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

# SQLAlchemy 2.1 Documentation

SQLAlchemy 1.4 / 2.0 Tutorial

This page is part of the SQLAlchemy Unified Tutorial.

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# Working with ORM Related Objects

In this section, we will cover one more essential ORM concept, which is how the ORM interacts with mapped classes that refer to other objects. In the section Declaring Mapped Classes, the mapped class examples made use of a construct called `relationship()`. This construct defines a linkage between two different mapped classes, or from a mapped class to itself, the latter of which is called a self-referential relationship.

To describe the basic idea of `relationship()`, first we’ll review the mapping in short form, omitting the `mapped_column()` mappings and other directives:

    from sqlalchemy.orm import Mapped
    from sqlalchemy.orm import relationship

    class User(Base):
        __tablename__ = "user_account"

        # ... mapped_column() mappings

        addresses: Mapped[List["Address"]] = relationship(back_populates="user")

    class Address(Base):
        __tablename__ = "address"

        # ... mapped_column() mappings

        user: Mapped["User"] = relationship(back_populates="addresses")

Above, the `User` class now has an attribute `User.addresses` and the `Address` class has an attribute `Address.user`. The `relationship()` construct, in conjunction with the `Mapped` construct to indicate typing behavior, will be used to inspect the table relationships between the `Table` objects that are mapped to the `User` and `Address` classes. As the `Table` object representing the `address` table has a `ForeignKeyConstraint` which refers to the `user_account` table, the `relationship()` can determine unambiguously that there is a one to many relationship from the `User` class to the `Address` class, along the `User.addresses` relationship; one particular row in the `user_account` table may be referenced by many rows in the `address` table.

All one-to-many relationships naturally correspond to a many to one relationship in the other direction, in this case the one noted by `Address.user`. The `relationship.back_populates` parameter, seen above configured on both `relationship()` objects referring to the other name, establishes that each of these two `relationship()` constructs should be considered to be complimentary to each other; we will see how this plays out in the next section.

## Persisting and Loading Relationships

We can start by illustrating what `relationship()` does to instances of objects. If we make a new `User` object, we can note that there is a Python list when we access the `.addresses` element:

    >>> u1 = User(name="pkrabs", fullname="Pearl Krabs")
    >>> u1.addresses
    []

This object is a SQLAlchemy-specific version of Python `list` which has the ability to track and respond to changes made to it. The collection also appeared automatically when we accessed the attribute, even though we never assigned it to the object. This is similar to the behavior noted at Inserting Rows using the ORM Unit of Work pattern where it was observed that column-based attributes to which we don’t explicitly assign a value also display as `None` automatically, rather than raising an `AttributeError` as would be Python’s usual behavior.

As the `u1` object is still transient and the `list` that we got from `u1.addresses` has not been mutated (i.e. appended or extended), it’s not actually associated with the object yet, but as we make changes to it, it will become part of the state of the `User` object.

The collection is specific to the `Address` class which is the only type of Python object that may be persisted within it. Using the `list.append()` method we may add an `Address` object:

    >>> a1 = Address(email_address="pearl.krabs@gmail.com")
    >>> u1.addresses.append(a1)

At this point, the `u1.addresses` collection as expected contains the new `Address` object:

    >>> u1.addresses
    [Address(id=None, email_address='pearl.krabs@gmail.com')]

As we associated the `Address` object with the `User.addresses` collection of the `u1` instance, another behavior also occurred, which is that the `User.addresses` relationship synchronized itself with the `Address.user` relationship, such that we can navigate not only from the `User` object to the `Address` object, we can also navigate from the `Address` object back to the “parent” `User` object:

    >>> a1.user
    User(id=None, name='pkrabs', fullname='Pearl Krabs')

This synchronization occurred as a result of our use of the `relationship.back_populates` parameter between the two `relationship()` objects. This parameter names another `relationship()` for which complementary attribute assignment / list mutation should occur. It will work equally well in the other direction, which is that if we create another `Address` object and assign to its `Address.user` attribute, that `Address` becomes part of the `User.addresses` collection on that `User` object:

    >>> a2 = Address(email_address="pearl@aol.com", user=u1)
    >>> u1.addresses
    [Address(id=None, email_address='pearl.krabs@gmail.com'), Address(id=None, email_address='pearl@aol.com')]

We actually made use of the `user` parameter as a keyword argument in the `Address` constructor, which is accepted just like any other mapped attribute that was declared on the `Address` class. It is equivalent to assignment of the `Address.user` attribute after the fact:

    # equivalent effect as a2 = Address(user=u1)
    >>> a2.user = u1

### Cascading Objects into the Session

We now have a `User` and two `Address` objects that are associated in a bidirectional structure in memory, but as noted previously in Inserting Rows using the ORM Unit of Work pattern , these objects are said to be in the transient state until they are associated with a `Session` object.

