Loading Inheritance Hierarchies

When classes are mapped in inheritance hierarchies using the “joined”, “single”, or “concrete” table inheritance styles as described at Mapping Class Inheritance Hierarchies, the usual behavior is that a query for a particular base class will also yield objects corresponding to subclasses as well. When a single query is capable of returning a result with a different class or subclasses per result row, we use the term “polymorphic loading”.

Within the realm of polymorphic loading, specifically with joined and single table inheritance, there is an additional problem of which subclass attributes are to be queried up front, and which are to be loaded later. When an attribute of a particular subclass is queried up front, we can use it in our query as something to filter on, and it also will be loaded when we get our objects back. If it’s not queried up front, it gets loaded later when we first need to access it. Basic control of this behavior is provided using the with_polymorphic() function, as well as two variants, the mapper configuration mapper.with_polymorphic in conjunction with the mapper.polymorphic_load option, and the Query -level Query.with_polymorphic() method. The “with_polymorphic” family each provide a means of specifying which specific subclasses of a particular base class should be included within a query, which implies what columns and tables will be available in the SELECT.

Using with_polymorphic

For the following sections, assume the Employee / Engineer / Manager examples introduced in Mapping Class Inheritance Hierarchies.

Normally, when a Query specifies the base class of an inheritance hierarchy, only the columns that are local to that base class are queried:

session.query(Employee).all()

Above, for both single and joined table inheritance, only the columns local to Employee will be present in the SELECT. We may get back instances of Engineer or Manager, however they will not have the additional attributes loaded until we first access them, at which point a lazy load is emitted.

Similarly, if we wanted to refer to columns mapped to Engineer or Manager in our query that’s against Employee, these columns aren’t available directly in either the single or joined table inheritance case, since the Employee entity does not refer to these columns (note that for single-table inheritance, this is common if Declarative is used, but not for a classical mapping).

To solve both of these issues, the with_polymorphic() function provides a special AliasedClass that represents a range of columns across subclasses. This object can be used in a Query like any other alias. When queried, it represents all the columns present in the classes given:

from sqlalchemy.orm import with_polymorphic
eng_plus_manager = with_polymorphic(Employee, [Engineer, Manager])
query = session.query(eng_plus_manager)

If the above mapping were using joined table inheritance, the SELECT statement for the above would be:

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
[]

Where above, the additional tables / columns for “engineer” and “manager” are included. Similar behavior occurs in the case of single table inheritance.

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

# include columns for Engineer
entity = with_polymorphic(Employee, Engineer)
# include columns for Engineer, Manager
entity = with_polymorphic(Employee, [Engineer, Manager])
# include columns for all mapped subclasses
entity = with_polymorphic(Employee, '*')

Using aliasing with with_polymorphic

The with_polymorphic() function also provides “aliasing” of the polymorphic selectable itself, meaning, two different with_polymorphic() entities, referring to the same class hierarchy, can be used together. This is available using the with_polymorphic.aliased flag. For a polymorphic selectable that is across multiple tables, the default behavior is to wrap the selectable into a subquery. Below we emit a query that will select for “employee or manager” paired with “employee or engineer” on employees with the same name:

engineer_employee = with_polymorphic(
    Employee, [Engineer], aliased=True)
manager_employee = with_polymorphic(
    Employee, [Manager], aliased=True)
q = s.query(engineer_employee, manager_employee).\
    join(
        manager_employee,
        and_(
            engineer_employee.id > manager_employee.id,
            engineer_employee.name == manager_employee.name
        )
)
q.all()
SELECT
    anon_1.employee_id AS anon_1_employee_id,
    anon_1.employee_name AS anon_1_employee_name,
    anon_1.employee_type AS anon_1_employee_type,
    anon_1.engineer_id AS anon_1_engineer_id,
    anon_1.engineer_engineer_name AS anon_1_engineer_engineer_name,
    anon_2.employee_id AS anon_2_employee_id,
    anon_2.employee_name AS anon_2_employee_name,
    anon_2.employee_type AS anon_2_employee_type,
    anon_2.manager_id AS anon_2_manager_id,
    anon_2.manager_manager_name AS anon_2_manager_manager_name
FROM (
    SELECT
        employee.id AS employee_id,
        employee.name AS employee_name,
        employee.type AS employee_type,
        engineer.id AS engineer_id,
        engineer.engineer_name AS engineer_engineer_name
    FROM employee
    LEFT OUTER JOIN engineer ON employee.id = engineer.id
) AS anon_1
JOIN (
    SELECT
        employee.id AS employee_id,
        employee.name AS employee_name,
        employee.type AS employee_type,
        manager.id AS manager_id,
         manager.manager_name AS manager_manager_name
    FROM employee
    LEFT OUTER JOIN manager ON employee.id = manager.id
) AS anon_2
ON anon_1.employee_id > anon_2.employee_id
AND anon_1.employee_name = anon_2.employee_name

