如果有多条数据需要同时插入，不要每次插入一条，然后分多次插入，因为每执行一次插入的操作，都要进行数据库的连接，多个操作就会连接多次，而一次批量操作只需要连接1次
因为Mysql默认每执行一次操作，就会提交一次事务，这样就会涉及到频繁的事务的开启与关闭
start transaction; insert into 表名 values(),(),(); insert into 表名 values(),(),(); insert into 表名 values(),(),(); commit;
主键一般是默认自增的，但是也可以手动增加，这里不建议手动乱序增加，而是使用默认的顺序增加，原因会在后面解释。
如果一次性需要插入大批量数据，使用insert语句插入性能较低，此时可以使用Mysql数据库提供的load指令进行插入，
首先在连接数据库的时候需要加上 --local-infile 参数
mysql --local-infile -u root -p
在使用本地文件加载功能的时候，需要先查看本地加载文件选项是否开启的
mysql> select @@local_infile; +----------------+ | @@local_infile | +----------------+ | 0 | +----------------+ 1 row in set (0.00 sec)
说明1：0表示本地加载文件并未开启
开启本地加载文件的语句
mysql> set global local_infile = 1; Query OK, 0 rows affected (0.01 sec) mysql> select @@local_infile; +----------------+ | @@local_infile | +----------------+ | 1 | +----------------+ 1 row in set (0.00 sec)
创建一个空表tb_user，其表结构如下
mysql> desc tb_user; +----------+-------------+------+-----+---------+----------------+ | Field | Type | Null | Key | Default | Extra | +----------+-------------+------+-----+---------+----------------+ | id | int | NO | PRI | NULL | auto_increment | | username | varchar(50) | NO | UNI | NULL | | | password | varchar(50) | NO | | NULL | | | name | varchar(20) | NO | | NULL | | | birthday | date | YES | | NULL | | | sex | char(1) | YES | | NULL | | +----------+-------------+------+-----+---------+----------------+ 6 rows in set (0.01 sec)
使用load加载本地文件 'tb_user_data.sql' 内容到新创建的表中,其中tb_user_data.sql中的测试数据如下
houlei@houleideMacBook-Pro Desktop % cat tb_user_data.sql 1,a,aa,aaa,2023-07-01,1 2,b,bb,bbb,2023-07-02,0 3,c,cc,ccc,2023-07-03,1 4,d,dd,ddd,2023-07-04,0 5,e,ee,eee,2023-07-05,1 6,f,ff,fff,2023-07-06,0 7,g,gg,ggg,2023-07-07,1 houlei@houleideMacBook-Pro Desktop %
使用load加载本地文件 'tb_user_data.sql' 内容到新创建的表中
mysql> load data local infile '/Users/houlei/Desktop/tb_user_data.sql' into table tb_user fields terminated by ',' lines terminated by '\n'; Query OK, 7 rows affected (0.01 sec) Records: 7 Deleted: 0 Skipped: 0 Warnings: 0
说明1： load data local infile 是加载本地文件的意思，
说明2：'/Users/houlei/Desktop/tb_user_data.sql'是文件路径
说明3：into table tb_user 是将文件中的数据，插入到tb_user表中
说明4：fields terminated by ',' 是说每个字段之间的数据是使用','分割的
说明5：lines terminated by '\n' 是说每一行之间的数据使用的是‘\n’分割的
说明6：本方法只是举例，在实际运用大数据量插入时100万条数据的插入至少要数分钟，如果使用load方法只需要十几秒
在InnoDB储存引擎中，表数据都是根据主键顺序组织存放的，这种存储方式的表称为索引组织表(index organized table）IOT
说明1：在索引的B+数中所有的数据保存在叶子节点上，非叶子节点只保存主键key的值
说明2：索引中的各个节点都是保存在逻辑结构页上面的，一页默认大小16K
页可以为空，也可以填充一半，也可以填充100%，每个页包含了2至N行数据，根据主键排列
情况1：主键顺序插入
说明1：row是行数据，每一页上可以存放多个行数据。
情况2：主键乱序插入
说明1：当我们想要在插入一个id=50的数据时，会发生页分裂
说明2：这时会将 1#page 页里面的数据超过 50% 的数据，移动到新开辟的 3#page 页中
说明3：然后将 id=50 的数据也拼接到 3#page 页中
说明4：这时就会出现一个问题，3#page 中的索引比 2#page 页中的索引小，所以还需要将 3#page 页前置，这就叫页分裂
当删除一行记录时，实际上记录并没有被物理删除，只是记录被标记(flaged)为删除并且它的空间变得允许被其他记录声明使用
当页中删除的记录达到 merge_threshold (默认为页的50%)，InnoDB 会开始寻找最靠近的页（前或者后）看看是否可以合并以优化空间使用
说明1：这时在 2#page 删除了13,14,15,16数据后，该页空余空间超过50%时就会寻找前一页或者后一页，是否同样有不满足50%,可以合并的
说明2：这时 1#page 页是满的，不能合并，3#page 页不满可以合并，所以 3#page 页迁移到 2#page 页中
说明3：这时如果在有数据20插入就可以直接插入到3#page页上了，这就是页合并。
通过表的索引或者全表扫描，读取满足条件的数据行，然后在排序缓冲区sort buffer 中完成排序操作，所有不是通过索引直接返回排序结果的排序都叫FileSort排序
通过有序索引顺序扫描直接返回有序数据，这种情况即为using index，不需要额外的排序，操作效率高，即排序的列表字段符合覆盖索引。
emp表结构：
