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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。
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