Release: 2.0.29 | Release Date: March 23, 2024

# SQLAlchemy 2.0 Documentation

# Configuring how Relationship Joins

`relationship()` will normally create a join between two tables by examining the foreign key relationship between the two tables to determine which columns should be compared. There are a variety of situations where this behavior needs to be customized.

## Handling Multiple Join Paths

One of the most common situations to deal with is when there are more than one foreign key path between two tables.

Consider a `Customer` class that contains two foreign keys to an `Address` class:

    from sqlalchemy import Integer, ForeignKey, String, Column
    from sqlalchemy.orm import DeclarativeBase
    from sqlalchemy.orm import relationship

    class Base(DeclarativeBase):
        pass

    class Customer(Base):
        __tablename__ = "customer"
        id = mapped_column(Integer, primary_key=True)
        name = mapped_column(String)

        billing_address_id = mapped_column(Integer, ForeignKey("address.id"))
        shipping_address_id = mapped_column(Integer, ForeignKey("address.id"))

        billing_address = relationship("Address")
        shipping_address = relationship("Address")

    class Address(Base):
        __tablename__ = "address"
        id = mapped_column(Integer, primary_key=True)
        street = mapped_column(String)
        city = mapped_column(String)
        state = mapped_column(String)
        zip = mapped_column(String)

The above mapping, when we attempt to use it, will produce the error:

    sqlalchemy.exc.AmbiguousForeignKeysError: Could not determine join
    condition between parent/child tables on relationship
    Customer.billing_address - there are multiple foreign key
    paths linking the tables.  Specify the 'foreign_keys' argument,
    providing a list of those columns which should be
    counted as containing a foreign key reference to the parent table.

The above message is pretty long. There are many potential messages that `relationship()` can return, which have been carefully tailored to detect a variety of common configurational issues; most will suggest the additional configuration that’s needed to resolve the ambiguity or other missing information.

In this case, the message wants us to qualify each `relationship()` by instructing for each one which foreign key column should be considered, and the appropriate form is as follows:

    class Customer(Base):
        __tablename__ = "customer"
        id = mapped_column(Integer, primary_key=True)
        name = mapped_column(String)

        billing_address_id = mapped_column(Integer, ForeignKey("address.id"))
        shipping_address_id = mapped_column(Integer, ForeignKey("address.id"))

        billing_address = relationship("Address", foreign_keys=[billing_address_id])
        shipping_address = relationship("Address", foreign_keys=[shipping_address_id])

Above, we specify the `foreign_keys` argument, which is a `Column` or list of `Column` objects which indicate those columns to be considered “foreign”, or in other words, the columns that contain a value referring to a parent table. Loading the `Customer.billing_address` relationship from a `Customer` object will use the value present in `billing_address_id` in order to identify the row in `Address` to be loaded; similarly, `shipping_address_id` is used for the `shipping_address` relationship. The linkage of the two columns also plays a role during persistence; the newly generated primary key of a just-inserted `Address` object will be copied into the appropriate foreign key column of an associated `Customer` object during a flush.

When specifying `foreign_keys` with Declarative, we can also use string names to specify, however it is important that if using a list, the list is part of the string:

    billing_address = relationship("Address", foreign_keys="[Customer.billing_address_id]")

In this specific example, the list is not necessary in any case as there’s only one `Column` we need:

    billing_address = relationship("Address", foreign_keys="Customer.billing_address_id")

Warning

When passed as a Python-evaluable string, the `relationship.foreign_keys` argument is interpreted using Python’s `eval()` function. DO NOT PASS UNTRUSTED INPUT TO THIS STRING. See Evaluation of relationship arguments for details on declarative evaluation of `relationship()` arguments.

## Specifying Alternate Join Conditions

The default behavior of `relationship()` when constructing a join is that it equates the value of primary key columns on one side to that of foreign-key-referring columns on the other. We can change this criterion to be anything we’d like using the `relationship.primaryjoin` argument, as well as the `relationship.secondaryjoin` argument in the case when a “secondary” table is used.

In the example below, using the `User` class as well as an `Address` class which stores a street address, we create a relationship `boston_addresses` which will only load those `Address` objects which specify a city of “Boston”:

    from sqlalchemy import Integer, ForeignKey, String, Column
    from sqlalchemy.orm import DeclarativeBase
    from sqlalchemy.orm import relationship

    class Base(DeclarativeBase):
        pass

    class User(Base):
        __tablename__ = "user"
        id = mapped_column(Integer, primary_key=True)
        name = mapped_column(String)
        boston_addresses = relationship(
            "Address",
            primaryjoin="and_(User.id==Address.user_id, Address.city=='Boston')",
        )

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

        street = mapped_column(String)
        city = mapped_column(String)
        state = mapped_column(String)
        zip = mapped_column(String)

Within this string SQL expression, we made use of the `and_()` conjunction construct to establish two distinct predicates for the join condition - joining both the `User.id` and `Address.user_id` columns to each other, as well as limiting rows in `Address` to just `city='Boston'`. When using Declarative, rudimentary SQL functions like `and_()` are automatically available in the evaluated namespace of a string `relationship()` argument.

Warning

When passed as a Python-evaluable string, the `relationship.primaryjoin` argument is interpreted using Python’s `eval()` function. DO NOT PASS UNTRUSTED INPUT TO THIS STRING. See Evaluation of relationship arguments for details on declarative evaluation of `relationship()` arguments.