mysql> desc emp; +-----------+-------------+------+-----+---------+----------------+ | Field | Type | Null | Key | Default | Extra | +-----------+-------------+------+-----+---------+----------------+ | id | int | NO | PRI | NULL | auto_increment | | name | varchar(20) | YES | | NULL | | | age | int | YES | | NULL | | | job | varchar(20) | YES | | NULL | | | salary | int | YES | | NULL | | | entrydate | date | YES | | NULL | | | managerid | int | YES | | NULL | | | dept_id | int | YES | MUL | NULL | | +-----------+-------------+------+-----+---------+----------------+ 8 rows in set (0.01 sec)
emp表中索引情况
mysql> show index from emp; +-------+------------+----------+--------------+-------------+-----------+-------------+----------+--------+------+------------+---------+---------------+---------+------------+ | Table | Non_unique | Key_name | Seq_in_index | Column_name | Collation | Cardinality | Sub_part | Packed | Null | Index_type | Comment | Index_comment | Visible | Expression | +-------+------------+----------+--------------+-------------+-----------+-------------+----------+--------+------+------------+---------+---------------+---------+------------+ | emp | 0 | PRIMARY | 1 | id | A | 7 | NULL | NULL | | BTREE | | | YES | NULL | | emp | 1 | fk_dept | 1 | dept_id | A | 3 | NULL | NULL | YES | BTREE | | | YES | NULL | +-------+------------+----------+--------------+-------------+-----------+-------------+----------+--------+------+------------+---------+---------------+---------+------------+
案例1：对查询结果进行按 salary 和 age 都进行升序排序
mysql> explain select salary,age from emp order by salary, age; +----+-------------+-------+------------+------+---------------+------+---------+------+------+----------+----------------+ | id | select_type | table | partitions | type | possible_keys | key | key_len | ref | rows | filtered | Extra | +----+-------------+-------+------------+------+---------------+------+---------+------+------+----------+----------------+ | 1 | SIMPLE | emp | NULL | ALL | NULL | NULL | NULL | NULL | 7 | 100.00 | Using filesort | +----+-------------+-------+------------+------+---------------+------+---------+------+------+----------+----------------+ 1 row in set, 1 warning (0.00 sec)
说明1：Extra 中值为 Using filesort 说明是先查出来需要的数据，然后再排序的,效率不高。
说明2：为什么会出现Using filesort呢？因为查询的这些字段在查询之前是无须的，索引需要先将数据查询出来，然后再做排序，这样才能得到想要的排序好的数据。
案例2：给 salary 和 age 添加一个联合排序
mysql> create index salary_age_idx on emp(salary,age); Query OK, 0 rows affected (0.07 sec) Records: 0 Duplicates: 0 Warnings: 0 mysql> show index from emp; +-------+------------+----------------+--------------+-------------+-----------+-------------+----------+--------+------+------------+---------+---------------+---------+------------+ | Table | Non_unique | Key_name | Seq_in_index | Column_name | Collation | Cardinality | Sub_part | Packed | Null | Index_type | Comment | Index_comment | Visible | Expression | +-------+------------+----------------+--------------+-------------+-----------+-------------+----------+--------+------+------------+---------+---------------+---------+------------+ | emp | 0 | PRIMARY | 1 | id | A | 7 | NULL | NULL | | BTREE | | | YES | NULL | | emp | 1 | fk_dept | 1 | dept_id | A | 3 | NULL | NULL | YES | BTREE | | | YES | NULL | | emp | 1 | salary_age_idx | 1 | salary | A | 7 | NULL | NULL | YES | BTREE | | | YES | NULL | | emp | 1 | salary_age_idx | 2 | age | A | 7 | NULL | NULL | YES | BTREE | | | YES | NULL | +-------+------------+----------------+--------------+-------------+-----------+-------------+----------+--------+------+------------+---------+---------------+---------+------------+ 4 rows in set (0.00 sec)
