Mapper Configuration

This section describes a variety of configurational patterns that are usable with mappers. It assumes you've worked through :ref:ormtutorial_toplevel and know how to construct and use rudimentary mappers and relationships.

Classical Mappings

A Classical Mapping refers to the configuration of a mapped class using the :func:.mapper function, without using the Declarative system. As an example, start with the declarative mapping introduced in :ref:ormtutorial_toplevel:

class User(Base):
__tablename__ = 'users'

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


In "classical" form, the table metadata is created separately with the :class:.Table construct, then associated with the User class via the :func:.mapper function:

from sqlalchemy import Table, MetaData, Column, ForeignKey, Integer, String
from sqlalchemy.orm import mapper

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

class User(object):
self.name = name
self.fullname = fullname

mapper(User, user)


Information about mapped attributes, such as relationships to other classes, are provided via the properties dictionary. The example below illustrates a second :class:.Table object, mapped to a class called Address, then linked to User via :func:.relationship:

address = Table('address', metadata,
Column('id', Integer, primary_key=True),
Column('user_id', Integer, ForeignKey('user.id')),
)

mapper(User, user, properties={
})



When using classical mappings, classes must be provided directly without the benefit of the "string lookup" system provided by Declarative. SQL expressions are typically specified in terms of the :class:.Table objects, i.e. address.c.id above for the Address relationship, and not Address.id, as Address may not yet be linked to table metadata, nor can we specify a string here.

Some examples in the documentation still use the classical approach, but note that the classical as well as Declarative approaches are fully interchangeable. Both systems ultimately create the same configuration, consisting of a :class:.Table, user-defined class, linked together with a :func:.mapper. When we talk about "the behavior of :func:.mapper", this includes when using the Declarative system as well - it's still used, just behind the scenes.

Customizing Column Properties

The default behavior of :func:~.orm.mapper is to assemble all the columns in the mapped :class:.Table into mapped object attributes, each of which are named according to the name of the column itself (specifically, the key attribute of :class:.Column). This behavior can be modified in several ways.

Naming Columns Distinctly from Attribute Names

A mapping by default shares the same name for a :class:.Column as that of the mapped attribute - specifically it matches the :attr:.Column.key attribute on :class:.Column, which by default is the same as the :attr:.Column.name.

The name assigned to the Python attribute which maps to :class:.Column can be different from either :attr:.Column.name or :attr:.Column.key just by assigning it that way, as we illustrate here in a Declarative mapping:

class User(Base):
__tablename__ = 'user'
id = Column('user_id', Integer, primary_key=True)
name = Column('user_name', String(50))


Where above User.id resolves to a column named user_id and User.name resolves to a column named user_name.

When mapping to an existing table, the :class:.Column object can be referenced directly:

class User(Base):
__table__ = user_table
id = user_table.c.user_id
name = user_table.c.user_name


Or in a classical mapping, placed in the properties dictionary with the desired key:

mapper(User, user_table, properties={
'id': user_table.c.user_id,
'name': user_table.c.user_name,
})


In the next section we'll examine the usage of .key more closely.

Automating Column Naming Schemes from Reflected Tables

In the previous section :ref:mapper_column_distinct_names, we showed how a :class:.Column explicitly mapped to a class can have a different attribute name than the column. But what if we aren't listing out :class:.Column objects explicitly, and instead are automating the production of :class:.Table objects using reflection (e.g. as described in :ref:metadata_reflection_toplevel)? In this case we can make use of the :meth:.DDLEvents.column_reflect event to intercept the production of :class:.Column objects and provide them with the :attr:.Column.key of our choice:

@event.listens_for(Table, "column_reflect")
def column_reflect(inspector, table, column_info):
# set column.key = "attr_<lower_case_name>"
column_info['key'] = "attr_%s" % column_info['name'].lower()


With the above event, the reflection of :class:.Column objects will be intercepted with our event that adds a new ".key" element, such as in a mapping as below:

class MyClass(Base):


If we want to qualify our event to only react for the specific :class:.MetaData object above, we can check for it in our event:

@event.listens_for(Table, "column_reflect")
def column_reflect(inspector, table, column_info):
# set column.key = "attr_<lower_case_name>"
column_info['key'] = "attr_%s" % column_info['name'].lower()


Naming All Columns with a Prefix

A quick approach to prefix column names, typically when mapping to an existing :class:.Table object, is to use column_prefix:

class User(Base):
__table__ = user_table
__mapper_args__ = {'column_prefix':'_'}


The above will place attribute names such as _user_id, _user_name, _password etc. on the mapped User class.

