本文目录
场景 1. LOAD 文件中的字段比数据表中的字段多 场景 2. LOAD 文件中的字段比数据表中的字段少 场景 3. LOAD 生成自定义字段数据 场景 4. LOAD 定长数据
一、LOAD 基本背景
我们在数据库运维过程中难免会涉及到需要对文本数据进行处理，并导入到数据库中，本文整理了一些导入导出时常见的场景进行示例演示。
二、LOAD 基础参数
文章后续示例均使用以下命令导出的 csv 格式样例数据（以
,逗号做分隔符，以"双引号作为界定符）
-- 导出基础参数
select * into outfile '/data/mysql/3306/tmp/employees.txt'
character set utf8mb4
fields terminated by ','
enclosed by '"'
lines terminated by '\n'
from employees.employees limit 10;
-- 导入基础参数
load data infile '/data/mysql/3306/tmp/employees.txt'
replace into table demo.emp
character set utf8mb4
fields terminated by ','
enclosed by '"'
lines terminated by '\n'
...三、LOAD 示例数据及示例表结构
以下为示例数据，表结构及对应关系信息
-- 导出的文件数据内容
[root@10-186-61-162 tmp]# cat employees.txt
"10001","1953-09-02","Georgi","Facello","M","1986-06-26"
"10002","1964-06-02","Bezalel","Simmel","F","1985-11-21"
"10003","1959-12-03","Parto","Bamford","M","1986-08-28"
"10004","1954-05-01","Chirstian","Koblick","M","1986-12-01"
"10005","1955-01-21","Kyoichi","Maliniak","M","1989-09-12"
"10006","1953-04-20","Anneke","Preusig","F","1989-06-02"
"10007","1957-05-23","Tzvetan","Zielinski","F","1989-02-10"
"10008","1958-02-19","Saniya","Kalloufi","M","1994-09-15"
"10009","1952-04-19","Sumant","Peac","F","1985-02-18"
"10010","1963-06-01","Duangkaew","Piveteau","F","1989-08-24"
-- 示例表结构
SQL > desc demo.emp;
+-------------+---------------+------+-----+---------+-------+
| Field | Type | Null | Key | Default | Extra |
+-------------+---------------+------+-----+---------+-------+
| emp_no | int | NO | PRI | NULL | |
| birth_date | date | NO | | NULL | |
| first_name | varchar(16) | NO | | NULL | |
| last_name | varchar(16) | NO | | NULL | |
| fullname | varchar(32) | YES | | NULL | | -- 表新增字段,导出数据文件中不存在
| gender | enum('M','F') | NO | | NULL | |
| hire_date | date | NO | | NULL | |
| modify_date | datetime | YES | | NULL | | -- 表新增字段,导出数据文件中不存在
| delete_flag | char(1) | YES | | NULL | | -- 表新增字段,导出数据文件中不存在
+-------------+---------------+------+-----+---------+-------+
-- 导出的数据与字段对应关系
emp_no birth_date first_name last_name gender hire_date
"10001" "1953-09-02" "Georgi" "Facello" "M" "1986-06-26"
"10002" "1964-06-02" "Bezalel" "Simmel" "F" "1985-11-21"
"10003" "1959-12-03" "Parto" "Bamford" "M" "1986-08-28"
"10004" "1954-05-01" "Chirstian" "Koblick" "M" "1986-12-01"
"10005" "1955-01-21" "Kyoichi" "Maliniak" "M" "1989-09-12"
"10006" "1953-04-20" "Anneke" "Preusig" "F" "1989-06-02"
"10007" "1957-05-23" "Tzvetan" "Zielinski" "F" "1989-02-10"
"10008" "1958-02-19" "Saniya" "Kalloufi" "M" "1994-09-15"
"10009" "1952-04-19" "Sumant" "Peac" "F" "1985-02-18"
"10010" "1963-06-01" "Duangkaew" "Piveteau" "F" "1989-08-24"四、LOAD 场景示例
场景 1. LOAD 文件中的字段比数据表中的字段多
只需要文本文件中部分数据导入到数据表中
-- 临时创建2个字段的表结构
SQL > create table emp_tmp select emp_no,hire_date from emp;
