Relationship Loading Techniques — SQLAlchemy 2.0 Documentation

Release: 2.0.50 | Release Date: May 24, 2026

# SQLAlchemy 2.0 Documentation

ORM Querying Guide

This page is part of the ORM Querying Guide.

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# Relationship Loading Techniques

About this Document

This section presents an in-depth view of how to load related objects. Readers should be familiar with Relationship Configuration and basic use.

Most examples here assume the “User/Address” mapping setup similar to the one illustrated at setup for selects.

A big part of SQLAlchemy is providing a wide range of control over how related objects get loaded when querying. By “related objects” we refer to collections or scalar associations configured on a mapper using `relationship()`. This behavior can be configured at mapper construction time using the `relationship.lazy` parameter to the `relationship()` function, as well as by using ORM loader options with the `Select` construct.

The loading of relationships falls into three categories; lazy loading, eager loading, and no loading. Lazy loading refers to objects that are returned from a query without the related objects loaded at first. When the given collection or reference is first accessed on a particular object, an additional SELECT statement is emitted such that the requested collection is loaded.

Eager loading refers to objects returned from a query with the related collection or scalar reference already loaded up front. The ORM achieves this either by augmenting the SELECT statement it would normally emit with a JOIN to load in related rows simultaneously, or by emitting additional SELECT statements after the primary one to load collections or scalar references at once.

“No” loading refers to the disabling of loading on a given relationship, either that the attribute is empty and is just never loaded, or that it raises an error when it is accessed, in order to guard against unwanted lazy loads.

## Summary of Relationship Loading Styles

The primary forms of relationship loading are:

- lazy loading - available via `lazy='select'` or the `lazyload()` option, this is the form of loading that emits a SELECT statement at attribute access time to lazily load a related reference on a single object at a time. Lazy loading is the default loading style for all `relationship()` constructs that don’t otherwise indicate the `relationship.lazy` option. Lazy loading is detailed at Lazy Loading.

- select IN loading - available via `lazy='selectin'` or the `selectinload()` option, this form of loading emits a second (or more) SELECT statement which assembles the primary key identifiers of the parent objects into an IN clause, so that all members of related collections / scalar references are loaded at once by primary key. Select IN loading is detailed at Select IN loading.

- joined loading - available via `lazy='joined'` or the `joinedload()` option, this form of loading applies a JOIN to the given SELECT statement so that related rows are loaded in the same result set. Joined eager loading is detailed at Joined Eager Loading.

- raise loading - available via `lazy='raise'`, `lazy='raise_on_sql'`, or the `raiseload()` option, this form of loading is triggered at the same time a lazy load would normally occur, except it raises an ORM exception in order to guard against the application making unwanted lazy loads. An introduction to raise loading is at Preventing unwanted lazy loads using raiseload.

- subquery loading - available via `lazy='subquery'` or the `subqueryload()` option, this form of loading emits a second SELECT statement which re-states the original query embedded inside of a subquery, then JOINs that subquery to the related table to be loaded to load all members of related collections / scalar references at once. Subquery eager loading is detailed at Subquery Eager Loading.

- write only loading - available via `lazy='write_only'`, or by annotating the left side of the `Relationship` object using the `WriteOnlyMapped` annotation. This collection-only loader style produces an alternative attribute instrumentation that never implicitly loads records from the database, instead only allowing `WriteOnlyCollection.add()`, `WriteOnlyCollection.add_all()` and `WriteOnlyCollection.remove()` methods. Querying the collection is performed by invoking a SELECT statement which is constructed using the `WriteOnlyCollection.select()` method. Write only loading is discussed at Write Only Relationships.

- dynamic loading - available via `lazy='dynamic'`, or by annotating the left side of the `Relationship` object using the `DynamicMapped` annotation. This is a legacy collection-only loader style which produces a `Query` object when the collection is accessed, allowing custom SQL to be emitted against the collection’s contents. However, dynamic loaders will implicitly iterate the underlying collection in various circumstances which makes them less useful for managing truly large collections. Dynamic loaders are superseded by “write only” collections, which will prevent the underlying collection from being implicitly loaded under any circumstances. Dynamic loaders are discussed at Dynamic Relationship Loaders.

## Configuring Loader Strategies at Mapping Time

The loader strategy for a particular relationship can be configured at mapping time to take place in all cases where an object of the mapped type is loaded, in the absence of any query-level options that modify it. This is configured using the `relationship.lazy` parameter to `relationship()`; common values for this parameter include `select`, `selectin` and `joined`.

The example below illustrates the relationship example at One To Many, configuring the `Parent.children` relationship to use Select IN loading when a SELECT statement for `Parent` objects is emitted:

    from typing import List

    from sqlalchemy import ForeignKey
    from sqlalchemy.orm import DeclarativeBase
    from sqlalchemy.orm import Mapped
    from sqlalchemy.orm import mapped_column
    from sqlalchemy.orm import relationship

    class Base(DeclarativeBase):
        pass

    class Parent(Base):
        __tablename__ = "parent"

        id: Mapped[int] = mapped_column(primary_key=True)
        children: Mapped[List["Child"]] = relationship(lazy="selectin")

    class Child(Base):
        __tablename__ = "child"

        id: Mapped[int] = mapped_column(primary_key=True)
        parent_id: Mapped[int] = mapped_column(ForeignKey("parent.id"))

Above, whenever a collection of `Parent` objects are loaded, each `Parent` will also have its `children` collection populated, using the `"selectin"` loader strategy that emits a second query.

The default value of the `relationship.lazy` argument is `"select"`, which indicates Lazy Loading.

## Relationship Loading with Loader Options

The other, and possibly more common way to configure loading strategies is to set them up on a per-query basis against specific attributes using the `Select.options()` method. Very detailed control over relationship loading is available using loader options; the most common are `joinedload()`, `selectinload()` and `lazyload()`. The option accepts a class-bound attribute referring to the specific class/attribute that should be targeted:

    from sqlalchemy import select
    from sqlalchemy.orm import lazyload

    # set children to load lazily
    stmt = select(Parent).options(lazyload(Parent.children))

    from sqlalchemy.orm import joinedload

    # set children to load eagerly with a join
    stmt = select(Parent).options(joinedload(Parent.children))

The loader options can also be “chained” using method chaining to specify how loading should occur further levels deep:

    from sqlalchemy import select
    from sqlalchemy.orm import joinedload

    stmt = select(Parent).options(
        joinedload(Parent.children).subqueryload(Child.subelements)
    )

Chained loader options can be applied against a “lazy” loaded collection. This means that when a collection or association is lazily loaded upon access, the specified option will then take effect:

    from sqlalchemy import select
    from sqlalchemy.orm import lazyload

    stmt = select(Parent).options(lazyload(Parent.children).subqueryload(Child.subelements))

Above, the query will return `Parent` objects without the `children` collections loaded. When the `children` collection on a particular `Parent` object is first accessed, it will lazy load the related objects, but additionally apply eager loading to the `subelements` collection on each member of `children`.

### Adding Criteria to loader options

The relationship attributes used to indicate loader options include the ability to add additional filtering criteria to the ON clause of the join that’s created, or to the WHERE criteria involved, depending on the loader strategy. This can be achieved using the `PropComparator.and_()` method which will pass through an option such that loaded results are limited to the given filter criteria:

    from sqlalchemy import select
    from sqlalchemy.orm import lazyload

    stmt = select(A).options(lazyload(A.bs.and_(B.id > 5)))

When using limiting criteria, if a particular collection is already loaded it won’t be refreshed; to ensure the new criteria takes place, apply the Populate Existing execution option:

    from sqlalchemy import select
    from sqlalchemy.orm import lazyload

    stmt = (
        select(A)
        .options(lazyload(A.bs.and_(B.id > 5)))
        .execution_options(populate_existing=True)
    )

In order to add filtering criteria to all occurrences of an entity throughout a query, regardless of loader strategy or where it occurs in the loading process, see the `with_loader_criteria()` function.

