是否有像Microsoft的“SQL Server Profiler”这样的工具用于MySQ

但是,所有希望都不会丢失.

自2007年以来,Percona为Developer和DBA想要的所有东西提供了一些绝对出色的工具,包括Query Profiling.

Percona的第一套工具,即MAATKIT,为严肃的MySQL用户创造了一个领域. It features many things,如：

>查询分析
>复制心跳
>复制从属管理
>表校验和和同步

Percona最近将MAATKIT分解为更新的工具集,known today as Percona Toolkit.这些工具通过扩展严肃的MySQL用户的活动领域而在MAATKIT停止的地方获得了包括以下内容：

>外键错误检查
>在线模式更改
>视觉解释计划
>等等……

回到原始问题,用于查询分析的工具是

> pt-query-advisor
> pt-query-digest
> mk-query-profiler(适用于MAATKIT用户)
> mk-query-digest(适用于MAATKIT用户)

以下是使用以下工具之一可以获得的丰富信息的示例：

我帮助客户实现mk-query-digest每20分钟报告20个性能最差的查询. I got the idea from this YouTube video.客户端会将任何错误查询的输出移动到memcached,从而降低了查询对数据库造成损失的可能性.

这是我调用mk-query-digest的脚本(仅检查进程列表)

#!/bin/sh

RUNFILE=/tmp/QueriesAreBeingDigested.txt
if [ -f ${RUNFILE} ] ; then exit ; fi

MKDQ=/usr/local/sbin/mk-query-digest
RUNTIME=${1}
COPIES_TO_KEEP=${2}
DBVIP=${3}

WHICH=/usr/bin/which
DATE=`${WHICH} date`
ECHO=`${WHICH} echo`
HEAD=`${WHICH} head`
TAIL=`${WHICH} tail`
AWK=`${WHICH} awk`
SED=`${WHICH} sed`
CAT=`${WHICH} cat`
WC=`${WHICH} wc`
RM=`${WHICH} rm | ${TAIL} -1 | ${AWK} '{print $1}'`
LS=`${WHICH} ls | ${TAIL} -1 | ${AWK} '{print $1}'`

HAS_THE_DBVIP=`/sbin/ip addr show | grep "scope global secondary" | grep -c "${DBVIP}"`
if [ ${HAS_THE_DBVIP} -eq 1 ] ; then exit ; fi

DT=`${DATE} +"%Y%m%d_%H%M%S"`
UNIQUETAG=`${ECHO} ${SSH_CLIENT}_${SSH_CONNECTION}_${DT} | ${SED} 's/./ /g' | ${SED} 's/ //g'`

cd /root/QueryDigest
OUTFILE=QP_${DT}.txt
HOSTADDR=${DBVIP}
${MKDQ} --processlist h=${HOSTADDR},u=queryprofiler,p=queryprofiler --run-time=${RUNTIME} > ${OUTFILE}

#
# Rotate out Old Copies
#

QPFILES=QPFiles.txt
QPFILES2ZAP=QPFiles2Zap.txt
${LS} QP_[0-9][0-9][0-9][0-9][0-9][0-9][0-9][0-9]_[0-9][0-9][0-9][0-9][0-9][0-9].txt > ${QPFILES}

LINECOUNT=`${WC} -l < ${QPFILES}`
if [ ${LINECOUNT} -gt ${COPIES_TO_KEEP} ]
then
        (( DIFF = LINECOUNT - COPIES_TO_KEEP ))
        ${HEAD} -${DIFF} < ${QPFILES} > ${QPFILES2ZAP}
        for QPFILETOZAP in `${CAT} ${QPFILES2ZAP}`
        do
                ${RM} ${QPFILETOZAP}
        done
fi

rm -f ${QPFILES2ZAP}
rm -f ${QPFILES}
rm -f ${RUNFILE}

这是我使用mk-query-digest连接到mysql的用户

GRANT PROCESS ON *.* TO 'queryprofiler'@'%' IDENTIFIED BY 'queryprofiler';

这是我每隔20分钟(不到10秒)运行的crontab,保留最后144份(这是48小时的分析)

