在Oracle中，对于数据库的修改操作都会记录redo，那么不同的操作会产生多少redo呢？可以通过以下一些方式来查询来统计产生的redo日志量。
（1）SQL*Plus中使用AUTOTRACE的使用。
当在SQL*Plus中启用autotrace跟踪后，在执行了特定的DML语句时，Oracle会显示该语句的统计信息，其中，redo Size一栏表示的就是该操作产生的redo的数量，其单位为Bytes：
SCOTT@seiang11g>set autotrace
traceonly statistics
注意：如果在启动autotrace跟踪的时候，出现如下报错：SP2-0618:
Cannot find the Session Identifier.
Check PLUSTRACE role is enabled.
解决方法请参考另一篇博文：SP2-0618: Cannot find the Session Identifier.Check PLUSTRACE role is enabled
SCOTT@seiang11g>create table emp1
as select * from emp;
Table created.
SCOTT@seiang11g>
SCOTT@seiang11g>insert into emp1
select * from emp1;
14 rows created.
Statistics
———————————————————-
15
recursive calls
22
db block gets
33
consistent gets
5
physical reads
1872 redo size
834
bytes sent via SQL*Net to client
791
bytes received via SQL*Net from client
3
SQL*Net roundtrips to/from client
2
sorts (memory)
0
sorts (disk)
14
rows processed
（2）通过v$mystat查询。
Oracle通过v$mystat视图记录当前session的统计信息，我们也可以从该视图中查询得到session的redo生成情况：
SCOTT@seiang11g>set autot off
SCOTT@seiang11g>
SCOTT@seiang11g>select
a.name,b.value from v$statname a,v$mystat b
2 where a.statistic# = b.statistic# and
a.name=’redo size’;
NAME
VALUE
—————————————————————-
———-
redo
size
29140
SCOTT@seiang11g>
SCOTT@seiang11g>insert into emp1
select * from emp1;
28 rows created.
SCOTT@seiang11g>
SCOTT@seiang11g>select
a.name,b.value from v$statname a,v$mystat b
2 where a.statistic# = b.statistic# and
a.name=’redo size’;
NAME
VALUE
—————————————————————-
———-
redo
size
30708
SCOTT@seiang11g>
SCOTT@seiang11g>select 30708-29140
from dual;
30708-29140
———–
1568
（3）通过v$sysstat查询。
对于数据库全局Redo的生成量，可以通过v$sysstat视图来查询得到：
SYS@seiang11g>select name,value
from v$sysstat where name=’redo size’;
NAME
VALUE
—————————————————————-
———-
redo
size 548518160
从v$sysstat视图中得到的是自数据库实例启动以来的累积日志生成量，可以根据实例启动时间大致估算每天数据库的日志生成量：
SYS@seiang11g>alter session set
nls_date_format=’yyyy-mm-dd hh34:mi:ss’;
Session altered.
SYS@seiang11g>
SYS@seiang11g>select
2 (select value/1024/1024/1024 from
v$sysstat where name=’redo size’
3 )/
4 (select round(sysdate-
5 (select startup_time from v$instance
6 )) from dual
7 ) redo_gb_per_day
8 from dual;
REDO_GB_PER_DAY
—————
.102173401
如果数据库运行在归档模式下，由于其他因素的影响，以上Redo生成量并不代表归档日志的大小，但是可以通过一定的加权提供参考。
至于归档日志的生成量，可以通过v$archived_log视图，根据一段时间的归档日志量进行估算得到。该视图中记录了归档日志的主要信息：
SYS@seiang11g>select
name,completion_time,blocks*block_size/1024/1024 MB
2 from v$archived_log where status = ‘A’;
NAME
COMPLETION_TIME MB
————————————————–
——————- ———-
/u01/app/oracle/arch/arch_1_949237404_8.log 2017-07-13 13:37:10 1.74072266
/u01/app/oracle/arch/arch_1_949237404_9.log 2017-09-13 17:09:40 35.9506836
/u01/app/oracle/arch/arch_1_949237404_10.log 2017-09-13 22:00:47 42.2592773