This approach is uncommon in modern usage. For dealing with reflected tables, a more flexible approach is to use that described in :ref:mapper_automated_reflection_schemes.

Using column_property for column level options

Options can be specified when mapping a :class:.Column using the :func:.column_property function. This function explicitly creates the :class:.ColumnProperty used by the :func:.mapper to keep track of the :class:.Column; normally, the :func:.mapper creates this automatically. Using :func:.column_property, we can pass additional arguments about how we'd like the :class:.Column to be mapped. Below, we pass an option active_history, which specifies that a change to this column's value should result in the former value being loaded first:

from sqlalchemy.orm import column_property

class User(Base):
__tablename__ = 'user'

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


:func:.column_property is also used to map a single attribute to multiple columns. This use case arises when mapping to a :func:~.expression.join which has attributes which are equated to each other:

class User(Base):

# assign "user.id", "address.user_id" to the
# "id" attribute


For more examples featuring this usage, see :ref:maptojoin.

Another place where :func:.column_property is needed is to specify SQL expressions as mapped attributes, such as below where we create an attribute fullname that is the string concatenation of the firstname and lastname columns:

class User(Base):
__tablename__ = 'user'
id = Column(Integer, primary_key=True)
firstname = Column(String(50))
lastname = Column(String(50))
fullname = column_property(firstname + " " + lastname)


See examples of this usage at :ref:mapper_sql_expressions.

Mapping a Subset of Table Columns

Sometimes, a :class:.Table object was made available using the reflection process described at :ref:metadata_reflection to load the table's structure from the database. For such a table that has lots of columns that don't need to be referenced in the application, the include_properties or exclude_properties arguments can specify that only a subset of columns should be mapped. For example:

class User(Base):
__table__ = user_table
__mapper_args__ = {
'include_properties' :['user_id', 'user_name']
}


...will map the User class to the user_table table, only including the user_id and user_name columns - the rest are not referenced. Similarly:

class Address(Base):
__mapper_args__ = {
'exclude_properties' : ['street', 'city', 'state', 'zip']
}


...will map the Address class to the address_table table, including all columns present except street, city, state, and zip.

When this mapping is used, the columns that are not included will not be referenced in any SELECT statements emitted by :class:.Query, nor will there be any mapped attribute on the mapped class which represents the column; assigning an attribute of that name will have no effect beyond that of a normal Python attribute assignment.

In some cases, multiple columns may have the same name, such as when mapping to a join of two or more tables that share some column name. include_properties and exclude_properties can also accommodate :class:.Column objects to more accurately describe which columns should be included or excluded:

class UserAddress(Base):
__mapper_args__ = {
'primary_key' : [user_table.c.id]
}


Note

insert and update defaults configured on individual :class:.Column objects, i.e. those described at :ref:metadata_defaults including those configured by the default, update, server_default and server_onupdate arguments, will continue to function normally even if those :class:.Column objects are not mapped. This is because in the case of default and update, the :class:.Column object is still present on the underlying :class:.Table, thus allowing the default functions to take place when the ORM emits an INSERT or UPDATE, and in the case of server_default and server_onupdate, the relational database itself maintains these functions.

