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MySQL大表深度优化实战策略

2024-11-29 20:02

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XX实例的慢查询数量最多(执行时间超过1s的sql会被记录),XX应用那方每天晚上在做删除一个月前数据的任务。

分析

使用pt-query-digest工具分析最近一周的mysql-slow.log。

pt-query-digest --since=148h mysql-slow.log | less

结果第一部分

最近一个星期内,总共记录的慢查询执行花费时间为25403s,最大的慢sql执行时间为266s,平均每个慢sql执行时间5s,平均扫描的行数为1766万

结果第二部分

select arrival_record操作记录的慢查询数量最多有4万多次,平均响应时间为4s,delete arrival_record记录了6次,平均响应时间258s。

select xxx_record语句

select arrival_record 慢查询语句都类似于如下所示,where语句中的参数字段是一样的,传入的参数值不一样 select count(*) from arrival_record where product_id=26 and receive_time between '2019-03-25 14:00:00' and '2019-03-25 15:00:00' and receive_spend_ms>=0\G

select arrival_record 语句在mysql中最多扫描的行数为5600万、平均扫描的行数为172万,推断由于扫描的行数多导致的执行时间长

查看执行计划

explain select count(*) from arrival_record where product_id=26 and receive_time between '2019-03-25 14:00:00' and '2019-03-25 15:00:00' and receive_spend_ms>=0\G;
*************************** 1. row ***************************
id: 1
select_type: SIMPLE
table: arrival_record
partitions: NULL
type: ref
possible_keys: IXFK_arrival_record
key: IXFK_arrival_record
key_len: 8
ref: const
rows: 32261320
filtered: 3.70
Extra: Using index condition; Using where
1 row in set, 1 warning (0.00 sec)

用到了索引IXFK_arrival_record,但预计扫描的行数很多有3000多w行。

show index from arrival_record;
+----------------+------------+---------------------+--------------+--------------+-----------+-------------+----------+--------+------+------------+---------+---------------+
| Table | Non_unique | Key_name | Seq_in_index | Column_name | Collation | Cardinality | Sub_part | Packed | Null | Index_type | Comment | Index_comment |
+----------------+------------+---------------------+--------------+--------------+-----------+-------------+----------+--------+------+------------+---------+---------------+
| arrival_record | 0 | PRIMARY | 1 | id | A | 107990720 | NULL | NULL | | BTREE | | |
| arrival_record | 1 | IXFK_arrival_record | 1 | product_id | A | 1344 | NULL | NULL | | BTREE | | |
| arrival_record | 1 | IXFK_arrival_record | 2 | station_no | A | 22161 | NULL | NULL | YES | BTREE | | |
| arrival_record | 1 | IXFK_arrival_record | 3 | sequence | A | 77233384 | NULL | NULL | | BTREE | | |
| arrival_record | 1 | IXFK_arrival_record | 4 | receive_time | A | 65854652 | NULL | NULL | YES | BTREE | | |
| arrival_record | 1 | IXFK_arrival_record | 5 | arrival_time | A | 73861904 | NULL | NULL | YES | BTREE | | |
+----------------+------------+---------------------+--------------+--------------+-----------+-------------+----------+--------+------+------------+---------+---------------+
show create table arrival_record;
..........
arrival_spend_ms bigint(20) DEFAULT NULL,
total_spend_ms bigint(20) DEFAULT NULL,
PRIMARY KEY (id),
KEY IXFK_arrival_record (product_id,station_no,sequence,receive_time,arrival_time) USING BTREE,
CONSTRAINT FK_arrival_record_product FOREIGN KEY (product_id) REFERENCES product (id) ON DELETE NO ACTION ON UPDATE NO ACTION
) ENGINE=InnoDB AUTO_INCREMENT=614538979 DEFAULT CHARSET=utf8 COLLATE=utf8_bin |

现在已经知道了在慢查询中记录的select arrival_record where语句传入的参数字段有 product_id,receive_time,receive_spend_ms,还想知道对该表的访问有没有通过其它字段来过滤了?

