蓝的成长记追逐DBA（8）：重拾SP报告，回忆oracle的STATSPAC
***********************************************声明*********************************************************************** 原创作品，出自 深蓝的blog 博客，欢迎转载，转载时请务必注明出处，否则追究版权法律责任。 深蓝的blog：http://blog.csdn.
***********************************************声明***********************************************************************
原创作品，出自 “深蓝的blog” 博客，欢迎转载，转载时请务必注明出处，否则追究版权法律责任。
深蓝的blog：http://blog.csdn.net/huangyanlong/article/details/39803995
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蓝的成长记——追逐DBA（8）：重拾SP报告，回忆oracle的STATSPACK实验
**************************************************简介********************************************************************
个人在oracle路上的成长记录，其中以蓝自喻，分享成长中的情感、眼界与技术的变化与成长。敏感信息均以英文形式代替，不会泄露任何企业机密，纯为技术分享。
创作灵感源于对自己的自省和记录。若能对刚刚起步的库友起到些许的帮助或共鸣，欣慰不已。
欢迎拍砖，如有关技术细节表述有错误之处，请您留言或邮件（hyldba@163.com）指明，不胜感激。
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今天有些慵懒，整理过往学习中的一个实验，忆起oracle的SP报告。
——深蓝
**************************************************前言********************************************************************
这是一部个人记录的成长杂记，既然步入到oracle的这片蓝海，免不了一路的奔波与不断的考验。借由此杂记与库友们分享蓝的成长历程。
不知何时起对蓝有了一种说不出来的痴迷，痴迷其广博，痴迷其深邃，痴迷于近在咫尺却又遥不可及。
而又说不清从何时起，注视于oracle的红色耀眼，照亮出眼前的一道光，未知与迷惑在自己的脚下开始初露些许人生的充实与青春的回馈。
在追逐于DBA梦想的道路上步步前行。
***************************************************************************************************************************
时间有些久了，有些淡忘了SP报告的方法了，今天就利用闲暇的时光，重新拾起熟悉又陌生的STATSPACK报告的实验。
实验计划：
1、模拟某业务环境，制定快照计划；
2、生成初始状态数据库的statspack报告，分析数据；
3、调整数据缓冲区尺寸，生成 statspack报告，分析数据；
4、创建索引，生成statspack报告，分析数据；
5、使用绑定变量，生成 statspack报告，分析数据。
******************************************************************************************
步骤一：模拟业务环境，制定快照计划
目标：
1、关闭sga自动管理，调整DB cache、sharepool大小；
2、部署statspack；
3、部署模拟现场环境；
*****************************************************************************************
1、关闭sga自动管理，调整DB cache、sharepool大小，模拟现场环境
SQL> alter system set memory_target=0 scope=spfile; --11g中关闭内存自动管理 SQL> alter system set sga_target=0; SQL> alter system set db_cache_size=30m scope=spfile; --修改DB cache大小 SQL> alter system set shared_pool_size=70m scope=spfile; --修改share pool大小 SQL> startup force; --重启数据库 SQL> select component,current_size/1024/1024 from v$sga_dynamic_components; --查询修改后的缓冲区大小 COMPONENT CURRENT_SIZE/1024/1024 ---------------------------------------- ---------------------- shared pool 72 DEFAULT buffer cache 32
2、部署statspack
SQL> create tablespace tools datafile '/u01/app/oracle/oradata/PROD/disk6/tools01.dbf' size 300m; --创建statspack专用的tools表空间 SQL> @?/rdbms/admin/spcreate.sql --以sysdba身份执行spcreate脚本，用于创建spcreate对象 输入值设置： Enter value for perfstat_password: oracle Enter value for default_tablespace: tools Enter value for temporary_tablespace:回车 $ vi /u01/app/oracle/product/11.2.0/db_1/rdbms/admin/spauto.sql --设置自动快照时间，间隔30分钟生成一次快照 编辑如下： begin select instance_number into :instno from v$instance; dbms_job.submit(:jobno, 'statspack.snap;', trunc(sysdate+1/48,'MI'), 'trunc(SYSDATE+1/48,''MI'')', TRUE, :instno); commit; end; SQL>exec statspack.modify_statspack_parameter(i_snap_level=>7); --设置快照默认级别为7 SQL> conn scott/tiger SQL>CREATE SEQUENCE emp2_empno INCREMENT BY 1 START WITH 1 MAXVALUE 100000000 CACHE 10000 NOCYCLE; --执行创建序列语句
3、部署模拟现场环境
SQL> create table emp2 as select * from emp where 1=2; --创建实验表emp2，结构同emp表
SQL> alter table emp2 modify empno number(10);
SQL> alter table emp2 modify ename varchar(30);
SQL> alter table emp2 nologging; --为加快数据插入速度，关闭日志记录
--插入2千万行数据
SQL>begin
