Mysql qps 数据统计和分析
1.工作准备
1.1 mysql相关 配置准备
#打开查询日志
Set global general_log = 1;
Set global general_log_file =’/data/mysqldata/data/localhost1.log’
注意：log_output 这个参数需要设置为file，如果为table就不会记录到文件了
#打开满查询日志（默认是开的）
Set gloabl log_slow_queries = 1;
#修改满查询阀值（默认1s改成0.5s）
Set global long_query_time = 0.5;
#打开未使用索引查询记录
Set global log_queries_not_using_indexes = 1;
以上配置是，打开相关日志记录的参数，慢查询，和不适用索引查询的sql
统计QPS
2.1 QPS= select + update+ insert+delete
这里我们只统计数据库每秒执行的select，update，insert和delete数量（增量数据）。
因为数据库里面这些参数对应的值都是当前值， 而不是每秒的增量，所以，我们需要自己写脚本去搜集
Select ------>Com_select
Update ------>Com_update
Insert ------>Com_insert
Delete ------>Com_delete
2.2 去qps增量值保存到文件
这里我们采用mysqladmin -u7roaddba -p -r -i 1 exttended-status来动态增量获取上面对应参数的值。
这里-r 是去增量值，-i 是刷新取值频率，这里是每秒
在mysql服务器，系统层面执行
mysqladmin -u7roaddba -p -r -i 1 ext |awk -F"|" "BEGIN{ count=0; }"'{ if($2 ~ /Variable_name/ && ++count == 1){\
print "---------- MySQL Command Status -- --";\
print "---Time---|select insert update delete";\
}\
else if ($2 ~ /Com_select /){com_select=$3;}\
else if ($2 ~ /Com_insert /){com_insert=$3;}\
else if ($2 ~ /Com_update /){com_update=$3;}\
else if ($2 ~ /Com_delete /){com_delete=$3;}\
else if ($2 ~ /Uptime / && count = 2){\
printf(" %s ",strftime("%Y-%m-%d %H:%M:%S"));\
printf(",%6d ,%6d ,%6d ,%6d\n",com_select,com_insert,com_update,com_delete);\
}}' |tee -a a.csv
这里会吧上面的几个指标值，按照‘，’ 分割来写入文件a.csv中保存下来
看下a.csv中的内容
3,收集慢查询和general log日志文件
3.1 将服务器的general log日志和慢查询日志下载到本地保存
如：
这里的慢查询日志名是 slow-query.log
General log 日志名是 localhost.log
差不多5个小时，可能general log的日志文件就会填充到1G 大小以上
4，处理和分析阶段
4.1 将qps 抓取的数据，导入到本地空闲的mysql服务器中
这里我们导入到test库中
创建表
Create table qps(ctime datetime,
svalue int(11),
uvalue int(11),
ivalue int(11),
dvalue int(11));
将搜集qps的文件a.csv 复制到mysql的数据目录下的test目录中
Mysql> load data infile ‘a.csv’ into qps fields terminated by ‘,’;
4.2 处理qps 表数据
这里我们通过select查询，获取分钟的数据量和分钟内平均每秒的数据量，然后将取得的数据导出到外部csv格式文件：
select ctime as ‘统计时间’,
sum(svalue) as ‘select/分钟’,
sum(svalue) div 60 as ‘select/秒’,
sum(ivalue) as ‘insrt/分钟’,
sum(ivalue) div 60 as ‘insert/秒’,
sum(uvalue) as ‘update/分钟’,
sum(uvalue) div 60 as ‘update/秒’,
sum(dvalue) as ‘delete/分钟’,
sum(dvalue) div 60 as ‘delete/秒’,
(sum(svalue)+sum(ivalue)+sum(uvalue)+sum(dvalue)) as ‘总量/分钟’,
(sum(svalue)+sum(ivalue)+sum(uvalue)+sum(dvalue)) div 60 as ‘总量/秒’
from fengcelog group by date_format(ctime,'%Y-%m-%d %H:%i:00')
into outfile 'db_log_hgtest_0001_20140815.csv' fields terminated by ','
最后到处csv文件，在windows中 excel打开，如下
这个就很形象的展示了，统计时间，各种指标的值了
4.3 general log 日志处理分析
这里使用mysqlsla 去分析genernal 日志文件
a.安装mysqlsla
.下载 mysqlsla
[root@localhost tmp]# wget http://hackmysql.com/scripts/mysqlsla-2.03.tar.gz
--19:45:45-- http://hackmysql.com/scripts/mysqlsla-2.03.tar.gz
Resolving hackmysql.com... 64.13.232.157
Connecting to hackmysql.com|64.13.232.157|:80... connected.
