原文出处：
潇湘隐者
在ORACLE数据库的管理、维护过程中，偶尔会遇到归档日志暴增的情况,也就是说一些SQL语句产生了大量的redo log，那么如何跟踪、定位哪些SQL语句生成了大量的redo log日志呢？ 下面这篇文章结合实际案例和官方文档“How to identify the causes of High Redo Generation (文档 ID 2265722.1)”来实验验证一下。
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"
FRO