The creation of subqueries above is very verbose. While it creates the best encapsulation of the two distinct queries, it may be inefficient. with_polymorphic() includes an additional flag to help with this situation, with_polymorphic.flat, which will “flatten” the subquery / join combination into straight joins, applying aliasing to the individual tables instead. Setting with_polymorphic.flat implies with_polymorphic.aliased, so only one flag is necessary:

engineer_employee = with_polymorphic(
    Employee, [Engineer], flat=True)
manager_employee = with_polymorphic(
    Employee, [Manager], flat=True)
q = s.query(engineer_employee, manager_employee).\
    join(
        manager_employee,
        and_(
            engineer_employee.id > manager_employee.id,
            engineer_employee.name == manager_employee.name
        )
)
q.all()
SELECT
    employee_1.id AS employee_1_id,
    employee_1.name AS employee_1_name,
    employee_1.type AS employee_1_type,
    engineer_1.id AS engineer_1_id,
    engineer_1.engineer_name AS engineer_1_engineer_name,
    employee_2.id AS employee_2_id,
    employee_2.name AS employee_2_name,
    employee_2.type AS employee_2_type,
    manager_1.id AS manager_1_id,
    manager_1.manager_name AS manager_1_manager_name
FROM employee AS employee_1
LEFT OUTER JOIN engineer AS engineer_1
ON employee_1.id = engineer_1.id
JOIN (
    employee AS employee_2
    LEFT OUTER JOIN manager AS manager_1
    ON employee_2.id = manager_1.id
)
ON employee_1.id > employee_2.id
AND employee_1.name = employee_2.name

Note above, when using with_polymorphic.flat, it is often the case when used in conjunction with joined table inheritance that we get a right-nested JOIN in our statement. Some older databases, in particular older versions of SQLite, may have a problem with this syntax, although virtually all modern database versions now support this syntax.

Note

The with_polymorphic.flat flag only applies to the use of with_polymorphic with joined table inheritance and when the with_polymorphic.selectable argument is not used.

Referring to Specific Subclass Attributes

The entity returned by 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 previous 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'
                )
            )

Setting with_polymorphic at mapper configuration time

The with_polymorphic() function serves the purpose of allowing “eager” loading of attributes from subclass tables, as well as the ability to refer to the attributes from subclass tables at query time. Historically, the “eager loading” of columns has been the more important part of the equation. So just as eager loading for relationships can be specified as a configurational option, the mapper.with_polymorphic configuration parameter allows an entity to use a polymorphic load by default. We can add the parameter to our Employee mapping first introduced at Joined Table Inheritance:

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,
        'with_polymorphic': '*'
    }

Above is a common setting for mapper.with_polymorphic, which is to indicate an asterisk to load all subclass columns. In the case of joined table inheritance, this option should be used sparingly, as it implies that the mapping will always emit a (often large) series of LEFT OUTER JOIN to many tables, which is not efficient from a SQL perspective. For single table inheritance, specifying the asterisk is often a good idea as the load is still against a single table only, but an additional lazy load of subclass-mapped columns will be prevented.

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

The mapper.with_polymorphic option also accepts a list of classes just like with_polymorphic() to polymorphically load among a subset of classes. However, when using Declarative, providing classes to this list is not directly possible as the subclasses we’d like to add are not available yet. Instead, we can specify on each subclass that they should individually participate in polymorphic loading by default using the mapper.polymorphic_load parameter:

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

Setting the mapper.polymorphic_load parameter to the value "inline" means that the Engineer and Manager classes above are part of the “polymorphic load” of the base Employee class by default, exactly as though they had been appended to the mapper.with_polymorphic list of classes.