The custom criteria we use in a `relationship.primaryjoin` is generally only significant when SQLAlchemy is rendering SQL in order to load or represent this relationship. That is, it’s used in the SQL statement that’s emitted in order to perform a per-attribute lazy load, or when a join is constructed at query time, such as via `Select.join()`, or via the eager “joined” or “subquery” styles of loading. When in-memory objects are being manipulated, we can place any `Address` object we’d like into the `boston_addresses` collection, regardless of what the value of the `.city` attribute is. The objects will remain present in the collection until the attribute is expired and re-loaded from the database where the criterion is applied. When a flush occurs, the objects inside of `boston_addresses` will be flushed unconditionally, assigning value of the primary key `user.id` column onto the foreign-key-holding `address.user_id` column for each row. The `city` criteria has no effect here, as the flush process only cares about synchronizing primary key values into referencing foreign key values.

## Creating Custom Foreign Conditions

Another element of the primary join condition is how those columns considered “foreign” are determined. Usually, some subset of `Column` objects will specify `ForeignKey`, or otherwise be part of a `ForeignKeyConstraint` that’s relevant to the join condition. `relationship()` looks to this foreign key status as it decides how it should load and persist data for this relationship. However, the `relationship.primaryjoin` argument can be used to create a join condition that doesn’t involve any “schema” level foreign keys. We can combine `relationship.primaryjoin` along with `relationship.foreign_keys` and `relationship.remote_side` explicitly in order to establish such a join.

Below, a class `HostEntry` joins to itself, equating the string `content` column to the `ip_address` column, which is a PostgreSQL type called `INET`. We need to use `cast()` in order to cast one side of the join to the type of the other:

    from sqlalchemy import cast, String, Column, Integer
    from sqlalchemy.orm import relationship
    from sqlalchemy.dialects.postgresql import INET

    from sqlalchemy.orm import DeclarativeBase

    class Base(DeclarativeBase):
        pass

    class HostEntry(Base):
        __tablename__ = "host_entry"

        id = mapped_column(Integer, primary_key=True)
        ip_address = mapped_column(INET)
        content = mapped_column(String(50))

        # relationship() using explicit foreign_keys, remote_side
        parent_host = relationship(
            "HostEntry",
            primaryjoin=ip_address == cast(content, INET),
            foreign_keys=content,
            remote_side=ip_address,
        )

The above relationship will produce a join like:

    SELECT host_entry.id, host_entry.ip_address, host_entry.content
    FROM host_entry JOIN host_entry AS host_entry_1
    ON host_entry_1.ip_address = CAST(host_entry.content AS INET)

An alternative syntax to the above is to use the `foreign()` and `remote()` annotations, inline within the `relationship.primaryjoin` expression. This syntax represents the annotations that `relationship()` normally applies by itself to the join condition given the `relationship.foreign_keys` and `relationship.remote_side` arguments. These functions may be more succinct when an explicit join condition is present, and additionally serve to mark exactly the column that is “foreign” or “remote” independent of whether that column is stated multiple times or within complex SQL expressions:

    from sqlalchemy.orm import foreign, remote

    class HostEntry(Base):
        __tablename__ = "host_entry"

        id = mapped_column(Integer, primary_key=True)
        ip_address = mapped_column(INET)
        content = mapped_column(String(50))

        # relationship() using explicit foreign() and remote() annotations
        # in lieu of separate arguments
        parent_host = relationship(
            "HostEntry",
            primaryjoin=remote(ip_address) == cast(foreign(content), INET),
        )

## Using custom operators in join conditions

Another use case for relationships is the use of custom operators, such as PostgreSQL’s “is contained within” `<<` operator when joining with types such as `INET` and `CIDR`. For custom boolean operators we use the `Operators.bool_op()` function:

    inet_column.bool_op("<<")(cidr_column)

A comparison like the above may be used directly with `relationship.primaryjoin` when constructing a `relationship()`:

    class IPA(Base):
        __tablename__ = "ip_address"

        id = mapped_column(Integer, primary_key=True)
        v4address = mapped_column(INET)

        network = relationship(
            "Network",
            primaryjoin="IPA.v4address.bool_op('<<')(foreign(Network.v4representation))",
            viewonly=True,
        )

    class Network(Base):
        __tablename__ = "network"

        id = mapped_column(Integer, primary_key=True)
        v4representation = mapped_column(CIDR)

Above, a query such as:

    select(IPA).join(IPA.network)

Will render as:

    SELECT ip_address.id AS ip_address_id, ip_address.v4address AS ip_address_v4address
    FROM ip_address JOIN network ON ip_address.v4address << network.v4representation

## Custom operators based on SQL functions

A variant to the use case for `Operators.op.is_comparison` is when we aren’t using an operator, but a SQL function. The typical example of this use case is the PostgreSQL PostGIS functions however any SQL function on any database that resolves to a binary condition may apply. To suit this use case, the `FunctionElement.as_comparison()` method can modify any SQL function, such as those invoked from the `func` namespace, to indicate to the ORM that the function produces a comparison of two expressions. The below example illustrates this with the Geoalchemy2 library:

    from geoalchemy2 import Geometry
    from sqlalchemy import Column, Integer, func
    from sqlalchemy.orm import relationship, foreign

    class Polygon(Base):
        __tablename__ = "polygon"
        id = mapped_column(Integer, primary_key=True)
        geom = mapped_column(Geometry("POLYGON", srid=4326))
        points = relationship(
            "Point",
            primaryjoin="func.ST_Contains(foreign(Polygon.geom), Point.geom).as_comparison(1, 2)",
            viewonly=True,
        )

    class Point(Base):
        __tablename__ = "point"
        id = mapped_column(Integer, primary_key=True)
        geom = mapped_column(Geometry("POINT", srid=4326))

Above, the `FunctionElement.as_comparison()` indicates that the `func.ST_Contains()` SQL function is comparing the `Polygon.geom` and `Point.geom` expressions. The `foreign()` annotation additionally notes which column takes on the “foreign key” role in this particular relationship.