*/20 * * * * /root/QueryDigest/ExecQueryDigest.sh 1190s 144 10.1.1.8

令人难以置信的部分：mk-query-digest的输出

这是2011-12-28 11:20:00运行1190秒(20分钟少于10秒)的个人资料

最后22行

# Rank Query ID           Response time    Calls   R/Call     Item
# ==== ================== ================ ======= ========== ====
#    1 0x5E994008E9543B29    40.3255 11.2%     101   0.399263 SELECT schedule_occurrence schedule_eventschedule schedule_event schedule_eventtype schedule_event schedule_eventtype schedule_occurrence.start
#    2 0x392F6DA628C7FEBD    33.9181  9.4%      17   1.995184 SELECT mt_entry mt_objecttag
#    3 0x6C6318E56E149036    26.4695  7.3%     102   0.259505 SELECT schedule_occurrence schedule_eventschedule schedule_event schedule_eventtype schedule_event schedule_eventtype schedule_occurrence.start
#    4 0x00F66961DAE6FFB2    25.5472  7.1%      55   0.464495 SELECT mt_entry mt_placement mt_category
#    5 0x99E13015BFF1E75E    22.3618  6.2%     199   0.112371 SELECT mt_entry mt_objecttag
#    6 0x84DD09F0FC444677    22.3516  6.2%      39   0.573118 SELECT mt_entry
#    7 0x440EBDBCEDB88725    21.1817  5.9%      36   0.588380 SELECT mt_entry
#    8 0x8D258C584B858811    17.2402  4.8%      37   0.465951 SELECT mt_entry mt_placement mt_category
#    9 0x4E2CB0F4CAFD1400    16.9768  4.7%      40   0.424419 SELECT mt_entry mt_placement mt_category
#   10 0x377E0D0898266FDD    16.6979  4.6%     150   0.111319 SELECT polls_pollquestion mt_category
#   11 0x3B9686D98BB8E054    16.2089  4.5%      32   0.506529 SELECT mt_entry mt_objecttag mt_tag
#   12 0x97F670B604A85608    15.6158  4.3%      34   0.459287 SELECT mt_entry mt_placement mt_category
#   13 0x3F5557DA231225EB    14.4309  4.0%      36   0.400859 SELECT mt_entry mt_placement mt_category
#   14 0x191D660A10738896    13.1220  3.6%      31   0.423290 SELECT mt_entry mt_placement mt_category
#   15 0xF88F7421DD88036D    12.1261  3.4%      61   0.198788 SELECT mt_entry mt_blog mt_objecttag mt_tag mt_author
#   16 0xA909BF76E7051792    10.3971  2.9%      53   0.196172 SELECT mt_entry mt_objecttag mt_tag
#   17 0x3D42D07A335ED983     9.1424  2.5%      20   0.457121 SELECT mt_entry mt_placement mt_category
#   18 0x59F43B57DD43F2BD     9.0533  2.5%      21   0.431111 SELECT mt_entry mt_placement mt_category
#   19 0x7961BD4C76277EB7     8.5564  2.4%      47   0.182052 INSERT UNION UPDATE UNION mt_session
#   20 0x173EB4903F3B6DAC     8.5394  2.4%      22   0.388153 SELECT mt_entry mt_placement mt_category

请注意,这是基于查询响应时间除以调用查询的次数的20个性能最差的查询的列表.

