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

# SQLAlchemy 2.1 Documentation

ORM Querying Guide

This page is part of the ORM Querying Guide.

Previous: ORM-Enabled INSERT, UPDATE, and DELETE statements | Next: Relationship Loading Techniques

# Column Loading Options

About this Document

This section presents additional options regarding the loading of columns. The mappings used include columns that would store large string values for which we may want to limit when they are loaded.

View the ORM setup for this page. Some of the examples below will redefine the `Book` mapper to modify some of the column definitions.

## Limiting which Columns Load with Column Deferral

Column deferral refers to ORM mapped columns that are omitted from a SELECT statement when objects of that type are queried. The general rationale here is performance, in cases where tables have seldom-used columns with potentially large data values, as fully loading these columns on every query may be time and/or memory intensive. SQLAlchemy ORM offers a variety of ways to control the loading of columns when entities are loaded.

Most examples in this section are illustrating ORM loader options. These are small constructs that are passed to the `Select.options()` method of the `Select` object, which are then consumed by the ORM when the object is compiled into a SQL string.

### Using `load_only()` to reduce loaded columns

The `load_only()` loader option is the most expedient option to use when loading objects where it is known that only a small handful of columns will be accessed. This option accepts a variable number of class-bound attribute objects indicating those column-mapped attributes that should be loaded, where all other column-mapped attributes outside of the primary key will not be part of the columns fetched . In the example below, the `Book` class contains columns `.title`, `.summary` and `.cover_photo`. Using `load_only()` we can instruct the ORM to only load the `.title` and `.summary` columns up front:

    >>> from sqlalchemy import select
    >>> from sqlalchemy.orm import load_only
    >>> stmt = select(Book).options(load_only(Book.title, Book.summary))
    >>> books = session.scalars(stmt).all()
    SELECT book.id, book.title, book.summary
    FROM book
    [...] ()
    >>> for book in books:
    ...     print(f"{book.title}  {book.summary}")
    100 Years of Krabby Patties  some long summary
    Sea Catch 22  another long summary
    The Sea Grapes of Wrath  yet another summary
    A Nut Like No Other  some long summary
    Geodesic Domes: A Retrospective  another long summary
    Rocketry for Squirrels  yet another summary

Above, the SELECT statement has omitted the `.cover_photo` column and included only `.title` and `.summary`, as well as the primary key column `.id`; the ORM will typically always fetch the primary key columns as these are required to establish the identity for the row.

Once loaded, the object will normally have lazy loading behavior applied to the remaining unloaded attributes, meaning that when any are first accessed, a SQL statement will be emitted within the current transaction in order to load the value. Below, accessing `.cover_photo` emits a SELECT statement to load its value:

    >>> img_data = books[0].cover_photo
    SELECT book.cover_photo
    FROM book
    WHERE book.id = ?
    [...] (1,)

Lazy loads are always emitted using the `Session` to which the object is in the persistent state. If the object is detached from any `Session`, the operation fails, raising an exception.

As an alternative to lazy loading on access, deferred columns may also be configured to raise an informative exception when accessed, regardless of their attachment state. When using the `load_only()` construct, this may be indicated using the `load_only.raiseload` parameter. See the section Using raiseload to prevent deferred column loads for background and examples.

Tip

as noted elsewhere, lazy loading is not available when using Asynchronous I/O (asyncio).

#### Using `load_only()` with multiple entities

`load_only()` limits itself to the single entity that is referred towards in its list of attributes (passing a list of attributes that span more than a single entity is currently disallowed). In the example below, the given `load_only()` option applies only to the `Book` entity. The `User` entity that’s also selected is not affected; within the resulting SELECT statement, all columns for `user_account` are present, whereas only `book.id` and `book.title` are present for the `book` table:

    >>> stmt = select(User, Book).join_from(User, Book).options(load_only(Book.title))
    >>> print(stmt)
    SELECT user_account.id, user_account.name, user_account.fullname,
    book.id AS id_1, book.title
    FROM user_account JOIN book ON user_account.id = book.owner_id

If we wanted to apply `load_only()` options to both `User` and `Book`, we would make use of two separate options:

    >>> stmt = (
    ...     select(User, Book)
    ...     .join_from(User, Book)
    ...     .options(load_only(User.name), load_only(Book.title))
    ... )
    >>> print(stmt)
    SELECT user_account.id, user_account.name, book.id AS id_1, book.title
    FROM user_account JOIN book ON user_account.id = book.owner_id