神器tcpdump出场的时候到了

使用tcpdump抓包一段时间对该表的select语句。

tcpdump -i bond0 -s 0 -l -w - dst port 3316 | strings | grep select | egrep -i 'arrival_record' >/tmp/select_arri.log

获取select 语句中from 后面的where条件语句。

IFS_OLD=$IFS
IFS=$'\n'
for i in `cat /tmp/select_arri.log `;do echo ${i#*'from'}; done | less
IFS=$IFS_OLD
arrival_record arrivalrec0_ where arrivalrec0_.sequence='2019-03-27 08:40' and arrivalrec0_.product_id=17 and arrivalrec0_.station_no='56742'
arrival_record arrivalrec0_ where arrivalrec0_.sequence='2019-03-27 08:40' and arrivalrec0_.product_id=22 and arrivalrec0_.station_no='S7100'
arrival_record arrivalrec0_ where arrivalrec0_.sequence='2019-03-27 08:40' and arrivalrec0_.product_id=24 and arrivalrec0_.station_no='V4631'
arrival_record arrivalrec0_ where arrivalrec0_.sequence='2019-03-27 08:40' and arrivalrec0_.product_id=22 and arrivalrec0_.station_no='S9466'
arrival_record arrivalrec0_ where arrivalrec0_.sequence='2019-03-27 08:40' and arrivalrec0_.product_id=24 and arrivalrec0_.station_no='V4205'
arrival_record arrivalrec0_ where arrivalrec0_.sequence='2019-03-27 08:40' and arrivalrec0_.product_id=24 and arrivalrec0_.station_no='V4105'
arrival_record arrivalrec0_ where arrivalrec0_.sequence='2019-03-27 08:40' and arrivalrec0_.product_id=24 and arrivalrec0_.station_no='V4506'
arrival_record arrivalrec0_ where arrivalrec0_.sequence='2019-03-27 08:40' and arrivalrec0_.product_id=24 and arrivalrec0_.station_no='V4617'
arrival_record arrivalrec0_ where arrivalrec0_.sequence='2019-03-27 08:40' and arrivalrec0_.product_id=22 and arrivalrec0_.station_no='S8356'
arrival_record arrivalrec0_ where arrivalrec0_.sequence='2019-03-27 08:40' and arrivalrec0_.product_id=22 and arrivalrec0_.station_no='S8356'
select 该表 where条件中有product_id,station_no,sequence字段,可以使用到复合索引IXFK_arrival_record的前三个字段

综上所示,优化方法为,删除复合索引IXFK_arrival_record,建立复合索引idx_sequence_station_no_product_id,并建立单独索引indx_receive_time。

delete xxx_record语句

该delete操作平均扫描行数为1.1亿行,平均执行时间是262s。

delete语句如下所示,每次记录的慢查询传入的参数值不一样。

delete from arrival_record where receive_time < STR_TO_DATE('2019-02-23', '%Y-%m-%d')\G

执行计划

explain select * from arrival_record where receive_time < STR_TO_DATE('2019-02-23', '%Y-%m-%d')\G
*************************** 1. row ***************************
id: 1
select_type: SIMPLE
table: arrival_record
partitions: NULL
type: ALL
possible_keys: NULL
key: NULL
key_len: NULL
ref: NULL
rows: 109501508
filtered: 33.33
Extra: Using where
1 row in set, 1 warning (0.00 sec)

该delete语句没有使用索引(没有合适的索引可用),走的全表扫描,导致执行时间长

优化方法也是 建立单独索引indx_receive_time(receive_time)

测试

拷贝arrival_record表到测试实例上进行删除重新索引操作XX实例arrival_record表信息

du -sh /datas/mysql/data/3316/cq_new_cimiss/arrival_record*
12K /datas/mysql/data/3316/cq_new_cimiss/arrival_record.frm
48G /datas/mysql/data/3316/cq_new_cimiss/arrival_record.ibd
select count() from cq_new_cimiss.arrival_record;
+-----------+
| count() |
+-----------+
| 112294946 |
+-----------+
1亿多记录数

