关于Oracle开启自动收集统计信息的SPA测试

主题：关于Oracle开启自动收集统计信息的SPA测试

环境：Oracle RAC 11.2.0.4（Primary + Standby）

需求：生产Primary库由于历史原因关闭了自动统计信息的收集，目前客户需求是想要重新开启统计信息的自动收集，虽然一般来说，有了更准确的统计信息，SQL会有更好的执行计划，但由于生产环境数据复杂，实际上还是需要评估哪些SQL会因为重新开启自动统计信息收集性能反而会下降。

方案：本着尽可能减少对生产Primary环境影响的原则，在Standby DG环境临时开启snapshot standby来进行SPA（SQL Performance Analyze）测试，比对开启统计信息自动收集前后的性能差异，给客户提供有价值的参考。

1.构造测试环境

2.DG备库开启snapshot模式

3.SPA测试准备

4.从AWR中采集SQL

5.SPA分析比较

6.获取性能比对分析报告

1.构造测试环境

检查自动统计信息的开启状态：

select client_name,status from dba_autotask_client;

确认自动统计信息的收集是关闭的，对于“auto optimizer stats collection”的状态应该是“DISABLED”。

SQL> select client_name,status from dba_autotask_client;

CLIENT_NAME                                                      STATUS
---------------------------------------------------------------- --------
auto optimizer stats collection                                  DISABLED
auto space advisor                                               ENABLED
sql tuning advisor                                               ENABLED

附：关闭数据库的自动统计信息收集：

--光闭自动统计信息收集，（慎用，除非有其他手工收集统计信息的完整方案，否则不建议关闭）
BEGIN
DBMS_AUTO_TASK_ADMIN.disable(
client_name => 'auto optimizer stats collection',
operation => NULL,
window_name => NULL);
END;
/

DG备库保持和主库同步，所以这些设置项也都是完全一样的。

2.DG备库开启snapshot模式

主要就是在mount模式下切换数据到snapshot Standby模式再read write打开库，为之后测试做准备。下面是核心步骤：

SQL> shutdown immediate
SQL> startup mount
SQL> alter database convert to snapshot standby;
SQL> shutdown immediate
SQL> startup

关于其他细节可参考下面文章，主要是为“开启11gR2 DG的快照模式”，“后续还原成备库” 等操作提供参考：

ORACLE 11gR2 DG(Physical Standby)日常维护02

3.SPA测试准备

进行SPA测试时，强烈建议在数据库中创建SPA测试专用用户，这样可以与其他用户区分开以及避免误操作。

SQL>
CREATE USER SPA IDENTIFIED BY SPA DEFAULT TABLESPACE SYSAUX;
GRANT DBA TO SPA;
GRANT ADVISOR TO SPA;
GRANT SELECT ANY DICTIONARY TO SPA;
GRANT ADMINISTER SQL TUNING SET TO SPA;

4.从AWR中采集SQL

备库从AWR中采集到SQL。

4.1 获取AWR快照的边界ID

SET LINES 188 PAGES 1000
COL SNAP_TIME FOR A22
COL MIN_ID NEW_VALUE MINID
COL MAX_ID NEW_VALUE MAXID
SELECT MIN(SNAP_ID) MIN_ID, MAX(SNAP_ID) MAX_ID
FROM DBA_HIST_SNAPSHOT
WHERE END_INTERVAL_TIME > trunc(sysdate)-10
ORDER BY 1;

我这里的结果是：

MIN_ID     MAX_ID
---------- ----------
2755       2848

4.2 新建SQL Set

注意：以下的规范部分都是引用之前同事编写的SPA操作规范。

参考规范：

EXEC DBMS_SQLTUNE.DROP_SQLSET ( -
SQLSET_NAME  => '${DBNAME}_SQLSET_${YYYYMMDD}',
SQLSET_OWNER => 'SPA');

EXEC DBMS_SQLTUNE.CREATE_SQLSET ( -
SQLSET_NAME  => '${DBNAME}_SQLSET_${YYYYMMDD}', -
DESCRIPTION  => 'SQL Set Create at : '||TO_CHAR(SYSDATE, 'YYYY-MM-DD HH24:MI:SS'), -
SQLSET_OWNER => 'SPA');