for i in 1..20000000 loop
insert into emp2
values (emp2_empno.nextval,'cuug'||i,'SALESMAN',7698,sysdate,1600,300,30);
if mod(i,1000)=0 then
commit;
end if;
end loop;
commit;
end;
/
SQL> alter table emp2 logging; --开启日志记录
$ vi script/bin/share_pool_sql_1.sh --编写查询的业务脚本
#!/bin/bash
CNT=1
while [ $CNT -lt 20000000 ]
do
sqlplus scott/tiger <<EOF
select * from emp2 where empno=$CNT;
exit
EOF
CNT=`expr $CNT + 1`
done
$ sh script/bin/share_pool_sql_1.sh --执行脚本，模拟“查询业务”*****************************************************************************************
步骤二：生成原始statspack报告，分析报告
目标：
1、开启自动快照；
2、生成、导出报告；
3、关闭job；
4、分析报告。
*****************************************************************************************
1、开启自动快照
<span style="font-size:12px;">SQL> conn perfstat/oracle --开启快照及查询相关业务时，需要以perfstat身份登录 SQL>@?/rdbms/admin/spauto --执行脚本，开启自动快照</span>
2、生成报告
SQL> alter session set nls_date_format='yyyy-mm-dd hh24:mi:ss'; --设置查看格式，便于查询 SQL> select snap_id,snap_time,snap_level from stats$snapshot order by snap_time; --查询快照数量，是否满足生成statspack报告条件 SQL> @?/rdbms/admin/spreport --生成statspack报告 手工设置： Enter value for begin_snap:快照起点 Enter value for end_snap: 快照终点 Enter value for report_name:默认或指定报告名称 --使用x-manager将报告拷贝到windows主机
3、关闭job
SQL> select job,log_user,last_date,next_date from user_jobs; --查询需要关闭的job号 SQL> exec dbms_job.remove('21'); --将job号为21的任务删除
4、分析报告
关注点：
①buffer hit
②library hit
③Top 5 Timed Events
④造成最大物理读的sql
⑤Buffer Pool Advisory
⑥time model system stats
⑦Latch Sleep breakdown
① buffer hit、②library hit
|
时间 |
Buffer Hit（%） |
Library Hit（%） |
|
17:42:01~ 18:12:00 |
99.76 |
86.56 |
|
18:12:00 ~ 18:42:00 |
99.87 |
86.55 |
|
18:42:00~ 19:12:05 |
99.74 |
86.55 |
|
19:12:05~ 19:42:03 |
99.86 |
86.90 |
|
avg |
99.81 |
86.64 |
分析： buffer hit高于95%符合数据正常性能标准。library hit低于95%，说明库缓存区命中率较低，需做相应调整。
③Top 5 Timed Events
|
时间 |
name |
waits |
Time (s) |
|
17:42:01~ 18:12:00 |
direct path read |
32,014,645 |
814 |
|
db file sequential read |
1,697 |
6 |
|
|
log file parallel write |
706 |
5 |
|
|
18:12:00 ~18:42:00 |
direct path read |
32,095,337 |
816 |
|
log file parallel write |
898 |
5 |
|
|
os thread startup |
50 |
9 |
|
|
18:42:00~ 19:12:05 |
direct path read |
32,438,303 |
816 |
|
log file parallel write |
816 |
7 |
|
|
control file parallel write |
493 |
1 |
|
|
19:12:05~ 19:42:03 |
direct path read |
32,255,547 |
816 |
|
log file parallel write |
716 |
5 |
|
|
control file parallel write |
491 |
1 |
分析：
direct path read的磁盘I/O产生量最大，db file sequential read、log file parallel write、control file parallel write也会产生部分磁盘I/O。④查出造成物理读最大的前几个sql语句，产生执行计划
SQL>select sql_text from v$sql where disk_reads=(select max(disk_reads) from v$sql); --查询造成最大物理读的sql语句
……
select * from emp2 where empno=2215
select * from emp2 where empno=2270
select * from emp2 where empno=2208
……
SQL> set autotrace on；
SQL> set timing on;
SQL> select * from emp2 where empno=2208； --执行一条语句，查看执行计划，可以发现方式为全表扫描，在oracle11g下全表扫描时，库缓冲区将直接从磁盘中查询数据,磁盘I/O较大。cost值、physical read较大
<span style="font-family:SimSun;font-size:12px;"> EMPNO ENAME JOB MGR HIREDATE SAL COMM DEPTNO
------------ --------------------- ------------------ ---------- ----------- ---------- ------------ --------------
2208 cuug2207 SALESMAN 7698 03-JUN-14 1600 300 30
</span>Elapsed: 00:00:00.94
Execution Plan
----------------------------------------------------------
Plan hash value: 2941272003
--------------------------------------------------------------------------
| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time |
