板凳-------Mysql cookbook学习 （九）
第4章：表管理
4.0 引言
MySQL ：： 员工样例数据库 ：： 3 安装
https://dev.mysql.com/doc/employee/en/employees-installation.html
Employees 数据库与几种不同的 存储引擎，默认情况下启用 InnoDB 引擎。编辑 文件并调整 comments 选择不同的存储引擎：employees.sql
SET default_storage_engine = InnoDB;
– set storage_engine = MyISAM;
– set storage_engine = Falcon;
– set storage_engine = PBXT;
– set storage_engine = Maria;
• MySQL 5.7 或更早：使用 storage_engine。
• MySQL 8.0+：使用 default_storage_engine。
D:\sql\Mysql_learning\employee\mysql\master>mysql -u root -p -t < employees.sql
Enter password: ****
+-----------------------------+
| INFO |
+-----------------------------+
| CREATING DATABASE STRUCTURE |
+-----------------------------+
+------------------------+
| INFO |
+------------------------+
| storage engine: InnoDB |
+------------------------+
+---------------------+
| INFO |
+---------------------+
| LOADING departments |
+---------------------+
+-------------------+
| INFO |
+-------------------+
| LOADING employees |
+-------------------+
+------------------+
| INFO |
+------------------+
| LOADING dept_emp |
+------------------+
+----------------------+
| INFO |
+----------------------+
| LOADING dept_manager |
+----------------------+
+----------------+
| INFO |
+----------------+
| LOADING titles |
+----------------+
+------------------+
| INFO |
+------------------+
| LOADING salaries |
+------------------+
+---------------------+
| data_load_time_diff |
+---------------------+
| 00:01:09 |
+---------------------+
mysql> SOURCE D:/sql/Mysql_learning/employee/mysql/master/test_employees_md5.sql;
Database changed
+----------------------+
| INFO |
+----------------------+
| TESTING INSTALLATION |
+----------------------+
1 row in set (0.00 sec)Query OK, 0 rows affected, 2 warnings (0.00 sec)Query OK, 0 rows affected (0.06 sec)Query OK, 0 rows affected (0.04 sec)Query OK, 6 rows affected (0.00 sec)
Records: 6 Duplicates: 0 Warnings: 0+--------------+------------------+----------------------------------+
| table_name | expected_records | expected_crc |
+--------------+------------------+----------------------------------+
| departments | 9 | d1af5e170d2d1591d776d5638d71fc5f |
| dept_emp | 331603 | ccf6fe516f990bdaa49713fc478701b7 |
| dept_manager | 24 | 8720e2f0853ac9096b689c14664f847e |
| employees | 300024 | 4ec56ab5ba37218d187cf6ab09ce1aa1 |
| salaries | 2844047 | fd220654e95aea1b169624ffe3fca934 |
| titles | 443308 | bfa016c472df68e70a03facafa1bc0a8 |
+--------------+------------------+----------------------------------+
6 rows in set (0.00 sec)Query OK, 0 rows affected, 1 warning (0.01 sec)Query OK, 0 rows affected (0.04 sec)Query OK, 0 rows affected (0.00 sec)Query OK, 300024 rows affected, 1 warning (6.19 sec)
Records: 300024 Duplicates: 0 Warnings: 1Query OK, 1 row affected (0.29 sec)Query OK, 0 rows affected (0.00 sec)Query OK, 9 rows affected, 1 warning (0.01 sec)
Records: 9 Duplicates: 0 Warnings: 1Query OK, 1 row affected (0.01 sec)Query OK, 0 rows affected (0.00 sec)Query OK, 24 rows affected, 1 warning (0.01 sec)
Records: 24 Duplicates: 0 Warnings: 1Query OK, 1 row affected (0.01 sec)Query OK, 0 rows affected (0.00 sec)Query OK, 331603 rows affected, 1 warning (6.28 sec)
Records: 331603 Duplicates: 0 Warnings: 1Query OK, 1 row affected (0.38 sec)Query OK, 0 rows affected (0.00 sec)Query OK, 443308 rows affected, 1 warning (9.02 sec)
