Oracle归档日志暴增排查优化
1、ORACLE归档日志介绍
归档日志暴增是oracle比较常见的问题,遇到归档日志暴增,我们该如何排查:
- 归档日志暴增一般都是应用或者人为引起的
- 理解归档日志存储的是什么
- 如何排查归档日志暴增原因
- 如何优化归档日志暴增
1.1 归档日志是什么
归档日志(Archive Log)是非活动的重做日志(redo)备份. 通过使用归档日志,可以保留所有重做历史记录,当数据库处于ARCHIVELOG模式并进行日志切换式,后台进程ARCH会将重做日志的内容保存到归档日志中. 当数据库出现介质失败时,使用数据文件备份,归档日志和重做日志可以完全恢复数据库。
1.2 归档日志存储的是什么
所有重做的历史记录,包括DML语句、数据改变等
1.3 归档日志暴增的原因
一般是DML操作大量的数据,导致归档日志暴增
1.4 排查归档日志暴增的方法
1.SQL语句 2.AWR 3.挖掘归档日志
2、归档日志暴增排查实战
2.1 制造归档日志暴增
create table scott.object as select * from dba_objects; -- 执行10次 -- insert insert into scott.object select * from scott.object; select count(1) from scott.object; -- 49384448 -- update update SCOTT.object set owner='aa'; -- delete delete from SCOTT.object; truncate table SCOTT.object;
2.2 查看归档日志切换
SELECT THREAD# id,SUBSTR(TO_CHAR(first_time, 'MM/DD/RR HH:MI:SS'),1,5) DAY , SUM(DECODE(SUBSTR(TO_CHAR(first_time, 'MM/DD/RR HH24:MI:SS'),10,2),'00',1,0)) H00 , SUM(DECODE(SUBSTR(TO_CHAR(first_time, 'MM/DD/RR HH24:MI:SS'),10,2),'01',1,0)) H01 , SUM(DECODE(SUBSTR(TO_CHAR(first_time, 'MM/DD/RR HH24:MI:SS'),10,2),'02',1,0)) H02 , SUM(DECODE(SUBSTR(TO_CHAR(first_time, 'MM/DD/RR HH24:MI:SS'),10,2),'03',1,0)) H03 , SUM(DECODE(SUBSTR(TO_CHAR(first_time, 'MM/DD/RR HH24:MI:SS'),10,2),'04',1,0)) H04 , SUM(DECODE(SUBSTR(TO_CHAR(first_time, 'MM/DD/RR HH24:MI:SS'),10,2),'05',1,0)) H05 , SUM(DECODE(SUBSTR(TO_CHAR(first_time, 'MM/DD/RR HH24:MI:SS'),10,2),'06',1,0)) H06 , SUM(DECODE(SUBSTR(TO_CHAR(first_time, 'MM/DD/RR HH24:MI:SS'),10,2),'07',1,0)) H07 , SUM(DECODE(SUBSTR(TO_CHAR(first_time, 'MM/DD/RR HH24:MI:SS'),10,2),'08',1,0)) H08 , SUM(DECODE(SUBSTR(TO_CHAR(first_time, 'MM/DD/RR HH24:MI:SS'),10,2),'09',1,0)) H09 , SUM(DECODE(SUBSTR(TO_CHAR(first_time, 'MM/DD/RR HH24:MI:SS'),10,2),'10',1,0)) H10 , SUM(DECODE(SUBSTR(TO_CHAR(first_time, 'MM/DD/RR HH24:MI:SS'),10,2),'11',1,0)) H11 , SUM(DECODE(SUBSTR(TO_CHAR(first_time, 'MM/DD/RR HH24:MI:SS'),10,2),'12',1,0)) H12 , SUM(DECODE(SUBSTR(TO_CHAR(first_time, 'MM/DD/RR HH24:MI:SS'),10,2),'13',1,0)) H13 , SUM(DECODE(SUBSTR(TO_CHAR(first_time, 'MM/DD/RR HH24:MI:SS'),10,2),'14',1,0)) H14 , SUM(DECODE(SUBSTR(TO_CHAR(first_time, 'MM/DD/RR HH24:MI:SS'),10,2),'15',1,0)) H15 , SUM(DECODE(SUBSTR(TO_CHAR(first_time, 'MM/DD/RR HH24:MI:SS'),10,2),'16',1,0)) H16 , SUM(DECODE(SUBSTR(TO_CHAR(first_time, 'MM/DD/RR HH24:MI:SS'),10,2),'17',1,0)) H17 , SUM(DECODE(SUBSTR(TO_CHAR(first_time, 'MM/DD/RR HH24:MI:SS'),10,2),'18',1,0)) H18 , SUM(DECODE(SUBSTR(TO_CHAR(first_time, 'MM/DD/RR HH24:MI:SS'),10,2),'19',1,0)) H19 , SUM(DECODE(SUBSTR(TO_CHAR(first_time, 'MM/DD/RR HH24:MI:SS'),10,2),'20',1,0)) H20 , SUM(DECODE(SUBSTR(TO_CHAR(first_time, 'MM/DD/RR HH24:MI:SS'),10,2),'21',1,0)) H21 , SUM(DECODE(SUBSTR(TO_CHAR(first_time, 'MM/DD/RR HH24:MI:SS'),10,2),'22',1,0)) H22 , SUM(DECODE(SUBSTR(TO_CHAR(first_time, 'MM/DD/RR HH24:MI:SS'),10,2),'23',1,0)) H23 FROM v$log_history a GROUP BY SUBSTR(TO_CHAR(first_time, 'MM/DD/RR HH:MI:SS'),1,5),THREAD# ORDER BY id,SUBSTR(TO_CHAR(first_time, 'MM/DD/RR HH:MI:SS'),1,5) /
![](https://hsop-doc.fsh.bcebos.com/doc_classify/0/HSOP-BAIDU/d0d112d4c23a4dac96e53eaee9b83c0f@image-20211219210649152.png)
