SQLAlchemy is a popular ORM provider for Python applications, and is widely used by Python developers for database access. YugabyteDB provides full support for SQLAlchemy ORM.

CRUD operations

Learn how to establish a connection to YugabyteDB database and begin basic CRUD operations using the steps in the Build an application page.

The following sections demonstrate how to perform common tasks required for Python application development using the SQLAlchemy ORM.

Add the SQLAlchemy ORM dependency

To download and install SQLAlchemy to your project, use the following command.

pip3 install sqlalchemy

You can verify the installation as follows:

  1. Open the Python prompt by executing the following command:

    python3
    
  2. From the Python prompt, execute the following commands to check the SQLAlchemy version:

    import sqlalchemy
    
    sqlalchemy.__version__
    

Implement ORM mapping for YugabyteDB

To start with SQLAlchemy, in your project directory, create 4 Python files - config.py,base.py,model.py, and main.py

  1. config.py contains the credentials to connect to your database. Copy the following sample code to the config.py file.

     db_user = 'yugabyte'
     db_password = 'yugabyte'
     database = 'yugabyte'
     db_host = 'localhost'
     db_port = 5433
    
  2. Next, declare a mapping. When using the ORM, the configuration process begins with describing the database tables you'll use, and then defining the classes which map to those tables. In modern SQLAlchemy, these two tasks are usually performed together, using a system known as Declarative Extensions. Classes mapped using the Declarative system are defined in terms of a base class which maintains a catalog of classes and tables relative to that base - this is known as the declarative base class. You create the base class using the declarative_base() function. Add the following code to the base.py file.

    from sqlalchemy.ext.declarative import declarative_base
    Base = declarative_base()
    
  3. Now that you have a base, you can define any number of mapped classes in terms of it. Start with a single table called employees, to store records for the end-users using your application. A new class called Employee maps to this table. In the class, you define details about the table to which you're mapping; primarily the table name, and names and datatypes of the columns. Add the following to the model.py file:

    from sqlalchemy.ext.declarative import declarative_base
    from sqlalchemy import Table, Column, Integer, String, DateTime, ForeignKey
    from base import Base
    
    class Employee(Base):
    
      __tablename__ = 'employees'
    
      id = Column(Integer, primary_key=True)
      name = Column(String(255), unique=True, nullable=False)
      age = Column(Integer)
      language = Column(String(255))
    
  4. After the setup is done, you can connect to the database and create a new session. In the main.py file, add the following:

    import config as cfg
    from sqlalchemy.orm import sessionmaker, relationship
    from sqlalchemy import create_engine
    from sqlalchemy import MetaData
    from model import Employee
    from base import Base
    from sqlalchemy import Table, Column, Integer, String, DateTime, ForeignKey
    
    # create connection
    engine = create_engine('postgresql://{0}:{1}@{2}:{3}/{4}'.format(cfg.db_user, cfg.db_password, cfg.db_host, cfg.db_port, cfg.database))
    
    # create metadata
    Base.metadata.create_all(engine)
    
    # create session
    Session = sessionmaker(bind=engine)
    session = Session()
    
    # insert data
    tag_1 = Employee(name='Bob', age=21, language='Python')
    tag_2 = Employee(name='John', age=35, language='Java')
    tag_3 = Employee(name='Ivy', age=27, language='C++')
    
    session.add_all([tag_1, tag_2, tag_3])
    
    # Read the inserted data
    
    print('Query returned:')
    for instance in session.query(Employee):
        print("Name: %s Age: %s Language: %s"%(instance.name, instance.age, instance.language))
    session.commit()
    

When you run the main.py file, you should get the output similar to the following:

Query returned:
Name: Bob Age: 21 Language: Python
Name: John Age: 35 Language: Java
Name: Ivy Age: 27 Language: C++

Next steps

Explore Scaling Python Applications with YugabyteDB.