We make use of the `Session` that’s still ongoing, and note that when we apply the `Session.add()` method to the lead `User` object, the related `Address` object also gets added to that same `Session`:

    >>> session.add(u1)
    >>> u1 in session
    True
    >>> a1 in session
    True
    >>> a2 in session
    True

The above behavior, where the `Session` received a `User` object, and followed along the `User.addresses` relationship to locate a related `Address` object, is known as the save-update cascade and is discussed in detail in the ORM reference documentation at Cascades.

The three objects are now in the pending state; this means they are ready to be the subject of an INSERT operation but this has not yet proceeded; all three objects have no primary key assigned yet, and in addition, the `a1` and `a2` objects have an attribute called `user_id` which refers to the `Column` that has a `ForeignKeyConstraint` referring to the `user_account.id` column; these are also `None` as the objects are not yet associated with a real database row:

    >>> print(u1.id)
    None
    >>> print(a1.user_id)
    None

It’s at this stage that we can see the very great utility that the unit of work process provides; recall in the section INSERT usually generates the “values” clause automatically, rows were inserted into the `user_account` and `address` tables using some elaborate syntaxes in order to automatically associate the `address.user_id` columns with those of the `user_account` rows. Additionally, it was necessary that we emit INSERT for `user_account` rows first, before those of `address`, since rows in `address` are dependent on their parent row in `user_account` for a value in their `user_id` column.

When using the `Session`, all this tedium is handled for us and even the most die-hard SQL purist can benefit from automation of INSERT, UPDATE and DELETE statements. When we `Session.commit()` the transaction all steps invoke in the correct order, and furthermore the newly generated primary key of the `user_account` row is applied to the `address.user_id` column appropriately:

    >>> session.commit()
    INSERT INTO user_account (name, fullname) VALUES (?, ?)
    [...] ('pkrabs', 'Pearl Krabs')
    INSERT INTO address (email_address, user_id) VALUES (?, ?) RETURNING id
    [... (insertmanyvalues) 1/2 (ordered; batch not supported)] ('pearl.krabs@gmail.com', 6)
    INSERT INTO address (email_address, user_id) VALUES (?, ?) RETURNING id
    [insertmanyvalues 2/2 (ordered; batch not supported)] ('pearl@aol.com', 6)
    COMMIT

## Loading Relationships

In the last step, we called `Session.commit()` which emitted a COMMIT for the transaction, and then per `Session.commit.expire_on_commit` expired all objects so that they refresh for the next transaction.

When we next access an attribute on these objects, we’ll see the SELECT emitted for the primary attributes of the row, such as when we view the newly generated primary key for the `u1` object:

    >>> u1.id
    BEGIN (implicit)
    SELECT user_account.id, user_account.name, user_account.fullname
    FROM user_account
    WHERE user_account.id = ?
    [...] (6,)
    6

The `u1` `User` object now has a persistent collection `User.addresses` that we may also access. As this collection consists of an additional set of rows from the `address` table, when we access this collection as well we again see a lazy load emitted in order to retrieve the objects:

    >>> u1.addresses
    SELECT address.id, address.email_address, address.user_id
    FROM address
    WHERE ? = address.user_id
    [...] (6,)
    [Address(id=4, email_address='pearl.krabs@gmail.com'), Address(id=5, email_address='pearl@aol.com')]

Collections and related attributes in the SQLAlchemy ORM are persistent in memory; once the collection or attribute is populated, SQL is no longer emitted until that collection or attribute is expired. We may access `u1.addresses` again as well as add or remove items and this will not incur any new SQL calls:

    >>> u1.addresses
    [Address(id=4, email_address='pearl.krabs@gmail.com'), Address(id=5, email_address='pearl@aol.com')]

While the loading emitted by lazy loading can quickly become expensive if we don’t take explicit steps to optimize it, the network of lazy loading at least is fairly well optimized to not perform redundant work; as the `u1.addresses` collection was refreshed, per the identity map these are in fact the same `Address` instances as the `a1` and `a2` objects we’ve been dealing with already, so we’re done loading all attributes in this particular object graph:

    >>> a1
    Address(id=4, email_address='pearl.krabs@gmail.com')
    >>> a2
    Address(id=5, email_address='pearl@aol.com')

The issue of how relationships load, or not, is an entire subject onto itself. Some additional introduction to these concepts is later in this section at Loader Strategies.

