前言
我负责的有几个零碎随着业务量的增长，存储在MySQL中的数据日益剧增，我过后就想当初的业务方不讲武德，搞偷袭，趁我没反馈过去把很多表，很快，很快啊都打到了亿级别，我粗心了，没有闪，这就导致跟其Join的表的SQL变得很慢，对的利用接口的response time也变长了，影响了用户体验。
预先我找到业务方，我批评了他们跟他们说要讲武德，连忙跟我赔罪，这个事件才就此作罢，走的时候我对他们说下次不要这样了，耗子尾汁，好好反思。
骂归骂，事件还是得解决，时候我剖析起因发现，发现有些表的数据量增长很快，对应SQL扫描了很多有效数据，导致SQL慢了下来，通过确认之后，这些大表都是一些流水、记录、日志类型数据，只须要保留1到3个月，此时须要对表做数据清理实现瘦身，个别都会想到用insert + delete的形式去清理。
这篇文章我会从InnoDB存储空间散布，delete对性能的影响，以及优化倡议方面解释为什么不倡议delete删除数据。
InnoDB存储架构
从这张图能够看到，InnoDB存储构造次要包含两局部：逻辑存储构造和物理存储构造。
逻辑上是由表空间tablespace —> 段segment或者inode —> 区Extent ——>数据页Page形成，Innodb逻辑治理单位是segment，空间调配的最小单位是extent，每个segment都会从表空间FREE_PAGE中调配32个page，当这32个page不够用时，会依照以下准则进行扩大：如果以后小于1个extent，则扩大到1个extent；当表空间小于32MB时，每次扩大一个extent；表空间大于32MB，每次扩大4个extent。
物理上次要由零碎用户数据文件，日志文件组成，数据文件次要存储MySQL字典数据和用户数据，日志文件记录的是data page的变更记录，用于MySQL Crash时的复原。
Innodb表空间
InnoDB存储包含三类表空间：零碎表空间，用户表空间，Undo表空间。
零碎表空间：次要存储MySQL外部的数据字典数据，如information_schema下的数据。
用户表空间：当开启innodb_file_per_table=1时，数据表从零碎表空间独立进去存储在以table_name.ibd命令的数据文件中，构造信息存储在table_name.frm文件中。
Undo表空间：存储Undo信息，如快照统一读和flashback都是利用undo信息。
从MySQL 8.0开始容许用户自定义表空间，具体语法如下：
CREATE TABLESPACE tablespace_name ADD DATAFILE 'file_name' #数据文件名 USE LOGFILE GROUP logfile_group #自定义日志文件组，个别每组2个logfile。 [EXTENT_SIZE [=] extent_size] #区大小 [INITIAL_SIZE [=] initial_size] #初始化大小 [AUTOEXTEND_SIZE [=] autoextend_size] #主动扩宽尺寸 [MAX_SIZE [=] max_size] #单个文件最大size，最大是32G。 [NODEGROUP [=] nodegroup_id] #节点组 [WAIT] [COMMENT [=] comment_text] ENGINE [=] engine_name复制代码这样的益处是能够做到数据的冷热拆散，别离用HDD和SSD来存储，既能实现数据的高效拜访，又能节约老本，比方能够增加两块500G硬盘，通过创立卷组vg，划分逻辑卷lv，创立数据目录并mount相应的lv，假如划分的两个目录别离是/hot_data 和 /cold_data。
这样就能够将外围的业务表如用户表，订单表存储在高性能SSD盘上，一些日志，流水表存储在一般的HDD上，次要的操作步骤如下：
#创立热数据表空间create tablespace tbs_data_hot add datafile '/hot_data/tbs_data_hot01.dbf' max_size 20G;#创立外围业务表存储在热数据表空间create table booking(id bigint not null primary key auto_increment, …… ) tablespace tbs_data_hot;#创立冷数据表空间create tablespace tbs_data_cold add datafile '/hot_data/tbs_data_cold01.dbf' max_size 20G;#创立日志，流水，备份类的表存储在冷数据表空间create table payment_log(id bigint not null primary key auto_increment, …… ) tablespace tbs_data_cold;#能够挪动表到另一个表空间alter table payment_log tablespace tbs_data_hot;复制代码Inndob存储散布
创立空表查看空间变动
mysql> create table user(id bigint not null primary key auto_increment, -> name varchar(20) not null default '' comment '姓名', -> age tinyint not null default 0 comment 'age', -> gender char(1) not null default 'M' comment '性别', -> phone varchar(16) not null default '' comment '手机号', -> create_time datetime NOT NULL DEFAULT CURRENT_TIMESTAMP COMMENT '创立工夫', -> update_time datetime NOT NULL DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP COMMENT '批改工夫' -> ) engine = InnoDB DEFAULT CHARSET=utf8mb4 COMMENT '用户信息表';Query OK, 0 rows affected (0.26 sec)复制代码# ls -lh user1.ibd -rw-r----- 1 mysql mysql 96K Nov 6 12:48 user.ibd复制代码设置参数innodb_file_per_table=1时，创立表时会主动创立一个segment，同时调配一个extent，蕴含32个data page的来存储数据，这样创立的空表默认大小就是96KB，extent应用完之后会申请64个连贯页，这样对于一些小表，或者undo segment，能够在开始时申请较少的空间，节俭磁盘容量的开销。
