# SQLAlchemy 0.8 Documentation

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Release: 0.8.1 | Release Date: April 27, 2013

# Relationship Configuration

This section describes the `relationship()` function and in depth discussion of its usage. The reference material here continues into the next section, Collection Configuration and Techniques, which has additional detail on configuration of collections via `relationship()`.

## Basic Relational Patterns

A quick walkthrough of the basic relational patterns.

The imports used for each of the following sections is as follows:

    from sqlalchemy import Table, Column, Integer, ForeignKey
    from sqlalchemy.orm import relationship, backref
    from sqlalchemy.ext.declarative import declarative_base

    Base = declarative_base()

### One To Many

A one to many relationship places a foreign key on the child table referencing the parent. `relationship()` is then specified on the parent, as referencing a collection of items represented by the child:

    class Parent(Base):
        __tablename__ = 'parent'
        id = Column(Integer, primary_key=True)
        children = relationship("Child")

    class Child(Base):
        __tablename__ = 'child'
        id = Column(Integer, primary_key=True)
        parent_id = Column(Integer, ForeignKey('parent.id'))

To establish a bidirectional relationship in one-to-many, where the “reverse” side is a many to one, specify the `backref` option:

    class Parent(Base):
        __tablename__ = 'parent'
        id = Column(Integer, primary_key=True)
        children = relationship("Child", backref="parent")

    class Child(Base):
        __tablename__ = 'child'
        id = Column(Integer, primary_key=True)
        parent_id = Column(Integer, ForeignKey('parent.id'))

`Child` will get a `parent` attribute with many-to-one semantics.

### Many To One

Many to one places a foreign key in the parent table referencing the child. `relationship()` is declared on the parent, where a new scalar-holding attribute will be created:

    class Parent(Base):
        __tablename__ = 'parent'
        id = Column(Integer, primary_key=True)
        child_id = Column(Integer, ForeignKey('child.id'))
        child = relationship("Child")

    class Child(Base):
        __tablename__ = 'child'
        id = Column(Integer, primary_key=True)

Bidirectional behavior is achieved by specifying `backref="parents"`, which will place a one-to-many collection on the `Child` class:

    class Parent(Base):
        __tablename__ = 'parent'
        id = Column(Integer, primary_key=True)
        child_id = Column(Integer, ForeignKey('child.id'))
        child = relationship("Child", backref="parents")

### One To One

One To One is essentially a bidirectional relationship with a scalar attribute on both sides. To achieve this, the `uselist=False` flag indicates the placement of a scalar attribute instead of a collection on the “many” side of the relationship. To convert one-to-many into one-to-one:

    class Parent(Base):
        __tablename__ = 'parent'
        id = Column(Integer, primary_key=True)
        child = relationship("Child", uselist=False, backref="parent")

    class Child(Base):
        __tablename__ = 'child'
        id = Column(Integer, primary_key=True)
        parent_id = Column(Integer, ForeignKey('parent.id'))

Or to turn a one-to-many backref into one-to-one, use the `backref()` function to provide arguments for the reverse side:

    class Parent(Base):
        __tablename__ = 'parent'
        id = Column(Integer, primary_key=True)
        child_id = Column(Integer, ForeignKey('child.id'))
        child = relationship("Child", backref=backref("parent", uselist=False))

    class Child(Base):
        __tablename__ = 'child'
        id = Column(Integer, primary_key=True)

### Many To Many

Many to Many adds an association table between two classes. The association table is indicated by the `secondary` argument to `relationship()`. Usually, the `Table` uses the `MetaData` object associated with the declarative base class, so that the `ForeignKey` directives can locate the remote tables with which to link:

    association_table = Table('association', Base.metadata,
        Column('left_id', Integer, ForeignKey('left.id')),
        Column('right_id', Integer, ForeignKey('right.id'))
    )

    class Parent(Base):
        __tablename__ = 'left'
        id = Column(Integer, primary_key=True)
        children = relationship("Child",
                        secondary=association_table)

    class Child(Base):
        __tablename__ = 'right'
        id = Column(Integer, primary_key=True)

For a bidirectional relationship, both sides of the relationship contain a collection. The `backref` keyword will automatically use the same `secondary` argument for the reverse relationship:

    association_table = Table('association', Base.metadata,
        Column('left_id', Integer, ForeignKey('left.id')),
        Column('right_id', Integer, ForeignKey('right.id'))
    )

    class Parent(Base):
        __tablename__ = 'left'
        id = Column(Integer, primary_key=True)
        children = relationship("Child",
                        secondary=association_table,
                        backref="parents")

    class Child(Base):
        __tablename__ = 'right'
        id = Column(Integer, primary_key=True)

The `secondary` argument of `relationship()` also accepts a callable that returns the ultimate argument, which is evaluated only when mappers are first used. Using this, we can define the `association_table` at a later point, as long as it’s available to the callable after all module initialization is complete:

    class Parent(Base):
        __tablename__ = 'left'
        id = Column(Integer, primary_key=True)
        children = relationship("Child",
                        secondary=lambda: association_table,
                        backref="parents")

With the declarative extension in use, the traditional “string name of the table” is accepted as well, matching the name of the table as stored in `Base.metadata.tables`:

    class Parent(Base):
        __tablename__ = 'left'
        id = Column(Integer, primary_key=True)
        children = relationship("Child",
                        secondary="association",
                        backref="parents")

#### Deleting Rows from the Many to Many Table

A behavior which is unique to the `secondary` argument to `relationship()` is that the `Table` which is specified here is automatically subject to INSERT and DELETE statements, as objects are added or removed from the collection. There is no need to delete from this table manually. The act of removing a record from the collection will have the effect of the row being deleted on flush:

    # row will be deleted from the "secondary" table
    # automatically
    myparent.children.remove(somechild)

A question which often arises is how the row in the “secondary” table can be deleted when the child object is handed directly to `Session.delete()`:

    session.delete(somechild)

There are several possibilities here:

- If there is a `relationship()` from `Parent` to `Child`, but there is not a reverse-relationship that links a particular `Child` to each `Parent`, SQLAlchemy will not have any awareness that when deleting this particular `Child` object, it needs to maintain the “secondary” table that links it to the `Parent`. No delete of the “secondary” table will occur.
- If there is a relationship that links a particular `Child` to each `Parent`, suppose it’s called `Child.parents`, SQLAlchemy by default will load in the `Child.parents` collection to locate all `Parent` objects, and remove each row from the “secondary” table which establishes this link. Note that this relationship does not need to be bidrectional; SQLAlchemy is strictly looking at every `relationship()` associated with the `Child` object being deleted.
- A higher performing option here is to use ON DELETE CASCADE directives with the foreign keys used by the database. Assuming the database supports this feature, the database itself can be made to automatically delete rows in the “secondary” table as referencing rows in “child” are deleted. SQLAlchemy can be instructed to forego actively loading in the `Child.parents` collection in this case using the `passive_deletes=True` directive on `relationship()`; see Using Passive Deletes for more details on this.

