sql优化
- - 数据库 - ITeye博客是对数据库(数据)进行操作的惟一途径；. 消耗了70%~90%的数据库资源；独立于程序设计逻辑，相对于对程序源代码的优化，对SQL语句的优化在时间成本和风险上的代价都很低；. 可以有不同的写法；易学，难精通. 固定的SQL书写习惯，相同的查询尽量保持相同，存储过程的效率较高. 应该编写与其格式一致的语句，包括字母的大小写、标点符号、换行的位置等都要一致.
转载一个 sql优化 （[精华] 一个查询优化的分析调整全过程！很值得一看 ）
标签：
sql
优化
优化
| 发表时间：2014-01-03 18:08 | 作者：cwqcwqmax9
出处：http://www.iteye.com
见 http://www.itpub.net/forum.php?mod=viewthread&tid=239011
Web翻页优化实例
提交时间: 2004-6-18 15:37:49 回复 发消息
环境：
Linux version 2.4.20-8custom (root@web2) (gcc version 3.2.2 20030222 (Red Hat Linux 3.2.2-5)) #3 SMP Thu Jun 5 22:03:36 CST 2003
Mem: 2113466368
Swap: 4194881536
CPU:两个超线程的Intel(R) Xeon(TM) CPU 2.40GHz
优化前语句在mysql里面查询15秒左右出来，转移到oracle后进行在不调整索引和语句的情况下执行时间大概是4-5秒，调整后执行时间小于0.5秒。
翻页语句：
SELECT * FROM (SELECT T1.*, rownum as linenum FROM (
SELECT /*+ index(a ind_old)*/
a.category FROM auction_auctions a WHERE a.category =' 170101 ' AND a.closed='0' AND ends > sysdate AND (a.approve_status>=0) ORDER BY a.ends) T1 WHERE rownum < 18681) WHERE linenum >= 18641
被查询的表：auction_auctions（产品表）
表结构：
SQL> desc auction_auctions;
Name Null? Type
----------------------------------------- -------- ----------------------------
ID NOT NULL VARCHAR2(32)
USERNAME VARCHAR2(32)
TITLE CLOB
GMT_MODIFIED NOT NULL DATE
STARTS NOT NULL DATE
DESCRIPTION CLOB
PICT_URL CLOB
CATEGORY NOT NULL VARCHAR2(11)
MINIMUM_BID NUMBER
RESERVE_PRICE NUMBER
BUY_NOW NUMBER
AUCTION_TYPE CHAR(1)
DURATION VARCHAR2(7)
INCREMENTNUM NOT NULL NUMBER
CITY VARCHAR2(30)
PROV VARCHAR2(20)
LOCATION VARCHAR2(40)
LOCATION_ZIP VARCHAR2(6)
SHIPPING CHAR(1)
PAYMENT CLOB
INTERNATIONAL CHAR(1)
ENDS NOT NULL DATE
CURRENT_BID NUMBER
CLOSED CHAR(2)
PHOTO_UPLOADED CHAR(1)
QUANTITY NUMBER(11)
STORY CLOB
HAVE_INVOICE NOT NULL NUMBER(1)
HAVE_GUARANTEE NOT NULL NUMBER(1)
STUFF_STATUS NOT NULL NUMBER(1)
APPROVE_STATUS NOT NULL NUMBER(1)
OLD_STARTS NOT NULL DATE
ZOO VARCHAR2(10)
PROMOTED_STATUS NOT NULL NUMBER(1)
REPOST_TYPE CHAR(1)
REPOST_TIMES NOT NULL NUMBER(4)
SECURE_TRADE_AGREE NOT NULL NUMBER(1)
SECURE_TRADE_TRANSACTION_FEE VARCHAR2(16)
SECURE_TRADE_ORDINARY_POST_FEE NUMBER
SECURE_TRADE_FAST_POST_FEE NUMBER
表记录数及大小
SQL> select count(*) from auction_auctions;
COUNT(*)
----------
537351
SQL> select segment_name,bytes,blocks from user_segments where segment_name ='AUCTION_AUCTIONS';
SEGMENT_NAME BYTES BLOCKS
AUCTION_AUCTIONS 1059061760 129280
表上原有的索引
create index ind_old on auction_auctions(closed,approve_status,category,ends) tablespace tbsindex compress 2;
SQL> select segment_name,bytes,blocks from user_segments where segment_name = 'IND_OLD';
SEGMENT_NAME BYTES BLOCKS
IND_OLD 20971520 2560
表和索引都已经分析过，我们来看一下sql执行的费用
SQL> set autotrace trace;
SQL> SELECT * FROM (SELECT T1.*, rownum as linenum FROM (SELECT a.* FROM auction_auctions a WHERE a.category like '18%' AND a.closed='0' AND ends > sysdate AND (a.approve_status>=0) ORDER BY a.ends) T1 WHERE rownum <18681) WHERE linenum >= 18641;
40 rows selected.
