), filter(nil), rowset=256 |
| access([STUDENT.CLASS], [STUDENT.S_DATE]), partitions(p0) |
| is_index_back=false, is_global_index=false, |
| range_key([STUDENT.CLASS], [STUDENT.S_DATE], [STUDENT.ID]), range(MIN,MIN,MIN ; MAX,MAX,MAX)always true |
| 3 - output([T_FUN_MAX(S2.NAME)]), filter(nil), rowset=256 |
| group(nil), agg_func([T_FUN_MAX(S2.NAME)]) |
| 4 - output([S2.NAME]), filter([ora_decode(cast(:0, VARCHAR2(1 BYTE)), cast(NULL, VARCHAR2(0 )), cast('bb', VARCHAR2(2 BYTE)), cast(:0, VARCHAR2(2 BYTE))) |
| = ora_decode(cast(S2.CLASS, VARCHAR2(1 BYTE)), cast(NULL, VARCHAR2(0 )), cast('bb', VARCHAR2(2 BYTE)), cast(S2.CLASS, VARCHAR2(2 BYTE)))]), rowset=256 |
| access([S2.ID], [S2.CLASS], [S2.NAME]), partitions(p0) |
| is_index_back=true, is_global_index=false, filter_before_indexback[true], |
| range_key([S2.S_DATE], [S2.CLASS], [S2.ID]), range(MIN,MIN,MIN ; MAX,MAX,MAX)always true, |
| range_cond([S2.S_DATE = :1]) |
| 5 - output([T_FUN_MIN(S2.NAME)]), filter(nil), rowset=256 |
| group(nil), agg_func([T_FUN_MIN(S2.NAME)]) |
| 6 - output([S2.NAME]), filter([ora_decode(cast(:2, VARCHAR2(1 BYTE)), cast(NULL, VARCHAR2(0 )), cast('bb', VARCHAR2(2 BYTE)), cast(:2, VARCHAR2(2 BYTE))) |
| = ora_decode(cast(S2.CLASS, VARCHAR2(1 BYTE)), cast(NULL, VARCHAR2(0 )), cast('bb', VARCHAR2(2 BYTE)), cast(S2.CLASS, VARCHAR2(2 BYTE)))]), rowset=256 |
| access([S2.ID], [S2.CLASS], [S2.NAME]), partitions(p0) |
| is_index_back=true, is_global_index=false, filter_before_indexback[true], |
| range_key([S2.S_DATE], [S2.CLASS], [S2.ID]), range(MIN,MIN,MIN ; MAX,MAX,MAX)always true, |
| range_cond([S2.S_DATE = :3]) |
+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
38 rows in set (0.008 sec)
2.3、keep 形式一改写left join
改成 left join 也是按照标量的逻辑，都比较简单。
SELECT s1.CLASS,
s1.MAX_S_DATE,
max(CASE WHEN s2.S_DATE = s1.MAX_S_DATE THEN s2.NAME END) max_name,
s1.MIN_S_DATE,
min(CASE WHEN s2.S_DATE = s1.MIN_S_DATE THEN s2.NAME END ) min_name
FROM (
SELECT
CLASS,
max(S_DATE) MAX_S_DATE,
min(S_DATE) MIN_S_DATE
FROM student
GROUP BY
CLASS
)s1 LEFT JOIN student s2 ON decode(s1.CLASS,null,'bb',s1.CLASS) = decode(s2.CLASS,null,'bb',s2.CLASS)
GROUP BY
s1.CLASS,
s1.MAX_S_DATE,
s1.MIN_S_DATE;
27 rows in set (0.213 sec)
+--------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| Query Plan |
+--------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| =================================================================================== |
| |ID|OPERATOR |NAME |EST.ROWS|EST.TIME(us)| |
| ----------------------------------------------------------------------------------- |
| |0 |HASH GROUP BY | |17 |200679 | |
| |1 |└─HASH OUTER JOIN | |65001 |193221 | |
| |2 | ├─SUBPLAN SCAN |S1 |26 |35450 | |
| |3 | │ └─MERGE GROUP BY | |26 |35450 | |
| |4 | │ └─TABLE FULL SCAN|STUDENT(IDX_CLASS_SDATE_STUDENT)|500001 |14409 | |
| |5 | └─TABLE FULL SCAN |S2 |500001 |22309 | |
| =================================================================================== |
| Outputs & filters: |
| ------------------------------------- |
| 0 - output([S1.CLASS], [S1.MAX_S_DATE], [T_FUN_MAX(CASE WHEN S2.S_DATE = S1.MAX_S_DATE THEN S2.NAME ELSE NULL END)], [S1.MIN_S_DATE], [T_FUN_MIN(CASE |
| WHEN S2.S_DATE = S1.MIN_S_DATE THEN S2.NAME ELSE NULL END)]), filter(nil), rowset=256 |
| group([S1.CLASS]), agg_func([T_FUN_MAX(CASE WHEN S2.S_DATE = S1.MAX_S_DATE THEN S2.NAME ELSE NULL END)], [T_FUN_MIN(CASE WHEN S2.S_DATE = S1.MIN_S_DATE |
| THEN S2.NAME ELSE NULL END)]) |
| 1 - output([S1.CLASS], [S2.S_DATE], [S2.NAME], [S1.MAX_S_DATE], [S1.MIN_S_DATE]), filter(nil), rowset=256 |
