SQLAlchemy 2.0 Documentation
SQLAlchemy ORM
- ORM Quick Start
- ORM Mapped Class Configuration
- Relationship Configuration
- ORM Querying Guide
- Writing SELECT statements for ORM Mapped Classes¶
- Selecting ORM Entities and Attributes
- Selecting ORM Entities
- Selecting Multiple ORM Entities Simultaneously
- Selecting Individual Attributes
- Grouping Selected Attributes with Bundles
- Selecting ORM Aliases
- Getting ORM Results from Textual Statements
- Selecting Entities from Subqueries
- Selecting Entities from UNIONs and other set operations
- Joins
- Simple Relationship Joins
- Chaining Multiple Joins
- Joins to a Target Entity
- Joins to a Target with an ON Clause
- Combining Relationship with Custom ON Criteria
- Using Relationship to join between aliased targets
- Joining to Subqueries
- Joining to Subqueries along Relationship paths
- Subqueries that Refer to Multiple Entities
- Setting the leftmost FROM clause in a join
- Relationship WHERE Operators
- Selecting ORM Entities and Attributes
- Writing SELECT statements for Inheritance Mappings
- ORM-Enabled INSERT, UPDATE, and DELETE statements
- Column Loading Options
- Relationship Loading Techniques
- ORM API Features for Querying
- Legacy Query API
- Writing SELECT statements for ORM Mapped Classes¶
- Using the Session
- Events and Internals
- ORM Extensions
- ORM Examples
Project Versions
- Previous: ORM Querying Guide
- Next: Writing SELECT statements for Inheritance Mappings
- Up: Home
- On this page:
- Writing SELECT statements for ORM Mapped Classes
- Selecting ORM Entities and Attributes
- Selecting ORM Entities
- Selecting Multiple ORM Entities Simultaneously
- Selecting Individual Attributes
- Grouping Selected Attributes with Bundles
- Selecting ORM Aliases
- Getting ORM Results from Textual Statements
- Selecting Entities from Subqueries
- Selecting Entities from UNIONs and other set operations
- Joins
- Simple Relationship Joins
- Chaining Multiple Joins
- Joins to a Target Entity
- Joins to a Target with an ON Clause
- Combining Relationship with Custom ON Criteria
- Using Relationship to join between aliased targets
- Joining to Subqueries
- Joining to Subqueries along Relationship paths
- Subqueries that Refer to Multiple Entities
- Setting the leftmost FROM clause in a join
- Relationship WHERE Operators
- Selecting ORM Entities and Attributes
Writing SELECT statements for ORM Mapped Classes¶
About this Document
This section makes use of ORM mappings first illustrated in the SQLAlchemy Unified Tutorial, shown in the section Declaring Mapped Classes.
SELECT statements are produced by the select()
function which
returns a Select
object. The entities and/or SQL expressions
to return (i.e. the “columns” clause) are passed positionally to the
function. From there, additional methods are used to generate the complete
statement, such as the Select.where()
method illustrated below:
>>> from sqlalchemy import select
>>> stmt = select(User).where(User.name == "spongebob")
Given a completed Select
object, in order to execute it within
the ORM to get rows back, the object is passed to
Session.execute()
, where a Result
object is then
returned:
>>> result = session.execute(stmt)
SELECT user_account.id, user_account.name, user_account.fullname
FROM user_account
WHERE user_account.name = ?
[...] ('spongebob',)
>>> for user_obj in result.scalars():
... print(f"{user_obj.name} {user_obj.fullname}")
spongebob Spongebob Squarepants
Selecting ORM Entities and Attributes¶
The select()
construct accepts ORM entities, including mapped
classes as well as class-level attributes representing mapped columns, which
are converted into ORM-annotated FromClause
and
ColumnElement
elements at construction time.
A Select
object that contains ORM-annotated entities is normally
executed using a Session
object, and not a Connection
object, so that ORM-related features may take effect, including that
instances of ORM-mapped objects may be returned. When using the
Connection
directly, result rows will only contain
column-level data.