依据我的实验环境，真实的示例为：

--连接用户
conn SPA/SPA

--如果之前有这个SQLSET的名字，可以这样删除
EXEC DBMS_SQLTUNE.DROP_SQLSET (SQLSET_NAME  => 'JYZHAO_SQLSET_20180106', SQLSET_OWNER => 'SPA');

--新建SQLSET：JYZHAO_SQLSET_20180106
EXEC DBMS_SQLTUNE.CREATE_SQLSET ( -
SQLSET_NAME  => 'JYZHAO_SQLSET_20180106', -
DESCRIPTION  => 'SQL Set Create at : '||TO_CHAR(SYSDATE, 'YYYY-MM-DD HH24:MI:SS'), -
SQLSET_OWNER => 'SPA');

4.3 转化AWR数据中的SQL，将其载入到SQL Set

从备库的AWR中提取SQL（这等同于主库历史的SQL）。

参考规范：

DECLARE
SQLSET_CUR DBMS_SQLTUNE.SQLSET_CURSOR;
BEGIN
OPEN SQLSET_CUR FOR
SELECT VALUE(P) FROM TABLE(
DBMS_SQLTUNE.SELECT_WORKLOAD_REPOSITORY( &MINID, &MAXID,
'PARSING_SCHEMA_NAME NOT IN (''SYS'', ''SYSTEM'')',
NULL, NULL, NULL, NULL, 1, NULL, 'ALL')) P;
DBMS_SQLTUNE.LOAD_SQLSET(
SQLSET_NAME => '${DBNAME}_SQLSET_${YYYYMMDD}',
SQLSET_OWNER => 'SPA',
POPULATE_CURSOR => SQLSET_CUR,
LOAD_OPTION => 'MERGE',
UPDATE_OPTION => 'ACCUMULATE');
CLOSE SQLSET_CUR;
END;
/

依据我的实验环境，真实的示例为：

DECLARE
SQLSET_CUR DBMS_SQLTUNE.SQLSET_CURSOR;
BEGIN
OPEN SQLSET_CUR FOR
SELECT VALUE(P) FROM TABLE(
DBMS_SQLTUNE.SELECT_WORKLOAD_REPOSITORY( 2755, 2848,
'PARSING_SCHEMA_NAME NOT IN (''SYS'', ''SYSTEM'')',
NULL, NULL, NULL, NULL, 1, NULL, 'ALL')) P;
DBMS_SQLTUNE.LOAD_SQLSET(
SQLSET_NAME => 'JYZHAO_SQLSET_20180106',
SQLSET_OWNER => 'SPA',
POPULATE_CURSOR => SQLSET_CUR,
LOAD_OPTION => 'MERGE',
UPDATE_OPTION => 'ACCUMULATE');
CLOSE SQLSET_CUR;
END;
/

4.4 打包SQL Set(可不做)

参考规范：

DROP TABLE SPA.${DBNAME}_SQLSETTAB_${YYYYMMDD};
EXEC DBMS_SQLTUNE.CREATE_STGTAB_SQLSET ('${DBNAME}_SQLSETTAB_${YYYYMMDD}', ‘SPA’, 'SYSAUX');
EXEC DBMS_SQLTUNE.PACK_STGTAB_SQLSET ( -
SQLSET_NAME          => '${DBNAME}_SQLSET_${YYYYMMDD}', -
SQLSET_OWNER         => ‘SPA’, -
STAGING_TABLE_NAME   => '${DBNAME}_SQLSETTAB_${YYYYMMDD}', -
STAGING_SCHEMA_OWNER => ‘SPA’);

依据我的实验环境，真实的示例为：

DROP TABLE SPA.JYZHAO_SQLSETTAB_20180106;
EXEC DBMS_SQLTUNE.CREATE_STGTAB_SQLSET ('JYZHAO_SQLSETTAB_20180106', 'SPA', 'SYSAUX');
EXEC DBMS_SQLTUNE.PACK_STGTAB_SQLSET ( -
SQLSET_NAME          => 'JYZHAO_SQLSET_20180106', -
SQLSET_OWNER         => 'SPA', -
STAGING_TABLE_NAME   => 'JYZHAO_SQLSETTAB_20180106', -
STAGING_SCHEMA_OWNER => 'SPA');