Note again, these behaviors are only relevant to the `secondary` option used with `relationship()`. If dealing with association tables that are mapped explicitly and are not present in the `secondary` option of a relevant `relationship()`, cascade rules can be used instead to automatically delete entities in reaction to a related entity being deleted - see Cascades for information on this feature.

### Association Object

The association object pattern is a variant on many-to-many: it’s used when your association table contains additional columns beyond those which are foreign keys to the left and right tables. Instead of using the `secondary` argument, you map a new class directly to the association table. The left side of the relationship references the association object via one-to-many, and the association class references the right side via many-to-one. Below we illustrate an association table mapped to the `Association` class which includes a column called `extra_data`, which is a string value that is stored along with each association between `Parent` and `Child`:

    class Association(Base):
        __tablename__ = 'association'
        left_id = Column(Integer, ForeignKey('left.id'), primary_key=True)
        right_id = Column(Integer, ForeignKey('right.id'), primary_key=True)
        extra_data = Column(String(50))
        child = relationship("Child")

    class Parent(Base):
        __tablename__ = 'left'
        id = Column(Integer, primary_key=True)
        children = relationship("Association")

    class Child(Base):
        __tablename__ = 'right'
        id = Column(Integer, primary_key=True)

The bidirectional version adds backrefs to both relationships:

    class Association(Base):
        __tablename__ = 'association'
        left_id = Column(Integer, ForeignKey('left.id'), primary_key=True)
        right_id = Column(Integer, ForeignKey('right.id'), primary_key=True)
        extra_data = Column(String(50))
        child = relationship("Child", backref="parent_assocs")

    class Parent(Base):
        __tablename__ = 'left'
        id = Column(Integer, primary_key=True)
        children = relationship("Association", backref="parent")

    class Child(Base):
        __tablename__ = 'right'
        id = Column(Integer, primary_key=True)

Working with the association pattern in its direct form requires that child objects are associated with an association instance before being appended to the parent; similarly, access from parent to child goes through the association object:

    # create parent, append a child via association
    p = Parent()
    a = Association(extra_data="some data")
    a.child = Child()
    p.children.append(a)

    # iterate through child objects via association, including association
    # attributes
    for assoc in p.children:
        print assoc.extra_data
        print assoc.child

To enhance the association object pattern such that direct access to the `Association` object is optional, SQLAlchemy provides the Association Proxy extension. This extension allows the configuration of attributes which will access two “hops” with a single access, one “hop” to the associated object, and a second to a target attribute.

Note

When using the association object pattern, it is advisable that the association-mapped table not be used as the `secondary` argument on a `relationship()` elsewhere, unless that `relationship()` contains the option `viewonly=True`. SQLAlchemy otherwise may attempt to emit redundant INSERT and DELETE statements on the same table, if similar state is detected on the related attribute as well as the associated object.

## Adjacency List Relationships

The adjacency list pattern is a common relational pattern whereby a table contains a foreign key reference to itself. This is the most common way to represent hierarchical data in flat tables. Other methods include nested sets, sometimes called “modified preorder”, as well as materialized path. Despite the appeal that modified preorder has when evaluated for its fluency within SQL queries, the adjacency list model is probably the most appropriate pattern for the large majority of hierarchical storage needs, for reasons of concurrency, reduced complexity, and that modified preorder has little advantage over an application which can fully load subtrees into the application space.

In this example, we’ll work with a single mapped class called `Node`, representing a tree structure:

    class Node(Base):
        __tablename__ = 'node'
        id = Column(Integer, primary_key=True)
        parent_id = Column(Integer, ForeignKey('node.id'))
        data = Column(String(50))
        children = relationship("Node")

With this structure, a graph such as the following:

    root --+---> child1
           +---> child2 --+--> subchild1
           |              +--> subchild2
           +---> child3

Would be represented with data such as:

    id       parent_id     data
    ---      -------       ----
    1        NULL          root
    2        1             child1
    3        1             child2
    4        3             subchild1
    5        3             subchild2
    6        1             child3

The `relationship()` configuration here works in the same way as a “normal” one-to-many relationship, with the exception that the “direction”, i.e. whether the relationship is one-to-many or many-to-one, is assumed by default to be one-to-many. To establish the relationship as many-to-one, an extra directive is added known as `remote_side`, which is a `Column` or collection of `Column` objects that indicate those which should be considered to be “remote”:

    class Node(Base):
        __tablename__ = 'node'
        id = Column(Integer, primary_key=True)
        parent_id = Column(Integer, ForeignKey('node.id'))
        data = Column(String(50))
        parent = relationship("Node", remote_side=[id])

Where above, the `id` column is applied as the `remote_side` of the `parent` `relationship()`, thus establishing `parent_id` as the “local” side, and the relationship then behaves as a many-to-one.

As always, both directions can be combined into a bidirectional relationship using the `backref()` function:

    class Node(Base):
        __tablename__ = 'node'
        id = Column(Integer, primary_key=True)
        parent_id = Column(Integer, ForeignKey('node.id'))
        data = Column(String(50))
        children = relationship("Node",
                    backref=backref('parent', remote_side=[id])
                )

There are several examples included with SQLAlchemy illustrating self-referential strategies; these include Adjacency List and XML Persistence.

### Composite Adjacency Lists

A sub-category of the adjacency list relationship is the rare case where a particular column is present on both the “local” and “remote” side of the join condition. An example is the `Folder` class below; using a composite primary key, the `account_id` column refers to itself, to indicate sub folders which are within the same account as that of the parent; while `folder_id` refers to a specific folder within that account:

    class Folder(Base):
        __tablename__ = 'folder'
        __table_args__ = (
          ForeignKeyConstraint(
              ['account_id', 'parent_id'],
              ['folder.account_id', 'folder.folder_id']),
        )

        account_id = Column(Integer, primary_key=True)
        folder_id = Column(Integer, primary_key=True)
        parent_id = Column(Integer)
        name = Column(String)

        parent_folder = relationship("Folder",
                            backref="child_folders",
                            remote_side=[account_id, folder_id]
                      )

Above, we pass `account_id` into the `remote_side` list. `relationship()` recognizes that the `account_id` column here is on both sides, and aligns the “remote” column along with the `folder_id` column, which it recognizes as uniquely present on the “remote” side.