Added in version 1.4.

### Specifying Sub-Options with Load.options()

Using method chaining, the loader style of each link in the path is explicitly stated. To navigate along a path without changing the existing loader style of a particular attribute, the `defaultload()` method/function may be used:

    from sqlalchemy import select
    from sqlalchemy.orm import defaultload

    stmt = select(A).options(defaultload(A.atob).joinedload(B.btoc))

A similar approach can be used to specify multiple sub-options at once, using the `Load.options()` method:

    from sqlalchemy import select
    from sqlalchemy.orm import defaultload
    from sqlalchemy.orm import joinedload

    stmt = select(A).options(
        defaultload(A.atob).options(joinedload(B.btoc), joinedload(B.btod))
    )

See also

Using load_only() on related objects and collections - illustrates examples of combining relationship and column-oriented loader options.

Note

The loader options applied to an object’s lazy-loaded collections are “sticky” to specific object instances, meaning they will persist upon collections loaded by that specific object for as long as it exists in memory. For example, given the previous example:

    stmt = select(Parent).options(lazyload(Parent.children).subqueryload(Child.subelements))

if the `children` collection on a particular `Parent` object loaded by the above query is expired (such as when a `Session` object’s transaction is committed or rolled back, or `Session.expire_all()` is used), when the `Parent.children` collection is next accessed in order to re-load it, the `Child.subelements` collection will again be loaded using subquery eager loading. This stays the case even if the above `Parent` object is accessed from a subsequent query that specifies a different set of options. To change the options on an existing object without expunging it and re-loading, they must be set explicitly in conjunction using the Populate Existing execution option:

    # change the options on Parent objects that were already loaded
    stmt = (
        select(Parent)
        .execution_options(populate_existing=True)
        .options(lazyload(Parent.children).lazyload(Child.subelements))
        .all()
    )

If the objects loaded above are fully cleared from the `Session`, such as due to garbage collection or that `Session.expunge_all()` were used, the “sticky” options will also be gone and the newly created objects will make use of new options if loaded again.

A future SQLAlchemy release may add more alternatives to manipulating the loader options on already-loaded objects.

## Lazy Loading

By default, all inter-object relationships are lazy loading. The scalar or collection attribute associated with a `relationship()` contains a trigger which fires the first time the attribute is accessed. This trigger typically issues a SQL call at the point of access in order to load the related object or objects:

    >>> spongebob.addresses
    SELECT
        addresses.id AS addresses_id,
        addresses.email_address AS addresses_email_address,
        addresses.user_id AS addresses_user_id
    FROM addresses
    WHERE ? = addresses.user_id
    [5]
    [<Address(u'spongebob@google.com')>, <Address(u'j25@yahoo.com')>]

The one case where SQL is not emitted is for a simple many-to-one relationship, when the related object can be identified by its primary key alone and that object is already present in the current `Session`. For this reason, while lazy loading can be expensive for related collections, in the case that one is loading lots of objects with simple many-to-ones against a relatively small set of possible target objects, lazy loading may be able to refer to these objects locally without emitting as many SELECT statements as there are parent objects.

This default behavior of “load upon attribute access” is known as “lazy” or “select” loading - the name “select” because a “SELECT” statement is typically emitted when the attribute is first accessed.

Lazy loading can be enabled for a given attribute that is normally configured in some other way using the `lazyload()` loader option:

    from sqlalchemy import select
    from sqlalchemy.orm import lazyload

    # force lazy loading for an attribute that is set to
    # load some other way normally
    stmt = select(User).options(lazyload(User.addresses))

### Preventing unwanted lazy loads using raiseload

The `lazyload()` strategy produces an effect that is one of the most common issues referred to in object relational mapping; the N plus one problem, which states that for any N objects loaded, accessing their lazy-loaded attributes means there will be N+1 SELECT statements emitted. In SQLAlchemy, the usual mitigation for the N+1 problem is to make use of its very capable eager load system. However, eager loading requires that the attributes which are to be loaded be specified with the `Select` up front. The problem of code that may access other attributes that were not eagerly loaded, where lazy loading is not desired, may be addressed using the `raiseload()` strategy; this loader strategy replaces the behavior of lazy loading with an informative error being raised:

    from sqlalchemy import select
    from sqlalchemy.orm import raiseload

    stmt = select(User).options(raiseload(User.addresses))

Above, a `User` object loaded from the above query will not have the `.addresses` collection loaded; if some code later on attempts to access this attribute, an ORM exception is raised.

`raiseload()` may be used with a so-called “wildcard” specifier to indicate that all relationships should use this strategy. For example, to set up only one attribute as eager loading, and all the rest as raise:

    from sqlalchemy import select
    from sqlalchemy.orm import joinedload
    from sqlalchemy.orm import raiseload

    stmt = select(Order).options(joinedload(Order.items), raiseload("*"))

The above wildcard will apply to all relationships not just on `Order` besides `items`, but all those on the `Item` objects as well. To set up `raiseload()` for only the `Order` objects, specify a full path with `Load`:

    from sqlalchemy import select
    from sqlalchemy.orm import joinedload
    from sqlalchemy.orm import Load

    stmt = select(Order).options(joinedload(Order.items), Load(Order).raiseload("*"))

Conversely, to set up the raise for just the `Item` objects:

    stmt = select(Order).options(joinedload(Order.items).raiseload("*"))

The `raiseload()` option applies only to relationship attributes. For column-oriented attributes, the `defer()` option supports the `defer.raiseload` option which works in the same way.

Tip

The “raiseload” strategies do not apply within the unit of work flush process. That means if the `Session.flush()` process needs to load a collection in order to finish its work, it will do so while bypassing any `raiseload()` directives.

See also

Wildcard Loading Strategies

Using raiseload to prevent deferred column loads

## Joined Eager Loading

Joined eager loading is the oldest style of eager loading included with the SQLAlchemy ORM. It works by connecting a JOIN (by default a LEFT OUTER join) to the SELECT statement emitted, and populates the target scalar/collection from the same result set as that of the parent.

At the mapping level, this looks like:

    class Address(Base):
        # ...

        user: Mapped[User] = relationship(lazy="joined")

Joined eager loading is usually applied as an option to a query, rather than as a default loading option on the mapping, in particular when used for collections rather than many-to-one-references. This is achieved using the `joinedload()` loader option:

    >>> from sqlalchemy import select
    >>> from sqlalchemy.orm import joinedload
    >>> stmt = select(User).options(joinedload(User.addresses)).filter_by(name="spongebob")
    >>> spongebob = session.scalars(stmt).unique().all()
    SELECT
        addresses_1.id AS addresses_1_id,
        addresses_1.email_address AS addresses_1_email_address,
        addresses_1.user_id AS addresses_1_user_id,
        users.id AS users_id, users.name AS users_name,
        users.fullname AS users_fullname,
        users.nickname AS users_nickname
    FROM users
    LEFT OUTER JOIN addresses AS addresses_1
        ON users.id = addresses_1.user_id
    WHERE users.name = ?
    ['spongebob']

Tip

When including `joinedload()` in reference to a one-to-many or many-to-many collection, the `Result.unique()` method must be applied to the returned result, which will uniquify the incoming rows by primary key that otherwise are multiplied out by the join. The ORM will raise an error if this is not present.