代表12月19号,H20(20-21时),共切换24个归档日志,如果每一个500M,那么总共约500M*24,对比其余时间,可以说该时间产生异常的归档日志,目标排查改时间段
2.3 SQL语句判断
with aa as (SELECT IID, USERNAME, to_char(BEGIN_TIME,'mm/dd hh24:mi') begin_time, SQL_ID, decode(COMMAND_TYPE,3,'SELECT',2,'INSERT',6,'UPDATE',7,'DELETE',189,'MERGE INTO','OTH') "SQL_TYPE", executions "EXEC_NUM", rows_processed "Change_NUM" FROM (SELECT s.INSTANCE_NUMBER IID, PARSING_SCHEMA_NAME USERNAME,COMMAND_TYPE, cast(BEGIN_INTERVAL_TIME as date) BEGIN_TIME, s.SQL_ID, executions_DELTA executions, rows_processed_DELTA rows_processed, (IOWAIT_DELTA) / 1000000 io_time, 100*ratio_to_report(rows_processed_DELTA) over(partition by s.INSTANCE_NUMBER, BEGIN_INTERVAL_TIME) RATIO, sum(rows_processed_DELTA) over(partition by s.INSTANCE_NUMBER, BEGIN_INTERVAL_TIME) totetime, elapsed_time_DELTA / 1000000 ETIME, CPU_TIME_DELTA / 1000000 CPU_TIME, (CLWAIT_DELTA+APWAIT_DELTA+CCWAIT_DELTA+PLSEXEC_TIME_DELTA+JAVEXEC_TIME_DELTA)/1000000 OTIME, row_number() over(partition by s.INSTANCE_NUMBER,BEGIN_INTERVAL_TIME order by rows_processed_DELTA desc) TOP_D FROM dba_hist_sqlstat s, dba_hist_snapshot sn,dba_hist_sqltext s2 where s.snap_id = sn.snap_id and s.INSTANCE_NUMBER = sn.INSTANCE_NUMBER and rows_processed_DELTA is not null and s.sql_id = s2.sql_id and COMMAND_TYPE in (2,6,7,189) and sn.BEGIN_INTERVAL_TIME > sysdate - nvl(180,1)/1440 and PARSING_SCHEMA_NAME<>'SYS') WHERE TOP_D <= nvl(20,1) ) select aa.*,s.sql_fulltext "FULL_SQL" from aa left join v$sql s on aa.sql_id=s.sql_id ORDER BY IID, BEGIN_TIME desc,"Change_NUM" desc
![](https://hsop-doc.fsh.bcebos.com/doc_classify/0/HSOP-BAIDU/e1dd56d4f7334a699a61dc0c15edad89@image-20211219211605881.png)
查看2小时的数据该变量,可以看出Change_NUM数据该变量和执行次数EXEC_NUM和SQL语句,update回滚了,所以没有该变量。 此时可以判断大量插入数据导致归档日志暴增,此时并不能判断update。此语句不一定有数据,只能做参考。
2.4 AWR
创建AWR报告
创建AWR报告 @?/rdbms/admin/awrrpt.sql
SQL> @?/rdbms/admin/awrrpt.sql Current Instance ~~~~~~~~~~~~~~~~ DB Id DB Name Inst Num Instance ----------- ------------ -------- ------------ 3830097027 ..... 1 ..... Specify the Report Type ~~~~~~~~~~~~~~~~~~~~~~~ Would you like an HTML report, or a plain text report? Enter 'html' for an HTML report, or 'text' for plain text Defaults to 'html' Enter value for report_type: html Type Specified: html Instances in this Workload Repository schema ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ DB Id Inst Num DB Name Instance Host ------------ -------- ------------ ------------ ------------ * 3830097027 1 ..... ..... dbserver01 Using 3830097027 for database Id Using 1 for instance number Specify the number of days of snapshots to choose from ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Entering the number of days (n) will result in the most recent (n) days of snapshots being listed. Pressing <return> without specifying a number lists all completed snapshots. Enter value for num_days: 1 Listing the last day's Completed Snapshots Snap Instance DB Name Snap Id Snap Started Level ------------ ------------ --------- ------------------ ----- ..... ..... 