New in version 0.8: Support for self-referential composite keys in `relationship()` where a column points to itself.

### Self-Referential Query Strategies

Querying of self-referential structures works like any other query:

    # get all nodes named 'child2'
    session.query(Node).filter(Node.data=='child2')

However extra care is needed when attempting to join along the foreign key from one level of the tree to the next. In SQL, a join from a table to itself requires that at least one side of the expression be “aliased” so that it can be unambiguously referred to.

Recall from Using Aliases in the ORM tutorial that the `orm.aliased()` construct is normally used to provide an “alias” of an ORM entity. Joining from `Node` to itself using this technique looks like:

    from sqlalchemy.orm import aliased

    nodealias = aliased(Node)
    sqlsession.query(Node).filter(Node.data=='subchild1').\
                    join(nodealias, Node.parent).\
                    filter(nodealias.data=="child2").\
                    all()
    SELECT node.id AS node_id,
            node.parent_id AS node_parent_id,
            node.data AS node_data
    FROM node JOIN node AS node_1
        ON node.parent_id = node_1.id
    WHERE node.data = ?
        AND node_1.data = ?
    ['subchild1', 'child2']

`Query.join()` also includes a feature known as `aliased=True` that can shorten the verbosity self-referential joins, at the expense of query flexibility. This feature performs a similar “aliasing” step to that above, without the need for an explicit entity. Calls to `Query.filter()` and similar subsequent to the aliased join will adapt the `Node` entity to be that of the alias:

    sqlsession.query(Node).filter(Node.data=='subchild1').\
            join(Node.parent, aliased=True).\
            filter(Node.data=='child2').\
            all()
    SELECT node.id AS node_id,
            node.parent_id AS node_parent_id,
            node.data AS node_data
    FROM node
        JOIN node AS node_1 ON node_1.id = node.parent_id
    WHERE node.data = ? AND node_1.data = ?
    ['subchild1', 'child2']

To add criterion to multiple points along a longer join, add `from_joinpoint=True` to the additional `join()` calls:

    # get all nodes named 'subchild1' with a
    # parent named 'child2' and a grandparent 'root'
    sqlsession.query(Node).\
            filter(Node.data=='subchild1').\
            join(Node.parent, aliased=True).\
            filter(Node.data=='child2').\
            join(Node.parent, aliased=True, from_joinpoint=True).\
            filter(Node.data=='root').\
            all()
    SELECT node.id AS node_id,
            node.parent_id AS node_parent_id,
            node.data AS node_data
    FROM node
        JOIN node AS node_1 ON node_1.id = node.parent_id
        JOIN node AS node_2 ON node_2.id = node_1.parent_id
    WHERE node.data = ?
        AND node_1.data = ?
        AND node_2.data = ?
    ['subchild1', 'child2', 'root']

`Query.reset_joinpoint()` will also remove the “aliasing” from filtering calls:

    session.query(Node).\
            join(Node.children, aliased=True).\
            filter(Node.data == 'foo').\
            reset_joinpoint().\
            filter(Node.data == 'bar')

For an example of using `aliased=True` to arbitrarily join along a chain of self-referential nodes, see XML Persistence.

### Configuring Self-Referential Eager Loading

Eager loading of relationships occurs using joins or outerjoins from parent to child table during a normal query operation, such that the parent and its immediate child collection or reference can be populated from a single SQL statement, or a second statement for all immediate child collections. SQLAlchemy’s joined and subquery eager loading use aliased tables in all cases when joining to related items, so are compatible with self-referential joining. However, to use eager loading with a self-referential relationship, SQLAlchemy needs to be told how many levels deep it should join and/or query; otherwise the eager load will not take place at all. This depth setting is configured via `join_depth`:

    class Node(Base):
        __tablename__ = 'node'
        id = Column(Integer, primary_key=True)
        parent_id = Column(Integer, ForeignKey('node.id'))
        data = Column(String(50))
        children = relationship("Node",
                        lazy="joined",
                        join_depth=2)

    sqlsession.query(Node).all()
    SELECT node_1.id AS node_1_id,
            node_1.parent_id AS node_1_parent_id,
            node_1.data AS node_1_data,
            node_2.id AS node_2_id,
            node_2.parent_id AS node_2_parent_id,
            node_2.data AS node_2_data,
            node.id AS node_id,
            node.parent_id AS node_parent_id,
            node.data AS node_data
    FROM node
        LEFT OUTER JOIN node AS node_2
            ON node.id = node_2.parent_id
        LEFT OUTER JOIN node AS node_1
            ON node_2.id = node_1.parent_id
    []

## Linking Relationships with Backref

The `backref` keyword argument was first introduced in Object Relational Tutorial, and has been mentioned throughout many of the examples here. What does it actually do ? Let’s start with the canonical `User` and `Address` scenario:

    from sqlalchemy import Integer, ForeignKey, String, Column
    from sqlalchemy.ext.declarative import declarative_base
    from sqlalchemy.orm import relationship

    Base = declarative_base()

    class User(Base):
        __tablename__ = 'user'
        id = Column(Integer, primary_key=True)
        name = Column(String)

        addresses = relationship("Address", backref="user")

    class Address(Base):
        __tablename__ = 'address'
        id = Column(Integer, primary_key=True)
        email = Column(String)
        user_id = Column(Integer, ForeignKey('user.id'))

The above configuration establishes a collection of `Address` objects on `User` called `User.addresses`. It also establishes a `.user` attribute on `Address` which will refer to the parent `User` object.

In fact, the `backref` keyword is only a common shortcut for placing a second `relationship` onto the `Address` mapping, including the establishment of an event listener on both sides which will mirror attribute operations in both directions. The above configuration is equivalent to:

    from sqlalchemy import Integer, ForeignKey, String, Column
    from sqlalchemy.ext.declarative import declarative_base
    from sqlalchemy.orm import relationship

    Base = declarative_base()

    class User(Base):
        __tablename__ = 'user'
        id = Column(Integer, primary_key=True)
        name = Column(String)

        addresses = relationship("Address", back_populates="user")

    class Address(Base):
        __tablename__ = 'address'
        id = Column(Integer, primary_key=True)
        email = Column(String)
        user_id = Column(Integer, ForeignKey('user.id'))

        user = relationship("User", back_populates="addresses")

Above, we add a `.user` relationship to `Address` explicitly. On both relationships, the `back_populates` directive tells each relationship about the other one, indicating that they should establish “bidirectional” behavior between each other. The primary effect of this configuration is that the relationship adds event handlers to both attributes which have the behavior of “when an append or set event occurs here, set ourselves onto the incoming attribute using this particular attribute name”. The behavior is illustrated as follows. Start with a `User` and an `Address` instance. The `.addresses` collection is empty, and the `.user` attribute is `None`:

    >>> u1 = User()
    >>> a1 = Address()
    >>> u1.addresses
    []
    >>> print a1.user
    None

However, once the `Address` is appended to the `u1.addresses` collection, both the collection and the scalar attribute have been populated:

    >>> u1.addresses.append(a1)
    >>> u1.addresses
    [<__main__.Address object at 0x12a6ed0>]
    >>> a1.user
    <__main__.User object at 0x12a6590>

This behavior of course works in reverse for removal operations as well, as well as for equivalent operations on both sides. Such as when `.user` is set again to `None`, the `Address` object is removed from the reverse collection:

    >>> a1.user = None
    >>> u1.addresses
    []

The manipulation of the `.addresses` collection and the `.user` attribute occurs entirely in Python without any interaction with the SQL database. Without this behavior, the proper state would be apparent on both sides once the data has been flushed to the database, and later reloaded after a commit or expiration operation occurs. The `backref`/`back_populates` behavior has the advantage that common bidirectional operations can reflect the correct state without requiring a database round trip.

Remember, when the `backref` keyword is used on a single relationship, it’s exactly the same as if the above two relationships were created individually using `back_populates` on each.

### Backref Arguments

We’ve established that the `backref` keyword is merely a shortcut for building two individual `relationship()` constructs that refer to each other. Part of the behavior of this shortcut is that certain configurational arguments applied to the `relationship()` will also be applied to the other direction - namely those arguments that describe the relationship at a schema level, and are unlikely to be different in the reverse direction. The usual case here is a many-to-many `relationship()` that has a `secondary` argument, or a one-to-many or many-to-one which has a `primaryjoin` argument (the `primaryjoin` argument is discussed in Specifying Alternate Join Conditions). Such as if we limited the list of `Address` objects to those which start with “tony”:

    from sqlalchemy import Integer, ForeignKey, String, Column
    from sqlalchemy.ext.declarative import declarative_base
    from sqlalchemy.orm import relationship

    Base = declarative_base()

    class User(Base):
        __tablename__ = 'user'
        id = Column(Integer, primary_key=True)
        name = Column(String)

        addresses = relationship("Address",
                        primaryjoin="and_(User.id==Address.user_id, "
                            "Address.email.startswith('tony'))",
                        backref="user")

    class Address(Base):
        __tablename__ = 'address'
        id = Column(Integer, primary_key=True)
        email = Column(String)
        user_id = Column(Integer, ForeignKey('user.id'))

We can observe, by inspecting the resulting property, that both sides of the relationship have this join condition applied:

    >>> print User.addresses.property.primaryjoin
    "user".id = address.user_id AND address.email LIKE :email_1 || '%%'
    >>>
    >>> print Address.user.property.primaryjoin
    "user".id = address.user_id AND address.email LIKE :email_1 || '%%'
    >>>

This reuse of arguments should pretty much do the “right thing” - it uses only arguments that are applicable, and in the case of a many-to-many relationship, will reverse the usage of `primaryjoin` and `secondaryjoin` to correspond to the other direction (see the example in Self-Referential Many-to-Many Relationship for this).

It’s very often the case however that we’d like to specify arguments that are specific to just the side where we happened to place the “backref”. This includes `relationship()` arguments like `lazy`, `remote_side`, `cascade` and `cascade_backrefs`. For this case we use the `backref()` function in place of a string:

    # <other imports>
    from sqlalchemy.orm import backref

    class User(Base):
        __tablename__ = 'user'
        id = Column(Integer, primary_key=True)
        name = Column(String)

        addresses = relationship("Address",
                        backref=backref("user", lazy="joined"))

Where above, we placed a `lazy="joined"` directive only on the `Address.user` side, indicating that when a query against `Address` is made, a join to the `User` entity should be made automatically which will populate the `.user` attribute of each returned `Address`. The `backref()` function formatted the arguments we gave it into a form that is interpreted by the receiving `relationship()` as additional arguments to be applied to the new relationship it creates.

### One Way Backrefs

An unusual case is that of the “one way backref”. This is where the “back-populating” behavior of the backref is only desirable in one direction. An example of this is a collection which contains a filtering `primaryjoin` condition. We’d like to append items to this collection as needed, and have them populate the “parent” object on the incoming object. However, we’d also like to have items that are not part of the collection, but still have the same “parent” association - these items should never be in the collection.

Taking our previous example, where we established a `primaryjoin` that limited the collection only to `Address` objects whose email address started with the word `tony`, the usual backref behavior is that all items populate in both directions. We wouldn’t want this behavior for a case like the following:

    >>> u1 = User()
    >>> a1 = Address(email='mary')
    >>> a1.user = u1
    >>> u1.addresses
    [<__main__.Address object at 0x1411910>]

Above, the `Address` object that doesn’t match the criterion of “starts with ‘tony’” is present in the `addresses` collection of `u1`. After these objects are flushed, the transaction committed and their attributes expired for a re-load, the `addresses` collection will hit the database on next access and no longer have this `Address` object present, due to the filtering condition. But we can do away with this unwanted side of the “backref” behavior on the Python side by using two separate `relationship()` constructs, placing `back_populates` only on one side:

    from sqlalchemy import Integer, ForeignKey, String, Column
    from sqlalchemy.ext.declarative import declarative_base
    from sqlalchemy.orm import relationship

    Base = declarative_base()

    class User(Base):
        __tablename__ = 'user'
        id = Column(Integer, primary_key=True)
        name = Column(String)
        addresses = relationship("Address",
                        primaryjoin="and_(User.id==Address.user_id, "
                            "Address.email.startswith('tony'))",
                        back_populates="user")

    class Address(Base):
        __tablename__ = 'address'
        id = Column(Integer, primary_key=True)
        email = Column(String)
        user_id = Column(Integer, ForeignKey('user.id'))
        user = relationship("User")

With the above scenario, appending an `Address` object to the `.addresses` collection of a `User` will always establish the `.user` attribute on that `Address`:

    >>> u1 = User()
    >>> a1 = Address(email='tony')
    >>> u1.addresses.append(a1)
    >>> a1.user
    <__main__.User object at 0x1411850>

However, applying a `User` to the `.user` attribute of an `Address`, will not append the `Address` object to the collection:

    >>> a2 = Address(email='mary')
    >>> a2.user = u1
    >>> a2 in u1.addresses
    False

Of course, we’ve disabled some of the usefulness of `backref` here, in that when we do append an `Address` that corresponds to the criteria of `email.startswith('tony')`, it won’t show up in the `User.addresses` collection until the session is flushed, and the attributes reloaded after a commit or expire operation. While we could consider an attribute event that checks this criterion in Python, this starts to cross the line of duplicating too much SQL behavior in Python. The backref behavior itself is only a slight transgression of this philosophy - SQLAlchemy tries to keep these to a minimum overall.

