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Chapter 19: Advanced Database Operations with SQLAlchemy

19.3 Understanding SQLAlchemy ORM

SQLAlchemy is a comprehensive and powerful toolkit that offers a wide range of enterprise-level persistence patterns, designed to enable efficient and high-performing database access. It provides a simple and Pythonic domain language that is easy to use and understand.

The concept of Object Relational Mapping, or ORM, is a technique that allows for the connection of the rich objects of an application to tables in a relational database management system. By using ORM, the properties and relationships of the objects in an application can be easily stored and retrieved from a database without the need to write SQL statements directly, thereby reducing the overall database access code.

In addition, the ORM in SQLAlchemy is constructed on top of the Core, providing a full suite of mapping capabilities between Python classes and relational databases. This means that SQLAlchemy provides a flexible and comprehensive approach to database access, which can be tailored to suit the specific needs of your application.

Example:

Let's start with a simple example of creating a SQLAlchemy Session, which is the main object used to interact with an ORM-mapped database:

from sqlalchemy import create_engine
from sqlalchemy.orm import sessionmaker

engine = create_engine('postgresql+psycopg2://myuser:mypassword@localhost:5432/mydatabase')

Session = sessionmaker(bind=engine)

session = Session()

Here we first create an engine that knows how to connect to the database, then define a Session class that will serve as a factory for new Session instances, and finally create a session that we can use to talk to the database.

This session is a handle to the database, similar to a cursor in a traditional database API, but with many more features. You can use it to query the database, modify the database, and transactionally persist changes to the database.

Now that we have a session, we can use it to execute SQL queries. But before we can do that, we need to define our data models.

from sqlalchemy import Column, Integer, String
from sqlalchemy.ext.declarative import declarative_base

Base = declarative_base()

class User(Base):
   __tablename__ = 'users'

   id = Column(Integer, primary_key=True)
   name = Column(String)
   fullname = Column(String)
   nickname = Column(String)

   def __repr__(self):
       return "<User(name='%s', fullname='%s', nickname='%s')>" % (
                            self.name, self.fullname, self.nickname)

In this code, we define a User class that includes fields for an id, name, fullname, and nickname. The class uses SQLAlchemy's Declarative system, which provides a convenient way to declare schemas and models in a single class declaration.

In the next section, we will look at how to use these models to perform database operations using the SQLAlchemy ORM.

19.3 Understanding SQLAlchemy ORM

SQLAlchemy is a comprehensive and powerful toolkit that offers a wide range of enterprise-level persistence patterns, designed to enable efficient and high-performing database access. It provides a simple and Pythonic domain language that is easy to use and understand.

The concept of Object Relational Mapping, or ORM, is a technique that allows for the connection of the rich objects of an application to tables in a relational database management system. By using ORM, the properties and relationships of the objects in an application can be easily stored and retrieved from a database without the need to write SQL statements directly, thereby reducing the overall database access code.

In addition, the ORM in SQLAlchemy is constructed on top of the Core, providing a full suite of mapping capabilities between Python classes and relational databases. This means that SQLAlchemy provides a flexible and comprehensive approach to database access, which can be tailored to suit the specific needs of your application.

Example:

Let's start with a simple example of creating a SQLAlchemy Session, which is the main object used to interact with an ORM-mapped database:

from sqlalchemy import create_engine
from sqlalchemy.orm import sessionmaker

engine = create_engine('postgresql+psycopg2://myuser:mypassword@localhost:5432/mydatabase')

Session = sessionmaker(bind=engine)

session = Session()

Here we first create an engine that knows how to connect to the database, then define a Session class that will serve as a factory for new Session instances, and finally create a session that we can use to talk to the database.

This session is a handle to the database, similar to a cursor in a traditional database API, but with many more features. You can use it to query the database, modify the database, and transactionally persist changes to the database.

Now that we have a session, we can use it to execute SQL queries. But before we can do that, we need to define our data models.

from sqlalchemy import Column, Integer, String
from sqlalchemy.ext.declarative import declarative_base

Base = declarative_base()

class User(Base):
   __tablename__ = 'users'

   id = Column(Integer, primary_key=True)
   name = Column(String)
   fullname = Column(String)
   nickname = Column(String)

   def __repr__(self):
       return "<User(name='%s', fullname='%s', nickname='%s')>" % (
                            self.name, self.fullname, self.nickname)

In this code, we define a User class that includes fields for an id, name, fullname, and nickname. The class uses SQLAlchemy's Declarative system, which provides a convenient way to declare schemas and models in a single class declaration.

In the next section, we will look at how to use these models to perform database operations using the SQLAlchemy ORM.