This is not automatic in modern SQLAlchemy, as it changes the behavior of the result set to return fewer ORM objects than the statement would normally return in terms of number of rows. Therefore SQLAlchemy keeps the use of `Result.unique()` explicit, so there’s no ambiguity that the returned objects are being uniqified on primary key.

The JOIN emitted by default is a LEFT OUTER JOIN, to allow for a lead object that does not refer to a related row. For an attribute that is guaranteed to have an element, such as a many-to-one reference to a related object where the referencing foreign key is NOT NULL, the query can be made more efficient by using an inner join; this is available at the mapping level via the `relationship.innerjoin` flag:

    class Address(Base):
        # ...

        user_id: Mapped[int] = mapped_column(ForeignKey("users.id"))
        user: Mapped[User] = relationship(lazy="joined", innerjoin=True)

At the query option level, via the `joinedload.innerjoin` flag:

    from sqlalchemy import select
    from sqlalchemy.orm import joinedload

    stmt = select(Address).options(joinedload(Address.user, innerjoin=True))

The JOIN will right-nest itself when applied in a chain that includes an OUTER JOIN:

    >>> from sqlalchemy import select
    >>> from sqlalchemy.orm import joinedload
    >>> stmt = select(User).options(
    ...     joinedload(User.addresses).joinedload(Address.widgets, innerjoin=True)
    ... )
    >>> results = session.scalars(stmt).unique().all()
    SELECT
        widgets_1.id AS widgets_1_id,
        widgets_1.name AS widgets_1_name,
        addresses_1.id AS addresses_1_id,
        addresses_1.email_address AS addresses_1_email_address,
        addresses_1.user_id AS addresses_1_user_id,
        users.id AS users_id, users.name AS users_name,
        users.fullname AS users_fullname,
        users.nickname AS users_nickname
    FROM users
    LEFT OUTER JOIN (
        addresses AS addresses_1 JOIN widgets AS widgets_1 ON
        addresses_1.widget_id = widgets_1.id
    ) ON users.id = addresses_1.user_id

Tip

If using database row locking techniques when emitting the SELECT, meaning the `Select.with_for_update()` method is being used to emit SELECT..FOR UPDATE, the joined table may be locked as well, depending on the behavior of the backend in use. It’s not recommended to use joined eager loading at the same time as SELECT..FOR UPDATE for this reason.

### The Zen of Joined Eager Loading

Since joined eager loading seems to have many resemblances to the use of `Select.join()`, it often produces confusion as to when and how it should be used. It is critical to understand the distinction that while `Select.join()` is used to alter the results of a query, `joinedload()` goes through great lengths to not alter the results of the query, and instead hide the effects of the rendered join to only allow for related objects to be present.

The philosophy behind loader strategies is that any set of loading schemes can be applied to a particular query, and the results don’t change - only the number of SQL statements required to fully load related objects and collections changes. A particular query might start out using all lazy loads. After using it in context, it might be revealed that particular attributes or collections are always accessed, and that it would be more efficient to change the loader strategy for these. The strategy can be changed with no other modifications to the query, the results will remain identical, but fewer SQL statements would be emitted. In theory (and pretty much in practice), nothing you can do to the `Select` would make it load a different set of primary or related objects based on a change in loader strategy.

How `joinedload()` in particular achieves this result of not impacting entity rows returned in any way is that it creates an anonymous alias of the joins it adds to your query, so that they can’t be referenced by other parts of the query. For example, the query below uses `joinedload()` to create a LEFT OUTER JOIN from `users` to `addresses`, however the `ORDER BY` added against `Address.email_address` is not valid - the `Address` entity is not named in the query:

    >>> from sqlalchemy import select
    >>> from sqlalchemy.orm import joinedload
    >>> stmt = (
    ...     select(User)
    ...     .options(joinedload(User.addresses))
    ...     .filter(User.name == "spongebob")
    ...     .order_by(Address.email_address)
    ... )
    >>> result = session.scalars(stmt).unique().all()
    SELECT
        addresses_1.id AS addresses_1_id,
        addresses_1.email_address AS addresses_1_email_address,
        addresses_1.user_id AS addresses_1_user_id,
        users.id AS users_id,
        users.name AS users_name,
        users.fullname AS users_fullname,
        users.nickname AS users_nickname
    FROM users
    LEFT OUTER JOIN addresses AS addresses_1
        ON users.id = addresses_1.user_id
    WHERE users.name = ?
    ORDER BY addresses.email_address   <-- this part is wrong !
    ['spongebob']

Above, `ORDER BY addresses.email_address` is not valid since `addresses` is not in the FROM list. The correct way to load the `User` records and order by email address is to use `Select.join()`:

    >>> from sqlalchemy import select
    >>> stmt = (
    ...     select(User)
    ...     .join(User.addresses)
    ...     .filter(User.name == "spongebob")
    ...     .order_by(Address.email_address)
    ... )
    >>> result = session.scalars(stmt).unique().all()
    SELECT
        users.id AS users_id,
        users.name AS users_name,
        users.fullname AS users_fullname,
        users.nickname AS users_nickname
    FROM users
    JOIN addresses ON users.id = addresses.user_id
    WHERE users.name = ?
    ORDER BY addresses.email_address
    ['spongebob']

The statement above is of course not the same as the previous one, in that the columns from `addresses` are not included in the result at all. We can add `joinedload()` back in, so that there are two joins - one is that which we are ordering on, the other is used anonymously to load the contents of the `User.addresses` collection:

    >>> stmt = (
    ...     select(User)
    ...     .join(User.addresses)
    ...     .options(joinedload(User.addresses))
    ...     .filter(User.name == "spongebob")
    ...     .order_by(Address.email_address)
    ... )
    >>> result = session.scalars(stmt).unique().all()
    SELECT
        addresses_1.id AS addresses_1_id,
        addresses_1.email_address AS addresses_1_email_address,
        addresses_1.user_id AS addresses_1_user_id,
        users.id AS users_id, users.name AS users_name,
        users.fullname AS users_fullname,
        users.nickname AS users_nickname
    FROM users JOIN addresses
        ON users.id = addresses.user_id
    LEFT OUTER JOIN addresses AS addresses_1
        ON users.id = addresses_1.user_id
    WHERE users.name = ?
    ORDER BY addresses.email_address
    ['spongebob']

What we see above is that our usage of `Select.join()` is to supply JOIN clauses we’d like to use in subsequent query criterion, whereas our usage of `joinedload()` only concerns itself with the loading of the `User.addresses` collection, for each `User` in the result. In this case, the two joins most probably appear redundant - which they are. If we wanted to use just one JOIN for collection loading as well as ordering, we use the `contains_eager()` option, described in Routing Explicit Joins/Statements into Eagerly Loaded Collections below. But to see why `joinedload()` does what it does, consider if we were filtering on a particular `Address`:

    >>> stmt = (
    ...     select(User)
    ...     .join(User.addresses)
    ...     .options(joinedload(User.addresses))
    ...     .filter(User.name == "spongebob")
    ...     .filter(Address.email_address == "someaddress@foo.com")
    ... )
    >>> result = session.scalars(stmt).unique().all()
    SELECT
        addresses_1.id AS addresses_1_id,
        addresses_1.email_address AS addresses_1_email_address,
        addresses_1.user_id AS addresses_1_user_id,
        users.id AS users_id, users.name AS users_name,
        users.fullname AS users_fullname,
        users.nickname AS users_nickname
    FROM users JOIN addresses
        ON users.id = addresses.user_id
    LEFT OUTER JOIN addresses AS addresses_1
        ON users.id = addresses_1.user_id
    WHERE users.name = ? AND addresses.email_address = ?
    ['spongebob', 'someaddress@foo.com']

Above, we can see that the two JOINs have very different roles. One will match exactly one row, that of the join of `User` and `Address` where `Address.email_address=='someaddress@foo.com'`. The other LEFT OUTER JOIN will match all `Address` rows related to `User`, and is only used to populate the `User.addresses` collection, for those `User` objects that are returned.

