案例：调整查询重写GUC参数rewrite_rule
rewrite_rule包含了多个查询重写规则：magicset、partialpush、uniquecheck、disablerep、intargetlist、predpush。下面简要说明一下其中重要的几个规则的使用场景：
目标列子查询提升参数intargetlist
通过将目标列中子查询提升，转为JOIN，往往可以极大提升查询性能。举例如下查询：
openGauss=# set rewrite_rule='none';
SET
openGauss=# create table t1(c1 int,c2 int);
CREATE TABLE
openGauss=# create table t2(c1 int,c2 int);
CREATE TABLE
openGauss=# explain (verbose on, costs off) select c1,(select avg(c2) from t2 where t2.c2=t1.c2) from t1 where t1.c1<100 order by t1.c2;
QUERY PLAN
-----------------------------------------------
Sort
Output: t1.c1, ((SubPlan 1)), t1.c2
Sort Key: t1.c2
-> Seq Scan on public.t1
Output: t1.c1, (SubPlan 1), t1.c2
Filter: (t1.c1 < 100)
SubPlan 1
-> Aggregate
Output: avg(t2.c2)
-> Seq Scan on public.t2
Output: t2.c1, t2.c2
Filter: (t2.c2 = t1.c2)
(12 rows)
由于目标列中的相关子查询(select avg(c2) from t2 where t2.c2=t1.c2)无法提升的缘故，导致每扫描t1的一行数据，就会触发子查询的一次执行，效率低下。如果打开intargetlist参数会把子查询提升转为JOIN，来提升查询的性能：
openGauss=# set rewrite_rule='intargetlist';
SET
openGauss=# explain (verbose on, costs off) select c1,(select avg(c2) from t2 where t2.c2=t1.c2) from t1 where t1.c1<100 order by t1.c2;
QUERY PLAN
-----------------------------------------------
Sort
Output: t1.c1, (avg(t2.c2)), t1.c2
Sort Key: t1.c2
-> Hash Left Join
Output: t1.c1, (avg(t2.c2)), t1.c2
Hash Cond: (t1.c2 = t2.c2)
-> Seq Scan on public.t1
Output: t1.c1, t1.c2
Filter: (t1.c1 < 100)
-> Hash
Output: (avg(t2.c2)), t2.c2
-> HashAggregate
Output: avg(t2.c2), t2.c2
Group By Key: t2.c2
-> Seq Scan on public.t2
Output: t2.c2
(16 rows)
提升无agg的子查询uniquecheck
子链接提升需要保证对于每个条件只有一行输出，对于有agg的子查询可以自动提升，对于无agg的子查询如：
select t1.c1 from t1 where t1.c1 = (select t2.c1 from t2 where t1.c1=t2.c2) ;
重写为：
select t1.c1 from t1 join (select t2.c1 from t2 where t2.c1 is not null group by t2.c1(unique check)) tt(c1) on tt.c1=t1.c1;
为了保证语义等价，子查询tt必须保证对于每个group by t2.c1只能有一行输出。打开uniquecheck查询重写参数保证可以提升并且等价，如果在运行时输出了多于一行的数据，就会报错。
openGauss=# set rewrite_rule='uniquecheck';
SET
openGauss=# explain verbose select t1.c1 from t1 where t1.c1 = (select t2.c1 from t2 where t1.c1=t2.c1);
QUERY PLAN
-------------------------------------------------------------------------------------
Hash Join (cost=43.36..104.40 rows=2149 distinct=[200, 200] width=4)
Output: t1.c1
Hash Cond: (t1.c1 = subquery."?column?")