--------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 1 | 48 | 40046 (1)| 00:08:01 |
|* 1 | TABLE ACCESS FULL| EMP2 | 1 | 48 | 40046 (1)| 00:08:01 |
--------------------------------------------------------------------------
Statistics
----------------------------------------------------------
0 recursive calls
0 db block gets
147357 consistent gets
147349 physical reads
0 redo size
869 bytes sent via SQL*Net to client
419 bytes received via SQL*Net from client
2 SQL*Net roundtrips to/from client
0 sorts (memory)
0 sorts (disk)
1 rows processed 分析： 未发现造成物理读最大的sql。但发现查询语句为全表扫描，每条语句物理读都相对较大。
⑤Buffer Pool Advisory
|
statistics |
||||||||
|
Time |
P |
Size for Est (M) |
Size Factr |
Buffers (thousands) |
Est Phys Read Factr |
Estimated Phys Reads (thousands) |
Est Phys Read Time
|
Est % dbtime for Rds
|
|
17:42:01~ 18:12:00 |
D |
32 |
1.0 |
4 |
1.0 |
17 |
15 |
.3 |
|
18:12:00 ~18:42:00 |
D |
32 |
1.0 |
4 |
1.0 |
18 |
18 |
.2 |
|
18:42:00~ 19:12:05 |
D |
32 |
1.0 |
4 |
1.0 |
20 |
18 |
.2 |
|
19:12:05~ 19:42:03 |
D |
32 |
1.0 |
4 |
1.0 |
21 |
18 |
.1 |
|
avg |
|
32 |
1.0 |
4 |
1.0 |
19 |
17.25 |
.2 |
|
example |
||||||||
|
Time :17:42:01~ 18:12:00 Est Phys Estimated Est Size for Size Buffers Read Phys Reads Est Phys % dbtime P Est (M) Factr (thousands) Factr (thousands) Read Time for Rds --- -------- ----- ------------ ------ -------------- ------------ -------- D 4 .1 0 1.2 21 20 .4 D 8 .3 1 1.1 19 17 .3 D 12 .4 1 1.1 18 16 .3 D 16 .5 2 1.0 18 16 .3 D 20 .6 2 1.0 18 15 .3 D 24 .8 3 1.0 17 15 .3 D 28 .9 3 1.0 17 15 .3 D 32 1.0 4 1.0 17 15 .3 D 36 1.1 4 1.0 17 15 .3 D 40 1.3 5 1.0 17 15 .3 D 44 1.4 5 1.0 17 15 .3 D 48 1.5 6 1.0 17 15 .3 D 52 1.6 6 1.0 17 15 .3 D 56 1.8 7 1.0 17 15 .3 D 60 1.9 7 1.0 17 15 .3 D 64 2.0 8 1.0 17 15 .3 |
||||||||
分析： 对比4个时间段中的最佳buffer pool建议及第一时间段下的详细趋势列表，buffer pool设置为32m并未影响到性能。
⑥time model system stats
time：17:42:01~ 18:12:00
Statistic Time (s) % DB time
----------------------------------- -------------------- ---------
sql execute elapsed time 1,772.4 99.3
DB CPU 1,747.0 97.9
parse time elapsed 62.4 3.5
hard parse elapsed time 58.0 3.3
connection management call elapsed 6.2 .3
PL/SQL execution elapsed time 6.1 .3
hard parse (sharing criteria) elaps 6.1 .3
hard parse (bind mismatch) elapsed 3.9 .2
PL/SQL compilation elapsed time 0.7 .0
repeated bind elapsed time 0.4 .0
sequence load elapsed time 0.1 .0
DB time 1,784.9
background elapsed time 26.5
background cpu time 3.7
time：18:12:00 ~18:42:00
Statistic Time (s) % DB time
----------------------------------- -------------------- ---------
sql execute elapsed time 2,549.1 99.5
DB CPU 1,752.4 68.4
parse time elapsed 60.2 2.4
hard parse elapsed time 57.0 2.2
PL/SQL execution elapsed time 6.2 .2
hard parse (sharing criteria) elaps 6.2 .2
connection management call elapsed 6.1 .2
hard parse (bind mismatch) elapsed 4.0 .2
PL/SQL compilation elapsed time 0.7 .0
repeated bind elapsed time 0.4 .0
sequence load elapsed time 0.1 .0
DB time 2,561.0
background elapsed time 21.2
background cpu time 1.9
time：18:42:00~ 19:12:05
Statistic Time (s) % DB time
----------------------------------- -------------------- ---------
sql execute elapsed time 3,548.9 99.6
DB CPU 1,751.7 49.2
parse time elapsed 37.7 1.1
hard parse elapsed time 34.9 1.0
connection management call elapsed 7.3 .2
hard parse (sharing criteria) elaps 3.6 .1
PL/SQL execution elapsed time 3.2 .1
hard parse (bind mismatch) elapsed 2.1 .1
PL/SQL compilation elapsed time 0.5 .0