SQL > desc emp_tmp;
+-----------+------+------+-----+---------+-------+
| Field | Type | Null | Key | Default | Extra |
+-----------+------+------+-----+---------+-------+
| emp_no | int | NO | | NULL | |
| hire_date | date | NO | | NULL | |
+-----------+------+------+-----+---------+-------+
-- 导入数据语句
load data infile '/data/mysql/3306/tmp/employees.txt'
replace into table demo.emp_tmp
character set utf8mb4
fields terminated by ','
enclosed by '"'
lines terminated by '\n'
(@C1,@C2,@C3,@C4,@C5,@C6) -- 该部分对应employees.txt文件中6列数据
-- 只对导出数据中指定的2个列与表中字段做匹配,mapping关系指定的顺序不影响导入结果
set hire_date=@C6,
emp_no=@C1;
-- 导入数据结果示例
SQL > select * from emp_tmp;
+--------+------------+
| emp_no | hire_date |
+--------+------------+
| 10001 | 1986-06-26 |
| 10002 | 1985-11-21 |
| 10003 | 1986-08-28 |
| 10004 | 1986-12-01 |
| 10005 | 1989-09-12 |
| 10006 | 1989-06-02 |
| 10007 | 1989-02-10 |
| 10008 | 1994-09-15 |
| 10009 | 1985-02-18 |
| 10010 | 1989-08-24 |
+--------+------------+
10 rows in set (0.0016 sec)场景 2. LOAD 文件中的字段比数据表中的字段少
表字段不仅包含文本文件中所有数据，还包含了额外的字段
-- 导入数据语句
load data infile '/data/mysql/3306/tmp/employees.txt'
replace into table demo.emp
character set utf8mb4
fields terminated by ','
enclosed by '"'
lines terminated by '\n'
(@C1,@C2,@C3,@C4,@C5,@C6) -- 该部分对应employees.txt文件中6列数据
-- 将文件中的字段与表中字段做mapping对应,表中多出的字段不做处理
set emp_no=@C1,
birth_date=@C2,
first_name=@C3,
last_name=@C4,
gender=@C5,
hire_date=@C6;
场景 3. LOAD 生成自定义字段数据
从场景 2 的验证可以看到，emp 表中新增的字段
fullname,modify_date,delete_flag字段在导入时并未做处理，被置为了 NULL 值，如果需要对其进行处理，可在 LOAD 时通过MySQL支持的函数或给定固定值自行定义数据，对于文件中存在的字段也可做函数处理，结合导入导出，实现简单的 ETL 功能，如下所示：
-- 导入数据语句
load data infile '/data/mysql/3306/tmp/employees.txt'
replace into table demo.emp
character set utf8mb4
fields terminated by ','
enclosed by '"'
lines terminated by '\n'
(@C1,@C2,@C3,@C4,@C5,@C6) -- 该部分对应employees.txt文件中6列数据
-- 以下部分明确对表中字段与数据文件中的字段做Mapping关系,不存在的数据通过函数处理生成(也可设置为固定值)
set emp_no=@C1,
birth_date=@C2,
first_name=upper(@C3), -- 将导入的数据转为大写
last_name=lower(@C4), -- 将导入的数据转为小写
fullname=concat(first_name,' ',last_name), -- 对first_name和last_name做拼接
gender=@C5,
hire_date=@C6 ,
modify_date=now(), -- 生成当前时间数据
delete_flag=if(hire_date<'1988-01-01','Y','N'); -- 对需要生成的值基于某一列做条件运算
场景 4. LOAD 定长数据
定长数据的特点如下所示，可以使用函数取出字符串中固定长度来生成指定列数据
SQL > select
c1 as sample_data,
substr(c1,1,3) as c1,
substr(c1,4,3) as c2,
substr(c1,7,2) as c3,
substr(c1,9,5) as c4,
substr(c1,14,3) as c5,
substr(c1,17,3) as c6 from t1
*************************** 1. row ***************************
sample_data: ABC余振兴CDMySQLEFG数据库
c1: ABC
c2: 余振兴
c3: CD
c4: MySQL
c5: EFG
c6: 数据库
定长数据导入需要明确每列数据占用的字符个数，以下直接使用 rpad 对现有的表数据填充空格的方式生成定长数据用作示例使用
-- 生成定长数据
SQL > select
concat(rpad(emp_no,10,' '),
rpad(birth_date,19,' '),
rpad(first_name,14,' '),