36 19 Dec 2021 14:03 1 37 19 Dec 2021 15:00 1 38 19 Dec 2021 16:00 1 39 19 Dec 2021 17:00 1 40 19 Dec 2021 18:00 1 41 19 Dec 2021 20:12 1 42 19 Dec 2021 21:03 1 Specify the Begin and End Snapshot Ids ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Enter value for begin_snap: 41 Begin Snapshot Id specified: 41 Enter value for end_snap: 42 End Snapshot Id specified: 42 Specify the Report Name ~~~~~~~~~~~~~~~~~~~~~~~ The default report file name is awrrpt_1_41_42.html. To use this name, press <return> to continue, otherwise enter an alternative. Enter value for report_name: /tmp/awrrpt_1_41_42.html
解析AWR报告
![](https://hsop-doc.fsh.bcebos.com/doc_classify/0/HSOP-BAIDU/e48ac209962a41609e73bc52dc9fffac@image-20211219212536034.png)
可以看出大量redo,该时间段总该变量3762494/1024/1024=3674,每秒约产生3.5M
![](https://hsop-doc.fsh.bcebos.com/doc_classify/0/HSOP-BAIDU/75fb12a00f4f41459275ae6a0004b0bf@image-20211219213223610.png)
产生块最多的是scott用户,object对象,改变量是44684992,占比99%,说明是该对象产生的
![](https://hsop-doc.fsh.bcebos.com/doc_classify/0/HSOP-BAIDU/67f5b23d9758488fa7182aef0bd31f1c@image-20211219213710365.png)
根据对象可以在AWR报告中查看是否有怀疑的SQL,发现update语句。
其实根据SQL语句和AWR报告可以排查出大部分归档日志暴增的问题,如果无法排查可以继续进行挖掘归档日志。
2.5 挖掘归档日志
-rw-r-----. 1 oracle oinstall 794697216 Dec 19 20:37 1_66_1077902149.dbf -rw-r-----. 1 oracle oinstall 794697216 Dec 19 20:37 1_67_1077902149.dbf -rw-r-----. 1 oracle oinstall 794697216 Dec 19 21:03 1_68_1077902149.dbf -rw-r-----. 1 oracle oinstall 733794304 Dec 19 21:03 1_69_1077902149.dbf -rw-r-----. 1 oracle oinstall 756531200 Dec 19 21:03 1_70_1077902149.dbf -rw-r-----. 1 oracle oinstall 761492480 Dec 19 21:14 1_71_1077902149.dbf -rw-r-----. 1 oracle oinstall 794697216 Dec 19 21:14 1_72_1077902149.dbf -rw-r-----. 1 oracle oinstall 265107968 Dec 19 21:14 1_73_1077902149.dbf
-- 最好sys或相关权限的用户,也可以使用toad工具 -- 第一次 @?/rdbms/admin/dbmslm.sql @?/rdbms/admin/dbmslmd.sql -- 开始执行 execute dbms_logmnr.add_logfile(logfilename=>'../../1_66_1077902149.dbf',options=>dbms_logmnr.new); execute dbms_logmnr.add_logfile(logfilename=>'../../1_67_1077902149.dbf',options=>dbms_logmnr.new); execute dbms_logmnr.add_logfile(logfilename=>'../../1_68_1077902149.dbf',options=>dbms_logmnr.new); execute dbms_logmnr.add_logfile(logfilename=>'../../1_69_1077902149.dbf',options=>dbms_logmnr.new); execute dbms_logmnr.add_logfile(logfilename=>'../../1_70_1077902149.dbf',options=>dbms_logmnr.new); execute dbms_logmnr.start_logmnr(options=>dbms_logmnr.dict_from_online_catalog); -- 依次类推小批量解析归档日志 -- 保存记录 create table scott.logmnr_contents as select * from v$logmnr_contents; -- 分批执行...循环执行上面记录 alter session set nls_date_format='yyyy-mm-dd hh24:mi:ss'; -- 最后释放pga execute dbms_logmnr.end_logmnr;
select sql_redo from scott.logmnr_contents where table_name='OBJECT'; select count(*) from scott.logmnr_contents where table_name='OBJECT';
![](https://hsop-doc.fsh.bcebos.com/doc_classify/0/HSOP-BAIDU/69a9ec01b2de4153a8654a2274639835@image-20211219221354761.png)
可以从归档日志中查看大量的update语句,此时基本可以排查出归档日志暴增原因
2.6 归档日志暴增优化
1.delete是否可以改造成truncate分区表(ps: truncate需谨慎,无法恢复相关数据) 2.dml可以适量使用临时表 3.避免大事务 4.避免大量for循环dml
本文来自博客园,作者:风光小磊,转载请注明原文链接:https://www.cnblogs.com/lei-z/p/16467177.html