repeated bind elapsed time 0.4 .0
sequence load elapsed time 0.1 .0
DB time 3,563.0
background elapsed time 30.8
background cpu time 3.7
time：19:12:05~ 19:42:03
Statistic Time (s) % DB time
----------------------------------- -------------------- ---------
sql execute elapsed time 3,541.3 99.7
DB CPU 1,746.9 49.2
parse time elapsed 37.9 1.1
hard parse elapsed time 35.2 1.0
connection management call elapsed 5.3 .2
hard parse (sharing criteria) elaps 3.7 .1
PL/SQL execution elapsed time 3.2 .1
hard parse (bind mismatch) elapsed 2.0 .1
PL/SQL compilation elapsed time 0.4 .0
repeated bind elapsed time 0.3 .0
sequence load elapsed time 0.1 .0
DB time 3,552.3
background elapsed time 22.0
background cpu time 1.4
分析： 对比4个时间段的time model system stats，发现有硬解析存在。
⑦Latch Sleep breakdown
time：17:42:01~ 18:12:00
Get Spin
Latch Name Requests Misses Sleeps Gets
-------------------------- --------------- ------------ ----------- -----------
qmn task queue latch 252 11 11 0
shared pool 669,111 7 7 0
space background task latc 1,114 6 6 0
cache buffers chains 1,415,617 1 1 0
JS Sh mem access 4 1 1 0
OS process allocation 4,354 1 1 0
FOB s.o list latch 7,177 1 1 0
messages 16,597 1 1 0
In memory undo latch 60,562 1 1 0
time：18:12:00 ~18:42:00
Get Spin
Latch Name Requests Misses Sleeps Gets
-------------------------- --------------- ------------ ----------- -----------
qmn task queue latch 260 13 13 0
space background task latc 1,109 4 4 0
SQL memory manager latch 3,316 1 1 0
JS Sh mem access 5 1 1 0
FOB s.o list latch 7,103 1 1 0
shared pool 666,763 1 1 0
time：18:42:00~ 19:12:05
Get Spin
Latch Name Requests Misses Sleeps Gets
-------------------------- --------------- ------------ ----------- -----------
qmn task queue latch 256 10 10 0
space background task latc 1,125 7 7 0
JS Sh mem access 6 2 2 0
shared pool 657,424 1 1 0
time：19:12:05~ 19:42:03
Get Spin
Latch Name Requests Misses Sleeps Gets
-------------------------- --------------- ------------ ----------- -----------
qmn task queue latch 256 10 10 0
space background task latc 1,109 5 5 0
JS Sh mem access 3 1 1 0
分析： 观察Latch Sleep breakdown，在4个时间段中出现有miss、sleep的次数没有明显突显趋势，对性能没产生根本性影响。
总结：
通过以上几点分析发现，影响性能的方面有： ① library hit 平均值为86.64%低于标准值95%； ② direct path read产生磁盘I/O量最大，生成执行计划后发现，查询数据的方式是全表扫描； ③ time model system stats信息显示出，业务中存在一定量的hard parse。
*****************************************************************************************
步骤三：调整数据缓冲区尺寸，生成statspack报告，分析数据
思路：
通过初始报告分析，需提高库缓冲区的命中率及加索引以改变全表扫描的查询方式。而加入索
引后，对于数据的查询，会由目前直接从磁盘读取变为通过数据缓冲区读取，I/O性能会得到提
高。会对数据缓冲区有一定要求，目前数据缓冲区设为32m，由于查询业务是通过全表扫描方式，
未能体现出缓冲区大小是否对性能有影响，但发现由于目前可分配内存资源较多，先将其调整
为64m，观察是否会对性能有影响，为之后继续调优做准备。
目标：
1、调整缓冲区尺寸；
2、生成statspack报告；
3、分析报告
*****************************************************************************************
1、调整缓冲区尺寸
SQL> alter system set db_cache_size=64m;
2、生成statspack报告
SQL> @?/rdbms/admin/spreport --重新生成statspack分析报告
3、分析报告
关注点：
①buffer hit
②library hit
③Top 5 Timed Events
④造成最大物理读的sql
⑤Buffer Pool Advisory
⑥time model system stats
⑦Latch Sleep breakdown
① buffer hit、②library hit
|
时间 |
Buffer Hit（%） |
Library Hit（%） |
|
05:52:00 ~ 06:22:03 |
99.99 |
86.34 |
|
06:22:03 ~ 06:52:02 |
99.96 |
86.73 |
|
06:52:02 ~ 07:22:01 |
99.99 |
86.34 |
|
07:22:01 ~ 07:52:04 |
100.00 |
86.56 |
|
avg |
99.99 |
86.49 |
分析：
buffer hit由之前平均99.81提升为99.99%，性能还是有一定的提升。library hit平均为86.49%与之前平均86.64%没有显著变化，依然低于95%，说明库缓存区命中率仍然较低，需做进一步调整。③Top 5 Timed Events
|
时间 |
name |
waits |
Time (s) |
|
05:52:00 ~06:22:03 |
direct path read |