Execution Plan
----------------------------------------------------------
0 SELECT STATEMENT Optimizer=CHOOSE (Cost=19152 Card=18347 Byt
es=190698718)
1 0 VIEW (Cost=19152 Card=18347 Bytes=190698718)
2 1 COUNT (STOPKEY)
3 2 VIEW (Cost=19152 Card=18347 Bytes=190460207)
4 3 TABLE ACCESS (BY INDEX ROWID) OF 'AUCTION_AUCTIONS'
(Cost=19152 Card=18347 Bytes=20860539)
5 4 INDEX (RANGE SCAN) OF 'IND_OLD' (NON-UNIQUE) (Cost
=810 Card=186003)
Statistics
----------------------------------------------------------
0 recursive calls
0 db block gets
19437 consistent gets
18262 physical reads
0 redo size
114300 bytes sent via SQL*Net to client
56356 bytes received via SQL*Net from client
435 SQL*Net roundtrips to/from client
0 sorts (memory)
0 sorts (disk)
40 rows processed
我们可以看到这条sql语句通过索引范围扫描找到最里面的结果集，然后通过两个view操作最后得出数据。其中18502 consistent gets，17901 physical reads
我们来看一下这个索引建的到底合不合理，先看下各个查寻列的distinct值
select count(distinct ends) from auction_auctions;
COUNT(DISTINCTENDS)
-------------------
338965
SQL> select count(distinct category) from auction_auctions;
COUNT(DISTINCTCATEGORY)
-----------------------
1148
SQL> select count(distinct closed) from auction_auctions;
COUNT(DISTINCTCLOSED)
---------------------
2
SQL> select count(distinct approve_status) from auction_auctions;
COUNT(DISTINCTAPPROVE_STATUS)
-----------------------------
5
页索引里列平均存储长度
SQL> select avg(vsize(ends)) from auction_auctions;
AVG(VSIZE(ENDS))
----------------
7
SQL> select avg(vsize(closed)) from auction_auctions;
AVG(VSIZE(CLOSED))
------------------
2
SQL> select avg(vsize(category)) from auction_auctions;
AVG(VSIZE(CATEGORY))
--------------------
5.52313106
SQL> select avg(vsize(approve_status)) from auction_auctions;
AVG(VSIZE(APPROVE_STATUS))
--------------------------
1.67639401
我们来估算一下各种组合索引的大小，可以看到closed,approve_status，category都是相对较低集势的列（重复值较多），下面我们来大概计算下各种页索引需要的空间
column distinct num column len
ends 338965 7
category 1148 5.5
closed 2 2
approve_status 5 1.7
index1: (ends,closed,category,approve_status) compress 2
ends:distinct number---338965
closed: distinct number---2
index size=338965*2*(9+2)+ 537351*(1.7+5.5+6)=14603998
index2: (closed,category,ends,approve_status)
closed: distinct number---2
category: distinct number---1148
index size=2*1148*(2+5.5)+537351*(7+1.7+6)=7916279
index3: (closed,approve_status,category,ends)
closed: distinct number---2
approve_status: distinct number―5
index size=2*5*(2+1.7)+537351*(7+5.5+6)=9941030
结果出来了，index2: (closed,category,ends,approve_status)的索引最小
我们再来看一下语句
