Release: 1.0.14 | Release Date: July 6, 2016

# SQLAlchemy 1.0 Documentation

# Basic Use

SQLAlchemy object-relational configuration involves the combination of `Table`, `mapper()`, and class objects to define a mapped class. `declarative` allows all three to be expressed at once within the class declaration. As much as possible, regular SQLAlchemy schema and ORM constructs are used directly, so that configuration between “classical” ORM usage and declarative remain highly similar.

As a simple example:

    from sqlalchemy.ext.declarative import declarative_base

    Base = declarative_base()

    class SomeClass(Base):
        __tablename__ = 'some_table'
        id = Column(Integer, primary_key=True)
        name =  Column(String(50))

Above, the `declarative_base()` callable returns a new base class from which all mapped classes should inherit. When the class definition is completed, a new `Table` and `mapper()` will have been generated.

The resulting table and mapper are accessible via `__table__` and `__mapper__` attributes on the `SomeClass` class:

    # access the mapped Table
    SomeClass.__table__

    # access the Mapper
    SomeClass.__mapper__

## Defining Attributes

In the previous example, the `Column` objects are automatically named with the name of the attribute to which they are assigned.

To name columns explicitly with a name distinct from their mapped attribute, just give the column a name. Below, column “some_table_id” is mapped to the “id” attribute of SomeClass, but in SQL will be represented as “some_table_id”:

    class SomeClass(Base):
        __tablename__ = 'some_table'
        id = Column("some_table_id", Integer, primary_key=True)

Attributes may be added to the class after its construction, and they will be added to the underlying `Table` and `mapper()` definitions as appropriate:

    SomeClass.data = Column('data', Unicode)
    SomeClass.related = relationship(RelatedInfo)

Classes which are constructed using declarative can interact freely with classes that are mapped explicitly with `mapper()`.

It is recommended, though not required, that all tables share the same underlying `MetaData` object, so that string-configured `ForeignKey` references can be resolved without issue.

## Accessing the MetaData

The `declarative_base()` base class contains a `MetaData` object where newly defined `Table` objects are collected. This object is intended to be accessed directly for `MetaData`-specific operations. Such as, to issue CREATE statements for all tables:

    engine = create_engine('sqlite://')
    Base.metadata.create_all(engine)

`declarative_base()` can also receive a pre-existing `MetaData` object, which allows a declarative setup to be associated with an already existing traditional collection of `Table` objects:

    mymetadata = MetaData()
    Base = declarative_base(metadata=mymetadata)

## Class Constructor

As a convenience feature, the `declarative_base()` sets a default constructor on classes which takes keyword arguments, and assigns them to the named attributes:

    e = Engineer(primary_language='python')

## Mapper Configuration

Declarative makes use of the `mapper()` function internally when it creates the mapping to the declared table. The options for `mapper()` are passed directly through via the `__mapper_args__` class attribute. As always, arguments which reference locally mapped columns can reference them directly from within the class declaration:

    from datetime import datetime

    class Widget(Base):
        __tablename__ = 'widgets'

        id = Column(Integer, primary_key=True)
        timestamp = Column(DateTime, nullable=False)

        __mapper_args__ = {
                        'version_id_col': timestamp,
                        'version_id_generator': lambda v:datetime.now()
                    }

## Defining SQL Expressions

See SQL Expressions as Mapped Attributes for examples on declaratively mapping attributes to SQL expressions.