This feature allows particular columns of a table be loaded only upon direct access, instead of when the entity is queried using :class:.Query. This feature is useful when one wants to avoid loading a large text or binary field into memory when it's not needed. Individual columns can be lazy loaded by themselves or placed into groups that lazy-load together, using the :func:.orm.deferred function to mark them as "deferred". In the example below, we define a mapping that will load each of .excerpt and .photo in separate, individual-row SELECT statements when each attribute is first referenced on the individual object instance:

from sqlalchemy.orm import deferred
from sqlalchemy import Integer, String, Text, Binary, Column

class Book(Base):
__tablename__ = 'book'

book_id = Column(Integer, primary_key=True)
title = Column(String(200), nullable=False)
summary = Column(String(2000))
excerpt = deferred(Column(Text))
photo = deferred(Column(Binary))


Classical mappings as always place the usage of :func:.orm.deferred in the properties dictionary against the table-bound :class:.Column:

mapper(Book, book_table, properties={
'photo':deferred(book_table.c.photo)
})


Deferred columns can be associated with a "group" name, so that they load together when any of them are first accessed. The example below defines a mapping with a photos deferred group. When one .photo is accessed, all three photos will be loaded in one SELECT statement. The .excerpt will be loaded separately when it is accessed:

class Book(Base):
__tablename__ = 'book'

book_id = Column(Integer, primary_key=True)
title = Column(String(200), nullable=False)
summary = Column(String(2000))
excerpt = deferred(Column(Text))
photo1 = deferred(Column(Binary), group='photos')
photo2 = deferred(Column(Binary), group='photos')
photo3 = deferred(Column(Binary), group='photos')


You can defer or undefer columns at the :class:~sqlalchemy.orm.query.Query level using the :func:.orm.defer and :func:.orm.undefer query options:

from sqlalchemy.orm import defer, undefer

query = session.query(Book)
query.options(defer('summary')).all()
query.options(undefer('excerpt')).all()


And an entire "deferred group", i.e. which uses the group keyword argument to :func:.orm.deferred, can be undeferred using :func:.orm.undefer_group, sending in the group name:

from sqlalchemy.orm import undefer_group

query = session.query(Book)
query.options(undefer_group('photos')).all()


SQL Expressions as Mapped Attributes

Attributes on a mapped class can be linked to SQL expressions, which can be used in queries.

Using a Hybrid

The easiest and most flexible way to link relatively simple SQL expressions to a class is to use a so-called "hybrid attribute", described in the section :ref:hybrids_toplevel. The hybrid provides for an expression that works at both the Python level as well as at the SQL expression level. For example, below we map a class User, containing attributes firstname and lastname, and include a hybrid that will provide for us the fullname, which is the string concatenation of the two:

from sqlalchemy.ext.hybrid import hybrid_property

class User(Base):
__tablename__ = 'user'
id = Column(Integer, primary_key=True)
firstname = Column(String(50))
lastname = Column(String(50))

@hybrid_property
def fullname(self):
return self.firstname + " " + self.lastname


Above, the fullname attribute is interpreted at both the instance and class level, so that it is available from an instance:

some_user = session.query(User).first()
print some_user.fullname


as well as usable wtihin queries:

some_user = session.query(User).filter(User.fullname == "John Smith").first()


The string concatenation example is a simple one, where the Python expression can be dual purposed at the instance and class level. Often, the SQL expression must be distinguished from the Python expression, which can be achieved using :meth:.hybrid_property.expression. Below we illustrate the case where a conditional needs to be present inside the hybrid, using the if statement in Python and the :func:.sql.expression.case construct for SQL expressions:

from sqlalchemy.ext.hybrid import hybrid_property
from sqlalchemy.sql import case

class User(Base):
__tablename__ = 'user'
id = Column(Integer, primary_key=True)
firstname = Column(String(50))
lastname = Column(String(50))

@hybrid_property
def fullname(self):
if self.firstname is not None:
return self.firstname + " " + self.lastname
else:
return self.lastname

@fullname.expression
def fullname(cls):
return case([
(cls.firstname != None, cls.firstname + " " + cls.lastname),
], else_ = cls.lastname)


Using column_property

The :func:.orm.column_property function can be used to map a SQL expression in a manner similar to a regularly mapped :class:.Column. With this technique, the attribute is loaded along with all other column-mapped attributes at load time. This is in some cases an advantage over the usage of hybrids, as the value can be loaded up front at the same time as the parent row of the object, particularly if the expression is one which links to other tables (typically as a correlated subquery) to access data that wouldn't normally be available on an already loaded object.

Disadvantages to using :func:.orm.column_property for SQL expressions include that the expression must be compatible with the SELECT statement emitted for the class as a whole, and there are also some configurational quirks which can occur when using :func:.orm.column_property from declarative mixins.