说明：其实在我这里的测试场景下，这一步是不需要做的。因为备库的SQL Set可以直接在后面引用，不需要像SPA经典场景中，是从生产源环境打包导出来后，在测试环境再导入进去，再解包为SQL Set。

5.SPA分析比较

5.1 创建SPA分析任务

参考规范：

VARIABLE SPA_TASK  VARCHAR2(64);
EXEC :SPA_TASK := DBMS_SQLPA.CREATE_ANALYSIS_TASK(  -
TASK_NAME    => 'SPA_TASK_${YYYYMMDD}', -
DESCRIPTION => 'SPA Analysis task at : '||TO_CHAR(SYSDATE, 'YYYY-MM-DD HH24:MI:SS'), -
SQLSET_NAME  => '${DBNAME}_SQLSET_${YYYYMMDD}', -
SQLSET_OWNER => ‘SPA’);

依据我的实验环境，真实的示例为：

--创建SPA分析任务：
VARIABLE SPA_TASK  VARCHAR2(64);
EXEC :SPA_TASK := DBMS_SQLPA.CREATE_ANALYSIS_TASK(  -
TASK_NAME    => 'SPA_TASK_20180106', -
DESCRIPTION => 'SPA Analysis task at : '||TO_CHAR(SYSDATE, 'YYYY-MM-DD HH24:MI:SS'), -
SQLSET_NAME  => 'JYZHAO_SQLSET_20180106', -
SQLSET_OWNER => 'SPA');

5.2 获取变更前的SQL执行效率

参考规范：

EXEC DBMS_SQLPA.EXECUTE_ANALYSIS_TASK( -
TASK_NAME      => 'SPA_TASK_${YYYYMMDD}', -
EXECUTION_NAME => 'EXEC_10G_${YYYYMMDD}', -
EXECUTION_TYPE => 'CONVERT SQLSET', -
EXECUTION_DESC => 'Convert 10g SQLSET for SPA Task at : '||TO_CHAR(SYSDATE, 'YYYY-MM-DD HH24:MI:SS'));

依据我的实验环境，真实的示例为：

EXEC DBMS_SQLPA.EXECUTE_ANALYSIS_TASK( -
TASK_NAME      => 'SPA_TASK_20180106', -
EXECUTION_NAME => 'EXEC_BEFORE_20180106', -
EXECUTION_TYPE => 'CONVERT SQLSET', -
EXECUTION_DESC => 'Convert Before gathering stats SQLSET for SPA Task at : '||TO_CHAR(SYSDATE, 'YYYY-MM-DD HH24:MI:SS'));

5.3 开启变更操作

变更内容：开启统计信息自动收集并确认已经成功收集了最新的统计信息。

这里首先需要开启统计信息自动收集，并可以把自动收集的窗口时间提前到现在，减少等待的时间。

--检查自动统计信息的开启状态：
select client_name,status from dba_autotask_client;

--启动自动统计信息收集
BEGIN
DBMS_AUTO_TASK_ADMIN.enable(
client_name => 'auto optimizer stats collection',
operation => NULL,
window_name => NULL);
END;
/

查看窗口任务和有关统计信息自动收集的任务执行状态：

select window_name,repeat_interval,duration,enabled from dba_scheduler_windows;
select owner, job_name, status, ACTUAL_START_DATE, RUN_DURATION from dba_scheduler_job_run_details where job_name like 'ORA$AT_OS_OPT_S%' order by 4;

调整窗口任务的下一次执行时间：

--需要确认JOB可以启动
alter system set job_queue_processes=1000;

--调整窗口任务的下一次执行时间
EXEC DBMS_SCHEDULER.SET_ATTRIBUTE('SATURDAY_WINDOW','repeat_interval','freq=daily;byday=SAT;byhour=17;byminute=10;bysecond=0');