说明1：联合索引 salary_age_idx 中 salary 是第一索引字段，age 是第二索引字段
说明2：Collation 中A 代表升序，D 代表降序
案例3：再次使用 order by 对 salary 和 age 进行升序排序
mysql> explain select salary,age from emp order by salary,age; +----+-------------+-------+------------+-------+---------------+----------------+---------+------+------+----------+-------------+ | id | select_type | table | partitions | type | possible_keys | key | key_len | ref | rows | filtered | Extra | +----+-------------+-------+------------+-------+---------------+----------------+---------+------+------+----------+-------------+ | 1 | SIMPLE | emp | NULL | index | NULL | salary_age_idx | 10 | NULL | 7 | 100.00 | Using index | +----+-------------+-------+------------+-------+---------------+----------------+---------+------+------+----------+-------------+ 1 row in set, 1 warning (0.00 sec) mysql> explain select salary,age from emp order by salary; +----+-------------+-------+------------+-------+---------------+----------------+---------+------+------+----------+-------------+ | id | select_type | table | partitions | type | possible_keys | key | key_len | ref | rows | filtered | Extra | +----+-------------+-------+------------+-------+---------------+----------------+---------+------+------+----------+-------------+ | 1 | SIMPLE | emp | NULL | index | NULL | salary_age_idx | 10 | NULL | 7 | 100.00 | Using index | +----+-------------+-------+------------+-------+---------------+----------------+---------+------+------+----------+-------------+ 1 row in set, 1 warning (0.00 sec)
说明1：在做升序排列时，无论 order by 后面是组合索引的全部字段，还是只有部分字段，这时 Extra 的值都是Usind index，所以其查询的结果直接就是排序好的结果
说明2：为什么呢？因为这个时候 salary和age是一个联合索引，索引在文件中是一个带顺序的b+数结构，所以将这个字段建立一个联合索引，就意味着使用索引查询的时候，就已经是带着顺序的数据了，所以这个时候就不需要在将数据从新在排序了，这样的查询效率就会更高。
案例4: order by 中的字段顺序和索引顺序不一致的情况
mysql> explain select salary,age from emp order by age, salary; +----+-------------+-------+------------+-------+---------------+----------------+---------+------+------+----------+-----------------------------+ | id | select_type | table | partitions | type | possible_keys | key | key_len | ref | rows | filtered | Extra | +----+-------------+-------+------------+-------+---------------+----------------+---------+------+------+----------+-----------------------------+ | 1 | SIMPLE | emp | NULL | index | NULL | salary_age_idx | 10 | NULL | 7 | 100.00 | Using index; Using filesort | +----+-------------+-------+------------+-------+---------------+----------------+---------+------+------+----------+-----------------------------+ 1 row in set, 1 warning (0.00 sec)
说明1：这个时候order by 是age在前，salary在后，和索引的顺序不一致，仍然会触发索引，使用Using index，但是也会使用Using filesort,所以推荐大家使用正确的索引顺序的字段来进行排序
案例5：对salary和age做降序查询
mysql> explain select salary,age from emp order by salary desc, age desc; +----+-------------+-------+------------+-------+---------------+----------------+---------+------+------+----------+----------------------------------+ | id | select_type | table | partitions | type | possible_keys | key | key_len | ref | rows | filtered | Extra | +----+-------------+-------+------------+-------+---------------+----------------+---------+------+------+----------+----------------------------------+ | 1 | SIMPLE | emp | NULL | index | NULL | salary_age_idx | 10 | NULL | 7 | 100.00 | Backward index scan; Using index | +----+-------------+-------+------------+-------+---------------+----------------+---------+------+------+----------+----------------------------------+ 1 row in set, 1 warning (0.00 sec)
