继昨天使用DB time能够快速灵活的定位sql语句之后,发现分析问题更快捷,高效了。今天就牛刀小试,把一个数据库从500%的负载调到不到100%的负载。前提是确实有可提升可改进的空间。<br />
首先查看了在快照57611的时间段里DB time很快,也收到了zabbix的邮件通知,<br />
<span style="line-height:1.5;" lang="EN-US">
<p class="MsoNormal">
<span lang="EN-US">ZABBIX-</span>监控系统<span lang="EN-US">: <br />
------------------------------------<br />
</span>报警内容<span lang="EN-US">: DB time is too high<br />
------------------------------------<br />
</span>报警级别<span lang="EN-US">: PROBLEM<br />
------------------------------------<br />
</span>监控项目<span lang="EN-US">: DBtime</span>:<span lang="EN-US">530 % <br />
------------------------------------ <br />
</span>报警时间<span lang="EN-US">:2015.10.04-07:58:55</span>
</p>
</span>
<p class="MsoNormal">
<span lang="EN-US"><br />
</span><span lang="EN-US"><!--?xml:namespace prefix = o /--><?xml:namespace prefix = o /><o:p></o:p></span>
</p>
对于OLAP目前设定的阀值是500%,相对来说是一个较高的阀值了。来分析一下看看是否由于sql的影响较大。<br />
$ ksh showsnapsql.sh 57611<br />
SNAP_ID SQL_ID EXECUTIONS_DELTA ELAPSED_TI PER_TOTAL<br />
---------- ------------- ---------------- ---------- ----------<br />
57611 8tmf11fvxy09j 30 2451s 32%<br />
57611 cy55p6nrd31db 30 2346s 31%<br />
57611 4rhpc838qfsmy 143 655s 8%<br />
57611 c7k4g2urpu1sc 0 426s 5%<br />
57611 29tdwfv5d9s4f 30 441s 5%<br />
可以看到在快照57611的时间段内,有两个sql是尤其需要关注的,占用了60%多的DB time,也就意味着如果这两个语句能够改进,对于DB time的提升就很高了,同理,对于系统的负载也会减轻很多。<br />
我们来看看sql_id <span style="line-height:16px;white-space:normal;">8tmf11fvxy09j这个语句。这个语句一个快照时间范围内执行了近30次,每次执行大概是80秒左右。</span>发现这个语句是一个看似简单的查询。<br />
$ ksh showsql.sh 8tmf11fvxy09j<br />
SQL_FULLTEXT<br />
----------------------------------------------------------------------------------------------------<br />
SELECT ROUND(AVG(SUM(END_TIME-START_TIME)*1440)) AVG FROM TES_BILLDETAIL WHERE END_TIME ><br />
TRUNC(SYSDATE - 30) AND CN = :1 GROUP BY TRUNC(END_TIME) <br />
而查看执行计划发现,指标还是正常的,可以看到这个表TES_BILLDETAIL是一个分区表,分区有900多个。<br />
Plan hash value: 2129377450<br />
--------------------------------------------------------------------------------------------------------<br />
| Id | Operation | Name | Rows | Bytes | Cost (%CPU)<br />
--------------------------------------------------------------------------------------------------------<br />
| 0 | SELECT STATEMENT | | | | 13 (100)<br />
| 1 | SORT AGGREGATE | | 1 | 32 | 13 (8)<br />
| 2 | HASH GROUP BY | | 1 | 32 | 13 (8)<br />
| 3 | PARTITION RANGE ITERATOR | | 1 | 32 | 12 (0)<br />
|* 4 | TABLE ACCESS BY LOCAL INDEX ROWID| TES_BILLDETAIL | 1 | 32 | 12 (0)<br />
|* 5 | INDEX RANGE SCAN | IND_TES_BILLDETAIL_END_TIME | 49 | | 1 (0)<br />
