SQLAlchemy Python Library Cheatsheet

SQLAlchemy is a powerful and widely-used Python library for working with relational databases. It provides a flexible and expressive way to interact with databases, allowing developers to build sophisticated and efficient database-driven applications. Whether you are a beginner or an experienced developer, having a cheatsheet for SQLAlchemy can be incredibly handy. In this blog post, we’ll provide a quick reference guide to help you navigate and use SQLAlchemy effectively.

1. Installation

pip install sqlalchemy

2. Importing SQLAlchemy

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

3. Creating an Engine

# SQLite in-memory database
engine = create_engine('sqlite:///:memory:')

# SQLite file database
engine = create_engine('sqlite:///example.db')

# PostgreSQL database
engine = create_engine('postgresql://user:password@localhost/dbname')

4. Defining a Model

Base = declarative_base()

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

5. Creating Tables


6. Session Configuration

Session = sessionmaker(bind=engine)
session = Session()

7. Adding Data

new_user = User(name='John Doe', age=25)

8. Querying Data

# Query all users
users = session.query(User).all()

# Filter users by age
young_users = session.query(User).filter(User.age < 30).all()

9. Updating Data

user = session.query(User).filter_by(name='John Doe').first()
user.age = 26

10. Deleting Data

user_to_delete = session.query(User).filter_by(name='John Doe').first()

11. Relationships

class Address(Base):
    __tablename__ = 'addresses'
    id = Column(Integer, primary_key=True)
    email = Column(String)
    user_id = Column(Integer, ForeignKey('users.id'))
    user = relationship('User', back_populates='addresses')

User.addresses = relationship('Address', order_by=Address.id, back_populates='user')

12. Joining Tables

result = session.query(User, Address).join(Address).filter(User.id == Address.user_id).all()

13. Rolling Back Changes

    # your code here

This cheatsheet provides a quick reference for common SQLAlchemy tasks. However, SQLAlchemy is a feature-rich library, and there is much more to explore. Refer to the official documentation for in-depth information and advanced features.


1. What is the main purpose of SQLAlchemy?

SQLAlchemy is an Object-Relational Mapping (ORM) library for Python that provides a set of high-level API for communicating with relational databases. Its primary purpose is to simplify database interactions by allowing developers to work with database entities as Python objects, abstracting away the complexities of SQL queries and database-specific syntax.

2. How does SQLAlchemy handle database connections and sessions?

SQLAlchemy uses an Engine to handle database connections. The Engine is responsible for managing a pool of database connections. Sessions, on the other hand, are used to manage transactions and provide a consistent unit of work. The Session class in SQLAlchemy helps organize and coordinate high-level operations on a set of objects.

3. What is the difference between declarative and classical mapping in SQLAlchemy?

SQLAlchemy supports two approaches to define database models: declarative and classical (also known as “classical mapping”). Declarative mapping uses a class-based approach where models are defined as Python classes with a declarative base. Classical mapping involves explicitly defining Table, Mapper, and Class constructs separately. Declarative mapping is more concise and is the recommended approach for most use cases.

4. How can I perform complex queries and joins with SQLAlchemy?

SQLAlchemy provides a powerful query API that allows developers to perform complex queries and joins. The query() method is used to create queries, and you can apply various filter and join operations to it. The join() method is commonly used for joining tables. Additionally, relationships between models are defined to simplify queries involving multiple tables.

5. Can SQLAlchemy be used with NoSQL databases?

While SQLAlchemy is primarily designed for relational databases, there are extensions and third-party libraries that allow it to work with some NoSQL databases. For example, SQLAlchemy has an extension called SQLAlchemy-Dynamo for working with Amazon DynamoDB. However, it’s important to note that SQLAlchemy’s strengths lie in relational databases, and the level of abstraction it provides may not be as seamless for NoSQL databases compared to traditional relational databases.