SQLAlchemy 2.0 Documentation
SQLAlchemy ORM
- ORM Quick Start
- ORM Mapped Class Configuration
- ORM Mapped Class Overview
- Mapping Classes with Declarative
- Integration with dataclasses and attrs
- SQL Expressions as Mapped Attributes
- Changing Attribute Behavior
- Composite Column Types
- Mapping Class Inheritance Hierarchies
- Non-Traditional Mappings¶
- Configuring a Version Counter
- Class Mapping API
- Mapping SQL Expressions
- Relationship Configuration
- ORM Querying Guide
- Using the Session
- Events and Internals
- ORM Extensions
- ORM Examples
Project Versions
Non-Traditional Mappings¶
Mapping a Class against Multiple Tables¶
Mappers can be constructed against arbitrary relational units (called
selectables) in addition to plain tables. For example, the 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 Table
:
from sqlalchemy import Table, Column, Integer, String, MetaData, join, ForeignKey
from sqlalchemy.orm import DeclarativeBase
from sqlalchemy.orm import column_property
metadata_obj = MetaData()
# define two Table objects
user_table = Table(
"user",
metadata_obj,
Column("id", Integer, primary_key=True),
Column("name", String),
)
address_table = Table(
"address",
metadata_obj,
Column("id", Integer, primary_key=True),
Column("user_id", Integer, ForeignKey("user.id")),
Column("email_address", String),
)
# define a join between them. This
# takes place across the user.id and address.user_id
# columns.
user_address_join = join(user_table, address_table)
class Base(DeclarativeBase):
metadata = metadata_obj
# map to it
class AddressUser(Base):
__table__ = user_address_join
id = column_property(user_table.c.id, address_table.c.user_id)
address_id = address_table.c.id
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 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)
.
When referring to the AddressUser.id
column, most SQL expressions will
make use of only the first column in the list of columns mapped, as the
two columns are synonymous. However, for the special use case such as
a GROUP BY expression where both columns must be referenced at the same
time while making use of the proper context, that is, accommodating for
aliases and similar, the accessor Comparator.expressions
may be used:
stmt = select(AddressUser).group_by(*AddressUser.id.expressions)
New in version 1.3.17: Added the
Comparator.expressions
accessor.
Note
A mapping against multiple tables as illustrated above supports persistence, that is, INSERT, UPDATE and DELETE of rows within the targeted tables. However, it does not support an operation that would UPDATE one table and perform INSERT or DELETE on others at the same time for one record. That is, if a record PtoQ is mapped to tables “p” and “q”, where it has a row based on a LEFT OUTER JOIN of “p” and “q”, if an UPDATE proceeds that is to alter data in the “q” table in an existing record, the row in “q” must exist; it won’t emit an INSERT if the primary key identity is already present. If the row does not exist, for most DBAPI drivers which support reporting the number of rows affected by an UPDATE, the ORM will fail to detect an updated row and raise an error; otherwise, the data would be silently ignored.
A recipe to allow for an on-the-fly “insert” of the related row might make use of the .MapperEvents.before_update event and look like:
from sqlalchemy import event
@event.listens_for(PtoQ, "before_update")
def receive_before_update(mapper, connection, target):
if target.some_required_attr_on_q is None:
connection.execute(q_table.insert(), {"id": target.id})
where above, a row is INSERTed into the q_table
table by creating an
INSERT construct with Table.insert()
, then executing it using the
given Connection
which is the same one being used to emit other
SQL for the flush process. The user-supplied logic would have to detect
that the LEFT OUTER JOIN from “p” to “q” does not have an entry for the “q”
side.
Mapping a Class against Arbitrary Subqueries¶
Similar to mapping against a join, a plain select()
object
can be used with a mapper as well. The example fragment below illustrates
mapping a class called Customer
to a 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)
.subquery()
)
customer_select = (
select(customers, subq)
.join_from(customers, subq, customers.c.id == subq.c.customer_id)
.subquery()
)
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 Mapper
construct was meant to represent the primary querying interface;
in modern usage, the 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 mapped by only one so-called
primary mapper at a time. This mapper is involved in three main areas of
functionality: querying, persistence, and instrumentation of the mapped class.
The rationale of the primary mapper relates to the fact that the
Mapper
modifies the class itself, not only persisting it towards a
particular Table
, but also instrumenting attributes upon the
class which are structured specifically according to the table metadata. It’s
not possible for more than one mapper to be associated with a class in equal
measure, since only one mapper can actually instrument the class.
The concept of a “non-primary” mapper had existed for many versions of
SQLAlchemy however as of version 1.3 this feature is deprecated. The
one case where such a non-primary mapper is useful is when constructing
a relationship to a class against an alternative selectable. This
use case is now suited using the aliased
construct and is described
at Relationship to Aliased Class.
As far as the use case of a class that can actually be fully persisted to different tables under different scenarios, very early versions of SQLAlchemy offered a feature for this adapted from Hibernate, known as the “entity name” feature. However, this use case became infeasible within SQLAlchemy once the mapped class itself became the source of SQL expression construction; that is, the class’ attributes themselves link directly to mapped table columns. The feature was removed and replaced with a simple recipe-oriented approach to accomplishing this task without any ambiguity of instrumentation - to create new subclasses, each mapped individually. This pattern is now available as a recipe at Entity Name.
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