SQLAlchemy Object Relational Mapper (ORM) State Management

Albert Byrone
3 min readDec 15, 2023


Managing the state of your data is crucial for robust applications, and SQLAlchemy ORM offers a powerful toolkit for handling this with elegance and efficiency. 💼

In SQLAlchemy, the ORM layer helps us bridge the gap between the relational database and our Python code, making it seamless to work with database records as Python objects. 🐍

Key points on state management with SQLAlchemy ORM:

1️⃣ Identity Map Pattern: SQLAlchemy keeps track of objects in a unit of work using the Identity Map pattern. This ensures that each database row is represented by a single Python object within a session.

2️⃣ Session State: The Session in SQLAlchemy acts as a staging area for all changes before committing them to the database. Understanding and managing session states is vital for a smooth workflow.

3️⃣ Dirty Tracking: SQLAlchemy automatically tracks changes to objects and only updates the fields that have been modified. This minimizes unnecessary database updates, optimizing performance.

4️⃣ Unit of Work Pattern: SQLAlchemy employs the Unit of Work pattern to coordinate changes and transactions, making it easier to manage complex operations involving multiple objects.

5️⃣ Flushing and Committing: Explicitly flushing the session synchronizes changes with the database, and committing finalizes the transaction. Understanding when to flush and commit is essential for data integrity.

6️⃣ Events and Hooks: SQLAlchemy provides a rich set of events and hooks, allowing customization of state management behaviors to suit specific application needs.

Assume you have a User model representing users in a database:

from sqlalchemy import create_engine, Column, Integer, String, Sequence
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import sessionmaker

# Define the User model
Base = declarative_base()

class User(Base):
__tablename__ = 'users'
id = Column(Integer, Sequence('user_id_seq'), primary_key=True)
name = Column(String(50))
age = Column(Integer)

# Create an SQLite in-memory database and bind the engine
engine = create_engine('sqlite:///:memory:')

# Create the 'users' table in the database

# Create a Session class to interact with the database
Session = sessionmaker(bind=engine)
session = Session()

Now, let’s explore state management:

# Create a new user and add it to the session
new_user = User(name='John Doe', age=25)

# At this point, the user is in the 'Pending' state, not yet committed to the database

# Query the user (this will trigger a flush to the database)
queried_user = session.query(User).filter_by(name='John Doe').first()

# The queried_user is now in the 'Persistent' state, representing a database record

# Modify the user's age
queried_user.age = 26

# At this point, SQLAlchemy has marked the user as 'Dirty' because it has been modified

# Commit the changes to the database

# Now, the changes are saved to the database, and the user is in the 'Committed' state

# The session is now in a 'Clean' state, and the user is no longer marked as 'Dirty'

In this example:

  • Pending: When a new user is added to the session, it is in a pending state.
  • Persistent: After querying a user from the database, it becomes persistent.
  • Dirty: If you modify a persistent object, it becomes dirty.
  • Committed: After committing changes, the object is in a committed state.
  • Clean: The session is clean after committing, and the object is no longer dirty.

Understanding these states helps you manage your data effectively when working with SQLAlchemy ORM. Feel free to experiment and build upon this example!