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怎么定位哪些SQL产生了大量的Redo日志

发表于：2024-11-24 作者：千家信息网编辑

千家信息网最后更新 2024年11月24日，本篇内容介绍了"怎么定位哪些SQL产生了大量的Redo日志"的有关知识，在实际案例的操作过程中，不少人都会遇到这样的困境，接下来就让小编带领大家学习一下如何处理这些情况吧！希望大家仔细阅读，能够学有所

千家信息网最后更新 2024年11月24日怎么定位哪些SQL产生了大量的Redo日志

本篇内容介绍了"怎么定位哪些SQL产生了大量的Redo日志"的有关知识，在实际案例的操作过程中，不少人都会遇到这样的困境，接下来就让小编带领大家学习一下如何处理这些情况吧！希望大家仔细阅读，能够学有所成！

首先，我们需要定位、判断那个时间段的日志突然暴增了，注意，有些时间段生成了大量的redo log是正常业务行为，有可能每天这个时间段都有大量归档日志生成，例如，有大量作业在这个时间段集中运行。而要分析突然、异常的大量redo log生成情况，就必须有数据分析对比，找到redo log大量产生的时间段，缩小分析的范围是第一步。合理的缩小范围能够方便快速准确定位问题SQL。下面SQL语句分别统计了redo log的切换次数的相关数据指标。这个可以间接判断那个时间段产生了大量归档日志。

/******统计每天redo log的切换次数汇总,以及与平均次数的对比*****/ WITH T AS  (     SELECT TO_CHAR(FIRST_TIME, 'YYYY-MM-DD')    AS LOG_GEN_DAY,             TO_CHAR(SUM(DECODE(TO_CHAR(FIRST_TIME, 'YYYY-MM-DD'),                         TO_CHAR(FIRST_TIME, 'YYYY-MM-DD'), 1, 0))                 , '999') AS "LOG_SWITCH_NUM"      FROM   V$LOG_HISTORY    WHERE FIRST_TIME < TRUNC(SYSDATE)  --排除当前这一天     GROUP  BY TO_CHAR(FIRST_TIME, 'YYYY-MM-DD')  ) SELECT  T.LOG_GEN_DAY       , T.LOG_SWITCH_NUM       , M.AVG_LOG_SWITCH_NUM       , (T.LOG_SWITCH_NUM-M.AVG_LOG_SWITCH_NUM) AS DIFF_SWITCH_NUM FROM  T CROSS JOIN  (     SELECT  TO_CHAR(AVG(T.LOG_SWITCH_NUM),'999') AS AVG_LOG_SWITCH_NUM     FROM T ) M ORDER BY T.LOG_GEN_DAY DESC;       SELECT    TO_CHAR(FIRST_TIME,'YYYY-MM-DD') DAY,                 TO_CHAR(SUM(DECODE(TO_CHAR(FIRST_TIME,'HH24'),'00',1,0)),'999') "00",                 TO_CHAR(SUM(DECODE(TO_CHAR(FIRST_TIME,'HH24'),'01',1,0)),'999') "01",                 TO_CHAR(SUM(DECODE(TO_CHAR(FIRST_TIME,'HH24'),'02',1,0)),'999') "02",                 TO_CHAR(SUM(DECODE(TO_CHAR(FIRST_TIME,'HH24'),'03',1,0)),'999') "03",                 TO_CHAR(SUM(DECODE(TO_CHAR(FIRST_TIME,'HH24'),'04',1,0)),'999') "04",                 TO_CHAR(SUM(DECODE(TO_CHAR(FIRST_TIME,'HH24'),'05',1,0)),'999') "05",                 TO_CHAR(SUM(DECODE(TO_CHAR(FIRST_TIME,'HH24'),'06',1,0)),'999') "06",                 TO_CHAR(SUM(DECODE(TO_CHAR(FIRST_TIME,'HH24'),'07',1,0)),'999') "07",                 TO_CHAR(SUM(DECODE(TO_CHAR(FIRST_TIME,'HH24'),'08',1,0)),'999') "08",                 TO_CHAR(SUM(DECODE(TO_CHAR(FIRST_TIME,'HH24'),'09',1,0)),'999') "09",                 TO_CHAR(SUM(DECODE(TO_CHAR(FIRST_TIME,'HH24'),'10',1,0)),'999') "10",                 TO_CHAR(SUM(DECODE(TO_CHAR(FIRST_TIME,'HH24'),'11',1,0)),'999') "11",                 TO_CHAR(SUM(DECODE(TO_CHAR(FIRST_TIME,'HH24'),'12',1,0)),'999') "12",                 TO_CHAR(SUM(DECODE(TO_CHAR(FIRST_TIME,'HH24'),'13',1,0)),'999') "13",                 TO_CHAR(SUM(DECODE(TO_CHAR(FIRST_TIME,'HH24'),'14',1,0)),'999') "14",                 TO_CHAR(SUM(DECODE(TO_CHAR(FIRST_TIME,'HH24'),'15',1,0)),'999') "15",                 TO_CHAR(SUM(DECODE(TO_CHAR(FIRST_TIME,'HH24'),'16',1,0)),'999') "16",                 TO_CHAR(SUM(DECODE(TO_CHAR(FIRST_TIME,'HH24'),'17',1,0)),'999') "17",                 TO_CHAR(SUM(DECODE(TO_CHAR(FIRST_TIME,'HH24'),'18',1,0)),'999') "18",                 TO_CHAR(SUM(DECODE(TO_CHAR(FIRST_TIME,'HH24'),'19',1,0)),'999') "19",                 TO_CHAR(SUM(DECODE(TO_CHAR(FIRST_TIME,'HH24'),'20',1,0)),'999') "20",                 TO_CHAR(SUM(DECODE(TO_CHAR(FIRST_TIME,'HH24'),'21',1,0)),'999') "21",                 TO_CHAR(SUM(DECODE(TO_CHAR(FIRST_TIME,'HH24'),'22',1,0)),'999') "22",                 TO_CHAR(SUM(DECODE(TO_CHAR(FIRST_TIME,'HH24'),'23',1,0)),'999') "23" FROM V$LOG_HISTORY GROUP BY TO_CHAR(FIRST_TIME,'YYYY-MM-DD')  ORDER BY 1 DESC;