更多有关调整窗口和自动任务的内容可参考文章：

Oracle的窗口和自动任务

5.4 变更后再次分析性能

测试运行SQL Tuning Set中的SQL语句，分析所有语句在收集统计信息之后的执行效率：

参考规范：

EXEC DBMS_SQLPA.EXECUTE_ANALYSIS_TASK( -
TASK_NAME      => 'SPA_TASK_${YYYYMMDD}', -
EXECUTION_NAME => 'EXEC_11G_${YYYYMMDD}', -
EXECUTION_TYPE => 'TEST EXECUTE', -
EXECUTION_DESC => 'Execute SQL in 11g for SPA Task at : '||TO_CHAR(SYSDATE, 'YYYY-MM-DD HH24:MI:SS'));

依据我的实验环境，真实的示例为：

EXEC DBMS_SQLPA.EXECUTE_ANALYSIS_TASK( -
TASK_NAME      => 'SPA_TASK_20180106', -
EXECUTION_NAME => 'EXEC_AFTER_20180106', -
EXECUTION_TYPE => 'TEST EXECUTE', -
EXECUTION_DESC => 'Execute SQL After gathering stats for SPA Task at : '||TO_CHAR(SYSDATE, 'YYYY-MM-DD HH24:MI:SS'));

5.5 变更前后性能对比

得到两次SQL Trail之后，可以对比两次Trial之间的SQL执行性能，可以从不同的维度对两次Trail中的所有SQL进行对比分析，主要关注的维度有：SQL执行时间，SQL执行的CPU时间，SQL执行的逻辑读。

参考规范：

1). 对比两次Trail中的SQL执行时间
EXEC DBMS_SQLPA.EXECUTE_ANALYSIS_TASK( -
TASK_NAME      => 'SPA_TASK_${YYYYMMDD}', -
EXECUTION_NAME => 'COMPARE_ET_${YYYYMMDD}', -
EXECUTION_TYPE => 'COMPARE PERFORMANCE', -
EXECUTION_PARAMS => DBMS_ADVISOR.ARGLIST( -
'COMPARISON_METRIC', 'ELAPSED_TIME', -
'EXECUTE_FULLDML', 'TRUE', -
'EXECUTION_NAME1','EXEC_10G_${YYYYMMDD}', -
'EXECUTION_NAME2','EXEC_11G_${YYYYMMDD}'), -
EXECUTION_DESC => 'Compare SQLs between 10g and 11g at :'||TO_CHAR(SYSDATE, 'YYYY-MM-DD HH24:MI:SS'));
2). 对比两次Trail中的SQL执行的CPU时间
EXEC DBMS_SQLPA.EXECUTE_ANALYSIS_TASK( -
TASK_NAME      => 'SPA_TASK_${YYYYMMDD}', -
EXECUTION_NAME => 'COMPARE_CT_${YYYYMMDD}', -
EXECUTION_TYPE => 'COMPARE PERFORMANCE', -
EXECUTION_PARAMS => DBMS_ADVISOR.ARGLIST( -
'COMPARISON_METRIC', 'CPU_TIME', -
'EXECUTION_NAME1','EXEC_10G_${YYYYMMDD}', -
'EXECUTION_NAME2','EXEC_11G_${YYYYMMDD}'), -
EXECUTION_DESC => 'Compare SQLs between 10g and 11g at :'||TO_CHAR(SYSDATE, 'YYYY-MM-DD HH24:MI:SS'));
3). 对比两次Trail中的SQL执行的逻辑读
EXEC DBMS_SQLPA.EXECUTE_ANALYSIS_TASK( -
TASK_NAME      => 'SPA_TASK_D', -
EXECUTION_NAME => 'COMPARE_BG_D', -
EXECUTION_TYPE => 'COMPARE PERFORMANCE', -
EXECUTION_PARAMS => DBMS_ADVISOR.ARGLIST( -
'COMPARISON_METRIC', 'BUFFER_GETS', -
'EXECUTION_NAME1','EXEC_10G_${YYYYMMDD}', -
'EXECUTION_NAME2','EXEC_11G_${YYYYMMDD}'), -
EXECUTION_DESC => 'Compare SQLs between 10g and 11g at :'||TO_CHAR(SYSDATE, 'YYYY-MM-DD HH24:MI:SS'));