-> Seq Scan on public.t1 (cost=0.00..31.49 rows=2149 width=4)
Output: t1.c1, t1.c2
-> Hash (cost=40.86..40.86 rows=200 width=8)
Output: subquery."?column?", subquery.c1
-> Subquery Scan on subquery (cost=36.86..40.86 rows=200 width=8)
Output: subquery."?column?", subquery.c1
-> HashAggregate (cost=36.86..38.86 rows=200 width=4)
Output: t2.c1, t2.c1
Group By Key: t2.c1
Filter: (t2.c1 IS NOT NULL)
Unique Check Required
-> Seq Scan on public.t2 (cost=0.00..31.49 rows=2149 width=4)
Output: t2.c1
(16 rows)
注意：因为分组group by t2.c1 unique check发生在过滤条件tt.c1=t1.c1之前，可能导致原来不报错的查询重写之后报错。举例：
有t1,t2表，其中的数据为：
openGauss=# select * from t1 order by c2;
c1 | c2
----+----
1 | 1
2 | 2
3 | 3
(3 rows)
openGauss=# select * from t2 order by c2;
c1 | c2
----+----
1 | 1
2 | 2
3 | 3
4 | 4
4 | 4
5 | 5
(6 rows)
分别关闭和打开uniquecheck参数对比，打开之后报错。
openGauss=# select t1.c1 from t1 where t1.c1 = (select t2.c1 from t2 where t1.c1=t2.c2) ;
c1
----
1
2
3
(3 rows)
openGauss=# set rewrite_rule='uniquecheck';
SET
openGauss=# select t1.c1 from t1 where t1.c1 = (select t2.c1 from t2 where t1.c1=t2.c2) ;
ERROR: more than one row returned by a subquery used as an expression
去除多余distinct和group by子句remove_redundant_distinct_group_by
子查询提升需要保证没有distinct、group by子句，而对于如下ANY_sublink的语句：
explain select t1.c1 from t1 where t1.c2 = 5 and t1.c1 in (select t2.c1 from t2 group by t2.c1);
或者
explain select t1.c1 from t1 where t1.c2 = 5 and t1.c1 in (select distinct t2.c1 from t2);
可以发现distinct或group by对子查询的结果并无影响，仅做了去重，在in场景下并无意义，可以直接去除，重写为：
explain select t1.c1 from t1 where t1.c2 = 5 and t1.c1 in (select t2.c1 from t2);
具体的执行计划变更如下：
openGauss=# explain select t1.c1 from t1 where t1.c2 = 5 and t1.c1 in (select t2.c1 from t2 group by t2.c1);
QUERY PLAN
----------------------------------------------------------------------------------
Hash Right Semi Join (cost=52.01..56.79 rows=6 width=4)
Hash Cond: (t2.c1 = t1.c1)
-> HashAggregate (cost=36.86..38.86 rows=200 width=4)
Group By Key: t2.c1
-> Seq Scan on t2 (cost=0.00..31.49 rows=2149 width=4)
-> Hash (cost=15.01..15.01 rows=11 width=4)
-> Bitmap Heap Scan on t1 (cost=4.34..15.01 rows=11 width=4)
Recheck Cond: (c2 = 5)
-> Bitmap Index Scan on t1_idx (cost=0.00..4.33 rows=11 width=0)
Index Cond: (c2 = 5)
(10 rows)
openGauss=# set rewrite_rule = 'remove_redundant_distinct_group_by';
SET
openGauss=# explain select t1.c1 from t1 where t1.c2 = 5 and t1.c1 in (select t2.c1 from t2 group by t2.c1);
QUERY PLAN
-----------------------------------------------------------------------------
Nested Loop Semi Join (cost=4.34..23.55 rows=6 width=4)
-> Bitmap Heap Scan on t1 (cost=4.34..15.01 rows=11 width=4)
Recheck Cond: (c2 = 5)
-> Bitmap Index Scan on t1_idx (cost=0.00..4.33 rows=11 width=0)
Index Cond: (c2 = 5)
-> Index Only Scan using t2_idx on t2 (cost=0.00..4.47 rows=11 width=4)
Index Cond: (c1 = t1.c1)
(7 rows)
原先扫描t2表时，由于存在group by子句，导致t2.c1 = t1.c1过滤条件无法下推到扫描中，需要做全表扫描。如果打开remove_redundant_distinct_group_by参数，去掉了group by子句，选择了参数化路径，提升了查询性能。
需要注意的是，即使打开了该参数，部分场景也不会做该优化，原因是可能会影响结果：
- 对于distinct场景，使用了distinct on子句或limit子句。
- 对于group by场景，使用了聚集函数、窗口函数、having子句、groupingSets或limit子句。