Our "fullname" example can be expressed using :func:.orm.column_property as follows:

from sqlalchemy.orm import column_property

class User(Base):
__tablename__ = 'user'
id = Column(Integer, primary_key=True)
firstname = Column(String(50))
lastname = Column(String(50))
fullname = column_property(firstname + " " + lastname)


Correlated subqueries may be used as well. Below we use the :func:.select construct to create a SELECT that links together the count of Address objects available for a particular User:

from sqlalchemy.orm import column_property
from sqlalchemy import select, func
from sqlalchemy import Column, Integer, String, ForeignKey

from sqlalchemy.ext.declarative import declarative_base

Base = declarative_base()

id = Column(Integer, primary_key=True)
user_id = Column(Integer, ForeignKey('user.id'))

class User(Base):
__tablename__ = 'user'
id = Column(Integer, primary_key=True)
)


In the above example, we define a :func:.select construct like the following:

select([func.count(Address.id)]).\


The meaning of the above statement is, select the count of Address.id rows where the Address.user_id column is equated to id, which in the context of the User class is the :class:.Column named id (note that id is also the name of a Python built in function, which is not what we want to use here - if we were outside of the User class definition, we'd use User.id).

The :meth:.select.correlate_except directive indicates that each element in the FROM clause of this :func:.select may be omitted from the FROM list (that is, correlated to the enclosing SELECT statement against User) except for the one corresponding to Address. This isn't strictly necessary, but prevents Address from being inadvertently omitted from the FROM list in the case of a long string of joins between User and Address tables where SELECT statements against Address are nested.

If import issues prevent the :func:.column_property from being defined inline with the class, it can be assigned to the class after both are configured. In Declarative this has the effect of calling :meth:.Mapper.add_property to add an additional property after the fact:

User.address_count = column_property(
)


For many-to-many relationships, use :func:.and_ to join the fields of the association table to both tables in a relation, illustrated here with a classical mapping:

from sqlalchemy import and_

mapper(Author, authors, properties={
'book_count': column_property(
select([func.count(books.c.id)],
and_(
book_authors.c.author_id==authors.c.id,
book_authors.c.book_id==books.c.id
)))
})


Using a plain descriptor

In cases where a SQL query more elaborate than what :func:.orm.column_property or :class:.hybrid_property can provide must be emitted, a regular Python function accessed as an attribute can be used, assuming the expression only needs to be available on an already-loaded instance. The function is decorated with Python's own @property decorator to mark it as a read-only attribute. Within the function, :func:.object_session is used to locate the :class:.Session corresponding to the current object, which is then used to emit a query:

from sqlalchemy.orm import object_session
from sqlalchemy import select, func

class User(Base):
__tablename__ = 'user'
id = Column(Integer, primary_key=True)
firstname = Column(String(50))
lastname = Column(String(50))

@property
return object_session(self).\
scalar(
)


The plain descriptor approach is useful as a last resort, but is less performant in the usual case than both the hybrid and column property approaches, in that it needs to emit a SQL query upon each access.

Changing Attribute Behavior

Simple Validators

A quick way to add a "validation" routine to an attribute is to use the :func:~sqlalchemy.orm.validates decorator. An attribute validator can raise an exception, halting the process of mutating the attribute's value, or can change the given value into something different. Validators, like all attribute extensions, are only called by normal userland code; they are not issued when the ORM is populating the object:

from sqlalchemy.orm import validates

id = Column(Integer, primary_key=True)
email = Column(String)

@validates('email')


Validators also receive collection events, when items are added to a collection:

from sqlalchemy.orm import validates

class User(Base):
# ...



Note that the :func:~.validates decorator is a convenience function built on top of attribute events. An application that requires more control over configuration of attribute change behavior can make use of this system, described at :class:~.AttributeEvents.