17,621,085 |
750 |
|
log file parallel write |
694 |
1 |
|
|
control file parallel write |
523 |
1 |
|
|
06:22:03 ~06:52:02 |
direct path read |
17,661,174 |
754 |
|
log file parallel write |
422 |
1 |
|
|
control file parallel write |
523 |
1 |
|
|
06:52:02 ~ 07:22:01 |
direct path read |
17,596,117 |
752 |
|
log file parallel write |
534 |
1 |
|
|
control file parallel write |
522 |
1 |
|
|
07:22:01 ~ 07:52:04 |
direct path read |
17,644,217 |
754 |
|
log file parallel write |
440 |
1 |
|
|
control file parallel write |
524 |
1 |
|
|
avg |
direct path read |
17,630,648 |
|
分析：
虽然direct path read的磁盘I/O产生量仍然为最大，但是数据比较于未调整缓冲区大小之前，由平均32,438,303下降为17,630,648，仍是有显著改善的。log file parallel write、control file parallel write仍会产生部分磁盘I/O。由此，可通过全表扫描的I/O等待事件高，预判出需要在经常访问的列上加索引，以通过减少磁盘I/O产生量最大的事件的I/O来降低磁盘的I/O，进而提升性能。④查出造成物理读最大的前几个sql语句，产生执行计划
SQL>select sql_text from v$sql where disk_reads=(select max(disk_reads) from v$sql); --查询造成最大物理读的sql语句
……
select * from emp2 where empno=4009
select * from emp2 where empno=4012
select * from emp2 where empno=4023
……
SQL> set autotrace on；
SQL> set timing on;
SQL> select * from emp2 where empno=4009； --执行一条语句，查看执行计划，可以发现方式为全表扫描,磁盘I/O较大。cost值、physical read较大
<span style="font-size:12px;"> EMPNO ENAME JOB MGR HIREDATE SAL COMM DEPTNO
------------ --------------------- ------------------ ---------- ----------- ---------- ------------ --------------
4009 cuug4008 SALESMAN 7698 03-JUN-14 1600 300 30
</span>Elapsed: 00:00:00.94
Execution Plan
----------------------------------------------------------
Plan hash value: 2941272003
--------------------------------------------------------------------------
| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time |
--------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 1 | 48 | 40046 (1)| 00:08:01 |
|* 1 | TABLE ACCESS FULL| EMP2 | 1 | 48 | 40046 (1)| 00:08:01 |
--------------------------------------------------------------------------
Statistics
----------------------------------------------------------
0 recursive calls
0 db block gets
147357 consistent gets
147349 physical reads
0 redo size
869 bytes sent via SQL*Net to client
419 bytes received via SQL*Net from client
2 SQL*Net roundtrips to/from client
0 sorts (memory)
0 sorts (disk)
1 rows processed 分析： 未发现造成物理读最大的sql。但发现查询语句为全表扫描，每条语句物理读都相对较大，预判需对该表添加索引。
⑤Buffer Pool Advisory
|
statistics |
||||||||
|
Time |
P |
Size for Est (M) |
Size Factr |
Buffers (thousands) |
Est Phys Read Factr |
Estimated Phys Reads (thousands) |
Est Phys Read Time
|
Est % dbtime for Rds
|
|
05:52:00 ~06:22:03 |
D |
64 |
1.0 |
8 |
1.0 |
10 |
7 |
.0 |
|
06:22:03 ~06:52:02 |
D |
64 |
1.0 |
8 |
1.0 |
10 |
7 |
.0 |
|
06:22:03 ~06:52:02 |
D |
64 |
1.0 |
8 |
1.0 |
10 |
7 |
.0 |
|
07:22:01 ~ 07:52:04 |
D |
64 |
1.0 |
4 |
1.0 |
10 |
7 |
.0 |
|
avg(now) |
|
64 |
1.0 |
6 |
1.0 |
10 |
7 |
.0 |
|
avg (last) |
|
32 |
1.0 |
4 |
1.0 |
19 |
17.25 |
.2 |
|
example |
||||||||
|
Time : 05:52:00 ~06:22:03 Est Phys Estimated Est Size for Size Buffers Read Phys Reads Est Phys % dbtime P Est (M) Factr (thousands) Factr (thousands) Read Time for Rds --- -------- ----- ------------ ------ -------------- ------------ -------- D 4 .1 0 31.1 300 206 1.0 D 8 .1 1 14.4 139 95 .5 D 12 .2 1 6.5 62 43 .2 D 16 .3 2 4.4 43 29 .1 D 20 .3 2 3.0 29 20 .1 D 24 .4 3 2.4 23 16 .1 D 28 .4 3 1.8 17 12 .1 D 32 .5 4 1.7 16 11 .1 D 36 .6 4 1.5 15 10 .1 D 40 .6 5 1.3 13 9 .0 D 44 .7 5 1.0 10 7 .0 D 48 .8 6 1.0 10 7 .0 D 52 .8 6 1.0 10 7 .0 D 56 .9 7 1.0 10 7 .0 D 60 .9 7 1.0 10 7 .0 D 64 1.0 8 1.0 10 7 .0 D 68 1.1 8 1.0 10 7 .0 D 72 1.1 9 1.0 10 7 .0 D 76 1.2 9 1.0 10 7 .0 D 80 1.3 10 1.0 10 7 .0 |