By changing the usage of `joinedload()` to another style of loading, we can change how the collection is loaded completely independently of SQL used to retrieve the actual `User` rows we want. Below we change `joinedload()` into `selectinload()`:

    >>> stmt = (
    ...     select(User)
    ...     .join(User.addresses)
    ...     .options(selectinload(User.addresses))
    ...     .filter(User.name == "spongebob")
    ...     .filter(Address.email_address == "someaddress@foo.com")
    ... )
    >>> result = session.scalars(stmt).all()
    SELECT
        users.id AS users_id,
        users.name AS users_name,
        users.fullname AS users_fullname,
        users.nickname AS users_nickname
    FROM users
    JOIN addresses ON users.id = addresses.user_id
    WHERE
        users.name = ?
        AND addresses.email_address = ?
    ['spongebob', 'someaddress@foo.com']
    # ... selectinload() emits a SELECT in order
    # to load all address records ...

When using joined eager loading, if the query contains a modifier that impacts the rows returned externally to the joins, such as when using DISTINCT, LIMIT, OFFSET or equivalent, the completed statement is first wrapped inside a subquery, and the joins used specifically for joined eager loading are applied to the subquery. SQLAlchemy’s joined eager loading goes the extra mile, and then ten miles further, to absolutely ensure that it does not affect the end result of the query, only the way collections and related objects are loaded, no matter what the format of the query is.

See also

Routing Explicit Joins/Statements into Eagerly Loaded Collections - using `contains_eager()`

## Select IN loading

In most cases, selectin loading is the most simple and efficient way to eagerly load collections of objects. The only scenario in which selectin eager loading is not feasible is when the model is using composite primary keys, and the backend database does not support tuples with IN, which currently includes SQL Server.

“Select IN” eager loading is provided using the `"selectin"` argument to `relationship.lazy` or by using the `selectinload()` loader option. This style of loading emits a SELECT that refers to the primary key values of the parent object, or in the case of a many-to-one relationship to those of the child objects, inside of an IN clause, in order to load related associations:

    >>> from sqlalchemy import select
    >>> from sqlalchemy.orm import selectinload
    >>> stmt = (
    ...     select(User)
    ...     .options(selectinload(User.addresses))
    ...     .filter(or_(User.name == "spongebob", User.name == "ed"))
    ... )
    >>> result = session.scalars(stmt).all()
    SELECT
        users.id AS users_id,
        users.name AS users_name,
        users.fullname AS users_fullname,
        users.nickname AS users_nickname
    FROM users
    WHERE users.name = ? OR users.name = ?
    ('spongebob', 'ed')
    SELECT
        addresses.id AS addresses_id,
        addresses.email_address AS addresses_email_address,
        addresses.user_id AS addresses_user_id
    FROM addresses
    WHERE addresses.user_id IN (?, ?)
    (5, 7)

Above, the second SELECT refers to `addresses.user_id IN (5, 7)`, where the “5” and “7” are the primary key values for the previous two `User` objects loaded; after a batch of objects are completely loaded, their primary key values are injected into the `IN` clause for the second SELECT. Because the relationship between `User` and `Address` has a simple primary join condition and provides that the primary key values for `User` can be derived from `Address.user_id`, the statement has no joins or subqueries at all.

For simple many-to-one loads, a JOIN is also not needed as the foreign key value from the parent object is used:

    >>> from sqlalchemy import select
    >>> from sqlalchemy.orm import selectinload
    >>> stmt = select(Address).options(selectinload(Address.user))
    >>> result = session.scalars(stmt).all()
    SELECT
        addresses.id AS addresses_id,
        addresses.email_address AS addresses_email_address,
        addresses.user_id AS addresses_user_id
        FROM addresses
    SELECT
        users.id AS users_id,
        users.name AS users_name,
        users.fullname AS users_fullname,
        users.nickname AS users_nickname
    FROM users
    WHERE users.id IN (?, ?)
    (1, 2)

Tip

by “simple” we mean that the `relationship.primaryjoin` condition expresses an equality comparison between the primary key of the “one” side and a straight foreign key of the “many” side, without any additional criteria.

Select IN loading also supports many-to-many relationships, where it currently will JOIN across all three tables to match rows from one side to the other.

Things to know about this kind of loading include:

- The strategy emits a SELECT for up to 500 parent primary key values at a time, as the primary keys are rendered into a large IN expression in the SQL statement. Some databases like Oracle Database have a hard limit on how large an IN expression can be, and overall the size of the SQL string shouldn’t be arbitrarily large.

- As “selectin” loading relies upon IN, for a mapping with composite primary keys, it must use the “tuple” form of IN, which looks like `WHERE (table.column_a, table.column_b) IN ((?, ?), (?, ?), (?, ?))`. This syntax is not currently supported on SQL Server and for SQLite requires at least version 3.15. There is no special logic in SQLAlchemy to check ahead of time which platforms support this syntax or not; if run against a non-supporting platform, the database will return an error immediately. An advantage to SQLAlchemy just running the SQL out for it to fail is that if a particular database does start supporting this syntax, it will work without any changes to SQLAlchemy (as was the case with SQLite).

## Subquery Eager Loading

Legacy Feature

The `subqueryload()` eager loader is mostly legacy at this point, superseded by the `selectinload()` strategy which is of much simpler design, more flexible with features such as Yield Per, and emits more efficient SQL statements in most cases. As `subqueryload()` relies upon re-interpreting the original SELECT statement, it may fail to work efficiently when given very complex source queries.

`subqueryload()` may continue to be useful for the specific case of an eager loaded collection for objects that use composite primary keys, on the Microsoft SQL Server backend that continues to not have support for the “tuple IN” syntax.

Subquery loading is similar in operation to selectin eager loading, however the SELECT statement which is emitted is derived from the original statement, and has a more complex query structure as that of selectin eager loading.

Subquery eager loading is provided using the `"subquery"` argument to `relationship.lazy` or by using the `subqueryload()` loader option.

The operation of subquery eager loading is to emit a second SELECT statement for each relationship to be loaded, across all result objects at once. This SELECT statement refers to the original SELECT statement, wrapped inside of a subquery, so that we retrieve the same list of primary keys for the primary object being returned, then link that to the sum of all the collection members to load them at once:

    >>> from sqlalchemy import select
    >>> from sqlalchemy.orm import subqueryload
    >>> stmt = select(User).options(subqueryload(User.addresses)).filter_by(name="spongebob")
    >>> results = session.scalars(stmt).all()
    SELECT
        users.id AS users_id,
        users.name AS users_name,
        users.fullname AS users_fullname,
        users.nickname AS users_nickname
    FROM users
    WHERE users.name = ?
    ('spongebob',)
    SELECT
        addresses.id AS addresses_id,
        addresses.email_address AS addresses_email_address,
        addresses.user_id AS addresses_user_id,
        anon_1.users_id AS anon_1_users_id
    FROM (
        SELECT users.id AS users_id
        FROM users
        WHERE users.name = ?) AS anon_1
    JOIN addresses ON anon_1.users_id = addresses.user_id
    ORDER BY anon_1.users_id, addresses.id
    ('spongebob',)

Things to know about this kind of loading include:

- The SELECT statement emitted by the “subquery” loader strategy, unlike that of “selectin”, requires a subquery, and will inherit whatever performance limitations are present in the original query. The subquery itself may also incur performance penalties based on the specifics of the database in use.

