Implementing optimistic concurrency control with SQL ORM

29 Sep 2023

Optimistic concurrency control is a technique used to handle concurrent updates in a database system. It allows multiple users to work on the same data simultaneously by assuming that conflicts are unlikely to occur. When a user tries to update a record, the system verifies whether the data has been changed by another user during the update process.

In this blog post, we will explore how to implement optimistic concurrency control using a SQL ORM (Object-Relational Mapping) framework, such as SQLAlchemy for Python.

What is Optimistic Concurrency Control?

Optimistic concurrency control operates on the assumption that conflicts are rare, and therefore locks on data are minimized. Instead of acquiring locks and blocking other users, it allows concurrent modifications to proceed independently. However, before committing the changes, it verifies if any conflicts occurred.

Implementing Optimistic Concurrency Control with SQLAlchemy

SQLAlchemy is a popular SQL ORM library that provides an expressive and flexible way to work with databases in Python. Here’s how you can implement optimistic concurrency control using SQLAlchemy:

Versioning the Model: Add a version column to your database table, which will be used to track changes. This column should be an integer field that increments with every update.

class MyModel(Base):
    __tablename__ = 'mymodel'
    id = Column(Integer, primary_key=True)
    data = Column(String)
    version = Column(Integer, nullable=False)

Updating the Model: Whenever you update a record, you also need to increment its version number.

session = Session()
record = session.query(MyModel).get(1)
record.data = "Updated data"
record.version += 1
session.commit()

Handling Concurrency Conflicts: When persisting the changes, SQLAlchemy’s flush() method is used. If another user has modified the record concurrently, a StaleDataError will be raised. You can catch this exception and handle the conflict as per your application’s requirements.
```
session = Session()
try:
    session.flush()
except sqlalchemy.exc.StaleDataError:
    # Handle concurrency conflict
    # e.g., Notify the user, reload the record, merge changes, etc.
    session.rollback()
```

Conclusion

Implementing optimistic concurrency control using a SQL ORM like SQLAlchemy allows you to handle concurrent updates in a more efficient and elegant way. By versioning the model and handling conflicts when they occur, you can ensure data integrity and improve application performance.

Remember to use optimistic concurrency control for situations where conflicts are infrequent. In highly concurrent systems, pessimistic concurrency control techniques may be more appropriate.

#SQL #ORM