MogDB/openGauss索引推荐及虚拟索引

索引推荐

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

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;

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

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)

real 0m0.016s
user 0m0.009s
sys 0m0.000s


虚拟索引

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 | 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
-----------------------------+------------+-----------+--------
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 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
--------------------