- “subquery” loading imposes some special ordering requirements in order to work correctly. A query which makes use of `subqueryload()` in conjunction with a limiting modifier such as `Select.limit()`, or `Select.offset()` should always include `Select.order_by()` against unique column(s) such as the primary key, so that the additional queries emitted by `subqueryload()` include the same ordering as used by the parent query. Without it, there is a chance that the inner query could return the wrong rows:

      # incorrect, no ORDER BY
      stmt = select(User).options(subqueryload(User.addresses).limit(1))

      # incorrect if User.name is not unique
      stmt = select(User).options(subqueryload(User.addresses)).order_by(User.name).limit(1)

      # correct
      stmt = (
          select(User)
          .options(subqueryload(User.addresses))
          .order_by(User.name, User.id)
          .limit(1)
      )

  See also

  Why is ORDER BY recommended with LIMIT (especially with subqueryload())? - detailed example

- “subquery” loading also incurs additional performance / complexity issues when used on a many-levels-deep eager load, as subqueries will be nested repeatedly.

- “subquery” loading is not compatible with the “batched” loading supplied by Yield Per, both for collection and scalar relationships.

For the above reasons, the “selectin” strategy should be preferred over “subquery”.

See also

Select IN loading

## What Kind of Loading to Use ?

Which type of loading to use typically comes down to optimizing the tradeoff between number of SQL executions, complexity of SQL emitted, and amount of data fetched.

One to Many / Many to Many Collection - The `selectinload()` is generally the best loading strategy to use. It emits an additional SELECT that uses as few tables as possible, leaving the original statement unaffected, and is most flexible for any kind of originating query. Its only major limitation is when using a table with composite primary keys on a backend that does not support “tuple IN”, which currently includes SQL Server and very old SQLite versions; all other included backends support it.

Many to One - The `joinedload()` strategy is the most general purpose strategy. In special cases, the `immediateload()` strategy may also be useful, if there are a very small number of potential related values, as this strategy will fetch the object from the local `Session` without emitting any SQL if the related object is already present.

## Polymorphic Eager Loading

Specification of polymorphic options on a per-eager-load basis is supported. See the section Eager Loading of Polymorphic Subtypes for examples of the `PropComparator.of_type()` method in conjunction with the `with_polymorphic()` function.

## Wildcard Loading Strategies

Each of `joinedload()`, `subqueryload()`, `lazyload()`, `selectinload()`, and `raiseload()` can be used to set the default style of `relationship()` loading for a particular query, affecting all `relationship()` -mapped attributes not otherwise specified in the statement. This feature is available by passing the string `'*'` as the argument to any of these options:

    from sqlalchemy import select
    from sqlalchemy.orm import lazyload

    stmt = select(MyClass).options(lazyload("*"))

Above, the `lazyload('*')` option will supersede the `lazy` setting of all `relationship()` constructs in use for that query, with the exception of those that use `lazy='write_only'` or `lazy='dynamic'`.

If some relationships specify `lazy='joined'` or `lazy='selectin'`, for example, using `lazyload('*')` will unilaterally cause all those relationships to use `'select'` loading, e.g. emit a SELECT statement when each attribute is accessed.

The option does not supersede loader options stated in the query, such as `joinedload()`, `selectinload()`, etc. The query below will still use joined loading for the `widget` relationship:

    from sqlalchemy import select
    from sqlalchemy.orm import lazyload
    from sqlalchemy.orm import joinedload

    stmt = select(MyClass).options(lazyload("*"), joinedload(MyClass.widget))

While the instruction for `joinedload()` above will take place regardless of whether it appears before or after the `lazyload()` option, if multiple options that each included `"*"` were passed, the last one will take effect.

### Per-Entity Wildcard Loading Strategies

A variant of the wildcard loader strategy is the ability to set the strategy on a per-entity basis. For example, if querying for `User` and `Address`, we can instruct all relationships on `Address` to use lazy loading, while leaving the loader strategies for `User` unaffected, by first applying the `Load` object, then specifying the `*` as a chained option:

    from sqlalchemy import select
    from sqlalchemy.orm import Load

    stmt = select(User, Address).options(Load(Address).lazyload("*"))

Above, all relationships on `Address` will be set to a lazy load.

## Routing Explicit Joins/Statements into Eagerly Loaded Collections

The behavior of `joinedload()` is such that joins are created automatically, using anonymous aliases as targets, the results of which are routed into collections and scalar references on loaded objects. It is often the case that a query already includes the necessary joins which represent a particular collection or scalar reference, and the joins added by the joinedload feature are redundant - yet you’d still like the collections/references to be populated.

For this SQLAlchemy supplies the `contains_eager()` option. This option is used in the same manner as the `joinedload()` option except it is assumed that the `Select` object will explicitly include the appropriate joins, typically using methods like `Select.join()`. Below, we specify a join between `User` and `Address` and additionally establish this as the basis for eager loading of `User.addresses`:

    from sqlalchemy.orm import contains_eager

    stmt = select(User).join(User.addresses).options(contains_eager(User.addresses))

If the “eager” portion of the statement is “aliased”, the path should be specified using `PropComparator.of_type()`, which allows the specific `aliased()` construct to be passed:

    # use an alias of the Address entity
    adalias = aliased(Address)

    # construct a statement which expects the "addresses" results

    stmt = (
        select(User)
        .outerjoin(User.addresses.of_type(adalias))
        .options(contains_eager(User.addresses.of_type(adalias)))
    )

    # get results normally
    r = session.scalars(stmt).unique().all()
    SELECT
        users.user_id AS users_user_id,
        users.user_name AS users_user_name,
        adalias.address_id AS adalias_address_id,
        adalias.user_id AS adalias_user_id,
        adalias.email_address AS adalias_email_address,
        (...other columns...)
    FROM users
    LEFT OUTER JOIN email_addresses AS email_addresses_1
    ON users.user_id = email_addresses_1.user_id

The path given as the argument to `contains_eager()` needs to be a full path from the starting entity. For example if we were loading `Users->orders->Order->items->Item`, the option would be used as:

    stmt = select(User).options(contains_eager(User.orders).contains_eager(Order.items))

### Using contains_eager() to load a custom-filtered collection result

When we use `contains_eager()`, we are constructing ourselves the SQL that will be used to populate collections. From this, it naturally follows that we can opt to modify what values the collection is intended to store, by writing our SQL to load a subset of elements for collections or scalar attributes.

Tip

SQLAlchemy now has a much simpler way to do this, by allowing WHERE criteria to be added directly to loader options such as `joinedload()` and `selectinload()` using `PropComparator.and_()`. See the section Adding Criteria to loader options for examples.

The techniques described here still apply if the related collection is to be queried using SQL criteria or modifiers more complex than a simple WHERE clause.

As an example, we can load a `User` object and eagerly load only particular addresses into its `.addresses` collection by filtering the joined data, routing it using `contains_eager()`, also using Populate Existing to ensure any already-loaded collections are overwritten:

    stmt = (
        select(User)
        .join(User.addresses)
        .filter(Address.email_address.like("%@aol.com"))
        .options(contains_eager(User.addresses))
        .execution_options(populate_existing=True)
    )

The above query will load only `User` objects which contain at least `Address` object that contains the substring `'aol.com'` in its `email` field; the `User.addresses` collection will contain only these `Address` entries, and not any other `Address` entries that are in fact associated with the collection.