# python2 py_innodb_page_info.py -v /data2/mysql/test/user.ibdpage offset 00000000, page type <File Space Header>page offset 00000001, page type <Insert Buffer Bitmap>page offset 00000002, page type <File Segment inode>page offset 00000003, page type <B-tree Node>, page level <0000>page offset 00000000, page type <Freshly Allocated Page>page offset 00000000, page type <Freshly Allocated Page>Total number of page: 6: #总共调配的页数Freshly Allocated Page: 2 #可用的数据页Insert Buffer Bitmap: 1 #插入缓冲页File Space Header: 1 #文件空间头B-tree Node: 1 #数据页File Segment inode: 1 #文件端inonde，如果是在ibdata1.ibd上会有多个inode。复制代码插入数据后的空间变动
mysql> DELIMITER $$mysql> CREATE PROCEDURE insert_user_data(num INTEGER) -> BEGIN -> DECLARE v_i int unsigned DEFAULT 0; -> set autocommit= 0; -> WHILE v_i < num DO -> insert into user(`name`, age, gender, phone) values (CONCAT('lyn',v_i), mod(v_i,120), 'M', CONCAT('152',ROUND(RAND(1)*100000000))); -> SET v_i = v_i+1; -> END WHILE; -> commit; -> END $$Query OK, 0 rows affected (0.01 sec)mysql> DELIMITER ;#插入10w数据mysql> call insert_user_data(100000);Query OK, 0 rows affected (6.69 sec)复制代码# ls -lh user.ibd-rw-r----- 1 mysql mysql 14M Nov 6 10:58 /data2/mysql/test/user.ibd # python2 py_innodb_page_info.py -v /data2/mysql/test/user.ibdpage offset 00000000, page type <File Space Header>page offset 00000001, page type <Insert Buffer Bitmap>page offset 00000002, page type <File Segment inode>page offset 00000003, page type <B-tree Node>, page level <0001> #减少了一个非叶子节点，树的高度从1变为2.........................................................page offset 00000000, page type <Freshly Allocated Page>Total number of page: 896:Freshly Allocated Page: 493Insert Buffer Bitmap: 1File Space Header: 1B-tree Node: 400File Segment inode: 1复制代码delete数据后的空间变动
mysql> select min(id),max(id),count(*) from user;+---------+---------+----------+| min(id) | max(id) | count(*) |+---------+---------+----------+| 1 | 100000 | 100000 |+---------+---------+----------+1 row in set (0.05 sec)#删除50000条数据，实践上空间应该从14MB变长7MB左右。mysql> delete from user limit 50000;Query OK, 50000 rows affected (0.25 sec)#数据文件大小仍然是14MB，没有放大。# ls -lh /data2/mysql/test/user1.ibd -rw-r----- 1 mysql mysql 14M Nov 6 13:22 /data2/mysql/test/user.ibd#数据页没有被回收。# python2 py_innodb_page_info.py -v /data2/mysql/test/user.ibdpage offset 00000000, page type <File Space Header>page offset 00000001, page type <Insert Buffer Bitmap>page offset 00000002, page type <File Segment inode>page offset 00000003, page type <B-tree Node>, page level <0001>........................................................page offset 00000000, page type <Freshly Allocated Page>Total number of page: 896:Freshly Allocated Page: 493Insert Buffer Bitmap: 1File Space Header: 1B-tree Node: 400File Segment inode: 1#在MySQL外部是标记删除，复制代码mysql> use information_schema;Database