SQL Tuning Advisor使用实例
2015-10-28 10:59
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在oracle10g之前，想要优化一个sql语句是比较麻烦，但是在oracle10g这个版本推出的SQL Tuning Advisor这个工具，能大大减少sql调优的工作量，不过要想使用SQL Tuning Advisor，一定要保证你的优化器是CBO模式。
1.首先需要创建一个用于调优的用户bamboo,并授予advisor给创建的用户
SQL> create user bamboo identified by bamboo;
User created.
SQL> grant connect,resource to bamboo;
Grant succeeded.
SQL> grant advisor to bamboo;
Grant succeeded.
2.创建用户做测试的2张表,大表里面插入500万条数据，小表里面插入10万条数据，其创建方法如下
SQL> create table bigtable (id number(10),name varchar2(100));
Table created.
SQL> begin
2 for i in 1..5000000 loop
3 insert into bigtable values(i,'test'||i);
4 end loop;
5 end;
6 /
PL/SQL procedure successfully completed.
SQL> commti;
SQL> create table smalltable (id number(10),name varchar2(100));
Table created.
SQL> begin
2 for i in 1..100000 loop
3 insert into smalltable values(i,'test'||i);
4 end loop;
5 end;
6 /
PL/SQL procedure successfully completed.
SQL> commti;
3.然后对bigtable和smalltable做一个等连接查询，然后跟踪其执行计划
SQL> select a.id,a.name,b.id,b.name from bigtable a,smalltable b where a.id=b.id and a.id=40000;
ID NAME ID NAME
---------- ---------------------------------------- ---------- ----------------------------------------
40000 test40000 40000 test40000
Execution Plan
----------------------------------------------------------
Plan hash value: 1703851322
---------------------------------------------------------------------------------
| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time |
---------------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 839 | 106K| 3656 (5)| 00:00:44 |
|* 1 | HASH JOIN | | 839 | 106K| 3656 (5)| 00:00:44 |
|* 2 | TABLE ACCESS FULL| SMALLTABLE | 5 | 325 | 71 (3)| 00:00:01 |
|* 3 | TABLE ACCESS FULL| BIGTABLE | 173 | 11245 | 3584 (5)| 00:00:44 |
---------------------------------------------------------------------------------
Predicate Information (identified by operation id):
---------------------------------------------------
1 - access("A"."ID"="B"."ID")
2 - filter("B"."ID"=40000
4000
)
3 - filter("A"."ID"=40000)
Note
-----
- dynamic sampling used for this statement
Statistics
----------------------------------------------------------
9 recursive calls
0 db block gets
16151 consistent gets
11469 physical reads
0 redo size
588 bytes sent via SQL*Net to client
385 bytes received via SQL*Net from client
2 SQL*Net roundtrips to/from client
2 sorts (memory)
0 sorts (disk)
1 rows processed
熟悉执行计划的就可以看出，这个sql执行是很慢的，2个表都做的是全表扫描，并且其物理读是11469，按照优化的经验，给2个表的id创建索引，减少查询时候的物理读,下面我们就看看通过优化器，oracle能我们什么样的建议呢？
4.下面就通过DBMS_SQLTUNE包的CREATE_TUNING_TASK来创建一个优化任务，然后通过DBMS_SQLTUNE.EXECUTE_TUNING_TASK来执行调优任务，生成调优建议
SQL> DECLARE
2 my_task_name VARCHAR2(30);
3 my_sqltext CLOB;
4 BEGIN
5 my_sqltext := 'select a.id,a.name,b.id,b.name from bigtable a,smalltable b where a.id=b.id and a.id=40000';
6
7 my_task_name := DBMS_SQLTUNE.CREATE_TUNING_TASK(
8 sql_text => my_sqltext,
9 user_name => 'SCOTT',
10 scope => 'COMPREHENSIVE',
11 time_limit => 60,
12 task_name => 'test_sql_tuning_task1',
13 description => 'Task to tune a query');
14 DBMS_SQLTUNE.EXECUTE_TUNING_TASK(task_name => 'test_sql_tuning_task1');
15 END;
16 /
5.执行的过程中，也可以通过user_advisor_tasks或者dba_advisor_tasks来查看调优任务执行的状况
SQL> select task_name,ADVISOR_NAME,STATUS from user_advisor_tasks;
TASK_NAME ADVISOR_NAME STATUS
------------------------------ ---------------------------------------- ---------------------------------
test_sql_tuning_task1 SQL Tuning Advisor COMPLETED
如果status是EXECUTING，则表示任务正在执行，如果为COMPLETED，则任务已经执行完毕
6.通过调用dbms_sqltune.report_tuning_task可以查询调优的结果，不过在查询结果之前，得设置sqlplus的环境，如果不设置，则查询的结果出不来
SQL> set long 999999
SQL> set LONGCHUNKSIZE 999999
SQL> set serveroutput on size 999999
SQL> set linesize 200
SQL> select dbms_sqltune.report_tuning_task('test_sql_tuning_task1') from dual;
SQL> select dbms_sqltune.report_tuning_task('test_sql_tuning_task1') from dual;
