mogdb/opengauss索引推荐及虚拟索引 -m6米乐安卓版下载

原创阎书利 2021-12-10

1385

在oracle的优化中，可能大家有接触过sql tuning advisor(sql调优顾问，sta)，类似的mogdb/opengauss的索引推荐(index-advisor)功能也可以对你的查询进行分析，并提出合理的创建索引的建议。oracle的sta输出是以一种意见或者建议的形式，以及对每一项建议和期望效益的理由。该建议涉及对象的统计收集，新索引的创建，sql语句的重组，或sql概要的创建。你可以选择该建议来完成sql语句的调优。mogdb/opengauss的索引推荐(index-advisor)在这也是比较类似，但可能结果不如oracle的sta的优化报告详尽。

如下为我对mogdb/opengauss的索引推荐(index-advisor)功能的使用测试，包括单条sql查询索引推荐、workload级别索引推荐(针对一批sql语句的索引推荐)等。

一、测试数据导入

postgres=# create database ysla;
create database
postgres=# \c ysla
non-ssl connection (ssl connection is recommended when requiring high-security)
you are now connected to database "ysla" as user "omm".
ysla=# create table tab_ysl_1 (col1 int, col2 int, col3 text);
create table
ysla=# insert into tab_ysl_1 values(generate_series(1, 3000),generate_series(1, 3000),repeat( chr(int4(random()*26) 65),4));
insert 0 3000
ysla=# analyze tab_ysl_1;
analyze
ysla=# create table tab_ysl_2 (col1 int, col2 int);
create table
ysla=# insert into tab_ysl_2 values(generate_series(1, 1000),generate_series(1, 1000));
insert 0 1000
ysla=# analyze tab_ysl_2;
analyze

二、单条sql查询索引推荐

如下面所示，用gs_index_advise函数即可使用索引推荐，结果中包含表和可以创建索引的列。

1.测试where

ysla=# select  * from gs_index_advise('select * from tab_ysl_1 where col1 = 10');
   table   | column
----------- --------
 tab_ysl_1 | (col1)
(1 row)

2.测试join

ysla=# select  * from gs_index_advise('select * from tab_ysl_1 join tab_ysl_2 on tab_ysl_1.col1 = tab_ysl_2.col1');
   table   | column
----------- --------
 tab_ysl_1 | (col1)
 tab_ysl_2 |
(2 rows)

3.测试多表

ysla=# select  * from gs_index_advise('select count(*), tab_ysl_2.col1 from tab_ysl_1 join tab_ysl_2 on tab_ysl_1.col2 = tab_ysl_2.col2 where tab_ysl_2.col2 > 2 group by tab_ysl_2.col1 order by tab_ysl_2.col1');
   table   | column
----------- --------
 tab_ysl_1 | (col2)
 tab_ysl_2 | (col1)
(2 rows)

4.测试order by

ysla=# select  * from gs_index_advise('select *, col2 from tab_ysl_1 order by 1, 3');
   table   | column
----------- --------
 tab_ysl_1 |
(1 row)
ysla=# select  * from gs_index_advise('select * from tab_ysl_1 where col1 > 10 order by 1,col2');
   table   | column
----------- --------
 tab_ysl_1 |
(1 row)

5.测试过长字符串

ysla=# select  * from gs_index_advise('select * from tab_ysl_1 where col3 in (''aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa'',''bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb'',''ccccccccccccccccccccccccccccccccccccccc'',''ddddddddddddddddddddddddddddddddddddddd'',''ffffffffffffffffffffffffffffffffffffffff'',''ggggggggggggggggggggggggggggggggggggggggggggggggggg'',''ttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttt'',''vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv'',''ggmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmm'')');
error:  index_advisor.cpp : 983 : the parameter destmax is too small or parameter count is larger than macro parameter securec_string_max_len. the second case only occures in functions strncat_s/strncpy_s.