## Using Relationships in Queries

The previous section introduced the behavior of the `relationship()` construct when working with instances of a mapped class, above, the `u1`, `a1` and `a2` instances of the `User` and `Address` classes. In this section, we introduce the behavior of `relationship()` as it applies to class level behavior of a mapped class, where it serves in several ways to help automate the construction of SQL queries.

### Using Relationships to Join

The sections Explicit FROM clauses and JOINs and Setting the ON Clause introduced the usage of the `Select.join()` and `Select.join_from()` methods to compose SQL JOIN clauses. In order to describe how to join between tables, these methods either infer the ON clause based on the presence of a single unambiguous `ForeignKeyConstraint` object within the table metadata structure that links the two tables, or otherwise we may provide an explicit SQL Expression construct that indicates a specific ON clause.

When using ORM entities, an additional mechanism is available to help us set up the ON clause of a join, which is to make use of the `relationship()` objects that we set up in our user mapping, as was demonstrated at Declaring Mapped Classes. The class-bound attribute corresponding to the `relationship()` may be passed as the single argument to `Select.join()`, where it serves to indicate both the right side of the join as well as the ON clause at once:

    >>> print(select(Address.email_address).select_from(User).join(User.addresses))
    SELECT address.email_address
    FROM user_account JOIN address ON user_account.id = address.user_id

The presence of an ORM `relationship()` on a mapping is not used by `Select.join()` or `Select.join_from()` to infer the ON clause if we don’t specify it. This means, if we join from `User` to `Address` without an ON clause, it works because of the `ForeignKeyConstraint` between the two mapped `Table` objects, not because of the `relationship()` objects on the `User` and `Address` classes:

    >>> print(select(Address.email_address).join_from(User, Address))
    SELECT address.email_address
    FROM user_account JOIN address ON user_account.id = address.user_id

See the section Joins in the ORM Querying Guide for many more examples of how to use `Select.join()` and `Select.join_from()` with `relationship()` constructs.

See also

Joins in the ORM Querying Guide

### Relationship WHERE Operators

There are some additional varieties of SQL generation helpers that come with `relationship()` which are typically useful when building up the WHERE clause of a statement. See the section Relationship WHERE Operators in the ORM Querying Guide.

See also

Relationship WHERE Operators in the ORM Querying Guide

## Loader Strategies

In the section Loading Relationships we introduced the concept that when we work with instances of mapped objects, accessing the attributes that are mapped using `relationship()` in the default case will emit a lazy load when the collection is not populated in order to load the objects that should be present in this collection.

Lazy loading is one of the most famous ORM patterns, and is also the one that is most controversial. When several dozen ORM objects in memory each refer to a handful of unloaded attributes, routine manipulation of these objects can spin off many additional queries that can add up (otherwise known as the N plus one problem), and to make matters worse they are emitted implicitly. These implicit queries may not be noticed, may cause errors when they are attempted after there’s no longer a database transaction available, or when using alternative concurrency patterns such as asyncio, they actually won’t work at all.

At the same time, lazy loading is a vastly popular and useful pattern when it is compatible with the concurrency approach in use and isn’t otherwise causing problems. For these reasons, SQLAlchemy’s ORM places a lot of emphasis on being able to control and optimize this loading behavior.

Above all, the first step in using ORM lazy loading effectively is to test the application, turn on SQL echoing, and watch the SQL that is emitted. If there seem to be lots of redundant SELECT statements that look very much like they could be rolled into one much more efficiently, if there are loads occurring inappropriately for objects that have been detached from their `Session`, that’s when to look into using loader strategies.

Loader strategies are represented as objects that may be associated with a SELECT statement using the `Select.options()` method, e.g.:

    for user_obj in session.execute(
        select(User).options(selectinload(User.addresses))
    ).scalars():
        user_obj.addresses  # access addresses collection already loaded

They may be also configured as defaults for a `relationship()` using the `relationship.lazy` option, e.g.:

    from sqlalchemy.orm import Mapped
    from sqlalchemy.orm import relationship

    class User(Base):
        __tablename__ = "user_account"

        addresses: Mapped[List["Address"]] = relationship(
            back_populates="user", lazy="selectin"
        )

Each loader strategy object adds some kind of information to the statement that will be used later by the `Session` when it is deciding how various attributes should be loaded and/or behave when they are accessed.

The sections below will introduce a few of the most prominently used loader strategies.

