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import_table数据导入 MySQL数据库Shell import_table数据导入

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MySQL Shell import_table数据导入

1. import_table介绍

这一期我们介绍一款高效的数据导入工具,MySQL Shell 工具集中的import_table,该工具的全称是Parallel Table Import Utility,顾名思义,支持并发数据导入,该工具在MySQL Shell 8.0.23版本后,功能更加完善, 以下列举该工具的核心功能

2. Load Data 与 import table功能示例

该部分针对import table和Load Data相同的功能做命令示例演示,我们依旧以导入employees表的示例数据为例,演示MySQL Load Data的综合场景

示例数据如下:

[root@10-186-61-162 tmp]# cat employees_01.csv
"10001","1953-09-02","Georgi","Facello","M","1986-06-26"
"10003","1959-12-03","Parto","Bamford","M","1986-08-28"
"10002","1964-06-02","Bezalel","Simmel","F","1985-11-21"
"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:

 10.186.61.162:3306  employees  SQL > desc emp;
+-------------+---------------+------+-----+---------+-------+
| Field       | Type          | Null | Key | Default | Extra | 
+-------------+---------------+------+-----+---------+-------+
| emp_no      | int           | NO   | PRI | NULL    |       |
| birth_date  | date          | NO   |     | NULL    |       |
| first_name  | varchar(14)   | NO   |     | NULL    |       |
| last_name   | varchar(16)   | NO   |     | NULL    |       |
| full_name   | varchar(64)   | 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 | varchar(1)    | YES  |     | NULL    |       |  -- 表新增字段,导出数据文件中不存在
+-------------+---------------+------+-----+---------+-------+

2.1 用Load Data方式导入数据

load data infile '/data/mysql/3306/tmp/employees_01.csv'
into table employees.emp
character set utf8mb4
fields terminated by ','
enclosed by '"'
lines terminated by '\n'
(@C1,@C2,@C3,@C4,@C5,@C6)
set emp_no=@C1,
    birth_date=@C2,
    first_name=upper(@C3),
    last_name=lower(@C4),
    full_name=concat(first_name,' ',last_name),
    gender=@C5,
    hire_date=@C6 ,
    modify_date=now(),
    delete_flag=if(hire_date<'1988-01-01','Y','N');

2.2 用import_table方式导入数据

util.import_table(
    [
        "/data/mysql/3306/tmp/employees_01.csv",
    ],
    {
        "schema": "employees", 
        "table": "emp",
        "dialect": "csv-unix",
        "skipRows": 0,
        "showProgress": True,
        "characterSet": "utf8mb4",
        "columns": [1,2,3,4,5,6],                   ## 文件中多少个列就用多少个序号标识就行
        "decodeColumns": {
            "emp_no":       "@1",                   ## 对应文件中的第1列
            "birth_date":   "@2",                   ## 对应文件中的第2个列
            "first_name":   "upper(@3)",            ## 对应文件中的第3个列,并做转为大写的处理
            "last_name":    "lower(@4)",            ## 对应文件中的第4个列,并做转为大写的处理
            "full_name":    "concat(@3,' ',@4)",    ## 将文件中的第3,4列合并成一列生成表中字段值
            "gender":       "@5",                   ## 对应文件中的第5个列
            "hire_date":    "@6",                   ## 对应文件中的第6个列
            "modify_date":  "now()",                ## 用函数生成表中字段值
            "delete_flag":  "if(@6<'1988-01-01','Y','N')"  ## 基于文件中第6列做逻辑判断,生成表中对应字段值
        }
    })

3. import_table特定功能

3.1 多文件导入(模糊匹配)

## 在导入前我生成好了3分单独的employees文件,导出的结构一致
[root@10-186-61-162 tmp]# ls -lh
总用量 1.9G
-rw-r----- 1 mysql mysql  579 3月  24 19:07 employees_01.csv
-rw-r----- 1 mysql mysql  584 3月  24 18:48 employees_02.csv
-rw-r----- 1 mysql mysql  576 3月  24 18:48 employees_03.csv
-rw-r----- 1 mysql mysql 1.9G 3月  26 17:15 sbtest1.csv

## 导入命令,其中对对文件用employees_*做模糊匹配
util.import_table(
    [
        "/data/mysql/3306/tmp/employees_*",
    ],
    {
        "schema": "employees", 
        "table": "emp",
        "dialect": "csv-unix",
        "skipRows": 0,
        "showProgress": True,
        "characterSet": "utf8mb4",
        "columns": [1,2,3,4,5,6],                   ## 文件中多少个列就用多少个序号标识就行
        "decodeColumns": {
            "emp_no":       "@1",                   ## 对应文件中的第1列
            "birth_date":   "@2",                   ## 对应文件中的第2个列
            "first_name":   "upper(@3)",            ## 对应文件中的第3个列,并做转为大写的处理
            "last_name":    "lower(@4)",            ## 对应文件中的第4个列,并做转为大写的处理
            "full_name":    "concat(@3,' ',@4)",    ## 将文件中的第3,4列合并成一列生成表中字段值
            "gender":       "@5",                   ## 对应文件中的第5个列
            "hire_date":    "@6",                   ## 对应文件中的第6个列
            "modify_date":  "now()",                ## 用函数生成表中字段值
            "delete_flag":  "if(@6<'1988-01-01','Y','N')"  ## 基于文件中第6列做逻辑判断,生成表中对应字段值
        }
    })
    