DBMS_SQLTUNE.REPORT_TUNING_TASK('TEST_SQL_TUNING_TASK1')
---------------------------------------------------------------------------------------------------------------------------------
GENERAL INFORMATION SECTION
-------------------------------------------------------------------------------
Tuning Task Name : test_sql_tuning_task1
Tuning Task Owner : BAMBOO
Scope : COMPREHENSIVE
Time Limit(seconds) : 60
Completion Status : COMPLETED
Started at : 10/13/2011 05:07:53
Completed at : 10/13/2011 05:08:18
Number of Statistic Findings : 2
Number of Index Findings : 1
DBMS_SQLTUNE.REPORT_TUNING_TASK('TEST_SQL_TUNING_TASK1')
----------------------------------------------------------------------------------------------------------------------------------
Schema Name: SCOTT
SQL ID : 7arau1k5a3mv1
SQL Text : select a.id,a.name,b.id,b.name from bigtable a,smalltable b
where a.id=b.id and a.id=40000
-------------------------------------------------------------------------------
FINDINGS SECTION (3 findings)
-------------------------------------------------------------------------------
DBMS_SQLTUNE.REPORT_TUNING_TASK('TEST_SQL_TUNING_TASK1')
----------------------------------------------------------------------------------------------------------------------------------
1- Statistics Finding
---------------------
Table "SCOTT"."SMALLTABLE" was not analyzed.
Recommendation
--------------
- Consider collecting optimizer statistics for this table.
execute dbms_stats.gather_table_stats(ownname => 'SCOTT', tabname =>
'SMALLTABLE', estimate_percent => DBMS_STATS.AUTO_SAMPLE_SIZE,
method_opt => 'FOR ALL COLUMNS SIZE AUTO');
DBMS_SQLTUNE.REPORT_TUNING_TASK('TEST_SQL_TUNING_TASK1')
----------------------------------------------------------------------------------------------------------------------------------
Rationale
---------
The optimizer requires up-to-date statistics for the table in order to
select a good execution plan.
2- Statistics Finding
---------------------
Table "SCOTT"."BIGTABLE" was not analyzed.
Recommendation
--------------
DBMS_SQLTUNE.REPORT_TUNING_TASK('TEST_SQL_TUNING_TASK1')
----------------------------------------------------------------------------------------------------------------------------------
- Consider collecting optimizer statistics for this table.
execute dbms_stats.gather_table_stats(ownname => 'SCOTT', tabname =>
'BIGTABLE', estimate_percent => DBMS_STATS.AUTO_SAMPLE_SIZE,
method_opt => 'FOR ALL COLUMNS SIZE AUTO');
Rationale
---------
The optimizer requires up-to-date statistics for the table in order to
select a good execution plan.
3- Index Finding (see explain plans section below)
DBMS_SQLTUNE.REPORT_TUNING_TASK('TEST_SQL_TUNING_TASK1')
---------------------------------------------------------------------------------------------------------------------------------
The execution plan of this statement can be improved by creating one or more
indices.
Recommendation (estimated benefit: 100%)
----------------------------------------
- Consider running the Access Advisor to improve the physical schema design
or creating the recommended index.
create index SCOTT.IDX$$_00790001 on SCOTT.SMALLTABLE('ID');
- Consider running the Access Advisor to improve the physical schema design
DBMS_SQLTUNE.REPORT_TUNING_TASK('TEST_SQL_TUNING_TASK1')
----------------------------------------------------------------------------------------------------------------------------------
or creating the recommended index.
create index SCOTT.IDX$$_00790002 on SCOTT.BIGTABLE('ID');
Rationale
---------
Creating the recommended indices significantly improves the execution plan
of this statement. However, it might be preferable to run "Access Advisor"
using a representative SQL workload as opposed to a single statement. This
will allow to get comprehensive index recommendations which takes into
account index maintenance overhead and additional space consumption.