New in version 1.3: Added `FunctionElement.as_comparison()`.

## Overlapping Foreign Keys

A rare scenario can arise when composite foreign keys are used, such that a single column may be the subject of more than one column referred to via foreign key constraint.

Consider an (admittedly complex) mapping such as the `Magazine` object, referred to both by the `Writer` object and the `Article` object using a composite primary key scheme that includes `magazine_id` for both; then to make `Article` refer to `Writer` as well, `Article.magazine_id` is involved in two separate relationships; `Article.magazine` and `Article.writer`:

    class Magazine(Base):
        __tablename__ = "magazine"

        id = mapped_column(Integer, primary_key=True)

    class Article(Base):
        __tablename__ = "article"

        article_id = mapped_column(Integer)
        magazine_id = mapped_column(ForeignKey("magazine.id"))
        writer_id = mapped_column()

        magazine = relationship("Magazine")
        writer = relationship("Writer")

        __table_args__ = (
            PrimaryKeyConstraint("article_id", "magazine_id"),
            ForeignKeyConstraint(
                ["writer_id", "magazine_id"], ["writer.id", "writer.magazine_id"]
            ),
        )

    class Writer(Base):
        __tablename__ = "writer"

        id = mapped_column(Integer, primary_key=True)
        magazine_id = mapped_column(ForeignKey("magazine.id"), primary_key=True)
        magazine = relationship("Magazine")

When the above mapping is configured, we will see this warning emitted:

    SAWarning: relationship 'Article.writer' will copy column
    writer.magazine_id to column article.magazine_id,
    which conflicts with relationship(s): 'Article.magazine'
    (copies magazine.id to article.magazine_id). Consider applying
    viewonly=True to read-only relationships, or provide a primaryjoin
    condition marking writable columns with the foreign() annotation.

What this refers to originates from the fact that `Article.magazine_id` is the subject of two different foreign key constraints; it refers to `Magazine.id` directly as a source column, but also refers to `Writer.magazine_id` as a source column in the context of the composite key to `Writer`. If we associate an `Article` with a particular `Magazine`, but then associate the `Article` with a `Writer` that’s associated with a different `Magazine`, the ORM will overwrite `Article.magazine_id` non-deterministically, silently changing which magazine to which we refer; it may also attempt to place NULL into this column if we de-associate a `Writer` from an `Article`. The warning lets us know this is the case.

To solve this, we need to break out the behavior of `Article` to include all three of the following features:

1. `Article` first and foremost writes to `Article.magazine_id` based on data persisted in the `Article.magazine` relationship only, that is a value copied from `Magazine.id`.

2. `Article` can write to `Article.writer_id` on behalf of data persisted in the `Article.writer` relationship, but only the `Writer.id` column; the `Writer.magazine_id` column should not be written into `Article.magazine_id` as it ultimately is sourced from `Magazine.id`.

3. `Article` takes `Article.magazine_id` into account when loading `Article.writer`, even though it doesn’t write to it on behalf of this relationship.

To get just #1 and #2, we could specify only `Article.writer_id` as the “foreign keys” for `Article.writer`:

    class Article(Base):
        # ...

        writer = relationship("Writer", foreign_keys="Article.writer_id")

However, this has the effect of `Article.writer` not taking `Article.magazine_id` into account when querying against `Writer`:

    SELECT article.article_id AS article_article_id,
        article.magazine_id AS article_magazine_id,
        article.writer_id AS article_writer_id
    FROM article
    JOIN writer ON writer.id = article.writer_id

Therefore, to get at all of #1, #2, and #3, we express the join condition as well as which columns to be written by combining `relationship.primaryjoin` fully, along with either the `relationship.foreign_keys` argument, or more succinctly by annotating with `foreign()`:

    class Article(Base):
        # ...

        writer = relationship(
            "Writer",
            primaryjoin="and_(Writer.id == foreign(Article.writer_id), "
            "Writer.magazine_id == Article.magazine_id)",
        )

## Non-relational Comparisons / Materialized Path

Warning

this section details an experimental feature.

Using custom expressions means we can produce unorthodox join conditions that don’t obey the usual primary/foreign key model. One such example is the materialized path pattern, where we compare strings for overlapping path tokens in order to produce a tree structure.