SELECT * FROM (SELECT T1.*, rownum as linenum FROM (SELECT a.* FROM auction_auctions a WHERE a.category like '18%' AND a.closed='0' AND ends > sysdate AND (a.approve_status>=0) ORDER BY a.ends) T1 WHERE rownum <18681) WHERE linenum >= 18641;
可以看出这个sql语句有很大优化余地，首先最里面的结果集SELECT a.* FROM auction_auctions a WHERE a.category like '18%' AND a.closed='0' AND ends > sysdate AND (a.approve_status>=0) ORDER BY a.ends，这里的话会走index range scan,然后table scan by rowid，这样的话如果符合条件的数据多的话相当耗资源，我们可以改写成
SELECT a.rowid FROM auction_auctions a WHERE a.category like '18%' AND a.closed='0' AND ends > sysdate AND (a.approve_status>=0) ORDER BY a.ends
这样的话最里面的结果集只需要index fast full scan就可以完成了，再改写一下得出以下语句
select * from auction_auctions where rowid in (SELECT rid FROM (
SELECT T1.rowid rid, rownum as linenum FROM
(SELECT a.rowid FROM auction_auctions a WHERE a.category like '18%' AND a.closed='0' AND ends > sysdate AND
(a.approve_status>=0) ORDER BY a.ends) T1 WHERE rownum < 18681) WHERE linenum >= 18641)
下面我们来测试一下这个索引的查询开销
select * from auction_auctions where rowid in (SELECT rid FROM (
SELECT T1.rowid rid, rownum as linenum FROM
(SELECT a.rowid FROM auction_auctions a WHERE a.category like '18%' AND a.closed='0' AND ends > sysdate AND
(a.approve_status>=0) ORDER BY a.closed,a.ends) T1 WHERE rownum < 18681) WHERE linenum >= 18641)
Execution Plan
----------------------------------------------------------
0 SELECT STATEMENT Optimizer=CHOOSE (Cost=18698 Card=18344 Byt
es=21224008)
1 0 NESTED LOOPS (Cost=18698 Card=18344 Bytes=21224008)
2 1 VIEW (Cost=264 Card=18344 Bytes=366880)
3 2 SORT (UNIQUE)
4 3 COUNT (STOPKEY)
5 4 VIEW (Cost=264 Card=18344 Bytes=128408)
6 5 SORT (ORDER BY STOPKEY) (Cost=264 Card=18344 Byt
es=440256)
7 6 INDEX (FAST FULL SCAN) OF 'IDX_AUCTION_BROWSE'
(NON-UNIQUE) (Cost=159 Card=18344 Bytes=440256)
8 1 TABLE ACCESS (BY USER ROWID) OF 'AUCTION_AUCTIONS' (Cost
=1 Card=1 Bytes=1137)
Statistics
----------------------------------------------------------
0 recursive calls
0 db block gets
2080 consistent gets
1516 physical reads
0 redo size
114840 bytes sent via SQL*Net to client
56779 bytes received via SQL*Net from client
438 SQL*Net roundtrips to/from client
2 sorts (memory)
0 sorts (disk)
40 rows processed
可以看到consistent gets从19437降到2080，physical reads从18262降到1516，查询时间也丛4秒左右下降到0。5秒，可以来说这次sql调整取得了预期的效果。
又修改了一下语句，
SQL> select * from auction_auctions where rowid in
2 (SELECT rid FROM (
3 SELECT T1.rowid rid, rownum as linenum FROM
4 (SELECT a.rowid FROM auction_auctions a
5 WHERE a.category like '18%' AND a.closed='0' AND ends > sysdate AND
a.approve_status>=0
6 7 ORDER BY a.closed,a.category,a.ends) T1
8 WHERE rownum < 18600) WHERE linenum >= 18560) ;
40 rows selected.