## 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.ext.declarative import declarative_base
    from sqlalchemy.orm import relationship

    Base = declarative_base()

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

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

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

    class Address(Base):
        __tablename__ = 'address'
        id = Column(Integer, primary_key=True)
        street = Column(String)
        city = Column(String)
        state = Column(String)
        zip = 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 = Column(Integer, primary_key=True)
        name = Column(String)

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

Changed in version 0.8: `relationship()` can resolve ambiguity between foreign key targets on the basis of the `foreign_keys` argument alone; the `primaryjoin` argument is no longer needed in this situation.

### 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 `primaryjoin` argument, as well as the `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.ext.declarative import declarative_base
    from sqlalchemy.orm import relationship

    Base = declarative_base()

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

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

        street = Column(String)
        city = Column(String)
        state = Column(String)
        zip = 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.

The custom criteria we use in a `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 `Query.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 `primaryjoin` argument can be used to create a join condition that doesn’t involve any “schema” level foreign keys. We can combine `primaryjoin` along with `foreign_keys` and `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.ext.declarative import declarative_base

    Base = declarative_base()

    class HostEntry(Base):
        __tablename__ = 'host_entry'

        id = Column(Integer, primary_key=True)
        ip_address = Column(INET)
        content = 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 `primaryjoin` expression. This syntax represents the annotations that `relationship()` normally applies by itself to the join condition given the `foreign_keys` and `remote_side` arguments; the functions are provided in the API in the rare case that `relationship()` can’t determine the exact location of these features on its own:

    from sqlalchemy.orm import foreign, remote

    class HostEntry(Base):
        __tablename__ = 'host_entry'

        id = Column(Integer, primary_key=True)
        ip_address = Column(INET)
        content = 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),
                        )

### Self-Referential Many-to-Many Relationship

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

    from sqlalchemy import Integer, ForeignKey, String, Column, Table
    from sqlalchemy.ext.declarative import declarative_base
    from sqlalchemy.orm import relationship

    Base = declarative_base()

    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 = Column(Integer, primary_key=True)
        label = Column(String)
        right_nodes = relationship("Node",
                            secondary=node_to_node,
                            primaryjoin=id==node_to_node.c.left_node_id,
                            secondaryjoin=id==node_to_node.c.right_node_id,
                            backref="left_nodes"
        )

Where above, SQLAlchemy can’t know automatically which columns should connect to which for the `right_nodes` and `left_nodes` relationships. The `primaryjoin` and `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.

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, mapper

    metadata = MetaData()

    node_to_node = Table("node_to_node", metadata,
        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,
        Column('id', Integer, primary_key=True),
        Column('label', String)
    )
    class Node(object):
        pass

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

### 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 `viewonly=True` 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 = Column(Integer, primary_key=True)

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

## Rows that point to themselves / Mutually Dependent Rows

This is a very specific case where relationship() must perform an INSERT and a second UPDATE in order to properly populate a row (and vice versa an UPDATE and DELETE in order to delete without violating foreign key constraints). The two use cases are:

- A table contains a foreign key to itself, and a single row will have a foreign key value pointing to its own primary key.
- Two tables each contain a foreign key referencing the other table, with a row in each table referencing the other.

For example:

              user
    ---------------------------------
    user_id    name   related_user_id
       1       'ed'          1

Or:

                 widget                                                  entry
    -------------------------------------------             ---------------------------------
    widget_id     name        favorite_entry_id             entry_id      name      widget_id
       1       'somewidget'          5                         5       'someentry'     1

In the first case, a row points to itself. Technically, a database that uses sequences such as PostgreSQL or Oracle can INSERT the row at once using a previously generated value, but databases which rely upon autoincrement-style primary key identifiers cannot. The `relationship()` always assumes a “parent/child” model of row population during flush, so unless you are populating the primary key/foreign key columns directly, `relationship()` needs to use two statements.

In the second case, the “widget” row must be inserted before any referring “entry” rows, but then the “favorite_entry_id” column of that “widget” row cannot be set until the “entry” rows have been generated. In this case, it’s typically impossible to insert the “widget” and “entry” rows using just two INSERT statements; an UPDATE must be performed in order to keep foreign key constraints fulfilled. The exception is if the foreign keys are configured as “deferred until commit” (a feature some databases support) and if the identifiers were populated manually (again essentially bypassing `relationship()`).

To enable the usage of a supplementary UPDATE statement, we use the `post_update` option of `relationship()`. This specifies that the linkage between the two rows should be created using an UPDATE statement after both rows have been INSERTED; it also causes the rows to be de-associated with each other via UPDATE before a DELETE is emitted. The flag should be placed on just one of the relationships, preferably the many-to-one side. Below we illustrate a complete example, including two `ForeignKey` constructs, one which specifies `use_alter=True` to help with emitting CREATE TABLE statements:

    from sqlalchemy import Integer, ForeignKey, Column
    from sqlalchemy.ext.declarative import declarative_base
    from sqlalchemy.orm import relationship

    Base = declarative_base()

    class Entry(Base):
        __tablename__ = 'entry'
        entry_id = Column(Integer, primary_key=True)
        widget_id = Column(Integer, ForeignKey('widget.widget_id'))
        name = Column(String(50))

    class Widget(Base):
        __tablename__ = 'widget'

        widget_id = Column(Integer, primary_key=True)
        favorite_entry_id = Column(Integer,
                                ForeignKey('entry.entry_id',
                                use_alter=True,
                                name="fk_favorite_entry"))
        name = Column(String(50))

        entries = relationship(Entry, primaryjoin=
                                        widget_id==Entry.widget_id)
        favorite_entry = relationship(Entry,
                                    primaryjoin=
                                        favorite_entry_id==Entry.entry_id,
                                    post_update=True)

When a structure against the above configuration is flushed, the “widget” row will be INSERTed minus the “favorite_entry_id” value, then all the “entry” rows will be INSERTed referencing the parent “widget” row, and then an UPDATE statement will populate the “favorite_entry_id” column of the “widget” table (it’s one row at a time for the time being):

