【MySQL】谈谈上亿大表的优化实践 |
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【MySQL】谈谈上亿大表的优化实践
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当MySQL单表记录数过大时，增删改查性能都会急剧下降，这个时候就需要进行优化了，那今天我们就来谈谈如何优化上亿数据的大表。 （说明：采用Mysql存储千亿级的数据，确实是一项非常大的挑战。Mysql单表确实可以存储10亿级的数据，只是这个时候性能非常差，项目中大量的实验证明，Mysql单表容量在500万左右，性能处于最佳状态。） 背景XX实例（一主一从）xxx告警中每天凌晨在报SLA报警，该报警的意思是存在一定的主从延迟（若在此时发生主从切换，需要长时间才可以完成切换，要追延迟来保证主从数据的一致性） 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.logpt-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
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: 1select_type: SIMPLEtable: arrival_recordpartitions: NULLtype: refpossible_keys: IXFK_arrival_recordkey: IXFK_arrival_recordkey_len: 8ref: constrows: 32261320filtered: 3.70Extra: Using index condition; Using where1 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 |--- 该表总记录数约1亿多条，表上只有一个复合索引，product_id字段基数很小，选择性不好 传入的过滤条件 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 没有station_nu字段，使用不到复合索引 IXFK_arrival_record的 product_id,station_no,sequence,receive_time 这几个字段 根据最左前缀原则，select arrival_record只用到了复合索引IXFK_arrival_record的第一个字段product_id，而该字段选择性很差，导致扫描的行数很多，执行时间长 receive_time字段的基数大，选择性好，可对该字段单独建立索引，select arrival_record sql就会使用到该索引 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_OLDarrival_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: 1select_type: SIMPLEtable: arrival_recordpartitions: NULLtype: ALLpossible_keys: NULLkey: NULLkey_len: NULLref: NULLrows: 109501508filtered: 33.33Extra: Using where1 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.frm48G /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 SELECTtable_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_timeFROMinformation_schema.tablesWHERE 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 $backupdir -t 32 -r 2000000 && echo `date +%T` &并行压缩备份所花时间（52s）和占用空间（1.2G，实际该表占用磁盘空间为48G，mydumper并行压缩备份压缩比相当高！） Started dump at: 2019-03-26 12:46:04 ........ Finished dump at: 2019-03-26 12:46:56 du -sh /datas/dump_arrival_record/ 1.2G /datas/dump_arrival_record/拷贝dump数据到测试节点scp -rp /datas/dump_arrival_record [email protected]:/datas 多线程导入数据 time myloader -u root -S /datas/mysql/data/3308/mysqld.sock -P 3308 -p root -B test -d /datas/dump_arrival_record -t 32 real 126m42.885suser 1m4.543ssys 0m4.267s 逻辑导入该表后磁盘占用空间 du -h -d 1 /datas/mysql/data/3308/test/arrival_record.12K /datas/mysql/data/3308/test/arrival_record.frm30G /datas/mysql/data/3308/test/arrival_record.ibd没有碎片，和mysql的该表的大小一致* cp -rp /datas/mysql/data/3308 /datas 分别使用online DDL和 pt-osc工具来做删除重建索引操作先删除外键，不删除外键，无法删除复合索引，外键列属于复合索引中第一列 nohup bash /tmp/ddl_index.sh &2019-04-04-10:41:39 begin stop mysqld_33082019-04-04-10:41:41 begin rm -rf datadir and cp -rp datadir_bak2019-04-04-10:46:53 start mysqld_33082019-04-04-10:46:59 online ddl begin2019-04-04-11:20:34 onlie ddl stop2019-04-04-11:20:34 begin stop mysqld_33082019-04-04-11:20:36 begin rm -rf datadir and cp -rp datadir_bak2019-04-04-11:22:48 start mysqld_33082019-04-04-11:22:53 pt-osc begin2019-04-04-12:19:15 pt-osc stoponline ddl 花费时间为34 分钟，pt-osc花费时间为57 分钟，使用onlne ddl时间约为pt-osc工具时间的一半 做DDL 参考 使用建议： 由于是一主一从实例，应用是连接的vip，删除重建索引采用online ddl来做。停止主从复制后，先在从实例上做（不记录binlog），主从切换，再在新切换的从实例上做（不记录binlog) function red_echo () { local what="$*" 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 slavemysql: [Warning] Using a password on the command line interface can be insecure.ddl_start2019-04-08 11:17:36ddl_stop2019-04-08 11:45:132019-04-08-11:45:13 onlie ddl stop2019-04-08-11:45:13 add foreign keymysql: [Warning] Using a password on the command line interface can be insecure.2019-04-08-12:33:48 add foreign key stop2019-04-08-12:33:48 start slave删除重建索引花费时间为28分钟，添加外键约束时间为48分钟 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: 1select_type: SIMPLEtable: arrival_recordpartitions: NULLtype: rangepossible_keys: idx_receive_timekey: idx_receive_timekey_len: 6ref: NULLrows: 7540948filtered: 100.00Extra: 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: 1select_type: SIMPLEtable: arrival_recordpartitions: NULLtype: rangepossible_keys: idx_product_id_sequence_station_no,idx_receive_timekey: idx_receive_timekey_len: 6ref: NULLrows: 291448filtered: 16.66Extra: 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')\Gdelete 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 |
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