HTTP request sent, awaiting response... 200 OK
Length: 33674 (33K) [application/x-tar]
Saving to: `mysqlsla-2.03.tar.gz.2'
100%[===========================================================================================>] 33,674 50.2K/s in 0.7s
19:45:47 (50.2 KB/s) - `mysqlsla-2.03.tar.gz.2' saved [33674/33674]
b..解压
[root@localhost tmp]# tar -zxvf mysqlsla-2.03.tar.gz
mysqlsla-2.03/
mysqlsla-2.03/Changes
mysqlsla-2.03/INSTALL
mysqlsla-2.03/README
mysqlsla-2.03/Makefile.PL
mysqlsla-2.03/bin/
mysqlsla-2.03/bin/mysqlsla
mysqlsla-2.03/META.yml
mysqlsla-2.03/lib/
mysqlsla-2.03/lib/mysqlsla.pm
mysqlsla-2.03/MANIFEST
[root@localhost tmp]# cd mysqlsla-2.03
[root@localhost mysqlsla-2.03]# ls
bin Changes INSTALL lib Makefile.PL MANIFEST META.yml README
c.执行perl脚本检查包依赖关系
[root@localhost mysqlsla-2.03]# perl Makefile.PL
Checking if your kit is complete...
Looks good
Writing Makefile for mysqlsla
d.安装
[root@localhost mysqlsla-2.03]# make && make install;
cp lib/mysqlsla.pm blib/lib/mysqlsla.pm
cp bin/mysqlsla blib/script/mysqlsla
/usr/bin/perl "-MExtUtils::MY" -e "MY->fixin(shift)" blib/script/mysqlsla
Manifying blib/man3/mysqlsla.3pm
Installing /usr/lib/perl5/site_perl/5.8.8/mysqlsla.pm
Installing /usr/share/man/man3/mysqlsla.3pm
Installing /usr/bin/mysqlsla
Writing /usr/lib/perl5/site_perl/5.8.8/i386-linux-thread-multi/auto/mysqlsla/.packlist
Appending installation info to /usr/lib/perl5/5.8.8/i386-linux-thread-multi/perllocal.pod
[root@localhost mysqlsla-2.03]#
使用mysqlsla 分析general log
mysqlsla -lt general --top 200 general_log.txt > top_200.txt
这里-lt 是指定后面要分析的日志类型，这里我们使用-lt general
展示下分析后的top_200.txt文件内容
Report for general logs: game_gerernal.log
1.56M queries total, 4.17k unique
Sorted by 'c_sum'
____________________________001 ___
Count : 735.58k (47.22%)
Connection ID : 2528823
Database :
Users :
[email protected] : 87.93% (646784) of query, 86.83% (1352745) of all users
@ : 12.07% (88798) of query, 13.07% (203592) of all users
Query abstract:
SET autocommit=N
Query sample:
SET autocommit=1
___________________________________________________ 002 ___
Count : 49.78k (3.20%)#这里统计sql执行了4.978万次
Connection ID : 2528823 #这里是connect id，从show processlist中可以找到
[email protected] : 84.11% (41868) of query, 86.83% (1352745) of all users
@ : 15.89% (7910) of query, 13.07% (203592) of all users
Query abstract:
SELECT * FROM t_u_newbie WHERE username='S' AND site='S' #执行的语句（通用写法）
Query sample:
select * from t_u_newbie where `username`='449' and `site`='hgtest_0001'#具体sql示例
_____________________________________________ 003 ___
Count : 43.84k (2.81%)
Connection ID : 2528826
[email protected] : 99.46% (43602) of query, 86.83% (1352745) of all users
@ : 0.54% (238) of query, 13.07% (203592) of all users
Query abstract:
SELECT * FROM t_u_item WHERE userid = N AND (bagtype = N OR bagtype = N) AND isexist = N AND place != -N
