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在oracle 官方文档中有如下一段话：
注意，oracle 可能将因子化的子查询作为临时表来处理，在一个表被多次引用的查询中，这可能是独特
的性能优势，因为oracle可以物化查询结果集 从而避免多次执行一些非常耗费
资源的数据库运算。在这里需要注意的是，这种优化只是可能的独特性能优势。需要牢记于心的一点是物化结果集需要创建
一个临时表并将数据行插入其中。如果同一个结果集会被多次引用，这样做可能是很值得的，否则就有可能极大的降低性能。
–用materialize提示将查询因子物化成临时表。（不加提示时，本例也会默认采用这种办法）
子查询因子化实例演示
--测试前 先在dba权限清除缓存
ALTER SYSTEM FLUSH SHARED_POOL;
ALTER SYSTEM FLUSH BUFFER_CACHE;
ALTER SYSTEM FLUSH GLOBAL CONTEXT;
1、使用子查询因子化
with cust as
(select /*+materialize gather_plan_statistics*/
b.cust_income_level, a.country_name
from customers b
join countries a
on a.country_id = b.country_id)
select country_name,
cust_income_level,
count(country_name) country_cust_count
from cust
having count (country_name) > (select count(1) * 0.01 from cust c2)
or count (cust_income_level) >=
--median 用于计算中位数
(select median(income_level_count) from (
select cust_income_level,count(1) * 0.25 income_level_count from cust
group by cust_income_level))
group by country_name,
cust_income_level
order by 1,2;
--根据第一列和第二列排序
既相当于 order by country_name, cust_income_level
执行后
获取sqlid
select t.SQL_TEXT,t.SQL_ID,t.CHILD_NUMBER from v$sql t where t.SQL_TEXT like '%materialize%';
查看执行计划：
SELECT * FROM TABLE(DBMS_XPLAN.DISPLAY_CURSOR('f26yd87mjkfwg',0,'ALLSTATS LAST'));
结果如下:
可以看到customers以及countries表的联结经过了一个临时表转化( TEMP TABLE TRANSFORMATION),接下来的查询都会用到这个临时表SYS_TEMP_0FD9D6609_2630CA，并且由于创建了临时表，产生了物理读取 reads 既IO
2、使用INLINE提示，查询因子做内联处理。
with cust as
(select /*+ inline gather_plan_statistics*/
b.cust_income_level, a.country_name
from customers b
join countries a
on a.country_id = b.country_id)
select country_name,
cust_income_level,
count(country_name) country_cust_count
from cust
having count (country_name) > (select count(1) * 0.01 from cust c2)
or count (cust_income_level) >=
(select median(income_level_count) from (
select cust_income_level,count(1) * 0.25 income_level_count from cust
group by cust_income_level))
group by country_name,
cust_income_level
order by 1,2;
执行完后查看 sql_id
select t.SQL_TEXT,t.SQL_ID,t.CHILD_NUMBER from v$sql t where t.SQL_TEXT like '%inline%';
执行计划
可以看到对customers表进行了三次全表扫描以及countries进行了一次全表扫描对cust子查询进行了两次索引全扫描
同时由于全表扫描也产生了物理读取，有意思的是相对物化既第一个sql中执行计划，少了300多个IO。而时间也是比 第一个提高了一半；
3、再增加一次结果集的引用
在查看执行计划时，发现需要执行两次sql，不然得不到执行计划 会有如下提示 未找到原因
with cust as
(select /*+materialize gather_plan_statistics*/
b.cust_income_level, a.country_name
from customers b
join countries a
on a.country_id = b.country_id),
median_income_set as
(select /*+materialize*/ cust_income_level,count(1) income_level_count from cust
group by cust_income_level
having count(cust_income_level)>(
select median(income_level_count) income_level_count from (
select cust_income_level,count(1) income_level_count from cust group by cust_income_level
)
))
select country_name,
cust_income_level,
count(country_name) country_cust_count
from cust c
having count (country_name) > (select count(1) * 0.01 from cust c2)
or cust_income_level in(select mis.cust_income_level from median_income_set mis)
group by country_name,
cust_income_level
order by 1,2
;
执行计划
4、增加一次结果集 使用内联 *+inline
with cust as
(select /*+inline gather_plan_statistics*/
b.cust_income_level, a.country_name
from customers b
join countries a
on a.country_id = b.country_id),
median_income_set as
(select /*+inline*/ cust_income_level,count(1) income_level_count from cust
group by cust_income_level
having count(cust_income_level)>(
select median(income_level_count) income_level_count from (
select cust_income_level,count(1) income_level_count from cust group by cust_income_level
)
))
select country_name,
cust_income_level,
count(country_name) country_cust_count
from cust c
having count (country_name) > (select count(1) * 0.01 from cust c2)
or cust_income_level in(select mis.cust_income_level from median_income_set mis)
group by country_name,
cust_income_level
order by 1,2;
执行计划
增加一次结果集的引用后发现，子查询因子话 的逻辑读取只有3000左右 比内联 少了仅2w，而且时间也是内联的一半。可见子查询因子话即将
结果集写入到临时表中的效率在多次重用的效率明显提高。