Setting with_polymorphic against a query

The with_polymorphic() function evolved from a query-level method Query.with_polymorphic(). This method has the same purpose as with_polymorphic(), except is not as flexible in its usage patterns in that it only applies to the first entity of the Query. It then takes effect for all occurrences of that entity, so that the entity (and its subclasses) can be referred to directly, rather than using an alias object. 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'
        )
    )

The Query.with_polymorphic() method has a more complicated job than the with_polymorphic() function, as it needs to correctly transform entities like Engineer and Manager appropriately, but not interfere with other entities. If its flexibility is lacking, switch to using with_polymorphic().

Polymorphic Selectin Loading

An alternative to using the with_polymorphic() family of functions to “eagerly” load the additional subclasses on an inheritance mapping, primarily when using joined table inheritance, is to use polymorphic “selectin” loading. This is an eager loading feature which works similarly to the Select IN loading feature of relationship loading. Given our example mapping, we can instruct a load of Employee to emit an extra SELECT per subclass by using the selectin_polymorphic() loader option:

from sqlalchemy.orm import selectin_polymorphic
query = session.query(Employee).options(
    selectin_polymorphic(Employee, [Manager, Engineer])
)

When the above query is run, two additional SELECT statements will be emitted:

query.all()
SELECT
    employee.id AS employee_id,
    employee.name AS employee_name,
    employee.type AS employee_type
FROM employee
()
SELECT
    engineer.id AS engineer_id,
    employee.id AS employee_id,
    employee.type AS employee_type,
    engineer.engineer_name AS engineer_engineer_name
FROM employee JOIN engineer ON employee.id = engineer.id
WHERE employee.id IN (?, ?) ORDER BY employee.id
(1, 2)
SELECT
    manager.id AS manager_id,
    employee.id AS employee_id,
    employee.type AS employee_type,
    manager.manager_name AS manager_manager_name
FROM employee JOIN manager ON employee.id = manager.id
WHERE employee.id IN (?) ORDER BY employee.id
(3,)

We can similarly establish the above style of loading to take place by default by specifying the mapper.polymorphic_load parameter, using the value "selectin" on a per-subclass basis:

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
    }
class Engineer(Employee):
    __tablename__ = 'engineer'
    id = Column(Integer, ForeignKey('employee.id'), primary_key=True)
    engineer_name = Column(String(30))
    __mapper_args__ = {
        'polymorphic_load': 'selectin',
        '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_load': 'selectin',
        'polymorphic_identity': 'manager',
    }

Unlike when using with_polymorphic(), when using the selectin_polymorphic() style of loading, we do not have the ability to refer to the Engineer or Manager entities within our main query as filter, order by, or other criteria, as these entities are not present in the initial query that is used to locate results. However, we can apply loader options that apply towards Engineer or Manager, which will take effect when the secondary SELECT is emitted. Below we assume Manager has an additional relationship Manager.paperwork, that we’d like to eagerly load as well. We can use any type of eager loading, such as joined eager loading via the joinedload() function:

from sqlalchemy.orm import joinedload
from sqlalchemy.orm import selectin_polymorphic
query = session.query(Employee).options(
    selectin_polymorphic(Employee, [Manager, Engineer]),
    joinedload(Manager.paperwork)
)

Using the query above, we get three SELECT statements emitted, however the one against Manager will be:

SELECT
    manager.id AS manager_id,
    employee.id AS employee_id,
    employee.type AS employee_type,
    manager.manager_name AS manager_manager_name,
    paperwork_1.id AS paperwork_1_id,
    paperwork_1.manager_id AS paperwork_1_manager_id,
    paperwork_1.data AS paperwork_1_data
FROM employee JOIN manager ON employee.id = manager.id
LEFT OUTER JOIN paperwork AS paperwork_1
ON manager.id = paperwork_1.manager_id
WHERE employee.id IN (?) ORDER BY employee.id
(3,)

Note that selectin polymorphic loading has similar caveats as that of selectin relationship loading; for entities that make use of a composite primary key, the database in use must support tuples with “IN”, currently known to work with MySQL and PostgreSQL.

New in version 1.2.

Warning

The selectin polymorphic loading feature should be considered as experimental within early releases of the 1.2 series.