依据我的实验环境，真实的示例为：

1). 对比两次Trail中的SQL执行时间
EXEC DBMS_SQLPA.EXECUTE_ANALYSIS_TASK( -
TASK_NAME      => 'SPA_TASK_20180106', -
EXECUTION_NAME => 'COMPARE_ET_20180106', -
EXECUTION_TYPE => 'COMPARE PERFORMANCE', -
EXECUTION_PARAMS => DBMS_ADVISOR.ARGLIST( -
'COMPARISON_METRIC', 'ELAPSED_TIME', -
'EXECUTE_FULLDML', 'TRUE', -
'EXECUTION_NAME1','EXEC_BEFORE_20180106', -
'EXECUTION_NAME2','EXEC_AFTER_20180106'), -
EXECUTION_DESC => 'Compare SQLs between 10g and 11g at :'||TO_CHAR(SYSDATE, 'YYYY-MM-DD HH24:MI:SS'));
2). 对比两次Trail中的SQL执行的CPU时间
EXEC DBMS_SQLPA.EXECUTE_ANALYSIS_TASK( -
TASK_NAME      => 'SPA_TASK_20180106', -
EXECUTION_NAME => 'COMPARE_CT_20180106}', -
EXECUTION_TYPE => 'COMPARE PERFORMANCE', -
EXECUTION_PARAMS => DBMS_ADVISOR.ARGLIST( -
'COMPARISON_METRIC', 'CPU_TIME', -
'EXECUTION_NAME1','EXEC_BEFORE_20180106', -
'EXECUTION_NAME2','EXEC_AFTER_20180106'), -
EXECUTION_DESC => 'Compare SQLs between 10g and 11g at :'||TO_CHAR(SYSDATE, 'YYYY-MM-DD HH24:MI:SS'));
3). 对比两次Trail中的SQL执行的逻辑读
EXEC DBMS_SQLPA.EXECUTE_ANALYSIS_TASK( -
TASK_NAME      => 'SPA_TASK_20180106', -
EXECUTION_NAME => 'COMPARE_BG_20180106', -
EXECUTION_TYPE => 'COMPARE PERFORMANCE', -
EXECUTION_PARAMS => DBMS_ADVISOR.ARGLIST( -
'COMPARISON_METRIC', 'BUFFER_GETS', -
'EXECUTION_NAME1','EXEC_BEFORE_20180106', -
'EXECUTION_NAME2','EXEC_AFTER_20180106'), -
EXECUTION_DESC => 'Compare SQLs between Before_STATS and After_STATS at :'||TO_CHAR(SYSDATE, 'YYYY-MM-DD HH24:MI:SS'));

