Showing posts with label Database. Show all posts
Showing posts with label Database. Show all posts

Friday, 5 April 2024

Create database connection using Python and SQLAlchemy

SQLAlchemy is an awesome Object-Relational Mapping (ORM) library that allows us to interact with databases using Python. I will provide you with my step-by-step notes on how to create a database using Python and Alchemy.

Prerequisites:

Before you begin you have to make sure you have the necessary tools installed.

  1. Python
  2. SQLAlchemy (install via pip: pip install sqlalchemy)

Awesome. Now let’s begin!

Step 1: Import necessary modules

Let's start by creating a new Python script and importing the necessary modules.

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

Step 2: Establish a database connection

Create a database connection using SQLAlchemy’s create_engine function. Replace 'your_database_url' with the URL of your database.

  • For PostgreSQL, the URL format is similar to MySQL:
# PostgreSQL database URL
database_url = 'postgresql://username:password@host/database_name'
  • For MySQL, you need to provide the necessary credentials (username, password, host, and database name) in the URL format:
# MySQL database URL
database_url = 'mysql+pymysql://username:password@host/database_name'

For MySQLite, he URL would be:

engine = create_engine('sqlite:///your_database_name.db')

Note: Replace 'username', 'password', 'host', and 'database_name' with your MySQL or PostgreSQL credentials and database information. It's good to remember the database needs to be installed and running before you are connecting to it.

Step 3: Define your data mode

To create your data model, make a Python class that inherits from the Base. Each attribute of the class represents a column in the relevant database table. You can include additional columns as you see fit.

class User(Base):
    __tablename__ = 'users'

    id = Column(Integer, primary_key=True)
    username = Column(String(50), unique=True, nullable=False)
    email = Column(String(100), unique=True, nullable=False
    password = Column(String(100), unique=True, nullable=False)
    created_at = Column(DateTime, default=datetime.datetime.utcnow)

Step 4: Create the database tables

With the data model defined, create the database tables using the create_all method of the Base class:

Base.metadata.create_all(engine)

Step 5: Insert data into the database

To insert data into your database, first, create a session using SQLAlchemy’s sessionmaker:

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

Step 6: Adding and creating objects

Hurray! Now, you can create objects and add them to your database.

# Example: Inserting a new user into the database
new_user = User(username='Sandy', email='sandy@gmail.com', password='cool-password')
session.add(new_user)
session.commit()

Step 7: Query data from the database

To retrieve data from the database, use the session’s query method:

# Example: Querying all users from the database
all_users = session.query(User).all()

# Example: Querying a specific user by their username
user = session.query(User).filter_by(username='Sandy').first()

Step 8: Close the session

Remember to close the session once you’re done working with the database:

session.close()

SQL vs SQLAlchemy

If we were to compare SQLAlchemy and plain SQL, we could notice the difference in how they interact with data and the advantages they offer.

  1. Abstraction level:
  • SQLAlchemy: Provides a high-level abstraction with Object-Relational Mapping (ORM). It eliminates the need to write raw SQL queries.
  • Plain SQL: Requires us to write raw SQL queries and manually handle the mapping between Python data and the database tables.

2. Portability:

  • SQLAlchemy: Offers database abstraction allowing us to easily switch between different database URLs.
  • Plain SQL: Often writes database-specific queries, requiring us to rewrite the queries to match a new database’s syntax when switching systems.

3. Safety and security:

  • SQLAlchemy: Provides protection against SQL injection attacks. SQL injection is a type of code injection that targets data-driven applications by inserting malicious SQL statements into an entry field. SQLAlchemy handles parameter binding and query construction to prevent these attacks.
  • Plain SQL: Vulnerable to SQL injection if user-provided data is not properly sanitized. To sanitize is to program our system to detect malicious bugs that can harm our database.

4. Code maintainability and readability:

  • SQLAlchemy: Enhances the maintainability and readability of code, just like regular Python code is used for interacting with databases.
  • Plain SQL: Queries can become long and complex, and potentially impact our code’s readability and maintainability.

5. Database schema management:

  • SQLAlchemy: Simplifies schema management and version control by providing tools for managing database schemas using Python classes.
  • Plain SQL: Requires manual handling of schema changes using SQL migration scripts or external tools.

Migration scripts are SQL scripts made by users in ApexSQL Source Control. They help configure changes, handle overrides and more.

After everything has been said and done, our SQLAlchemy code should resemble the following example:

# Step 1: Import the necessary modules

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


# Step 2: Establish a database connection

database_url = 'sqlite:///your_database_name.db'

# Create an engine to connect to a SQLite database
engine = create_engine(database_url)

#will return engine instance
Base = declarative_base()

# Step 3: Define your data model
class User(Base):
    __tablename__ = 'users'

    id = Column(Integer, primary_key=True)
    username = Column(String(50), unique=True, nullable=False)
    email = Column(String(100), unique=True, nullable=False)
    created_at = Column(DateTime, default=datetime.datetime.utcnow)

# Step 4: Create the database tables
Base.metadata.create_all(engine)

# Step 5: Insert data into the database
Session = sessionmaker(bind=engine)
session = Session()

# Example: Inserting a new user into the database
new_user = User(username='Sandy', email='sandy@gmail.com', password='cool-password')
session.add(new_user)
session.commit()

# Step 6: Query data from the database
# Example: Querying all users from the database
all_users = session.query(User).all()

# Example: Querying a specific user by their username
user = session.query(User).filter_by(username='Sandy').first()

# Step 7: Close the session
session.close()

Conclusion:

Great job! You have successfully mastered the art of creating a database with Python and SQLAlchemy.

Things to remember:

  • Engine: It establishes a connection to the database and handles the communication behind the scenes for us.
  • Declarative Base: We utilize SQLAlchemy's declarative base to establish a link between our Python classes and the corresponding database tables.
  • Sessions: We use this tool to manage our database interactions and it offers us methods to effectively manage CRUD operations.
  • Querying: This tool allows us to create intricate database queries through Python methods.
  • Relationships: With SQLAlchemy, we can establish connections between various classes that represent database relationships such as one-to-many, many-to-one, and many-to-many.
  • Mapping: Using SQLAlchemy, we also have the ability to map attributes of our Python classes to columns in our corresponding database tables.

If you are looking for a flexible library to manage databases in your Python projects, SQLAlchemy is a great choice. It provides a user-friendly and robust interface to handle all your database-related tasks with ease.


Source : https://medium.com/@sandyjtech/creating-a-database-using-python-and-sqlalchemy-422b7ba39d7e

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