Release: 1.4.31 | Release Date: January 20, 2022

# SQLAlchemy 1.4 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.

See also

Inheritance Mapping Recipes - complete examples of joined, single and concrete inheritance

## Joined Table Inheritance

In joined table inheritance, each class along a hierarchy of classes is represented by a distinct table. Querying for a particular subclass in the hierarchy will render as a SQL JOIN along all tables in its inheritance path. If the queried class is the base class, the default behavior is to include only the base table in a SELECT statement. In all cases, the ultimate class to instantiate for a given row is determined by a discriminator column or an expression that works against the base table. When a subclass is loaded only against a base table, resulting objects will have base attributes populated at first; attributes that are local to the subclass will lazy load when they are accessed. Alternatively, there are options which can change the default behavior, allowing the query to include columns corresponding to multiple tables/subclasses up front.

The base class in a joined inheritance hierarchy is configured with additional arguments that will refer to the polymorphic discriminator column as well as the identifier for the base class:

    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
        }

Above, an additional column `type` is established to act as the discriminator, configured as such using the `mapper.polymorphic_on` parameter. This column will store 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 actual data value to be applied to this column for a particular row in the database is specified using the `mapper.polymorphic_identity` parameter, described below.

While a polymorphic discriminator expression is not strictly necessary, it is required if polymorphic loading is desired. Establishing a simple column on the base table is the easiest way to achieve this, however very sophisticated inheritance mappings may even configure a SQL expression such as a CASE statement as the polymorphic discriminator.

Note

Currently, only one discriminator column or SQL expression may be configured for the entire inheritance hierarchy, typically on the base- most class in the hierarchy. “Cascading” polymorphic discriminator expressions are not yet supported.

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), as well as 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',
        }

In the above example, each mapping specifies the `mapper.polymorphic_identity` parameter within its mapper arguments. This value populates the column designated by the `mapper.polymorphic_on` parameter established on the base mapper. The `mapper.polymorphic_identity` parameter should be unique to each mapped class across the whole hierarchy, and there should only be one “identity” per mapped class; as noted above, “cascading” identities where some subclasses introduce a second identity are not supported.

The ORM uses the value set up by `mapper.polymorphic_identity` in order to determine which class a row belongs towards when loading rows polymorphically. In the example above, every row which represents an `Employee` will have the value `'employee'` in its `type` row; similarly, every `Engineer` will get the value `'engineer'`, and each `Manager` will get the value `'manager'`. Regardless of whether the inheritance mapping uses distinct joined tables for subclasses as in joined table inheritance, or all one table as in single table inheritance, this value is expected to be persisted and available to the ORM when querying. The `mapper.polymorphic_identity` parameter also applies to concrete table inheritance, but is not actually persisted; see the later section at Concrete Table Inheritance for details.

In a polymorphic setup, it is most common that the foreign key constraint is established on the same column or columns as the primary key itself, however this is not required; a column distinct from the primary key may also be made to refer to the parent via foreign key. The way that a JOIN is constructed from the base table to subclasses is also directly customizable, however this is rarely necessary.

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 rows in the base table alone. 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 the correct “discriminator” value in this case `"engineer"`, `"manager"`, or `"employee"`, as appropriate.

### Relationships with Joined Inheritance

Relationships are fully supported with joined table inheritance. The relationship involving a joined-inheritance class should target the class in the hierarchy that also corresponds to the foreign key constraint; below, as the `employee` table has a foreign key constraint back to the `company` table, the relationships are set up between `Company` and `Employee`:

    class Company(Base):
        __tablename__ = 'company'
        id = Column(Integer, primary_key=True)
        name = Column(String(50))
        employees = relationship("Employee", back_populates="company")

    class Employee(Base):
        __tablename__ = 'employee'
        id = Column(Integer, primary_key=True)
        name = Column(String(50))
        type = Column(String(50))
        company_id = Column(ForeignKey('company.id'))
        company = relationship("Company", back_populates="employees")

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

    class Manager(Employee):
        # ...

    class Engineer(Employee):
        # ...