6.获取性能比对分析报告

参考规范：

--a)  获取执行时间全部报告
ALTER SESSION SET EVENTS='31156 TRACE NAME CONTEXT FOREVER, LEVEL 0X400';
SET LINES 1111 PAGES 50000 LONG 1999999999 TRIM ON TRIMS ON SERVEROUTPUT ON SIZE UNLIMITED
SPOOL elapsed_all.html
SELECT XMLTYPE(DBMS_SQLPA.REPORT_ANALYSIS_TASK('SPA_TASK_${YYYYMMDD}','HTML','ALL','ALL',NULL,1000,'SPA_TASK_${YYYYMMDD}_COMP_ET')).GETCLOBVAL(0,0) FROM DUAL;
--b)  获取执行时间下降报告
ALTER SESSION SET EVENTS='31156 TRACE NAME CONTEXT FOREVER, LEVEL 0X400';
SET LINES 1111 PAGES 50000 LONG 1999999999 TRIM ON TRIMS ON SERVEROUTPUT ON SIZE UNLIMITED
SPOOL elapsed_regressed.html
SELECT XMLTYPE(DBMS_SQLPA.REPORT_ANALYSIS_TASK('SPA_TASK_${YYYYMMDD}','HTML','REGRESSED','ALL',NULL,1000,'SPA_TASK_${YYYYMMDD}_COMP_ET')).GETCLOBVAL(0,0) FROM DUAL;
--c)  获取逻辑读全部报告
ALTER SESSION SET EVENTS='31156 TRACE NAME CONTEXT FOREVER, LEVEL 0X400';
SET LINES 1111 PAGES 50000 LONG 1999999999 TRIM ON TRIMS ON SERVEROUTPUT ON SIZE UNLIMITED
SPOOL buffer_all.html
SELECT XMLTYPE(DBMS_SQLPA.REPORT_ANALYSIS_TASK('SPA_TASK_${YYYYMMDD}','HTML','ALL','ALL',NULL,1000,'SPA_TASK_${YYYYMMDD}_COMP_BG')).GETCLOBVAL(0,0) FROM DUAL;
--d)  获取逻辑读下降报告
ALTER SESSION SET EVENTS='31156 TRACE NAME CONTEXT FOREVER, LEVEL 0X400';
SET LINES 1111 PAGES 50000 LONG 1999999999 TRIM ON TRIMS ON SERVEROUTPUT ON SIZE UNLIMITED
SPOOL buffer_regressed.html
SELECT XMLTYPE(DBMS_SQLPA.REPORT_ANALYSIS_TASK('SPA_TASK_${YYYYMMDD}','HTML','REGRESSED','ALL',NULL,1000,'SPA_TASK_${YYYYMMDD}_COMP_BG')).GETCLOBVAL(0,0) FROM DUAL;
--e)  获取错误报告
ALTER SESSION SET EVENTS='31156 TRACE NAME CONTEXT FOREVER, LEVEL 0X400';
SET LINES 1111 PAGES 50000 LONG 1999999999 TRIM ON TRIMS ON SERVEROUTPUT ON SIZE UNLIMITED
SPOOL error.html
SELECT XMLTYPE(DBMS_SQLPA.REPORT_ANALYSIS_TASK('SPA_TASK_${YYYYMMDD}','HTML','ERRORS','ALL',NULL,1000,'SPA_TASK_${YYYYMMDD}_COMP_ET')).GETCLOBVAL(0,0) FROM DUAL;
--f)  获取不支持报告
ALTER SESSION SET EVENTS='31156 TRACE NAME CONTEXT FOREVER, LEVEL 0X400';
SET LINES 1111 PAGES 50000 LONG 1999999999 TRIM ON TRIMS ON SERVEROUTPUT ON SIZE UNLIMITED
SPOOL unsupported.html
SELECT XMLTYPE(DBMS_SQLPA.REPORT_ANALYSIS_TASK('SPA_TASK_${YYYYMMDD}','HTML','UNSUPPORTED','ALL',NULL,1000,'SPA_TASK_${YYYYMMDD}_COMP_ET')).GETCLOBVAL(0,0) FROM DUAL;
--g)  获取执行计划变化报告
ALTER SESSION SET EVENTS='31156 TRACE NAME CONTEXT FOREVER, LEVEL 0X400';
SET LINES 1111 PAGES 50000 LONG 1999999999 TRIM ON TRIMS ON SERVEROUTPUT ON SIZE UNLIMITED
SPOOL changed_plans.html
SELECT XMLTYPE(DBMS_SQLPA.REPORT_ANALYSIS_TASK('SPA_TASK_${YYYYMMDD}','HTML','CHANGED_PLANS','ALL',NULL,1000,'SPA_TASK_${YYYYMMDD}_COMP_ET')).GETCLOBVAL(0,0) FROM DUAL;