查看查询ID#1,即0x5E994008E9543B29,我们在输出文件中找到该查询ID,这里是该特定查询的报告：

# Query 1: 0.09 QPS,0.03x concurrency,ID 0x5E994008E9543B29 at byte 0 __
# This item is included in the report because it matches --limit.
#              pct   total     min     max     avg     95%  stddev  median
# Count          4     101
# Exec time      7     40s   303ms      1s   399ms   992ms   198ms   293ms
# Lock time      0       0       0       0       0       0       0       0
# Users                  1      mt
# Hosts                101 10.64.95.73:33750 (1),10.64.95.73:34452 (1),10.64.95.73:38440 (1)... 97 more
# Databases              1     mt1
# Time range 1325089201 to 1325090385
# bytes          0 273.60k   2.71k   2.71k   2.71k   2.62k       0   2.62k
# id             4 765.11M   7.57M   7.58M   7.58M   7.29M    0.12   7.29M
# Query_time distribution
#   1us
#  10us
# 100us
#   1ms
#  10ms
# 100ms  ################################################################
#    1s  ######
#  10s+
# Tables
#    SHOW TABLE STATUS FROM `mt1` LIKE 'schedule_occurrence'G
#    SHOW CREATE TABLE `mt1`.`schedule_occurrence`G
#    SHOW TABLE STATUS FROM `mt1` LIKE 'schedule_eventschedule'G
#    SHOW CREATE TABLE `mt1`.`schedule_eventschedule`G
#    SHOW TABLE STATUS FROM `mt1` LIKE 'schedule_event'G
#    SHOW CREATE TABLE `mt1`.`schedule_event`G
#    SHOW TABLE STATUS FROM `mt1` LIKE 'schedule_eventtype'G
#    SHOW CREATE TABLE `mt1`.`schedule_eventtype`G
#    SHOW TABLE STATUS FROM `schedule_occurrence` LIKE 'start'G
#    SHOW CREATE TABLE `schedule_occurrence`.`start`G
# EXPLAIN
SELECT `schedule_occurrence`.`id`,`schedule_occurrence`.`schedule_id`,`schedule_occurrence`.`event_id`,`schedule_occurrence`.`start`,`schedule_occurrence`.`end`,`schedule_occurrence`.`cancelled`,`schedule_occurrence`.`original_start`,`schedule_occurrence`.`original_end`,`schedule_occurrence`.`all_day`,`schedule_occurrence`.`ongoing`,`schedule_occurrence`.`featured`,`schedule_eventschedule`.`id`,`schedule_eventschedule`.`event_id`,`schedule_eventschedule`.`start`,`schedule_eventschedule`.`end`,`schedule_eventschedule`.`all_day`,`schedule_eventschedule`.`ongoing`,`schedule_eventschedule`.`min_date_calculated`,`schedule_eventschedule`.`max_date_calculated`,`schedule_eventschedule`.`rule`,`schedule_eventschedule`.`end_recurring_period`,`schedule_eventschedule`.`textual_description`,`schedule_event`.`id`,`schedule_event`.`title`,`schedule_event`.`slug`,`schedule_event`.`description`,`schedule_event`.`host_id`,`schedule_event`.`cost`,`schedule_event`.`age_restrictions`,`schedule_event`.`more_info`,`schedule_event`.`photo_id`,`schedule_event`.`contact_email`,`schedule_event`.`event_type_id`,`schedule_event`.`featured`,`schedule_event`.`staff_pick`,`schedule_event`.`futuremost`,`schedule_event`.`creator_id`,`schedule_event`.`created_on`,`schedule_event`.`allow_comments`,`schedule_event`.`mt_entry`,`schedule_eventtype`.`id`,`schedule_eventtype`.`parent_id`,`schedule_eventtype`.`name`,`schedule_eventtype`.`slug`,`schedule_eventtype`.`lft`,`schedule_eventtype`.`rght`,`schedule_eventtype`.`tree_id`,`schedule_eventtype`.`level`,T5.`id`,T5.`title`,T5.`slug`,T5.`description`,T5.`host_id`,T5.`cost`,T5.`age_restrictions`,T5.`more_info`,T5.`photo_id`,T5.`contact_email`,T5.`event_type_id`,T5.`featured`,T5.`staff_pick`,T5.`futuremost`,T5.`creator_id`,T5.`created_on`,T5.`allow_comments`,T5.`mt_entry`,T6.`id`,T6.`parent_id`,T6.`name`,T6.`slug`,T6.`lft`,T6.`rght`,T6.`tree_id`,T6.`level` FROM `schedule_occurrence` INNER JOIN `schedule_eventschedule` ON (`schedule_occurrence`.`schedule_id` = `schedule_eventschedule`.`id`) INNER JOIN `schedule_event` ON (`schedule_eventschedule`.`event_id` = `schedule_event`.`id`) INNER JOIN `schedule_eventtype` ON (`schedule_event`.`event_type_id` = `schedule_eventtype`.`id`) INNER JOIN `schedule_event` T5 ON (`schedule_occurrence`.`event_id` = T5.`id`) INNER JOIN `schedule_eventtype` T6 ON (T5.`event_type_id` = T6.`id`) WHERE (EXTRACT(MONTH FROM `schedule_occurrence`.`start`) = 8 AND EXTRACT(DAY FROM `schedule_occurrence`.`start`) = 6 AND `schedule_occurrence`.`start` BETWEEN '2011-01-01 00:00:00' and '2011-12-31 23:59:59.99') ORDER BY `schedule_occurrence`.`ongoing` ASC,`schedule_occurrence`.`all_day` DESC,`schedule_occurrence`.`start` ASCG

尽管直方图是基于文本的,但它可以准确显示查询的整体性能,有时会超过1秒,大部分时间在0.01到0.1秒之间.从这里开始,可以通过重构查询,将查询结果放入memcached,添加缺失或覆盖索引等来进行性能调整.

结论

恕我直言如果Percona曾将探查器工具放入Windows GUI,它很容易与微软的SQL Server Profiler相媲美.

防守休息!!!