If the foreign key constraint is on a table corresponding to a subclass, the relationship should target that subclass instead. In the example below, there is a foreign key constraint from `manager` to `company`, so the relationships are established between the `Manager` and `Company` classes:

    class Company(Base):
        __tablename__ = 'company'
        id = Column(Integer, primary_key=True)
        name = Column(String(50))
        managers = relationship("Manager", back_populates="company")

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

        company_id = Column(ForeignKey('company.id'))
        company = relationship("Company", back_populates="managers")

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

    class Engineer(Employee):
        # ...

Above, the `Manager` class will have a `Manager.company` attribute; `Company` will have a `Company.managers` attribute that always loads against a join of the `employee` and `manager` tables together.

### Loading Joined Inheritance Mappings

See the sections Loading Inheritance Hierarchies and Loading objects with joined table inheritance for background on inheritance loading techniques, including configuration of tables to be queried both at mapper configuration time as well as query time.

## Single Table Inheritance

Single table inheritance represents all attributes of all subclasses within a single table. A particular subclass that has attributes unique to that class will persist them within columns in the table that are otherwise NULL if the row refers to a different kind of object.

Querying for a particular subclass in the hierarchy will render as a SELECT against the base table, which will include a WHERE clause that limits rows to those with a particular value or values present in the discriminator column or expression.

Single table inheritance has the advantage of simplicity compared to joined table inheritance; queries are much more efficient as only one table needs to be involved in order to load objects of every represented class.

Single-table inheritance configuration looks much like joined-table inheritance, except only the base class specifies `__tablename__`. A discriminator column is also required on the base table so that classes can be differentiated from each other.

Even though subclasses share the base table for all of their attributes, when using Declarative, `Column` objects may still be specified on subclasses, indicating that the column is to be mapped only to that subclass; the `Column` will be applied to the same base `Table` object:

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

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

    class Manager(Employee):
        manager_data = Column(String(50))

        __mapper_args__ = {
            'polymorphic_identity':'manager'
        }

    class Engineer(Employee):
        engineer_info = Column(String(50))

        __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.

### Resolving Column Conflicts

Note in the previous section that the `manager_name` and `engineer_info` columns are “moved up” to be applied to `Employee.__table__`, as a result of their declaration on a subclass that has no table of its own. A tricky case comes up when two subclasses want to specify the same column, as below:

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

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

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

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

Above, the `start_date` column declared on both `Engineer` and `Manager` will result in an error:

    sqlalchemy.exc.ArgumentError: Column 'start_date' on class
    <class '__main__.Manager'> conflicts with existing
    column 'employee.start_date'

The above scenario presents an ambiguity to the Declarative mapping system that may be resolved by using `declared_attr` to define the `Column` conditionally, taking care to return the existing column via the parent `__table__` if it already exists:

    from sqlalchemy.orm import declared_attr

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

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

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

        @declared_attr
        def start_date(cls):
            "Start date column, if not present already."
            return Employee.__table__.c.get('start_date', Column(DateTime))

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

        @declared_attr
        def start_date(cls):
            "Start date column, if not present already."
            return Employee.__table__.c.get('start_date', Column(DateTime))

Above, when `Manager` is mapped, the `start_date` column is already present on the `Employee` class; by returning the existing `Column` object, the declarative system recognizes that this is the same column to be mapped to the two different subclasses separately.

A similar concept can be used with mixin classes (see Composing Mapped Hierarchies with Mixins) to define a particular series of columns and/or other mapped attributes from a reusable mixin class:

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

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

    class HasStartDate:
        @declared_attr
        def start_date(cls):
            return cls.__table__.c.get('start_date', Column(DateTime))

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

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

### Relationships with Single Table Inheritance

Relationships are fully supported with single table inheritance. Configuration is done in the same manner as that of joined inheritance; a foreign key attribute should be on the same class that’s the “foreign” side of the relationship:

    class Company(Base):
        __tablename__ = 'company'
        id = Column(Integer, primary_key=True)
        name = Column(String(50))
        employees = relationship("Employee", back_populates="company")

    class Employee(Base):
        __tablename__ = 'employee'
        id = Column(Integer, primary_key=True)
        name = Column(String(50))
        type = Column(String(50))
        company_id = Column(ForeignKey('company.id'))
        company = relationship("Company", back_populates="employees")

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

    class Manager(Employee):
        manager_data = Column(String(50))