mysql> explain select id,salary,age from emp order by salary desc;
+----+-------------+-------+------------+-------+---------------+----------------+---------+------+------+----------+----------------------------------+
| id | select_type | table | partitions | type | possible_keys | key | key_len | ref | rows | filtered | Extra |
+----+-------------+-------+------------+-------+---------------+----------------+---------+------+------+----------+----------------------------------+
| 1 | SIMPLE | emp | NULL | index | NULL | salary_age_idx | 10 | NULL | 7 | 100.00 | Backward index scan; Using index |
+----+-------------+-------+------------+-------+---------------+----------------+---------+------+------+----------+----------------------------------+
1 row in set, 1 warning (0.00 sec)
说明1：无论是对salary和age同时做降序还是对其中一个字段做降序排列，都会出现 Backward index scan; Using index，其中 Backward index scan 是反向扫描索引
说明2：这是因为索引中默认的顺序是升序的，而做降序排列，就需要反向扫描索引了
案例7：创建一个 salary 和 age 都是降序的索引
create index salary_age_desc_idx on emp(salary desc, age desc); Query OK, 0 rows affected (0.02 sec) Records: 0 Duplicates: 0 Warnings: 0
查询目前所有的索引
mysql> show index from emp; +-------+------------+---------------------+--------------+-------------+-----------+-------------+----------+--------+------+------------+---------+---------------+---------+------------+ | Table | Non_unique | Key_name | Seq_in_index | Column_name | Collation | Cardinality | Sub_part | Packed | Null | Index_type | Comment | Index_comment | Visible | Expression | +-------+------------+---------------------+--------------+-------------+-----------+-------------+----------+--------+------+------------+---------+---------------+---------+------------+ | emp | 0 | PRIMARY | 1 | id | A | 7 | NULL | NULL | | BTREE | | | YES | NULL | | emp | 1 | fk_dept | 1 | dept_id | A | 3 | NULL | NULL | YES | BTREE | | | YES | NULL | | emp | 1 | salary_age_desc_idx | 1 | salary | D | 7 | NULL | NULL | YES | BTREE | | | YES | NULL | | emp | 1 | salary_age_desc_idx | 2 | age | D | 7 | NULL | NULL | YES | BTREE | | | YES | NULL | | emp | 1 | salary_age_idx | 1 | salary | A | 7 | NULL | NULL | YES | BTREE | | | YES | NULL | | emp | 1 | salary_age_idx | 2 | age | A | 7 | NULL | NULL | YES | BTREE | | | YES | NULL | +-------+------------+---------------------+--------------+-------------+-----------+-------------+----------+--------+------+------------+---------+---------------+---------+------------+ 6 rows in set (0.01 sec)
说明1：这里 salary_age_desc_idx 就是根据 salary 和 age 做的降序索引，其Collation中的D即降序的意思
案例8：使用salary_age_desc_idx索引然后在使用order by降序查询
mysql> explain select salary,age from emp use index(salary_age_desc_idx) order by salary desc, age desc; +----+-------------+-------+------------+-------+---------------+---------------------+---------+------+------+----------+-------------+ | id | select_type | table | partitions | type | possible_keys | key | key_len | ref | rows | filtered | Extra | +----+-------------+-------+------------+-------+---------------+---------------------+---------+------+------+----------+-------------+ | 1 | SIMPLE | emp | NULL | index | NULL | salary_age_desc_idx | 10 | NULL | 7 | 100.00 | Using index | +----+-------------+-------+------------+-------+---------------+---------------------+---------+------+------+----------+-------------+ 1 row in set, 1 warning (0.01 sec)
说明1：这个时候的Extra 中显示的 Using index，效率就会比较高了