Records: 443308 Duplicates: 0 Warnings: 1Query OK, 1 row affected (0.43 sec)Query OK, 0 rows affected (0.00 sec)Query OK, 2844047 rows affected, 1 warning (51.55 sec)
Records: 2844047 Duplicates: 0 Warnings: 1Query OK, 1 row affected (2.60 sec)Query OK, 0 rows affected (0.07 sec)+--------------+------------------+----------------------------------+
| table_name | found_records | found_crc |
+--------------+------------------+----------------------------------+
| departments | 9 | d1af5e170d2d1591d776d5638d71fc5f |
| dept_emp | 331603 | ccf6fe516f990bdaa49713fc478701b7 |
| dept_manager | 24 | 8720e2f0853ac9096b689c14664f847e |
| employees | 300024 | 4ec56ab5ba37218d187cf6ab09ce1aa1 |
| salaries | 2844047 | fd220654e95aea1b169624ffe3fca934 |
| titles | 443308 | bfa016c472df68e70a03facafa1bc0a8 |
+--------------+------------------+----------------------------------+
6 rows in set (0.00 sec)+--------------+---------------+-----------+
| table_name | records_match | crc_match |
+--------------+---------------+-----------+
| departments | OK | ok |
| dept_emp | OK | ok |
| dept_manager | OK | ok |
| employees | OK | ok |
| salaries | OK | ok |
| titles | OK | ok |
+--------------+---------------+-----------+
6 rows in set (0.00 sec)Query OK, 0 rows affected (0.00 sec)Query OK, 0 rows affected (0.00 sec)+------------------+
| computation_time |
+------------------+
| 00:01:18 |
+------------------+
1 row in set (0.01 sec)Query OK, 0 rows affected (0.04 sec)+---------+--------+
| summary | result |
+---------+--------+
| CRC | OK |
| count | OK |
+---------+--------+
2 rows in set (0.00 sec)
mysql>
SQL CREATE TABLE LIKE 用法及示例
在 MySQL 中，CREATE TABLE LIKE 是一种用于创建新表的语法，该新表的结构与现有表完全相同，但不包含源表中的数据1。以下是详细的用法和示例：
import mysql.connector
from pathlib import Path
import redef merge_sql_files(main_sql_path):"""递归合并所有被引用的SQL文件"""main_path = Path(main_sql_path)base_dir = main_path.parentmerged_sql = []with open(main_path, 'r', encoding='utf-8') as f:for line in f:# 处理source命令if line.strip().startswith('source'):# 提取被引用的文件名match = re.search(r'source\s+[\'"](.+?)[\'"]|source\s+(\S+)', line.strip())ref_file = match.group(1) or match.group(2)ref_path = base_dir / ref_fileif ref_path.exists():merged_sql.append(f"\n-- 开始导入: {ref_file}\n")merged_sql.append(merge_sql_files(str(ref_path)))merged_sql.append(f"\n-- 完成导入: {ref_file}\n")else:raise FileNotFoundError(f"无法找到引用的SQL文件: {ref_path}")else:# 移除DELIMITER等客户端命令if not line.strip().upper().startswith(('DELIMITER', 'USE ', 'CONNECT ')):merged_sql.append(line)return ''.join(merged_sql)def execute_mega_sql(db_config, full_sql):"""执行大型SQL脚本"""cnx = mysql.connector.connect(**db_config)cursor = cnx.cursor()try:# 设置更大的缓冲区cursor.execute("SET GLOBAL max_allowed_packet=256*1024*1024")cursor.execute("SET GLOBAL net_buffer_length=1000000")# 分割并执行SQL语句statements = [s.strip() for s in full_sql.split(';') if s.strip()]for stmt in statements:try:# 确保语句以分号结尾sql = stmt if stmt.endswith(';') else stmt + ';'for result in cursor.execute(sql, multi=True):if result.with_rows:result.fetchall() # 消费结果集print(f"执行成功: {result.statement[:100]}...")cnx.commit()except mysql.connector.Error as err:print(f"跳过错误: {err}\n语句: {stmt[:200]}...")cnx.rollback()except Exception as e:print(f"意外错误: {e}")cnx.rollback()finally:cursor.close()cnx.close()# 配置信息
db_config = {'host': 'localhost','user': 'root','password': 'root','database': 'cookbook'
}# 主SQL文件路径