Tip

In all cases, the SQLAlchemy ORM does not overwrite already loaded attributes and collections unless told to do so. As there is an identity map in use, it is often the case that an ORM query is returning objects that were in fact already present and loaded in memory. Therefore, when using `contains_eager()` to populate a collection in an alternate way, it is usually a good idea to use Populate Existing as illustrated above so that an already-loaded collection is refreshed with the new data. The `populate_existing` option will reset all attributes that were already present, including pending changes, so make sure all data is flushed before using it. Using the `Session` with its default behavior of autoflush is sufficient.

Note

The customized collection we load using `contains_eager()` is not “sticky”; that is, the next time this collection is loaded, it will be loaded with its usual default contents. The collection is subject to being reloaded if the object is expired, which occurs whenever the `Session.commit()`, `Session.rollback()` methods are used assuming default session settings, or the `Session.expire_all()` or `Session.expire()` methods are used.

See also

Adding Criteria to loader options - modern API allowing WHERE criteria directly within any relationship loader option

## Relationship Loader API

Object Name | Description
--- | ---
contains_eager(*keys, **kw) | Indicate that the given attribute should be eagerly loaded from columns stated manually in the query.
defaultload(*keys) | Indicate an attribute should load using its predefined loader style.
immediateload(*keys, [recursion_depth]) | Indicate that the given attribute should be loaded using an immediate load with a per-attribute SELECT statement.
joinedload(*keys, **kw) | Indicate that the given attribute should be loaded using joined eager loading.
lazyload(*keys) | Indicate that the given attribute should be loaded using “lazy” loading.
Load | Represents loader options which modify the state of a ORM-enabled `Select` or a legacy `Query` in order to affect how various mapped attributes are loaded.
noload(*keys) | Indicate that the given relationship attribute should remain unloaded.
raiseload(*keys, **kw) | Indicate that the given attribute should raise an error if accessed.
selectinload(*keys, [recursion_depth]) | Indicate that the given attribute should be loaded using SELECT IN eager loading.
subqueryload(*keys) | Indicate that the given attribute should be loaded using subquery eager loading.

function sqlalchemy.orm.contains_eager(*keys: Literal['*'] | QueryableAttribute[Any], **kw: Any) → _AbstractLoad

  Indicate that the given attribute should be eagerly loaded from columns stated manually in the query.

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

  The option is used in conjunction with an explicit join that loads the desired rows, i.e.:

      sess.query(Order).join(Order.user).options(contains_eager(Order.user))

  The above query would join from the `Order` entity to its related `User` entity, and the returned `Order` objects would have the `Order.user` attribute pre-populated.

  It may also be used for customizing the entries in an eagerly loaded collection; queries will normally want to use the Populate Existing execution option assuming the primary collection of parent objects may already have been loaded:

      sess.query(User).join(User.addresses).filter(
          Address.email_address.like("%@aol.com")
      ).options(contains_eager(User.addresses)).populate_existing()

  See the section Routing Explicit Joins/Statements into Eagerly Loaded Collections for complete usage details.

  See also

  Relationship Loading Techniques

  Routing Explicit Joins/Statements into Eagerly Loaded Collections

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

  Indicate an attribute should load using its predefined loader style.

  The behavior of this loading option is to not change the current loading style of the attribute, meaning that the previously configured one is used or, if no previous style was selected, the default loading will be used.

  This method is used to link to other loader options further into a chain of attributes without altering the loader style of the links along the chain. For example, to set joined eager loading for an element of an element:

      session.query(MyClass).options(
          defaultload(MyClass.someattribute).joinedload(
              MyOtherClass.someotherattribute
          )
      )

  `defaultload()` is also useful for setting column-level options on a related class, namely that of `defer()` and `undefer()`:

      session.scalars(
          select(MyClass).options(
              defaultload(MyClass.someattribute)
              .defer("some_column")
              .undefer("some_other_column")
          )
      )

  See also

  Specifying Sub-Options with Load.options()

  `Load.options()`

function sqlalchemy.orm.immediateload(*keys: Literal['*'] | QueryableAttribute[Any], recursion_depth: int | None = None) → _AbstractLoad

  Indicate that the given attribute should be loaded using an immediate load with a per-attribute SELECT statement.

  The load is achieved using the “lazyloader” strategy and does not fire off any additional eager loaders.

  The `immediateload()` option is superseded in general by the `selectinload()` option, which performs the same task more efficiently by emitting a SELECT for all loaded objects.

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

  Parameters:

    recursion_depth –

    optional int; when set to a positive integer in conjunction with a self-referential relationship, indicates “selectin” loading will continue that many levels deep automatically until no items are found.

    Note

    The `immediateload.recursion_depth` option currently supports only self-referential relationships. There is not yet an option to automatically traverse recursive structures with more than one relationship involved.

    Warning

    This parameter is new and experimental and should be treated as “alpha” status

    Added in version 2.0: added `immediateload.recursion_depth`

  See also

  Relationship Loading Techniques

  Select IN loading

function sqlalchemy.orm.joinedload(*keys: Literal['*'] | QueryableAttribute[Any], **kw: Any) → _AbstractLoad

  Indicate that the given attribute should be loaded using joined eager loading.

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

  examples:

      # joined-load the "orders" collection on "User"
      select(User).options(joinedload(User.orders))

      # joined-load Order.items and then Item.keywords
      select(Order).options(joinedload(Order.items).joinedload(Item.keywords))

      # lazily load Order.items, but when Items are loaded,
      # joined-load the keywords collection
      select(Order).options(lazyload(Order.items).joinedload(Item.keywords))

  Parameters:

    innerjoin –

    if `True`, indicates that the joined eager load should use an inner join instead of the default of left outer join:

        select(Order).options(joinedload(Order.user, innerjoin=True))

  In order to chain multiple eager joins together where some may be OUTER and others INNER, right-nested joins are used to link them:

      select(A).options(
          joinedload(A.bs, innerjoin=False).joinedload(B.cs, innerjoin=True)
      )

  The above query, linking A.bs via “outer” join and B.cs via “inner” join would render the joins as “a LEFT OUTER JOIN (b JOIN c)”. When using older versions of SQLite (< 3.7.16), this form of JOIN is translated to use full subqueries as this syntax is otherwise not directly supported.

  The `innerjoin` flag can also be stated with the term `"unnested"`. This indicates that an INNER JOIN should be used, unless the join is linked to a LEFT OUTER JOIN to the left, in which case it will render as LEFT OUTER JOIN. For example, supposing `A.bs` is an outerjoin:

      select(A).options(joinedload(A.bs).joinedload(B.cs, innerjoin="unnested"))

  The above join will render as “a LEFT OUTER JOIN b LEFT OUTER JOIN c”, rather than as “a LEFT OUTER JOIN (b JOIN c)”.

  Note

  The “unnested” flag does not affect the JOIN rendered from a many-to-many association table, e.g. a table configured as `relationship.secondary`, to the target table; for correctness of results, these joins are always INNER and are therefore right-nested if linked to an OUTER join.

  Note

  The joins produced by `joinedload()` are anonymously aliased. The criteria by which the join proceeds cannot be modified, nor can the ORM-enabled `Select` or legacy `Query` refer to these joins in any way, including ordering. See The Zen of Joined Eager Loading for further detail.

  To produce a specific SQL JOIN which is explicitly available, use `Select.join()` and `Query.join()`. To combine explicit JOINs with eager loading of collections, use `contains_eager()`; see Routing Explicit Joins/Statements into Eagerly Loaded Collections.

  See also

  Relationship Loading Techniques

  Joined Eager Loading

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

  Indicate that the given attribute should be loaded using “lazy” loading.

