Release: 1.0.0 | Release Date: Not released

SQLAlchemy 1.0 Documentation

Mapping Class Inheritance Hierarchies

SQLAlchemy supports three forms of inheritance: single table inheritance, where several types of classes are represented by a single table, concrete table inheritance, where each type of class is represented by independent tables, and joined table inheritance, where the class hierarchy is broken up among dependent tables, each class represented by its own table that only includes those attributes local to that class.

The most common forms of inheritance are single and joined table, while concrete inheritance presents more configurational challenges.

When mappers are configured in an inheritance relationship, SQLAlchemy has the ability to load elements polymorphically, meaning that a single query can return objects of multiple types.

Joined Table Inheritance

In joined table inheritance, each class along a particular classes’ list of parents is represented by a unique table. The total set of attributes for a particular instance is represented as a join along all tables in its inheritance path. Here, we first define the Employee class. This table will contain a primary key column (or columns), and a column for each attribute that’s represented by Employee. In this case it’s just name:

class Employee(Base):
    __tablename__ = 'employee'
    id = Column(Integer, primary_key=True)
    name = Column(String(50))
    type = Column(String(50))

    __mapper_args__ = {
        'polymorphic_identity':'employee',
        'polymorphic_on':type
    }

The mapped table also has a column called type. The purpose of this column is to act as the discriminator, and stores a value which indicates the type of object represented within the row. The column may be of any datatype, though string and integer are the most common.

The discriminator column is only needed if polymorphic loading is desired, as is usually the case. It is not strictly necessary that it be present directly on the base mapped table, and can instead be defined on a derived select statement that’s used when the class is queried; however, this is a much more sophisticated configuration scenario.

The mapping receives additional arguments via the __mapper_args__ dictionary. Here the type column is explicitly stated as the discriminator column, and the polymorphic identity of employee is also given; this is the value that will be stored in the polymorphic discriminator column for instances of this class.

We next define Engineer and Manager subclasses of Employee. Each contains columns that represent the attributes unique to the subclass they represent. Each table also must contain a primary key column (or columns), and in most cases a foreign key reference to the parent table:

class Engineer(Employee):
    __tablename__ = 'engineer'
    id = Column(Integer, ForeignKey('employee.id'), primary_key=True)
    engineer_name = Column(String(30))

    __mapper_args__ = {
        'polymorphic_identity':'engineer',
    }

class Manager(Employee):
    __tablename__ = 'manager'
    id = Column(Integer, ForeignKey('employee.id'), primary_key=True)
    manager_name = Column(String(30))

    __mapper_args__ = {
        'polymorphic_identity':'manager',
    }

It is standard practice that the same column is used for both the role of primary key as well as foreign key to the parent table, and that the column is also named the same as that of the parent table. However, both of these practices are optional. Separate columns may be used for primary key and parent-relationship, the column may be named differently than that of the parent, and even a custom join condition can be specified between parent and child tables instead of using a foreign key.

Joined inheritance primary keys

One natural effect of the joined table inheritance configuration is that the identity of any mapped object can be determined entirely from the base table. This has obvious advantages, so SQLAlchemy always considers the primary key columns of a joined inheritance class to be those of the base table only. In other words, the id columns of both the engineer and manager tables are not used to locate Engineer or Manager objects - only the value in employee.id is considered. engineer.id and manager.id are still of course critical to the proper operation of the pattern overall as they are used to locate the joined row, once the parent row has been determined within a statement.

With the joined inheritance mapping complete, querying against Employee will return a combination of Employee, Engineer and Manager objects. Newly saved Engineer, Manager, and Employee objects will automatically populate the employee.type column with engineer, manager, or employee, as appropriate.