changedmysql> SELECT A.SPACE AS TBL_SPACEID, A.TABLE_ID, A.NAME AS TABLE_NAME, FILE_FORMAT, ROW_FORMAT, SPACE_TYPE, B.INDEX_ID , B.NAME AS INDEX_NAME, PAGE_NO, B.TYPE AS INDEX_TYPE FROM INNODB_SYS_TABLES A LEFT JOIN INNODB_SYS_INDEXES B ON A.TABLE_ID =B.TABLE_ID WHERE A.NAME = 'test/user1';+-------------+----------+------------+-------------+------------+------------+----------+------------+---------+------------+| TBL_SPACEID | TABLE_ID | TABLE_NAME | FILE_FORMAT | ROW_FORMAT | SPACE_TYPE | INDEX_ID | INDEX_NAME | PAGE_NO | INDEX_TYPE |+-------------+----------+------------+-------------+------------+------------+----------+------------+---------+------------+| 1283 | 1207 | test/user | Barracuda | Dynamic | Single | 2236 | PRIMARY | 3 | 3 |+-------------+----------+------------+-------------+------------+------------+----------+------------+---------+------------+1 row in set (0.01 sec)PAGE_NO = 3 标识B-tree的root page是3号页，INDEX_TYPE = 3是汇集索引。 INDEX_TYPE取值如下：0 = nonunique secondary index; 1 = automatically generated clustered index (GEN_CLUST_INDEX); 2 = unique nonclustered index; 3 = clustered index; 32 = full-text index;#膨胀空间再后进行察看复制代码MySQL外部不会真正删除空间，而且做标记删除，行将delflag:N批改为delflag:Y，commit之后会会被purge进入删除链表，如果下一次insert更大的记录，delete之后的空间不会被重用，如果插入的记录小于等于delete的记录空会被重用，这块内容能够通过知数堂的innblock工具进行剖析。
Innodb中的碎片
碎片的产生
咱们晓得数据存储在文件系统上的，总是不能100%利用调配给它的物理空间，删除数据会在页面上留下一些”空洞”，或者随机写入（汇集索引非线性减少）会导致页决裂，页决裂导致页面的利用空间少于50%，另外对表进行增删改会引起对应的二级索引值的随机的增删改，也会导致索引构造中的数据页面上留下一些"空洞"，尽管这些空洞有可能会被反复利用，但终究会导致局部物理空间未被应用，也就是碎片。
同时，即使是设置了填充因子为100%，Innodb也会被动留下page页面1/16的空间作为预留应用（An innodb_fill_factor setting of 100 leaves 1/16 of the space in clustered index pages free for future index growth）避免update带来的行溢出。
mysql> select table_schema, -> table_name,ENGINE, -> round(DATA_LENGTH/1024/1024+ INDEX_LENGTH/1024/1024) total_mb,TABLE_ROWS, -> round(DATA_LENGTH/1024/1024) data_mb, round(INDEX_LENGTH/1024/1024) index_mb, round(DATA_FREE/1024/1024) free_mb, round(DATA_FREE/DATA_LENGTH*100,2) free_ratio -> from information_schema.TABLES where TABLE_SCHEMA= 'test' -> and TABLE_NAME= 'user';+--------------+------------+--------+----------+------------+---------+----------+---------+------------+| table_schema | table_name | ENGINE | total_mb | TABLE_ROWS | data_mb | index_mb | free_mb | free_ratio |+--------------+------------+--------+----------+------------+---------+----------+---------+------------+| test | user | InnoDB | 4 | 50000 | 4 | 0 | 6 | 149.42 |+--------------+------------+--------+----------+------------+---------+----------+---------+------------+1 row in set (0.00 sec)复制代码其中data_free是调配了未应用的字节数，并不能阐明齐全是碎片空间。
碎片的回收
对于InnoDB的表，能够通过以下命令来回收碎片，开释空间，这个是随机读IO操作，会比拟耗时，也会阻塞表上失常的DML运行，同时须要占用额定更多的磁盘空间，对于RDS来说，可能会导致磁盘空间霎时爆满，实例霎时被锁定，利用无奈做DML操作，所以禁止在线上环境去执行。