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

  See also

  Relationship Loading Techniques

  Lazy Loading

class sqlalchemy.orm.Load

  inherits from `sqlalchemy.orm.strategy_options._AbstractLoad`

  Represents loader options which modify the state of a ORM-enabled `Select` or a legacy `Query` in order to affect how various mapped attributes are loaded.

  The `Load` object is in most cases used implicitly behind the scenes when one makes use of a query option like `joinedload()`, `defer()`, or similar. It typically is not instantiated directly except for in some very specific cases.

  See also

  Per-Entity Wildcard Loading Strategies - illustrates an example where direct use of `Load` may be useful

  Member Name | Description
  --- | ---
  contains_eager() | Produce a new `Load` object with the `contains_eager()` option applied.
  defaultload() | Produce a new `Load` object with the `defaultload()` option applied.
  defer() | Produce a new `Load` object with the `defer()` option applied.
  get_children() | Return immediate child `HasTraverseInternals` elements of this `HasTraverseInternals`.
  immediateload() | Produce a new `Load` object with the `immediateload()` option applied.
  inherit_cache | Indicate if this `HasCacheKey` instance should make use of the cache key generation scheme used by its immediate superclass.
  joinedload() | Produce a new `Load` object with the `joinedload()` option applied.
  lazyload() | Produce a new `Load` object with the `lazyload()` option applied.
  load_only() | Produce a new `Load` object with the `load_only()` option applied.
  noload() | Produce a new `Load` object with the `noload()` option applied.
  options() | Apply a series of options as sub-options to this `Load` object.
  process_compile_state() | Apply a modification to a given `ORMCompileState`.
  process_compile_state_replaced_entities() | Apply a modification to a given `ORMCompileState`, given entities that were replaced by with_only_columns() or with_entities().
  propagate_to_loaders | if True, indicate this option should be carried along to “secondary” SELECT statements that occur for relationship lazy loaders as well as attribute load / refresh operations.
  raiseload() | Produce a new `Load` object with the `raiseload()` option applied.
  selectin_polymorphic() | Produce a new `Load` object with the `selectin_polymorphic()` option applied.
  selectinload() | Produce a new `Load` object with the `selectinload()` option applied.
  subqueryload() | Produce a new `Load` object with the `subqueryload()` option applied.
  undefer() | Produce a new `Load` object with the `undefer()` option applied.
  undefer_group() | Produce a new `Load` object with the `undefer_group()` option applied.
  with_expression() | Produce a new `Load` object with the `with_expression()` option applied.

  method `sqlalchemy.orm.Load.` contains_eager(attr: _AttrType, alias: _FromClauseArgument | None = None, _is_chain: bool = False, _propagate_to_loaders: bool = False) → Self

    inherited from the `sqlalchemy.orm.strategy_options._AbstractLoad.contains_eager` method of `sqlalchemy.orm.strategy_options._AbstractLoad`

    Produce a new `Load` object with the `contains_eager()` option applied.

    See `contains_eager()` for usage examples.

  method `sqlalchemy.orm.Load.` defaultload(attr: Literal['*'] | QueryableAttribute[Any]) → Self

    inherited from the `sqlalchemy.orm.strategy_options._AbstractLoad.defaultload` method of `sqlalchemy.orm.strategy_options._AbstractLoad`

    Produce a new `Load` object with the `defaultload()` option applied.

    See `defaultload()` for usage examples.

  method `sqlalchemy.orm.Load.` defer(key: Literal['*'] | QueryableAttribute[Any], raiseload: bool = False) → Self

    inherited from the `sqlalchemy.orm.strategy_options._AbstractLoad.defer` method of `sqlalchemy.orm.strategy_options._AbstractLoad`

    Produce a new `Load` object with the `defer()` option applied.

    See `defer()` for usage examples.

  method `sqlalchemy.orm.Load.` get_children(*, omit_attrs: Tuple[str, ...] = (), **kw: Any) → Iterable[HasTraverseInternals]

    inherited from the `HasTraverseInternals.get_children()` method of `HasTraverseInternals`

    Return immediate child `HasTraverseInternals` elements of this `HasTraverseInternals`.

    This is used for visit traversal.

    **kw may contain flags that change the collection that is returned, for example to return a subset of items in order to cut down on larger traversals, or to return child items from a different context (such as schema-level collections instead of clause-level).

  method `sqlalchemy.orm.Load.` immediateload(attr: Literal['*'] | QueryableAttribute[Any], recursion_depth: int | None = None) → Self

    inherited from the `sqlalchemy.orm.strategy_options._AbstractLoad.immediateload` method of `sqlalchemy.orm.strategy_options._AbstractLoad`

    Produce a new `Load` object with the `immediateload()` option applied.

    See `immediateload()` for usage examples.

  attribute `sqlalchemy.orm.Load.` inherit_cache = None

    inherited from the `HasCacheKey.inherit_cache` attribute of `HasCacheKey`

    Indicate if this `HasCacheKey` instance should make use of the cache key generation scheme used by its immediate superclass.

    The attribute defaults to `None`, which indicates that a construct has not yet taken into account whether or not its appropriate for it to participate in caching; this is functionally equivalent to setting the value to `False`, except that a warning is also emitted.

    This flag can be set to `True` on a particular class, if the SQL that corresponds to the object does not change based on attributes which are local to this class, and not its superclass.

    See also

    Enabling Caching Support for Custom Constructs - General guideslines for setting the `HasCacheKey.inherit_cache` attribute for third-party or user defined SQL constructs.

  method `sqlalchemy.orm.Load.` joinedload(attr: Literal['*'] | QueryableAttribute[Any], innerjoin: bool | None = None) → Self

    inherited from the `sqlalchemy.orm.strategy_options._AbstractLoad.joinedload` method of `sqlalchemy.orm.strategy_options._AbstractLoad`

    Produce a new `Load` object with the `joinedload()` option applied.

    See `joinedload()` for usage examples.

  method `sqlalchemy.orm.Load.` lazyload(attr: Literal['*'] | QueryableAttribute[Any]) → Self

    inherited from the `sqlalchemy.orm.strategy_options._AbstractLoad.lazyload` method of `sqlalchemy.orm.strategy_options._AbstractLoad`

    Produce a new `Load` object with the `lazyload()` option applied.

    See `lazyload()` for usage examples.

  method `sqlalchemy.orm.Load.` load_only(*attrs: Literal['*'] | QueryableAttribute[Any], raiseload: bool = False) → Self

    inherited from the `sqlalchemy.orm.strategy_options._AbstractLoad.load_only` method of `sqlalchemy.orm.strategy_options._AbstractLoad`

    Produce a new `Load` object with the `load_only()` option applied.

    See `load_only()` for usage examples.

  method `sqlalchemy.orm.Load.` noload(attr: Literal['*'] | QueryableAttribute[Any]) → Self

    inherited from the `sqlalchemy.orm.strategy_options._AbstractLoad.noload` method of `sqlalchemy.orm.strategy_options._AbstractLoad`

    Produce a new `Load` object with the `noload()` option applied.

    See `noload()` for usage examples.

  method `sqlalchemy.orm.Load.` options(*opts: _AbstractLoad) → Self

    Apply a series of options as sub-options to this `Load` object.

    E.g.:

        query = session.query(Author)
        query = query.options(
            joinedload(Author.book).options(
                load_only(Book.summary, Book.excerpt),
                joinedload(Book.citations).options(joinedload(Citation.author)),
            )
        )

    Parameters:

      *opts – A series of loader option objects (ultimately `Load` objects) which should be applied to the path specified by this `Load` object.

    Added in version 1.3.6.