--------------------------------------------------------------------------------------------------------<br />
Query Block Name / Object Alias (identified by operation id):<br />
-------------------------------------------------------------<br />
1 - SEL$1<br />
4 - SEL$1 / TES_BILLDETAIL@SEL$1<br />
5 - SEL$1 / TES_BILLDETAIL@SEL$1<br />
Predicate Information (identified by operation id):<br />
---------------------------------------------------<br />
4 - filter("CN"=:1)<br />
5 - access("END_TIME">TRUNC(SYSDATE@!-30))<br />
通过谓词信息可以看到走了end_date相关的索引。<br />
<br />
执行计划看起来还是不错的,但是执行的情况确实还是不够理想,那么我们怎么查看执行的细节呢,如果是11g的库,sqlmonitor就是一个利器,使用下面的语句抓取了正在执行的sql情况。<br />
set pages 0<br />
set long 99999999<br />
set linesize 150<br />
col comm format a200<br />
set long 99999<br />
SELECT dbms_sqltune.report_sql_monitor(<br />
sql_id => '8tmf11fvxy09j',<br />
report_level => 'ALL',<br />
type=>'HTML'<br />
) comm <br />
FROM dual;<br />
可以看到<br />
<span class="s9"><b>SQL Plan Monitoring Details (Plan Hash Value=2129377450)</b></span><br />
<table style="width:491pt;" class="ke-zeroborder" border="2" cellspacing="1" cellpadding="1" width="652">
<tbody>
<tr>
<td height="57" rowspan="2" width="77" colspan="2">
Id
</td>
<td rowspan="2" width="214">
Operation
</td>
<td rowspan="2" width="125">
Name
</td>
<td width="72">
Estimated
</td>
<td rowspan="2" width="53">
Cost
</td>
<td rowspan="2" width="53">
Execs
</td>
<td rowspan="2" width="58">
Rows
</td>
</tr>
<tr>
<td height="19" width="72">
Rows
</td>
</tr>
<tr>
<td height="45" width="49">
.
</td>
<td width="28">
0
</td>
<td width="214">
SELECT STATEMENT
</td>
<td width="125">
.
</td>
<td width="72">
.
</td>
<td width="53">
.
</td>
<td width="53">
1
</td>
<td width="58">
.
</td>
</tr>
<tr>
<td height="45" width="49">
.
</td>
<td width="28">
1
</td>
<td width="214">
.<span> SORT AGGREGATE</span>
</td>
<td width="125">
.
</td>
<td width="72">
1
</td>
<td width="53">
13
</td>
<td width="53">
1
</td>
<td width="58">
.
</td>
</tr>
<tr>
<td height="36" rowspan="2" width="49">
->
</td>
<td rowspan="2" width="28">
2
</td>
<td rowspan="2" width="214">
..<span> HASH GROUP BY</span>
</td>
<td rowspan="2" width="125">
.
</td>
<td rowspan="2" width="72">
1
</td>
<td rowspan="2" width="53">
13
</td>
<td rowspan="2" width="53">
1
</td>
<td rowspan="2" width="58">
0
</td>
</tr>
<tr>
</tr>
<tr>
<td height="60" rowspan="2" width="49">
->
</td>
<td rowspan="2" width="28">
3
</td>
<td rowspan="2" width="214">
...<span> PARTITION RANGE ITERATOR</span>
</td>
<td rowspan="2" width="125">
.