#执行InnoDB的碎片回收mysql> alter table user engine=InnoDB;Query OK, 0 rows affected (9.00 sec)Records: 0 Duplicates: 0 Warnings: 0 ##执行完之后，数据文件大小从14MB升高到10M。# ls -lh /data2/mysql/test/user1.ibd -rw-r----- 1 mysql mysql 10M Nov 6 16:18 /data2/mysql/test/user.ibd复制代码mysql> select table_schema, table_name,ENGINE, round(DATA_LENGTH/1024/1024+ INDEX_LENGTH/1024/1024) total_mb,TABLE_ROWS, round(DATA_LENGTH/1024/1024) data_mb, round(INDEX_LENGTH/1024/1024) index_mb, round(DATA_FREE/1024/1024) free_mb, round(DATA_FREE/DATA_LENGTH*100,2) free_ratio from information_schema.TABLES where TABLE_SCHEMA= 'test' and TABLE_NAME= 'user';+--------------+------------+--------+----------+------------+---------+----------+---------+------------+| table_schema | table_name | ENGINE | total_mb | TABLE_ROWS | data_mb | index_mb | free_mb | free_ratio |+--------------+------------+--------+----------+------------+---------+----------+---------+------------+| test | user | InnoDB | 5 | 50000 | 5 | 0 | 2 | 44.29 |+--------------+------------+--------+----------+------------+---------+----------+---------+------------+1 row in set (0.00 sec)复制代码delete对SQL的影响
未删除前的SQL执行状况
#插入100W数据mysql> call insert_user_data(1000000);Query OK, 0 rows affected (35.99 sec)#增加相干索引mysql> alter table user add index idx_name(name), add index idx_phone(phone);Query OK, 0 rows affected (6.00 sec)Records: 0 Duplicates: 0 Warnings: 0#表上索引统计信息mysql> show index from user;+-------+------------+-----------+--------------+-------------+-----------+-------------+----------+--------+------+------------+---------+---------------+| Table | Non_unique | Key_name | Seq_in_index | Column_name | Collation | Cardinality | Sub_part | Packed | Null | Index_type | Comment | Index_comment |+-------+------------+-----------+--------------+-------------+-----------+-------------+----------+--------+------+------------+---------+---------------+| user | 0 | PRIMARY | 1 | id | A | 996757 | NULL | NULL | | BTREE | | || user | 1 | idx_name | 1 | name | A | 996757 | NULL | NULL | | BTREE | | || user | 1 | idx_phone | 1 | phone | A | 2 | NULL | NULL | | BTREE | | |+-------+------------+-----------+--------------+-------------+-----------+-------------+----------+--------+------+------------+---------+---------------+3 rows in set (0.00 sec)#重置状态变量计数mysql> flush status;Query OK, 0 rows affected (0.00 sec)#执行SQL语句mysql> select id, age ,phone from user where name like 'lyn12%';+--------+-----+-------------+| id | age | phone |+--------+-----+-------------+| 124 | 3 | 15240540354 || 1231 | 30 | 15240540354 || 12301 | 60 | 15240540354 |.............................