    See also

    `defaultload()`

    Specifying Sub-Options with Load.options()

  method `sqlalchemy.orm.Load.` process_compile_state(compile_state: ORMCompileState) → None

    inherited from the `sqlalchemy.orm.strategy_options._AbstractLoad.process_compile_state` method of `sqlalchemy.orm.strategy_options._AbstractLoad`

    Apply a modification to a given `ORMCompileState`.

    This method is part of the implementation of a particular `CompileStateOption` and is only invoked internally when an ORM query is compiled.

  method `sqlalchemy.orm.Load.` process_compile_state_replaced_entities(compile_state: ORMCompileState, mapper_entities: Sequence[_MapperEntity]) → None

    inherited from the `sqlalchemy.orm.strategy_options._AbstractLoad.process_compile_state_replaced_entities` method of `sqlalchemy.orm.strategy_options._AbstractLoad`

    Apply a modification to a given `ORMCompileState`, given entities that were replaced by with_only_columns() or with_entities().

    This method is part of the implementation of a particular `CompileStateOption` and is only invoked internally when an ORM query is compiled.

    Added in version 1.4.19.

  attribute `sqlalchemy.orm.Load.` propagate_to_loaders

    inherited from the `sqlalchemy.orm.strategy_options._AbstractLoad.propagate_to_loaders` attribute of `sqlalchemy.orm.strategy_options._AbstractLoad`

    if True, indicate this option should be carried along to “secondary” SELECT statements that occur for relationship lazy loaders as well as attribute load / refresh operations.

  method `sqlalchemy.orm.Load.` raiseload(attr: Literal['*'] | QueryableAttribute[Any], sql_only: bool = False) → Self

    inherited from the `sqlalchemy.orm.strategy_options._AbstractLoad.raiseload` method of `sqlalchemy.orm.strategy_options._AbstractLoad`

    Produce a new `Load` object with the `raiseload()` option applied.

    See `raiseload()` for usage examples.

  method `sqlalchemy.orm.Load.` selectin_polymorphic(classes: Iterable[Type[Any]]) → Self

    inherited from the `sqlalchemy.orm.strategy_options._AbstractLoad.selectin_polymorphic` method of `sqlalchemy.orm.strategy_options._AbstractLoad`

    Produce a new `Load` object with the `selectin_polymorphic()` option applied.

    See `selectin_polymorphic()` for usage examples.

  method `sqlalchemy.orm.Load.` selectinload(attr: Literal['*'] | QueryableAttribute[Any], recursion_depth: int | None = None) → Self

    inherited from the `sqlalchemy.orm.strategy_options._AbstractLoad.selectinload` method of `sqlalchemy.orm.strategy_options._AbstractLoad`

    Produce a new `Load` object with the `selectinload()` option applied.

    See `selectinload()` for usage examples.

  method `sqlalchemy.orm.Load.` subqueryload(attr: Literal['*'] | QueryableAttribute[Any]) → Self

    inherited from the `sqlalchemy.orm.strategy_options._AbstractLoad.subqueryload` method of `sqlalchemy.orm.strategy_options._AbstractLoad`

    Produce a new `Load` object with the `subqueryload()` option applied.

    See `subqueryload()` for usage examples.

  method `sqlalchemy.orm.Load.` undefer(key: Literal['*'] | QueryableAttribute[Any]) → Self

    inherited from the `sqlalchemy.orm.strategy_options._AbstractLoad.undefer` method of `sqlalchemy.orm.strategy_options._AbstractLoad`

    Produce a new `Load` object with the `undefer()` option applied.

    See `undefer()` for usage examples.

  method `sqlalchemy.orm.Load.` undefer_group(name: str) → Self

    inherited from the `sqlalchemy.orm.strategy_options._AbstractLoad.undefer_group` method of `sqlalchemy.orm.strategy_options._AbstractLoad`

    Produce a new `Load` object with the `undefer_group()` option applied.

    See `undefer_group()` for usage examples.

  method `sqlalchemy.orm.Load.` with_expression(key: _AttrType, expression: _ColumnExpressionArgument[Any]) → Self

    inherited from the `sqlalchemy.orm.strategy_options._AbstractLoad.with_expression` method of `sqlalchemy.orm.strategy_options._AbstractLoad`

    Produce a new `Load` object with the `with_expression()` option applied.

    See `with_expression()` for usage examples.

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

  Indicate that the given relationship attribute should remain unloaded.

  The relationship attribute will return `None` when accessed without producing any loading effect.

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

  `noload()` applies to `relationship()` attributes only.

  Legacy Feature

  The `noload()` option is legacy. As it forces collections to be empty, which invariably leads to non-intuitive and difficult to predict results. There are no legitimate uses for this option in modern SQLAlchemy.

  See also

  Relationship Loading Techniques

function sqlalchemy.orm.raiseload(*keys: Literal['*'] | QueryableAttribute[Any], **kw: Any) → _AbstractLoad

  Indicate that the given attribute should raise an error if accessed.

  A relationship attribute configured with `raiseload()` will raise an `InvalidRequestError` upon access. The typical way this is useful is when an application is attempting to ensure that all relationship attributes that are accessed in a particular context would have been already loaded via eager loading. Instead of having to read through SQL logs to ensure lazy loads aren’t occurring, this strategy will cause them to raise immediately.

  `raiseload()` applies to `relationship()` attributes only. In order to apply raise-on-SQL behavior to a column-based attribute, use the `defer.raiseload` parameter on the `defer()` loader option.

  Parameters:

    sql_only – if True, raise only if the lazy load would emit SQL, but not if it is only checking the identity map, or determining that the related value should just be None due to missing keys. When False, the strategy will raise for all varieties of relationship loading.

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

  See also

  Relationship Loading Techniques

  Preventing unwanted lazy loads using raiseload

  Using raiseload to prevent deferred column loads

function sqlalchemy.orm.selectinload(*keys: Literal['*'] | QueryableAttribute[Any], recursion_depth: int | None = None) → _AbstractLoad

  Indicate that the given attribute should be loaded using SELECT IN eager loading.

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

  examples:

      # selectin-load the "orders" collection on "User"
      select(User).options(selectinload(User.orders))

      # selectin-load Order.items and then Item.keywords
      select(Order).options(
          selectinload(Order.items).selectinload(Item.keywords)
      )

      # lazily load Order.items, but when Items are loaded,
      # selectin-load the keywords collection
      select(Order).options(lazyload(Order.items).selectinload(Item.keywords))

  Parameters:

    recursion_depth –

    optional int; when set to a positive integer in conjunction with a self-referential relationship, indicates “selectin” loading will continue that many levels deep automatically until no items are found.

    Note

    The `selectinload.recursion_depth` option currently supports only self-referential relationships. There is not yet an option to automatically traverse recursive structures with more than one relationship involved.

    Additionally, the `selectinload.recursion_depth` parameter is new and experimental and should be treated as “alpha” status for the 2.0 series.

    Added in version 2.0: added `selectinload.recursion_depth`

  See also

  Relationship Loading Techniques

  Select IN loading

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

  Indicate that the given attribute should be loaded using subquery eager loading.

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

  examples:

      # subquery-load the "orders" collection on "User"
      select(User).options(subqueryload(User.orders))

      # subquery-load Order.items and then Item.keywords
      select(Order).options(
          subqueryload(Order.items).subqueryload(Item.keywords)
      )

      # lazily load Order.items, but when Items are loaded,
      # subquery-load the keywords collection
      select(Order).options(lazyload(Order.items).subqueryload(Item.keywords))

  See also

  Relationship Loading Techniques

  Subquery Eager Loading

ORM Querying Guide

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