#### Using `load_only()` on related objects and collections

When using relationship loaders to control the loading of related objects, the `Load.load_only()` method of any relationship loader may be used to apply `load_only()` rules to columns on the sub-entity. In the example below, `selectinload()` is used to load the related `books` collection on each `User` object. By applying `Load.load_only()` to the resulting option object, when objects are loaded for the relationship, the SELECT emitted will only refer to the `title` column in addition to primary key column:

    >>> from sqlalchemy.orm import selectinload
    >>> stmt = select(User).options(selectinload(User.books).load_only(Book.title))
    >>> for user in session.scalars(stmt):
    ...     print(f"{user.fullname}   {[b.title for b in user.books]}")
    SELECT user_account.id, user_account.name, user_account.fullname
    FROM user_account
    [...] ()
    SELECT book.owner_id, book.id, book.title
    FROM book
    WHERE book.owner_id IN (?, ?)
    [...] (1, 2)
    Spongebob Squarepants   ['100 Years of Krabby Patties', 'Sea Catch 22', 'The Sea Grapes of Wrath']
    Sandy Cheeks   ['A Nut Like No Other', 'Geodesic Domes: A Retrospective', 'Rocketry for Squirrels']

`load_only()` may also be applied to sub-entities without needing to state the style of loading to use for the relationship itself. If we didn’t want to change the default loading style of `User.books` but still apply load only rules to `Book`, we would link using the `defaultload()` option, which in this case will retain the default relationship loading style of `"lazy"`, and applying our custom `load_only()` rule to the SELECT statement emitted for each `User.books` collection:

    >>> from sqlalchemy.orm import defaultload
    >>> stmt = select(User).options(defaultload(User.books).load_only(Book.title))
    >>> for user in session.scalars(stmt):
    ...     print(f"{user.fullname}   {[b.title for b in user.books]}")
    SELECT user_account.id, user_account.name, user_account.fullname
    FROM user_account
    [...] ()
    SELECT book.id, book.title
    FROM book
    WHERE ? = book.owner_id
    [...] (1,)
    Spongebob Squarepants   ['100 Years of Krabby Patties', 'Sea Catch 22', 'The Sea Grapes of Wrath']
    SELECT book.id, book.title
    FROM book
    WHERE ? = book.owner_id
    [...] (2,)
    Sandy Cheeks   ['A Nut Like No Other', 'Geodesic Domes: A Retrospective', 'Rocketry for Squirrels']

### Using `defer()` to omit specific columns

The `defer()` loader option is a more fine grained alternative to `load_only()`, which allows a single specific column to be marked as “dont load”. In the example below, `defer()` is applied directly to the `.cover_photo` column, leaving the behavior of all other columns unchanged:

    >>> from sqlalchemy.orm import defer
    >>> stmt = select(Book).where(Book.owner_id == 2).options(defer(Book.cover_photo))
    >>> books = session.scalars(stmt).all()
    SELECT book.id, book.owner_id, book.title, book.summary
    FROM book
    WHERE book.owner_id = ?
    [...] (2,)
    >>> for book in books:
    ...     print(f"{book.title}: {book.summary}")
    A Nut Like No Other: some long summary
    Geodesic Domes: A Retrospective: another long summary
    Rocketry for Squirrels: yet another summary

As is the case with `load_only()`, unloaded columns by default will load themselves when accessed using lazy loading:

    >>> img_data = books[0].cover_photo
    SELECT book.cover_photo
    FROM book
    WHERE book.id = ?
    [...] (4,)

Multiple `defer()` options may be used in one statement in order to mark several columns as deferred.

As is the case with `load_only()`, the `defer()` option also includes the ability to have a deferred attribute raise an exception on access rather than lazy loading. This is illustrated in the section Using raiseload to prevent deferred column loads.

### Using raiseload to prevent deferred column loads

When using the `load_only()` or `defer()` loader options, attributes marked as deferred on an object have the default behavior that when first accessed, a SELECT statement will be emitted within the current transaction in order to load their value. It is often necessary to prevent this load from occurring, and instead raise an exception when the attribute is accessed, indicating that the need to query the database for this column was not expected. A typical scenario is an operation where objects are loaded with all the columns that are known to be required for the operation to proceed, which are then passed onto a view layer. Any further SQL operations that emit within the view layer should be caught, so that the up-front loading operation can be adjusted to accommodate for that additional data up front, rather than incurring additional lazy loading.