Execution Plan
----------------------------------------------------------
0 SELECT STATEMENT Optimizer=CHOOSE (Cost=17912 Card=17604 Byt
es=20367828)
1 0 NESTED LOOPS (Cost=17912 Card=17604 Bytes=20367828)
2 1 VIEW (Cost=221 Card=17604 Bytes=352080)
3 2 SORT (UNIQUE)
4 3 COUNT (STOPKEY)
5 4 VIEW (Cost=221 Card=17604 Bytes=123228)
6 5 INDEX (RANGE SCAN) OF 'IDX_AUCTION_BROWSE' (NON-
UNIQUE) (Cost=221 Card=17604 Bytes=422496)
7 1 TABLE ACCESS (BY USER ROWID) OF 'AUCTION_AUCTIONS' (Cost
=1 Card=1 Bytes=1137)
Statistics
----------------------------------------------------------
0 recursive calls
0 db block gets
550 consistent gets
14 physical reads
0 redo size
117106 bytes sent via SQL*Net to client
56497 bytes received via SQL*Net from client
436 SQL*Net roundtrips to/from client
1 sorts (memory)
0 sorts (disk)
40 rows processed
在order by里加上索引前导列，消除了
6 5 SORT (ORDER BY STOPKEY) (Cost=264 Card=18344 Byt
es=440256)
，把consistent gets从2080降到550
已有 0 人发表留言，猛击->> 这里<<-参与讨论
ITeye推荐
Web翻页优化实例
提交时间: 2004-6-18 15:37:49 回复 发消息
环境：
Linux version 2.4.20-8custom (root@web2) (gcc version 3.2.2 20030222 (Red Hat Linux 3.2.2-5)) #3 SMP Thu Jun 5 22:03:36 CST 2003
Mem: 2113466368
Swap: 4194881536
CPU:两个超线程的Intel(R) Xeon(TM) CPU 2.40GHz
优化前语句在mysql里面查询15秒左右出来，转移到oracle后进行在不调整索引和语句的情况下执行时间大概是4-5秒，调整后执行时间小于0.5秒。
翻页语句：
SELECT * FROM (SELECT T1.*, rownum as linenum FROM (
SELECT /*+ index(a ind_old)*/
a.category FROM auction_auctions a WHERE a.category =' 170101 ' AND a.closed='0' AND ends > sysdate AND (a.approve_status>=0) ORDER BY a.ends) T1 WHERE rownum < 18681) WHERE linenum >= 18641
被查询的表：auction_auctions（产品表）
表结构：
SQL> desc auction_auctions;
Name Null? Type
----------------------------------------- -------- ----------------------------
ID NOT NULL VARCHAR2(32)
USERNAME VARCHAR2(32)
TITLE CLOB
GMT_MODIFIED NOT NULL DATE
STARTS NOT NULL DATE
DESCRIPTION CLOB
PICT_URL CLOB
CATEGORY NOT NULL VARCHAR2(11)
MINIMUM_BID NUMBER
RESERVE_PRICE NUMBER
BUY_NOW NUMBER
AUCTION_TYPE CHAR(1)
DURATION VARCHAR2(7)
INCREMENTNUM NOT NULL NUMBER
CITY VARCHAR2(30)
PROV VARCHAR2(20)
LOCATION VARCHAR2(40)
LOCATION_ZIP VARCHAR2(6)
SHIPPING CHAR(1)
PAYMENT CLOB
INTERNATIONAL CHAR(1)
ENDS NOT NULL DATE
CURRENT_BID NUMBER
CLOSED CHAR(2)
PHOTO_UPLOADED CHAR(1)
QUANTITY NUMBER(11)
STORY CLOB
HAVE_INVOICE NOT NULL NUMBER(1)
HAVE_GUARANTEE NOT NULL NUMBER(1)
STUFF_STATUS NOT NULL NUMBER(1)
APPROVE_STATUS NOT NULL NUMBER(1)
OLD_STARTS NOT NULL DATE
ZOO VARCHAR2(10)
PROMOTED_STATUS NOT NULL NUMBER(1)
REPOST_TYPE CHAR(1)
REPOST_TIMES NOT NULL NUMBER(4)
SECURE_TRADE_AGREE NOT NULL NUMBER(1)
SECURE_TRADE_TRANSACTION_FEE VARCHAR2(16)
SECURE_TRADE_ORDINARY_POST_FEE NUMBER
SECURE_TRADE_FAST_POST_FEE NUMBER
表记录数及大小
SQL> select count(*) from auction_auctions;
COUNT(*)
----------
537351
SQL> select segment_name,bytes,blocks from user_segments where segment_name ='AUCTION_AUCTIONS';
SEGMENT_NAME BYTES BLOCKS
AUCTION_AUCTIONS 1059061760 129280
表上原有的索引
create index ind_old on auction_auctions(closed,approve_status,category,ends) tablespace tbsindex compress 2;
SQL> select segment_name,bytes,blocks from user_segments where segment_name = 'IND_OLD';
SEGMENT_NAME BYTES BLOCKS
IND_OLD 20971520 2560
表和索引都已经分析过，我们来看一下sql执行的费用
SQL> set autotrace trace;
SQL> SELECT * FROM (SELECT T1.*, rownum as linenum FROM (SELECT a.* FROM auction_auctions a WHERE a.category like '18%' AND a.closed='0' AND ends > sysdate AND (a.approve_status>=0) ORDER BY a.ends) T1 WHERE rownum <18681) WHERE linenum >= 18641;
40 rows selected.