Through careful use of `foreign()` and `remote()`, we can build a relationship that effectively produces a rudimentary materialized path system. Essentially, when `foreign()` and `remote()` are on the same side of the comparison expression, the relationship is considered to be “one to many”; when they are on different sides, the relationship is considered to be “many to one”. For the comparison we’ll use here, we’ll be dealing with collections so we keep things configured as “one to many”:

    class Element(Base):
        __tablename__ = "element"

        path = mapped_column(String, primary_key=True)

        descendants = relationship(
            "Element",
            primaryjoin=remote(foreign(path)).like(path.concat("/%")),
            viewonly=True,
            order_by=path,
        )

Above, if given an `Element` object with a path attribute of `"/foo/bar2"`, we seek for a load of `Element.descendants` to look like:

    SELECT element.path AS element_path
    FROM element
    WHERE element.path LIKE ('/foo/bar2' || '/%') ORDER BY element.path

## Self-Referential Many-to-Many Relationship

See also

This section documents a two-table variant of the “adjacency list” pattern, which is documented at Adjacency List Relationships. Be sure to review the self-referential querying patterns in subsections Self-Referential Query Strategies and Configuring Self-Referential Eager Loading which apply equally well to the mapping pattern discussed here.

Many to many relationships can be customized by one or both of `relationship.primaryjoin` and `relationship.secondaryjoin` - the latter is significant for a relationship that specifies a many-to-many reference using the `relationship.secondary` argument. A common situation which involves the usage of `relationship.primaryjoin` and `relationship.secondaryjoin` is when establishing a many-to-many relationship from a class to itself, as shown below:

    from typing import List

    from sqlalchemy import Integer, ForeignKey, Column, Table
    from sqlalchemy.orm import DeclarativeBase, Mapped
    from sqlalchemy.orm import mapped_column, relationship

    class Base(DeclarativeBase):
        pass

    node_to_node = Table(
        "node_to_node",
        Base.metadata,
        Column("left_node_id", Integer, ForeignKey("node.id"), primary_key=True),
        Column("right_node_id", Integer, ForeignKey("node.id"), primary_key=True),
    )

    class Node(Base):
        __tablename__ = "node"
        id: Mapped[int] = mapped_column(primary_key=True)
        label: Mapped[str]
        right_nodes: Mapped[List["Node"]] = relationship(
            "Node",
            secondary=node_to_node,
            primaryjoin=id == node_to_node.c.left_node_id,
            secondaryjoin=id == node_to_node.c.right_node_id,
            back_populates="left_nodes",
        )
        left_nodes: Mapped[List["Node"]] = relationship(
            "Node",
            secondary=node_to_node,
            primaryjoin=id == node_to_node.c.right_node_id,
            secondaryjoin=id == node_to_node.c.left_node_id,
            back_populates="right_nodes",
        )

Where above, SQLAlchemy can’t know automatically which columns should connect to which for the `right_nodes` and `left_nodes` relationships. The `relationship.primaryjoin` and `relationship.secondaryjoin` arguments establish how we’d like to join to the association table. In the Declarative form above, as we are declaring these conditions within the Python block that corresponds to the `Node` class, the `id` variable is available directly as the `Column` object we wish to join with.

Alternatively, we can define the `relationship.primaryjoin` and `relationship.secondaryjoin` arguments using strings, which is suitable in the case that our configuration does not have either the `Node.id` column object available yet or the `node_to_node` table perhaps isn’t yet available. When referring to a plain `Table` object in a declarative string, we use the string name of the table as it is present in the `MetaData`:

    class Node(Base):
        __tablename__ = "node"
        id = mapped_column(Integer, primary_key=True)
        label = mapped_column(String)
        right_nodes = relationship(
            "Node",
            secondary="node_to_node",
            primaryjoin="Node.id==node_to_node.c.left_node_id",
            secondaryjoin="Node.id==node_to_node.c.right_node_id",
            backref="left_nodes",
        )

Warning

When passed as a Python-evaluable string, the `relationship.primaryjoin` and `relationship.secondaryjoin` arguments are interpreted using Python’s `eval()` function. DO NOT PASS UNTRUSTED INPUT TO THESE STRINGS. See Evaluation of relationship arguments for details on declarative evaluation of `relationship()` arguments.

A classical mapping situation here is similar, where `node_to_node` can be joined to `node.c.id`:

    from sqlalchemy import Integer, ForeignKey, String, Column, Table, MetaData
    from sqlalchemy.orm import relationship, registry

    metadata_obj = MetaData()
    mapper_registry = registry()

    node_to_node = Table(
        "node_to_node",
        metadata_obj,
        Column("left_node_id", Integer, ForeignKey("node.id"), primary_key=True),
        Column("right_node_id", Integer, ForeignKey("node.id"), primary_key=True),
    )

    node = Table(
        "node",
        metadata_obj,
        Column("id", Integer, primary_key=True),
        Column("label", String),
    )

    class Node:
        pass

    mapper_registry.map_imperatively(
        Node,
        node,
        properties={
            "right_nodes": relationship(
                Node,
                secondary=node_to_node,
                primaryjoin=node.c.id == node_to_node.c.left_node_id,
                secondaryjoin=node.c.id == node_to_node.c.right_node_id,
                backref="left_nodes",
            )
        },
    )

Note that in both examples, the `relationship.backref` keyword specifies a `left_nodes` backref - when `relationship()` creates the second relationship in the reverse direction, it’s smart enough to reverse the `relationship.primaryjoin` and `relationship.secondaryjoin` arguments.

See also

- Adjacency List Relationships - single table version

- Self-Referential Query Strategies - tips on querying with self-referential mappings

- Configuring Self-Referential Eager Loading - tips on eager loading with self- referential mapping

## Composite “Secondary” Joins

Note

This section features far edge cases that are somewhat supported by SQLAlchemy, however it is recommended to solve problems like these in simpler ways whenever possible, by using reasonable relational layouts and / or in-Python attributes.