说明2：这是因为salary_age_desc_idx索引的顺序就是降序排列的，所以使用该索引做降序排列的时候，就不需要在做反向扫描
说明3：在实际的业务中，我们可以根据自己的查询需要，创建升序或者降序的索引。
为了测试数据的准确性，这是我先把除了主键以外的索引都删除了，然后根据需要在重新创建
mysql> show index from emp; +-------+------------+----------+--------------+-------------+-----------+-------------+----------+--------+------+------------+---------+---------------+---------+------------+ | Table | Non_unique | Key_name | Seq_in_index | Column_name | Collation | Cardinality | Sub_part | Packed | Null | Index_type | Comment | Index_comment | Visible | Expression | +-------+------------+----------+--------------+-------------+-----------+-------------+----------+--------+------+------------+---------+---------------+---------+------------+ | emp | 0 | PRIMARY | 1 | id | A | 7 | NULL | NULL | | BTREE | | | YES | NULL | +-------+------------+----------+--------------+-------------+-----------+-------------+----------+--------+------+------------+---------+---------------+---------+------------+ 2 rows in set (0.01 sec)
案例1：根据job做聚合查询
mysql> select job, count(*) from emp group by job;; +--------------+----------+ | job | count(*) | +--------------+----------+ | 董事长 | 1 | | 项目经理 | 1 | | 开发 | 3 | | 财务 | 1 | | 出纳 | 1 | | 人事 | 1 | +--------------+----------+ 6 rows in set (0.00 sec)
我们使用explain查看一下执行计划
mysql> explain select job, count(*) from emp group by job; +----+-------------+-------+------------+------+---------------+------+---------+------+------+----------+-----------------+ | id | select_type | table | partitions | type | possible_keys | key | key_len | ref | rows | filtered | Extra | +----+-------------+-------+------------+------+---------------+------+---------+------+------+----------+-----------------+ | 1 | SIMPLE | emp | NULL | ALL | NULL | NULL | NULL | NULL | 7 | 100.00 | Using temporary | +----+-------------+-------+------------+------+---------------+------+---------+------+------+----------+-----------------+ 1 row in set, 1 warning (0.00 sec)
说明1：通过Extra字段：Using temporary,说明在这次的查询中创建了一张临时表，这是无论是空间上还是速度上都会影响到查询效率的。
这时我们给 job 创建一个索引，再次使用explain查看一下执行计划
mysql> create index job_idx on emp(job); Query OK, 0 rows affected (0.02 sec) Records: 0 Duplicates: 0 Warnings: 0 mysql> show index from emp; +-------+------------+----------+--------------+-------------+-----------+-------------+----------+--------+------+------------+---------+---------------+---------+------------+ | Table | Non_unique | Key_name | Seq_in_index | Column_name | Collation | Cardinality | Sub_part | Packed | Null | Index_type | Comment | Index_comment | Visible | Expression | +-------+------------+----------+--------------+-------------+-----------+-------------+----------+--------+------+------------+---------+---------------+---------+------------+ | emp | 0 | PRIMARY | 1 | id | A | 7 | NULL | NULL | | BTREE | | | YES | NULL | | emp | 1 | job_idx | 1 | job | A | 6 | NULL | NULL | YES | BTREE | | | YES | NULL | +-------+------------+----------+--------------+-------------+-----------+-------------+----------+--------+------+------------+---------+---------------+---------+------------+
mysql> explain select job, count(*) from emp group by job; +----+-------------+-------+------------+-------+---------------+---------+---------+------+------+----------+-------------+ | id | select_type | table | partitions | type | possible_keys | key | key_len | ref | rows | filtered | Extra | +----+-------------+-------+------------+-------+---------------+---------+---------+------+------+----------+-------------+ | 1 | SIMPLE | emp | NULL | index | job_idx | job_idx | 83 | NULL | 7 | 100.00 | Using index | +----+-------------+-------+------------+-------+---------------+---------+---------+------+------+----------+-------------+ 1 row in set, 1 warning (0.00 sec)