SELECT
table_name,
CONCAT(FORMAT(SUM(data_length) / 1024 / 1024,2),'M') AS dbdata_size,
CONCAT(FORMAT(SUM(index_length) / 1024 / 1024,2),'M') AS dbindex_size,
CONCAT(FORMAT(SUM(data_length + index_length) / 1024 / 1024 / 1024,2),'G') AS table_size(G),
AVG_ROW_LENGTH,table_rows,update_time
FROM
information_schema.tables
WHERE table_schema = 'cq_new_cimiss' and table_name='arrival_record';
+----------------+-------------+--------------+------------+----------------+------------+---------------------+
| table_name | dbdata_size | dbindex_size | table_size(G) | AVG_ROW_LENGTH | table_rows | update_time |
+----------------+-------------+--------------+------------+----------------+------------+---------------------+
| arrival_record | 18,268.02M | 13,868.05M | 31.38G | 175 | 109155053 | 2019-03-26 12:40:17 |
+----------------+-------------+--------------+------------+----------------+------------+---------------------+

磁盘占用空间48G,mysql中该表大小为31G,存在17G左右的碎片,大多由于删除操作造成的(记录被删除了,空间没有回收)

备份还原该表到新的实例中,删除原来的复合索引,重新添加索引进行测试

mydumper并行压缩备份

user=root
passwd=xxxx
socket=/datas/mysql/data/3316/mysqld.sock
db=cq_new_cimiss
table_name=arrival_record
backupdir=/datas/dump_$table_name
mkdir -p $backupdir
  nohup echo `date +%T` && mydumper -u $user -p $passwd -S $socket -B $db -c -T $table_name -o $backupdir -t 32 -r 2000000 && echo `date +%T` &

并行压缩备份所花时间(52s)和占用空间(1.2G,实际该表占用磁盘空间为48G,mydumper并行压缩备份压缩比相当高!)

Started dump at: 2019-03-26 12:46:04
........

Finished dump at: 2019-03-26 12:46:56

du -sh   /datas/dump_arrival_record/
1.2G  /datas/dump_arrival_record/

拷贝dump数据到测试节点

scp -rp /datas/dump_arrival_record root@10.230.124.19:/datas

多线程导入数据

time myloader -u root -S /datas/mysql/data/3308/mysqld.sock -P 3308 -p root -B test -d /datas/dump_arrival_record -t 32

real 126m42.885s user 1m4.543s sys 0m4.267s

逻辑导入该表后磁盘占用空间

du -h -d 1 /datas/mysql/data/3308/test/arrival_record.*
12K /datas/mysql/data/3308/test/arrival_record.frm
30G /datas/mysql/data/3308/test/arrival_record.ibd
没有碎片,和mysql的该表的大小一致
cp -rp /datas/mysql/data/3308 /datas

分别使用online DDL和 pt-osc工具来做删除重建索引操作 先删除外键,不删除外键,无法删除复合索引,外键列属于复合索引中第一列

nohup bash /tmp/ddl_index.sh &
2019-04-04-10:41:39 begin stop mysqld_3308
2019-04-04-10:41:41 begin rm -rf datadir and cp -rp datadir_bak
2019-04-04-10:46:53 start mysqld_3308
2019-04-04-10:46:59 online ddl begin
2019-04-04-11:20:34 onlie ddl stop
2019-04-04-11:20:34 begin stop mysqld_3308
2019-04-04-11:20:36 begin rm -rf datadir and cp -rp datadir_bak
2019-04-04-11:22:48 start mysqld_3308
2019-04-04-11:22:53 pt-osc begin
2019-04-04-12:19:15 pt-osc stop
online ddl 花费时间为34 分钟,pt-osc花费时间为57 分钟,使用onlne ddl时间约为pt-osc工具时间的一半