DBMS_SQLTUNE.REPORT_TUNING_TASK('TEST_SQL_TUNING_TASK1')
----------------------------------------------------------------------------------------------------------------------------------
-------------------------------------------------------------------------------
EXPLAIN PLANS SECTION
-------------------------------------------------------------------------------
1- Original
-----------
Plan hash value: 1703851322
---------------------------------------------------------------------------------
| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time |
---------------------------------------------------------------------------------
DBMS_SQLTUNE.REPORT_TUNING_TASK('TEST_SQL_TUNING_TASK1')
----------------------------------------------------------------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 839 | 106K| 3656 (5)| 00:00:44 |
|* 1 | HASH JOIN | | 839 | 106K| 3656 (5)| 00:00:44 |
|* 2 | TABLE ACCESS FULL| SMALLTABLE | 5 | 325 | 71 (3)| 00:00:01 |
|* 3 | TABLE ACCESS FULL| BIGTABLE | 173 | 11245 | 3584 (5)| 00:00:44 |
---------------------------------------------------------------------------------
Predicate Information (identified by operation id):
---------------------------------------------------
1 - access("A"."ID"="B"."ID")
2 - filter("B"."ID"=40000)
DBMS_SQLTUNE.REPORT_TUNING_TASK('TEST_SQL_TUNING_TASK1')
---------------------------------------------------------------------------------------------------------------------------------
3 - filter("A"."ID"=40000)
2- Using New Indices
--------------------
Plan hash value: 3720188830
------------------------------------------------------------------------------------------------
| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time |
------------------------------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 1 | 130 | 5 (0)| 00:00:01 |
| 1 | TABLE ACCESS BY INDEX ROWID | BIGTABLE | 1 | 65 | 3 (0)| 00:00:01 |
DBMS_SQLTUNE.REPORT_TUNING_TASK('TEST_SQL_TUNING_TASK1')
---------------------------------------------------------------------------------------------------------------------------------
| 2 | NESTED LOOPS | | 1 | 130 | 5 (0)| 00:00:01 |
| 3 | TABLE ACCESS BY INDEX ROWID| SMALLTABLE | 1 | 65 | 2 (0)| 00:00:01 |
|* 4 | INDEX RANGE SCAN | IDX$$_00790001 | 1 | | 1 (0)| 00:00:01 |
|* 5 | INDEX RANGE SCAN | IDX$$_00790002 | 1 | | 2 (0)| 00:00:01 |
------------------------------------------------------------------------------------------------
Predicate Information (identified by operation id):
---------------------------------------------------
4 - access("B"."ID"=40000)
5 - access("A"."ID"=40000)
从上面的结果可以看到oracle的调优顾问给我们3条建议：
（1）SCOTT.SMALLTABLE表没有做分析，需要做一下表结构的分析，并且给出一个分析的建议，如下所示
execute dbms_stats.gather_table_stats(ownname => 'SCOTT', tabname =>
'SMALLTABLE', estimate_percent => DBMS_STATS.AUTO_SAMPLE_SIZE,
method_opt => 'FOR ALL COLUMNS SIZE AUTO');
（2）SCOTT.BIGTABLE表没有做分析，需要做一下表结构的分析，并且给出一个分析的建议，如下所示