/u01/app/oracle/arch/arch_1_949237404_11.log 2017-09-14 05:00:33 36.9936523
/u01/app/oracle/arch/arch_1_949237404_12.log 2017-09-14 19:00:36 36.9335938
/u01/app/oracle/arch/arch_1_949237404_13.log 2017-09-15 01:06:21 35.8876953
/u01/app/oracle/arch/arch_1_949237404_14.log 2017-09-15 15:00:10 35.8935547
/u01/app/oracle/arch/arch_1_949237404_15.log 2017-09-15 22:00:37 37.5634766
/u01/app/oracle/arch/arch_1_949237404_16.log 2017-09-16 06:00:28 42.2397461
/u01/app/oracle/arch/arch_1_949237404_17.log 2017-09-16 14:00:16 43.9946289
/u01/app/oracle/arch/arch_1_949237404_18.log 2017-09-16 22:00:25 44.0483398
/u01/app/oracle/arch/arch_1_949237404_19.log 2017-09-17 06:00:25 40.4213867
/u01/app/oracle/arch/arch_1_949237404_20.log 2017-09-17 14:00:25 42.0063477
/u01/app/oracle/arch/arch_1_949237404_21.log 2017-09-17 22:00:28 42.7241211
/u01/app/oracle/arch/arch_1_949237404_22.log 2017-09-18 11:00:07 36.0229492
某日全天的日志生成可以通过如下查询计算：
SYS@seiang11g>select
trunc(completion_time),
2 sum(Mb)/1024 DAY_GB
3 from
4 (select name,
5 completion_time,
6 blocks*block_size/1024/1024 Mb
7 from v$archived_log
8 where completion_time between
trunc(sysdate)-2 and trunc(sysdate)-1
9 )
10 group by trunc(completion_time);
TRUNC(COMPLETION_TI DAY_GB
——————- ———-
2017-09-16
00:00:00 .127229214
最近日期的日志生成统计：
SYS@seiang11g>select
trunc(completion_time),
2 sum(mb)/1024 day_gb
3 from
4 (select name,
5 completion_time,
6 blocks*block_size/1024/1024 mb
7 from v$archived_log
8 )
9 group by trunc(completion_time);
TRUNC(COMPLETION_TI DAY_GB
——————- ———-
2017-09-15 00:00:00 .10678196
2017-09-18 00:00:00 .035178661
2017-09-13 00:00:00 .076376915
2017-09-17 00:00:00 .122218609
2017-07-13 00:00:00 .065961361
2017-09-16 00:00:00 .127229214
2017-09-14 00:00:00 .072194576
根据每日归档的生成量，我们也可以反过来估计每日的数据库活动性及周期性，并决定空间分配等问题。
拓展：
（一）以下脚本可以用于列出最近Oracle数据库每小时估算的redo重做日志产生量，因为估算数据来源于archivelog的产生量和大小，所以数据是近似值，可供参考:
WITH
times AS
(SELECT /*+ MATERIALIZE */
hour_end_time
FROM (SELECT (TRUNC(SYSDATE, ‘HH’) + (2 /
24)) – (ROWNUM / 24) hour_end_time
FROM DUAL
CONNECT BY ROWNUM <= (1 * 24) +
3),
v$database
WHERE log_mode = ‘ARCHIVELOG’)
SELECT
hour_end_time, NVL(ROUND(SUM(size_mb), 3), 0) size_mb, i.instance_name
FROM(
SELECT
hour_end_time, CASE WHEN(hour_end_time – (1 / 24)) > lag_next_time
THEN(next_time + (1 / 24) – hour_end_time) * (size_mb / (next_time –
lag_next_time)) ELSE 0 END + CASE WHEN hour_end_time < lead_next_time
THEN(hour_end_time – next_time) * (lead_size_mb / (lead_next_time – next_time))
ELSE 0 END + CASE WHEN lag_next_time > (hour_end_time – (1 / 24)) THEN
size_mb ELSE 0 END + CASE WHEN next_time IS NULL THEN(1 / 24) * LAST_VALUE(CASE
WHEN next_time IS NOT NULL AND lag_next_time IS NULL THEN 0 ELSE(size_mb /