| equal_conds([ora_decode(cast(S1.CLASS, VARCHAR2(1 BYTE)), cast(NULL, VARCHAR2(0 )), cast('bb', VARCHAR2(2 BYTE)), cast(S1.CLASS, VARCHAR2(2 BYTE))) |
| = ora_decode(cast(S2.CLASS, VARCHAR2(1 BYTE)), cast(NULL, VARCHAR2(0 )), cast('bb', VARCHAR2(2 BYTE)), cast(S2.CLASS, VARCHAR2(2 BYTE)))]), other_conds(nil) |
| 2 - output([S1.CLASS], [S1.MAX_S_DATE], [S1.MIN_S_DATE]), filter(nil), rowset=256 |
| access([S1.CLASS], [S1.MAX_S_DATE], [S1.MIN_S_DATE]) |
| 3 - output([STUDENT.CLASS], [T_FUN_MAX(STUDENT.S_DATE)], [T_FUN_MIN(STUDENT.S_DATE)]), filter(nil), rowset=256 |
| group([STUDENT.CLASS]), agg_func([T_FUN_MAX(STUDENT.S_DATE)], [T_FUN_MIN(STUDENT.S_DATE)]) |
| 4 - output([STUDENT.CLASS], [STUDENT.S_DATE]), filter(nil), rowset=256 |
| access([STUDENT.CLASS], [STUDENT.S_DATE]), partitions(p0) |
| is_index_back=false, is_global_index=false, |
| range_key([STUDENT.CLASS], [STUDENT.S_DATE], [STUDENT.ID]), range(MIN,MIN,MIN ; MAX,MAX,MAX)always true |
| 5 - output([S2.CLASS], [S2.S_DATE], [S2.NAME]), filter(nil), rowset=256 |
| access([S2.CLASS], [S2.S_DATE], [S2.NAME]), partitions(p0) |
| is_index_back=false, is_global_index=false, |
| range_key([S2.ID]), range(MIN ; MAX)always true |
+--------------------------------------------------------------------------------------------------------------------------------------------------------------------+
31 rows in set (0.005 sec)
2.4、keep 形式二
SELECT
DEPARTMENT_ID,
HIRE_DATE,
MAX(FIRST_NAME) KEEP (DENSE_RANK LAST ORDER BY HIRE_DATE) over(PARTITION BY DEPARTMENT_ID) max_name,
MIN(FIRST_NAME) KEEP (DENSE_RANK FIRST ORDER BY HIRE_DATE) over(PARTITION BY DEPARTMENT_ID) min_name
FROM EMPLOYEES ;
107 rows in set (0.002 sec)
2.5、keep 形式二改写left join
SELECT
e1.DEPARTMENT_ID,
e1.HIRE_DATE,
e2.max_name,
e2.min_name
FROM EMPLOYEES e1 LEFT JOIN (
SELECT DEPARTMENT_ID,
MAX(CASE WHEN max_rn = 1 THEN FIRST_NAME ELSE NULL END) max_name,
MIN(CASE WHEN min_rn = 1 THEN FIRST_NAME ELSE NULL END) min_name
FROM (
SELECT
FIRST_NAME,
HIRE_DATE,
DEPARTMENT_ID,
DENSE_RANK() OVER(PARTITION BY DEPARTMENT_ID ORDER BY HIRE_DATE DESC) max_rn,
DENSE_RANK() OVER(PARTITION BY DEPARTMENT_ID ORDER BY HIRE_DATE ) min_rn
FROM EMPLOYEES
)
GROUP BY DEPARTMENT_ID
) e2 ON // 最后关联
decode(e1.DEPARTMENT_ID, null, 'aaa', e1.DEPARTMENT_ID) =
decode(e2.DEPARTMENT_ID, null, 'aaa', e2.DEPARTMENT_ID);
107 rows in set (0.007 sec)
2.6、keep 形式二改写标量子查询
SELECT DEPARTMENT_ID,
HIRE_DATE,
(SELECT e2.max_name
FROM
(SELECT DEPARTMENT_ID,
max(FIRST_NAME) max_name
FROM
(SELECT x.*,
DENSE_RANK() OVER(PARTITION BY DEPARTMENT_ID
ORDER BY HIRE_DATE DESC) max_rn
FROM EMPLOYEES x )
WHERE max_rn = 1
GROUP BY DEPARTMENT_ID ) e2
WHERE decode(e1.department_id, null, 'aaa', e1.department_id) = decode(e2.department_id, null, 'aaa', e2.department_id) ) max_name,
(SELECT e2.min_name
FROM
(SELECT DEPARTMENT_ID,
min(FIRST_NAME) min_name
FROM
(SELECT x.*,
DENSE_RANK() OVER(PARTITION BY DEPARTMENT_ID
ORDER BY HIRE_DATE ) min_rn
FROM EMPLOYEES x )
WHERE min_rn = 1
GROUP BY DEPARTMENT_ID ) e2
WHERE decode(e1.department_id, null, 'aaa', e1.department_id) = decode(e2.department_id, null, 'aaa', e2.department_id) ) min_name
FROM EMPLOYEES e1;
107 rows in set (0.030 sec)
/* 改SQL 逻辑等价上面join sql, 如果会写join版本改成表量子查询是更简单的操作 */
3、rank() over() 函数逻辑实现
rank() over() 函数会跳号，如果符合over()逻辑的数据有两条的话，那么这两条会并列第一，然后第二条数据会加二（即显示第三）。
这个函数在业务逻辑上比较少使用到，但是我们也可以尝试使用其他逻辑来实现rank() over() 函数的功能。
******** 注意：以下实现的rank() over() 逻辑的代码性能，会比原来rank() over() 的性能更差，这里只做功能实现，不考虑性能问题 ********
SELECT * FROM (
SELECT
FIRST_NAME,
DEPARTMENT_ID,
