Release: 2.1.0b1 | Release Date: January 21, 2026

# SQLAlchemy 2.1 Documentation

# SQL Expressions as Mapped Attributes

Attributes on a mapped class can be linked to SQL expressions, which can be used in queries.

## Using a Hybrid

The easiest and most flexible way to link relatively simple SQL expressions to a class is to use a so-called “hybrid attribute”, described in the section Hybrid Attributes. The hybrid provides for an expression that works at both the Python level as well as at the SQL expression level. For example, below we map a class `User`, containing attributes `firstname` and `lastname`, and include a hybrid that will provide for us the `fullname`, which is the string concatenation of the two:

    from sqlalchemy.ext.hybrid import hybrid_property

    class User(Base):
        __tablename__ = "user"
        id = mapped_column(Integer, primary_key=True)
        firstname = mapped_column(String(50))
        lastname = mapped_column(String(50))

        @hybrid_property
        def fullname(self):
            return self.firstname + " " + self.lastname

Above, the `fullname` attribute is interpreted at both the instance and class level, so that it is available from an instance:

    some_user = session.scalars(select(User).limit(1)).first()
    print(some_user.fullname)

as well as usable within queries:

    some_user = session.scalars(
        select(User).where(User.fullname == "John Smith").limit(1)
    ).first()

The string concatenation example is a simple one, where the Python expression can be dual purposed at the instance and class level. Often, the SQL expression must be distinguished from the Python expression, which can be achieved using `hybrid_property.expression()`. Below we illustrate the case where a conditional needs to be present inside the hybrid, using the `if` statement in Python and the `case()` construct for SQL expressions:

    from sqlalchemy.ext.hybrid import hybrid_property
    from sqlalchemy.sql import case

    class User(Base):
        __tablename__ = "user"
        id = mapped_column(Integer, primary_key=True)
        firstname = mapped_column(String(50))
        lastname = mapped_column(String(50))

        @hybrid_property
        def fullname(self):
            if self.firstname is not None:
                return self.firstname + " " + self.lastname
            else:
                return self.lastname

        @fullname.expression
        def fullname(cls):
            return case(
                (cls.firstname != None, cls.firstname + " " + cls.lastname),
                else_=cls.lastname,
            )

## Using column_property

The `column_property()` function can be used to map a SQL expression in a manner similar to a regularly mapped `Column`. With this technique, the attribute is loaded along with all other column-mapped attributes at load time. This is in some cases an advantage over the usage of hybrids, as the value can be loaded up front at the same time as the parent row of the object, particularly if the expression is one which links to other tables (typically as a correlated subquery) to access data that wouldn’t normally be available on an already loaded object.

Disadvantages to using `column_property()` for SQL expressions include that the expression must be compatible with the SELECT statement emitted for the class as a whole, and there are also some configurational quirks which can occur when using `column_property()` from declarative mixins.

Our “fullname” example can be expressed using `column_property()` as follows:

    from sqlalchemy.orm import column_property

    class User(Base):
        __tablename__ = "user"
        id = mapped_column(Integer, primary_key=True)
        firstname = mapped_column(String(50))
        lastname = mapped_column(String(50))
        fullname = column_property(firstname + " " + lastname)

Correlated subqueries may be used as well. Below we use the `select()` construct to create a `ScalarSelect`, representing a column-oriented SELECT statement, that links together the count of `Address` objects available for a particular `User`:

    from sqlalchemy.orm import column_property
    from sqlalchemy import select, func
    from sqlalchemy import Column, Integer, String, ForeignKey

    from sqlalchemy.orm import DeclarativeBase

    class Base(DeclarativeBase):
        pass

    class Address(Base):
        __tablename__ = "address"
        id = mapped_column(Integer, primary_key=True)
        user_id = mapped_column(Integer, ForeignKey("user.id"))

    class User(Base):
        __tablename__ = "user"
        id = mapped_column(Integer, primary_key=True)
        address_count = column_property(
            select(func.count(Address.id))
            .where(Address.user_id == id)
            .correlate_except(Address)
            .scalar_subquery()
        )

In the above example, we define a `ScalarSelect()` construct like the following:

    stmt = (
        select(func.count(Address.id))
        .where(Address.user_id == id)
        .correlate_except(Address)
        .scalar_subquery()
    )

Above, we first use `select()` to create a `Select` construct, which we then convert into a scalar subquery using the `Select.scalar_subquery()` method, indicating our intent to use this `Select` statement in a column expression context.

Within the `Select` itself, we select the count of `Address.id` rows where the `Address.user_id` column is equated to `id`, which in the context of the `User` class is the `Column` named `id` (note that `id` is also the name of a Python built in function, which is not what we want to use here - if we were outside of the `User` class definition, we’d use `User.id`).

The `Select.correlate_except()` method indicates that each element in the FROM clause of this `select()` may be omitted from the FROM list (that is, correlated to the enclosing SELECT statement against `User`) except for the one corresponding to `Address`. This isn’t strictly necessary, but prevents `Address` from being inadvertently omitted from the FROM list in the case of a long string of joins between `User` and `Address` tables where SELECT statements against `Address` are nested.