See also

Two sections in Relationship Loading Techniques:

- Configuring Loader Strategies at Mapping Time - details on configuring the strategy on `relationship()`

- Relationship Loading with Loader Options - details on using query-time loader strategies

### Selectin Load

The most useful loader in modern SQLAlchemy is the `selectinload()` loader option. This option solves the most common form of the “N plus one” problem which is that of a set of objects that refer to related collections. `selectinload()` will ensure that a particular collection for a full series of objects are loaded up front using a single query. It does this using a SELECT form that in most cases can be emitted against the related table alone, without the introduction of JOINs or subqueries, and only queries for those parent objects for which the collection isn’t already loaded. Below we illustrate `selectinload()` by loading all of the `User` objects and all of their related `Address` objects; while we invoke `Session.execute()` only once, given a `select()` construct, when the database is accessed, there are in fact two SELECT statements emitted, the second one being to fetch the related `Address` objects:

    >>> from sqlalchemy.orm import selectinload
    >>> stmt = select(User).options(selectinload(User.addresses)).order_by(User.id)
    >>> for row in session.execute(stmt):
    ...     print(
    ...         f"{row.User.name}  ({', '.join(a.email_address for a in row.User.addresses)})"
    ...     )
    SELECT user_account.id, user_account.name, user_account.fullname
    FROM user_account ORDER BY user_account.id
    [...] ()
    SELECT address.user_id, address.id, address.email_address
    FROM address
    WHERE address.user_id IN (?, ?, ?, ?, ?, ?)
    [...] (1, 2, 3, 4, 5, 6)
    spongebob  (spongebob@sqlalchemy.org)
    sandy  (sandy@sqlalchemy.org, sandy@squirrelpower.org)
    patrick  ()
    squidward  ()
    ehkrabs  ()
    pkrabs  (pearl.krabs@gmail.com, pearl@aol.com)

See also

Select IN loading - in Relationship Loading Techniques

### Joined Load

The `joinedload()` eager load strategy is the oldest eager loader in SQLAlchemy, which augments the SELECT statement that’s being passed to the database with a JOIN (which may be an outer or an inner join depending on options), which can then load in related objects.

The `joinedload()` strategy is best suited towards loading related many-to-one objects, as this only requires that additional columns are added to a primary entity row that would be fetched in any case. For greater efficiency, it also accepts an option `joinedload.innerjoin` so that an inner join instead of an outer join may be used for a case such as below where we know that all `Address` objects have an associated `User`:

    >>> from sqlalchemy.orm import joinedload
    >>> stmt = (
    ...     select(Address)
    ...     .options(joinedload(Address.user, innerjoin=True))
    ...     .order_by(Address.id)
    ... )
    >>> for row in session.execute(stmt):
    ...     print(f"{row.Address.email_address} {row.Address.user.name}")
    SELECT address.id, address.email_address, address.user_id, user_account_1.id AS id_1,
    user_account_1.name, user_account_1.fullname
    FROM address
    JOIN user_account AS user_account_1 ON user_account_1.id = address.user_id
    ORDER BY address.id
    [...] ()
    spongebob@sqlalchemy.org spongebob
    sandy@sqlalchemy.org sandy
    sandy@squirrelpower.org sandy
    pearl.krabs@gmail.com pkrabs
    pearl@aol.com pkrabs

`joinedload()` also works for collections, meaning one-to-many relationships, however it has the effect of multiplying out primary rows per related item in a recursive way that grows the amount of data sent for a result set by orders of magnitude for nested collections and/or larger collections, so its use vs. another option such as `selectinload()` should be evaluated on a per-case basis.

It’s important to note that the WHERE and ORDER BY criteria of the enclosing `Select` statement do not target the table rendered by joinedload(). Above, it can be seen in the SQL that an anonymous alias is applied to the `user_account` table such that is not directly addressable in the query. This concept is discussed in more detail in the section The Zen of Joined Eager Loading.

Tip

It’s important to note that many-to-one eager loads are often not necessary, as the “N plus one” problem is much less prevalent in the common case. When many objects all refer to the same related object, such as many `Address` objects that each refer to the same `User`, SQL will be emitted only once for that `User` object using normal lazy loading. The lazy load routine will look up the related object by primary key in the current `Session` without emitting any SQL when possible.