依据我的实验环境，真实的示例为：

--a)  获取执行时间全部报告
ALTER SESSION SET EVENTS='31156 TRACE NAME CONTEXT FOREVER, LEVEL 0X400';
SET LINES 1111 PAGES 50000 LONG 1999999999 TRIM ON TRIMS ON SERVEROUTPUT ON SIZE UNLIMITED
SPOOL elapsed_all.html
SELECT XMLTYPE(DBMS_SQLPA.REPORT_ANALYSIS_TASK('SPA_TASK_20180106','HTML','ALL','ALL',NULL,1000,'COMPARE_ET_20180106')).GETCLOBVAL(0,0) FROM DUAL;
--b)  获取执行时间下降报告
ALTER SESSION SET EVENTS='31156 TRACE NAME CONTEXT FOREVER, LEVEL 0X400';
SET LINES 1111 PAGES 50000 LONG 1999999999 TRIM ON TRIMS ON SERVEROUTPUT ON SIZE UNLIMITED
SPOOL elapsed_regressed.html
SELECT XMLTYPE(DBMS_SQLPA.REPORT_ANALYSIS_TASK('SPA_TASK_20180106','HTML','REGRESSED','ALL',NULL,1000,'COMPARE_ET_20180106')).GETCLOBVAL(0,0) FROM DUAL;
--c)  获取逻辑读全部报告
ALTER SESSION SET EVENTS='31156 TRACE NAME CONTEXT FOREVER, LEVEL 0X400';
SET LINES 1111 PAGES 50000 LONG 1999999999 TRIM ON TRIMS ON SERVEROUTPUT ON SIZE UNLIMITED
SPOOL buffer_all.html
SELECT XMLTYPE(DBMS_SQLPA.REPORT_ANALYSIS_TASK('SPA_TASK_20180106','HTML','ALL','ALL',NULL,1000,'COMPARE_BG_20180106')).GETCLOBVAL(0,0) FROM DUAL;
--d)  获取逻辑读下降报告
ALTER SESSION SET EVENTS='31156 TRACE NAME CONTEXT FOREVER, LEVEL 0X400';
SET LINES 1111 PAGES 50000 LONG 1999999999 TRIM ON TRIMS ON SERVEROUTPUT ON SIZE UNLIMITED
SPOOL buffer_regressed.html
SELECT XMLTYPE(DBMS_SQLPA.REPORT_ANALYSIS_TASK('SPA_TASK_20180106','HTML','REGRESSED','ALL',NULL,1000,'COMPARE_BG_20180106')).GETCLOBVAL(0,0) FROM DUAL;
--e)  获取错误报告
ALTER SESSION SET EVENTS='31156 TRACE NAME CONTEXT FOREVER, LEVEL 0X400';
SET LINES 1111 PAGES 50000 LONG 1999999999 TRIM ON TRIMS ON SERVEROUTPUT ON SIZE UNLIMITED
SPOOL error.html
SELECT XMLTYPE(DBMS_SQLPA.REPORT_ANALYSIS_TASK('SPA_TASK_20180106','HTML','ERRORS','ALL',NULL,1000,'COMPARE_ET_20180106')).GETCLOBVAL(0,0) FROM DUAL;
--f)  获取不支持报告
ALTER SESSION SET EVENTS='31156 TRACE NAME CONTEXT FOREVER, LEVEL 0X400';
SET LINES 1111 PAGES 50000 LONG 1999999999 TRIM ON TRIMS ON SERVEROUTPUT ON SIZE UNLIMITED
SPOOL unsupported.html
SELECT XMLTYPE(DBMS_SQLPA.REPORT_ANALYSIS_TASK('SPA_TASK_20180106','HTML','UNSUPPORTED','ALL',NULL,1000,'COMPARE_ET_20180106')).GETCLOBVAL(0,0) FROM DUAL;
--g)  获取执行计划变化报告
ALTER SESSION SET EVENTS='31156 TRACE NAME CONTEXT FOREVER, LEVEL 0X400';
SET LINES 1111 PAGES 50000 LONG 1999999999 TRIM ON TRIMS ON SERVEROUTPUT ON SIZE UNLIMITED
SPOOL changed_plans.html
SELECT XMLTYPE(DBMS_SQLPA.REPORT_ANALYSIS_TASK('SPA_TASK_20180106','HTML','CHANGED_PLANS','ALL',NULL,1000,'COMPARE_ET_20180106')).GETCLOBVAL(0,0) FROM DUAL;

这样就得到了各类的性能对比报告，以执行时间的全部报告为例，生成的报告概要头部类似这样：



当然，具体获取到的这些性能对比报告，针对那些有性能下降的SQL，还需要人工干预，评估如何优化处理那些性能下降的SQL。