    >>> w1 = Widget(name='somewidget')
    >>> e1 = Entry(name='someentry')
    >>> w1.favorite_entry = e1
    >>> w1.entries = [e1]
    >>> session.add_all([w1, e1])
    sql>>> session.commit()
    BEGIN (implicit)
    INSERT INTO widget (favorite_entry_id, name) VALUES (?, ?)
    (None, 'somewidget')
    INSERT INTO entry (widget_id, name) VALUES (?, ?)
    (1, 'someentry')
    UPDATE widget SET favorite_entry_id=? WHERE widget.widget_id = ?
    (1, 1)
    COMMIT

An additional configuration we can specify is to supply a more comprehensive foreign key constraint on `Widget`, such that it’s guaranteed that `favorite_entry_id` refers to an `Entry` that also refers to this `Widget`. We can use a composite foreign key, as illustrated below:

    from sqlalchemy import Integer, ForeignKey, String, \
            Column, UniqueConstraint, ForeignKeyConstraint
    from sqlalchemy.ext.declarative import declarative_base
    from sqlalchemy.orm import relationship

    Base = declarative_base()

    class Entry(Base):
        __tablename__ = 'entry'
        entry_id = Column(Integer, primary_key=True)
        widget_id = Column(Integer, ForeignKey('widget.widget_id'))
        name = Column(String(50))
        __table_args__ = (
            UniqueConstraint("entry_id", "widget_id"),
        )

    class Widget(Base):
        __tablename__ = 'widget'

        widget_id = Column(Integer, autoincrement='ignore_fk', primary_key=True)
        favorite_entry_id = Column(Integer)

        name = Column(String(50))

        __table_args__ = (
            ForeignKeyConstraint(
                ["widget_id", "favorite_entry_id"],
                ["entry.widget_id", "entry.entry_id"],
                name="fk_favorite_entry", use_alter=True
            ),
        )

        entries = relationship(Entry, primaryjoin=
                                        widget_id==Entry.widget_id,
                                        foreign_keys=Entry.widget_id)
        favorite_entry = relationship(Entry,
                                    primaryjoin=
                                        favorite_entry_id==Entry.entry_id,
                                    foreign_keys=favorite_entry_id,
                                    post_update=True)

The above mapping features a composite `ForeignKeyConstraint` bridging the `widget_id` and `favorite_entry_id` columns. To ensure that `Widget.widget_id` remains an “autoincrementing” column we specify `autoincrement='ignore_fk'` on `Column`, and additionally on each `relationship()` we must limit those columns considered as part of the foreign key for the purposes of joining and cross-population.

New in version 0.7.4: `autoincrement='ignore_fk'` on `Column`.

## Mutable Primary Keys / Update Cascades

When the primary key of an entity changes, related items which reference the primary key must also be updated as well. For databases which enforce referential integrity, it’s required to use the database’s ON UPDATE CASCADE functionality in order to propagate primary key changes to referenced foreign keys - the values cannot be out of sync for any moment.

For databases that don’t support this, such as SQLite and MySQL without their referential integrity options turned on, the `passive_updates` flag can be set to `False`, most preferably on a one-to-many or many-to-many `relationship()`, which instructs SQLAlchemy to issue UPDATE statements individually for objects referenced in the collection, loading them into memory if not already locally present. The `passive_updates` flag can also be `False` in conjunction with ON UPDATE CASCADE functionality, although in that case the unit of work will be issuing extra SELECT and UPDATE statements unnecessarily.

A typical mutable primary key setup might look like:

    class User(Base):
        __tablename__ = 'user'

        username = Column(String(50), primary_key=True)
        fullname = Column(String(100))

        # passive_updates=False *only* needed if the database
        # does not implement ON UPDATE CASCADE
        addresses = relationship("Address", passive_updates=False)

    class Address(Base):
        __tablename__ = 'address'

        email = Column(String(50), primary_key=True)
        username = Column(String(50),
                    ForeignKey('user.username', onupdate="cascade")
                )

`passive_updates` is set to `True` by default, indicating that ON UPDATE CASCADE is expected to be in place in the usual case for foreign keys that expect to have a mutating parent key.

`passive_updates=False` may be configured on any direction of relationship, i.e. one-to-many, many-to-one, and many-to-many, although it is much more effective when placed just on the one-to-many or many-to-many side. Configuring the `passive_updates=False` only on the many-to-one side will have only a partial effect, as the unit of work searches only through the current identity map for objects that may be referencing the one with a mutating primary key, not throughout the database.

## Relationships API

`sqlalchemy.orm.``relationship`(argument, secondary=None, **kwargs)

  Provide a relationship of a primary Mapper to a secondary Mapper.

  This corresponds to a parent-child or associative table relationship. The constructed class is an instance of `RelationshipProperty`.

  A typical `relationship()`, used in a classical mapping:

      mapper(Parent, properties={
        'children': relationship(Child)
      })

  Some arguments accepted by `relationship()` optionally accept a callable function, which when called produces the desired value. The callable is invoked by the parent `Mapper` at “mapper initialization” time, which happens only when mappers are first used, and is assumed to be after all mappings have been constructed. This can be used to resolve order-of-declaration and other dependency issues, such as if `Child` is declared below `Parent` in the same file:

      mapper(Parent, properties={
          "children":relationship(lambda: Child,
                              order_by=lambda: Child.id)
      })

  When using the Declarative extension, the Declarative initializer allows string arguments to be passed to `relationship()`. These string arguments are converted into callables that evaluate the string as Python code, using the Declarative class-registry as a namespace. This allows the lookup of related classes to be automatic via their string name, and removes the need to import related classes at all into the local module space:

      from sqlalchemy.ext.declarative import declarative_base

      Base = declarative_base()

      class Parent(Base):
          __tablename__ = 'parent'
          id = Column(Integer, primary_key=True)
          children = relationship("Child", order_by="Child.id")

  A full array of examples and reference documentation regarding `relationship()` is at Relationship Configuration.