HIRE_DATE,
RANK() OVER(PARTITION BY DEPARTMENT_ID ORDER BY HIRE_DATE DESC) MAX_RN
FROM EMPLOYEES ) WHERE DEPARTMENT_ID = 80;
34 rows in set (0.004 sec)
+--------------------------------------------------------------------------------------------------------------------------------------------------------------+
| Query Plan |
+--------------------------------------------------------------------------------------------------------------------------------------------------------------+
| ======================================================== |
| |ID|OPERATOR |NAME |EST.ROWS|EST.TIME(us)| |
| -------------------------------------------------------- |
| |0 |WINDOW FUNCTION | |34 |42 | |
| |1 |└─SORT | |34 |16 | |
| |2 | └─TABLE FULL SCAN|EMPLOYEES|34 |10 | |
| ======================================================== |
| Outputs & filters: |
| ------------------------------------- |
| 0 - output([EMPLOYEES.FIRST_NAME], [EMPLOYEES.DEPARTMENT_ID], [EMPLOYEES.HIRE_DATE], [T_WIN_FUN_RANK()]), filter(nil), rowset=256 |
| win_expr(T_WIN_FUN_RANK()), partition_by(nil), order_by([EMPLOYEES.HIRE_DATE, DESC]), window_type(RANGE), upper(UNBOUNDED PRECEDING), lower(UNBOUNDED |
| FOLLOWING) |
| 1 - output([EMPLOYEES.FIRST_NAME], [EMPLOYEES.DEPARTMENT_ID], [EMPLOYEES.HIRE_DATE]), filter(nil), rowset=256 |
| sort_keys([EMPLOYEES.HIRE_DATE, DESC]) |
| 2 - output([EMPLOYEES.FIRST_NAME], [EMPLOYEES.DEPARTMENT_ID], [EMPLOYEES.HIRE_DATE]), filter([EMPLOYEES.DEPARTMENT_ID = 80]), rowset=256 |
| access([EMPLOYEES.FIRST_NAME], [EMPLOYEES.DEPARTMENT_ID], [EMPLOYEES.HIRE_DATE]), partitions(p0) |
| is_index_back=false, is_global_index=false, filter_before_indexback[false], |
| range_key([EMPLOYEES.EMPLOYEE_ID]), range(MIN ; MAX)always true |
+--------------------------------------------------------------------------------------------------------------------------------------------------------------+
18 rows in set (0.004 sec)
3.1、改写方式一：标量子查询逻辑实现
SELECT
e.FIRST_NAME,
e.DEPARTMENT_ID,
e.HIRE_DATE,
(
SELECT count(HIRE_DATE) + 1 FROM EMPLOYEES e1 WHERE
DECODE(e1.DEPARTMENT_ID,NULL,'8080',e1.DEPARTMENT_ID) = DECODE(e.DEPARTMENT_ID,NULL,'8080',e.DEPARTMENT_ID) AND
e1.HIRE_DATE > e.HIRE_DATE
) MAX_RN
FROM EMPLOYEES e WHERE e.DEPARTMENT_ID = 80;
/*
重点是在标量子查询内的 e1.HIRE_DATE > e.HIRE_DATE 逻辑，可以将 e1 表理解成一个滑动的窗口。
*/
34 rows in set (0.006 sec)
+---------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| Query Plan |
+---------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| ========================================================= |
| |ID|OPERATOR |NAME|EST.ROWS|EST.TIME(us)| |
| --------------------------------------------------------- |
| |0 |MERGE GROUP BY | |34 |29 | |
| |1 |└─NESTED-LOOP OUTER JOIN | |34 |22 | |
| |2 | ├─TABLE FULL SCAN |E |34 |11 | |
| |3 | └─MATERIAL | |1 |11 | |
| |4 | └─TABLE FULL SCAN |E1 |1 |11 | |
| ========================================================= |
| Outputs & filters: |
| ------------------------------------- |
| 0 - output([E.FIRST_NAME], [E.DEPARTMENT_ID], [E.HIRE_DATE], [T_FUN_COUNT(E1.HIRE_DATE) + 1]), filter(nil), rowset=256 |
| group([E.EMPLOYEE_ID]), agg_func([T_FUN_COUNT(E1.HIRE_DATE)]) |
| 1 - output([E.EMPLOYEE_ID], [E.FIRST_NAME], [E.DEPARTMENT_ID], [E.HIRE_DATE], [E1.HIRE_DATE]), filter(nil), rowset=256 |
| conds([E1.HIRE_DATE > E.HIRE_DATE]), nl_params_(nil), use_batch=false |
| 2 - output([E.EMPLOYEE_ID], [E.DEPARTMENT_ID], [E.FIRST_NAME], [E.HIRE_DATE]), filter([E.DEPARTMENT_ID = 80]), rowset=256 |