main_sql_path = r"D:\sql\Mysql_learning\employee\mysql\dataset_small\employee.sql"try:print("开始合并SQL文件...")full_sql = merge_sql_files(main_sql_path)# 可选：保存合并后的文件供检查with open('merged_script.sql', 'w', encoding='utf-8') as f:f.write(full_sql)print("开始执行SQL脚本...")execute_mega_sql(db_config, full_sql)print("数据导入成功完成！")except Exception as e:
print(f"导入失败: {str(e)}")
def verify_import():tables = ['employee', 'department', 'dept_emp','dept_manager', 'title', 'salary']cnx = mysql.connector.connect(**db_config)cursor = cnx.cursor(dictionary=True)for table in tables:cursor.execute(f"SELECT COUNT(*) as count FROM {table}")result = cursor.fetchone()print(f"{table}表记录数: {result['count']}")cursor.close()cnx.close()verify_import()
employee表记录数: 1000
department表记录数: 9
dept_emp表记录数: 1103
dept_manager表记录数: 16
title表记录数: 1470
salary表记录数: 9488
mysql> SHOW TABLES;
+-----------------------+
| Tables_in_cookbook |
+-----------------------+
| actors |
| adcount |
| al_winner |
| app_log |
| artist |
| book_authors |
| book_vendor |
| booksales |
| catalog_list |
| cd |
| city |
| color |
| cow_color |
| cow_order |
| current_dept_emp |
| customers |
| date_val |
| datetbl |
| datetime_val |
| department |
| dept_emp |
| dept_emp_latest_date |
| dept_manager |
| dialogue |
| dialogue_analysis |
| die |
| doremi |
| drawing |
| driver_log |
| employee |
| expt |
| formula1 |
| goods_characteristics |
| goods_shops |
| groceries |
| groceries_order_items |
| hitcount |
| hitlog |
| hostip |
| hostname |
| housewares |
| housewares2 |
| housewares3 |
| housewares4 |
| httpdlog |
| httpdlog2 |
| hw_category |
| image |
| ingredient |
| insect |
| inv_item |
| invoice |
| item |
| limbs |
| limbs_backup |
| limbs_stats |
| mail |
| mail2 |
| mail_view |
| marathon |
| mark_log |
| metal |
| money |
| movies |
| movies_actors |
| movies_actors_link |
| mytable |
| name |
| news |
| newsstaff |
| numbers |
| obs |
| occasion |
| painting |
| passtbl |
| passwd |
| patients |
| perl_session |
| person |
| php_session |
| phrase |
| player_stats |
| player_stats2 |
| poi |
| poll_vote |
| profile |
| profile_contact |
| rainfall |
| rand_names |
| rank |
| ranks |
| reviews |
| roster |
| ruby_session |
| salary |
| sales_region |
| sales_tax_rate |
| sales_volume |
| sibling |
| some table |
| standings1 |
| standings2 |
| states |
| str_val |
| sundays |
| taxpayer |
| testscore |
| testscore_withmisses |
| testscore_withmisses2 |
| time_val |
| title |
| tmp |
| tomcat_role |
| tomcat_session |
| tomcat_user |
| top_names |
| trip_leg |
| trip_log |
| tsdemo |
| weatherdata |
| weekday |
+-----------------------+
121 rows in set (0.01 sec)
mysql> SELECT * FROM department LIMIT 5;
+---------+------------------+
| dept_no | dept_name |
+---------+------------------+
| d009 | Customer Service |
| d005 | Development |
| d002 | Finance |
| d003 | Human Resources |
| d001 | Marketing |
+---------+------------------+
5 rows in set (0.00 sec)mysql> SELECT COUNT(*) FROM employee;
+----------+
| COUNT(*) |
+----------+
| 1000 |
+----------+
1 row in set (0.01 sec)
基本语法
CREATE TABLE new_table_name LIKE existing_table_name;
new_table_name：要创建的新表的名称。
existing_table_name：作为模板的现有表的名称。
此语法会复制 existing_table_name 表的所有列定义、索引以及约束到 new_table_name 表中1。
示例代码
以下是一个具体的例子，展示如何使用 CREATE TABLE LIKE 创建一个新表：
– 假设有一个名为 employees 的表
CREATE TABLE employees_backup LIKE employees;
上述语句将创建一个名为 employees_backup 的新表，其结构与 employees 表完全一致，但不包含任何数据1。
如果需要同时复制数据，则可以结合 INSERT INTO SELECT 使用：
– 复制数据到新表