Query sample:
select * from t_u_item where userId = 1002011 and (bagType = 2 or bagType = 18) and IsExist = 1 and place != -1
上面 简单的展示了general log中记录执行次数最多的3个sql
4.4 ，慢查询日志分析
同样适用mysqlsla可以分析慢查询中出现频率最高的sql，
当然也要注意关注那些没有适应索引查询的sql，（有些很明显字段是有加索引的，但是还出现在慢查询日志中）
root]#mysqlsla -lt slow -top 200 query_slow.log > slow.log
1.04k queries total, 7 unique
Sorted by 't_sum'
Grand Totals: Time 0 s, Lock 0 s, Rows sent 18, Rows Examined 38.52
_____________________________________ 001 ___
Count : 865 (82.93%)
Time : 213.588 ms total, 247 avg, 188 to 388 max (57.17%)
95% of Time : 197.697 ms total, 241 avg, 188 to 351 max
Lock Time (s) : 79.913 ms total, 92 avg, 59 to 162 max (57.97%)
95% of Lock : 73.55 ms total, 90 avg, 59 to 134 max
Rows sent : 0 avg, 0 to 0 max (0.00%)
Rows examined : 33 avg, 33 to 33 max (74.10%)
Database : db_game_hgtest_0002
Users :
g_hgtest_0002@ 10.34.148.18 : 100.00% (865) of query, 99.81% (1041) of all users
Query abstract:
SET timestamp=N; UPDATE t_p_macrodrop SET currentcount = maxcount, resetdate = now() WHERE timestampdiff(hour,resetdate,now()) >= time;
Query sample:
SET timestamp=1408031756;
update t_p_macrodrop set `currentCount` = `maxCount`, `resetDate` = now() where TIMESTAMPDIFF(HOUR,resetDate,now()) >= time;
_______________________________ 002 ___
Count : 173 (16.59%)
Time : 153.938 ms total, 890 avg, 826 to 989 max (41.20%)
95% of Time : 145.608 ms total, 888 avg, 826 to 906 max #执行消耗的总时间，平均时间，和最慢的一条执行时间
Lock Time (s) : 57.457 ms total, 332 avg, 310 to 365 max (41.68%) #锁时间
95% of Lock : 54.246 ms total, 331 avg, 310 to 349 max
Rows sent : 0 avg, 0 to 0 max (0.00%)
Rows examined : 41 avg, 41 to 41 max (18.41%)
Database :
Users :
g_hgtest_0002@ 10.34.148.18 : 100.00% (173) of query, 99.81% (1041) of all users
Query abstract:
SET timestamp=N; SELECT *, t_mv.membercount membercount_ FROM t_u_guild g, t_u_combine_load_guild c, (SELECT COUNT( mv.guildid) membercount,mv.guildid FROM v_guild_member_list mv GROUP BY mv.guildid ) t_mv WHERE g.guildid = t_mv.guildid AND g.guildid=c.guildid AND g.state !=N AND c.states = N LIMIT N;
Query sample:
SET timestamp=1408031999;
SELECT *, t_mv.memberCount memberCount_ FROM t_u_guild g, t_u_combine_load_guild c, (SELECT COUNT( mv.guildID) memberCount,mv.guildID FROM v_guild_member_list mv GROUP BY mv.guildID ) t_mv WHERE g.guildID = t_mv.guildID AND g.guildID=c.guildID AND g.state !=3 AND c.states = 1 limit 100;
4，查找数据库中最大的表
4.1 根据最多数据的表，查看刚刚分析的general log中的某个insert 次数是否相对应（insert 次数多导致表大，这里insert次数并不是跟表数据量相等）
扫描大表：
Select TABLE_SCHEMA,TABLE_NAME,TABLE_ROWS from information_schema.tables where TABLE_SCHEMA=’TABLENAME’ order by TABLE_ROWS desc limit 10;
Qps分析:
1，通过mysqladmin 每秒获取数据库的com_select com_update等值
2，打开genenral log， 保存下来，使用mysqlsla统计
3，打开慢查询日志，设定满查询时间为1秒， 分析慢查询
4，统计数据库中每个表的行数
5，跟踪一个玩家的所有sql
6，压测期间，让开发记住当天的操作