*做DDL 参考 *

实施

由于是一主一从实例,应用是连接的vip,删除重建索引采用online ddl来做。停止主从复制后,先在从实例上做(不记录binlog),主从切换,再在新切换的从实例上做(不记录binlog)

function red_echo () {

        local what="$*"
        echo -e "$(date +%F-%T)  ${what}"
}

function check_las_comm(){
    if [ "$1" != "0" ];then
        red_echo "$2"
        echo "exit 1"
        exit 1
    fi
}

red_echo "stop slave"
mysql -uroot -p$passwd --socket=/datas/mysql/data/${port}/mysqld.sock -e"stop slave"
check_las_comm "$?" "stop slave failed"

red_echo "online ddl begin"
 mysql -uroot -p$passwd --socket=/datas/mysql/data/${port}/mysqld.sock -e"set sql_log_bin=0;select now() as  ddl_start;ALTER TABLE $db_.\`${table_name}\` DROP FOREIGN KEY FK_arrival_record_product,drop index IXFK_arrival_record,add index idx_product_id_sequence_station_no(product_id,sequence,station_no),add index idx_receive_time(receive_time);select now() as ddl_stop" >>${log_file} 2>& 1
 red_echo "onlie ddl stop"
 red_echo "add foreign key"
 mysql -uroot -p$passwd --socket=/datas/mysql/data/${port}/mysqld.sock -e"set sql_log_bin=0;ALTER TABLE $db_.${table_name} ADD CONSTRAINT _FK_${table_name}_product FOREIGN KEY (product_id) REFERENCES cq_new_cimiss.product (id) ON DELETE NO ACTION ON UPDATE NO ACTION;" >>${log_file} 2>& 1
 check_las_comm "$?" "add foreign key error"
 red_echo "add foreign key stop"

red_echo "start slave"
mysql -uroot -p$passwd --socket=/datas/mysql/data/${port}/mysqld.sock -e"start slave"
check_las_comm "$?" "start slave failed"

*执行时间 *

2019-04-08-11:17:36 stop slave mysql: [Warning] Using a password on the command line interface can be insecure. ddl_start 2019-04-08 11:17:36ddl_stop 2019-04-08 11:45:132019-04-08-11:45:13 onlie ddl stop 2019-04-08-11:45:13 add foreign key mysql: [Warning] Using a password on the command line interface can be insecure. 2019-04-08-12:33:48 add foreign key stop 2019-04-08-12:33:48 start slave

*再次查看delete 和select语句的执行计划 *

explain select count(*) from arrival_record where receive_time < STR_TO_DATE('2019-03-10', '%Y-%m-%d')\G
*************************** 1. row ***************************
id: 1
select_type: SIMPLE
table: arrival_record
partitions: NULL
type: range
possible_keys: idx_receive_time
key: idx_receive_time
key_len: 6
ref: NULL
rows: 7540948
filtered: 100.00
Extra: Using where; Using index
explain select count(*) from arrival_record where product_id=26 and receive_time between '2019-03-25 14:00:00' and '2019-03-25 15:00:00' and receive_spend_ms>=0\G;
*************************** 1. row ***************************
id: 1
select_type: SIMPLE
table: arrival_record
partitions: NULL
type: range
possible_keys: idx_product_id_sequence_station_no,idx_receive_time
key: idx_receive_time
key_len: 6
ref: NULL
rows: 291448
filtered: 16.66
Extra: Using index condition; Using where
都使用到了idx_receive_time 索引,扫描的行数大大降低

索引优化后

delete 还是花费了77s时间

delete from arrival_record where receive_time < STR_TO_DATE('2019-03-10', '%Y-%m-%d')\G

delete 语句通过receive_time的索引删除300多万的记录花费77s时间*

delete大表优化为小批量删除

应用端已优化成每次删除10分钟的数据(每次执行时间1s左右),xxx中没在出现SLA(主从延迟告警) 

另一个方法是通过主键的顺序每次删除20000条记录 

#得到满足时间条件的最大主键ID
#通过按照主键的顺序去 顺序扫描小批量删除数据
#先执行一次以下语句
 SELECT MAX(id) INTO @need_delete_max_id FROM `arrival_record` WHERE receive_time<'2019-03-01' ;
 DELETE FROM arrival_record WHERE id<@need_delete_max_id LIMIT 20000;
 select ROW_COUNT();  #返回20000


#执行小批量delete后会返回row_count(), 删除的行数
#程序判断返回的row_count()是否为0,不为0执行以下循环,为0退出循环,删除操作完成
 DELETE FROM arrival_record WHERE id<@need_delete_max_id LIMIT 20000;
 select ROW_COUNT();
#程序睡眠0.5s

总结

来源:一安未来内容投诉

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