When migrating MySQL data, how to efficiently handle primary key updates and migration of associated fields of 80 tables?

Efficient migration of MySQL database: primary key update and associated field processing of 80 tables

Faced with the MySQL database migration, especially complex scenarios involving 80 tables, primary keys and related fields updates, it is crucial to efficiently complete data migration. This article discusses a Python script-based solution for migrating specific user data from MySQL 5.5 database to a new database and regenerate auto-added primary keys and update associated fields.

Migration steps and strategies

1. Data security: Backup first

   Be sure to fully back up the original database before any migration operations to prevent data loss. This step is crucial.

2. Python script automation migration

   To improve efficiency, it is recommended to use Python scripts to automate the entire migration process. The following example script simplifies the core logic and needs to be adjusted according to the specific table structure in actual applications:

    import pymysql
   
   # Database connection information (replace with your actual information)
   src_conn_params = {
       'host': 'src_host',
       'user': 'src_user',
       'password': 'src_password',
       'db': 'src_db'
   }
   dst_conn_params = {
       'host': 'dst_host',
       'user': 'dst_user',
       'password': 'dst_password',
       'db': 'dst_db'
   }
   
   def migrate_data(table_name, src_conn, dst_conn):
       """Migrate data from a single table and update primary key map"""
       src_cursor = src_conn.cursor()
       dst_cursor = dst_conn.cursor()
       id_mapping = {} # Store the mapping of the old primary key and the new primary key # Get data (please modify the SQL statement based on the actual table structure)
       src_cursor.execute(f"SELECT * FROM {table_name}")
       data = src_cursor.fetchall()
   
       # Insert data into the target database and record the primary key map for row in data:
           # Assuming the primary key is the first column, the other fields are arranged in order old_id = row[0]
           new_row = row[1:] # Remove the old primary key dst_cursor.execute(f"INSERT INTO {table_name} VALUES ({','.join(['%s'] * len(new_row))})", new_row)
           new_id = dst_cursor.lastrowid
           id_mapping[old_id] = new_id
   
       return id_mapping
   
   def update_foreign_keys(table_name, field_name, id_mapping, dst_conn):
       """Update foreign keys in association table"""
       dst_cursor = dst_conn.cursor()
       for old_id, new_id in id_mapping.items():
           dst_cursor.execute(f"UPDATE {table_name} SET {field_name} = %s WHERE {field_name} = %s", (new_id, old_id))
   
   try:
       with pymysql.connect(**src_conn_params) as src_conn, pymysql.connect(**dst_conn_params) as dst_conn:
           # Migrate all 80 tables for table_name in ['table1', 'table2', ..., 'table80']: # Replace with your 80 table names id_map = migrate_data(table_name, src_conn, dst_conn)
               # Update the foreign keys of the associated table (please modify the table name and field name according to the actual situation)
               update_foreign_keys('related_table1', 'foreign_key1', id_map, dst_conn)
               dst_conn.commit()
   except Exception as e:
       print(f"Migration failed: {e}")

   This script provides a basic framework that needs to be modified and improved based on the actual table structure and association relationship. Pay special attention to the correctness of SQL statements and consider batch processing to improve efficiency.

Through the above steps, combined with the automated processing capabilities of Python scripts, the MySQL database migration of 80 tables can be efficiently completed, and the primary key update and associated fields can be properly handled to ensure data integrity and consistency. Remember, in actual applications, you need to adjust and optimize according to your database structure and data volume. For example, it may be considered to use transaction processing to ensure data consistency and use connection pools to improve database connection efficiency.

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