Using Descriptors and Hybrids

A more comprehensive way to produce modified behavior for an attribute is to use descriptors. These are commonly used in Python using the property() function. The standard SQLAlchemy technique for descriptors is to create a plain descriptor, and to have it read/write from a mapped attribute with a different name. Below we illustrate this using Python 2.6-style properties:

class EmailAddress(Base):

id = Column(Integer, primary_key=True)

# name the attribute with an underscore,
# different from the column name
_email = Column("email", String)

# then create an ".email" attribute
# to get/set "._email"
@property
def email(self):
return self._email

@email.setter
def email(self, email):
self._email = email


The approach above will work, but there's more we can add. While our EmailAddress object will shuttle the value through the email descriptor and into the _email mapped attribute, the class level EmailAddress.email attribute does not have the usual expression semantics usable with :class:.Query. To provide these, we instead use the :mod:~sqlalchemy.ext.hybrid extension as follows:

from sqlalchemy.ext.hybrid import hybrid_property

id = Column(Integer, primary_key=True)

_email = Column("email", String)

@hybrid_property
def email(self):
return self._email

@email.setter
def email(self, email):
self._email = email


The .email attribute, in addition to providing getter/setter behavior when we have an instance of EmailAddress, also provides a SQL expression when used at the class level, that is, from the EmailAddress class directly:

from sqlalchemy.orm import Session
session = Session()

one()
{stop}

{sql}session.commit()
COMMIT
{stop}


The :class:~.hybrid_property also allows us to change the behavior of the attribute, including defining separate behaviors when the attribute is accessed at the instance level versus at the class/expression level, using the :meth:.hybrid_property.expression modifier. Such as, if we wanted to add a host name automatically, we might define two sets of string manipulation logic:

class EmailAddress(Base):

id = Column(Integer, primary_key=True)

_email = Column("email", String)

@hybrid_property
def email(self):
"""Return the value of _email up until the last twelve
characters."""

return self._email[:-12]

@email.setter
def email(self, email):
"""Set the value of _email, tacking on the twelve character
value @example.com."""

self._email = email + "@example.com"

@email.expression
def email(cls):
"""Produce a SQL expression that represents the value
of the _email column, minus the last twelve characters."""

return func.substr(cls._email, 0, func.length(cls._email) - 12)


Above, accessing the email property of an instance of EmailAddress will return the value of the _email attribute, removing or adding the hostname @example.com from the value. When we query against the email attribute, a SQL function is rendered which produces the same effect:

{sql}address = session.query(EmailAddress).filter(EmailAddress.email == 'address').one()
{stop}


Read more about Hybrids at :ref:hybrids_toplevel.

Synonyms

Synonyms are a mapper-level construct that applies expression behavior to a descriptor based attribute.

Operator Customization

The "operators" used by the SQLAlchemy ORM and Core expression language are fully customizable. For example, the comparison expression User.name == 'ed' makes usage of an operator built into Python itself called operator.eq - the actual SQL construct which SQLAlchemy associates with such an operator can be modified. New operations can be associated with column expressions as well. The operators which take place for column expressions are most directly redefined at the type level - see the section :ref:types_operators for a description.

ORM level functions like :func:.column_property, :func:.relationship, and :func:.composite also provide for operator redefinition at the ORM level, by passing a :class:.PropComparator subclass to the comparator_factory argument of each function. Customization of operators at this level is a rare use case. See the documentation at :class:.PropComparator for an overview.

Composite Column Types

Sets of columns can be associated with a single user-defined datatype. The ORM provides a single attribute which represents the group of columns using the class you provide.

A simple example represents pairs of columns as a Point object. Point represents such a pair as .x and .y:

class Point(object):
def __init__(self, x, y):
self.x = x
self.y = y

def __composite_values__(self):
return self.x, self.y

def __repr__(self):
return "Point(x=%r, y=%r)" % (self.x, self.y)

def __eq__(self, other):
return isinstance(other, Point) and \
other.x == self.x and \
other.y == self.y

def __ne__(self, other):
return not self.__eq__(other)


The requirements for the custom datatype class are that it have a constructor which accepts positional arguments corresponding to its column format, and also provides a method __composite_values__() which returns the state of the object as a list or tuple, in order of its column-based attributes. It also should supply adequate __eq__() and __ne__() methods which test the equality of two instances.