For this use case the `defer()` and `load_only()` options include a boolean parameter `defer.raiseload`, which when set to `True` will cause the affected attributes to raise on access. In the example below, the deferred column `.cover_photo` will disallow attribute access:

    >>> book = session.scalar(
    ...     select(Book).options(defer(Book.cover_photo, raiseload=True)).where(Book.id == 4)
    ... )
    SELECT book.id, book.owner_id, book.title, book.summary
    FROM book
    WHERE book.id = ?
    [...] (4,)
    >>> book.cover_photo
    Traceback (most recent call last):
    ...
    sqlalchemy.exc.InvalidRequestError: 'Book.cover_photo' is not available due to raiseload=True

When using `load_only()` to name a specific set of non-deferred columns, `raiseload` behavior may be applied to the remaining columns using the `load_only.raiseload` parameter, which will be applied to all deferred attributes:

    >>> session.expunge_all()
    >>> book = session.scalar(
    ...     select(Book).options(load_only(Book.title, raiseload=True)).where(Book.id == 5)
    ... )
    SELECT book.id, book.title
    FROM book
    WHERE book.id = ?
    [...] (5,)
    >>> book.summary
    Traceback (most recent call last):
    ...
    sqlalchemy.exc.InvalidRequestError: 'Book.summary' is not available due to raiseload=True

Note

It is not yet possible to mix `load_only()` and `defer()` options which refer to the same entity together in one statement in order to change the `raiseload` behavior of certain attributes; currently, doing so will produce undefined loading behavior of attributes.

See also

The `defer.raiseload` feature is the column-level version of the same “raiseload” feature that’s available for relationships. For “raiseload” with relationships, see Preventing unwanted lazy loads using raiseload in the Relationship Loading Techniques section of this guide.

## Configuring Column Deferral on Mappings

The functionality of `defer()` is available as a default behavior for mapped columns, as may be appropriate for columns that should not be loaded unconditionally on every query. To configure, use the `mapped_column.deferred` parameter of `mapped_column()`. The example below illustrates a mapping for `Book` which applies default column deferral to the `summary` and `cover_photo` columns:

    >>> class Book(Base):
    ...     __tablename__ = "book"
    ...     id: Mapped[int] = mapped_column(primary_key=True)
    ...     owner_id: Mapped[int] = mapped_column(ForeignKey("user_account.id"))
    ...     title: Mapped[str]
    ...     summary: Mapped[str] = mapped_column(Text, deferred=True)
    ...     cover_photo: Mapped[bytes] = mapped_column(LargeBinary, deferred=True)
    ...
    ...     def __repr__(self) -> str:
    ...         return f"Book(id={self.id!r}, title={self.title!r})"

Using the above mapping, queries against `Book` will automatically not include the `summary` and `cover_photo` columns:

    >>> book = session.scalar(select(Book).where(Book.id == 2))
    SELECT book.id, book.owner_id, book.title
    FROM book
    WHERE book.id = ?
    [...] (2,)

As is the case with all deferral, the default behavior when deferred attributes on the loaded object are first accessed is that they will lazy load their value:

    >>> img_data = book.cover_photo
    SELECT book.cover_photo
    FROM book
    WHERE book.id = ?
    [...] (2,)

As is the case with the `defer()` and `load_only()` loader options, mapper level deferral also includes an option for `raiseload` behavior to occur, rather than lazy loading, when no other options are present in a statement. This allows a mapping where certain columns will not load by default and will also never load lazily without explicit directives used in a statement. See the section Configuring mapper-level “raiseload” behavior for background on how to configure and use this behavior.

### Using `deferred()` for imperative mappers, mapped SQL expressions

The `deferred()` function is the earlier, more general purpose “deferred column” mapping directive that precedes the introduction of the `mapped_column()` construct in SQLAlchemy.

`deferred()` is used when configuring ORM mappers, and accepts arbitrary SQL expressions or `Column` objects. As such it’s suitable to be used with non-declarative imperative mappings, passing it to the `map_imperatively.properties` dictionary:

    from sqlalchemy import Blob
    from sqlalchemy import Column
    from sqlalchemy import ForeignKey
    from sqlalchemy import Integer
    from sqlalchemy import String
    from sqlalchemy import Table
    from sqlalchemy import Text
    from sqlalchemy.orm import registry

    mapper_registry = registry()

    book_table = Table(
        "book",
        mapper_registry.metadata,
        Column("id", Integer, primary_key=True),
        Column("title", String(50)),
        Column("summary", Text),
        Column("cover_image", Blob),
    )

    class Book:
        pass

    mapper_registry.map_imperatively(
        Book,
        book_table,
        properties={
            "summary": deferred(book_table.c.summary),
            "cover_image": deferred(book_table.c.cover_image),
        },
    )

`deferred()` may also be used in place of `column_property()` when mapped SQL expressions should be loaded on a deferred basis:

    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 column_property - in the section SQL Expressions as Mapped Attributes

Applying Load, Persistence and Mapping Options for Imperative Table Columns - in the section Table Configuration with Declarative

### Using `undefer()` to “eagerly” load deferred columns

With columns configured on mappings to defer by default, the `undefer()` option will cause any column that is normally deferred to be undeferred, that is, to load up front with all the other columns of the mapping. For example we may apply `undefer()` to the `Book.summary` column, which is indicated in the previous mapping as deferred:

    >>> from sqlalchemy.orm import undefer
    >>> book = session.scalar(select(Book).where(Book.id == 2).options(undefer(Book.summary)))
    SELECT book.id, book.owner_id, book.title, book.summary
    FROM book
    WHERE book.id = ?
    [...] (2,)

The `Book.summary` column was now eagerly loaded, and may be accessed without additional SQL being emitted:

    >>> print(book.summary)
    another long summary

### Loading deferred columns in groups

Normally when a column is mapped with `mapped_column(deferred=True)`, when the deferred attribute is accessed on an object, SQL will be emitted to load only that specific column and no others, even if the mapping has other columns that are also marked as deferred. In the common case that the deferred attribute is part of a group of attributes that should all load at once, rather than emitting SQL for each attribute individually, the `mapped_column.deferred_group` parameter may be used, which accepts an arbitrary string which will define a common group of columns to be undeferred:

    >>> class Book(Base):
    ...     __tablename__ = "book"
    ...     id: Mapped[int] = mapped_column(primary_key=True)
    ...     owner_id: Mapped[int] = mapped_column(ForeignKey("user_account.id"))
    ...     title: Mapped[str]
    ...     summary: Mapped[str] = mapped_column(
    ...         Text, deferred=True, deferred_group="book_attrs"
    ...     )
    ...     cover_photo: Mapped[bytes] = mapped_column(
    ...         LargeBinary, deferred=True, deferred_group="book_attrs"
    ...     )
    ...
    ...     def __repr__(self) -> str:
    ...         return f"Book(id={self.id!r}, title={self.title!r})"

Using the above mapping, accessing either `summary` or `cover_photo` will load both columns at once using just one SELECT statement:

    >>> book = session.scalar(select(Book).where(Book.id == 2))
    SELECT book.id, book.owner_id, book.title
    FROM book
    WHERE book.id = ?
    [...] (2,)
    >>> img_data, summary = book.cover_photo, book.summary
    SELECT book.summary, book.cover_photo
    FROM book
    WHERE book.id = ?
    [...] (2,)

### Undeferring by group with `undefer_group()`

If deferred columns are configured with `mapped_column.deferred_group` as introduced in the preceding section, the entire group may be indicated to load eagerly using the `undefer_group()` option, passing the string name of the group to be eagerly loaded:

    >>> from sqlalchemy.orm import undefer_group
    >>> book = session.scalar(
    ...     select(Book).where(Book.id == 2).options(undefer_group("book_attrs"))
    ... )
    SELECT book.id, book.owner_id, book.title, book.summary, book.cover_photo
    FROM book
    WHERE book.id = ?
    [...] (2,)

Both `summary` and `cover_photo` are available without additional loads:

    >>> img_data, summary = book.cover_photo, book.summary

### Undeferring on wildcards

Most ORM loader options accept a wildcard expression, indicated by `"*"`, which indicates that the option should be applied to all relevant attributes. If a mapping has a series of deferred columns, all such columns can be undeferred at once, without using a group name, by indicating a wildcard:

    >>> book = session.scalar(select(Book).where(Book.id == 3).options(undefer("*")))
    SELECT book.id, book.owner_id, book.title, book.summary, book.cover_photo
    FROM book
    WHERE book.id = ?
    [...] (3,)

### Configuring mapper-level “raiseload” behavior

The “raiseload” behavior first introduced at Using raiseload to prevent deferred column loads may also be applied as a default mapper-level behavior, using the `mapped_column.deferred_raiseload` parameter of `mapped_column()`. When using this parameter, the affected columns will raise on access in all cases unless explicitly “undeferred” using `undefer()` or `load_only()` at query time:

    >>> class Book(Base):
    ...     __tablename__ = "book"
    ...     id: Mapped[int] = mapped_column(primary_key=True)
    ...     owner_id: Mapped[int] = mapped_column(ForeignKey("user_account.id"))
    ...     title: Mapped[str]
    ...     summary: Mapped[str] = mapped_column(Text, deferred=True, deferred_raiseload=True)
    ...     cover_photo: Mapped[bytes] = mapped_column(
    ...         LargeBinary, deferred=True, deferred_raiseload=True
    ...     )
    ...
    ...     def __repr__(self) -> str:
    ...         return f"Book(id={self.id!r}, title={self.title!r})"