Sometimes, when one seeks to build a `relationship()` between two tables there is a need for more than just two or three tables to be involved in order to join them. This is an area of `relationship()` where one seeks to push the boundaries of what’s possible, and often the ultimate solution to many of these exotic use cases needs to be hammered out on the SQLAlchemy mailing list.

In more recent versions of SQLAlchemy, the `relationship.secondary` parameter can be used in some of these cases in order to provide a composite target consisting of multiple tables. Below is an example of such a join condition (requires version 0.9.2 at least to function as is):

    class A(Base):
        __tablename__ = "a"

        id = mapped_column(Integer, primary_key=True)
        b_id = mapped_column(ForeignKey("b.id"))

        d = relationship(
            "D",
            secondary="join(B, D, B.d_id == D.id).join(C, C.d_id == D.id)",
            primaryjoin="and_(A.b_id == B.id, A.id == C.a_id)",
            secondaryjoin="D.id == B.d_id",
            uselist=False,
            viewonly=True,
        )

    class B(Base):
        __tablename__ = "b"

        id = mapped_column(Integer, primary_key=True)
        d_id = mapped_column(ForeignKey("d.id"))

    class C(Base):
        __tablename__ = "c"

        id = mapped_column(Integer, primary_key=True)
        a_id = mapped_column(ForeignKey("a.id"))
        d_id = mapped_column(ForeignKey("d.id"))

    class D(Base):
        __tablename__ = "d"

        id = mapped_column(Integer, primary_key=True)

In the above example, we provide all three of `relationship.secondary`, `relationship.primaryjoin`, and `relationship.secondaryjoin`, in the declarative style referring to the named tables `a`, `b`, `c`, `d` directly. A query from `A` to `D` looks like:

    sess.scalars(select(A).join(A.d)).all()

    SELECT a.id AS a_id, a.b_id AS a_b_id
    FROM a JOIN (
        b AS b_1 JOIN d AS d_1 ON b_1.d_id = d_1.id
            JOIN c AS c_1 ON c_1.d_id = d_1.id)
        ON a.b_id = b_1.id AND a.id = c_1.a_id JOIN d ON d.id = b_1.d_id

In the above example, we take advantage of being able to stuff multiple tables into a “secondary” container, so that we can join across many tables while still keeping things “simple” for `relationship()`, in that there’s just “one” table on both the “left” and the “right” side; the complexity is kept within the middle.

Warning

A relationship like the above is typically marked as `viewonly=True`, using `relationship.viewonly`, and should be considered as read-only. While there are sometimes ways to make relationships like the above writable, this is generally complicated and error prone.

See also

Notes on using the viewonly relationship parameter

## Relationship to Aliased Class

In the previous section, we illustrated a technique where we used `relationship.secondary` in order to place additional tables within a join condition. There is one complex join case where even this technique is not sufficient; when we seek to join from `A` to `B`, making use of any number of `C`, `D`, etc. in between, however there are also join conditions between `A` and `B` directly. In this case, the join from `A` to `B` may be difficult to express with just a complex `relationship.primaryjoin` condition, as the intermediary tables may need special handling, and it is also not expressible with a `relationship.secondary` object, since the `A->secondary->B` pattern does not support any references between `A` and `B` directly. When this extremely advanced case arises, we can resort to creating a second mapping as a target for the relationship. This is where we use `AliasedClass` in order to make a mapping to a class that includes all the additional tables we need for this join. In order to produce this mapper as an “alternative” mapping for our class, we use the `aliased()` function to produce the new construct, then use `relationship()` against the object as though it were a plain mapped class.

Below illustrates a `relationship()` with a simple join from `A` to `B`, however the primaryjoin condition is augmented with two additional entities `C` and `D`, which also must have rows that line up with the rows in both `A` and `B` simultaneously:

    class A(Base):
        __tablename__ = "a"

        id = mapped_column(Integer, primary_key=True)
        b_id = mapped_column(ForeignKey("b.id"))

    class B(Base):
        __tablename__ = "b"

        id = mapped_column(Integer, primary_key=True)

    class C(Base):
        __tablename__ = "c"

        id = mapped_column(Integer, primary_key=True)
        a_id = mapped_column(ForeignKey("a.id"))

        some_c_value = mapped_column(String)

    class D(Base):
        __tablename__ = "d"

        id = mapped_column(Integer, primary_key=True)
        c_id = mapped_column(ForeignKey("c.id"))
        b_id = mapped_column(ForeignKey("b.id"))

        some_d_value = mapped_column(String)

    # 1. set up the join() as a variable, so we can refer
    # to it in the mapping multiple times.
    j = join(B, D, D.b_id == B.id).join(C, C.id == D.c_id)

    # 2. Create an AliasedClass to B
    B_viacd = aliased(B, j, flat=True)

    A.b = relationship(B_viacd, primaryjoin=A.b_id == j.c.b_id)

With the above mapping, a simple join looks like:

    sess.scalars(select(A).join(A.b)).all()

    SELECT a.id AS a_id, a.b_id AS a_b_id
    FROM a JOIN (b JOIN d ON d.b_id = b.id JOIN c ON c.id = d.c_id) ON a.b_id = b.id

### Integrating AliasedClass Mappings with Typing and Avoiding Early Mapper Configuration

The creation of the `aliased()` construct against a mapped class forces the `configure_mappers()` step to proceed, which will resolve all current classes and their relationships. This may be problematic if unrelated mapped classes needed by the current mappings have not yet been declared, or if the configuration of the relationship itself needs access to as-yet undeclared classes. Additionally, SQLAlchemy’s Declarative pattern works with Python typing most effectively when relationships are declared up front.