We will create a mapping to a table vertice, which represents two points as x1/y1 and x2/y2. These are created normally as :class:.Column objects. Then, the :func:.composite function is used to assign new attributes that will represent sets of columns via the Point class:

from sqlalchemy import Column, Integer
from sqlalchemy.orm import composite
from sqlalchemy.ext.declarative import declarative_base

Base = declarative_base()

class Vertex(Base):
__tablename__ = 'vertice'

id = Column(Integer, primary_key=True)
x1 = Column(Integer)
y1 = Column(Integer)
x2 = Column(Integer)
y2 = Column(Integer)

start = composite(Point, x1, y1)
end = composite(Point, x2, y2)


A classical mapping above would define each :func:.composite against the existing table:

mapper(Vertex, vertice_table, properties={
'start':composite(Point, vertice_table.c.x1, vertice_table.c.y1),
'end':composite(Point, vertice_table.c.x2, vertice_table.c.y2),
})


We can now persist and use Vertex instances, as well as query for them, using the .start and .end attributes against ad-hoc Point instances:

>>> v = Vertex(start=Point(3, 4), end=Point(5, 6))
>>> q = session.query(Vertex).filter(Vertex.start == Point(3, 4))
{sql}>>> print q.first().start
BEGIN (implicit)
INSERT INTO vertice (x1, y1, x2, y2) VALUES (?, ?, ?, ?)
(3, 4, 5, 6)
SELECT vertice.id AS vertice_id,
vertice.x1 AS vertice_x1,
vertice.y1 AS vertice_y1,
vertice.x2 AS vertice_x2,
vertice.y2 AS vertice_y2
FROM vertice
WHERE vertice.x1 = ? AND vertice.y1 = ?
LIMIT ? OFFSET ?
(3, 4, 1, 0)
{stop}Point(x=3, y=4)


Tracking In-Place Mutations on Composites

In-place changes to an existing composite value are not tracked automatically. Instead, the composite class needs to provide events to its parent object explicitly. This task is largely automated via the usage of the :class:.MutableComposite mixin, which uses events to associate each user-defined composite object with all parent associations. Please see the example in :ref:mutable_composites.

Redefining Comparison Operations for Composites

The "equals" comparison operation by default produces an AND of all corresponding columns equated to one another. This can be changed using the comparator_factory argument to :func:.composite, where we specify a custom :class:.CompositeProperty.Comparator class to define existing or new operations. Below we illustrate the "greater than" operator, implementing the same expression that the base "greater than" does:

from sqlalchemy.orm.properties import CompositeProperty
from sqlalchemy import sql

class PointComparator(CompositeProperty.Comparator):
def __gt__(self, other):
"""redefine the 'greater than' operation"""

return sql.and_(*[a>b for a, b in
zip(self.__clause_element__().clauses,
other.__composite_values__())])

class Vertex(Base):
___tablename__ = 'vertice'

id = Column(Integer, primary_key=True)
x1 = Column(Integer)
y1 = Column(Integer)
x2 = Column(Integer)
y2 = Column(Integer)

start = composite(Point, x1, y1,
comparator_factory=PointComparator)
end = composite(Point, x2, y2,
comparator_factory=PointComparator)


Mapping a Class against Multiple Tables

Mappers can be constructed against arbitrary relational units (called selectables) in addition to plain tables. For example, the :func:~.expression.join function creates a selectable unit comprised of multiple tables, complete with its own composite primary key, which can be mapped in the same way as a :class:.Table:

from sqlalchemy import Table, Column, Integer, \
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import column_property

# define two Table objects
Column('id', Integer, primary_key=True),
Column('name', String),
)

Column('id', Integer, primary_key=True),
Column('user_id', Integer, ForeignKey('user.id')),
)

# define a join between them.  This
# takes place across the user.id and address.user_id
# columns.

Base = declarative_base()

# map to it



In the example above, the join expresses columns for both the user and the address table. The user.id and address.user_id columns are equated by foreign key, so in the mapping they are defined as one attribute, AddressUser.id, using :func:.column_property to indicate a specialized column mapping. Based on this part of the configuration, the mapping will copy new primary key values from user.id into the address.user_id column when a flush occurs.

Additionally, the address.id column is mapped explicitly to an attribute named address_id. This is to disambiguate the mapping of the address.id column from the same-named AddressUser.id attribute, which here has been assigned to refer to the user table combined with the address.user_id foreign key.

