Difference between psycopg2 and SQLAlchemy in Python

Comparing SQLAlchemy and psycopg2, the choice between the two is primarily dictated by the project at hand. SQLAlchemy is perfect for the situations when a developer does not only need an ORM to interact with the database, but would also like the comfort of choosing the database type. On the other hand, psycopg2 will suit well for developers who are in search of simple and efficient instrument that works specifically with PostgreSQL.

Comparison Between SQLAlchemhy and Psycopg2

Let use see the difference between SQLAlchemy and Psycopg2 for a better understanding.

What is SQLAlchemy?

SQLAlchemy is a Library for writing SQL-compliant commands in Python and an Object Relational mapping tool for Python. It offers a complete range of the most widely used high-performance business-level persistence patterns. SQLAlchemy helps the developers to write the code in Python Objects rather than the raw SQL, thus providing an interface or layer to manipulate the databases which can be useful in controlling the database transactions.

Key Features

• ORM Capabilities: This is implemented in SQLAlchemy’s ORM where you can map Python classes to database tables and this will enable you work with record in the database as Python objects.
• SQL Expression Language: For those who wish to be closer to the database, thus targeted at direct manipulation, the library provides a SQL Expression Layer.
• Cross-Database Support: Dialects are the implemented engines that are used by SQLAlchemy to connect with various SQL database systems; for instance MySQL, PostgreSQL, SQLite.
• Declarative Mapping: It enables depiction of the database schema using the classes under Python.

Example:

Let's consider a simple example where we have a User model that represents a user in a database. We first connect to a PostgreSQL database using create_engine() and set up a base class for ORM models with declarative_base().

Then, a 'User' class is defined, representing the users table with columns for 'id', 'name', and 'email'. The Base.metadata.create_all(engine) command creates the users table in the database. Lastly, a session is created to interact with the database. A new user is added to the users table, and then a query is made to retrieve and print the email.

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

# Define the database engine
engine = create_engine('postgresql://username:password@localhost/mydatabase')

# Base class for the ORM models
Base = declarative_base()

# Define the User model
class User(Base):
    __tablename__ = 'users'
    id = Column(Integer, primary_key=True)
    name = Column(String)
    email = Column(String)

# Create the database table
Base.metadata.create_all(engine)

# Create a new session
Session = sessionmaker(bind=engine)
session = Session()

# Add a new user to the database
new_user = User(name='John Doe', email='[email protected]')
session.add(new_user)
session.commit()

# Query the user
user = session.query(User).filter_by(name='John Doe').first()
print(user.email)

What is psycopg2?

psycopg2 can be described as a PostgreSQL database adapter for the use in Python applications, which means that it is in fact an interface that enables the proper communication between Python and PostgreSQL database. This is the most regularly used PostgreSQL database adapter and can be used for executing simple Create, Read, Update, Delete operations with PostgreSQL.

Key Features:

• Native PostgreSQL Support: psycopg2 is a PostgreSQL adapter distinguished by high performance and strict compliance with the database.
• Thread Safety: It is created to work in multiple threads and can thus be used in web applications, where the client can simultaneously access many databases.
• Simple to Use: psycopg2 is easy to use and on some occasions one can run simple raw SQL commands directly on the database hence its popularity especially in basic applications.
• Support for Advanced PostgreSQL Features: This includes; Large objects, Asynchronous commands and Queries, Notifications.

Example:

Here's an example of how you can achieve the same functionality as the SQLAlchemy example using psycopg2. We first connect to a PostgreSQL database using psycopg2.connect(), specifying the database name, username, password, and host. Then a cursor is created to execute SQL commands. The code executes a SQL command to create a users table with columns 'id', 'name', and 'email' if it doesn't already exist.

A new user is inserted into the users table. The code queries the table to find the email of the user and prints it. Finally, the cursor and database connection are closed to free resources.

import psycopg2

# Connect to the PostgreSQL database
connection = psycopg2.connect(
    dbname="mydatabase",
    user="username",
    password="password",
    host="localhost"
)
cursor = connection.cursor()

# Create the users table
cursor.execute("""
    CREATE TABLE IF NOT EXISTS users (
        id SERIAL PRIMARY KEY,
        name VARCHAR(100),
        email VARCHAR(100)
    )
""")
connection.commit()

# Insert a new user
cursor.execute("""
    INSERT INTO users (name, email)
    VALUES (%s, %s)
""", ('John Doe', '[email protected]'))
connection.commit()

# Query the user
cursor.execute("SELECT email FROM users WHERE name = %s", ('John Doe',))
user_email = cursor.fetchone()[0]
print(user_email)

# Close the connection
cursor.close()
connection.close()

Use Cases of SQLAlchemy and psycopg2

SQLAlchemy and Psycopg2 have various use cases.

When to Use SQLAlchemy:

• When you are using a number of databases or if you need constant and easy to implement database changes.
• In cases where it is advisable to apply ORM in the given project, for example, in the case of applications with high levels of data entanglement.
• In case you don’t like writing raw SQL queries, preferably if you are comfortable with objects.

When to Use psycopg2:

• When developing an application using only PostgreSQL and where it is necessary to have direct calls to the most functions of the database.
• A scenario where the use of an ORM might be disadvantageous is in performance-sensitive application environments.
• When you require raw access to the database and want to write your own SQL statements.

Pros and Cons

The following table shows the Pros and Cons of SQLAlchemy and Psycopy2.