19.3 Understanding SQLAlchemy ORM

SQLAlchemy is a comprehensive and powerful toolkit that offers a wide range of enterprise-level persistence patterns, designed to enable efficient and high-performing database access. It provides a simple and Pythonic domain language that is easy to use and understand.

The concept of Object Relational Mapping, or ORM, is a technique that allows for the connection of the rich objects of an application to tables in a relational database management system. By using ORM, the properties and relationships of the objects in an application can be easily stored and retrieved from a database without the need to write SQL statements directly, thereby reducing the overall database access code.

In addition, the ORM in SQLAlchemy is constructed on top of the Core, providing a full suite of mapping capabilities between Python classes and relational databases. This means that SQLAlchemy provides a flexible and comprehensive approach to database access, which can be tailored to suit the specific needs of your application.

Example:

Let's start with a simple example of creating a SQLAlchemy Session, which is the main object used to interact with an ORM-mapped database:

from sqlalchemy import create_engine
from sqlalchemy.orm import sessionmaker

engine = create_engine('postgresql+psycopg2://myuser:mypassword@localhost:5432/mydatabase')

Session = sessionmaker(bind=engine)

session = Session()

Here we first create an engine that knows how to connect to the database, then define a Session class that will serve as a factory for new Session instances, and finally create a session that we can use to talk to the database.

This session is a handle to the database, similar to a cursor in a traditional database API, but with many more features. You can use it to query the database, modify the database, and transactionally persist changes to the database.

Now that we have a session, we can use it to execute SQL queries. But before we can do that, we need to define our data models.

from sqlalchemy import Column, Integer, String
from sqlalchemy.ext.declarative import declarative_base

Base = declarative_base()

class User(Base):
   __tablename__ = 'users'

   id = Column(Integer, primary_key=True)
   name = Column(String)
   fullname = Column(String)
   nickname = Column(String)

   def __repr__(self):
       return "<User(name='%s', fullname='%s', nickname='%s')>" % (
                            self.name, self.fullname, self.nickname)

In this code, we define a User class that includes fields for an id, name, fullname, and nickname. The class uses SQLAlchemy's Declarative system, which provides a convenient way to declare schemas and models in a single class declaration.

In the next section, we will look at how to use these models to perform database operations using the SQLAlchemy ORM.

19.3 Understanding SQLAlchemy ORM

SQLAlchemy is a comprehensive and powerful toolkit that offers a wide range of enterprise-level persistence patterns, designed to enable efficient and high-performing database access. It provides a simple and Pythonic domain language that is easy to use and understand.

The concept of Object Relational Mapping, or ORM, is a technique that allows for the connection of the rich objects of an application to tables in a relational database management system. By using ORM, the properties and relationships of the objects in an application can be easily stored and retrieved from a database without the need to write SQL statements directly, thereby reducing the overall database access code.

In addition, the ORM in SQLAlchemy is constructed on top of the Core, providing a full suite of mapping capabilities between Python classes and relational databases. This means that SQLAlchemy provides a flexible and comprehensive approach to database access, which can be tailored to suit the specific needs of your application.

Example:

Let's start with a simple example of creating a SQLAlchemy Session, which is the main object used to interact with an ORM-mapped database:

from sqlalchemy import create_engine
from sqlalchemy.orm import sessionmaker

engine = create_engine('postgresql+psycopg2://myuser:mypassword@localhost:5432/mydatabase')

Session = sessionmaker(bind=engine)

session = Session()

Here we first create an engine that knows how to connect to the database, then define a Session class that will serve as a factory for new Session instances, and finally create a session that we can use to talk to the database.

This session is a handle to the database, similar to a cursor in a traditional database API, but with many more features. You can use it to query the database, modify the database, and transactionally persist changes to the database.

Now that we have a session, we can use it to execute SQL queries. But before we can do that, we need to define our data models.

from sqlalchemy import Column, Integer, String
from sqlalchemy.ext.declarative import declarative_base

Base = declarative_base()

class User(Base):
   __tablename__ = 'users'

   id = Column(Integer, primary_key=True)
   name = Column(String)
   fullname = Column(String)
   nickname = Column(String)

   def __repr__(self):
       return "<User(name='%s', fullname='%s', nickname='%s')>" % (
                            self.name, self.fullname, self.nickname)

In this code, we define a User class that includes fields for an id, name, fullname, and nickname. The class uses SQLAlchemy's Declarative system, which provides a convenient way to declare schemas and models in a single class declaration.

In the next section, we will look at how to use these models to perform database operations using the SQLAlchemy ORM.