||||||||
分析： 对比4个时间段中的最佳buffer pool建议，再与未调整缓冲区之前比较，虽然最佳尺寸设置建议均为人为指定的值（第一次为32m、第二次为64m）。但当缓冲区由32m调高到64m时，缓冲区的使用平均由4000升高到6000，预估的物理读由之前平均为19000降低到10000，预估的物理读时间由原来的平均17.25降低到7。通过物理读的下降，发现将缓冲区调大，对I/O性能有明显的改善。
⑥time model system stats
time：05:52:00 ~06:22:03
Statistic Time (s) % DB time
----------------------------------- -------------------- ---------
sql execute elapsed time 1,767.9 99.3
DB CPU 1,751.9 98.4
parse time elapsed 56.9 3.2
hard parse elapsed time 53.4 3.0
hard parse (sharing criteria) elaps 6.8 .4
connection management call elapsed 6.6 .4
PL/SQL execution elapsed time 5.9 .3
hard parse (bind mismatch) elapsed 3.9 .2
PL/SQL compilation elapsed time 0.6 .0
repeated bind elapsed time 0.4 .0
sequence load elapsed time 0.1 .0
DB time 1,780.4
background elapsed time 12.4
background cpu time 1.6
time：06:22:03 ~06:52:02
Statistic Time (s) % DB time
----------------------------------- -------------------- ---------
sql execute elapsed time 1,756.0 99.2
DB CPU 1,749.6 98.9
parse time elapsed 48.7 2.8
hard parse elapsed time 45.0 2.5
connection management call elapsed 6.4 .4
hard parse (sharing criteria) elaps 6.0 .3
PL/SQL execution elapsed time 5.5 .3
hard parse (bind mismatch) elapsed 4.3 .2
PL/SQL compilation elapsed time 0.8 .0
repeated bind elapsed time 0.4 .0
sequence load elapsed time 0.1 .0
DB time 1,769.8
background elapsed time 10.8
background cpu time 1.6
time：06:52:02~07:22:01
Statistic Time (s) % DB time
----------------------------------- -------------------- ---------
sql execute elapsed time 1,755.9 99.3
DB CPU 1,745.2 98.7
parse time elapsed 49.7 2.8
hard parse elapsed time 45.7 2.6
connection management call elapsed 7.3 .4
PL/SQL execution elapsed time 6.1 .3
hard parse (sharing criteria) elaps 6.1 .3
hard parse (bind mismatch) elapsed 3.5 .2
PL/SQL compilation elapsed time 1.4 .1
repeated bind elapsed time 0.5 .0
sequence load elapsed time 0.1 .0
DB time 1,767.4
background elapsed time 16.3
background cpu time 3.4
time：07:22:01 ~ 07:52:04
Statistic Time (s) % DB time
----------------------------------- -------------------- ---------
sql execute elapsed time 1,767.9 99.3
DB CPU 1,751.9 98.4
parse time elapsed 56.9 3.2
hard parse elapsed time 53.4 3.0
hard parse (sharing criteria) elaps 6.8 .4
connection management call elapsed 6.6 .4
PL/SQL execution elapsed time 5.9 .3
hard parse (bind mismatch) elapsed 3.9 .2
PL/SQL compilation elapsed time 0.6 .0
repeated bind elapsed time 0.4 .0
sequence load elapsed time 0.1 .0
DB time 1,780.4
background elapsed time 12.4
background cpu time 1.6
分析： 对比4个时间段的time model system stats，发现硬解析存在，较之前平均为46.28上升为平均的49.38，近似没有太大变化，但其值已经占据大部分解析，需要进一步分析对其优化，应重点关注。
⑦Latch Sleep breakdown
time：05:52:00 ~06:22:03
Get Spin
Latch Name Requests Misses Sleeps Gets
-------------------------- --------------- ------------ ----------- -----------
qmn task queue latch 252 9 9 0
shared pool 646,354 2 2 0
redo allocation 1,831 1 1 0
JS Sh mem access 5 1 1 0
FOB s.o list latch 7,824 1 1 0
SQL memory manager latch 3,271 1 1 0
time：06:22:03 ~06:52:02
Get Spin
Latch Name Requests Misses Sleeps Gets
-------------------------- --------------- ------------ ----------- -----------
shared pool 598,719 4 4 0
qmn task queue latch 260 3 3 0
shared pool simulator 94,784 2 2 0
row cache objects 807,801 2 2 0
JS Sh mem access 3 1 1 0
time：06:52:02~07:22:01
Get Spin