INSERT INTO employees_backup SELECT * FROM employees;
注意事项
CREATE TABLE LIKE 不会复制触发器或视图等其他数据库对象3。
在不同数据库系统中，LIKE 的支持情况可能有所不同。例如，PostgreSQL 支持类似的 CREATE TABLE AS 功能，但行为略有差异2。
结合 INSERT INTO SELECT 的完整示例
以下是一个完整的示例，展示如何结合 CREATE TABLE LIKE 和 INSERT INTO SELECT 创建并填充新表：
– 创建结构相同的空表
CREATE TABLE employees_backup LIKE employees;
– 将数据从原表插入到新表
INSERT INTO employees_backup SELECT * FROM employees WHERE department = ‘Sales’;
上述代码将创建一个名为 employees_backup 的新表，并仅复制属于 Sales 部门的数据
4.1 克隆表
mysql> insert into mail2 select * from mail where srcuser = 'barb';
Query OK, 3 rows affected (0.01 sec)
Records: 3 Duplicates: 0 Warnings: 0mysql> select * from mail2;
+---------------------+---------+---------+---------+---------+-------+
| t | srcuser | srchost | dstuser | dsthost | size |
+---------------------+---------+---------+---------+---------+-------+
| 2006-05-11 10:15:08 | barb | saturn | tricia | mars | 58274 |
| 2006-05-13 13:59:18 | barb | saturn | tricia | venus | 271 |
| 2006-05-14 14:42:21 | barb | venus | barb | venus | 98151 |
+---------------------+---------+---------+---------+---------+-------+
3 rows in set (0.00 sec)
4.2 将查询结果保存到表中
一、CREATE TABLE … LIKE + INSERT INTO … SELECT
原理
结构复制：通过 LIKE 关键字复制源表的完整结构（包括字段、索引、约束等），但不复制数据。
数据筛选插入：使用 INSERT INTO … SELECT 将源表中符合条件的数据插入新表。
实例
场景：从 employees 表复制 2023 年入职的员工到新表 employees_2023。
sql
– 1. 创建结构相同的新表
mysql> CREATE TABLE employees_2023 LIKE employee;
Query OK, 0 rows affected (0.04 sec)
– 2. 插入符合条件的数据
INSERT INTO employees_2023
SELECT *
FROM employee
WHERE hire_date >= ‘2023-01-01’ AND hire_date < ‘2024-01-01’;
Query OK, 0 rows affected (0.01 sec)
Records: 0 Duplicates: 0 Warnings: 0
关键点：
新表 employees_2023 包含与 employees 相同的字段、索引和约束。
SELECT * 表示复制所有字段，需确保源表与目标表结构完全一致。
二、CREATE TABLE … AS SELECT
原理
结构与数据一次性创建：根据 SELECT 子句的字段动态创建新表，并将查询结果直接插入。
新表结构：仅包含 SELECT 中显式指定的字段，不继承源表的索引和约束（除非手动声明）。
实例
场景：创建 high_salary_employees 表，仅包含高薪员工的姓名和薪资。
sql
CREATE TABLE high_salary_employees AS
SELECT
emp_no, – 显式指定字段
first_name+last_name,
salary
FROM employee
WHERE salary > (SELECT AVG(salary) FROM employee); – 筛选高薪员工
关键点：
新表 high_salary_employees 仅包含 emp_id、full_name、salary 三个字段。
不继承源表的索引和约束，如需需手动添加（如 PRIMARY KEY）。
三、两种方法对比
特性 CREATE TABLE … LIKE + INSERT CREATE TABLE … AS SELECT
结构完整性 完全复制源表结构（含索引、约束） 仅复制 SELECT 中指定的字段
数据筛选灵活性 支持复杂 WHERE 条件和多表连接 支持 WHERE，但需提前规划字段列表
性能（大数据量） 分两步执行，可能稍慢 一次性完成，通常更快
适用场景 需要完整保留源表结构，后续可能修改数据 快速创建临时表或统计结果表
四、注意事项
字段匹配：
使用 INSERT INTO … SELECT 时，SELECT 的字段顺序和类型必须与目标表严格一致。
示例（指定字段避免顺序问题）：
sql
INSERT INTO employees_2023 (emp_id, full_name, hire_date)
SELECT id, name, hired_at FROM employees WHERE …;
约束与索引：
CREATE TABLE … AS 不会复制源表的约束（如 NOT NULL）和索引，需手动添加：
sql
CREATE TABLE high_salary_employees AS
SELECT emp_id, salary FROM employees WHERE …;
ALTER TABLE high_salary_employees ADD PRIMARY KEY (emp_id); – 手动添加主键
临时表优化：
若需快速统计，可结合 TEMPORARY 关键字：
sql
CREATE TEMPORARY TABLE temp_stats AS
SELECT dept_id, COUNT(*) FROM employees GROUP BY dept_id;
五、实战案例
需求：从 orders 表复制 2023 年 1 月的订单到新表，并添加额外统计字段。
sql
– 方法一：LIKE + INSERT
CREATE TABLE jan2023_orders LIKE orders;
INSERT INTO jan2023_orders
SELECT *
FROM orders
WHERE order_date BETWEEN ‘2023-01-01’ AND ‘2023-01-31’;
– 方法二：AS SELECT（带计算字段）
CREATE TABLE jan2023_orders_stats AS
SELECT
order_id,
customer_id,
amount,
amount * 0.9 AS discounted_amount – 计算字段
FROM orders
WHERE order_date BETWEEN ‘2023-01-01’ AND ‘2023-01-31’;
通过上述方法，你可以灵活复制表结构和数据，满足不同场景需求。
https://www.modb.pro/db/408221
MySQL 样例数据库 Employee 的制作过程