</td>
<td rowspan="2" width="72">
1
</td>
<td rowspan="2" width="53">
12
</td>
<td rowspan="2" width="53">
1
</td>
<td rowspan="2" width="58">
3
</td>
</tr>
<tr>
</tr>
<tr>
<td height="75" rowspan="2" width="49">
->
</td>
<td rowspan="2" width="28">
4
</td>
<td rowspan="2" width="214">
....<span> TABLE ACCESS BY LOCAL INDEX ROWID</span>
</td>
<td rowspan="2" width="125">
TES_BILLDETAIL
</td>
<td rowspan="2" width="72">
1
</td>
<td rowspan="2" width="53">
12
</td>
<td rowspan="2" width="53">
5
</td>
<td rowspan="2" width="58">
3
</td>
</tr>
<tr>
</tr>
<tr>
<td height="45" rowspan="2" width="49">
->
</td>
<td rowspan="2" width="28">
5
</td>
<td rowspan="2" width="214">
.....<span> INDEX RANGE SCAN</span>
</td>
<td rowspan="2" width="125">
IND_TES_BILLDETAIL<br />
_END_TIME
</td>
<td rowspan="2" width="72">
49
</td>
<td rowspan="2" width="53">
1
</td>
<td rowspan="2" width="53">
5
</td>
<td rowspan="2" width="58">
9M
</td>
</tr>
<tr>
</tr>
</tbody>
</table>
<br />
绑定变量值也能抓取到。<span class="s9"><br />
<hr align="left" size="1" />
</span>
<table border="1" cellspacing="0" cellpadding="0">
<tbody>
<tr class="s16">
<th align="middle">
Name
</th>
<th align="middle">
Position
</th>
<th align="middle">
Type
</th>
<th align="middle">
Value
</th>
</tr>
<tr class="s17a">
<td align="left">
:1
</td>
<td align="middle">
1
</td>
<td align="left">
VARCHAR2(128)
</td>
<td align="left">
582891946
</td>
</tr>
</tbody>
</table>
带着疑问查看了下数据的情况,发现使用字段CN来过滤,只有1000多条记录,相比通过时间来过滤的900多万记录来说差别实在是太大了。<br />
SQL> select count(*)from TES_BILLDETAIL where cn='582891946';<br />
1052<br />
Elapsed: 00:00:00.01<br />
<br />
关于这个表的索引情况如下:<br />
INDEX_NAME TABLESPACE INDEX_TYPE UNIQUENES PAR COLUMN_LIST TABLE_TYPE STATUS NUM_ROWS LAST_ANALYZED G<br />
------------------------------ ---------- ---------- --------- --- ------------------------------ ---------- ------ ---------- ------------------- -<br />
IND_TES_BILLDETAIL_CN NORMAL NONUNIQUE YES CN TABLE N/A 3145619347 2015-06-26 04:52:11 N<br />
IND_TES_BILLDETAIL_END_TIME NORMAL NONUNIQUE YES END_TIME TABLE N/A 2926456256 2015-06-26 04:52:23 N<br />
<br />
在字段CN上也是有索引的,同时还有一个索引是end_time上,但是通过end_time过滤的数据实在是有点多,尽管相比整张表几十亿的记录来说确实是很大的改进,但是相比于字段CN过滤的数据结果来说,差别更加的悬殊。<br />
所以采用CN来过滤着实是一个不错的选择。可以简单打一个比方,比如我去网上购物,购物的所以记录都存放在一张表里,如果需要把我最近这些天的消费记录抓取出来,肯定是通过我的id号直接关联,然后再过滤时间要好一些,如果直接来过滤时间,先把全国人民购物的这些天的数据都抓出来再过滤,这样的差别着实很大,也不高效。<br />
为什么走了end_time的索引而不是CN字段的索引,一个原因就是分区字段是根据end_time来做的,刚好有prefix的local index,加上直方图的信息,所以优化器做评判的时候根据这些信息分析觉得还是end_time的索引要好一些,而CN字段的信息是non-prefix的,CN的信息在各个分区中就很难评估,这个时候优化器评判的基准也有一定的限制了。<br />
<br />
当然,查看这个索引的并行度的时候发现竟然是12,可见之前也有同学尝试改进过这个问题,似乎从并行上下工夫了。<br />