To organize the construction of the relationship to work with these issues, a configure level event hook like `MapperEvents.before_mapper_configured()` may be used, which will invoke the configuration code only when all mappings are ready for configuration:

    from sqlalchemy import event

    class A(Base):
        __tablename__ = "a"

        id = mapped_column(Integer, primary_key=True)
        b_id = mapped_column(ForeignKey("b.id"))

    @event.listens_for(A, "before_mapper_configured")
    def _configure_ab_relationship(mapper, cls):
        # do the above configuration in a configuration hook

        j = join(B, D, D.b_id == B.id).join(C, C.id == D.c_id)
        B_viacd = aliased(B, j, flat=True)
        A.b = relationship(B_viacd, primaryjoin=A.b_id == j.c.b_id)

Above, the function `_configure_ab_relationship()` will be invoked only when a fully configured version of `A` is requested, at which point the classes `B`, `D` and `C` would be available.

For an approach that integrates with inline typing, a similar technique can be used to effectively generate a “singleton” creation pattern for the aliased class where it is late-initialized as a global variable, which can then be used in the relationship inline:

    from typing import Any

    B_viacd: Any = None
    b_viacd_join: Any = None

    class A(Base):
        __tablename__ = "a"

        id: Mapped[int] = mapped_column(primary_key=True)
        b_id: Mapped[int] = mapped_column(ForeignKey("b.id"))

        # 1. the relationship can be declared using lambdas, allowing it to resolve
        #    to targets that are late-configured
        b: Mapped[B] = relationship(
            lambda: B_viacd, primaryjoin=lambda: A.b_id == b_viacd_join.c.b_id
        )

    # 2. configure the targets of the relationship using a before_mapper_configured
    #    hook.
    @event.listens_for(A, "before_mapper_configured")
    def _configure_ab_relationship(mapper, cls):
        # 3. set up the join() and AliasedClass as globals from within
        #    the configuration hook.

        global B_viacd, b_viacd_join

        b_viacd_join = join(B, D, D.b_id == B.id).join(C, C.id == D.c_id)
        B_viacd = aliased(B, b_viacd_join, flat=True)

### Using the AliasedClass target in Queries

In the previous example, the `A.b` relationship refers to the `B_viacd` entity as the target, and not the `B` class directly. To add additional criteria involving the `A.b` relationship, it’s typically necessary to reference the `B_viacd` directly rather than using `B`, especially in a case where the target entity of `A.b` is to be transformed into an alias or a subquery. Below illustrates the same relationship using a subquery, rather than a join:

    subq = select(B).join(D, D.b_id == B.id).join(C, C.id == D.c_id).subquery()

    B_viacd_subquery = aliased(B, subq)

    A.b = relationship(B_viacd_subquery, primaryjoin=A.b_id == subq.c.id)

A query using the above `A.b` relationship will render a subquery:

    sess.scalars(select(A).join(A.b)).all()

    SELECT a.id AS a_id, a.b_id AS a_b_id
    FROM a JOIN (SELECT b.id AS id, b.some_b_column AS some_b_column
    FROM b JOIN d ON d.b_id = b.id JOIN c ON c.id = d.c_id) AS anon_1 ON a.b_id = anon_1.id

If we want to add additional criteria based on the `A.b` join, we must do so in terms of `B_viacd_subquery` rather than `B` directly:

    sess.scalars(
        select(A)
        .join(A.b)
        .where(B_viacd_subquery.some_b_column == "some b")
        .order_by(B_viacd_subquery.id)
    ).all()

    SELECT a.id AS a_id, a.b_id AS a_b_id
    FROM a JOIN (SELECT b.id AS id, b.some_b_column AS some_b_column
    FROM b JOIN d ON d.b_id = b.id JOIN c ON c.id = d.c_id) AS anon_1 ON a.b_id = anon_1.id
    WHERE anon_1.some_b_column = ? ORDER BY anon_1.id

## Row-Limited Relationships with Window Functions

Another interesting use case for relationships to `AliasedClass` objects are situations where the relationship needs to join to a specialized SELECT of any form. One scenario is when the use of a window function is desired, such as to limit how many rows should be returned for a relationship. The example below illustrates a non-primary mapper relationship that will load the first ten items for each collection:

    class A(Base):
        __tablename__ = "a"

        id = mapped_column(Integer, primary_key=True)

    class B(Base):
        __tablename__ = "b"
        id = mapped_column(Integer, primary_key=True)
        a_id = mapped_column(ForeignKey("a.id"))

    partition = select(
        B, func.row_number().over(order_by=B.id, partition_by=B.a_id).label("index")
    ).alias()

    partitioned_b = aliased(B, partition)

    A.partitioned_bs = relationship(
        partitioned_b, primaryjoin=and_(partitioned_b.a_id == A.id, partition.c.index < 10)
    )

We can use the above `partitioned_bs` relationship with most of the loader strategies, such as `selectinload()`:

    for a1 in session.scalars(select(A).options(selectinload(A.partitioned_bs))):
        print(a1.partitioned_bs)  # <-- will be no more than ten objects