| access([E.EMPLOYEE_ID], [E.DEPARTMENT_ID], [E.FIRST_NAME], [E.HIRE_DATE]), partitions(p0) |
| is_index_back=false, is_global_index=false, filter_before_indexback[false], |
| range_key([E.EMPLOYEE_ID]), range(MIN ; MAX)always true |
| 3 - output([E1.HIRE_DATE]), filter(nil), rowset=256 |
| 4 - output([E1.HIRE_DATE]), filter([ora_decode(cast(E1.DEPARTMENT_ID, VARCHAR2(1048576 )), cast(NULL, VARCHAR2(0 )), cast('8080', VARCHAR2(40 BYTE)), |
| cast(E1.DEPARTMENT_ID, VARCHAR2(40 BYTE))) = ora_decode(cast(cast(80, NUMBER(4, 0)), VARCHAR2(1048576 )), cast(NULL, VARCHAR2(0 )), cast('8080', VARCHAR2(40 |
| BYTE)), cast(cast(80, NUMBER(4, 0)), VARCHAR2(40 BYTE)))]), rowset=256 |
| access([E1.DEPARTMENT_ID], [E1.HIRE_DATE]), partitions(p0) |
| is_index_back=false, is_global_index=false, filter_before_indexback[false], |
| range_key([E1.EMPLOYEE_ID]), range(MIN ; MAX)always true |
+---------------------------------------------------------------------------------------------------------------------------------------------------------------------+
26 rows in set (0.006 sec)
SELECT * FROM (
SELECT
e.FIRST_NAME,
e.DEPARTMENT_ID,
e.HIRE_DATE,
(
SELECT count(HIRE_DATE) +1 FROM EMPLOYEES e1 WHERE
DECODE(e1.DEPARTMENT_ID,NULL,'8080',e1.DEPARTMENT_ID) = DECODE(e.DEPARTMENT_ID,NULL,'8080',e.DEPARTMENT_ID) AND
e1.HIRE_DATE > e.HIRE_DATE
) MAX_RN,
(
SELECT LISTAGG(e1.HIRE_DATE, '，') WITHIN GROUP (ORDER BY HIRE_DATE) FROM EMPLOYEES e1 WHERE
DECODE(e1.DEPARTMENT_ID,NULL,'8080',e1.DEPARTMENT_ID) = DECODE(e.DEPARTMENT_ID,NULL,'8080',e.DEPARTMENT_ID) AND
e1.HIRE_DATE > e.HIRE_DATE
) e1_HIRE_DATE
FROM EMPLOYEES e
) WHERE DEPARTMENT_ID = 80 ORDER BY MAX_RN ;
3.2、改写方式二：LEFT JOIN 逻辑实现
会写标量子查询的情况下，改写成LEFT JOIN 是很简单的事，逻辑一样不再赘述。
SELECT
e.FIRST_NAME,
e.DEPARTMENT_ID,
e.HIRE_DATE,
count(e1.HIRE_DATE) + 1 MAX_RN
FROM EMPLOYEES e LEFT JOIN EMPLOYEES e1 ON (
DECODE(e1.DEPARTMENT_ID,NULL,'8080',e1.DEPARTMENT_ID) = DECODE(e.DEPARTMENT_ID,NULL,'8080',e.DEPARTMENT_ID) AND e1.HIRE_DATE > e.HIRE_DATE)
WHERE e.DEPARTMENT_ID = 80
GROUP BY
e.FIRST_NAME,
e.DEPARTMENT_ID,
e.HIRE_DATE;
34 rows in set (0.012 sec)
+----------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| Query Plan |
+----------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| ========================================================== |
| |ID|OPERATOR |NAME |EST.ROWS|EST.TIME(us)| |
| ---------------------------------------------------------- |
| |0 |HASH GROUP BY | |34 |49 | |
| |1 |└─NESTED-LOOP OUTER JOIN | |34 |33 | |
| |2 | ├─SUBPLAN SCAN |VIEW1|34 |22 | |
| |3 | │ └─HASH GROUP BY | |34 |22 | |
| |4 | │ └─TABLE FULL SCAN |E |34 |10 | |
| |5 | └─MATERIAL | |1 |11 | |
| |6 | └─SUBPLAN SCAN |VIEW2|1 |11 | |
| |7 | └─HASH GROUP BY | |1 |11 | |
| |8 | └─TABLE FULL SCAN|E1 |1 |11 | |
| ========================================================== |
| Outputs & filters: |
| ------------------------------------- |
| 0 - output([VIEW1.E.FIRST_NAME], [VIEW1.E.DEPARTMENT_ID], [VIEW1.E.HIRE_DATE], [T_FUN_COUNT_SUM(VIEW1.T_FUN_COUNT(*) * CASE WHEN VIEW2.T_FUN_COUNT(E1.HIRE_DATE) |
| IS NOT NULL THEN VIEW2.T_FUN_COUNT(E1.HIRE_DATE) ELSE 0 END) + 1]), filter(nil), rowset=256 |
| group([VIEW1.E.HIRE_DATE], [VIEW1.E.FIRST_NAME]), agg_func([T_FUN_COUNT_SUM(VIEW1.T_FUN_COUNT(*) * CASE WHEN VIEW2.T_FUN_COUNT(E1.HIRE_DATE) IS NOT |
| NULL THEN VIEW2.T_FUN_COUNT(E1.HIRE_DATE) ELSE 0 END)]) |
| 1 - output([VIEW1.E.HIRE_DATE], [VIEW1.E.FIRST_NAME], [VIEW1.E.DEPARTMENT_ID], [VIEW2.T_FUN_COUNT(E1.HIRE_DATE)], [VIEW1.T_FUN_COUNT(*)]), filter(nil), rowset=256 |
| conds([VIEW2.E1.HIRE_DATE > VIEW1.E.HIRE_DATE]), nl_params_(nil), use_batch=false |