        __mapper_args__ = {
            'polymorphic_identity':'manager'
        }

    class Engineer(Employee):
        engineer_info = Column(String(50))

        __mapper_args__ = {
            'polymorphic_identity':'engineer'
        }

Also, like the case of joined inheritance, we can create relationships that involve a specific subclass. When queried, the SELECT statement will include a WHERE clause that limits the class selection to that subclass or subclasses:

    class Company(Base):
        __tablename__ = 'company'
        id = Column(Integer, primary_key=True)
        name = Column(String(50))
        managers = relationship("Manager", back_populates="company")

    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 Manager(Employee):
        manager_name = Column(String(30))

        company_id = Column(ForeignKey('company.id'))
        company = relationship("Company", back_populates="managers")

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

    class Engineer(Employee):
        engineer_info = Column(String(50))

        __mapper_args__ = {
            'polymorphic_identity':'engineer'
        }

Above, the `Manager` class will have a `Manager.company` attribute; `Company` will have a `Company.managers` attribute that always loads against the `employee` with an additional WHERE clause that limits rows to those with `type = 'manager'`.

### Loading Single Inheritance Mappings

The loading techniques for single-table inheritance are mostly identical to those used for joined-table inheritance, and a high degree of abstraction is provided between these two mapping types such that it is easy to switch between them as well as to intermix them in a single hierarchy (just omit `__tablename__` from whichever subclasses are to be single-inheriting). See the sections Loading Inheritance Hierarchies and Loading objects with single table inheritance for documentation on inheritance loading techniques, including configuration of classes to be queried both at mapper configuration time as well as query time.

## Concrete Table Inheritance

Concrete inheritance maps each subclass to its own distinct table, each of which contains all columns necessary to produce an instance of that class. A concrete inheritance configuration by default queries non-polymorphically; a query for a particular class will only query that class’ table and only return instances of that class. Polymorphic loading of concrete classes is enabled by configuring within the mapper a special SELECT that typically is produced as a UNION of all the tables.

Warning

Concrete table inheritance is much more complicated than joined or single table inheritance, and is much more limited in functionality especially pertaining to using it with relationships, eager loading, and polymorphic loading. When used polymorphically it produces very large queries with UNIONS that won’t perform as well as simple joins. It is strongly advised that if flexibility in relationship loading and polymorphic loading is required, that joined or single table inheritance be used if at all possible. If polymorphic loading isn’t required, then plain non-inheriting mappings can be used if each class refers to its own table completely.

Whereas joined and single table inheritance are fluent in “polymorphic” loading, it is a more awkward affair in concrete inheritance. For this reason, concrete inheritance is more appropriate when polymorphic loading is not required. Establishing relationships that involve concrete inheritance classes is also more awkward.

To establish a class as using concrete inheritance, add the `mapper.concrete` parameter within the `__mapper_args__`. This indicates to Declarative as well as the mapping that the superclass table should not be considered as part of the mapping:

    class Employee(Base):
        __tablename__ = 'employee'

        id = Column(Integer, primary_key=True)
        name = Column(String(50))

    class Manager(Employee):
        __tablename__ = 'manager'

        id = Column(Integer, primary_key=True)
        name = Column(String(50))
        manager_data = Column(String(50))

        __mapper_args__ = {
            'concrete': True
        }

    class Engineer(Employee):
        __tablename__ = 'engineer'

        id = Column(Integer, primary_key=True)
        name = Column(String(50))
        engineer_info = Column(String(50))

        __mapper_args__ = {
            'concrete': True
        }

Two critical points should be noted:

- We must define all columns explicitly on each subclass, even those of the same name. A column such as `Employee.name` here is not copied out to the tables mapped by `Manager` or `Engineer` for us.

- while the `Engineer` and `Manager` classes are mapped in an inheritance relationship with `Employee`, they still do not include polymorphic loading. Meaning, if we query for `Employee` objects, the `manager` and `engineer` tables are not queried at all.

### Concrete Polymorphic Loading Configuration

Polymorphic loading with concrete inheritance requires that a specialized SELECT is configured against each base class that should have polymorphic loading. This SELECT needs to be capable of accessing all the mapped tables individually, and is typically a UNION statement that is constructed using a SQLAlchemy helper `polymorphic_union()`.