Basic Control of Which Tables are Queried

The orm.with_polymorphic() function and the with_polymorphic() method of Query affects the specific tables which the Query selects from. Normally, a query such as this:

session.query(Employee).all()

...selects only from the employee table. When loading fresh from the database, our joined-table setup will query from the parent table only, using SQL such as this:

SELECT employee.id AS employee_id, employee.name AS employee_name, employee.type AS employee_type FROM employee []

As attributes are requested from those Employee objects which are represented in either the engineer or manager child tables, a second load is issued for the columns in that related row, if the data was not already loaded. So above, after accessing the objects you’d see further SQL issued along the lines of:

SELECT manager.id AS manager_id, manager.manager_data AS manager_manager_data FROM manager WHERE ? = manager.id [5] SELECT engineer.id AS engineer_id, engineer.engineer_info AS engineer_engineer_info FROM engineer WHERE ? = engineer.id [2]

This behavior works well when issuing searches for small numbers of items, such as when using Query.get(), since the full range of joined tables are not pulled in to the SQL statement unnecessarily. But when querying a larger span of rows which are known to be of many types, you may want to actively join to some or all of the joined tables. The with_polymorphic feature provides this.

Telling our query to polymorphically load Engineer and Manager objects, we can use the orm.with_polymorphic() function to create a new aliased class which represents a select of the base table combined with outer joins to each of the inheriting tables:

from sqlalchemy.orm import with_polymorphic

eng_plus_manager = with_polymorphic(Employee, [Engineer, Manager])

query = session.query(eng_plus_manager)

The above produces a query which joins the employee table to both the engineer and manager tables like the following:

query.all()
SELECT employee.id AS employee_id, engineer.id AS engineer_id, manager.id AS manager_id, employee.name AS employee_name, employee.type AS employee_type, engineer.engineer_info AS engineer_engineer_info, manager.manager_data AS manager_manager_data FROM employee LEFT OUTER JOIN engineer ON employee.id = engineer.id LEFT OUTER JOIN manager ON employee.id = manager.id []

The entity returned by orm.with_polymorphic() is an AliasedClass object, which can be used in a Query like any other alias, including named attributes for those attributes on the Employee class. In our example, eng_plus_manager becomes the entity that we use to refer to the three-way outer join above. It also includes namespaces for each class named in the list of classes, so that attributes specific to those subclasses can be called upon as well. The following example illustrates calling upon attributes specific to Engineer as well as Manager in terms of eng_plus_manager:

eng_plus_manager = with_polymorphic(Employee, [Engineer, Manager])
query = session.query(eng_plus_manager).filter(
                or_(
                    eng_plus_manager.Engineer.engineer_info=='x',
                    eng_plus_manager.Manager.manager_data=='y'
                )
            )

orm.with_polymorphic() accepts a single class or mapper, a list of classes/mappers, or the string '*' to indicate all subclasses:

# join to the engineer table
entity = with_polymorphic(Employee, Engineer)

# join to the engineer and manager tables
entity = with_polymorphic(Employee, [Engineer, Manager])

# join to all subclass tables
entity = query.with_polymorphic(Employee, '*')

# use with Query
session.query(entity).all()

It also accepts a third argument selectable which replaces the automatic join creation and instead selects directly from the selectable given. This feature is normally used with “concrete” inheritance, described later, but can be used with any kind of inheritance setup in the case that specialized SQL should be used to load polymorphically:

# custom selectable
employee = Employee.__table__
manager = Manager.__table__
engineer = Engineer.__table__
entity = with_polymorphic(
            Employee,
            [Engineer, Manager],
            employee.outerjoin(manager).outerjoin(engineer)
        )

# use with Query
session.query(entity).all()

Note that if you only need to load a single subtype, such as just the Engineer objects, orm.with_polymorphic() is not needed since you would query against the Engineer class directly.

Query.with_polymorphic() has the same purpose as orm.with_polymorphic(), except is not as flexible in its usage patterns in that it only applies to the first full mapping, which then impacts all occurrences of that class or the target subclasses within the Query. For simple cases it might be considered to be more succinct:

session.query(Employee).with_polymorphic([Engineer, Manager]).\
    filter(or_(Engineer.engineer_info=='w', Manager.manager_data=='q'))

New in version 0.8: orm.with_polymorphic(), an improved version of Query.with_polymorphic() method.