SQL> SELECT DEGREE FROM DBA_INDEXES WHERE INDEX_NAME='IND_TES_BILLDETAIL_CN';<br />
12<br />
当然这个的效果也有,但是解决的力度还是不大。<br />
我们加入hint先来看看效果。<br />
<span style="line-height:16px;white-space:normal;">EXPLAIN PLAN FOR SELECT /*+INDEX(TES_BILLDETAIL IND_TES_BILLDETAIL_CN)*/ROUND(AVG(SUM(END_TIME-START_TIME)*1440)) AVG FROM TES_BILLDETAIL WHERE END_TIME > TRUNC(SYSDATE - 30) AND CN = :1 GROUP BY TRUNC(END_TIME) </span><br style="line-height:16px;white-space:normal;" />
<span style="line-height:16px;">发现索引能够正常启用,而且cost也不高,就按照这个方子来试试。</span><br />
如果想有一种更好更全面的改进,就是添加复合索引,但是有一些隐患,一来这个表实在太大了,加索引时间,资源都是很大的消耗,二来更重要的是不确定添加索引之后会对其它的语句有很大的影响,所以为了保险起见,我先修改这两个相关的sql,修改它的执行计划,在线就可以做,如果不合适,撤销就可以。<br />
修改执行计划可以使用sqlt来做,coe提供的这套工具还是很不错的。<br />
因为里面有绑定变量,所以尝试用下面的方式来做。<br />
declare<br />
cursor temp_cur is select '582891946' id from dual;<br />
begin<br />
for i in temp_cur loop<br />
execute immediate 'SELECT /*+INDEX(TES_BILLDETAIL IND_TES_BILLDETAIL_CN)*/ROUND(AVG(SUM(END_TIME-START_TIME)*1440)) AVG FROM TES_BILLDETAIL WHERE END_TIME > TRUNC(SYSDATE - 30) AND CN = :1 GROUP BY TRUNC(END_TIME) '<br />
using i.id;<br />
end loop;<br />
end;<br />
/<br />
<br />
SQL> @a.sql<br />
PL/SQL procedure successfully completed.<br />
然后从v$sql里面去抓取,可以找到修改后的sql的sql_id<br />
<br />
SQL_ID SQL_FULLTEXT<br />
------------- ----------------------------------------------------------------------------------------------------<br />
9h0kr4s3365mt SELECT /*+INDEX(TES_BILLDETAIL IND_TES_BILLDETAIL_CN)*/ROUND(AVG(SUM(END_TIME-START_TIME)*1440)) AVG<br />
FROM TES_BILLDETAIL WHERE END_TIME > TRUNC(SYSDATE - 30) AND CN = :1 GROUP BY TRUNC(END_TIME)<br />
<br />
所以sql_id <span style="line-height:16px;white-space:normal;">9h0kr4s3365mt就是我们努力的目标,使用提供的脚本来得到执行计划的profile信息。输入sql_id和hash_value即可。</span><br />
SQL>@coe_xfr_sql_profile.sql<br />
Parameter 1:<br />
SQL_ID (required)<br />
Enter value for 1: 8tmf11fvxy09j<br />
PLAN_HASH_VALUE AVG_ET_SECS<br />
--------------- -----------<br />
2129377450 70.743<br />
Parameter 2:<br />
PLAN_HASH_VALUE (required)<br />
Enter value for 2: 2129377450<br />
Values passed to coe_xfr_sql_profile:<br />
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~<br />
SQL_ID : "8tmf11fvxy09j"<br />
PLAN_HASH_VALUE: "2129377450"<br />
然后输入修改后的sql_id,也得到一个执行计划的proifle信息,我们需要做一个置换。<br />
SQL> @coe_xfr_sql_profile.sql<br />
Parameter 1:<br />
SQL_ID (required)<br />
Enter value for 1: 9h0kr4s3365mt<br />
PLAN_HASH_VALUE AVG_ET_SECS<br />
--------------- -----------<br />
3361841277 .003<br />
Parameter 2:<br />
PLAN_HASH_VALUE (required)<br />
Enter value for 2: 3361841277<br />
Values passed to coe_xfr_sql_profile:<br />
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~<br />
