Oracle SQL调优系列之体系结构学习笔记

oracle体系结构由实例和一组数据文件组成，实例由SGA内存区，SGA意思是共享内存区，由share pool(共享池)、data buffer(数据缓冲区)、log buffer(日志缓冲区)组成

SGA内存区的share pool是解析SQL并保存执行计划的，然后SQL根据执行计划获取数据时先看data buffer里是否有数据，没数据才从磁盘读，然后还是读到data buffer里，下次就直接读data buffer的，当SQL更新时，data buffer的数据就必须写入磁盘备份，为了保护这些数据，才有log buffer，这就是大概的原理简介

系统结构关系图如图，图来自《收获，不止SQL优化》一书：

下面介绍共享池、数据缓冲、日志缓冲方面调优的例子

共享池相关例子

未使用使用绑定变量的情况，进行一下批量写数据，在登录系统，经常用的sql是select * from sys_users where username='admin'或者什么什么的，假如有很多用户登录，就需要执行很多次这样类似的sql，能不能用一条SQL代表？意思是不需要Oracle优化器每次都解析sql获取执行计划，对于这种类似的sql是没必要的，Oracle提供了绑定变量的方法，可以用于调优sql，然后一堆sql就可以用

select * from sys_users where username=:x

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这里用一个变量x表示，具体例子如下，

新建一张表来测试

create table t (x int);

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不使用绑定遍历，批量写数据

begin for i in 1 .. 1000 loop execute immediate 'insert into t values('|| i ||')'; commit; end loop; end; /

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输出

已用时间: 00: 00: 00.80

加上绑定遍历，绑定变量是用:x的形式

begin for i in 1 .. 100 loop execute immediate 'insert into t values( :x )' using i; commit; end loop; end; /

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已用时间: 00: 00: 00.05

数据缓冲相关例子

这里介绍和数据缓存相关例子

(1) 清解析缓存

//创建一个表来测试 SQL> create table t as select * from dba_objects; 表已创建。 //设置打印行数 SQL> set linesize 1000 //设置执行计划开启 SQL> set autotrace on //打印出时间 SQL> set timing on //查询一下数据 SQL> select count(1) from t; COUNT(1) ---------- 72043 已用时间: 00: 00: 00.10 //清一下缓冲区缓存(ps:这个sql不能随便在生产环境执行) SQL> alter system flush buffer_cache; 系统已更改。 已用时间: 00: 00: 00.08 //清一下共享池缓存(ps:这个sql不能随便在生产环境执行) SQL> alter system flush shared_pool; //再次查询，发现查询快了 SQL> select count(1) from t; COUNT(1) ---------- 72043 已用时间: 00: 00: 00.12 SQL>

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日志缓冲相关例子

这里说明一下，日志关闭是可以提供性能的，不过在生生产环境还是不能随便用，只能说是一些特定创建，SQL如：

alter table [表名] nologging;

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调优拓展知识

这些是看《收获，不止SQL优化》一书的小记

(1) 批量写数据事务问题

对于循环批量事务提交的问题，commit放在循环内和放在循环外的区别，

放在循环内，每次执行就提交一次事务，这种时间相对比较少的

begin for i in 1 .. 1000 loop execute immediate 'insert into t values('|| i ||')'; commit; end loop; end;

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放在循环外，sql循环成功，再提交一次事务，这种时间相对比较多一点

begin for i in 1 .. 1000 loop execute immediate 'insert into t values('|| i ||')'; end loop; commit; end;