Using the above mapping, the `.summary` and `.cover_photo` columns are by default not loadable:

    >>> book = session.scalar(select(Book).where(Book.id == 2))
    SELECT book.id, book.owner_id, book.title
    FROM book
    WHERE book.id = ?
    [...] (2,)
    >>> book.summary
    Traceback (most recent call last):
    ...
    sqlalchemy.exc.InvalidRequestError: 'Book.summary' is not available due to raiseload=True

Only by overriding their behavior at query time, typically using `undefer()` or `undefer_group()`, or less commonly `defer()`, may the attributes be loaded. The example below applies `undefer('*')` to undefer all attributes, also making use of Populate Existing to refresh the already-loaded object’s loader options:

    >>> book = session.scalar(
    ...     select(Book)
    ...     .where(Book.id == 2)
    ...     .options(undefer("*"))
    ...     .execution_options(populate_existing=True)
    ... )
    SELECT book.id, book.owner_id, book.title, book.summary, book.cover_photo
    FROM book
    WHERE book.id = ?
    [...] (2,)
    >>> book.summary
    'another long summary'

## Loading Arbitrary SQL Expressions onto Objects

As discussed Selecting ORM Entities and Attributes and elsewhere, the `select()` construct may be used to load arbitrary SQL expressions in a result set. Such as if we wanted to issue a query that loads `User` objects, but also includes a count of how many books each `User` owned, we could use `func.count(Book.id)` to add a “count” column to a query which includes a JOIN to `Book` as well as a GROUP BY owner id. This will yield `Row` objects that each contain two entries, one for `User` and one for `func.count(Book.id)`:

    >>> from sqlalchemy import func
    >>> stmt = select(User, func.count(Book.id)).join_from(User, Book).group_by(Book.owner_id)
    >>> for user, book_count in session.execute(stmt):
    ...     print(f"Username: {user.name}  Number of books: {book_count}")
    SELECT user_account.id, user_account.name, user_account.fullname,
    count(book.id) AS count_1
    FROM user_account JOIN book ON user_account.id = book.owner_id
    GROUP BY book.owner_id
    [...] ()
    Username: spongebob  Number of books: 3
    Username: sandy  Number of books: 3

In the above example, the `User` entity and the “book count” SQL expression are returned separately. However, a popular use case is to produce a query that will yield `User` objects alone, which can be iterated for example using `Session.scalars()`, where the result of the `func.count(Book.id)` SQL expression is applied dynamically to each `User` entity. The end result would be similar to the case where an arbitrary SQL expression were mapped to the class using `column_property()`, except that the SQL expression can be modified at query time. For this use case SQLAlchemy provides the `with_expression()` loader option, which when combined with the mapper level `query_expression()` directive may produce this result.

To apply `with_expression()` to a query, the mapped class must have pre-configured an ORM mapped attribute using the `query_expression()` directive; this directive will produce an attribute on the mapped class that is suitable for receiving query-time SQL expressions. Below we add a new attribute `User.book_count` to `User`. This ORM mapped attribute is read-only and has no default value; accessing it on a loaded instance will normally produce `None`:

    >>> from sqlalchemy.orm import query_expression
    >>> class User(Base):
    ...     __tablename__ = "user_account"
    ...     id: Mapped[int] = mapped_column(primary_key=True)
    ...     name: Mapped[str]
    ...     fullname: Mapped[Optional[str]]
    ...     book_count: Mapped[int] = query_expression()
    ...
    ...     def __repr__(self) -> str:
    ...         return f"User(id={self.id!r}, name={self.name!r}, fullname={self.fullname!r})"

With the `User.book_count` attribute configured in our mapping, we may populate it with data from a SQL expression using the `with_expression()` loader option to apply a custom SQL expression to each `User` object as it’s loaded:

    >>> from sqlalchemy.orm import with_expression
    >>> stmt = (
    ...     select(User)
    ...     .join_from(User, Book)
    ...     .group_by(Book.owner_id)
    ...     .options(with_expression(User.book_count, func.count(Book.id)))
    ... )
    >>> for user in session.scalars(stmt):
    ...     print(f"Username: {user.name}  Number of books: {user.book_count}")
    SELECT count(book.id) AS count_1, user_account.id, user_account.name,
    user_account.fullname
    FROM user_account JOIN book ON user_account.id = book.owner_id
    GROUP BY book.owner_id
    [...] ()
    Username: spongebob  Number of books: 3
    Username: sandy  Number of books: 3

Above, we moved our `func.count(Book.id)` expression out of the columns argument of the `select()` construct and into the `with_expression()` loader option. The ORM then considers this to be a special column load option that’s applied dynamically to the statement.

