關於Oracle開啓自動收集統計信息的SPA測試

時間  2019-12-01
標籤 關於 oracle 開啓 自動 收集 統計 信息 spa 測試 欄目 Oracle 简体版
原文   原文鏈接

主題：關於Oracle開啓自動收集統計信息的SPA測試
環境：Oracle RAC 11.2.0.4（Primary + Standby）
需求：生產Primary庫因爲歷史緣由關閉了自動統計信息的收集，目前客戶需求是想要從新開啓統計信息的自動收集，雖然通常來講，有了更準確的統計信息，SQL會有更好的執行計劃，但因爲生產環境數據複雜，實際上仍是須要評估哪些SQL會由於從新開啓自動統計信息收集性能反而會降低。
方案：本着儘量減小對生產Primary環境影響的原則，在Standby DG環境臨時開啓snapshot standby來進行SPA（SQL Performance Analyze）測試，比對開啓統計信息自動收集先後的性能差別，給客戶提供有價值的參考。html

1.構造測試環境

檢查自動統計信息的開啓狀態：
select client_name,status from dba_autotask_client;
確認自動統計信息的收集是關閉的，對於「auto optimizer stats collection」的狀態應該是「DISABLED」。sql

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：關閉數據庫的自動統計信息收集：數據庫

--光閉自動統計信息收集，（慎用，除非有其餘手工收集統計信息的完整方案，不然不建議關閉）
BEGIN
  DBMS_AUTO_TASK_ADMIN.disable(
    client_name => 'auto optimizer stats collection',
    operation => NULL,
    window_name => NULL);
END;
/

DG備庫保持和主庫同步，因此這些設置項也都是徹底同樣的。windows

2.DG備庫開啓snapshot模式

主要就是在mount模式下切換數據到snapshot Standby模式再read write打開庫，爲以後測試作準備。下面是核心步驟：性能

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

關於其餘細節可參考下面文章，主要是爲「開啓11gR2 DG的快照模式」，「後續還原成備庫」 等操做提供參考：測試

3.SPA測試準備

進行SPA測試時，強烈建議在數據庫中建立SPA測試專用用戶，這樣能夠與其餘用戶區分開以及避免誤操做。優化

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.從AWR中採集SQL

備庫從AWR中採集到SQL。
4.1 獲取AWR快照的邊界IDspa

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;

我這裏的結果是：3d

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

4.2 新建SQL Set
注意：如下的規範部分都是引用以前同事編寫的SPA操做規範。code

參考規範：

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');

依據個人實驗環境，真實的示例爲：

--鏈接用戶
conn SPA/SPA

--若是以前有這個SQLSET的名字，能夠這樣刪除
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 轉化AWR數據中的SQL，將其載入到SQL Set
從備庫的AWR中提取SQL（這等同於主庫歷史的SQL）。
參考規範：

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;
/

依據個人實驗環境，真實的示例爲：

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(可不作)
參考規範：

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’);

依據個人實驗環境，真實的示例爲：

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');

說明：其實在我這裏的測試場景下，這一步是不須要作的。由於備庫的SQL Set能夠直接在後面引用，不須要像SPA經典場景中，是從生產源環境打包導出來後，在測試環境再導入進去，再解包爲SQL Set。

5.SPA分析比較

5.1 建立SPA分析任務
參考規範：

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’);

依據個人實驗環境，真實的示例爲：

--建立SPA分析任務：
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 獲取變動前的SQL執行效率
參考規範：

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'));

依據個人實驗環境，真實的示例爲：

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 開啓變動操做
變動內容：開啓統計信息自動收集並確認已經成功收集了最新的統計信息。
這裏首先須要開啓統計信息自動收集，並能夠把自動收集的窗口時間提早到如今，減小等待的時間。

--檢查自動統計信息的開啓狀態：
select client_name,status from dba_autotask_client;

--啓動自動統計信息收集
BEGIN
  DBMS_AUTO_TASK_ADMIN.enable(
    client_name => 'auto optimizer stats collection',
    operation => NULL,
    window_name => NULL);
END;
/

查看窗口任務和有關統計信息自動收集的任務執行狀態：

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;

調整窗口任務的下一次執行時間：

--須要確認JOB能夠啓動
alter system set job_queue_processes=1000;

--調整窗口任務的下一次執行時間
EXEC DBMS_SCHEDULER.SET_ATTRIBUTE('SATURDAY_WINDOW','repeat_interval','freq=daily;byday=SAT;byhour=17;byminute=10;bysecond=0');

更多有關調整窗口和自動任務的內容可參考文章：

5.4 變動後再次分析性能
測試運行SQL Tuning Set中的SQL語句，分析全部語句在收集統計信息以後的執行效率：
參考規範：

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'));

依據個人實驗環境，真實的示例爲：

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 變動先後性能對比
獲得兩次SQL Trail以後，能夠對比兩次Trial之間的SQL執行性能，能夠從不一樣的維度對兩次Trail中的全部SQL進行對比分析，主要關注的維度有：SQL執行時間，SQL執行的CPU時間，SQL執行的邏輯讀。
參考規範：

1). 對比兩次Trail中的SQL執行時間
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). 對比兩次Trail中的SQL執行的CPU時間
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). 對比兩次Trail中的SQL執行的邏輯讀
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'));

依據個人實驗環境，真實的示例爲：

1). 對比兩次Trail中的SQL執行時間
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). 對比兩次Trail中的SQL執行的CPU時間
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). 對比兩次Trail中的SQL執行的邏輯讀
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.獲取性能比對分析報告

參考規範：

--a)  獲取執行時間所有報告
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)  獲取執行時間降低報告
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)  獲取邏輯讀所有報告
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)  獲取邏輯讀降低報告
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)  獲取錯誤報告
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)  獲取不支持報告
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)  獲取執行計劃變化報告
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;

依據個人實驗環境，真實的示例爲：

--a)  獲取執行時間所有報告
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)  獲取執行時間降低報告
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)  獲取邏輯讀所有報告
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)  獲取邏輯讀降低報告
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)  獲取錯誤報告
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)  獲取不支持報告
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)  獲取執行計劃變化報告
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;

這樣就獲得了各種的性能對比報告，以執行時間的所有報告爲例，生成的報告概要頭部相似這樣：
固然，具體獲取到的這些性能對比報告，針對那些有性能降低的SQL，還須要人工干預，評估如何優化處理那些性能降低的SQL。

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