| 129998 | 37 | 15240540354 || 129999 | 38 | 15240540354 || 130000 | 39 | 15240540354 |+--------+-----+-------------+11111 rows in set (0.03 sec)mysql> explain select id, age ,phone from user where name like 'lyn12%';+----+-------------+-------+-------+---------------+----------+---------+------+-------+-----------------------+| id | select_type | table | type | possible_keys | key | key_len | ref | rows | Extra |+----+-------------+-------+-------+---------------+----------+---------+------+-------+-----------------------+| 1 | SIMPLE | user | range | idx_name | idx_name | 82 | NULL | 22226 | Using index condition |+----+-------------+-------+-------+---------------+----------+---------+------+-------+-----------------------+1 row in set (0.00 sec)#查看相干状态呢变量mysql> select * from information_schema.session_status where variable_name in('Last_query_cost','Handler_read_next','Innodb_pages_read','Innodb_data_reads','Innodb_pages_read');+-------------------+----------------+| VARIABLE_NAME | VARIABLE_VALUE |+-------------------+----------------+| HANDLER_READ_NEXT | 11111 | #申请读的行数| INNODB_DATA_READS | 7868409 | #数据物理读的总数| INNODB_PAGES_READ | 7855239 | #逻辑读的总数| LAST_QUERY_COST | 10.499000 | #SQL语句的老本COST，次要包含IO_COST和CPU_COST。+-------------------+----------------+4 rows in set (0.00 sec)复制代码删除后的SQL执行状况
#删除50w数据mysql> delete from user limit 500000;Query OK, 500000 rows affected (3.70 sec)#剖析表统计信息mysql> analyze table user;+-----------+---------+----------+----------+| Table | Op | Msg_type | Msg_text |+-----------+---------+----------+----------+| test.user | analyze | status | OK |+-----------+---------+----------+----------+1 row in set (0.01 sec)#重置状态变量计数mysql> flush status;Query OK, 0 rows affected (0.01 sec)mysql> select id, age ,phone from user where name like 'lyn12%';Empty set (0.05 sec)mysql> explain select id, age ,phone from user where name like 'lyn12%';+----+-------------+-------+-------+---------------+----------+---------+------+-------+-----------------------+| id | select_type | table | type | possible_keys | key | key_len | ref | rows | Extra |+----+-------------+-------+-------+---------------+----------+---------+------+-------+-----------------------+| 1 | SIMPLE | user | range | idx_name | idx_name | 82 | NULL | 22226 | Using index condition |+----+-------------+-------+-------+---------------+----------+---------+------+-------+-----------------------+1 row in set (0.00 sec)mysql> select * from information_schema.session_status where variable_name in('Last_query_cost','Handler_read_next','Innodb_pages_read','Innodb_data_reads','Innodb_pages_read');+-------------------+----------------+| VARIABLE_NAME | VARIABLE_VALUE |+-------------------+----------------+| HANDLER_READ_NEXT | 0 || INNODB_DATA_READS | 7868409 || INNODB_PAGES_READ | 7855239 || LAST_QUERY_COST | 10.499000 |+-------------------+----------------+4 rows in set (0.00 sec)复制代码后果统计分析
操作
COST
物理读次数
逻辑读次数
扫描行数
返回行数
执行工夫
初始化插入100W
10.499000
7868409
7855239
22226
11111
30ms
100W随机删除50W
10.499000
7868409
7855239
22226
0
50ms
这也阐明对一般的大表，想要通过delete数据来对表进行瘦身是不事实的，所以在任何时候不要用delete去删除数据，应该应用优雅的标记删除。
delete优化倡议
管制业务账号权限
对于一个大的零碎来说，须要依据业务特点去拆分子系统，每个子系统能够看做是一个service，例如美团APP，下面有很多服务，外围的服务有用户服务user-service，搜寻服务search-service，商品product-service，位置服务location-service，价格服务price-service等。每个服务对应一个数据库，为该数据库创立独自账号，同时只授予DML权限且没有delete权限，同时禁止跨库拜访。