The `query_expression()` mapping has these caveats:

- On an object where `with_expression()` were not used to populate the attribute, the attribute on an object instance will have the value `None`, unless on the mapping the `query_expression.default_expr` parameter is set to a default SQL expression.

- The `with_expression()` value does not populate on an object that is already loaded, unless Populate Existing is used. The example below will not work, as the `A` object is already loaded:

      # load the first A
      obj = session.scalars(select(A).order_by(A.id)).first()

      # load the same A with an option; expression will **not** be applied
      # to the already-loaded object
      obj = session.scalars(select(A).options(with_expression(A.expr, some_expr))).first()

  To ensure the attribute is re-loaded on an existing object, use the Populate Existing execution option to ensure all columns are re-populated:

      obj = session.scalars(
          select(A)
          .options(with_expression(A.expr, some_expr))
          .execution_options(populate_existing=True)
      ).first()

- The `with_expression()` SQL expression is lost when the object is expired. Once the object is expired, either via `Session.expire()` or via the expire_on_commit behavior of `Session.commit()`, the SQL expression and its value is no longer associated with the attribute and will return `None` on subsequent access.

- `with_expression()`, as an object loading option, only takes effect on the outermost part of a query and only for a query against a full entity, and not for arbitrary column selects, within subqueries, or the elements of a compound statement such as a UNION. See the next section Using with_expression() with UNIONs, other subqueries for an example.

- The mapped attribute cannot be applied to other parts of the query, such as the WHERE clause, the ORDER BY clause, and make use of the ad-hoc expression; that is, this won’t work:

      # can't refer to A.expr elsewhere in the query
      stmt = (
          select(A)
          .options(with_expression(A.expr, A.x + A.y))
          .filter(A.expr > 5)
          .order_by(A.expr)
      )

  The `A.expr` expression will resolve to NULL in the above WHERE clause and ORDER BY clause. To use the expression throughout the query, assign to a variable and use that:

      # assign desired expression up front, then refer to that in
      # the query
      a_expr = A.x + A.y
      stmt = (
          select(A)
          .options(with_expression(A.expr, a_expr))
          .filter(a_expr > 5)
          .order_by(a_expr)
      )

See also

The `with_expression()` option is a special option used to apply SQL expressions to mapped classes dynamically at query time. For ordinary fixed SQL expressions configured on mappers, see the section SQL Expressions as Mapped Attributes.

### Using `with_expression()` with UNIONs, other subqueries

The `with_expression()` construct is an ORM loader option, and as such may only be applied to the outermost level of a SELECT statement which is to load a particular ORM entity. It does not have any effect if used inside of a `select()` that will then be used as a subquery or as an element within a compound statement such as a UNION.

In order to use arbitrary SQL expressions in subqueries, normal Core-style means of adding expressions should be used. To assemble a subquery-derived expression onto the ORM entity’s `query_expression()` attributes, `with_expression()` is used at the top layer of ORM object loading, referencing the SQL expression within the subquery.

In the example below, two `select()` constructs are used against the ORM entity `A` with an additional SQL expression labeled in `expr`, and combined using `union_all()`. Then, at the topmost layer, the `A` entity is SELECTed from this UNION, using the querying technique described at Selecting Entities from UNIONs and other set operations, adding an option with `with_expression()` to extract this SQL expression onto newly loaded instances of `A`:

    >>> from sqlalchemy import union_all
    >>> s1 = (
    ...     select(User, func.count(Book.id).label("book_count"))
    ...     .join_from(User, Book)
    ...     .where(User.name == "spongebob")
    ... )
    >>> s2 = (
    ...     select(User, func.count(Book.id).label("book_count"))
    ...     .join_from(User, Book)
    ...     .where(User.name == "sandy")
    ... )
    >>> union_stmt = union_all(s1, s2)
    >>> orm_stmt = (
    ...     select(User)
    ...     .from_statement(union_stmt)
    ...     .options(with_expression(User.book_count, union_stmt.selected_columns.book_count))
    ... )
    >>> for user in session.scalars(orm_stmt):
    ...     print(f"Username: {user.name}  Number of books: {user.book_count}")
    SELECT user_account.id, user_account.name, user_account.fullname, count(book.id) AS book_count
    FROM user_account JOIN book ON user_account.id = book.owner_id
    WHERE user_account.name = ?
    UNION ALL
    SELECT user_account.id, user_account.name, user_account.fullname, count(book.id) AS book_count
    FROM user_account JOIN book ON user_account.id = book.owner_id
    WHERE user_account.name = ?
    [...] ('spongebob', 'sandy')
    Username: spongebob  Number of books: 3
    Username: sandy  Number of books: 3