| 2 - output([VIEW1.E.DEPARTMENT_ID], [VIEW1.E.HIRE_DATE], [VIEW1.E.FIRST_NAME], [VIEW1.T_FUN_COUNT(*)]), filter(nil), rowset=256 |
| access([VIEW1.E.DEPARTMENT_ID], [VIEW1.E.HIRE_DATE], [VIEW1.E.FIRST_NAME], [VIEW1.T_FUN_COUNT(*)]) |
| 3 - output([E.DEPARTMENT_ID], [E.HIRE_DATE], [E.FIRST_NAME], [T_FUN_COUNT(*)]), filter(nil), rowset=256 |
| group([E.HIRE_DATE], [E.FIRST_NAME]), agg_func([T_FUN_COUNT(*)]) |
| 4 - output([E.DEPARTMENT_ID], [E.HIRE_DATE], [E.FIRST_NAME]), filter([E.DEPARTMENT_ID = 80]), rowset=256 |
| access([E.DEPARTMENT_ID], [E.HIRE_DATE], [E.FIRST_NAME]), partitions(p0) |
| is_index_back=false, is_global_index=false, filter_before_indexback[false], |
| range_key([E.EMPLOYEE_ID]), range(MIN ; MAX)always true |
| 5 - output([VIEW2.T_FUN_COUNT(E1.HIRE_DATE)], [VIEW2.E1.HIRE_DATE]), filter(nil), rowset=256 |
| 6 - output([VIEW2.E1.HIRE_DATE], [VIEW2.T_FUN_COUNT(E1.HIRE_DATE)]), filter(nil), rowset=256 |
| access([VIEW2.E1.HIRE_DATE], [VIEW2.T_FUN_COUNT(E1.HIRE_DATE)]) |
| 7 - output([E1.HIRE_DATE], [T_FUN_COUNT(E1.HIRE_DATE)]), filter(nil), rowset=256 |
| group([E1.DEPARTMENT_ID], [E1.HIRE_DATE]), agg_func([T_FUN_COUNT(E1.HIRE_DATE)]) |
| 8 - output([E1.DEPARTMENT_ID], [E1.HIRE_DATE]), filter([ora_decode(cast(E1.DEPARTMENT_ID, VARCHAR2(1048576 )), cast(NULL, VARCHAR2(0 )), cast('8080', |
| VARCHAR2(40 BYTE)), cast(E1.DEPARTMENT_ID, VARCHAR2(40 BYTE))) = ora_decode(cast(cast(80, NUMBER(4, 0)), VARCHAR2(1048576 )), cast(NULL, VARCHAR2(0 )), |
| cast('8080', VARCHAR2(40 BYTE)), cast(cast(80, NUMBER(4, 0)), VARCHAR2(40 BYTE)))]), rowset=256 |
| access([E1.DEPARTMENT_ID], [E1.HIRE_DATE]), partitions(p0) |
| is_index_back=false, is_global_index=false, filter_before_indexback[false], |
| range_key([E1.EMPLOYEE_ID]), range(MIN ; MAX)always true |
+----------------------------------------------------------------------------------------------------------------------------------------------------------------------+
40 rows in set (0.011 sec)
4、 dense_rank() over() 函数逻辑实现
dense_rank() over() 函数不会跳号，如果符合over()逻辑的数据有两条的话，那么这两条会并列第一，然后第二条数据会递增加一（即显示第二）。
这个函数在业务逻辑上也比较少用到，但是比rank()用得多，但是我们也可以尝试使用其他逻辑来实现dense_rank() over() 函数的功能。
******** 注意：以下实现的dense_rank() over() 逻辑的代码性能，会比原来dense_rank() over() 的性能更差，这里只做功能实现，不考虑性能问题 ********
SELECT * FROM (
SELECT
FIRST_NAME,
DEPARTMENT_ID,
HIRE_DATE,
dense_rank() OVER(PARTITION BY DEPARTMENT_ID ORDER BY HIRE_DATE DESC) MAX_RN
FROM EMPLOYEES ) WHERE DEPARTMENT_ID = 80;
34 rows in set (0.001 sec)
+--------------------------------------------------------------------------------------------------------------------------------------------------------------+
| Query Plan |
+--------------------------------------------------------------------------------------------------------------------------------------------------------------+
| ======================================================== |
| |ID|OPERATOR |NAME |EST.ROWS|EST.TIME(us)| |
| -------------------------------------------------------- |
| |0 |WINDOW FUNCTION | |107 |115 | |
| |1 |└─PARTITION SORT | |107 |33 | |
| |2 | └─TABLE FULL SCAN|EMPLOYEES|107 |9 | |
| ======================================================== |
| Outputs & filters: |
| ------------------------------------- |
| 0 - output([EMPLOYEES.FIRST_NAME], [EMPLOYEES.DEPARTMENT_ID], [EMPLOYEES.HIRE_DATE], [T_WIN_FUN_DENSE_RANK()]), filter(nil), rowset=256 |
| win_expr(T_WIN_FUN_DENSE_RANK()), partition_by([EMPLOYEES.DEPARTMENT_ID]), order_by([EMPLOYEES.HIRE_DATE, DESC]), window_type(RANGE), upper(UNBOUNDED |