Combining selectin and with_polymorphic

Note

works as of 1.2.0b3

With careful planning, selectin loading can be applied against a hierarchy that itself uses “with_polymorphic”. A particular use case is that of using selectin loading to load a joined-inheritance subtable, which then uses “with_polymorphic” to refer to further sub-classes, which may be joined- or single-table inheritance. If we added a class VicePresident that extends Manager using single-table inheritance, we could ensure that a load of Manager also fully loads VicePresident subtypes at the same time:

# use "Employee" example from the enclosing section
class Manager(Employee):
    __tablename__ = 'manager'
    id = Column(Integer, ForeignKey('employee.id'), primary_key=True)
    manager_name = Column(String(30))
    __mapper_args__ = {
        'polymorphic_load': 'selectin',
        'polymorphic_identity': 'manager',
    }
class VicePresident(Manager):
    vp_info = Column(String(30))
    __mapper_args__ = {
        "polymorphic_load": "inline",
        "polymorphic_identity": "vp"
    }

Above, we add a vp_info column to the manager table, local to the VicePresident subclass. This subclass is linked to the polymorphic identity "vp" which refers to rows which have this data. By setting the load style to “inline”, it means that a load of Manager objects will also ensure that the vp_info column is queried for in the same SELECT statement. A query against Employee that encounters a Manager row would emit similarly to the following:

SELECT employee.id AS employee_id, employee.name AS employee_name,
       employee.type AS employee_type
FROM employee
)
SELECT manager.id AS manager_id, employee.id AS employee_id,
       employee.type AS employee_type,
       manager.manager_name AS manager_manager_name,
       manager.vp_info AS manager_vp_info
FROM employee JOIN manager ON employee.id = manager.id
WHERE employee.id IN (?) ORDER BY employee.id
(1,)

Combining “selectin” polymorphic loading with query-time with_polymorphic() usage is also possible (though this is very outer-space stuff!); assuming the above mappings had no polymorphic_load set up, we could get the same result as follows:

from sqlalchemy.orm import with_polymorphic, selectin_polymorphic
manager_poly = with_polymorphic(Manager, [VicePresident])
s.query(Employee).options(
    selectin_polymorphic(Employee, [manager_poly])).all()

Referring to specific subtypes on relationships

Mapped attributes which correspond to a relationship() are used in querying in order to refer to the linkage between two mappings. Common uses for this are to refer to a relationship() in Query.join() as well as in loader options like joinedload(). When using relationship() where the target class is an inheritance hierarchy, the API allows that the join, eager load, or other linkage should target a specific subclass, alias, or with_polymorphic() alias, of that class hierarchy, rather than the class directly targeted by the relationship().

The of_type() method allows the construction of joins along relationship() paths while narrowing the criterion to specific derived aliases or 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')
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',
    }
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 Query.join() method as well as operators like PropComparator.any() and PropComparator.has() 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 set of columns representing the subclass using the PropComparator.of_type() operator:

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

Similarly, to join from Company to the polymorphic entity that includes both Engineer and Manager columns:

manager_and_engineer = with_polymorphic(
                            Employee, [Manager, Engineer])
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 PropComparator.any() and PropComparator.has() operators also can be used with of_type(), such as 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()

Eager Loading of Specific or Polymorphic Subtypes

The joinedload(), subqueryload(), contains_eager() and other eagerloader options 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(Engineer.machines)
        )
    )

As is the case with Query.join(), PropComparator.of_type() can be used to combine eager loading and with_polymorphic(), so that all sub-attributes of all referenced subtypes can be loaded:

manager_and_engineer = with_polymorphic(
                            Employee, [Manager, Engineer],
                            flat=True)
session.query(Company).\
    options(
        joinedload(
            Company.employees.of_type(manager_and_engineer)
        )
    )

Note

When using with_polymorphic() in conjunction with joinedload(), the with_polymorphic() object must be against an “aliased” object, that is an instance of Alias, so that the polymorphic selectable is aliased (an informative error message is raised otherwise).

The typical way to do this is to include the with_polymorphic.aliased or flat flag, which will apply this aliasing automatically. However, if the with_polymorphic.selectable argument is being used to pass an object that is already an Alias object then this flag should not be set. The “flat” option implies the “aliased” option and is an alternate form of aliasing against join objects that produces fewer subqueries.

