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关于Oracle开启自动收集统计信息的SPA测试

[日期:2018-01-08] 来源:cnblogs.com/jyzhao  作者:jyzhao [字体: ]

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。

更多Oracle相关信息见Oracle 专题页面 http://www.chineselinuxuniversity.net/topicnews.aspx?tid=12

本文永久更新链接地址http://www.chineselinuxuniversity.net/Linux/2018-01/150160.htm

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相关资讯       Oracle自动收集统计信息  Oracle SPA 
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