| PRECEDING), lower(UNBOUNDED FOLLOWING) |
| 1 - output([EMPLOYEES.FIRST_NAME], [EMPLOYEES.DEPARTMENT_ID], [EMPLOYEES.HIRE_DATE]), filter(nil), rowset=256 |
| sort_keys([HASH(EMPLOYEES.DEPARTMENT_ID), ASC], [EMPLOYEES.DEPARTMENT_ID, ASC], [EMPLOYEES.HIRE_DATE, DESC]) |
| 2 - output([EMPLOYEES.FIRST_NAME], [EMPLOYEES.DEPARTMENT_ID], [EMPLOYEES.HIRE_DATE]), filter(nil), rowset=256 |
| access([EMPLOYEES.FIRST_NAME], [EMPLOYEES.DEPARTMENT_ID], [EMPLOYEES.HIRE_DATE]), partitions(p0) |
| is_index_back=false, is_global_index=false, |
| range_key([EMPLOYEES.EMPLOYEE_ID]), range(MIN ; MAX)always true |
+--------------------------------------------------------------------------------------------------------------------------------------------------------------+
18 rows in set (0.004 sec)
4.1、改写方式一：标量子查询逻辑实现
SELECT
e.FIRST_NAME,
e.DEPARTMENT_ID,
e.HIRE_DATE,
(SELECT COUNT(DISTINCT HIRE_DATE) +1 FROM EMPLOYEES e1 WHERE DECODE(e1.DEPARTMENT_ID,NULL,'8080',e1.DEPARTMENT_ID) = DECODE(e.DEPARTMENT_ID,NULL,'8080',e.DEPARTMENT_ID) AND e1.HIRE_DATE > e.HIRE_DATE ) MAX_RN
FROM EMPLOYEES e WHERE DEPARTMENT_ID = 80 ;
/*
COUNT(DISTINCT HIRE_DATE) 逻辑是求 e1.HIRE_DATE > e.HIRE_DATE 符合这个逻辑的 e1表的 HIRE_DATE 数据，等价 dense_rank。
*/
34 rows in set (0.006 sec)
+---------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| Query Plan |
+---------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| ========================================================= |
| |ID|OPERATOR |NAME|EST.ROWS|EST.TIME(us)| |
| --------------------------------------------------------- |
| |0 |MERGE GROUP BY | |34 |29 | |
| |1 |└─NESTED-LOOP OUTER JOIN | |34 |22 | |
| |2 | ├─TABLE FULL SCAN |E |34 |11 | |
| |3 | └─MATERIAL | |1 |11 | |
| |4 | └─TABLE FULL SCAN |E1 |1 |11 | |
| ========================================================= |
| Outputs & filters: |
| ------------------------------------- |
| 0 - output([E.FIRST_NAME], [E.DEPARTMENT_ID], [E.HIRE_DATE], [T_FUN_COUNT(distinct E1.HIRE_DATE) + 1]), filter(nil), rowset=256 |
| group([E.EMPLOYEE_ID]), agg_func([T_FUN_COUNT(distinct E1.HIRE_DATE)]) |
| 1 - output([E.EMPLOYEE_ID], [E.FIRST_NAME], [E.DEPARTMENT_ID], [E.HIRE_DATE], [E1.HIRE_DATE]), filter(nil), rowset=256 |
| conds([E1.HIRE_DATE > E.HIRE_DATE]), nl_params_(nil), use_batch=false |
| 2 - output([E.EMPLOYEE_ID], [E.DEPARTMENT_ID], [E.FIRST_NAME], [E.HIRE_DATE]), filter([E.DEPARTMENT_ID = 80]), rowset=256 |
| access([E.EMPLOYEE_ID], [E.DEPARTMENT_ID], [E.FIRST_NAME], [E.HIRE_DATE]), partitions(p0) |
| is_index_back=false, is_global_index=false, filter_before_indexback[false], |
| range_key([E.EMPLOYEE_ID]), range(MIN ; MAX)always true |
| 3 - output([E1.HIRE_DATE]), filter(nil), rowset=256 |
| 4 - output([E1.HIRE_DATE]), filter([ora_decode(cast(E1.DEPARTMENT_ID, VARCHAR2(1048576 )), cast(NULL, VARCHAR2(0 )), cast('8080', VARCHAR2(40 BYTE)), |
| cast(E1.DEPARTMENT_ID, VARCHAR2(40 BYTE))) = ora_decode(cast(cast(80, NUMBER(4, 0)), VARCHAR2(1048576 )), cast(NULL, VARCHAR2(0 )), cast('8080', VARCHAR2(40 |
| BYTE)), cast(cast(80, NUMBER(4, 0)), VARCHAR2(40 BYTE)))]), rowset=256 |
| access([E1.DEPARTMENT_ID], [E1.HIRE_DATE]), partitions(p0) |
| is_index_back=false, is_global_index=false, filter_before_indexback[false], |
| range_key([E1.EMPLOYEE_ID]), range(MIN ; MAX)always true |
+---------------------------------------------------------------------------------------------------------------------------------------------------------------------+
26 rows in set (0.006 sec)
SELECT * FROM (
SELECT
e.FIRST_NAME,
e.DEPARTMENT_ID,