(next_time – lag_next_time)) END IGNORE NULLS) OVER(
ORDER BY hour_end_time DESC, next_time DESC)
ELSE 0 END size_mb
FROM(
SELECT
t.hour_end_time, arc.next_time, arc.lag_next_time, LEAD(arc.next_time) OVER(
ORDER BY arc.next_time ASC) lead_next_time,
arc.size_mb, LEAD(arc.size_mb) OVER(
ORDER BY arc.next_time ASC) lead_size_mb
FROM times t,(
SELECT
next_time, size_mb, LAG(next_time) OVER(
ORDER BY next_time) lag_next_time
FROM(
SELECT
next_time, SUM(size_mb) size_mb
FROM(
SELECT
DISTINCT a.sequence#, a.next_time, ROUND(a.blocks * a.block_size / 1024 / 1024)
size_mb
FROM v$archived_log a,(
SELECT
/*+ no_merge */
CASE
WHEN TO_NUMBER(pt.VALUE) = 0 THEN 1 ELSE TO_NUMBER(pt.VALUE) END VALUE
FROM v$parameter pt
WHERE pt.name = ‘thread’) pt
WHERE a.next_time > SYSDATE – 3 AND
a.thread# = pt.VALUE AND ROUND(a.blocks * a.block_size / 1024 / 1024) > 0)
GROUP BY next_time)) arc
WHERE t.hour_end_time =
(TRUNC(arc.next_time(+), ‘HH’) + (1 / 24)))
WHERE hour_end_time > TRUNC(SYSDATE, ‘HH’)
– 1 – (1 / 24)), v$instance i
WHERE hour_end_time <= TRUNC(SYSDATE, ‘HH’)
GROUP BY hour_end_time, i.instance_name
ORDER BY hour_end_time
/
执行结果：
HOUR_END_TIME SIZE_MB INSTANCE_NAME
——————-
———- —————-
2017-09-17
14:00:00 5.25 seiang11g
2017-09-17
15:00:00 5.374 seiang11g
2017-09-17
16:00:00 5.374 seiang11g
2017-09-17
17:00:00 5.374 seiang11g
2017-09-17
18:00:00 5.374 seiang11g
2017-09-17
19:00:00 5.374 seiang11g
2017-09-17
20:00:00 5.374 seiang11g
2017-09-17
21:00:00 5.374 seiang11g
2017-09-17
22:00:00 5.374 seiang11g
2017-09-17
23:00:00 2.79 seiang11g
2017-09-18
00:00:00 2.77 seiang11g
2017-09-18
01:00:00 2.77 seiang11g
2017-09-18
02:00:00 2.77 seiang11g
2017-09-18
03:00:00 2.77 seiang11g
2017-09-18
04:00:00 2.77 seiang11g
2017-09-18
05:00:00 2.77 seiang11g
2017-09-18
06:00:00 2.77 seiang11g
2017-09-18
07:00:00 2.77 seiang11g
2017-09-18
08:00:00 2.77 seiang11g
2017-09-18
09:00:00 2.77 seiang11g
2017-09-18
10:00:00 2.77 seiang11g
2017-09-18
11:00:00 2.77 seiang11g
2017-09-18
12:00:00 .005 seiang11g
2017-09-18
13:00:00 0 seiang11g
2017-09-18
14:00:00 0 seiang11g
（二）Oracle查询最近几天每小时归档日志产生数量的脚本，脚本内容如下所示：
SELECT 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,
COUNT(*) TOTAL
FROM v$log_history a
WHERE first_time>=to_char(sysdate-10)
GROUP BY SUBSTR(TO_CHAR(first_time,
‘MM/DD/RR HH:MI:SS’),1,5)
ORDER BY SUBSTR(TO_CHAR(first_time,
‘MM/DD/RR HH:MI:SS’),1,5) DESC;
修改天数，可以修改WHERE
first_time>=to_char(sysdate-11)
执行结果：
参考链接：
http://www.dbtan.com/2009/12/how-many-redo-has-produced.html
http://www.askmaclean.com/archives/script%E5%88%97%E5%87%BAoracle%E6%AF%8F%E5%B0%8F%E6%97%B6%E7%9A%84redo%E9%87%8D%E5%81%9A%E6%97%A5%E5%BF%97%E4%BA%A7%E7%94%9F%E9%87%8F.html
http://www.jb51.net/article/119200.htm
作者：SEian.G（苦练七十二变，笑对八十一难）