execute dbms_stats.gather_table_stats(ownname => 'SCOTT', tabname =>
'BIGTABLE', estimate_percent => DBMS_STATS.AUTO_SAMPLE_SIZE,
method_opt => 'FOR ALL COLUMNS SIZE AUTO');
（3）oracle建议我们在表SCOTT.SMALLTABLE，SCOTT.BIGTABLE的id列创建一个bitree索引，给的建议如下
create index SCOTT.IDX$$_00790002 on SCOTT.BIGTABLE('ID');
create index SCOTT.IDX$$_00790001 on SCOTT.SMALLTABLE('ID');
当然创建索引的名字可以改成别的名字
通过以上查看oracle的调优顾问给的建议，基本和我们在前面给出的调优方案是一致，因此当我们给一个大的ＳＱＬ做优化的时候，可以先使用oracle调优顾问，得到一些调优方案，然后根据实际情况做一些调整就可以。
以下就是执行oracle调优顾问的建议，重新执行select a.id,a.name,b.id,b.name from bigtable a,smalltable b where a.id=b.id and a.id=40000这天语句得到的执行计划，可以看出查询时间和物理读大大减少
SQL> select a.id,a.name,b.id,b.name from bigtable a,smalltable b where a.id=b.id and a.id=40000;
ID NAME ID NAME
---------- ---------------------------------------- ---------- ----------------------------------------
40000 test40000 40000 test40000
Execution Plan
----------------------------------------------------------
Plan hash value: 777647921
-------------------------------------------------------------------------------------------------
| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time |
-------------------------------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 1 | 31 | 5 (0)| 00:00:01 |
| 1 | TABLE ACCESS BY INDEX ROWID | BIGTABLE | 1 | 17 | 3 (0)| 00:00:01 |
| 2 | NESTED LOOPS | | 1 | 31 | 5 (0)| 00:00:01 |
| 3 | TABLE ACCESS BY INDEX ROWID| SMALLTABLE | 1 | 14 | 2 (0)| 00:00:01 |
|* 4 | INDEX RANGE SCAN | I_ID_SAMLLTABLE | 1 | | 1 (0)| 00:00:01 |
|* 5 | INDEX RANGE SCAN | I_ID_BIGTABLE | 1 | | 2 (0)| 00:00:01 |
-------------------------------------------------------------------------------------------------
Predicate Information (identified by operation id):
---------------------------------------------------
4 - access("B"."ID"=40000)
5 - access("A"."ID"=40000)
Statistics
----------------------------------------------------------
0 recursive calls
0 db block gets
9 consistent gets
0 physical reads
0 redo size
588 bytes sent via SQL*Net to client
385 bytes received via SQL*Net from client
2 SQL*Net roundtrips to/from client
0 sorts (memory)
0 sorts (disk)
1 rows processed
在oracle10g之前，想要优化一个sql语句是比较麻烦，但是在oracle10g这个版本推出的SQL Tuning Advisor这个工具，能大大减少sql调优的工作量，不过要想使用SQL Tuning Advisor，一定要保证你的优化器是CBO模式。
1.首先需要创建一个用于调优的用户bamboo,并授予advisor给创建的用户
SQL> create user bamboo identified by bamboo;
User created.
SQL> grant connect,resource to bamboo;
Grant succeeded.
SQL> grant advisor to bamboo;
Grant succeeded.
2.创建用户做测试的2张表,大表里面插入500万条数据，小表里面插入10万条数据，其创建方法如下
SQL> create table bigtable (id number(10),name varchar2(100));
Table created.
SQL> begin
2 for i in 1..5000000 loop
3 insert into bigtable values(i,'test'||i);
4 end loop;
5 end;
6 /
PL/SQL procedure successfully completed.
SQL> commti;
SQL> create table smalltable (id number(10),name varchar2(100));
Table created.
SQL> begin
2 for i in 1..100000 loop
3 insert into smalltable values(i,'test'||i);
4 end loop;
5 end;
6 /
PL/SQL procedure successfully completed.