  - Parameters: | argument – a mapped class, or actual `Mapper` instance, representing the target of the relationship. `argument` may also be passed as a callable function which is evaluated at mapper initialization time, and may be passed as a Python-evaluable string when using Declarative. secondary – for a many-to-many relationship, specifies the intermediary table, and is an instance of `Table`. The `secondary` keyword argument should generally only be used for a table that is not otherwise expressed in any class mapping, unless this relationship is declared as view only, otherwise conflicting persistence operations can occur. `secondary` may also be passed as a callable function which is evaluated at mapper initialization time. active_history=False – When `True`, indicates that the “previous” value for a many-to-one reference should be loaded when replaced, if not already loaded. Normally, history tracking logic for simple many-to-ones only needs to be aware of the “new” value in order to perform a flush. This flag is available for applications that make use of `attributes.get_history()` which also need to know the “previous” value of the attribute. backref – indicates the string name of a property to be placed on the related mapper’s class that will handle this relationship in the other direction. The other property will be created automatically when the mappers are configured. Can also be passed as a `backref()` object to control the configuration of the new relationship. back_populates – Takes a string name and has the same meaning as `backref`, except the complementing property is not created automatically, and instead must be configured explicitly on the other mapper. The complementing property should also indicate `back_populates` to this relationship to ensure proper functioning. cascade – a comma-separated list of cascade rules which determines how Session operations should be “cascaded” from parent to child. This defaults to `False`, which means the default cascade should be used. The default value is `"save-update, merge"`. Available cascades are: `save-update` - cascade the `Session.add()` operation. This cascade applies both to future and past calls to `add()`, meaning new items added to a collection or scalar relationship get placed into the same session as that of the parent, and also applies to items which have been removed from this relationship but are still part of unflushed history. `merge` - cascade the `merge()` operation `expunge` - cascade the `Session.expunge()` operation `delete` - cascade the `Session.delete()` operation `delete-orphan` - if an item of the child’s type is detached from its parent, mark it for deletion. Changed in version 0.7: This option does not prevent a new instance of the child object from being persisted without a parent to start with; to constrain against that case, ensure the child’s foreign key column(s) is configured as NOT NULL `refresh-expire` - cascade the `Session.expire()` and `refresh()` operations `all` - shorthand for “save-update,merge, refresh-expire, expunge, delete” See the section Cascades for more background on configuring cascades. cascade_backrefs=True – a boolean value indicating if the `save-update` cascade should operate along an assignment event intercepted by a backref. When set to `False`, the attribute managed by this relationship will not cascade an incoming transient object into the session of a persistent parent, if the event is received via backref. That is:
        mapper(A, a_table, properties={
            'bs':relationship(B, backref="a", cascade_backrefs=False)
        })
    If an `A()` is present in the session, assigning it to the “a” attribute on a transient `B()` will not place the `B()` into the session. To set the flag in the other direction, i.e. so that `A().bs.append(B())` won’t add a transient `A()` into the session for a persistent `B()`:
        mapper(A, a_table, properties={
            'bs':relationship(B,
                    backref=backref("a", cascade_backrefs=False)
                )
        })
  See the section Cascades for more background on configuring cascades. collection_class – a class or callable that returns a new list-holding object. will be used in place of a plain list for storing elements. Behavior of this attribute is described in detail at Customizing Collection Access. comparator_factory – a class which extends `RelationshipProperty.Comparator` which provides custom SQL clause generation for comparison operations. doc – docstring which will be applied to the resulting descriptor. extension – an `AttributeExtension` instance, or list of extensions, which will be prepended to the list of attribute listeners for the resulting descriptor placed on the class. Deprecated. Please see `AttributeEvents`. foreign_keys – a list of columns which are to be used as “foreign key” columns. Normally, `relationship()` uses the `ForeignKey` and `ForeignKeyConstraint` objects present within the mapped or secondary `Table` to determine the “foreign” side of the join condition. This is used to construct SQL clauses in order to load objects, as well as to “synchronize” values from primary key columns to referencing foreign key columns. The `foreign_keys` parameter overrides the notion of what’s “foreign” in the table metadata, allowing the specification of a list of `Column` objects that should be considered part of the foreign key. There are only two use cases for `foreign_keys` - one, when it is not convenient for `Table` metadata to contain its own foreign key metadata (which should be almost never, unless reflecting a large amount of tables from a MySQL MyISAM schema, or a schema that doesn’t actually have foreign keys on it). The other is for extremely rare and exotic composite foreign key setups where some columns should artificially not be considered as foreign. `foreign_keys` may also be passed as a callable function which is evaluated at mapper initialization time, and may be passed as a Python-evaluable string when using Declarative. Changed in version 0.8: The `foreign()` annotation can also be applied directly to the `primaryjoin` expression, which is an alternate, more specific system of describing which columns in a particular `primaryjoin` should be considered “foreign”. info – Optional data dictionary which will be populated into the `MapperProperty.info` attribute of this object. New in version 0.8. innerjoin=False – when `True`, joined eager loads will use an inner join to join against related tables instead of an outer join. The purpose of this option is generally one of performance, as inner joins generally perform better than outer joins. Another reason can be the use of `with_lockmode`, which does not support outer joins. This flag can be set to `True` when the relationship references an object via many-to-one using local foreign keys that are not nullable, or when the reference is one-to-one or a collection that is guaranteed to have one or at least one entry. join_depth – when non-`None`, an integer value indicating how many levels deep “eager” loaders should join on a self-referring or cyclical relationship. The number counts how many times the same Mapper shall be present in the loading condition along a particular join branch. When left at its default of `None`, eager loaders will stop chaining when they encounter a the same target mapper which is already higher up in the chain. This option applies both to joined- and subquery- eager loaders. lazy=’select’ – specifies how the related items should be loaded. Default value is `select`. Values include: `select` - items should be loaded lazily when the property is first accessed, using a separate SELECT statement, or identity map fetch for simple many-to-one references. `immediate` - items should be loaded as the parents are loaded, using a separate SELECT statement, or identity map fetch for simple many-to-one references. New in version 0.6.5. `joined` - items should be loaded “eagerly” in the same query as that of the parent, using a JOIN or LEFT OUTER JOIN. Whether the join is “outer” or not is determined by the `innerjoin` parameter. `subquery` - items should be loaded “eagerly” as the parents are loaded, using one additional SQL statement, which issues a JOIN to a subquery of the original statement, for each collection requested. `noload` - no loading should occur at any time. This is to support “write-only” attributes, or attributes which are populated in some manner specific to the application. `dynamic` - the attribute will return a pre-configured `Query` object for all read operations, onto which further filtering operations can be applied before iterating the results. See the section Dynamic Relationship Loaders for more details. True - a synonym for ‘select’ False - a synonym for ‘joined’ None - a synonym for ‘noload’ Detailed discussion of loader strategies is at Relationship Loading Techniques. load_on_pending=False – Indicates loading behavior for transient or pending parent objects. Changed in version 0.8: load_on_pending is superseded by `Session.enable_relationship_loading()`. When set to `True`, causes the lazy-loader to issue a query for a parent object that is not persistent, meaning it has never been flushed. This may take effect for a pending object when autoflush is disabled, or for a transient object that has been “attached” to a `Session` but is not part of its pending collection. The load_on_pending flag does not improve behavior when the ORM is used normally - object references should be constructed at the object level, not at the foreign key level, so that they are present in an ordinary way before flush() proceeds. This flag is not not intended for general use. New in version 0.6.5. order_by – indicates the ordering that should be applied when loading these items. `order_by` is expected to refer to one of the `Column` objects to which the target class is mapped, or the attribute itself bound to the target class which refers to the column. `order_by` may also be passed as a callable function which is evaluated at mapper initialization time, and may be passed as a Python-evaluable string when using Declarative. passive_deletes=False – Indicates loading behavior during delete operations. A value of True indicates that unloaded child items should not be loaded during a delete operation on the parent. Normally, when a parent item is deleted, all child items are loaded so that they can either be marked as deleted, or have their foreign key to the parent set to NULL. Marking this flag as True usually implies an ON DELETE <CASCADE|SET NULL> rule is in place which will handle updating/deleting child rows on the database side. Additionally, setting the flag to the string value ‘all’ will disable the “nulling out” of the child foreign keys, when there is no delete or delete-orphan cascade enabled. This is typically used when a triggering or error raise scenario is in place on the database side. Note that the foreign key attributes on in-session child objects will not be changed after a flush occurs so this is a very special use-case setting. passive_updates=True – Indicates loading and INSERT/UPDATE/DELETE behavior when the source of a foreign key value changes (i.e. an “on update” cascade), which are typically the primary key columns of the source row. When True, it is assumed that ON UPDATE CASCADE is configured on the foreign key in the database, and that the database will handle propagation of an UPDATE from a source column to dependent rows. Note that with databases which enforce referential integrity (i.e. PostgreSQL, MySQL with InnoDB tables), ON UPDATE CASCADE is required for this operation. The relationship() will update the value of the attribute on related items which are locally present in the session during a flush. When False, it is assumed that the database does not enforce referential integrity and will not be issuing its own CASCADE operation for an update. The relationship() will issue the appropriate UPDATE statements to the database in response to the change of a referenced key, and items locally present in the session during a flush will also be refreshed. This flag should probably be set to False if primary key changes are expected and the database in use doesn’t support CASCADE (i.e. SQLite, MySQL MyISAM tables). Also see the passive_updates flag on `mapper()`. A future SQLAlchemy release will provide a “detect” feature for this flag. post_update – this indicates that the relationship should be handled by a second UPDATE statement after an INSERT or before a DELETE. Currently, it also will issue an UPDATE after the instance was UPDATEd as well, although this technically should be improved. This flag is used to handle saving bi-directional dependencies between two individual rows (i.e. each row references the other), where it would otherwise be impossible to INSERT or DELETE both rows fully since one row exists before the other. Use this flag when a particular mapping arrangement will incur two rows that are dependent on each other, such as a table that has a one-to-many relationship to a set of child rows, and also has a column that references a single child row within that list (i.e. both tables contain a foreign key to each other). If a `flush()` operation returns an error that a “cyclical dependency” was detected, this is a cue that you might want to use `post_update` to “break” the cycle. primaryjoin – a SQL expression that will be used as the primary join of this child object against the parent object, or in a many-to-many relationship the join of the primary object to the association table. By default, this value is computed based on the foreign key relationships of the parent and child tables (or association table). `primaryjoin` may also be passed as a callable function which is evaluated at mapper initialization time, and may be passed as a Python-evaluable string when using Declarative. remote_side – used for self-referential relationships, indicates the column or list of columns that form the “remote side” of the relationship. `remote_side` may also be passed as a callable function which is evaluated at mapper initialization time, and may be passed as a Python-evaluable string when using Declarative. Changed in version 0.8: The `remote()` annotation can also be applied directly to the `primaryjoin` expression, which is an alternate, more specific system of describing which columns in a particular `primaryjoin` should be considered “remote”. query_class – a `Query` subclass that will be used as the base of the “appender query” returned by a “dynamic” relationship, that is, a relationship that specifies `lazy="dynamic"` or was otherwise constructed using the `orm.dynamic_loader()` function. secondaryjoin – a SQL expression that will be used as the join of an association table to the child object. By default, this value is computed based on the foreign key relationships of the association and child tables. `secondaryjoin` may also be passed as a callable function which is evaluated at mapper initialization time, and may be passed as a Python-evaluable string when using Declarative. single_parent=(True|False) – when True, installs a validator which will prevent objects from being associated with more than one parent at a time. This is used for many-to-one or many-to-many relationships that should be treated either as one-to-one or one-to-many. Its usage is optional unless delete-orphan cascade is also set on this relationship(), in which case its required. uselist=(True|False) – a boolean that indicates if this property should be loaded as a list or a scalar. In most cases, this value is determined automatically by `relationship()`, based on the type and direction of the relationship - one to many forms a list, many to one forms a scalar, many to many is a list. If a scalar is desired where normally a list would be present, such as a bi-directional one-to-one relationship, set uselist to False. viewonly=False – when set to True, the relationship is used only for loading objects within the relationship, and has no effect on the unit-of-work flush process. Relationships with viewonly can specify any kind of join conditions to provide additional views of related objects onto a parent object. Note that the functionality of a viewonly relationship has its limits - complicated join conditions may not compile into eager or lazy loaders properly. If this is the case, use an alternative method.