As discussed in Loading Inheritance Hierarchies, mapper inheritance configurations of any type can be configured to load from a special selectable by default using the `mapper.with_polymorphic` argument. Current public API requires that this argument is set on a `Mapper` when it is first constructed.

However, in the case of Declarative, both the mapper and the `Table` that is mapped are created at once, the moment the mapped class is defined. This means that the `mapper.with_polymorphic` argument cannot be provided yet, since the `Table` objects that correspond to the subclasses haven’t yet been defined.

There are a few strategies available to resolve this cycle, however Declarative provides helper classes `ConcreteBase` and `AbstractConcreteBase` which handle this issue behind the scenes.

Using `ConcreteBase`, we can set up our concrete mapping in almost the same way as we do other forms of inheritance mappings:

    from sqlalchemy.ext.declarative import ConcreteBase

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

        __mapper_args__ = {
            'polymorphic_identity': 'employee',
            'concrete': True
        }

    class Manager(Employee):
        __tablename__ = 'manager'
        id = Column(Integer, primary_key=True)
        name = Column(String(50))
        manager_data = Column(String(40))

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

    class Engineer(Employee):
        __tablename__ = 'engineer'
        id = Column(Integer, primary_key=True)
        name = Column(String(50))
        engineer_info = Column(String(40))

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

Above, Declarative sets up the polymorphic selectable for the `Employee` class at mapper “initialization” time; this is the late-configuration step for mappers that resolves other dependent mappers. The `ConcreteBase` helper uses the `polymorphic_union()` function to create a UNION of all concrete-mapped tables after all the other classes are set up, and then configures this statement with the already existing base-class mapper.

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

    session.query(Employee).all()
    SELECT
        pjoin.id AS pjoin_id,
        pjoin.name AS pjoin_name,
        pjoin.type AS pjoin_type,
        pjoin.manager_data AS pjoin_manager_data,
        pjoin.engineer_info AS pjoin_engineer_info
    FROM (
        SELECT
            employee.id AS id,
            employee.name AS name,
            CAST(NULL AS VARCHAR(50)) AS manager_data,
            CAST(NULL AS VARCHAR(50)) AS engineer_info,
            'employee' AS type
        FROM employee
        UNION ALL
        SELECT
            manager.id AS id,
            manager.name AS name,
            manager.manager_data AS manager_data,
            CAST(NULL AS VARCHAR(50)) AS engineer_info,
            'manager' AS type
        FROM manager
        UNION ALL
        SELECT
            engineer.id AS id,
            engineer.name AS name,
            CAST(NULL AS VARCHAR(50)) AS manager_data,
            engineer.engineer_info AS engineer_info,
            'engineer' AS type
        FROM engineer
    ) AS pjoin

The above UNION query needs to manufacture “NULL” columns for each subtable in order to accommodate for those columns that aren’t members of that particular subclass.

### Abstract Concrete Classes

The concrete mappings illustrated thus far show both the subclasses as well as the base class mapped to individual tables. In the concrete inheritance use case, it is common that the base class is not represented within the database, only the subclasses. In other words, the base class is “abstract”.

Normally, when one would like to map two different subclasses to individual tables, and leave the base class unmapped, this can be achieved very easily. When using Declarative, just declare the base class with the `__abstract__` indicator:

    class Employee(Base):
        __abstract__ = True

    class Manager(Employee):
        __tablename__ = 'manager'
        id = Column(Integer, primary_key=True)
        name = Column(String(50))
        manager_data = Column(String(40))

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

    class Engineer(Employee):
        __tablename__ = 'engineer'
        id = Column(Integer, primary_key=True)
        name = Column(String(50))
        engineer_info = Column(String(40))

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

Above, we are not actually making use of SQLAlchemy’s inheritance mapping facilities; we can load and persist instances of `Manager` and `Engineer` normally. The situation changes however when we need to query polymorphically, that is, we’d like to emit `session.query(Employee)` and get back a collection of `Manager` and `Engineer` instances. This brings us back into the domain of concrete inheritance, and we must build a special mapper against `Employee` in order to achieve this.

Mappers can always SELECT

In SQLAlchemy, a mapper for a class always has to refer to some “selectable”, which is normally a `Table` but may also refer to any `select()` object as well. While it may appear that a “single table inheritance” mapper does not map to a table, these mappers in fact implicitly refer to the table that is mapped by a superclass.