三、workload级别索引推荐

这种方式可以针对多条sql,可以将待优化的sql写到文件里，通过脚本获得推荐索引。

脚本目录在安装目录的bin/dbmind/index_advisor下边，我的目录为

/opt/gaussdb/app/bin/dbmind/index_advisor/index_advisor_workload.py

将待优化的sql放到文件里

[omm@node1 index_advisor]$ cat 1.sql
select * from tab_ysl_1 where col1 = 10;
select count(*), tab_ysl_2.col1 from tab_ysl_1 join tab_ysl_2 on tab_ysl_1.col2 = tab_ysl_2.col2 where tab_ysl_2.col2 > 2 group by tab_ysl_2.col1 order by tab_ysl_2.col1;
select * from tab_ysl_1 join tab_ysl_2 on tab_ysl_1.col1 = tab_ysl_2.col1;

使用如下方式调用脚本，可以批量获取推荐索引，26000为我的数据库端口，ysla为我的数据库名，1.sql为我待优化的sql存放的文件

[omm@node1 index_advisor]$ pwd
/opt/gaussdb/app/bin/dbmind/index_advisor
[omm@node1 index_advisor]$ python3 ./index_advisor_workload.py 26000 ysla 1.sql
###### ############################################################## generate candidate indexes
table:  tab_ysl_1 columns:  col1
table:  tab_ysl_1 columns:  col2
table:  tab_ysl_2 columns:  col1
###### ############################################################### determine optimal indexes
create index ind0 on tab_ysl_1(col1);

四、索引效率查看

这里验证下索引推荐给我们推荐的索引究竟是否起到优化作用。

[omm@node1 index_advisor]$ cat 1.sql
select * from tab_ysl_1 where col1 = 10;
[omm@node1 index_advisor]$  time gsql -d ysla -p 26000 -f 1.sql
 col1 | col2 | col3
------ ------ ------
   10 |   10 | ssss
(1 row)
total time: 35  ms
real    0m0.050s
user    0m0.007s
sys     0m0.002s

可以看到上边未优化的sql执行时间为0m0.050s

[omm@node1 index_advisor]$ python3 ./index_advisor_workload.py 26000 ysla 1.sql
###### ############################################################## generate candidate indexes
table:  tab_ysl_1 columns:  col1
###### ############################################################### determine optimal indexes
create index ind0 on tab_ysl_1(col1);

通过index-advisor获取推荐索引。并创建索引

ysla=# create index ind0 on tab_ysl_1(col1);
create index

可以看到查询的时间明显减少。

[omm@node1 index_advisor]$  time gsql -d ysla -p 26000 -f 1.sql
 col1 | col2 | col3
------ ------ ------
   10 |   10 | ssss
(1 row)
total time: 0  ms
real    0m0.016s
user    0m0.009s
sys     0m0.000s

一般在加索引时，会堵塞dml（不过pg支持并发加索引，不堵塞dml）。只有索引真正能起到优化作用，我们建立索引才是有意义的。虚拟索引是一个很有用的东西，没有副作用，只是虚拟的索引，建立虚拟索引后，可以通过explain来查看加索引后的成本估算，判断是否加索引cost会降低。

可以用虚拟索引检验索引的效果，根据效果可选择是否创建真实的索引优化查询。

#测试建立虚拟索引（hypopg_create_index）
ysla=# select * from hypopg_create_index('create index on tab_ysl_1(col1)');
 indexrelid |          indexname
------------ -----------------------------
      41453 | <41453>btree_tab_ysl_1_col1
(1 row)
#显示所有创建的虚拟索引信息（enable_hypo_index）
ysla=# select * from hypopg_display_index();
          indexname          | indexrelid |   table   | column
----------------------------- ------------ ----------- --------
 <41454>btree_tab_ysl_1_col1 |      41454 | tab_ysl_1 | (col1)
(1 row)
ysla=# set enable_hypo_index = on;explain select * from tab_ysl_1 where col1 = 100;
set
                                          query plan
----------------------------------------------------------------------------------------------
 index scan using <41453>btree_tab_ysl_1_col1 on tab_ysl_1  (cost=0.00..8.27 rows=1 width=13)
   index cond: (col1 = 100)
(2 rows)
#测试删除指定虚拟索引（hypopg_display_index）
使用函数hypopg_drop_index删除指定oid的虚拟索引
ysla=# select * from hypopg_drop_index(41454);
 hypopg_drop_index
-------------------
 t
(1 row)
#使用函数hypopg_reset_index一次性清除所有创建的虚拟索引
ysla=# select * from hypopg_reset_index();
 hypopg_reset_index
--------------------