## Column Loading API

Object Name | Description
--- | ---
defer(key, *, [raiseload]) | Indicate that the given column-oriented attribute should be deferred, e.g. not loaded until accessed.
deferred(column, *additional_columns, [group, raiseload, comparator_factory, init, repr, default, default_factory, compare, kw_only, hash, active_history, expire_on_flush, info, doc, dataclass_metadata]) | Indicate a column-based mapped attribute that by default will not load unless accessed.
load_only(*attrs, [raiseload]) | Indicate that for a particular entity, only the given list of column-based attribute names should be loaded; all others will be deferred.
query_expression([default_expr], *, [repr, compare, expire_on_flush, info, doc]) | Indicate an attribute that populates from a query-time SQL expression.
undefer(key) | Indicate that the given column-oriented attribute should be undeferred, e.g. specified within the SELECT statement of the entity as a whole.
undefer_group(name) | Indicate that columns within the given deferred group name should be undeferred.
with_expression(key, expression) | Apply an ad-hoc SQL expression to a “deferred expression” attribute.

function sqlalchemy.orm.defer(key: Literal['*'] | QueryableAttribute[Any], *, raiseload: bool = False) → _AbstractLoad

  Indicate that the given column-oriented attribute should be deferred, e.g. not loaded until accessed.

  This function is part of the `Load` interface and supports both method-chained and standalone operation.

  e.g.:

      from sqlalchemy.orm import defer

      session.query(MyClass).options(
          defer(MyClass.attribute_one), defer(MyClass.attribute_two)
      )

  To specify a deferred load of an attribute on a related class, the path can be specified one token at a time, specifying the loading style for each link along the chain. To leave the loading style for a link unchanged, use `defaultload()`:

      session.query(MyClass).options(
          defaultload(MyClass.someattr).defer(RelatedClass.some_column)
      )

  Multiple deferral options related to a relationship can be bundled at once using `Load.options()`:

      select(MyClass).options(
          defaultload(MyClass.someattr).options(
              defer(RelatedClass.some_column),
              defer(RelatedClass.some_other_column),
              defer(RelatedClass.another_column),
          )
      )

  Parameters:

    - key – Attribute to be deferred.

    - raiseload – raise `InvalidRequestError` rather than lazy loading a value when the deferred attribute is accessed. Used to prevent unwanted SQL from being emitted.

  Added in version 1.4.

  See also

  Limiting which Columns Load with Column Deferral - in the ORM Querying Guide

  `load_only()`

  `undefer()`

function sqlalchemy.orm.deferred(column: _ORMColumnExprArgument[_T], *additional_columns: _ORMColumnExprArgument[Any], group: str | None = None, raiseload: bool = False, comparator_factory: Type[PropComparator[_T]] | None = None, init: _NoArg | bool = _NoArg.NO_ARG, repr: _NoArg | bool = _NoArg.NO_ARG, default: Any | None = _NoArg.NO_ARG, default_factory: _NoArg | Callable[[], _T] = _NoArg.NO_ARG, compare: _NoArg | bool = _NoArg.NO_ARG, kw_only: _NoArg | bool = _NoArg.NO_ARG, hash: _NoArg | bool | None = _NoArg.NO_ARG, active_history: bool = False, expire_on_flush: bool = True, info: _InfoType | None = None, doc: str | None = None, dataclass_metadata: _NoArg | Mapping[Any, Any] | None = _NoArg.NO_ARG) → MappedSQLExpression[_T]

  Indicate a column-based mapped attribute that by default will not load unless accessed.

  When using `mapped_column()`, the same functionality as that of `deferred()` construct is provided by using the `mapped_column.deferred` parameter.

  Parameters:

    - *columns – columns to be mapped. This is typically a single `Column` object, however a collection is supported in order to support multiple columns mapped under the same attribute.

    - raiseload –

      boolean, if True, indicates an exception should be raised if the load operation is to take place.

      Added in version 1.4.

  Additional arguments are the same as that of `column_property()`.