Where above, the “selectinload” query looks like:

    SELECT
        a_1.id AS a_1_id, anon_1.id AS anon_1_id, anon_1.a_id AS anon_1_a_id,
        anon_1.data AS anon_1_data, anon_1.index AS anon_1_index
    FROM a AS a_1
    JOIN (
        SELECT b.id AS id, b.a_id AS a_id, b.data AS data,
        row_number() OVER (PARTITION BY b.a_id ORDER BY b.id) AS index
        FROM b) AS anon_1
    ON anon_1.a_id = a_1.id AND anon_1.index < %(index_1)s
    WHERE a_1.id IN ( ... primary key collection ...)
    ORDER BY a_1.id

Above, for each matching primary key in “a”, we will get the first ten “bs” as ordered by “b.id”. By partitioning on “a_id” we ensure that each “row number” is local to the parent “a_id”.

Such a mapping would ordinarily also include a “plain” relationship from “A” to “B”, for persistence operations as well as when the full set of “B” objects per “A” is desired.

## Building Query-Enabled Properties

Very ambitious custom join conditions may fail to be directly persistable, and in some cases may not even load correctly. To remove the persistence part of the equation, use the flag `relationship.viewonly` on the `relationship()`, which establishes it as a read-only attribute (data written to the collection will be ignored on flush()). However, in extreme cases, consider using a regular Python property in conjunction with `Query` as follows:

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

        @property
        def addresses(self):
            return object_session(self).query(Address).with_parent(self).filter(...).all()

In other cases, the descriptor can be built to make use of existing in-Python data. See the section on Using Descriptors and Hybrids for more general discussion of special Python attributes.

See also

Using Descriptors and Hybrids

## Notes on using the viewonly relationship parameter

The `relationship.viewonly` parameter when applied to a `relationship()` construct indicates that this `relationship()` will not take part in any ORM unit of work operations, and additionally that the attribute does not expect to participate within in-Python mutations of its represented collection. This means that while the viewonly relationship may refer to a mutable Python collection like a list or set, making changes to that list or set as present on a mapped instance will have no effect on the ORM flush process.

To explore this scenario consider this mapping:

    from __future__ import annotations

    import datetime

    from sqlalchemy import and_
    from sqlalchemy import ForeignKey
    from sqlalchemy import func
    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 User(Base):
        __tablename__ = "user_account"

        id: Mapped[int] = mapped_column(primary_key=True)
        name: Mapped[str | None]

        all_tasks: Mapped[list[Task]] = relationship()

        current_week_tasks: Mapped[list[Task]] = relationship(
            primaryjoin=lambda: and_(
                User.id == Task.user_account_id,
                # this expression works on PostgreSQL but may not be supported
                # by other database engines
                Task.task_date >= func.now() - datetime.timedelta(days=7),
            ),
            viewonly=True,
        )

    class Task(Base):
        __tablename__ = "task"

        id: Mapped[int] = mapped_column(primary_key=True)
        user_account_id: Mapped[int] = mapped_column(ForeignKey("user_account.id"))
        description: Mapped[str | None]
        task_date: Mapped[datetime.datetime] = mapped_column(server_default=func.now())

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

The following sections will note different aspects of this configuration.

### In-Python mutations including backrefs are not appropriate with viewonly=True

The above mapping targets the `User.current_week_tasks` viewonly relationship as the backref target of the `Task.user` attribute. This is not currently flagged by SQLAlchemy’s ORM configuration process, however is a configuration error. Changing the `.user` attribute on a `Task` will not affect the `.current_week_tasks` attribute:

    >>> u1 = User()
    >>> t1 = Task(task_date=datetime.datetime.now())
    >>> t1.user = u1
    >>> u1.current_week_tasks
    []

There is another parameter called `relationship.sync_backrefs` which can be turned on here to allow `.current_week_tasks` to be mutated in this case, however this is not considered to be a best practice with a viewonly relationship, which instead should not be relied upon for in-Python mutations.

In this mapping, backrefs can be configured between `User.all_tasks` and `Task.user`, as these are both not viewonly and will synchronize normally.

Beyond the issue of backref mutations being disabled for viewonly relationships, plain changes to the `User.all_tasks` collection in Python are also not reflected in the `User.current_week_tasks` collection until changes have been flushed to the database.

Overall, for a use case where a custom collection should respond immediately to in-Python mutations, the viewonly relationship is generally not appropriate. A better approach is to use the Hybrid Attributes feature of SQLAlchemy, or for instance-only cases to use a Python `@property`, where a user-defined collection that is generated in terms of the current Python instance can be implemented. To change our example to work this way, we repair the `relationship.back_populates` parameter on `Task.user` to reference `User.all_tasks`, and then illustrate a simple `@property` that will deliver results in terms of the immediate `User.all_tasks` collection:

    class User(Base):
        __tablename__ = "user_account"

        id: Mapped[int] = mapped_column(primary_key=True)
        name: Mapped[str | None]

        all_tasks: Mapped[list[Task]] = relationship(back_populates="user")

        @property
        def current_week_tasks(self) -> list[Task]:
            past_seven_days = datetime.datetime.now() - datetime.timedelta(days=7)
            return [t for t in self.all_tasks if t.task_date >= past_seven_days]

    class Task(Base):
        __tablename__ = "task"

        id: Mapped[int] = mapped_column(primary_key=True)
        user_account_id: Mapped[int] = mapped_column(ForeignKey("user_account.id"))
        description: Mapped[str | None]
        task_date: Mapped[datetime.datetime] = mapped_column(server_default=func.now())