SQL> commti;
3.然后对bigtable和smalltable做一个等连接查询，然后跟踪其执行计划
SQL> select a.id,a.name,b.id,b.name from bigtable a,smalltable b where a.id=b.id and a.id=40000;
ID NAME ID NAME
---------- ---------------------------------------- ---------- ----------------------------------------
40000 test40000 40000 test40000
Execution Plan
----------------------------------------------------------
Plan hash value: 1703851322
---------------------------------------------------------------------------------
| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time |
---------------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 839 | 106K| 3656 (5)| 00:00:44 |
|* 1 | HASH JOIN | | 839 | 106K| 3656 (5)| 00:00:44 |
|* 2 | TABLE ACCESS FULL| SMALLTABLE | 5 | 325 | 71 (3)| 00:00:01 |
|* 3 | TABLE ACCESS FULL| BIGTABLE | 173 | 11245 | 3584 (5)| 00:00:44 |
---------------------------------------------------------------------------------
Predicate Information (identified by operation id):
---------------------------------------------------
1 - access("A"."ID"="B"."ID")
2 - filter("B"."ID"=40000
4000
)
3 - filter("A"."ID"=40000)
Note
-----
- dynamic sampling used for this statement
Statistics
----------------------------------------------------------
9 recursive calls
0 db block gets
16151 consistent gets
11469 physical reads
0 redo size
588 bytes sent via SQL*Net to client
385 bytes received via SQL*Net from client
2 SQL*Net roundtrips to/from client
2 sorts (memory)
0 sorts (disk)
1 rows processed
熟悉执行计划的就可以看出，这个sql执行是很慢的，2个表都做的是全表扫描，并且其物理读是11469，按照优化的经验，给2个表的id创建索引，减少查询时候的物理读,下面我们就看看通过优化器，oracle能我们什么样的建议呢？
4.下面就通过DBMS_SQLTUNE包的CREATE_TUNING_TASK来创建一个优化任务，然后通过DBMS_SQLTUNE.EXECUTE_TUNING_TASK来执行调优任务，生成调优建议
SQL> DECLARE
2 my_task_name VARCHAR2(30);
3 my_sqltext CLOB;
4 BEGIN
5 my_sqltext := 'select a.id,a.name,b.id,b.name from bigtable a,smalltable b where a.id=b.id and a.id=40000';
6
7 my_task_name := DBMS_SQLTUNE.CREATE_TUNING_TASK(
8 sql_text => my_sqltext,
9 user_name => 'SCOTT',
10 scope => 'COMPREHENSIVE',
11 time_limit => 60,
12 task_name => 'test_sql_tuning_task1',
13 description => 'Task to tune a query');
14 DBMS_SQLTUNE.EXECUTE_TUNING_TASK(task_name => 'test_sql_tuning_task1');
15 END;
16 /
5.执行的过程中，也可以通过user_advisor_tasks或者dba_advisor_tasks来查看调优任务执行的状况
SQL> select task_name,ADVISOR_NAME,STATUS from user_advisor_tasks;
TASK_NAME ADVISOR_NAME STATUS
------------------------------ ---------------------------------------- ---------------------------------
test_sql_tuning_task1 SQL Tuning Advisor COMPLETED
如果status是EXECUTING，则表示任务正在执行，如果为COMPLETED，则任务已经执行完毕
6.通过调用dbms_sqltune.report_tuning_task可以查询调优的结果，不过在查询结果之前，得设置sqlplus的环境，如果不设置，则查询的结果出不来
SQL> set long 999999
SQL> set LONGCHUNKSIZE 999999
SQL> set serveroutput on size 999999
SQL> set linesize 200
SQL> select dbms_sqltune.report_tuning_task('test_sql_tuning_task1') from dual;
SQL> select dbms_sqltune.report_tuning_task('test_sql_tuning_task1') from dual;
DBMS_SQLTUNE.REPORT_TUNING_TASK('TEST_SQL_TUNING_TASK1')
---------------------------------------------------------------------------------------------------------------------------------
GENERAL INFORMATION SECTION
-------------------------------------------------------------------------------
Tuning Task Name : test_sql_tuning_task1
Tuning Task Owner : BAMBOO
Scope : COMPREHENSIVE
Time Limit(seconds) : 60
Completion Status : COMPLETED
Started at : 10/13/2011 05:07:53
Completed at : 10/13/2011 05:08:18
Number of Statistic Findings : 2
Number of Index Findings : 1
DBMS_SQLTUNE.REPORT_TUNING_TASK('TEST_SQL_TUNING_TASK1')
----------------------------------------------------------------------------------------------------------------------------------
Schema Name: SCOTT
SQL ID : 7arau1k5a3mv1
SQL Text : select a.id,a.name,b.id,b.name from bigtable a,smalltable b
where a.id=b.id and a.id=40000
-------------------------------------------------------------------------------
FINDINGS SECTION (3 findings)
-------------------------------------------------------------------------------
DBMS_SQLTUNE.REPORT_TUNING_TASK('TEST_SQL_TUNING_TASK1')
----------------------------------------------------------------------------------------------------------------------------------
1- Statistics Finding
---------------------
Table "SCOTT"."SMALLTABLE" was not analyzed.