e.HIRE_DATE,
(
SELECT count(DISTINCT HIRE_DATE) +1 FROM EMPLOYEES e1 WHERE
DECODE(e1.DEPARTMENT_ID,NULL,'8080',e1.DEPARTMENT_ID) = DECODE(e.DEPARTMENT_ID,NULL,'8080',e.DEPARTMENT_ID) AND
e1.HIRE_DATE > e.HIRE_DATE
) MAX_RN,
(
SELECT LISTAGG(e1.HIRE_DATE, '，') WITHIN GROUP (ORDER BY HIRE_DATE) FROM EMPLOYEES e1 WHERE
DECODE(e1.DEPARTMENT_ID,NULL,'8080',e1.DEPARTMENT_ID) = DECODE(e.DEPARTMENT_ID,NULL,'8080',e.DEPARTMENT_ID) AND
e1.HIRE_DATE > e.HIRE_DATE
) e1_HIRE_DATE
FROM EMPLOYEES e
) WHERE DEPARTMENT_ID = 80 ORDER BY MAX_RN ;
4.2、改写方式二：LEFT JOIN 逻辑实现
会写标量子查询的情况下，改写成LEFT JOIN 是很简单的事，逻辑一样不再赘述。
SELECT
e.FIRST_NAME,
e.DEPARTMENT_ID,
e.HIRE_DATE,
count(DISTINCT e1.HIRE_DATE) + 1 MAX_RN
FROM EMPLOYEES e LEFT JOIN EMPLOYEES e1 ON (
DECODE(e1.DEPARTMENT_ID,NULL,'8080',e1.DEPARTMENT_ID) = DECODE(e.DEPARTMENT_ID,NULL,'8080',e.DEPARTMENT_ID) AND e1.HIRE_DATE > e.HIRE_DATE)
WHERE e.DEPARTMENT_ID = 80
GROUP BY
e.FIRST_NAME,
e.DEPARTMENT_ID,
e.HIRE_DATE;
34 rows in set (0.006 sec)
+---------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| Query Plan |
+---------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| ===================================================== |
| |ID|OPERATOR |NAME|EST.ROWS|EST.TIME(us)| |
| ----------------------------------------------------- |
| |0 |MERGE GROUP BY | |34 |76 | |
| |1 |└─PARTITION SORT | |34 |70 | |
| |2 | └─HASH OUTER JOIN | |34 |62 | |
| |3 | ├─TABLE FULL SCAN|E |34 |10 | |
| |4 | └─TABLE FULL SCAN|E1 |107 |9 | |
| ===================================================== |
| Outputs & filters: |
| ------------------------------------- |
| 0 - output([E.FIRST_NAME], [E.DEPARTMENT_ID], [E.HIRE_DATE], [T_FUN_COUNT(distinct E1.HIRE_DATE) + 1]), filter(nil), rowset=256 |
| group([E.FIRST_NAME], [E.HIRE_DATE]), agg_func([T_FUN_COUNT(distinct E1.HIRE_DATE)]) |
| 1 - output([E.FIRST_NAME], [E.HIRE_DATE], [E.DEPARTMENT_ID], [E1.HIRE_DATE]), filter(nil), rowset=256 |
| sort_keys([HASH(E.FIRST_NAME, E.HIRE_DATE), ASC], [E.FIRST_NAME, ASC], [E.HIRE_DATE, ASC]) |
| 2 - output([E.FIRST_NAME], [E.HIRE_DATE], [E.DEPARTMENT_ID], [E1.HIRE_DATE]), filter(nil), rowset=256 |
| equal_conds([ora_decode(cast(E1.DEPARTMENT_ID, VARCHAR2(1048576 )), cast(NULL, VARCHAR2(0 )), cast('8080', VARCHAR2(40 BYTE)), cast(E1.DEPARTMENT_ID, |
| VARCHAR2(40 BYTE))) = ora_decode(cast(E.DEPARTMENT_ID, VARCHAR2(1048576 )), cast(NULL, VARCHAR2(0 )), cast('8080', VARCHAR2(40 BYTE)), cast(E.DEPARTMENT_ID, |
| VARCHAR2(40 BYTE)))]), other_conds([E1.HIRE_DATE > E.HIRE_DATE]) |
| 3 - output([E.DEPARTMENT_ID], [E.HIRE_DATE], [E.FIRST_NAME]), filter([E.DEPARTMENT_ID = 80]), rowset=256 |
| access([E.DEPARTMENT_ID], [E.HIRE_DATE], [E.FIRST_NAME]), partitions(p0) |
| is_index_back=false, is_global_index=false, filter_before_indexback[false], |
| range_key([E.EMPLOYEE_ID]), range(MIN ; MAX)always true |
| 4 - output([E1.DEPARTMENT_ID], [E1.HIRE_DATE]), filter(nil), rowset=256 |
| access([E1.DEPARTMENT_ID], [E1.HIRE_DATE]), partitions(p0) |
| is_index_back=false, is_global_index=false, |
| range_key([E1.EMPLOYEE_ID]), range(MIN ; MAX)always true |
+---------------------------------------------------------------------------------------------------------------------------------------------------------------------+
27 rows in set (0.005 sec)
5、 总结
SQL语句虽然很简单，但是要培养出SQL思维可不是一件容易的事，还是得多看，多写，多思考。
这个是要通过不同的开发需求日积月累形成的思维逻辑，而不是一蹴而就。
SQL Thinking的更多相关文章
- 最近帮客户实施的基于SQL Server AlwaysOn跨机房切换项目
最近帮客户实施的基于SQL Server AlwaysOn跨机房切换项目 最近一个来自重庆的客户找到走起君,客户的业务是做移动互联网支付,是微信支付收单渠道合作伙伴,数据库里存储的是支付流水和交易流水 ...