Execution Plan
----------------------------------------------------------
0 SELECT STATEMENT Optimizer=CHOOSE (Cost=19152 Card=18347 Byt
es=190698718)
1 0 VIEW (Cost=19152 Card=18347 Bytes=190698718)
2 1 COUNT (STOPKEY)
3 2 VIEW (Cost=19152 Card=18347 Bytes=190460207)
4 3 TABLE ACCESS (BY INDEX ROWID) OF 'AUCTION_AUCTIONS'
(Cost=19152 Card=18347 Bytes=20860539)
5 4 INDEX (RANGE SCAN) OF 'IND_OLD' (NON-UNIQUE) (Cost
=810 Card=186003)
Statistics
----------------------------------------------------------
0 recursive calls
0 db block gets
19437 consistent gets
18262 physical reads
0 redo size
114300 bytes sent via SQL*Net to client
56356 bytes received via SQL*Net from client
435 SQL*Net roundtrips to/from client
0 sorts (memory)
0 sorts (disk)
40 rows processed
我们可以看到这条sql语句通过索引范围扫描找到最里面的结果集，然后通过两个view操作最后得出数据。其中18502 consistent gets，17901 physical reads
我们来看一下这个索引建的到底合不合理，先看下各个查寻列的distinct值
select count(distinct ends) from auction_auctions;
COUNT(DISTINCTENDS)
-------------------
338965
SQL> select count(distinct category) from auction_auctions;
COUNT(DISTINCTCATEGORY)
-----------------------
1148
SQL> select count(distinct closed) from auction_auctions;
COUNT(DISTINCTCLOSED)
---------------------
2
SQL> select count(distinct approve_status) from auction_auctions;
COUNT(DISTINCTAPPROVE_STATUS)
-----------------------------
5
页索引里列平均存储长度
SQL> select avg(vsize(ends)) from auction_auctions;
AVG(VSIZE(ENDS))
----------------
7
SQL> select avg(vsize(closed)) from auction_auctions;
AVG(VSIZE(CLOSED))
------------------
2
SQL> select avg(vsize(category)) from auction_auctions;
AVG(VSIZE(CATEGORY))
--------------------
5.52313106
SQL> select avg(vsize(approve_status)) from auction_auctions;
AVG(VSIZE(APPROVE_STATUS))
--------------------------
1.67639401
我们来估算一下各种组合索引的大小，可以看到closed,approve_status，category都是相对较低集势的列（重复值较多），下面我们来大概计算下各种页索引需要的空间
column distinct num column len
ends 338965 7
category 1148 5.5
closed 2 2
approve_status 5 1.7
index1: (ends,closed,category,approve_status) compress 2
ends:distinct number---338965
closed: distinct number---2
index size=338965*2*(9+2)+ 537351*(1.7+5.5+6)=14603998
index2: (closed,category,ends,approve_status)
closed: distinct number---2
category: distinct number---1148
index size=2*1148*(2+5.5)+537351*(7+1.7+6)=7916279
index3: (closed,approve_status,category,ends)
closed: distinct number---2
approve_status: distinct number―5
index size=2*5*(2+1.7)+537351*(7+5.5+6)=9941030
结果出来了，index2: (closed,category,ends,approve_status)的索引最小
我们再来看一下语句
SELECT * FROM (SELECT T1.*, rownum as linenum FROM (SELECT a.* FROM auction_auctions a WHERE a.category like '18%' AND a.closed='0' AND ends > sysdate AND (a.approve_status>=0) ORDER BY a.ends) T1 WHERE rownum <18681) WHERE linenum >= 18641;
可以看出这个sql语句有很大优化余地，首先最里面的结果集SELECT a.* FROM auction_auctions a WHERE a.category like '18%' AND a.closed='0' AND ends > sysdate AND (a.approve_status>=0) ORDER BY a.ends，这里的话会走index range scan,然后table scan by rowid，这样的话如果符合条件的数据多的话相当耗资源，我们可以改写成
SELECT a.rowid FROM auction_auctions a WHERE a.category like '18%' AND a.closed='0' AND ends > sysdate AND (a.approve_status>=0) ORDER BY a.ends
这样的话最里面的结果集只需要index fast full scan就可以完成了，再改写一下得出以下语句