Recommendation
--------------
- Consider collecting optimizer statistics for this table.
execute dbms_stats.gather_table_stats(ownname => 'SCOTT', tabname =>
'SMALLTABLE', estimate_percent => DBMS_STATS.AUTO_SAMPLE_SIZE,
method_opt => 'FOR ALL COLUMNS SIZE AUTO');
DBMS_SQLTUNE.REPORT_TUNING_TASK('TEST_SQL_TUNING_TASK1')
----------------------------------------------------------------------------------------------------------------------------------
Rationale
---------
The optimizer requires up-to-date statistics for the table in order to
select a good execution plan.
2- Statistics Finding
---------------------
Table "SCOTT"."BIGTABLE" was not analyzed.
Recommendation
--------------
DBMS_SQLTUNE.REPORT_TUNING_TASK('TEST_SQL_TUNING_TASK1')
----------------------------------------------------------------------------------------------------------------------------------
- Consider collecting optimizer statistics for this table.
execute dbms_stats.gather_table_stats(ownname => 'SCOTT', tabname =>
'BIGTABLE', estimate_percent => DBMS_STATS.AUTO_SAMPLE_SIZE,
method_opt => 'FOR ALL COLUMNS SIZE AUTO');
Rationale
---------
The optimizer requires up-to-date statistics for the table in order to
select a good execution plan.
3- Index Finding (see explain plans section below)
DBMS_SQLTUNE.REPORT_TUNING_TASK('TEST_SQL_TUNING_TASK1')
---------------------------------------------------------------------------------------------------------------------------------
The execution plan of this statement can be improved by creating one or more
indices.
Recommendation (estimated benefit: 100%)
----------------------------------------
- Consider running the Access Advisor to improve the physical schema design
or creating the recommended index.
create index SCOTT.IDX$$_00790001 on SCOTT.SMALLTABLE('ID');
- Consider running the Access Advisor to improve the physical schema design
DBMS_SQLTUNE.REPORT_TUNING_TASK('TEST_SQL_TUNING_TASK1')
----------------------------------------------------------------------------------------------------------------------------------
or creating the recommended index.
create index SCOTT.IDX$$_00790002 on SCOTT.BIGTABLE('ID');
Rationale
---------
Creating the recommended indices significantly improves the execution plan
of this statement. However, it might be preferable to run "Access Advisor"
using a representative SQL workload as opposed to a single statement. This
will allow to get comprehensive index recommendations which takes into
account index maintenance overhead and additional space consumption.
DBMS_SQLTUNE.REPORT_TUNING_TASK('TEST_SQL_TUNING_TASK1')
----------------------------------------------------------------------------------------------------------------------------------
-------------------------------------------------------------------------------
EXPLAIN PLANS SECTION
-------------------------------------------------------------------------------
1- Original
-----------
Plan hash value: 1703851322
---------------------------------------------------------------------------------
| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time |
---------------------------------------------------------------------------------
DBMS_SQLTUNE.REPORT_TUNING_TASK('TEST_SQL_TUNING_TASK1')
----------------------------------------------------------------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 839 | 106K| 3656 (5)| 00:00:44 |
|* 1 | HASH JOIN | | 839 | 106K| 3656 (5)| 00:00:44 |
|* 2 | TABLE ACCESS FULL| SMALLTABLE | 5 | 325 | 71 (3)| 00:00:01 |
|* 3 | TABLE ACCESS FULL| BIGTABLE | 173 | 11245 | 3584 (5)| 00:00:44 |
---------------------------------------------------------------------------------
Predicate Information (identified by operation id):
---------------------------------------------------
1 - access("A"."ID"="B"."ID")
2 - filter("B"."ID"=40000)