如下案例所示，2018-03-26日有一个归档日志暴增的情况，我们可以横向、纵向对比分析，然后判定在17点到18点这段时间出现异常，这个时间段与往常对比，生成了大量的redo log。

这里分享一个非常不错的分析redo log 历史信息的SQL

------------------------------------------------------------------------------------------------ REM Author: Riyaj Shamsudeen @OraInternals, LLC REM         www.orainternals.com REM REM Functionality: This script is to print redo size rates in a RAC claster REM ************** REM REM Source  : AWR tables REM REM Exectution type: Execute from sqlplus or any other tool. REM REM Parameters: No parameters. Uses Last snapshot and the one prior snap REM No implied or explicit warranty REM REM Please send me an email to rshamsud@orainternals.com, if you enhance this script :-) REM  This is a open Source code and it is free to use and modify. REM Version 1.20 REM ------------------------------------------------------------------------------------------------   set colsep '|' set lines 220 alter session set nls_date_format='YYYY-MM-DD HH24:MI'; set pagesize 10000 with redo_data as ( SELECT instance_number,        to_date(to_char(redo_date,'DD-MON-YY-HH24:MI'), 'DD-MON-YY-HH24:MI') redo_dt,        trunc(redo_size/(1024 * 1024),2) redo_size_mb  FROM  (   SELECT dbid, instance_number, redo_date, redo_size , startup_time  FROM  (     SELECT  sysst.dbid,sysst.instance_number, begin_interval_time redo_date, startup_time,   VALUE -     lag (VALUE) OVER     ( PARTITION BY  sysst.dbid, sysst.instance_number, startup_time       ORDER BY begin_interval_time ,sysst.instance_number      ) redo_size   FROM sys.wrh$_sysstat sysst , DBA_HIST_SNAPSHOT snaps WHERE sysst.stat_id =        ( SELECT stat_id FROM sys.wrh$_stat_name WHERE  stat_name='redo size' )   AND snaps.snap_id = sysst.snap_id   AND snaps.dbid =sysst.dbid   AND sysst.instance_number  = snaps.instance_number   AND snaps.begin_interval_time> sysdate-30    ORDER BY snaps.snap_id )   ) ) select  instance_number,  redo_dt, redo_size_mb,     sum (redo_size_mb) over (partition by  trunc(redo_dt)) total_daily,     trunc(sum (redo_size_mb) over (partition by  trunc(redo_dt))/24,2) hourly_rate    from redo_Data order by redo_dt, instance_number /