## 导入命令,其中对要导入的文件均明确指定其路径
util.import_table(
    [
        "/data/mysql/3306/tmp/employees_01.csv",
        "/data/mysql/3306/tmp/employees_02.csv",
        "/data/mysql/3306/tmp/employees_03.csv"
    ],
    {
        "schema": "employees", 
        "table": "emp",
        "dialect": "csv-unix",
        "skipRows": 0,
        "showProgress": True,
        "characterSet": "utf8mb4",
        "columns": [1,2,3,4,5,6],                   ## 文件中多少个列就用多少个序号标识就行
        "decodeColumns": {
            "emp_no":       "@1",                   ## 对应文件中的第1列
            "birth_date":   "@2",                   ## 对应文件中的第2个列
            "first_name":   "upper(@3)",            ## 对应文件中的第3个列,并做转为大写的处理
            "last_name":    "lower(@4)",            ## 对应文件中的第4个列,并做转为大写的处理
            "full_name":    "concat(@3,' ',@4)",    ## 将文件中的第3,4列合并成一列生成表中字段值
            "gender":       "@5",                   ## 对应文件中的第5个列
            "hire_date":    "@6",                   ## 对应文件中的第6个列
            "modify_date":  "now()",                ## 用函数生成表中字段值
            "delete_flag":  "if(@6<'1988-01-01','Y','N')"  ## 基于文件中第6列做逻辑判断,生成表中对应字段值
        }
    })

 

3.2 并发导入

在实验并发导入前我们创建一张1000W的sbtest1表(大约2G数据),做并发模拟,import_table用threads参数作为并发配置, 默认为8个并发.

## 导出测试需要的sbtest1数据
[root@10-186-61-162 tmp]# ls -lh
总用量 1.9G
-rw-r----- 1 mysql mysql  579 3月  24 19:07 employees_01.csv
-rw-r----- 1 mysql mysql  584 3月  24 18:48 employees_02.csv
-rw-r----- 1 mysql mysql  576 3月  24 18:48 employees_03.csv
-rw-r----- 1 mysql mysql 1.9G 3月  26 17:15 sbtest1.csv

## 开启threads为8个并发
util.import_table(
    [
        "/data/mysql/3306/tmp/sbtest1.csv",
    ],
    {
        "schema": "demo", 
        "table": "sbtest1",
        "dialect": "csv-unix",
        "skipRows": 0,
        "showProgress": True,
        "characterSet": "utf8mb4",
        "threads": "8"
    })

 

3.3 导入速率控制

可以通过maxRate和threads来控制每个并发线程的导入数据,如,当前配置线程为4个,每个线程的速率为2M/s,则最高不会超过8M/s

util.import_table(
    [
        "/data/mysql/3306/tmp/sbtest1.csv",
    ],
    {
        "schema": "demo", 
        "table": "sbtest1",
        "dialect": "csv-unix",
        "skipRows": 0,
        "showProgress": True,
        "characterSet": "utf8mb4",
        "threads": "4",
        "maxRate": "2M"
    })

3.4 自定义chunk大小

默认的chunk大小为50M,我们可以调整chunk的大小,减少事务大小,如我们将chunk大小调整为1M,则每个线程每次导入的数据量也相应减少

util.import_table(
    [
        "/data/mysql/3306/tmp/sbtest1.csv",
    ],
    {
        "schema": "demo", 
        "table": "sbtest1",
        "dialect": "csv-unix",
        "skipRows": 0,
        "showProgress": True,
        "characterSet": "utf8mb4",
        "threads": "4",
        "bytesPerChunk": "1M",
        "maxRate": "2M"
    })

4. Load Data vs import_table性能对比

-- Load Data语句
load data infile '/data/mysql/3306/tmp/sbtest1.csv'
into table demo.sbtest1
character set utf8mb4
fields terminated by ','
enclosed by '"'
lines terminated by '\n'

-- import_table语句
util.import_table(
    [
        "/data/mysql/3306/tmp/sbtest1.csv",
    ],
    {
        "schema": "demo", 
        "table": "sbtest1",
        "dialect": "csv-unix",
        "skipRows": 0,
        "showProgress": True,
        "characterSet": "utf8mb4"
    })

可以看到,Load Data耗时约5分钟,而import_table则只要不到一半的时间即可完成数据导入,效率高一倍以上(虚拟机环境磁盘IO能力有限情况下)

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