Latch Name Requests Misses Sleeps Gets
-------------------------- --------------- ------------ ----------- -----------
shared pool 598,719 4 4 0
qmn task queue latch 260 3 3 0
shared pool simulator 94,784 2 2 0
row cache objects 807,801 2 2 0
JS Sh mem access 3 1 1 0
time：07:22:01 ~ 07:52:04
Latch Name Requests Misses Sleeps Gets
-------------------------- --------------- ------------ ----------- -----------
qmn task queue latch 252 9 9 0
shared pool 646,354 2 2 0
redo allocation 1,831 1 1 0
JS Sh mem access 5 1 1 0
FOB s.o list latch 7,824 1 1 0
SQL memory manager latch 3,271 1 1 0
分析： 观察“Latch Sleep breakdown”，比较调整之前，“qmn task queue latch”在4个时间段中出现有miss、sleep的次数稍有增加，其余项很少有misss、sleeps。
总结：
通过以上几点分析发现，将缓冲区调大后，性能有所提升，体现在如下：
① buffer hit由之前平均99.81提高为99.99%，稍有提升；
② direct path read磁盘I/O产生量虽然仍为产生I/O的最大来源，但比较于之前I/O量，由平
均32,438,303下降为17,596,117，I/O性能上已有显著改善；
③ Buffer Pool Advisory中发现，预估的物理读由之前平均为19000降低到10000，物理读的下
降说明了性能的提升；
通过分析后，性能上仍存在需改善的方面，如下：
① library hit仍低于95%，库缓冲区命中率较低；
② 以全表扫描的方式查询数据，direct path read产生较多的磁盘I/O；
③ 仍存在部分硬解析。*****************************************************************************************
步骤四：创建索引，生成statspack报告，分析数据
目标：
1、创建索引；
2、生成、导出报告；
3、分析报告。
*****************************************************************************************
1、创建索引
SQL> create index ind_empno on emp2(empno); --添加索引 $ sh script/bin/share_pool_sql_1.sh； --执行查询业务
2、生成报告
SQL>@?/rdbms/admin/spauto --开启自动快照 SQL> @?/rdbms/admin/spreport --生成报告
3、分析报告
关注点：
①buffer hit
②library hit
③Top 5 Timed Events
④造成最大物理读的sql
⑤Buffer Pool Advisory
⑥time model system stats
⑦Latch Sleep breakdown
① buffer hit、②library hit
|
时间 |
Buffer Hit（%） |
Library Hit（%） |
|
12:43:00 ~ 12:58:02 |
99.98 |
89.65 |
|
12:58:02 ~ 13:13:03 |
99.98 |
88.92 |
|
13:13:03 ~ 13:28:03 |
99.96 |
90.62 |
|
13:28:03 ~ 13:43:05 |
99.97 |
90.56 |
|
avg（now） |
99.97 |
89.94 |
|
avg（last） |
99.99 |
86.49 |
分析：
buffer hit较上次变化不大，其值已满足高于90%的标准。library hit由之前平均86.49%提高为平均89.94%，稍有提升，但仍低于95%标准值，说明库缓存区命中率仍然较低，仍需做进一步调整。③Top 5 Timed Events
|
时间 |
name |
waits |
Time (s) |
|
12:43:00 ~12:58:02 |
Disk file operations I/O |
18,849 |
1 |
|
log file parallel write |
280 |
1 |
|
|
control file parallel write |
251 |
1 |
|
|
12:58:02 ~13:13:03 |
Disk file operations I/O |
18,641 |
1 |
|
log file parallel write |
412 |
1 |
|
|
cursor: pin S wait on X |
35 |
2 |
|
|
13:13:03 ~13:28:03 |
Disk file operations I/O |
19,002 |
1 |
|
log file parallel write |
190 |
1 |
|
|
control file parallel write |
251 |
1 |
|
|
13:28:03 ~13:43:05 |
Disk file operations I/O |
19,146 |
1 |
|
log file parallel write |
224 |
1 |
|
|
control file parallel write |
250 |
0 |
|
|
avg |
Disk file operations I/O |
18,909 |
|
分析：
对于磁盘I/O的产生已由direct path read变为了Disk file operations，说明由全表扫描变成走索引了，waits值由之前平均17,630,648下降为18,909，性能上实现了显著的提高。并且log file parallel write、control file parallel write产生的部分磁盘I/O也相应下降了。由此，可以看出通过加索引显著地降低了I/O，提升了性能。④查出造成物理读最大的前几个sql语句，产生执行计划
SQL>select sql_text from v$sql where disk_reads=(select max(disk_reads) from v$sql); --查询造成最大物理读的sql语句 SQL_ID SQL_TEXT ------------- ---------------------------------------- 3wmbtk9vt5pbq SELECT * FROM EMP3 WHERE EMPNO=:B1 Plan hash value: 2607329332 ----------------------------------------------------------------------------------------- | Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time | ----------------------------------------------------------------------------------------- | 0 | SELECT STATEMENT | | 1 | 41 | 4 (0)| 00:00:01 | | 1 | TABLE ACCESS BY INDEX ROWID| EMP3 | 1 | 41 | 4 (0)| 00:00:01 | |* 2 | INDEX RANGE SCAN | IND_EMPNO | 1 | | 3 (0)| 00:00:01 | -----------------------------------------------------------------------------------------