Once PropComparator.of_type() is the target of the eager load, that’s the entity we would use for subsequent chaining, not the original class or derived class. If we wanted to further eager load a collection on the eager-loaded Engineer class, we access this class from the namespace of the with_polymorphic() object:

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

Loading objects with joined table inheritance

When using joined table inheritance, if we query for a specific subclass that represents a JOIN of two tables such as our Engineer example from the inheritance section, the SQL emitted is a join:

session.query(Engineer).all()

The above query will emit SQL like:

SELECT employee.id AS employee_id,
    employee.name AS employee_name, employee.type AS employee_type,
    engineer.name AS engineer_name
FROM employee JOIN engineer
ON employee.id = engineer.id

We will then get a collection of Engineer objects back, which will contain all columns from employee and engineer loaded.

However, when emitting a Query against a base class, the behavior is to load only from the base table:

session.query(Employee).all()

Above, the default behavior would be to SELECT only from the employee table and not from any “sub” tables (engineer and manager, in our previous examples):

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

After a collection of Employee objects has been returned from the query, and 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]

The with_polymorphic() function and related configuration options allow us to instead emit a JOIN up front which will conditionally load against employee, engineer, or manager, very much like joined eager loading works for relationships, removing the necessity for a second per-entity load:

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 section Using with_polymorphic discusses the with_polymorphic() function and its configurational variants.

See also

Using with_polymorphic

Loading objects with single table inheritance

In modern Declarative, single inheritance mappings produce Column objects that are mapped only to a subclass, and not available from the superclass, even though they are present on the same table. In our example from Single Table Inheritance, the Manager mapping for example had a Column specified:

class Manager(Employee):
    manager_data = Column(String(50))
    __mapper_args__ = {
        'polymorphic_identity':'manager'
    }

Above, there would be no Employee.manager_data attribute, even though the employee table has a manager_data column. A query against Manager will include this column in the query, as well as an IN clause to limit rows only to Manager objects:

session.query(Manager).all()
SELECT
    employee.id AS employee_id,
    employee.name AS employee_name,
    employee.type AS employee_type,
    employee.manager_data AS employee_manager_data
FROM employee
WHERE employee.type IN (?)
('manager',)

However, in a similar way to that of joined table inheritance, a query against Employee will only query for columns mapped to Employee:

session.query(Employee).all()
SELECT employee.id AS employee_id,
    employee.name AS employee_name,
    employee.type AS employee_type
FROM employee

If we get back an instance of Manager from our result, accessing additional columns only mapped to Manager emits a lazy load for those columns, in a similar way to joined inheritance:

SELECT employee.manager_data AS employee_manager_data
FROM employee
WHERE employee.id = ? AND employee.type IN (?)

The with_polymorphic() function serves a similar role as joined inheritance in the case of single inheritance; it allows both for eager loading of subclass attributes as well as specification of subclasses in a query, just without the overhead of using OUTER JOIN:

employee_poly = with_polymorphic(Employee, '*')
q = session.query(employee_poly).filter(
    or_(
        employee_poly.name == 'a',
        employee_poly.Manager.manager_data == 'b'
    )
)

Above, our query remains against a single table however we can refer to the columns present in Manager or Engineer using the “polymorphic” namespace. Since we specified "*" for the entities, both Engineer and Manager will be loaded at once. SQL emitted would be:

q.all()
SELECT
    employee.id AS employee_id, employee.name AS employee_name,
    employee.type AS employee_type,
    employee.manager_data AS employee_manager_data,
    employee.engineer_info AS employee_engineer_info
FROM employee
WHERE employee.name = :name_1
OR employee.manager_data = :manager_data_1

Inheritance Loading API

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

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

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 also

Using with_polymorphic - full discussion of with_polymorphic().

• 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 aliased. For a JOIN, this means the JOIN will be SELECTed from inside of a subquery unless the with_polymorphic.flat flag is set to True, which is recommended for simpler use cases.

  • flat – Boolean, will be passed through to the FromClause.alias() call so that aliases of Join objects will alias the individual tables inside the join, rather than creating a subquery. This is generally supported by all modern databases with regards to right-nested joins and generally produces more efficient queries. Setting this flag is recommended as long as the resulting SQL is functional.

  • selectable – a table or subquery 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

function sqlalchemy.orm.``selectin_polymorphic(base_cls, classes)

Indicate an eager load should take place for all attributes specific to a subclass.

This uses an additional SELECT with IN against all matched primary key values, and is the per-query analogue to the "selectin" setting on the mapper.polymorphic_load parameter.

New in version 1.2.

See also

Polymorphic Selectin Loading