梳理MySQL数据逻辑和元数据的技巧

• MySQL元数据概览
• • TABLES
  • COLUMNS
  • 联TABLES和COLUMNS
  • 查建表语句
• E-R图
• 批量表抽样，查看具体数据（Python程序）

查看表注释、行数

SELECt
  `TABLE_NAME`       -- 表名
  ,`TABLE_TYPE`      -- 表类型
  ,`TABLE_COMMENT`   -- 表注释
  ,`TABLE_ROWS`      -- 行数
  ,`AVG_ROW_LENGTH`  -- 平均行长
  ,`CREATE_TIME`     -- 创建时间
  ,`UPDATE_TIME`     -- 更新时间
FROM `information_schema`.`TABLES`
WHERe `TABLE_SCHEMA`='库名';

查看列注释

SELECt
  `COLUMN_NAME`     -- 列名
  ,`COLUMN_DEFAULT` -- 列默认值
  ,`IS_NULLABLE`    -- 是否允许NULL
  ,`COLUMN_TYPE`    -- 类型
  ,`COLUMN_KEY`     -- 列键
  ,`COLUMN_COMMENT` -- 列注释
FROM `information_schema`.`COLUMNS`
WHERe `TABLE_SCHEMA`='库名'
  AND `TABLE_NAME`='表名';

查看表的主键、时间字段、列数、注释缺失数量

SELECt
 `TABLE_NAME` -- 表名
 ,COUNT(1) -- 列数
 ,TRIm("," FROM GROUP_CONCAt(DISTINCT IF(`COLUMN_KEY`='PRI',`COLUMN_NAME`,""))) -- 主键
 ,TRIm("," FROM GROUP_CONCAt(DISTINCT IF(`DATA_TYPE` IN ('timestamp','datetime'),`COLUMN_NAME`,""))) -- 时间字段
 ,SUM(ISNULL(`COLUMN_COMMENT`))+SUM(IF(COLUMN_COMMENT='',1,0)) -- 注释缺失数量
FROM `information_schema`.`COLUMNS`
WHERe `TABLE_SCHEMA`='库名'
GROUP BY `TABLE_NAME`;

GROUP_CONCAT为连接字符串的聚合函数，默认连接符是英文逗号

SELECt
  a.`TABLE_NAME` AS `表名`
  ,a.`TABLE_COMMENT` AS `表注释`
  ,a.`TABLE_ROWS` AS `行数`
  ,a.`AVG_ROW_LENGTH` AS `平均行长`
  ,a.`CREATE_TIME` AS `创建时间`
  ,a.`UPDATE_TIME` AS `更新时间`
  ,b.`col_cnt` AS `列数`
  ,b.`pk` AS `主键`
  ,b.`t` AS `时间字段`
  ,b.`null_cnt` AS `缺失注释的列数`
FROM `information_schema`.`TABLES` a
INNER JOIN (
  SELECt
    `TABLE_NAME`
    ,COUNT(1) AS `col_cnt`
    ,TRIm("," FROM GROUP_CONCAt(DISTINCT IF(`COLUMN_KEY`='PRI',`COLUMN_NAME`,""))) AS `pk`
    ,TRIm("," FROM GROUP_CONCAt(DISTINCT IF(`DATA_TYPE` IN ('timestamp','datetime'),`COLUMN_NAME`,""))) AS `t`
    ,SUM(ISNULL(`COLUMN_COMMENT`))+SUM(IF(COLUMN_COMMENT='',1,0)) AS `null_cnt`
  FROM `information_schema`.`COLUMNS`
  WHERe `TABLE_SCHEMA`='库名'
  GROUP BY `TABLE_NAME`
)b ON b.`TABLE_NAME`=a.`TABLE_NAME`
WHERe `TABLE_TYPE`='BASE TABLE';

效果

单表

SHOW CREATE TABLE `表名`;

导出某个库的全部建表语句

mysqldump --no-data '库名' -u'root' -p > 文件名.sql

利用一些数据库工具来创建 E-R图，下面以 DBeaver 为例

from pymysql import Connection  # conda install pymysql
import pandas as pd  # conda install pandas openpyxl


class Mysql:
    def __init__(self, **kwargs):
        self.db = Connection(
            user=kwargs.pop('user', 'root'),
            password=kwargs.pop('password'),
            host=kwargs.pop('host', 'localhost'),
            database=kwargs.pop('database'),
            port=kwargs.pop('port', 3306),
            charset=kwargs.pop('charset', 'UTF8'))

    def __del__(self):
        self.db.close()

    def db2sheets(self, queries, prefix):
        """
        保存为多sheets的Excel文件
        :param queries: 字典{sheet名:sql}
        :param prefix: 文件名前缀
        """
        writer = pd.ExcelWriter(prefix.replace('.xlsx', '') + '.xlsx')
        for sheet_name, query in queries.items():
            df = pd.read_sql_query(query, self.db)
            # if 'url' in df.columns:df['url'] = "'" + df['url']
            df.to_excel(writer, sheet_name=sheet_name, index=False)
        writer.save()


if __name__ == '__main__':
    db = '库名'
    # 批量的表，粘到这里
    tables = '''
    tb1
    tb2
    tb3
    '''.strip().split()
    # 抽样SQL（可选择乱序或正序）
    _queries = {t: 'SELECT * FROM {} ORDER BY RAND() LIMIT 99'.format(t) for t in tables}
    # _queries = {t: 'SELECt * FROM {} LIMIT 99'.format(t) for t in tables}
    # 导出为Excel
    Mysql(pwd='密码', database=db).db2sheets(_queries, db)