To modify our concrete inheritance example to illustrate an “abstract” base that is capable of polymorphic loading, we will have only an `engineer` and a `manager` table and no `employee` table, however the `Employee` mapper will be mapped directly to the “polymorphic union”, rather than specifying it locally to the `mapper.with_polymorphic` parameter.

To help with this, Declarative offers a variant of the `ConcreteBase` class called `AbstractConcreteBase` which achieves this automatically:

    from sqlalchemy.ext.declarative import AbstractConcreteBase

    class Employee(AbstractConcreteBase, Base):
        pass

    class Manager(Employee):
        __tablename__ = 'manager'
        id = Column(Integer, primary_key=True)
        name = Column(String(50))
        manager_data = Column(String(40))

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

    class Engineer(Employee):
        __tablename__ = 'engineer'
        id = Column(Integer, primary_key=True)
        name = Column(String(50))
        engineer_info = Column(String(40))

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

The `AbstractConcreteBase` helper class has a more complex internal process than that of `ConcreteBase`, in that the entire mapping of the base class must be delayed until all the subclasses have been declared. With a mapping like the above, only instances of `Manager` and `Engineer` may be persisted; querying against the `Employee` class will always produce `Manager` and `Engineer` objects.

### Classical and Semi-Classical Concrete Polymorphic Configuration

The Declarative configurations illustrated with `ConcreteBase` and `AbstractConcreteBase` are equivalent to two other forms of configuration that make use of `polymorphic_union()` explicitly. These configurational forms make use of the `Table` object explicitly so that the “polymorphic union” can be created first, then applied to the mappings. These are illustrated here to clarify the role of the `polymorphic_union()` function in terms of mapping.

A semi-classical mapping for example makes use of Declarative, but establishes the `Table` objects separately:

    metadata_obj = Base.metadata

    employees_table = Table(
        'employee', metadata_obj,
        Column('id', Integer, primary_key=True),
        Column('name', String(50)),
    )

    managers_table = Table(
        'manager', metadata_obj,
        Column('id', Integer, primary_key=True),
        Column('name', String(50)),
        Column('manager_data', String(50)),
    )

    engineers_table = Table(
        'engineer', metadata_obj,
        Column('id', Integer, primary_key=True),
        Column('name', String(50)),
        Column('engineer_info', String(50)),
    )

Next, the UNION is produced using `polymorphic_union()`:

    from sqlalchemy.orm import polymorphic_union

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

With the above `Table` objects, the mappings can be produced using “semi-classical” style, where we use Declarative in conjunction with the `__table__` argument; our polymorphic union above is passed via `__mapper_args__` to the `mapper.with_polymorphic` parameter:

    class Employee(Base):
        __table__ = employee_table
        __mapper_args__ = {
            'polymorphic_on': pjoin.c.type,
            'with_polymorphic': ('*', pjoin),
            'polymorphic_identity': 'employee'
        }

    class Engineer(Employee):
        __table__ = engineer_table
        __mapper_args__ = {
            'polymorphic_identity': 'engineer',
            'concrete': True}

    class Manager(Employee):
        __table__ = manager_table
        __mapper_args__ = {
            'polymorphic_identity': 'manager',
            'concrete': True}

Alternatively, the same `Table` objects can be used in fully “classical” style, without using Declarative at all. A constructor similar to that supplied by Declarative is illustrated:

    class Employee(object):
        def __init__(self, **kw):
            for k in kw:
                setattr(self, k, kw[k])

    class Manager(Employee):
        pass

    class Engineer(Employee):
        pass

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

The “abstract” example can also be mapped using “semi-classical” or “classical” style. The difference is that instead of applying the “polymorphic union” to the `mapper.with_polymorphic` parameter, we apply it directly as the mapped selectable on our basemost mapper. The semi-classical mapping is illustrated below:

    from sqlalchemy.orm import polymorphic_union

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

    class Employee(Base):
        __table__ = pjoin
        __mapper_args__ = {
            'polymorphic_on': pjoin.c.type,
            'with_polymorphic': '*',
            'polymorphic_identity': 'employee'
        }

    class Engineer(Employee):
        __table__ = engineer_table
        __mapper_args__ = {
            'polymorphic_identity': 'engineer',
            'concrete': True}

    class Manager(Employee):
        __table__ = manager_table
        __mapper_args__ = {
            'polymorphic_identity': 'manager',
            'concrete': True}

Above, we use `polymorphic_union()` in the same manner as before, except that we omit the `employee` table.