select * from auction_auctions where rowid in (SELECT rid FROM (
SELECT T1.rowid rid, rownum as linenum FROM
(SELECT a.rowid FROM auction_auctions a WHERE a.category like '18%' AND a.closed='0' AND ends > sysdate AND
(a.approve_status>=0) ORDER BY a.ends) T1 WHERE rownum < 18681) WHERE linenum >= 18641)
下面我们来测试一下这个索引的查询开销
select * from auction_auctions where rowid in (SELECT rid FROM (
SELECT T1.rowid rid, rownum as linenum FROM
(SELECT a.rowid FROM auction_auctions a WHERE a.category like '18%' AND a.closed='0' AND ends > sysdate AND
(a.approve_status>=0) ORDER BY a.closed,a.ends) T1 WHERE rownum < 18681) WHERE linenum >= 18641)
Execution Plan
----------------------------------------------------------
0 SELECT STATEMENT Optimizer=CHOOSE (Cost=18698 Card=18344 Byt
es=21224008)
1 0 NESTED LOOPS (Cost=18698 Card=18344 Bytes=21224008)
2 1 VIEW (Cost=264 Card=18344 Bytes=366880)
3 2 SORT (UNIQUE)
4 3 COUNT (STOPKEY)
5 4 VIEW (Cost=264 Card=18344 Bytes=128408)
6 5 SORT (ORDER BY STOPKEY) (Cost=264 Card=18344 Byt
es=440256)
7 6 INDEX (FAST FULL SCAN) OF 'IDX_AUCTION_BROWSE'
(NON-UNIQUE) (Cost=159 Card=18344 Bytes=440256)
8 1 TABLE ACCESS (BY USER ROWID) OF 'AUCTION_AUCTIONS' (Cost
=1 Card=1 Bytes=1137)
Statistics
----------------------------------------------------------
0 recursive calls
0 db block gets
2080 consistent gets
1516 physical reads
0 redo size
114840 bytes sent via SQL*Net to client
56779 bytes received via SQL*Net from client
438 SQL*Net roundtrips to/from client
2 sorts (memory)
0 sorts (disk)
40 rows processed
可以看到consistent gets从19437降到2080，physical reads从18262降到1516，查询时间也丛4秒左右下降到0。5秒，可以来说这次sql调整取得了预期的效果。
又修改了一下语句，
SQL> select * from auction_auctions where rowid in
2 (SELECT rid FROM (
3 SELECT T1.rowid rid, rownum as linenum FROM
4 (SELECT a.rowid FROM auction_auctions a
5 WHERE a.category like '18%' AND a.closed='0' AND ends > sysdate AND
a.approve_status>=0
6 7 ORDER BY a.closed,a.category,a.ends) T1
8 WHERE rownum < 18600) WHERE linenum >= 18560) ;
40 rows selected.
Execution Plan
----------------------------------------------------------
0 SELECT STATEMENT Optimizer=CHOOSE (Cost=17912 Card=17604 Byt
es=20367828)
1 0 NESTED LOOPS (Cost=17912 Card=17604 Bytes=20367828)
2 1 VIEW (Cost=221 Card=17604 Bytes=352080)
3 2 SORT (UNIQUE)
4 3 COUNT (STOPKEY)
5 4 VIEW (Cost=221 Card=17604 Bytes=123228)
6 5 INDEX (RANGE SCAN) OF 'IDX_AUCTION_BROWSE' (NON-
UNIQUE) (Cost=221 Card=17604 Bytes=422496)
7 1 TABLE ACCESS (BY USER ROWID) OF 'AUCTION_AUCTIONS' (Cost
=1 Card=1 Bytes=1137)
Statistics
----------------------------------------------------------
0 recursive calls
0 db block gets
550 consistent gets
14 physical reads
0 redo size
117106 bytes sent via SQL*Net to client
56497 bytes received via SQL*Net from client
436 SQL*Net roundtrips to/from client
1 sorts (memory)
0 sorts (disk)
40 rows processed
在order by里加上索引前导列，消除了
6 5 SORT (ORDER BY STOPKEY) (Cost=264 Card=18344 Byt
es=440256)
，把consistent gets从2080降到550
已有 0 人发表留言，猛击->> 这里<<-参与讨论
ITeye推荐
相关 [sql 优化 优化] 推荐：
sql 优化
- - SQL - 编程语言 - ITeye博客转：数据库SQL优化大总结之 百万级数据库优化方案. 2014-07-18 09:33 雲霏霏 雲霏霏的博客 字号：. 网上关于SQL优化的教程很多，但是比较杂乱. 近日有空整理了一下，写出来跟大家分享一下，其中有错误和不足的地方，还请大家纠正补充. 网上关于SQL优化的教程很多，但是比较杂乱. 近日有空整理了一下，写出来跟大家分享一下，其中有错误和不足的地方，还请大家纠正补充.