The mapper also accepts with_polymorphic as a configurational argument so that the joined-style load will be issued automatically. This argument may be the string '*', a list of classes, or a tuple consisting of either, followed by a selectable:

class Employee(Base):
    __tablename__ = 'employee'
    id = Column(Integer, primary_key=True)
    type = Column(String(20))

    __mapper_args__ = {
        'polymorphic_on':type,
        'polymorphic_identity':'employee',
        'with_polymorphic':'*'
    }

class Engineer(Employee):
    __tablename__ = 'engineer'
    id = Column(Integer, ForeignKey('employee.id'), primary_key=True)
    __mapper_args__ = {'polymorphic_identity':'engineer'}

class Manager(Employee):
    __tablename__ = 'manager'
    id = Column(Integer, ForeignKey('employee.id'), primary_key=True)
    __mapper_args__ = {'polymorphic_identity':'manager'}

The above mapping will produce a query similar to that of with_polymorphic('*') for every query of Employee objects.

Using orm.with_polymorphic() or Query.with_polymorphic() will override the mapper-level with_polymorphic setting.

sqlalchemy.orm.with_polymorphic(base, classes, selectable=False, flat=False, polymorphic_on=None, aliased=False, innerjoin=False, _use_mapper_path=False)

Produce an AliasedClass construct which specifies columns for descendant mappers of the given base.

New in version 0.8: orm.with_polymorphic() is in addition to the existing Query method Query.with_polymorphic(), which has the same purpose but is not as flexible in its usage.

Using this method will ensure that each descendant mapper’s tables are included in the FROM clause, and will allow filter() criterion to be used against those tables. The resulting instances will also have those columns already loaded so that no “post fetch” of those columns will be required.

See the examples at Basic Control of Which Tables are Queried.

Parameters:
  • base – Base class to be aliased.
  • classes – a single class or mapper, or list of class/mappers, which inherit from the base class. Alternatively, it may also be the string '*', in which case all descending mapped classes will be added to the FROM clause.
  • aliased – when True, the selectable will be wrapped in an alias, that is (SELECT * FROM <fromclauses>) AS anon_1. This can be important when using the with_polymorphic() to create the target of a JOIN on a backend that does not support parenthesized joins, such as SQLite and older versions of MySQL.
  • flat
    Boolean, will be passed through to the
    FromClause.alias() call so that aliases of Join objects don’t include an enclosing SELECT. This can lead to more efficient queries in many circumstances. A JOIN against a nested JOIN will be rewritten as a JOIN against an aliased SELECT subquery on backends that don’t support this syntax.

    Setting flat to True implies the aliased flag is also True.

    New in version 0.9.0.

    See also

    Join.alias()

  • selectable – a table or select() statement that will be used in place of the generated FROM clause. This argument is required if any of the desired classes use concrete table inheritance, since SQLAlchemy currently cannot generate UNIONs among tables automatically. If used, the selectable argument must represent the full set of tables and columns mapped by every mapped class. Otherwise, the unaccounted mapped columns will result in their table being appended directly to the FROM clause which will usually lead to incorrect results.
  • polymorphic_on – a column to be used as the “discriminator” column for the given selectable. If not given, the polymorphic_on attribute of the base classes’ mapper will be used, if any. This is useful for mappings that don’t have polymorphic loading behavior by default.
  • innerjoin – if True, an INNER JOIN will be used. This should only be specified if querying for one specific subtype only

Advanced Control of Which Tables are Queried

The with_polymorphic functions work fine for simplistic scenarios. However, direct control of table rendering is called for, such as the case when one wants to render to only the subclass table and not the parent table.

This use case can be achieved by using the mapped Table objects directly. For example, to query the name of employees with particular criterion:

engineer = Engineer.__table__
manager = Manager.__table__

session.query(Employee.name).\
    outerjoin((engineer, engineer.c.employee_id==Employee.employee_id)).\
    outerjoin((manager, manager.c.employee_id==Employee.employee_id)).\
    filter(or_(Engineer.engineer_info=='w', Manager.manager_data=='q'))

The base table, in this case the “employees” table, isn’t always necessary. A SQL query is always more efficient with fewer joins. Here, if we wanted to just load information specific to manager or engineer, we can instruct Query to use only those tables. The FROM clause is determined by what’s specified in the Session.query(), Query.filter(), or Query.select_from() methods:

session.query(Manager.manager_data).select_from(manager)

session.query(engineer.c.id).\
        filter(engineer.c.engineer_info==manager.c.manager_data)