See also

Joined Eager Loading - in Relationship Loading Techniques

### Explicit Join + Eager load

If we were to load `Address` rows while joining to the `user_account` table using a method such as `Select.join()` to render the JOIN, we could also leverage that JOIN in order to eagerly load the contents of the `Address.user` attribute on each `Address` object returned. This is essentially that we are using “joined eager loading” but rendering the JOIN ourselves. This common use case is achieved by using the `contains_eager()` option. This option is very similar to `joinedload()`, except that it assumes we have set up the JOIN ourselves, and it instead only indicates that additional columns in the COLUMNS clause should be loaded into related attributes on each returned object, for example:

    >>> from sqlalchemy.orm import contains_eager
    >>> stmt = (
    ...     select(Address)
    ...     .join(Address.user)
    ...     .where(User.name == "pkrabs")
    ...     .options(contains_eager(Address.user))
    ...     .order_by(Address.id)
    ... )
    >>> for row in session.execute(stmt):
    ...     print(f"{row.Address.email_address} {row.Address.user.name}")
    SELECT user_account.id, user_account.name, user_account.fullname,
    address.id AS id_1, address.email_address, address.user_id
    FROM address JOIN user_account ON user_account.id = address.user_id
    WHERE user_account.name = ? ORDER BY address.id
    [...] ('pkrabs',)
    pearl.krabs@gmail.com pkrabs
    pearl@aol.com pkrabs

Above, we both filtered the rows on `user_account.name` and also loaded rows from `user_account` into the `Address.user` attribute of the returned rows. If we had applied `joinedload()` separately, we would get a SQL query that unnecessarily joins twice:

    >>> stmt = (
    ...     select(Address)
    ...     .join(Address.user)
    ...     .where(User.name == "pkrabs")
    ...     .options(joinedload(Address.user))
    ...     .order_by(Address.id)
    ... )
    >>> print(stmt)  # SELECT has a JOIN and LEFT OUTER JOIN unnecessarily
    SELECT address.id, address.email_address, address.user_id,
    user_account_1.id AS id_1, user_account_1.name, user_account_1.fullname
    FROM address JOIN user_account ON user_account.id = address.user_id
    LEFT OUTER JOIN user_account AS user_account_1 ON user_account_1.id = address.user_id
    WHERE user_account.name = :name_1 ORDER BY address.id

See also

Two sections in Relationship Loading Techniques:

- The Zen of Joined Eager Loading - describes the above problem in detail

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

### Raiseload

One additional loader strategy worth mentioning is `raiseload()`. This option is used to completely block an application from having the N plus one problem at all by causing what would normally be a lazy load to raise an error instead. It has two variants that are controlled via the `raiseload.sql_only` option to block either lazy loads that require SQL, versus all “load” operations including those which only need to consult the current `Session`.

One way to use `raiseload()` is to configure it on `relationship()` itself, by setting `relationship.lazy` to the value `"raise_on_sql"`, so that for a particular mapping, a certain relationship will never try to emit SQL:

    >>> from sqlalchemy.orm import Mapped
    >>> from sqlalchemy.orm import relationship

    >>> class User(Base):
    ...     __tablename__ = "user_account"
    ...     id: Mapped[int] = mapped_column(primary_key=True)
    ...     addresses: Mapped[List["Address"]] = relationship(
    ...         back_populates="user", lazy="raise_on_sql"
    ...     )

    >>> class Address(Base):
    ...     __tablename__ = "address"
    ...     id: Mapped[int] = mapped_column(primary_key=True)
    ...     user_id: Mapped[int] = mapped_column(ForeignKey("user_account.id"))
    ...     user: Mapped["User"] = relationship(back_populates="addresses", lazy="raise_on_sql")

Using such a mapping, the application is blocked from lazy loading, indicating that a particular query would need to specify a loader strategy:

    >>> u1 = session.execute(select(User)).scalars().first()
    SELECT user_account.id FROM user_account
    [...] ()
    >>> u1.addresses
    Traceback (most recent call last):
    ...
    sqlalchemy.exc.InvalidRequestError: 'User.addresses' is not available due to lazy='raise_on_sql'

The exception would indicate that this collection should be loaded up front instead:

    >>> u1 = (
    ...     session.execute(select(User).options(selectinload(User.addresses)))
    ...     .scalars()
    ...     .first()
    ... )
    SELECT user_account.id
    FROM user_account
    [...] ()
    SELECT address.user_id, address.id
    FROM address
    WHERE address.user_id IN (?, ?, ?, ?, ?, ?)
    [...] (1, 2, 3, 4, 5, 6)

The `lazy="raise_on_sql"` option tries to be smart about many-to-one relationships as well; above, if the `Address.user` attribute of an `Address` object were not loaded, but that `User` object were locally present in the same `Session`, the “raiseload” strategy would not raise an error.

See also

Preventing unwanted lazy loads using raiseload - in Relationship Loading Techniques

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