rpad(last_name,16,' '),
rpad(gender,2,' '),
rpad(hire_date,19,' ')) as fixed_length_data
from employees.employees limit 10;
+----------------------------------------------------------------------------------+
| fixed_length_data |
+----------------------------------------------------------------------------------+
| 10001 1953-09-02 Georgi Facello M 1986-06-26 |
| 10002 1964-06-02 Bezalel Simmel F 1985-11-21 |
| 10003 1959-12-03 Parto Bamford M 1986-08-28 |
| 10004 1954-05-01 Chirstian Koblick M 1986-12-01 |
| 10005 1955-01-21 Kyoichi Maliniak M 1989-09-12 |
| 10006 1953-04-20 Anneke Preusig F 1989-06-02 |
| 10007 1957-05-23 Tzvetan Zielinski F 1989-02-10 |
| 10008 1958-02-19 Saniya Kalloufi M 1994-09-15 |
| 10009 1952-04-19 Sumant Peac F 1985-02-18 |
| 10010 1963-06-01 Duangkaew Piveteau F 1989-08-24 |
+----------------------------------------------------------------------------------+
-- 导出定长数据
select
concat(rpad(emp_no,10,' '),
rpad(birth_date,19,' '),
rpad(first_name,14,' '),
rpad(last_name,16,' '),
rpad(gender,2,' '),
rpad(hire_date,19,' ')) as fixed_length_data
into outfile '/data/mysql/3306/tmp/employees_fixed.txt'
character set utf8mb4
lines terminated by '\n'
from employees.employees limit 10;
-- 导出数据示例
[root@10-186-61-162 tmp]# cat employees_fixed.txt
10001 1953-09-02 Georgi Facello M 1986-06-26
10002 1964-06-02 Bezalel Simmel F 1985-11-21
10003 1959-12-03 Parto Bamford M 1986-08-28
10004 1954-05-01 Chirstian Koblick M 1986-12-01
10005 1955-01-21 Kyoichi Maliniak M 1989-09-12
10006 1953-04-20 Anneke Preusig F 1989-06-02
10007 1957-05-23 Tzvetan Zielinski F 1989-02-10
10008 1958-02-19 Saniya Kalloufi M 1994-09-15
10009 1952-04-19 Sumant Peac F 1985-02-18
10010 1963-06-01 Duangkaew Piveteau F 1989-08-24
-- 导入定长数据
load data infile '/data/mysql/3306/tmp/employees_fixed.txt'
replace into table demo.emp
character set utf8mb4
fields terminated by ','
enclosed by '"'
lines terminated by '\n'
(@row) -- 对一行数据定义为一个整体
set emp_no = trim(substr(@row,1,10)),-- 使用substr取前10个字符,并去除头尾空格数据
birth_date = trim(substr(@row,11,19)),-- 后续字段以此类推
first_name = trim(substr(@row,30,14)),
last_name = trim(substr(@row,44,16)),
fullname = concat(first_name,' ',last_name), -- 对first_name和last_name做拼接
gender = trim(substr(@row,60,2)),
hire_date = trim(substr(@row,62,19)),
modify_date = now(),
delete_flag = if(hire_date<'1988-01-01','Y','N'); -- 对需要生成的值基于某一列做条件运算
五、LOAD 总结
1. 默认情况下导入的顺序以文本文件 列-从左到右，行-从上到下 的顺序导入
2. 如果表结构和文本数据不一致，建议将文本文件中的各列依次顺序编号并与表中字段建立 mapping 关系，以防数据导入到错误的字段
3. 对于待导入的文本文件较大的场景，建议将文件 按行拆分 为多个小文件，如用 split 拆分
4. 对文件导入后建议执行以下语句验证导入的数据是否有 Warning,ERROR 以及导入的数据量
GET DIAGNOSTICS @p1=NUMBER,@p2=ROW_COUNT;select @p1 AS ERROR_COUNT,@p2 as ROW_COUNT;
5. 文本文件数据与表结构存在过大的差异或数据需要做清洗转换，建议还是用专业的 ETL 工具或先粗略导入 MySQL 中再进行加工转换处理。