Creating Joins to Specific Subtypes

The of_type() method is a helper which allows the construction of joins along relationship() paths while narrowing the criterion to specific subclasses. Suppose the employees table represents a collection of employees which are associated with a Company object. We’ll add a company_id column to the employees table and a new table companies:

class Company(Base):
    __tablename__ = 'company'
    id = Column(Integer, primary_key=True)
    name = Column(String(50))
    employees = relationship("Employee",
                    backref='company',
                    cascade='all, delete-orphan')

class Employee(Base):
    __tablename__ = 'employee'
    id = Column(Integer, primary_key=True)
    type = Column(String(20))
    company_id = Column(Integer, ForeignKey('company.id'))
    __mapper_args__ = {
        'polymorphic_on':type,
        'polymorphic_identity':'employee',
        'with_polymorphic':'*'
    }

class Engineer(Employee):
    __tablename__ = 'engineer'
    id = Column(Integer, ForeignKey('employee.id'), primary_key=True)
    engineer_info = Column(String(50))
    __mapper_args__ = {'polymorphic_identity':'engineer'}

class Manager(Employee):
    __tablename__ = 'manager'
    id = Column(Integer, ForeignKey('employee.id'), primary_key=True)
    manager_data = Column(String(50))
    __mapper_args__ = {'polymorphic_identity':'manager'}

When querying from Company onto the Employee relationship, the join() method as well as the any() and has() operators will create a join from company to employee, without including engineer or manager in the mix. If we wish to have criterion which is specifically against the Engineer class, we can tell those methods to join or subquery against the joined table representing the subclass using the of_type() operator:

session.query(Company).\
    join(Company.employees.of_type(Engineer)).\
    filter(Engineer.engineer_info=='someinfo')

A longhand version of this would involve spelling out the full target selectable within a 2-tuple:

employee = Employee.__table__
engineer = Engineer.__table__

session.query(Company).\
    join((employee.join(engineer), Company.employees)).\
    filter(Engineer.engineer_info=='someinfo')

of_type() accepts a single class argument. More flexibility can be achieved either by joining to an explicit join as above, or by using the orm.with_polymorphic() function to create a polymorphic selectable:

manager_and_engineer = with_polymorphic(
                            Employee, [Manager, Engineer],
                            aliased=True)

session.query(Company).\
    join(manager_and_engineer, Company.employees).\
    filter(
        or_(manager_and_engineer.Engineer.engineer_info=='someinfo',
            manager_and_engineer.Manager.manager_data=='somedata')
    )

Above, we use the aliased=True argument with orm.with_polymorhpic() so that the right hand side of the join between Company and manager_and_engineer is converted into an aliased subquery. Some backends, such as SQLite and older versions of MySQL can’t handle a FROM clause of the following form:

FROM x JOIN (y JOIN z ON <onclause>) ON <onclause>

Using aliased=True instead renders it more like:

FROM x JOIN (SELECT * FROM y JOIN z ON <onclause>) AS anon_1 ON <onclause>

The above join can also be expressed more succinctly by combining of_type() with the polymorphic construct:

manager_and_engineer = with_polymorphic(
                            Employee, [Manager, Engineer],
                            aliased=True)

session.query(Company).\
    join(Company.employees.of_type(manager_and_engineer)).\
    filter(
        or_(manager_and_engineer.Engineer.engineer_info=='someinfo',
            manager_and_engineer.Manager.manager_data=='somedata')
    )

The any() and has() operators also can be used with of_type() when the embedded criterion is in terms of a subclass:

session.query(Company).\
        filter(
            Company.employees.of_type(Engineer).
                any(Engineer.engineer_info=='someinfo')
            ).all()

Note that the any() and has() are both shorthand for a correlated EXISTS query. To build one by hand looks like:

session.query(Company).filter(
    exists([1],
        and_(Engineer.engineer_info=='someinfo',
            employees.c.company_id==companies.c.company_id),
        from_obj=employees.join(engineers)
    )
).all()

The EXISTS subquery above selects from the join of employees to engineers, and also specifies criterion which correlates the EXISTS subselect back to the parent companies table.

New in version 0.8: of_type() accepts orm.aliased() and orm.with_polymorphic() constructs in conjunction with Query.join(), any() and has().