SQL_ID : "9h0kr4s3365mt"<br />
PLAN_HASH_VALUE: "3361841277"<br />
得到的关于hint部分的信息如下,原来的sql中的信息为:<br />
h := SYS.SQLPROF_ATTR(<br />
q'[BEGIN_OUTLINE_DATA]',<br />
q'[IGNORE_OPTIM_EMBEDDED_HINTS]',<br />
q'[OPTIMIZER_FEATURES_ENABLE('11.2.0.3')]',<br />
q'[DB_VERSION('11.2.0.3')]',<br />
q'[OPT_PARAM('optimizer_index_cost_adj' 30)]',<br />
q'[OPT_PARAM('optimizer_index_caching' 90)]',<br />
q'[ALL_ROWS]',<br />
q'[OUTLINE_LEAF(@"SEL$1")]',<br />
q'[INDEX_RS_ASC(@"SEL$1" "TES_BILLDETAIL"@"SEL$1" ("TES_BILLDETAIL"."CN"))]',<br />
q'[USE_HASH_AGGREGATION(@"SEL$1")]',<br />
q'[END_OUTLINE_DATA]');<br />
<br />
添加了hint之后启用了CN相关的索引的hint信息如下:<br />
h := SYS.SQLPROF_ATTR(<br />
q'[BEGIN_OUTLINE_DATA]',<br />
q'[IGNORE_OPTIM_EMBEDDED_HINTS]',<br />
q'[OPTIMIZER_FEATURES_ENABLE('11.2.0.3')]',<br />
q'[DB_VERSION('11.2.0.3')]',<br />
q'[OPT_PARAM('optimizer_index_cost_adj' 30)]',<br />
q'[OPT_PARAM('optimizer_index_caching' 90)]',<br />
q'[ALL_ROWS]',<br />
q'[OUTLINE_LEAF(@"SEL$1")]',<br />
q'[INDEX_RS_ASC(@"SEL$1" "TES_BILLDETAIL"@"SEL$1" ("TES_BILLDETAIL"."CN"))]',<br />
q'[USE_HASH_AGGREGATION(@"SEL$1")]',<br />
q'[END_OUTLINE_DATA]');<br />
需要把这部分内容替换原来sqlprofile中的hint部分。<br />
拷贝替换之后,就可以生成sqlprofile了,运行脚本的结果如下:<br />
SQL> DECLARE<br />
2 sql_txt CLOB;<br />
3 h SYS.SQLPROF_ATTR;<br />
4 PROCEDURE wa (p_line IN VARCHAR2) IS<br />
5 BEGIN<br />
6 DBMS_LOB.WRITEAPPEND(sql_txt, LENGTH(p_line), p_line);<br />
7 END wa;<br />
8 BEGIN<br />
9 DBMS_LOB.CREATETEMPORARY(sql_txt, TRUE);<br />
10 DBMS_LOB.OPEN(sql_txt, DBMS_LOB.LOB_READWRITE);<br />
11 -- SQL Text pieces below do not have to be of same length.<br />
12 -- So if you edit SQL Text (i.e. removing temporary Hints),<br />
13 -- there is no need to edit or re-align unmodified pieces.<br />
14 wa(q'[ SELECT ROUND(AVG(SUM(END_TIME-START_TIME)*1440)) AVG FROM TES_B]');<br />
15 wa(q'[ILLDETAIL WHERE END_TIME > TRUNC(SYSDATE - 30) AND CN ]');<br />
16 wa(q'[= :1 GROUP BY TRUNC(END_TIME) ]');<br />
17 DBMS_LOB.CLOSE(sql_txt);<br />
18 h := SYS.SQLPROF_ATTR(<br />
19 q'[BEGIN_OUTLINE_DATA]',<br />
20 q'[IGNORE_OPTIM_EMBEDDED_HINTS]',<br />
21 q'[OPTIMIZER_FEATURES_ENABLE('11.2.0.3')]',<br />
22 q'[DB_VERSION('11.2.0.3')]',<br />
23 q'[OPT_PARAM('optimizer_index_cost_adj' 30)]',<br />
24 q'[OPT_PARAM('optimizer_index_caching' 90)]',<br />
25 q'[ALL_ROWS]',<br />
26 q'[OUTLINE_LEAF(@"SEL$1")]',<br />
27 q'[INDEX_RS_ASC(@"SEL$1" "TES_BILLDETAIL"@"SEL$1" ("TES_BILLDETAIL"."CN"))]',<br />
28 q'[USE_HASH_AGGREGATION(@"SEL$1")]',<br />
29 q'[END_OUTLINE_DATA]');<br />
30 :signature := DBMS_SQLTUNE.SQLTEXT_TO_SIGNATURE(sql_txt);<br />
31 :signaturef := DBMS_SQLTUNE.SQLTEXT_TO_SIGNATURE(sql_txt, TRUE);<br />
