如何定位那些 SQL 產生了大量的 redo 日誌-知識星球

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來源：瀟湘隱者，

www.cnblogs.com/kerrycode/p/8660931.html

在ORACLE資料庫的管理、維護過程中，偶爾會遇到歸檔日誌暴增的情況,也就是說一些SQL陳述句產生了大量的redo log，那麼如何跟蹤、定位哪些SQL陳述句生成了大量的redo log日誌呢？下麵這篇文章結合實際案例和官方檔案“How to identify the causes of High Redo Generation (檔案 ID 2265722.1)”來實驗驗證一下。

首先，我們需要定位、判斷那個時間段的日誌突然暴增了，註意，有些時間段生成了大量的redo log是正常業務行為，有可能每天這個時間段都有大量歸檔日誌生成，例如，有大量作業在這個時間段集中執行。而要分析突然、異常的大量redo log生成情況，就必須有資料分析對比，找到redo log大量產生的時間段，縮小分析的範圍是第一步。合理的縮小範圍能夠方便快速準確定位問題SQL。下麵SQL陳述句分別統計了redo log的切換次數的相關資料指標。這個可以間接判斷那個時間段產生了大量歸檔日誌。

/******統計每天redo log的切換次數彙總,以及與平均次數的對比*****/

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) --排除當前這一天

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日有一個歸檔日誌暴增的情況，我們可以橫向、縱向對比分析，然後判定在17點到18點這段時間出現異常，這個時間段與往常對比，生成了大量的redo log。

這裡分享一個非常不錯的分析redo log 歷史資訊的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

/

image

分析到這個階段，我們還只獲取了那個時間段歸檔日誌異常（歸檔日誌暴增），那麼要如何定位到相關的SQL陳述句呢？我們可以用下麵SQL來定位：在這個時間段，哪些物件有大量資料塊變化情況。如下所示，這兩個物件(當然，物件有可能是表或索引，這個案例中，這兩個物件其實是同一個表和其主鍵索引)有大量的資料塊修改情況。基本上我們可以判斷是涉及這個物件的DML陳述句生成了大量的redo log，當然有可能有些場景會比較複雜，不是那麼容易定位。

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;

此時，我們可以生成這個時間段的AWR報告，那些產生大量redo log的SQL一般是來自TOP Gets、TOP Execution中某個DML SQL陳述句或一些DML SQL陳述句，結合上面SQL定位到的物件和下麵相關SQL陳述句，基本上就可以判斷就是下麵這兩個SQL產生了大量的redo log。（第一個SQL是呼叫包，包裡面有對這個表做大量的DELETE、INSERT操作）

如果你此時還不能完全斷定，也可以使用下麵SQL來輔佐判斷那些SQL生成了大量的redo log。在這個案例中，上面AWR報告中發現的SQL陳述句和下麵SQL捕獲的SQL基本一致。那麼可以進一步佐證。

註意，該SQL陳述句執行較慢，執行時需要修改相關條件：時間和具體段物件。

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 ‘%%’ –此處用具體的段物件替換

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’)

其實上面分析已經基本完全定位到SQL陳述句，剩下的就是和開發人員或Support人員溝通、瞭解是正常業務邏輯變更還是異常行為。如果需要進一步挖掘深入，我們可以使用日誌挖掘工具Log Miner深入分析。在此不做展開分析。其實個人在判斷分析時生成了正常時段和出現問題時段的AWR對比報告（WORKLOAD REPOSITORY COMPARE PERIOD REPORT），如下所示，其中一些資訊也可以供分析、對比參考。可以為複雜場景做對比分析（因為複雜場景，僅僅透過最上面的AWR報告可能無法準確定位SQL）