- SQL Server 大数据搬迁之文件组备份还原实战
一.本文所涉及的内容(Contents) 本文所涉及的内容(Contents) 背景(Contexts) 解决方案(Solution) 搬迁步骤(Procedure) 搬迁脚本(SQL Codes) ...
- Sql Server系列：分区表操作
1. 分区表简介 分区表在逻辑上是一个表,而物理上是多个表.从用户角度来看,分区表和普通表是一样的.使用分区表的主要目的是为改善大型表以及具有多个访问模式的表的可伸缩性和可管理性. 分区表是把数据按设 ...
- SQL Server中的高可用性（2）----文件与文件组
在谈到SQL Server的高可用性之前,我们首先要谈一谈单实例的高可用性.在单实例的高可用性中,不可忽略的就是文件和文件组的高可用性.SQL Server允许在某些文件损坏或离线的情况下,允 ...
- EntityFramework Core Raw SQL
前言 本节我们来讲讲EF Core中的原始查询,目前在项目中对于简单的查询直接通过EF就可以解决,但是涉及到多表查询时为了一步到位就采用了原始查询的方式进行.下面我们一起来看看. EntityFram ...
- 从0开始搭建SQL Server AlwaysOn 第一篇（配置域控）
从0开始搭建SQL Server AlwaysOn 第一篇(配置域控) 第一篇http://www.cnblogs.com/lyhabc/p/4678330.html第二篇http://www.cnb ...
- 从0开始搭建SQL Server AlwaysOn 第二篇（配置故障转移集群）
从0开始搭建SQL Server AlwaysOn 第二篇(配置故障转移集群) 第一篇http://www.cnblogs.com/lyhabc/p/4678330.html第二篇http://www ...
- 从0开始搭建SQL Server AlwaysOn 第三篇（配置AlwaysOn）
从0开始搭建SQL Server AlwaysOn 第三篇(配置AlwaysOn) 第一篇http://www.cnblogs.com/lyhabc/p/4678330.html第二篇http://w ...
- 从0开始搭建SQL Server AlwaysOn 第四篇（配置异地机房节点）
从0开始搭建SQL Server AlwaysOn 第四篇(配置异地机房节点) 第一篇http://www.cnblogs.com/lyhabc/p/4678330.html第二篇http://www ...
- SQL Server on Linux 理由浅析
SQL Server on Linux 理由浅析 今天的爆炸性新闻<SQL Server on Linux>基本上在各大科技媒体上刷屏了 大家看到这个新闻都觉得非常震精,而美股,今天微软开 ...
随机推荐
- selenium遇到手机验证码怎么解决
完整代码在: selenium使用案例 解决思路,点击发送送验证码,程序用input方法去和人进行交互,手动输入验证码,按回车键,这样程序就接收到手机验证码了,再把验证码赋值给验证码框,继续往下操作 ...
- WPF,Frame控件的一个BUG
我使用WPF默认的frame <Frame Style="{DynamicResource FrameStyle1}" x:Name="frame" He ...
- C# winform GDI+ 五子棋 （一）：基本界面和胜负判断
棋盘和棋子采用GDI+画上去的.棋盘18*18.棋子是用DrawElipse画的,白棋和黑棋分两个List集合存储,方便判断五子连线的情况. 主要说一下,五子连线的思路,把集合按行和按列以及按正斜和反 ...
- PHP 中使用 ElasticSearch 的最佳实践 (下)
引言 上一篇文章,我们使用同步的方式将数据,同步写入到 ElasticSearch 中.接下来的这篇文章,主要介绍使用 RabbitMQ 的方式,异步的将数据同步到 ElasticSearch . 部 ...
- webview2 示例 Samples Selenium
https://github.com/MicrosoftEdge/WebView2Samples https://learn.microsoft.com/zh-cn/microsoft-edge/we ...
- WPF实现轮播图
1.效果图 2.前端代码 <Window x:Class="LiveChartDemo.View.CarouselView" xmlns="http://schem ...
- vue-cli3 项目路由 history 模式部署到 nginx 服务器
1.项目修改vue.config.js增加 publicPath: '/' 2.nginx配置 location / {#访问前端页面 root /data/dist;#vue项目存放路径 index ...
- 授权调用: 介绍 Transformers 智能体 2.0
简要概述 我们推出了 Transformers 智能体 2.0! ⇒ 在现有智能体类型的基础上,我们新增了两种能够 根据历史观察解决复杂任务的智能体. ⇒ 我们致力于让代码 清晰.模块化,并确保最终提 ...
- RTMP 直播 H265 推流适配总结
1.在iOS11的系统之上,苹果逐渐放开H265硬编硬解的能力,硬解码的能力只要升级到iOS11之后,iPhone6+以上的机型就支持了(印象中): H265硬编码的能力对设备要求较高,不仅要求系统版 ...
- 箭头函数 函数中的this指向
// 箭头函数 // 在匿名函数中,使用 => 箭头来替换 关键词 function // 箭头定义下 () 和 {} 之间 // 等于在使 ...