The natural primary key of the above mapping is the composite of (user.id, address.id), as these are the primary key columns of the user and address table combined together. The identity of an AddressUser object will be in terms of these two values, and is represented from an AddressUser object as (AddressUser.id, AddressUser.address_id).

Mapping a Class against Arbitrary Selects

Similar to mapping against a join, a plain :func:~.expression.select object can be used with a mapper as well. The example fragment below illustrates mapping a class called Customer to a :func:~.expression.select which includes a join to a subquery:

from sqlalchemy import select, func

subq = select([
func.count(orders.c.id).label('order_count'),
func.max(orders.c.price).label('highest_order'),
orders.c.customer_id
]).group_by(orders.c.customer_id).alias()

customer_select = select([customers, subq]).\
select_from(
join(customers, subq,
customers.c.id == subq.c.customer_id)
).alias()

class Customer(Base):
__table__ = customer_select


Above, the full row represented by customer_select will be all the columns of the customers table, in addition to those columns exposed by the subq subquery, which are order_count, highest_order, and customer_id. Mapping the Customer class to this selectable then creates a class which will contain those attributes.

When the ORM persists new instances of Customer, only the customers table will actually receive an INSERT. This is because the primary key of the orders table is not represented in the mapping; the ORM will only emit an INSERT into a table for which it has mapped the primary key.

Note

The practice of mapping to arbitrary SELECT statements, especially complex ones as above, is almost never needed; it necessarily tends to produce complex queries which are often less efficient than that which would be produced by direct query construction. The practice is to some degree based on the very early history of SQLAlchemy where the :func:.mapper construct was meant to represent the primary querying interface; in modern usage, the :class:.Query object can be used to construct virtually any SELECT statement, including complex composites, and should be favored over the "map-to-selectable" approach.

Multiple Mappers for One Class

In modern SQLAlchemy, a particular class is only mapped by one :func:.mapper at a time. The rationale here is that the :func:.mapper modifies the class itself, not only persisting it towards a particular :class:.Table, but also instrumenting attributes upon the class which are structured specifically according to the table metadata.

One potential use case for another mapper to exist at the same time is if we wanted to load instances of our class not just from the immediate :class:.Table to which it is mapped, but from another selectable that is a derivation of that :class:.Table. While there technically is a way to create such a :func:.mapper, using the non_primary=True option, this approach is virtually never needed. Instead, we use the functionality of the :class:.Query object to achieve this, using a method such as :meth:.Query.select_from or :meth:.Query.from_statement to specify a derived selectable.

Another potential use is if we genuinely want instances of our class to be persisted into different tables at different times; certain kinds of data sharding configurations may persist a particular class into tables that are identical in structure except for their name. For this kind of pattern, Python offers a better approach than the complexity of mapping the same class multiple times, which is to instead create new mapped classes for each target table. SQLAlchemy refers to this as the "entity name" pattern, which is described as a recipe at Entity Name.

Constructors and Object Initialization

Mapping imposes no restrictions or requirements on the constructor (__init__) method for the class. You are free to require any arguments for the function that you wish, assign attributes to the instance that are unknown to the ORM, and generally do anything else you would normally do when writing a constructor for a Python class.

The SQLAlchemy ORM does not call __init__ when recreating objects from database rows. The ORM's process is somewhat akin to the Python standard library's pickle module, invoking the low level __new__ method and then quietly restoring attributes directly on the instance rather than calling __init__.

If you need to do some setup on database-loaded instances before they're ready to use, you can use the @reconstructor decorator to tag a method as the ORM counterpart to __init__. SQLAlchemy will call this method with no arguments every time it loads or reconstructs one of your instances. This is useful for recreating transient properties that are normally assigned in your __init__:

from sqlalchemy import orm

class MyMappedClass(object):
def __init__(self, data):
self.data = data
# we need stuff on all instances, but not in the database.
self.stuff = []

@orm.reconstructor
self.stuff = []


When obj = MyMappedClass() is executed, Python calls the __init__ method as normal and the data argument is required. When instances are loaded during a :class:~sqlalchemy.orm.query.Query operation as in query(MyMappedClass).one(), init_on_load is called.