32 DBMS_SQLTUNE.IMPORT_SQL_PROFILE (<br />
33 sql_text => sql_txt,<br />
34 profile => h,<br />
35 name => 'coe_8tmf11fvxy09j_2129377450',<br />
36 description => 'coe 8tmf11fvxy09j 2129377450 '||:signature||' '||:signaturef||'',<br />
37 category => 'DEFAULT',<br />
38 validate => TRUE,<br />
39 replace => TRUE,<br />
40 force_match => FALSE /* TRUE:FORCE (match even when different literals in SQL). FALSE:EXACT (similar to CURSOR_SHARING) */ );<br />
41 DBMS_LOB.FREETEMPORARY(sql_txt);<br />
42 END;<br />
43 /<br />
PL/SQL procedure successfully completed.<br />
SQL> WHENEVER SQLERROR CONTINUE<br />
SQL> SET ECHO OFF;<br />
SIGNATURE<br />
---------------------<br />
4228059721940760383<br />
SIGNATUREF<br />
---------------------<br />
4228059721940760383<br />
... manual custom SQL Profile has been created<br />
COE_XFR_SQL_PROFILE_8tmf11fvxy09j_2129377450 completed<br />
sql profile已经启用了,再次查看就可以看到已经生效了。<br />
SQL> SELECT *FROM TABLE(DBMS_XPLAN.DISPLAY);<br />
PLAN_TABLE_OUTPUT<br />
---------------------------------------------------------------------<br />
Plan hash value: 3361841277<br />
------------------------------------------------------------------------------------------------------------------------------<br />
| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time | Pstart| Pstop |<br />
------------------------------------------------------------------------------------------------------------------------------<br />
| 0 | SELECT STATEMENT | | 1 | 32 | 41 (3)| 00:00:01 | | |<br />
| 1 | SORT AGGREGATE | | 1 | 32 | 41 (3)| 00:00:01 | | |<br />
| 2 | HASH GROUP BY | | 1 | 32 | 41 (3)| 00:00:01 | | |<br />
| 3 | PARTITION RANGE ITERATOR | | 1 | 32 | 40 (0)| 00:00:01 | KEY | 970 |<br />
|* 4 | TABLE ACCESS BY LOCAL INDEX ROWID| TES_BILLDETAIL | 1 | 32 | 40 (0)| 00:00:01 | KEY | 970 |<br />
|* 5 | INDEX RANGE SCAN | IND_TES_BILLDETAIL_CN | 42 | | 35 (0)| 00:00:01 | KEY | 970 |<br />
PLAN_TABLE_OUTPUT<br />
Predicate Information (identified by operation id):<br />
---------------------------------------------------<br />
4 - filter("END_TIME">TRUNC(SYSDATE@!-30))<br />
5 - access("CN"=:1)<br />
Note<br />
-----<br />
- SQL profile "coe_8tmf11fvxy09j_2129377450" used for this statement<br />
按照这个思路,又对另外两个sql进行类类似的分析,结果也是使用Hint来调整,使用sql monitor来查看实时的执行计划情况,使用sqlt来修改执行计划。<br />
调优之后的效果还是很明显的。DB time大大降低,逻辑读的效果更加明显。<br />
<img alt="" src="/attachment/201510/5/23718752_1444059250krLv.png" width="700" height="237" /><br />
<img alt="" src="/attachment/201510/6/23718752_1444102664kuQK.png" width="700" height="244" /><br />
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