  Changed in version 0.6: `relationship()` was renamed from its previous name `relation()`.

`sqlalchemy.orm.``backref`(name, **kwargs)

  Create a back reference with explicit keyword arguments, which are the same arguments one can send to `relationship()`.

  Used with the `backref` keyword argument to `relationship()` in place of a string argument, e.g.:

      'items':relationship(SomeItem, backref=backref('parent', lazy='subquery'))

`sqlalchemy.orm.``relation`(*arg, **kw)

  A synonym for `relationship()`.

`sqlalchemy.orm.``dynamic_loader`(argument, **kw)

  Construct a dynamically-loading mapper property.

  This is essentially the same as using the `lazy='dynamic'` argument with `relationship()`:

      dynamic_loader(SomeClass)

      # is the same as

      relationship(SomeClass, lazy="dynamic")

  See the section Dynamic Relationship Loaders for more details on dynamic loading.

`sqlalchemy.orm.``foreign`(expr)

  Annotate a portion of a primaryjoin expression with a ‘foreign’ annotation.

  See the section Creating Custom Foreign Conditions for a description of use.

  New in version 0.8.

  See also

  Creating Custom Foreign Conditions

  `remote()`

`sqlalchemy.orm.``remote`(expr)

  Annotate a portion of a primaryjoin expression with a ‘remote’ annotation.

  See the section Creating Custom Foreign Conditions for a description of use.

  New in version 0.8.

  See also

  Creating Custom Foreign Conditions

  `foreign()`