DBMS_SQLTUNE.REPORT_TUNING_TASK('TEST_SQL_TUNING_TASK1')
---------------------------------------------------------------------------------------------------------------------------------
3 - filter("A"."ID"=40000)
2- Using New Indices
--------------------
Plan hash value: 3720188830
------------------------------------------------------------------------------------------------
| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time |
------------------------------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 1 | 130 | 5 (0)| 00:00:01 |
| 1 | TABLE ACCESS BY INDEX ROWID | BIGTABLE | 1 | 65 | 3 (0)| 00:00:01 |
DBMS_SQLTUNE.REPORT_TUNING_TASK('TEST_SQL_TUNING_TASK1')
---------------------------------------------------------------------------------------------------------------------------------
| 2 | NESTED LOOPS | | 1 | 130 | 5 (0)| 00:00:01 |
| 3 | TABLE ACCESS BY INDEX ROWID| SMALLTABLE | 1 | 65 | 2 (0)| 00:00:01 |
|* 4 | INDEX RANGE SCAN | IDX$$_00790001 | 1 | | 1 (0)| 00:00:01 |
|* 5 | INDEX RANGE SCAN | IDX$$_00790002 | 1 | | 2 (0)| 00:00:01 |
------------------------------------------------------------------------------------------------
Predicate Information (identified by operation id):
---------------------------------------------------
4 - access("B"."ID"=40000)
5 - access("A"."ID"=40000)
从上面的结果可以看到oracle的调优顾问给我们3条建议：
（1）SCOTT.SMALLTABLE表没有做分析，需要做一下表结构的分析，并且给出一个分析的建议，如下所示
execute dbms_stats.gather_table_stats(ownname => 'SCOTT', tabname =>
'SMALLTABLE', estimate_percent => DBMS_STATS.AUTO_SAMPLE_SIZE,
method_opt => 'FOR ALL COLUMNS SIZE AUTO');
（2）SCOTT.BIGTABLE表没有做分析，需要做一下表结构的分析，并且给出一个分析的建议，如下所示
execute dbms_stats.gather_table_stats(ownname => 'SCOTT', tabname =>
'BIGTABLE', estimate_percent => DBMS_STATS.AUTO_SAMPLE_SIZE,
method_opt => 'FOR ALL COLUMNS SIZE AUTO');
（3）oracle建议我们在表SCOTT.SMALLTABLE，SCOTT.BIGTABLE的id列创建一个bitree索引，给的建议如下
create index SCOTT.IDX$$_00790002 on SCOTT.BIGTABLE('ID');
create index SCOTT.IDX$$_00790001 on SCOTT.SMALLTABLE('ID');
当然创建索引的名字可以改成别的名字
通过以上查看oracle的调优顾问给的建议，基本和我们在前面给出的调优方案是一致，因此当我们给一个大的ＳＱＬ做优化的时候，可以先使用oracle调优顾问，得到一些调优方案，然后根据实际情况做一些调整就可以。
以下就是执行oracle调优顾问的建议，重新执行select a.id,a.name,b.id,b.name from bigtable a,smalltable b where a.id=b.id and a.id=40000这天语句得到的执行计划，可以看出查询时间和物理读大大减少
SQL> select a.id,a.name,b.id,b.name from bigtable a,smalltable b where a.id=b.id and a.id=40000;
ID NAME ID NAME
---------- ---------------------------------------- ---------- ----------------------------------------
40000 test40000 40000 test40000
Execution Plan
----------------------------------------------------------
Plan hash value: 777647921
-------------------------------------------------------------------------------------------------
| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time |
-------------------------------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 1 | 31 | 5 (0)| 00:00:01 |
| 1 | TABLE ACCESS BY INDEX ROWID | BIGTABLE | 1 | 17 | 3 (0)| 00:00:01 |
| 2 | NESTED LOOPS | | 1 | 31 | 5 (0)| 00:00:01 |
| 3 | TABLE ACCESS BY INDEX ROWID| SMALLTABLE | 1 | 14 | 2 (0)| 00:00:01 |
|* 4 | INDEX RANGE SCAN | I_ID_SAMLLTABLE | 1 | | 1 (0)| 00:00:01 |
|* 5 | INDEX RANGE SCAN | I_ID_BIGTABLE | 1 | | 2 (0)| 00:00:01 |
-------------------------------------------------------------------------------------------------
Predicate Information (identified by operation id):
---------------------------------------------------
4 - access("B"."ID"=40000)
5 - access("A"."ID"=40000)
Statistics
----------------------------------------------------------
0 recursive calls
0 db block gets
9 consistent gets
0 physical reads
0 redo size
588 bytes sent via SQL*Net to client
385 bytes received via SQL*Net from client
2 SQL*Net roundtrips to/from client
0 sorts (memory)
0 sorts (disk)
1 rows processed
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