说明2：这是查询中就使用到了索引查询，而没有建立临时表
这时我们在对 job 和 age 同时做分组查询
mysql> explain select job,age, count(*) from emp group by job,age; +----+-------------+-------+------------+------+---------------+------+---------+------+------+----------+-----------------+ | id | select_type | table | partitions | type | possible_keys | key | key_len | ref | rows | filtered | Extra | +----+-------------+-------+------------+------+---------------+------+---------+------+------+----------+-----------------+ | 1 | SIMPLE | emp | NULL | ALL | NULL | NULL | NULL | NULL | 7 | 100.00 | Using temporary | +----+-------------+-------+------------+------+---------------+------+---------+------+------+----------+-----------------+ 1 row in set, 1 warning (0.00 sec)
说明3：这时Extra字段的值，仍然是Using temporary，那是因为没有一个与之对应的联合索引。
我们继续创建一个 job 和 age 的联合索引，然后再看一下 explain 的执行计划
mysql> create index job_age_idx on emp(job,age); Query OK, 0 rows affected (0.05 sec) Records: 0 Duplicates: 0 Warnings: 0 mysql> show index from emp; +-------+------------+-------------+--------------+-------------+-----------+-------------+----------+--------+------+------------+---------+---------------+---------+------------+ | Table | Non_unique | Key_name | Seq_in_index | Column_name | Collation | Cardinality | Sub_part | Packed | Null | Index_type | Comment | Index_comment | Visible | Expression | +-------+------------+-------------+--------------+-------------+-----------+-------------+----------+--------+------+------------+---------+---------------+---------+------------+ | emp | 0 | PRIMARY | 1 | id | A | 7 | NULL | NULL | | BTREE | | | YES | NULL | | emp | 1 | job_idx | 1 | job | A | 6 | NULL | NULL | YES | BTREE | | | YES | NULL | | emp | 1 | job_age_idx | 1 | job | A | 6 | NULL | NULL | YES | BTREE | | | YES | NULL | | emp | 1 | job_age_idx | 2 | age | A | 6 | NULL | NULL | YES | BTREE | | | YES | NULL | +-------+------------+-------------+--------------+-------------+-----------+-------------+----------+--------+------+------------+---------+---------------+---------+------------+ 5 rows in set (0.00 sec)
mysql> select job,age,count(*) from emp group by job,age; +--------------+------+----------+ | job | age | count(*) | +--------------+------+----------+ | 人事 | 27 | 1 | | 出纳 | 25 | 1 | | 开发 | 22 | 2 | | 开发 | 24 | 1 | | 董事长 | 43 | 1 | | 财务 | 25 | 1 | | 项目经理 | 38 | 1 | +--------------+------+----------+ 7 rows in set (0.00 sec) mysql> explain select job,age,count(*) from emp group by job,age; +----+-------------+-------+------------+-------+---------------+-------------+---------+------+------+----------+-------------+ | id | select_type | table | partitions | type | possible_keys | key | key_len | ref | rows | filtered | Extra | +----+-------------+-------+------------+-------+---------------+-------------+---------+------+------+----------+-------------+ | 1 | SIMPLE | emp | NULL | index | job_age_idx | job_age_idx | 88 | NULL | 7 | 100.00 | Using index | +----+-------------+-------+------------+-------+---------------+-------------+---------+------+------+----------+-------------+ 1 row in set, 1 warning (0.00 sec)
说明4：当我们group by后面的分组字段，存在于某一个联合索引中的时候，group by会使用索引查询，而不会建立临时表