#创立用户数据库并受权create database mt_user charset utf8mb4;grant USAGE, SELECT, INSERT, UPDATE ON mt_user.* to 'w_user'@'%' identified by 't$W*g@gaHTGi123456';flush privileges;复制代码delete改为标记删除
在MySQL数据库建模标准中有4个公共字段，基本上每个表必须有的，同时在create_time列要创立索引，有两方面的益处：
- 一些查问业务场景都会有一个默认的时间段，比方7天或者一个月，都是通过create_time去过滤，走索引扫描更快。
- 一些外围的业务表须要以T +1的形式抽取数据仓库中，比方每天晚上00:30抽取前一天的数据，都是通过create_time过滤的。
`id` bigint(20) NOT NULL AUTO_INCREMENT COMMENT '主键id',`is_deleted` tinyint(4) NOT NULL DEFAULT '0' COMMENT '是否逻辑删除：0：未删除，1：已删除',`create_time` timestamp NOT NULL DEFAULT CURRENT_TIMESTAMP COMMENT '创立工夫',`update_time` timestamp NOT NULL DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP COMMENT '批改工夫'#有了删除标记，业务接口的delete操作就能够转换为updateupdate user set is_deleted = 1 where user_id = 1213;#查问的时候须要带上is_deleted过滤select id, age ,phone from user where is_deleted = 0 and name like 'lyn12%';复制代码数据归档形式
通用数据归档办法
#1. 创立归档表，个别在原表名前面增加_bak。CREATE TABLE `ota_order_bak` ( `id` bigint(11) NOT NULL AUTO_INCREMENT COMMENT '主键', `order_id` varchar(255) DEFAULT NULL COMMENT '订单id', `ota_id` varchar(255) DEFAULT NULL COMMENT 'ota', `check_in_date` varchar(255) DEFAULT NULL COMMENT '入住日期', `check_out_date` varchar(255) DEFAULT NULL COMMENT '离店日期', `hotel_id` varchar(255) DEFAULT NULL COMMENT '酒店ID', `guest_name` varchar(255) DEFAULT NULL COMMENT '顾客', `purcharse_time` timestamp NULL DEFAULT NULL COMMENT '购买工夫', `create_time` datetime DEFAULT NULL, `update_time` datetime DEFAULT NULL, `create_user` varchar(255) DEFAULT NULL, `update_user` varchar(255) DEFAULT NULL, `status` int(4) DEFAULT '1' COMMENT '状态 ： 1 失常 ， 0 删除', `hotel_name` varchar(255) DEFAULT NULL, `price` decimal(10,0) DEFAULT NULL, `remark` longtext, PRIMARY KEY (`id`), KEY `IDX_order_id` (`order_id`) USING BTREE, KEY `hotel_name` (`hotel_name`) USING BTREE, KEY `ota_id` (`ota_id`) USING BTREE, KEY `IDX_purcharse_time` (`purcharse_time`) USING BTREE, KEY `IDX_create_time` (`create_time`) USING BTREE) ENGINE=InnoDB DEFAULT CHARSET=utf8PARTITION BY RANGE (to_days(create_time)) ( PARTITION p201808 VALUES LESS THAN (to_days('2018-09-01')), PARTITION p201809 VALUES LESS THAN (to_days('2018-10-01')), PARTITION p201810 VALUES LESS THAN (to_days('2018-11-01')), PARTITION p201811 VALUES LESS THAN (to_days('2018-12-01')), PARTITION p201812 VALUES LESS THAN (to_days('2019-01-01')), PARTITION p201901 VALUES LESS THAN (to_days('2019-02-01')), PARTITION p201902 VALUES LESS THAN (to_days('2019-03-01')), PARTITION p201903 VALUES LESS THAN (to_days('2019-04-01')), PARTITION p201904 VALUES LESS THAN (to_days('2019-05-01')), PARTITION p201905 VALUES LESS THAN (to_days('2019-06-01')), PARTITION p201906 VALUES LESS THAN (to_days('2019-07-01')), PARTITION p201907 VALUES LESS THAN (to_days('2019-08-01')), PARTITION p201908 VALUES LESS THAN (to_days('2019-09-01')), PARTITION p201909 VALUES LESS THAN (to_days('2019-10-01')), PARTITION p201910 VALUES LESS THAN (to_days('2019-11-01')), PARTITION p201911 VALUES LESS THAN (to_days('2019-12-01')), PARTITION p201912 VALUES LESS THAN (to_days('2020-01-01')));#2. 