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")

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)
    boston_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. 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 = 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),
                    )

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 postgresql.INET and postgresql.CIDR. For custom operators we use the Operators.op() function:

inet_column.op("<<")(cidr_column)

However, if we construct a primaryjoin using this operator, relationship() will still need more information. This is because when it examines our primaryjoin condition, it specifically looks for operators used for comparisons, and this is typically a fixed list containing known comparison operators such as ==, <, etc. So for our custom operator to participate in this system, we need it to register as a comparison operator using the is_comparison parameter:

inet_column.op("<<", is_comparison=True)(cidr_column)

A complete example:

class IPA(Base):
    __tablename__ = 'ip_address'

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

    network = relationship("Network",
                        primaryjoin="IPA.v4address.op('<<', is_comparison=True)"
                            "(foreign(Network.v4representation))",
                        viewonly=True
                    )
class Network(Base):
    __tablename__ = 'network'

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

Above, a query such as:

session.query(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

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 = Column(Integer, primary_key=True)


class Article(Base):
    __tablename__ = 'article'

    article_id = Column(Integer)
    magazine_id = Column(ForeignKey('magazine.id'))
    writer_id = 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 = Column(Integer, primary_key=True)
    magazine_id = 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 we refer towards; it may also attempt to place NULL into this columnn 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 primaryjoin fully, along with either the 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¶

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 = 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¶

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.

Alternatively, we can define the primaryjoin and 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 = Column(Integer, primary_key=True)
    label = 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"
    )

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.

Composite “Secondary” Joins¶

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 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 = Column(Integer, primary_key=True)
    b_id = 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
                )

class B(Base):
    __tablename__ = 'b'

    id = Column(Integer, primary_key=True)
    d_id = Column(ForeignKey('d.id'))

class C(Base):
    __tablename__ = 'c'

    id = Column(Integer, primary_key=True)
    a_id = Column(ForeignKey('a.id'))
    d_id = Column(ForeignKey('d.id'))

class D(Base):
    __tablename__ = 'd'

    id = Column(Integer, primary_key=True)

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

sess.query(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.

Relationship to Non Primary Mapper¶

In the previous section, we illustrated a technique where we used 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 primaryjoin condition, as the intermediary tables may need special handling, and it is also not expressable with a 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 mapper() 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 non_primary flag.

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 = Column(Integer, primary_key=True)
    b_id = Column(ForeignKey('b.id'))

class B(Base):
    __tablename__ = 'b'

    id = Column(Integer, primary_key=True)

class C(Base):
    __tablename__ = 'c'

    id = Column(Integer, primary_key=True)
    a_id = Column(ForeignKey('a.id'))

class D(Base):
    __tablename__ = 'd'

    id = Column(Integer, primary_key=True)
    c_id = Column(ForeignKey('c.id'))
    b_id = Column(ForeignKey('b.id'))

# 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 a new mapper() to B, with non_primary=True.
# Columns in the join with the same name must be
# disambiguated within the mapping, using named properties.
B_viacd = mapper(B, j, non_primary=True, properties={
    "b_id": [j.c.b_id, j.c.d_b_id],
    "d_id": j.c.d_id
    })

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

In the above case, our non-primary mapper for B will emit for additional columns when we query; these can be ignored:

sess.query(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

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 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)