案例2：我们根据job做过滤然后再根据age排序
mysql> select job,age from emp where job="开发" group by age; +--------+------+ | job | age | +--------+------+ | 开发 | 22 | | 开发 | 24 | +--------+------+ 2 rows in set (0.01 sec) mysql> explain select job,age from emp where job="开发" group by age; +----+-------------+-------+------------+------+---------------------+-------------+---------+-------+------+----------+-------------+ | id | select_type | table | partitions | type | possible_keys | key | key_len | ref | rows | filtered | Extra | +----+-------------+-------+------------+------+---------------------+-------------+---------+-------+------+----------+-------------+ | 1 | SIMPLE | emp | NULL | ref | job_idx,job_age_idx | job_age_idx | 83 | const | 3 | 100.00 | Using index | +----+-------------+-------+------------+------+---------------------+-------------+---------+-------+------+----------+-------------+ 1 row in set, 1 warning (0.00 sec)
说明1：当where后面的条件和group by 后面的条件一起组合成连锁索引，也不会建立临时表，也会直接走连个查询索引的。效率同样比较高
总结：
account_transaction表数据量展示
mysql> select count(*) from account_transaction; +----------+ | count(*) | +----------+ | 2261942 | +----------+ 1 row in set (8.40 sec)
说明1：account_transaction总数据量有226万+
案例1：分别采用分页查询，第一页，第1万页，200万页的数据
mysql> select * from account_transaction limit 1,2; +----+--------------------+--------+--------+----------------------------+---------------+--------------+--------+---------+-----------------+-------------------+-----------+--------+ | id | trade_no | type | method | time | payment | out_trade_no | amount | balance | trader_staff_id | operator_staff_id | device_id | remark | +----+--------------------+--------+--------+----------------------------+---------------+--------------+--------+---------+-----------------+-------------------+-----------+--------+ | 2 | 156384294742000250 | TOP_UP | CASH | 2019-07-23 00:49:07.072256 | LOCAL_ACCOUNT | | 10000 | 10000 | 250 | 12 | 6 | | | 3 | 156384301875000251 | TOP_UP | CASH | 2019-07-23 00:50:18.059192 | LOCAL_ACCOUNT | | 10000 | 10000 | 251 | 12 | 6 | | +----+--------------------+--------+--------+----------------------------+---------------+--------------+--------+---------+-----------------+-------------------+-----------+--------+ 2 rows in set (0.00 sec) mysql> select * from account_transaction limit 10000,2; +-------+--------------------+---------------+--------+----------------------------+---------------+--------------+--------+---------+-----------------+-------------------+-----------+--------+ | id | trade_no | type | method | time | payment | out_trade_no | amount | balance | trader_staff_id | operator_staff_id | device_id | remark | +-------+--------------------+---------------+--------+----------------------------+---------------+--------------+--------+---------+-----------------+-------------------+-----------+--------+ | 10054 | 156506391300003827 | CONSUME_LUNCH | | 2019-08-06 03:58:33.000000 | LOCAL_ACCOUNT | | 200 | 9800 | 3827 | 0 | 27 | | | 10055 | 156506391300002816 | CONSUME_LUNCH | | 2019-08-06 03:58:33.000000 | LOCAL_ACCOUNT | | 200 | 9800 | 2816 | 0 | 19 | | +-------+--------------------+---------------+--------+----------------------------+---------------+--------------+--------+---------+-----------------+-------------------+-----------+--------+ 2 rows in set (0.02 sec) mysql> select * from account_transaction limit 