分析：
通过对比物理读最大的sql的执行计划，发现有相同的执行计划，预判可以使用绑定变量以提高性能。⑤Buffer Pool Advisory
|
statistics |
||||||||
|
Time |
P |
Size for Est (M) |
Size Factr |
Buffers (thousands) |
Est Phys Read Factr |
Estimated Phys Reads (thousands) |
Est Phys Read Time
|
Est % dbtime for Rds
|
|
12:43:00 ~12:58:02 |
D |
64 |
1.0 |
8 |
1.0 |
10 |
7 |
.1 |
|
12:58:02 ~13:13:03 |
D |
64 |
1.0 |
8 |
1.0 |
10 |
7 |
.0 |
|
13:13:03 ~13:28:03 |
D |
64 |
1.0 |
8 |
1.0 |
10 |
8 |
.1 |
|
13:28:03 ~13:43:05 |
D |
64 |
1.0 |
4 |
1.0 |
10 |
8 |
.1 |
|
avg(last) |
|
64 |
1.0 |
6 |
1.0 |
10 |
7 |
.0 |
|
avg (now) |
|
64 |
1.0 |
7 |
1.0 |
10 |
7.5 |
.0 |
|
example |
||||||||
|
Time : 12:43:00 ~12:58:02 Est Phys Estimated Est Size for Size Buffers Read Phys Reads Est Phys % dbtime P Est (M) Factr (thousands) Factr (thousands) Read Time for Rds --- -------- ----- ------------ ------ -------------- ------------ -------- D 4 .1 0 21.6 207 196 2.5 D 8 .1 1 9.7 93 87 1.1 D 12 .2 1 4.7 45 41 .5 D 16 .3 2 3.2 31 27 .4 D 20 .3 2 2.0 19 16 .2 D 24 .4 3 1.7 16 13 .2 D 28 .4 3 1.5 14 11 .1 D 32 .5 4 1.3 12 9 .1 D 36 .6 4 1.1 11 8 .1 D 40 .6 5 1.1 11 8 .1 D 44 .7 5 1.1 10 7 .1 D 48 .8 6 1.0 10 7 .1 D 52 .8 6 1.0 10 7 .1 D 56 .9 7 1.0 10 7 .1 D 60 .9 7 1.0 10 7 .1 D 64 1.0 8 1.0 10 7 .1 D 68 1.1 8 1.0 10 7 .1 D 72 1.1 9 1.0 10 7 .1 D 76 1.2 9 1.0 10 7 .1 D 80 1.3 10 1.0 10 7 .1 |
||||||||
分析：
对比4个时间段中的最佳buffer pool建议，各指标数值近似与调整前持平。⑥time model system stats
time：12:43:00 ~12:58:02
Statistic Time (s) % DB time
----------------------------------- -------------------- ---------
DB CPU 393.3 159.2
parse time elapsed 84.6 34.3
sql execute elapsed time 67.0 27.1
hard parse elapsed time 65.3 26.5
connection management call elapsed 51.8 21.0
PL/SQL execution elapsed time 7.3 3.0
hard parse (sharing criteria) elaps 3.8 1.6
hard parse (bind mismatch) elapsed 2.7 1.1
PL/SQL compilation elapsed time 1.1 .5
repeated bind elapsed time 0.3 .1
sequence load elapsed time 0.3 .1
DB time 247.0
background elapsed time 7.7
background cpu time 2.2
time：12:58:02 ~13:13:03
Statistic Time (s) % DB time
----------------------------------- -------------------- ---------
DB CPU 395.9 151.5
parse time elapsed 100.5 38.5
sql execute elapsed time 83.8 32.1
hard parse elapsed time 80.5 30.8
connection management call elapsed 51.8 19.8
PL/SQL execution elapsed time 7.6 2.9
hard parse (sharing criteria) elaps 6.0 2.3
hard parse (bind mismatch) elapsed 3.1 1.2
PL/SQL compilation elapsed time 2.3 .9
sequence load elapsed time 0.7 .3
repeated bind elapsed time 0.5 .2
DB time 261.2
background elapsed time 9.8
background cpu time 4.3
time：13:13:03 ~13:28:03
Statistic Time (s) % DB time
----------------------------------- -------------------- ---------
DB CPU 378.0 171.7
parse time elapsed 62.6 28.4
connection management call elapsed 47.4 21.6
sql execute elapsed time 44.8 20.4
hard parse elapsed time 43.3 19.6
PL/SQL execution elapsed time 6.7 3.0
hard parse (sharing criteria) elaps 2.5 1.1
hard pars
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오라클에서 문자열을 바꾸는 방법
May 08, 2024 pm 07:24 PM
Oracle에서 문자열을 바꾸는 방법은 REPLACE 함수를 사용하는 것입니다. 이 함수의 구문은 REPLACE(string, search_string, replacement_string)입니다. 사용 단계: 1. 대체할 하위 문자열을 식별합니다. 2. 하위 문자열을 대체할 새 문자열을 결정합니다. 3. 대체할 REPLACE 함수를 사용합니다. 고급 사용법에는 여러 대체, 대소문자 구분, 특수 문자 대체 등이 포함됩니다.