  See also

  Using deferred() for imperative mappers, mapped SQL expressions

function sqlalchemy.orm.query_expression(default_expr: _ORMColumnExprArgument[_T] = <sqlalchemy.sql.elements.Null object>, *, repr: Union[_NoArg, bool]=_NoArg.NO_ARG, compare: Union[_NoArg, bool]=_NoArg.NO_ARG, expire_on_flush: bool = True, info: _InfoType | None = None, doc: str | None = None) → MappedSQLExpression[_T]

  Indicate an attribute that populates from a query-time SQL expression.

  Parameters:

    default_expr – Optional SQL expression object that will be used in all cases if not assigned later with `with_expression()`.

  See also

  Loading Arbitrary SQL Expressions onto Objects - background and usage examples

function sqlalchemy.orm.load_only(*attrs: Literal['*'] | QueryableAttribute[Any], raiseload: bool = False) → _AbstractLoad

  Indicate that for a particular entity, only the given list of column-based attribute names should be loaded; all others will be deferred.

  This function is part of the `Load` interface and supports both method-chained and standalone operation.

  Example - given a class `User`, load only the `name` and `fullname` attributes:

      session.query(User).options(load_only(User.name, User.fullname))

  Example - given a relationship `User.addresses -> Address`, specify subquery loading for the `User.addresses` collection, but on each `Address` object load only the `email_address` attribute:

      session.query(User).options(
          subqueryload(User.addresses).load_only(Address.email_address)
      )

  For a statement that has multiple entities, the lead entity can be specifically referred to using the `Load` constructor:

      stmt = (
          select(User, Address)
          .join(User.addresses)
          .options(
              Load(User).load_only(User.name, User.fullname),
              Load(Address).load_only(Address.email_address),
          )
      )

  When used together with the populate_existing execution option only the attributes listed will be refreshed.

  Parameters:

    - *attrs – Attributes to be loaded, all others will be deferred.

    - raiseload –

      raise `InvalidRequestError` rather than lazy loading a value when a deferred attribute is accessed. Used to prevent unwanted SQL from being emitted.

      Added in version 2.0.

  See also

  Limiting which Columns Load with Column Deferral - in the ORM Querying Guide

  Parameters:

    - *attrs – Attributes to be loaded, all others will be deferred.

    - raiseload –

      raise `InvalidRequestError` rather than lazy loading a value when a deferred attribute is accessed. Used to prevent unwanted SQL from being emitted.

      Added in version 2.0.

function sqlalchemy.orm.undefer(key: Literal['*'] | QueryableAttribute[Any]) → _AbstractLoad

  Indicate that the given column-oriented attribute should be undeferred, e.g. specified within the SELECT statement of the entity as a whole.

  The column being undeferred is typically set up on the mapping as a `deferred()` attribute.

  This function is part of the `Load` interface and supports both method-chained and standalone operation.

  Examples:

      # undefer two columns
      session.query(MyClass).options(
          undefer(MyClass.col1), undefer(MyClass.col2)
      )

      # undefer all columns specific to a single class using Load + *
      session.query(MyClass, MyOtherClass).options(Load(MyClass).undefer("*"))

      # undefer a column on a related object
      select(MyClass).options(defaultload(MyClass.items).undefer(MyClass.text))

  Parameters:

    key – Attribute to be undeferred.

  See also

  Limiting which Columns Load with Column Deferral - in the ORM Querying Guide

  `defer()`

  `undefer_group()`

function sqlalchemy.orm.undefer_group(name: str) → _AbstractLoad

  Indicate that columns within the given deferred group name should be undeferred.

  The columns being undeferred are set up on the mapping as `deferred()` attributes and include a “group” name.

  E.g:

      session.query(MyClass).options(undefer_group("large_attrs"))

  To undefer a group of attributes on a related entity, the path can be spelled out using relationship loader options, such as `defaultload()`:

      select(MyClass).options(
          defaultload("someattr").undefer_group("large_attrs")
      )

  See also

  Limiting which Columns Load with Column Deferral - in the ORM Querying Guide

  `defer()`

  `undefer()`

function sqlalchemy.orm.with_expression(key: _AttrType, expression: _ColumnExpressionArgument[Any]) → _AbstractLoad

  Apply an ad-hoc SQL expression to a “deferred expression” attribute.

  This option is used in conjunction with the `query_expression()` mapper-level construct that indicates an attribute which should be the target of an ad-hoc SQL expression.

  E.g.:

      stmt = select(SomeClass).options(
          with_expression(SomeClass.x_y_expr, SomeClass.x + SomeClass.y)
      )

  Parameters:

    - key – Attribute to be populated

    - expr – SQL expression to be applied to the attribute.

  See also

  Loading Arbitrary SQL Expressions onto Objects - background and usage examples

ORM Querying Guide

Next Query Guide Section: Relationship Loading Techniques