分析到这个阶段，我们还只获取了那个时间段归档日志异常(归档日志暴增)，那么要如何定位到相关的SQL语句呢?我们可以用下面SQL来定位：在这个时间段，哪些对象有大量数据块变化情况。如下所示，这两个对象(当然，对象有可能是表或索引，这个案例中，这两个对象其实是同一个表和其主键索引)有大量的数据块修改情况。基本上我们可以判断是涉及这个对象的DML语句生成了大量的redo log，当然有可能有些场景会比较复杂，不是那么容易定位。

SELECT TO_CHAR(BEGIN_INTERVAL_TIME, 'YYYY-MM-DD HH24') SNAP_TIME,         DHSO.OBJECT_NAME,         SUM(DB_BLOCK_CHANGES_DELTA)                     BLOCK_CHANGED  FROM   DBA_HIST_SEG_STAT DHSS,         DBA_HIST_SEG_STAT_OBJ DHSO,         DBA_HIST_SNAPSHOT DHS  WHERE  DHS.SNAP_ID = DHSS.SNAP_ID         AND DHS.INSTANCE_NUMBER = DHSS.INSTANCE_NUMBER         AND DHSS.OBJ# = DHSO.OBJ#         AND DHSS.DATAOBJ# = DHSO.DATAOBJ#         AND BEGIN_INTERVAL_TIME BETWEEN TO_DATE('2018-03-26 17:00',                                         'YYYY-MM-DD HH24:MI')                                         AND             TO_DATE('2018-03-26 18:00', 'YYYY-MM-DD HH24:MI')  GROUP  BY TO_CHAR(BEGIN_INTERVAL_TIME, 'YYYY-MM-DD HH24'),            DHSO.OBJECT_NAME  HAVING SUM(DB_BLOCK_CHANGES_DELTA) > 0  ORDER  BY SUM(DB_BLOCK_CHANGES_DELTA) DESC;

此时，我们可以生成这个时间段的AWR报告，那些产生大量redo log的SQL一般是来自TOP Gets、TOP Execution中某个DML SQL语句或一些DML SQL语句，结合上面SQL定位到的对象和下面相关SQL语句，基本上就可以判断就是下面这两个SQL产生了大量的redo log。(第一个SQL是调用包，包里面有对这个表做大量的DELETE、INSERT操作)

如果你此时还不能完全断定，也可以使用下面SQL来辅佐判断那些SQL生成了大量的redo log。在这个案例中，上面AWR报告中发现的SQL语句和下面SQL捕获的SQL基本一致。那么可以进一步佐证。

注意，该SQL语句执行较慢，执行时需要修改相关条件：时间和具体段对象。

SELECT TO_CHAR(BEGIN_INTERVAL_TIME,'YYYY_MM_DD HH24') WHEN,        DBMS_LOB.SUBSTR(SQL_TEXT,4000,1) SQL,        DHSS.INSTANCE_NUMBER INST_ID,        DHSS.SQL_ID,        EXECUTIONS_DELTA EXEC_DELTA,        ROWS_PROCESSED_DELTA ROWS_PROC_DELTA FROM DBA_HIST_SQLSTAT DHSS,      DBA_HIST_SNAPSHOT DHS,      DBA_HIST_SQLTEXT DHST WHERE UPPER(DHST.SQL_TEXT) LIKE '%%'  --此处用具体的段对象替换   AND LTRIM(UPPER(DHST.SQL_TEXT)) NOT LIKE 'SELECT%'   AND DHSS.SNAP_ID=DHS.SNAP_ID   AND DHSS.INSTANCE_NUMBER=DHS.INSTANCE_NUMBER   AND DHSS.SQL_ID=DHST.SQL_ID   AND BEGIN_INTERVAL_TIME BETWEEN TO_DATE('2018-03-26 17:00','YYYY-MM-DD HH24:MI')   AND TO_DATE('2018-03-26 18:00','YYYY-MM-DD HH24:MI')

其实上面分析已经基本完全定位到SQL语句，剩下的就是和开发人员或Support人员沟通、了解是正常业务逻辑变更还是异常行为。如果需要进一步挖掘深入，我们可以使用日志挖掘工具Log Miner深入分析。在此不做展开分析。其实个人在判断分析时生成了正常时段和出现问题时段的AWR对比报告(WORKLOAD REPOSITORY COMPARE PERIOD REPORT)，如下所示，其中一些信息也可以供分析、对比参考。可以为复杂场景做对比分析(因为复杂场景，仅仅通过最上面的AWR报告可能无法准确定位SQL)