Eager Loading of Specific or Polymorphic Subtypes

The joinedload() and subqueryload() options also support paths which make use of of_type(). Below we load Company rows while eagerly loading related Engineer objects, querying the employee and engineer tables simultaneously:

session.query(Company).\
    options(
        subqueryload(Company.employees.of_type(Engineer)).
        subqueryload("machines")
        )
    )

As is the case with Query.join(), of_type() also can be used with eager loading and orm.with_polymorphic() at the same time, so that all sub-attributes of all referenced subtypes can be loaded:

manager_and_engineer = with_polymorphic(
                            Employee, [Manager, Engineer],
                            aliased=True)

session.query(Company).\
    options(
        joinedload(Company.employees.of_type(manager_and_engineer))
        )
    )

New in version 0.8: joinedload() and subqueryload() support paths that are qualified with of_type(), supporting single target types as well as orm.with_polymorphic() targets.

Single Table Inheritance

Single table inheritance is where the attributes of the base class as well as all subclasses are represented within a single table. A column is present in the table for every attribute mapped to the base class and all subclasses; the columns which correspond to a single subclass are nullable. This configuration looks much like joined-table inheritance except there’s only one table. In this case, a type column is required, as there would be no other way to discriminate between classes. The table is specified in the base mapper only; for the inheriting classes, leave their table parameter blank:

class Employee(Base):
    __tablename__ = 'employee'
    id = Column(Integer, primary_key=True)
    name = Column(String(50))
    manager_data = Column(String(50))
    engineer_info = Column(String(50))
    type = Column(String(20))

    __mapper_args__ = {
        'polymorphic_on':type,
        'polymorphic_identity':'employee'
    }

class Manager(Employee):
    __mapper_args__ = {
        'polymorphic_identity':'manager'
    }

class Engineer(Employee):
    __mapper_args__ = {
        'polymorphic_identity':'engineer'
    }

Note that the mappers for the derived classes Manager and Engineer omit the __tablename__, indicating they do not have a mapped table of their own.

Concrete Table Inheritance

Note

this section is currently using classical mappings. The Declarative system fully supports concrete inheritance however. See the links below for more information on using declarative with concrete table inheritance.

This form of inheritance maps each class to a distinct table, as below:

employees_table = Table('employees', metadata,
    Column('employee_id', Integer, primary_key=True),
    Column('name', String(50)),
)

managers_table = Table('managers', metadata,
    Column('employee_id', Integer, primary_key=True),
    Column('name', String(50)),
    Column('manager_data', String(50)),
)

engineers_table = Table('engineers', metadata,
    Column('employee_id', Integer, primary_key=True),
    Column('name', String(50)),
    Column('engineer_info', String(50)),
)

Notice in this case there is no type column. If polymorphic loading is not required, there’s no advantage to using inherits here; you just define a separate mapper for each class.

mapper(Employee, employees_table)
mapper(Manager, managers_table)
mapper(Engineer, engineers_table)

To load polymorphically, the with_polymorphic argument is required, along with a selectable indicating how rows should be loaded. In this case we must construct a UNION of all three tables. SQLAlchemy includes a helper function to create these called polymorphic_union(), which will map all the different columns into a structure of selects with the same numbers and names of columns, and also generate a virtual type column for each subselect:

pjoin = polymorphic_union({
    'employee': employees_table,
    'manager': managers_table,
    'engineer': engineers_table
}, 'type', 'pjoin')

employee_mapper = mapper(Employee, employees_table,
                                    with_polymorphic=('*', pjoin),
                                    polymorphic_on=pjoin.c.type,
                                    polymorphic_identity='employee')
manager_mapper = mapper(Manager, managers_table,
                                    inherits=employee_mapper,
                                    concrete=True,
                                    polymorphic_identity='manager')
engineer_mapper = mapper(Engineer, engineers_table,
                                    inherits=employee_mapper,
                                    concrete=True,
                                    polymorphic_identity='engineer')

