在使用zabbix的过程中，随着时间的推移，数据库中的历史数据会越来越多，会发现打开页面，查询数据等会变慢。zabbix 自带的 housekeeper会定时清理（默认一小时清理一次）旧的数据。不过在 housekeeper清理过中，会导致数据库负载极具增加。这里介绍另外一种办法，就是对几个历史数据表做分区表（partition table）可按照时间（每天）为维度，把历史数据存到各个分区表中，这样做有以下好处： 1.加快查询速度 2.快速清理过去一时间的历史数据（删除分区表）
配置
这里以zabbix3.4版本为例。使用itnihao的脚本即可（本人做了稍加修改）: zabbix 2.2分区脚本 https://dl.cactifans.com/zabbix/partitiontables.sh zabbix3.4分区脚本 https://dl.cactifans.com/zabbix/partitiontables_gt_zbx34.sh 下载脚本到并赋予可执行权限，默认脚本详情数据保留30天，趋势数据保留12个月，可根据实际情况修改。修改脚本以下地方即可
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daily_history_min=30
monthly_history_min=12
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脚本里默认的zabbix数据库主机为localhost，用户为zabbix，密码为zabbix，如果你的信息不是这个，可以修改脚本里的如下对应信息
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DBHOST=localhost
DBUSER=zabbix
DBPASS=zabbix
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修改好之后，赋予可执行权限。
分区
注意：执行分区表之前建议先停止zabbix server，如果不停止可能导致意外事件，备份好zabbix server数据库。因此建议安装好zabbix server之后就进行分区表。
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./partitiontables_gt_zbx34.sh
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完整执行过程如下
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[root@localhost opt]# ./partitiontables_gt_zbx34.sh
Ready to partition tables.
Ready to update permissions of Zabbix user to create routines
Enter root DB user: root
Enter root password: root
Warning: Using a password on the command line interface can be insecure.
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以上要输入数据库的root用户和root密码
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Do you want to backup the database (recommended) (Y/n): n
Are you certain you have a backup (y/N):
y
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是否需要备份数据库，这里我选n（之前备份过） 是否确认已经备份，y
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Ready to proceed:
Starting yearly partioning at: 2018
and ending at: 2018
With 30 days of daily history
Ready to proceed (Y/n):
y
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分区表基本信息，确认执行
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Altering table: history
Altering table: history_log
Altering table: history_str
Altering table: history_text
Altering table: history_uint
Altering table: trends
Altering table: trends_uint
Creating monthly partitions for table: trends
Creating monthly partitions for table: trends_uint
Creating daily partitions for table: history
Creating daily partitions for table: history_log
Creating daily partitions for table: history_str
Creating daily partitions for table: history_text
Creating daily partitions for table: history_uint
Ready to apply script to database, this may take a while.(Y/n):
y
Warning: Using a password on the command line interface can be insecure.
Altering tables
history
history_log
history_str
history_text
history_uint
trends
trends_uint
trends
trends_uint
history
history_log
history_str
history_text
history_uint
Installing procedures
If Zabbix Version = 2.0
Do you want to update the /etc/zabbix/zabbix_server.conf
to disable housekeeping (Y/n): n
Do you want to update the crontab (Y/n): y
The crontab entry can be either in /etc/cron.daily, or added
to the crontab for root
Do you want to add this to the /etc/cron.daily directory (Y/n): y
Enter email of who should get the daily housekeeping reports: test@126.com
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如果无错误，表示分区表创建成功。
确认
查看/etc/cron.daily/目录下是否有zabbixhousekeeping脚本，内容为
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/usr/local/zabbix/cron.d/housekeeping.sh
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查看housekeeping.sh脚本,内容如下
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MAILTO=test@126.com
tmpfile=/tmp/housekeeping$$
date >$tmpfile
/usr/bin/mysql --skip-column-names -B -h localhost -uzabbix -pzabbix zabbix -e "CALL create_zabbix_partitions();" >>$
tmpfile 2>&1
/usr/bin/mail -s "Zabbix MySql Partition Housekeeping" $MAILTO <$tmpfile
rm -f $tmpfile
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每次分区大约在每晚凌晨3点左右执行，执行成功后，脚本会用mail程序把执行结果发送到上面的邮箱，如果不需要可以删除mail 一行即可。 邮件内容大概如图 
即可看到删除过期的数据分区表，并建立新的分区表。
查看
如果要具体查看分区表的情况，可以进入zabbix数据库，执行以下SQL语句查看histroy表的分区情况
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use zabbix;
select
partition_name part,
partition_expression expr,
partition_description descr,
table_rows
from information_schema.partitions where
table_schema = schema()
and table_name='history';
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结果如下
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+-----------+--------+------------+------------+
| part | expr | descr | table_rows |
+-----------+--------+------------+------------+
| p20180126 | clock | 1516982400 | 0 |
| p20180127 | clock | 1517068800 | 0 |
| p20180128 | clock | 1517155200 | 0 |
| p20180129 | clock | 1517241600 | 0 |
| p20180130 | clock | 1517328000 | 0 |
| p20180131 | clock | 1517414400 | 0 |
| p20180201 | clock | 1517500800 | 0 |
| p20180202 | clock | 1517587200 | 0 |
| p20180203 | clock | 1517673600 | 0 |
| p20180204 | clock | 1517760000 | 0 |
| p20180205 | clock | 1517846400 | 0 |
| p20180206 | clock | 1517932800 | 0 |
| p20180207 | clock | 1518019200 | 0 |
| p20180208 | clock | 1518105600 | 0 |
| p20180209 | clock | 1518192000 | 0 |
| p20180210 | clock | 1518278400 | 0 |
| p20180211 | clock | 1518364800 | 0 |
| p20180212 | clock | 1518451200 | 0 |
| p20180213 | clock | 1518537600 | 0 |
| p20180214 | clock | 1518624000 | 0 |
| p20180215 | clock | 1518710400 | 0 |
| p20180216 | clock | 1518796800 | 0 |
| p20180217 | clock | 1518883200 | 0 |
| p20180218 | clock | 1518969600 | 0 |
| p20180219 | clock | 1519056000 | 0 |
| p20180220 | clock | 1519142400 | 0 |
| p20180221 | clock | 1519228800 | 0 |
| p20180222 | clock | 1519315200 | 0 |
| p20180223 | clock | 1519401600 | 0 |
| p20180224 | clock | 1519488000 | 0 |
| p20180225 | clock | 1519574400 | 204 |
| p20180226 | clock | 1519660800 | 0 |
| p20180227 | clock | 1519747200 | 0 |
| p20180228 | clock | 1519833600 | 0 |
| p20180301 | clock | 1519920000 | 0 |
| p20180302 | clock | 1520006400 | 0 |
| p20180303 | clock | 1520092800 | 0 |
| p20180304 | clock | 1520179200 | 0 |
+-----------+--------+------------+------------+
38 rows in set (0.00 sec)
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可以看到已经按照事件维度分区，和表中数据行数，分区表成功。