Adjacency List Relationships

The adjacency list pattern is a common relational pattern whereby a table contains a foreign key reference to itself, in other words is a self referential relationship. 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.

See also

This section details the single-table version of a self-referential relationship. For a self-referential relationship that uses a second table as an association table, see the section Self-Referential Many-to-Many Relationship.

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 relationship.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 relationship.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 relationship.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.

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 Selecting ORM Aliases in the ORM tutorial that the 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)
session.query(Node).filter(Node.data == "subchild1").join(
    Node.parent.of_type(nodealias)
).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']

For an example of using aliased() to join across an arbitrarily long 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 relationships.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)


session.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
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