Upon select, the polymorphic union produces a query like this:

session.query(Employee).all()
SELECT pjoin.type AS pjoin_type, pjoin.manager_data AS pjoin_manager_data, pjoin.employee_id AS pjoin_employee_id, pjoin.name AS pjoin_name, pjoin.engineer_info AS pjoin_engineer_info FROM ( SELECT employees.employee_id AS employee_id, CAST(NULL AS VARCHAR(50)) AS manager_data, employees.name AS name, CAST(NULL AS VARCHAR(50)) AS engineer_info, 'employee' AS type FROM employees UNION ALL SELECT managers.employee_id AS employee_id, managers.manager_data AS manager_data, managers.name AS name, CAST(NULL AS VARCHAR(50)) AS engineer_info, 'manager' AS type FROM managers UNION ALL SELECT engineers.employee_id AS employee_id, CAST(NULL AS VARCHAR(50)) AS manager_data, engineers.name AS name, engineers.engineer_info AS engineer_info, 'engineer' AS type FROM engineers ) AS pjoin []

Concrete Inheritance with Declarative

New in version 0.7.3: The Declarative module includes helpers for concrete inheritance. See Using the Concrete Helpers for more information.

Using Relationships with Inheritance

Both joined-table and single table inheritance scenarios produce mappings which are usable in relationship() functions; that is, it’s possible to map a parent object to a child object which is polymorphic. Similarly, inheriting mappers can have relationship() objects of their own at any level, which are inherited to each child class. The only requirement for relationships is that there is a table relationship between parent and child. An example is the following modification to the joined table inheritance example, which sets a bi-directional relationship between Employee and Company:

employees_table = Table('employees', metadata,
    Column('employee_id', Integer, primary_key=True),
    Column('name', String(50)),
    Column('company_id', Integer, ForeignKey('companies.company_id'))
)

companies = Table('companies', metadata,
   Column('company_id', Integer, primary_key=True),
   Column('name', String(50)))

class Company(object):
    pass

mapper(Company, companies, properties={
   'employees': relationship(Employee, backref='company')
})

Relationships with Concrete Inheritance

In a concrete inheritance scenario, mapping relationships is more challenging since the distinct classes do not share a table. In this case, you can establish a relationship from parent to child if a join condition can be constructed from parent to child, if each child table contains a foreign key to the parent:

companies = Table('companies', metadata,
   Column('id', Integer, primary_key=True),
   Column('name', String(50)))

employees_table = Table('employees', metadata,
    Column('employee_id', Integer, primary_key=True),
    Column('name', String(50)),
    Column('company_id', Integer, ForeignKey('companies.id'))
)

managers_table = Table('managers', metadata,
    Column('employee_id', Integer, primary_key=True),
    Column('name', String(50)),
    Column('manager_data', String(50)),
    Column('company_id', Integer, ForeignKey('companies.id'))
)

engineers_table = Table('engineers', metadata,
    Column('employee_id', Integer, primary_key=True),
    Column('name', String(50)),
    Column('engineer_info', String(50)),
    Column('company_id', Integer, ForeignKey('companies.id'))
)

mapper(Employee, employees_table,
                with_polymorphic=('*', pjoin),
                polymorphic_on=pjoin.c.type,
                polymorphic_identity='employee')

mapper(Manager, managers_table,
                inherits=employee_mapper,
                concrete=True,
                polymorphic_identity='manager')

mapper(Engineer, engineers_table,
                inherits=employee_mapper,
                concrete=True,
                polymorphic_identity='engineer')

mapper(Company, companies, properties={
    'employees': relationship(Employee)
})

The big limitation with concrete table inheritance is that relationship() objects placed on each concrete mapper do not propagate to child mappers. If you want to have the same relationship() objects set up on all concrete mappers, they must be configured manually on each. To configure back references in such a configuration the back_populates keyword may be used instead of backref, such as below where both A(object) and B(A) bidirectionally reference C:

ajoin = polymorphic_union({
        'a':a_table,
        'b':b_table
    }, 'type', 'ajoin')

mapper(A, a_table, with_polymorphic=('*', ajoin),
    polymorphic_on=ajoin.c.type, polymorphic_identity='a',
    properties={
        'some_c':relationship(C, back_populates='many_a')
})
mapper(B, b_table,inherits=A, concrete=True,
    polymorphic_identity='b',
    properties={
        'some_c':relationship(C, back_populates='many_a')
})
mapper(C, c_table, properties={
    'many_a':relationship(A, collection_class=set,
                                back_populates='some_c'),
})

Using Inheritance with Declarative

Declarative makes inheritance configuration more intuitive. See the docs at Inheritance Configuration.