Any method may be tagged as the :func:~sqlalchemy.orm.reconstructor, even the __init__ method. SQLAlchemy will call the reconstructor method with no arguments. Scalar (non-collection) database-mapped attributes of the instance will be available for use within the function. Eagerly-loaded collections are generally not yet available and will usually only contain the first element. ORM state changes made to objects at this stage will not be recorded for the next flush() operation, so the activity within a reconstructor should be conservative.

:func:~sqlalchemy.orm.reconstructor is a shortcut into a larger system of "instance level" events, which can be subscribed to using the event API - see :class:.InstanceEvents for the full API description of these events.

Configuring a Version Counter

The :class:.Mapper supports management of a :term:version id column, which is a single table column that increments or otherwise updates its value each time an UPDATE to the mapped table occurs. This value is checked each time the ORM emits an UPDATE or DELETE against the row to ensure that the value held in memory matches the database value.

The purpose of this feature is to detect when two concurrent transactions are modifying the same row at roughly the same time, or alternatively to provide a guard against the usage of a "stale" row in a system that might be re-using data from a previous transaction without refreshing (e.g. if one sets expire_on_commit=False with a :class:.Session, it is possible to re-use the data from a previous transaction).

When detecting concurrent updates within transactions, it is typically the case that the database's transaction isolation level is below the level of :term:repeatable read; otherwise, the transaction will not be exposed to a new row value created by a concurrent update which conflicts with the locally updated value. In this case, the SQLAlchemy versioning feature will typically not be useful for in-transaction conflict detection, though it still can be used for cross-transaction staleness detection.

The database that enforces repeatable reads will typically either have locked the target row against a concurrent update, or is employing some form of multi version concurrency control such that it will emit an error when the transaction is committed. SQLAlchemy's version_id_col is an alternative which allows version tracking to occur for specific tables within a transaction that otherwise might not have this isolation level set.

Simple Version Counting

The most straightforward way to track versions is to add an integer column to the mapped table, then establish it as the version_id_col within the mapper options:

class User(Base):
__tablename__ = 'user'

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

__mapper_args__ = {
"version_id_col": version_id
}


Above, the User mapping tracks integer versions using the column version_id. When an object of type User is first flushed, the version_id column will be given a value of "1". Then, an UPDATE of the table later on will always be emitted in a manner similar to the following:

UPDATE user SET version_id=:version_id, name=:name
WHERE user.id = :user_id AND user.version_id = :user_version_id
{"name": "new name", "version_id": 2, "user_id": 1, "user_version_id": 1}


The above UPDATE statement is updating the row that not only matches user.id = 1, it also is requiring that user.version_id = 1, where "1" is the last version identifier we've been known to use on this object. If a transaction elsewhere has modifed the row independently, this version id will no longer match, and the UPDATE statement will report that no rows matched; this is the condition that SQLAlchemy tests, that exactly one row matched our UPDATE (or DELETE) statement. If zero rows match, that indicates our version of the data is stale, and a :class:.StaleDataError is raised.

Custom Version Counters / Types

Other kinds of values or counters can be used for versioning. Common types include dates and GUIDs. When using an alternate type or counter scheme, SQLAlchemy provides a hook for this scheme using the version_id_generator argument, which accepts a version generation callable. This callable is passed the value of the current known version, and is expected to return the subsequent version.

For example, if we wanted to track the versioning of our User class using a randomly generated GUID, we could do this (note that some backends support a native GUID type, but we illustrate here using a simple string):

import uuid

class User(Base):
__tablename__ = 'user'

id = Column(Integer, primary_key=True)
version_uuid = Column(String(32))
name = Column(String(50), nullable=False)

__mapper_args__ = {
'version_id_col':version_uuid,
'version_id_generator':lambda version: uuid.uuid4().hex
}


The persistence engine will call upon uuid.uuid4() each time a User object is subject to an INSERT or an UPDATE. In this case, our version generation function can disregard the incoming value of version, as the uuid4() function generates identifiers without any prerequisite value. If we were using a sequential versioning scheme such as numeric or a special character system, we could make use of the given version in order to help determine the subsequent value.

Class Mapping API

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