索引SQL优化
- - SQL - 编程语言 - ITeye博客（一）深入浅出理解索引SQL优化 (转). 实际上，您可以把索引理解为一种特殊的目录. 微软的SQL SERVER提供了两种索引：聚集索引（clustered index，也称聚类索引、簇集索引）和非聚集索引（nonclustered index，也称非聚类索引、非簇集索引）. 下面，我们举例来说明一下聚集索引和非聚集索引的区别： .
优化sql查询
- - 数据库 - ITeye博客1、 首先要搞明白什么叫执行计划. 执行计划是数据库根据SQL语句和相关表的统计信息作出的一个查询方案，这个方案是由查询优化器自动分析产生的，比如一条SQL语句如果用来从一个 10万条记录的表中查1条记录，那查询优化器会选择“索引查找”方式，如果该表进行了归档，当前只剩下5000条记录了，那查询优化器就会改变方案，采用 “全表扫描”方式.
sql语句优化
- - 数据库 - ITeye博客性能不理想的系统中除了一部分是因为应用程序的负载确实超过了服务器的实际处理能力外,更多的是因为系统存在大量的SQL语句需要优化. 为了获得稳定的执行性能，SQL语句越简单越好. 对复杂的SQL语句，要设法对之进行简化. 1）不要有超过5个以上的表连接（JOIN）. 2）考虑使用临时表或表变量存放中间结果.
SQL Server优化50法
- - CSDN博客推荐文章虽然查询速度慢的原因很多，但是如果通过一定的优化，也可以使查询问题得到一定程度的解决. 查询速度慢的原因很多，常见如下几种：没有索引或者没有用到索引（这是查询慢最常见的问题，是程序设计的缺陷）. I/O吞吐量小，形成了瓶颈效应. 没有创建计算列导致查询不优化. 内存不足网络速度慢查询出的数据量过大（可以采用多次查询，其他的方法降低数据量）.
oracle sql 优化大全
- - Oracle - 数据库 - ITeye博客最近遇到了oracle sql优化的问题，找了一下，发现这文章实在不错，跟大家分享一下，如果以后有什么新的改进也会继续补充的. 1 前言… 2 . 2 总纲… 2 . 3 降龙十八掌… 3 . 第一掌 避免对列的操作… 3 . 第二掌 避免不必要的类型转换… 4 . 第三掌 增加查询的范围限制… 4 .
Oracle SQL性能优化
- - 数据库 - ITeye博客（1） 选择最有效率的表名顺序(只在基于规则的优化器中有效)：. ORACLE的解析器按照从右到左的顺序处理FROM子句中的表名，FROM子句中写在最后的表(基础表 driving table)将被最先处理，在FROM子句中包含多个表的情况下,你必须选择记录条数最少的表作为基础表. 如果有3个以上的表连接查询, 那就需要选择交叉表(intersection table)作为基础表, 交叉表是指那个被其他表所引用的表.
SQL Server优化50法
- - CSDN博客数据库推荐文章 虽然查询速度慢的原因很多，但是如果通过一定的优化，也可以使查询问题得到一定程度的解决. 查询速度慢的原因很多，常见如下几种：. 没有索引或者没有用到索引(这是查询慢最常见的问题，是程序设计的缺陷). I/O吞吐量小，形成了瓶颈效应. 查询出的数据量过大(可以采用多次查询，其他的方法降低数据量).
Oracle SQL性能优化
- - 数据库 - ITeye博客（1） 选择最有效率的表名顺序(只在基于规则的优化器中有效)：. ORACLE的解析器按照从右到左的顺序处理FROM子句中的表名，FROM子句中写在最后的表(基础表 driving table)将被最先处理，在FROM子句中包含多个表的情况下,你必须选择记录条数最少的表作为基础表. 如果有3个以上的表连接查询, 那就需要选择交叉表(intersection table)作为基础表, 交叉表是指那个被其他表所引用的表.