In modern applications, it is important to keep track of changes made to data in order to provide auditing, rollbacks, and to maintain data integrity. One way to implement change tracking is by using an Object-Relational Mapping (ORM) library with SQL.

In this blog post, we will explore how to implement change tracking using an ORM in SQL, focusing on the popular SQLAlchemy library in Python.

Change Tracking with SQLAlchemy

SQLAlchemy is a powerful and flexible ORM library that provides a high-level API for interacting with databases in Python. It supports different database engines including PostgreSQL, MySQL, SQLite, and more.

To implement change tracking with SQLAlchemy, we can take advantage of SQLAlchemy’s event system. We can register event handlers for specific database events, such as INSERT, UPDATE, and DELETE, and perform custom logic to track the changes.

Here’s an example of how to implement change tracking using SQLAlchemy:

from sqlalchemy import create_engine, event
from sqlalchemy.orm import sessionmaker
from sqlalchemy.ext.declarative import declarative_base

# Create the SQLAlchemy engine and session
engine = create_engine('sqlite:///mydatabase.db')
Session = sessionmaker(bind=engine)
session = Session()

# Create the base model
Base = declarative_base()

# Define a model to track changes
class ChangeLog(Base):
    __tablename__ = 'changelog'
    
    id = Column(Integer, primary_key=True)
    table_name = Column(String)
    row_id = Column(Integer)
    event_type = Column(String)
    timestamp = Column(DateTime)

# Register an event handler for the INSERT event
@event.listens_for(Base, 'after_insert')
def track_insert(mapper, connection, target):
    change_log = ChangeLog(
        table_name=target.__class__.__tablename__,
        row_id=target.id,
        event_type='INSERT',
        timestamp=datetime.now()
    )
    session.add(change_log)
    session.commit()

# Register an event handler for the UPDATE event
@event.listens_for(Base, 'after_update')
def track_update(mapper, connection, target):
    change_log = ChangeLog(
        table_name=target.__class__.__tablename__,
        row_id=target.id,
        event_type='UPDATE',
        timestamp=datetime.now()
    )
    session.add(change_log)
    session.commit()

# Register an event handler for the DELETE event
@event.listens_for(Base, 'after_delete')
def track_delete(mapper, connection, target):
    change_log = ChangeLog(
        table_name=target.__class__.__tablename__,
        row_id=target.id,
        event_type='DELETE',
        timestamp=datetime.now()
    )
    session.add(change_log)
    session.commit()

In this example, we create a ChangeLog model that represents a log entry for each change made to any table. We register event handlers for the after_insert, after_update, and after_delete events, and create a new ChangeLog record for each event.

The ChangeLog model has fields such as table_name, row_id, event_type, and timestamp. These fields capture information about the changed table, row identifier, type of change, and the timestamp of the change.

By using SQLAlchemy’s event system, we can easily implement change tracking in our application to keep track of data modifications.

Conclusion

Implementing change tracking with an ORM like SQLAlchemy can greatly simplify the process of tracking changes made to data in a database. By leveraging SQLAlchemy’s event system, we can easily capture INSERT, UPDATE, and DELETE events and record them in a separate change log table.

This approach allows us to implement auditing, rollbacks, and other data integrity measures in our applications. With proper change tracking, we can gain insights into our data and ensure its integrity over time.

#SQL #ORM