插入原表中有效的数据（须要跟开发同学确认数据保留范畴）create table tbl_p201808 as select * from ota_order where create_time between '2018-08-01 00:00:00' and '2018-08-31 23:59:59';#3. 跟归档表分区做分区替换alter table ota_order_bak exchange partition p201808 with table tbl_p201808; #4. 删除原表中曾经标准的数据delete from ota_order where create_time between '2018-08-01 00:00:00' and '2018-08-31 23:59:59' limit 3000;复制代码优化后的归档形式
#1. 创立两头表CREATE TABLE `ota_order_2020` (........) ENGINE=InnoDB DEFAULT CHARSET=utf8PARTITION BY RANGE (to_days(create_time)) ( PARTITION p201808 VALUES LESS THAN (to_days('2018-09-01')), PARTITION p201809 VALUES LESS THAN (to_days('2018-10-01')), PARTITION p201810 VALUES LESS THAN (to_days('2018-11-01')), PARTITION p201811 VALUES LESS THAN (to_days('2018-12-01')), PARTITION p201812 VALUES LESS THAN (to_days('2019-01-01')), PARTITION p201901 VALUES LESS THAN (to_days('2019-02-01')), PARTITION p201902 VALUES LESS THAN (to_days('2019-03-01')), PARTITION p201903 VALUES LESS THAN (to_days('2019-04-01')), PARTITION p201904 VALUES LESS THAN (to_days('2019-05-01')), PARTITION p201905 VALUES LESS THAN (to_days('2019-06-01')), PARTITION p201906 VALUES LESS THAN (to_days('2019-07-01')), PARTITION p201907 VALUES LESS THAN (to_days('2019-08-01')), PARTITION p201908 VALUES LESS THAN (to_days('2019-09-01')), PARTITION p201909 VALUES LESS THAN (to_days('2019-10-01')), PARTITION p201910 VALUES LESS THAN (to_days('2019-11-01')), PARTITION p201911 VALUES LESS THAN (to_days('2019-12-01')), PARTITION p201912 VALUES LESS THAN (to_days('2020-01-01')));#2. 插入原表中无效的数据，如果数据量在100W左右能够在业务低峰期直接插入，如果比拟大，倡议采纳dataX来做，能够管制频率和大小，之前我这边用Go封装了dataX能够实现主动生成json文件，自定义大小去执行。insert into ota_order_2020 select * from ota_order where create_time between '2020-08-01 00:00:00' and '2020-08-31 23:59:59';#3. 表重命名alter table ota_order rename to ota_order_bak; alter table ota_order_2020 rename to ota_order;#4. 插入差别数据insert into ota_order select * from ota_order_bak a where not exists (select 1 from ota_order b where a.id = b.id);#5. ota_order_bak革新成分区表，如果表比拟大不倡议间接革新，能够先创立好分区表，通过dataX把导入进去即可。#6. 后续的归档办法#创立两头广泛表create table ota_order_mid like ota_order;#替换原表有效数据分区到一般表alter table ota_order exchange partition p201808 with table ota_order_mid; ##替换一般表数据到归档表的相应分区alter table ota_order_bak exchange partition p201808 with table ota_order_mid; 复制代码这样原表和归档表都是按月的分区表，只须要创立一个两头一般表，在业务低峰期做两次分区替换，既能够删除有效数据，又能回收空，而且没有空间碎片，不会影响表上的索引及SQL的执行打算。
总结
通过从InnoDB存储空间散布，delete对性能的影响能够看到，delete物理删除既不能开释磁盘空间，而且会产生大量的碎片，导致索引频繁决裂，影响SQL执行打算的稳定性；
同时在碎片回收时，会耗用大量的CPU，磁盘空间，影响表上失常的DML操作。
在业务代码层面，应该做逻辑标记删除，防止物理删除；为了实现数据归档需要，能够用采纳MySQL分区表个性来实现，都是DDL操作，没有碎片产生。
另外一个比拟好的计划采纳Clickhouse，对有生命周期的数据表能够应用Clickhouse存储，利用其TTL个性实现有效数据主动清理。