2000000,2; +---------+--------------------+---------------+--------+----------------------------+---------------+--------------+--------+---------+-----------------+-------------------+-----------+--------+ | id | trade_no | type | method | time | payment | out_trade_no | amount | balance | trader_staff_id | operator_staff_id | device_id | remark | +---------+--------------------+---------------+--------+----------------------------+---------------+--------------+--------+---------+-----------------+-------------------+-----------+--------+ | 5524352 | 163539315991003043 | CONSUME_LUNCH | | 2021-10-28 03:52:39.000000 | LOCAL_ACCOUNT | | 200 | 3800 | 3043 | 0 | 34 | | | 5524354 | 163539342290003077 | CONSUME_LUNCH | | 2021-10-28 03:57:02.000000 | LOCAL_ACCOUNT | | 200 | 1500 | 3077 | 0 | 19 | | +---------+--------------------+---------------+--------+----------------------------+---------------+--------------+--------+---------+-----------------+-------------------+-----------+--------+ 2 rows in set (2.51 sec)
说明1：我们对1页，1万页，200万页的数据分别查询，发现随着查询数据量的增加，查询的时间也在增加
说明2：当我们查询limit 2000000,2时，此时需要Mysql排序钱2000002条记录，但是仅仅需要返回200001-20002的记录，前2000000条记录丢弃，查询排序的代价非常大
查询优化
mysql> select a.* from account_transaction as a, (select id from account_transaction order by id limit 2000000,2) as at where a.id = at.id; +---------+--------------------+---------------+--------+----------------------------+---------------+--------------+--------+---------+-----------------+-------------------+-----------+--------+ | id | trade_no | type | method | time | payment | out_trade_no | amount | balance | trader_staff_id | operator_staff_id | device_id | remark | +---------+--------------------+---------------+--------+----------------------------+---------------+--------------+--------+---------+-----------------+-------------------+-----------+--------+ | 5524352 | 163539315991003043 | CONSUME_LUNCH | | 2021-10-28 03:52:39.000000 | LOCAL_ACCOUNT | | 200 | 3800 | 3043 | 0 | 34 | | | 5524354 | 163539342290003077 | CONSUME_LUNCH | | 2021-10-28 03:57:02.000000 | LOCAL_ACCOUNT | | 200 | 1500 | 3077 | 0 | 19 | | +---------+--------------------+---------------+--------+----------------------------+---------------+--------------+--------+---------+-----------------+-------------------+-----------+--------+ 2 rows in set (0.51 sec)
说明3：同样是分页查询2000000页以后的数据，该查询仅好事0.51秒，比直接使用limit分页查询快了几倍
说明4：Mysql官方针对大数据量的分页查询给出的方案是，建议使用覆盖查询加子查询形式进行优化
说明5：该插叙的子查询：select id from account_transaction order by id limit 2000000,2，首先这是根据id查询到需要数据的id，本身根据id查找就是比较快的。
mysql> select id from account_transaction order by id limit 2000000,2; +---------+ | id | +---------+ | 5524352 | | 5524354 | +---------+ 2 rows in set (0.45 sec)
说明6：将该子查询的结果当做一张表，与account_trasaction做子查询，这样效率就会比直接使用limit速度快很多。
是一个聚合函数，对于返回的结果集，一行一行的判断，如果count函数的参数不为NULL,累计值就+1，否则不加1，最后返回累计值
count(*)：
InnoDB引擎并不会把全部的字段取出来，而是专门做了优化，不取值，服务层直接按行进行累加,mysql 对count(*)做了优化。
count(主键)
InnoDB引擎会遍历整张表，把每一行的主键id值都取出来，返回给服务层，服务层那个主键后，直接按行进行累加（主键不可能为空）
count(普通字段)：
没有not null 约束：InnoDB引擎会遍历整张表，把每一行的字段值都取出来，返回给服务层，服务层判断是否为null，不为null,计数+1.
有not null 约束：InnofDB引擎会遍历整张表，把每一行的字段值都取出来，返回给服务层，直接按行累加
count(1）
InnoDB引擎遍历整张表，但不取值，服务层对于返回的每一行，放一个数字“1”进去，直接按行进行累加。
count(*) ≈count(1)>count(id)>count(普通字段)
说明1：因为左边和右边都是根据id修改的不同数据，这时id是主键索引，所以这里的修改都只会触发行锁，不会影响其他行的修改。
说明1：update的时候，如果条件是索引字段，则只会触发行索引
说明2：updae的时候，如果条件是非索引字段，则会触发表索引，即在update的时候，整张表处于锁住的状态。
说明3：主需要对update的字段创建一个索引值，就可以在update的时候将表锁降低为行锁。
InnoDB的行锁是针对索引加的锁，不是针对记录加的锁，并且该索引不能失效，否则会从行锁升级为表锁。