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《收获，不止SQL优化》一书提供的脚本，用于查看逻辑读、解析、事务数等等情况：

select s.snap_date, decode(s.redosize, null, '--shutdown or end--', s.currtime) "TIME", to_char(round(s.seconds / 60, 2)) "elapse(min)", round(t.db_time / 1000000 / 60, 2) "DB time(min)", s.redosize redo, round(s.redosize / s.seconds, 2) "redo/s", s.logicalreads logical, round(s.logicalreads / s.seconds, 2) "logical/s", physicalreads physical, round(s.physicalreads / s.seconds, 2) "phy/s", s.executes execs, round(s.executes / s.seconds, 2) "execs/s", s.parse, round(s.parse / s.seconds, 2) "parse/s", s.hardparse, round(s.hardparse / s.seconds, 2) "hardparse/s", s.transactions trans, round(s.transactions / s.seconds, 2) "trans/s" from (select curr_redo - last_redo redosize, curr_logicalreads - last_logicalreads logicalreads, curr_physicalreads - last_physicalreads physicalreads, curr_executes - last_executes executes, curr_parse - last_parse parse, curr_hardparse - last_hardparse hardparse, curr_transactions - last_transactions transactions, round(((currtime + 0) - (lasttime + 0)) * 3600 * 24, 0) seconds, to_char(currtime, 'yy/mm/dd') snap_date, to_char(currtime, 'hh24:mi') currtime, currsnap_id endsnap_id, to_char(startup_time, 'yyyy-mm-dd hh24:mi:ss') startup_time from (select a.redo last_redo, a.logicalreads last_logicalreads, a.physicalreads last_physicalreads, a.executes last_executes, a.parse last_parse, a.hardparse last_hardparse, a.transactions last_transactions, lead(a.redo, 1, null) over(partition by b.startup_time order by b.end_interval_time) curr_redo, lead(a.logicalreads, 1, null) over(partition by b.startup_time order by b.end_interval_time) curr_logicalreads, lead(a.physicalreads, 1, null) over(partition by b.startup_time order by b.end_interval_time) curr_physicalreads, lead(a.executes, 1, null) over(partition by b.startup_time order by b.end_interval_time) curr_executes, lead(a.parse, 1, null) over(partition by b.startup_time order by b.end_interval_time) curr_parse, lead(a.hardparse, 1, null) over(partition by b.startup_time order by b.end_interval_time) curr_hardparse, lead(a.transactions, 1, null) over(partition by b.startup_time order by b.end_interval_time) curr_transactions, b.end_interval_time lasttime, lead(b.end_interval_time, 1, null) over(partition by b.startup_time order by b.end_interval_time) currtime, lead(b.snap_id, 1, null) over(partition by b.startup_time order by b.end_interval_time) currsnap_id, b.startup_time from (select snap_id, dbid, instance_number, sum(decode(stat_name, 'redo size', value, 0)) redo, sum(decode(stat_name, 'session logical reads', value, 0)) logicalreads, sum(decode(stat_name, 'physical reads', value, 0)) physicalreads, sum(decode(stat_name, 'execute count', value, 0)) executes, sum(decode(stat_name, 'parse count (total)', value, 0)) parse, sum(decode(stat_name, 'parse count (hard)', value, 0)) hardparse, sum(decode(stat_name, 'user rollbacks', value, 'user commits', value, 0)) transactions from dba_hist_sysstat where stat_name in ('redo size', 'session logical reads', 'physical reads', 'execute count', 'user rollbacks', 'user commits', 'parse count (hard)', 'parse count (total)') group by snap_id, dbid, instance_number) a, dba_hist_snapshot b where a.snap_id = b.snap_id and a.dbid = b.dbid and a.instance_number = b.instance_number order by end_interval_time)) s, (select lead(a.value, 1, null) over(partition by b.startup_time order by b.end_interval_time) - a.value db_time, lead(b.snap_id, 1, null) over(partition by b.startup_time order by b.end_interval_time) endsnap_id from dba_hist_sys_time_model a, dba_hist_snapshot b where a.snap_id = b.snap_id and a.dbid = b.dbid and a.instance_number = b.instance_number and a.stat_name = 'DB time') t where s.endsnap_id = t.endsnap_id order by s.snap_date, time desc;

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Oracle SQL

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