For a `column_property()` that refers to columns linked from a many-to-many relationship, use `and_()` to join the fields of the association table to both tables in a relationship:

    from sqlalchemy import and_

    class Author(Base):
        # ...

        book_count = column_property(
            select(func.count(books.c.id))
            .where(
                and_(
                    book_authors.c.author_id == authors.c.id,
                    book_authors.c.book_id == books.c.id,
                )
            )
            .scalar_subquery()
        )

### Adding column_property() to an existing Declarative mapped class

If import issues prevent the `column_property()` from being defined inline with the class, it can be assigned to the class after both are configured. When using mappings that make use of a Declarative base class (i.e. produced by the `DeclarativeBase` superclass or legacy functions such as `declarative_base()`), this attribute assignment has the effect of calling `Mapper.add_property()` to add an additional property after the fact:

    # only works if a declarative base class is in use
    User.address_count = column_property(
        select(func.count(Address.id)).where(Address.user_id == User.id).scalar_subquery()
    )

When using mapping styles that don’t use Declarative base classes such as the `registry.mapped()` decorator, the `Mapper.add_property()` method may be invoked explicitly on the underlying `Mapper` object, which can be obtained using `inspect()`:

    from sqlalchemy.orm import registry

    reg = registry()

    @reg.mapped
    class User:
        __tablename__ = "user"

        # ... additional mapping directives

    # later ...

    # works for any kind of mapping
    from sqlalchemy import inspect

    inspect(User).add_property(
        column_property(
            select(func.count(Address.id))
            .where(Address.user_id == User.id)
            .scalar_subquery()
        )
    )

See also

Appending additional columns to an existing Declarative mapped class

### Composing from Column Properties at Mapping Time

It is possible to create mappings that combine multiple `ColumnProperty` objects together. The `ColumnProperty` will be interpreted as a SQL expression when used in a Core expression context, provided that it is targeted by an existing expression object; this works by the Core detecting that the object has a `__clause_element__()` method which returns a SQL expression. However, if the `ColumnProperty` is used as a lead object in an expression where there is no other Core SQL expression object to target it, the `ColumnProperty.expression` attribute will return the underlying SQL expression so that it can be used to build SQL expressions consistently. Below, the `File` class contains an attribute `File.path` that concatenates a string token to the `File.filename` attribute, which is itself a `ColumnProperty`:

    class File(Base):
        __tablename__ = "file"

        id = mapped_column(Integer, primary_key=True)
        name = mapped_column(String(64))
        extension = mapped_column(String(8))
        filename = column_property(name + "." + extension)
        path = column_property("C:/" + filename.expression)

When the `File` class is used in expressions normally, the attributes assigned to `filename` and `path` are usable directly. The use of the `ColumnProperty.expression` attribute is only necessary when using the `ColumnProperty` directly within the mapping definition:

    stmt = select(File.path).where(File.filename == "foo.txt")

### Using Column Deferral with `column_property()`

The column deferral feature introduced in the ORM Querying Guide at Limiting which Columns Load with Column Deferral may be applied at mapping time to a SQL expression mapped by `column_property()` by using the `deferred()` function in place of `column_property()`:

    from sqlalchemy.orm import deferred

    class User(Base):
        __tablename__ = "user"

        id: Mapped[int] = mapped_column(primary_key=True)
        firstname: Mapped[str] = mapped_column()
        lastname: Mapped[str] = mapped_column()
        fullname: Mapped[str] = deferred(firstname + " " + lastname)

See also

Using deferred() for imperative mappers, mapped SQL expressions

## Using a plain descriptor

In cases where a SQL query more elaborate than what `column_property()` or `hybrid_property` can provide must be emitted, a regular Python function accessed as an attribute can be used, assuming the expression only needs to be available on an already-loaded instance. The function is decorated with Python’s own `@property` decorator to mark it as a read-only attribute. Within the function, `object_session()` is used to locate the `Session` corresponding to the current object, which is then used to emit a query:

    from sqlalchemy.orm import object_session
    from sqlalchemy import select, func

    class User(Base):
        __tablename__ = "user"
        id = mapped_column(Integer, primary_key=True)
        firstname = mapped_column(String(50))
        lastname = mapped_column(String(50))

        @property
        def address_count(self):
            return object_session(self).scalar(
                select(func.count(Address.id)).where(Address.user_id == self.id)
            )

The plain descriptor approach is useful as a last resort, but is less performant in the usual case than both the hybrid and column property approaches, in that it needs to emit a SQL query upon each access.

## Query-time SQL expressions as mapped attributes

In addition to being able to configure fixed SQL expressions on mapped classes, the SQLAlchemy ORM also includes a feature wherein objects may be loaded with the results of arbitrary SQL expressions which are set up at query time as part of their state. This behavior is available by configuring an ORM mapped attribute using `query_expression()` and then using the `with_expression()` loader option at query time. See the section Loading Arbitrary SQL Expressions onto Objects for an example mapping and usage.