See also

Imperative (a.k.a. Classical) Mappings - background information on “classical” mappings

### Relationships with Concrete Inheritance

In a concrete inheritance scenario, mapping relationships is challenging since the distinct classes do not share a table. If the relationships only involve specific classes, such as a relationship between `Company` in our previous examples and `Manager`, special steps aren’t needed as these are just two related tables.

However, if `Company` is to have a one-to-many relationship to `Employee`, indicating that the collection may include both `Engineer` and `Manager` objects, that implies that `Employee` must have polymorphic loading capabilities and also that each table to be related must have a foreign key back to the `company` table. An example of such a configuration is as follows:

    from sqlalchemy.ext.declarative import ConcreteBase

    class Company(Base):
        __tablename__ = 'company'
        id = Column(Integer, primary_key=True)
        name = Column(String(50))
        employees = relationship("Employee")

    class Employee(ConcreteBase, Base):
        __tablename__ = 'employee'
        id = Column(Integer, primary_key=True)
        name = Column(String(50))
        company_id = Column(ForeignKey('company.id'))

        __mapper_args__ = {
            'polymorphic_identity': 'employee',
            'concrete': True
        }

    class Manager(Employee):
        __tablename__ = 'manager'
        id = Column(Integer, primary_key=True)
        name = Column(String(50))
        manager_data = Column(String(40))
        company_id = Column(ForeignKey('company.id'))

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

    class Engineer(Employee):
        __tablename__ = 'engineer'
        id = Column(Integer, primary_key=True)
        name = Column(String(50))
        engineer_info = Column(String(40))
        company_id = Column(ForeignKey('company.id'))

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

The next complexity with concrete inheritance and relationships involves when we’d like one or all of `Employee`, `Manager` and `Engineer` to themselves refer back to `Company`. For this case, SQLAlchemy has special behavior in that a `relationship()` placed on `Employee` which links to `Company` does not work against the `Manager` and `Engineer` classes, when exercised at the instance level. Instead, a distinct `relationship()` must be applied to each class. In order to achieve bi-directional behavior in terms of three separate relationships which serve as the opposite of `Company.employees`, the `relationship.back_populates` parameter is used between each of the relationships:

    from sqlalchemy.ext.declarative import ConcreteBase

    class Company(Base):
        __tablename__ = 'company'
        id = Column(Integer, primary_key=True)
        name = Column(String(50))
        employees = relationship("Employee", back_populates="company")

    class Employee(ConcreteBase, Base):
        __tablename__ = 'employee'
        id = Column(Integer, primary_key=True)
        name = Column(String(50))
        company_id = Column(ForeignKey('company.id'))
        company = relationship("Company", back_populates="employees")

        __mapper_args__ = {
            'polymorphic_identity': 'employee',
            'concrete': True
        }

    class Manager(Employee):
        __tablename__ = 'manager'
        id = Column(Integer, primary_key=True)
        name = Column(String(50))
        manager_data = Column(String(40))
        company_id = Column(ForeignKey('company.id'))
        company = relationship("Company", back_populates="employees")

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

    class Engineer(Employee):
        __tablename__ = 'engineer'
        id = Column(Integer, primary_key=True)
        name = Column(String(50))
        engineer_info = Column(String(40))
        company_id = Column(ForeignKey('company.id'))
        company = relationship("Company", back_populates="employees")

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

The above limitation is related to the current implementation, including that concrete inheriting classes do not share any of the attributes of the superclass and therefore need distinct relationships to be set up.

### Loading Concrete Inheritance Mappings

The options for loading with concrete inheritance are limited; generally, if polymorphic loading is configured on the mapper using one of the declarative concrete mixins, it can’t be modified at query time in current SQLAlchemy versions. Normally, the `with_polymorphic()` function would be able to override the style of loading used by concrete, however due to current limitations this is not yet supported.