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

Using an in-Python collection calculated on the fly each time, we are guaranteed to have the correct answer at all times, without the need to use a database at all:

    >>> u1 = User()
    >>> t1 = Task(task_date=datetime.datetime.now())
    >>> t1.user = u1
    >>> u1.current_week_tasks
    [<__main__.Task object at 0x7f3d699523c0>]

### viewonly=True collections / attributes do not get re-queried until expired

Continuing with the original viewonly attribute, if we do in fact make changes to the `User.all_tasks` collection on a persistent object, the viewonly collection can only show the net result of this change after two things occur. The first is that the change to `User.all_tasks` is flushed, so that the new data is available in the database, at least within the scope of the local transaction. The second is that the `User.current_week_tasks` attribute is expired and reloaded via a new SQL query to the database.

To support this requirement, the simplest flow to use is one where the viewonly relationship is consumed only in operations that are primarily read only to start with. Such as below, if we retrieve a `User` fresh from the database, the collection will be current:

    >>> with Session(e) as sess:
    ...     u1 = sess.scalar(select(User).where(User.id == 1))
    ...     print(u1.current_week_tasks)
    [<__main__.Task object at 0x7f8711b906b0>]

When we make modifications to `u1.all_tasks`, if we want to see these changes reflected in the `u1.current_week_tasks` viewonly relationship, these changes need to be flushed and the `u1.current_week_tasks` attribute needs to be expired, so that it will lazy load on next access. The simplest approach to this is to use `Session.commit()`, keeping the `Session.expire_on_commit` parameter set at its default of `True`:

    >>> with Session(e) as sess:
    ...     u1 = sess.scalar(select(User).where(User.id == 1))
    ...     u1.all_tasks.append(Task(task_date=datetime.datetime.now()))
    ...     sess.commit()
    ...     print(u1.current_week_tasks)
    [<__main__.Task object at 0x7f8711b90ec0>, <__main__.Task object at 0x7f8711b90a10>]

Above, the call to `Session.commit()` flushed the changes to `u1.all_tasks` to the database, then expired all objects, so that when we accessed `u1.current_week_tasks`, a :term:` lazy load` occurred which fetched the contents for this attribute freshly from the database.

To intercept operations without actually committing the transaction, the attribute needs to be explicitly expired first. A simplistic way to do this is to just call it directly. In the example below, `Session.flush()` sends pending changes to the database, then `Session.expire()` is used to expire the `u1.current_week_tasks` collection so that it re-fetches on next access:

    >>> with Session(e) as sess:
    ...     u1 = sess.scalar(select(User).where(User.id == 1))
    ...     u1.all_tasks.append(Task(task_date=datetime.datetime.now()))
    ...     sess.flush()
    ...     sess.expire(u1, ["current_week_tasks"])
    ...     print(u1.current_week_tasks)
    [<__main__.Task object at 0x7fd95a4c8c50>, <__main__.Task object at 0x7fd95a4c8c80>]

We can in fact skip the call to `Session.flush()`, assuming a `Session` that keeps `Session.autoflush` at its default value of `True`, as the expired `current_week_tasks` attribute will trigger autoflush when accessed after expiration:

    >>> with Session(e) as sess:
    ...     u1 = sess.scalar(select(User).where(User.id == 1))
    ...     u1.all_tasks.append(Task(task_date=datetime.datetime.now()))
    ...     sess.expire(u1, ["current_week_tasks"])
    ...     print(u1.current_week_tasks)  # triggers autoflush before querying
    [<__main__.Task object at 0x7fd95a4c8c50>, <__main__.Task object at 0x7fd95a4c8c80>]

Continuing with the above approach to something more elaborate, we can apply the expiration programmatically when the related `User.all_tasks` collection changes, using event hooks. This an advanced technique, where simpler architectures like `@property` or sticking to read-only use cases should be examined first. In our simple example, this would be configured as:

    from sqlalchemy import event, inspect

    @event.listens_for(User.all_tasks, "append")
    @event.listens_for(User.all_tasks, "remove")
    @event.listens_for(User.all_tasks, "bulk_replace")
    def _expire_User_current_week_tasks(target, value, initiator):
        inspect(target).session.expire(target, ["current_week_tasks"])

With the above hooks, mutation operations are intercepted and result in the `User.current_week_tasks` collection to be expired automatically:

    >>> with Session(e) as sess:
    ...     u1 = sess.scalar(select(User).where(User.id == 1))
    ...     u1.all_tasks.append(Task(task_date=datetime.datetime.now()))
    ...     print(u1.current_week_tasks)
    [<__main__.Task object at 0x7f66d093ccb0>, <__main__.Task object at 0x7f66d093cce0>]

The `AttributeEvents` event hooks used above are also triggered by backref mutations, so with the above hooks a change to `Task.user` is also intercepted:

    >>> with Session(e) as sess:
    ...     u1 = sess.scalar(select(User).where(User.id == 1))
    ...     t1 = Task(task_date=datetime.datetime.now())
    ...     t1.user = u1
    ...     sess.add(t1)
    ...     print(u1.current_week_tasks)
    [<__main__.Task object at 0x7f3b0c070d10>, <__main__.Task object at 0x7f3b0c057d10>]