Selecting ORM Entities¶
Below we select from the User
entity, producing a Select
that selects from the mapped Table
to which User
is mapped:
>>> result = session.execute(select(User).order_by(User.id))
SELECT user_account.id, user_account.name, user_account.fullname
FROM user_account ORDER BY user_account.id
[...] ()
When selecting from ORM entities, the entity itself is returned in the result
as a row with a single element, as opposed to a series of individual columns;
for example above, the Result
returns Row
objects that have just a single element per row, that element holding onto a
User
object:
>>> result.all()
[(User(id=1, name='spongebob', fullname='Spongebob Squarepants'),),
(User(id=2, name='sandy', fullname='Sandy Cheeks'),),
(User(id=3, name='patrick', fullname='Patrick Star'),),
(User(id=4, name='squidward', fullname='Squidward Tentacles'),),
(User(id=5, name='ehkrabs', fullname='Eugene H. Krabs'),)]
When selecting a list of single-element rows containing ORM entities, it is
typical to skip the generation of Row
objects and instead
receive ORM entities directly. This is most easily achieved by using the
Session.scalars()
method to execute, rather than the
Session.execute()
method, so that a ScalarResult
object
which yields single elements rather than rows is returned:
>>> session.scalars(select(User).order_by(User.id)).all()
SELECT user_account.id, user_account.name, user_account.fullname
FROM user_account ORDER BY user_account.id
[...] ()
[User(id=1, name='spongebob', fullname='Spongebob Squarepants'),
User(id=2, name='sandy', fullname='Sandy Cheeks'),
User(id=3, name='patrick', fullname='Patrick Star'),
User(id=4, name='squidward', fullname='Squidward Tentacles'),
User(id=5, name='ehkrabs', fullname='Eugene H. Krabs')]
Calling the Session.scalars()
method is the equivalent to calling
upon Session.execute()
to receive a Result
object,
then calling upon Result.scalars()
to receive a
ScalarResult
object.
Selecting Multiple ORM Entities Simultaneously¶
The select()
function accepts any number of ORM classes and/or
column expressions at once, including that multiple ORM classes may be
requested. When SELECTing from multiple ORM classes, they are named
in each result row based on their class name. In the example below,
the result rows for a SELECT against User
and Address
will
refer to them under the names User
and Address
:
>>> stmt = select(User, Address).join(User.addresses).order_by(User.id, Address.id)
>>> for row in session.execute(stmt):
... print(f"{row.User.name} {row.Address.email_address}")
SELECT user_account.id, user_account.name, user_account.fullname,
address.id AS id_1, address.user_id, address.email_address
FROM user_account JOIN address ON user_account.id = address.user_id
ORDER BY user_account.id, address.id
[...] ()
spongebob spongebob@sqlalchemy.org
sandy sandy@sqlalchemy.org
sandy squirrel@squirrelpower.org
patrick pat999@aol.com
squidward stentcl@sqlalchemy.org
If we wanted to assign different names to these entities in the rows, we would
use the aliased()
construct using the aliased.name
parameter to alias them with an explicit name:
>>> from sqlalchemy.orm import aliased
>>> user_cls = aliased(User, name="user_cls")
>>> email_cls = aliased(Address, name="email")
>>> stmt = (
... select(user_cls, email_cls)
... .join(user_cls.addresses.of_type(email_cls))
... .order_by(user_cls.id, email_cls.id)
... )
>>> row = session.execute(stmt).first()
SELECT user_cls.id, user_cls.name, user_cls.fullname,
email.id AS id_1, email.user_id, email.email_address
FROM user_account AS user_cls JOIN address AS email
ON user_cls.id = email.user_id ORDER BY user_cls.id, email.id
[...] ()
>>> print(f"{row.user_cls.name} {row.email.email_address}")
spongebob spongebob@sqlalchemy.org
The aliased form above is discussed further at Using Relationship to join between aliased targets.
An existing Select
construct may also have ORM classes and/or
column expressions added to its columns clause using the
Select.add_columns()
method. We can produce the same statement as
above using this form as well:
>>> stmt = (
... select(User).join(User.addresses).add_columns(Address).order_by(User.id, Address.id)
... )
>>> print(stmt)
SELECT user_account.id, user_account.name, user_account.fullname,
address.id AS id_1, address.user_id, address.email_address
FROM user_account JOIN address ON user_account.id = address.user_id
ORDER BY user_account.id, address.id
Selecting Individual Attributes¶
The attributes on a mapped class, such as User.name
and
Address.email_address
, can be used just like Column
or
other SQL expression objects when passed to select()
. Creating a
select()
that is against specific columns will return Row
objects, and not entities like User
or Address
objects.
Each Row
will have each column represented individually:
>>> result = session.execute(
... select(User.name, Address.email_address)
... .join(User.addresses)
... .order_by(User.id, Address.id)
... )
SELECT user_account.name, address.email_address
FROM user_account JOIN address ON user_account.id = address.user_id
ORDER BY user_account.id, address.id
[...] ()
The above statement returns Row
objects with name
and
email_address
columns, as illustrated in the runtime demonstration below:
>>> for row in result:
... print(f"{row.name} {row.email_address}")
spongebob spongebob@sqlalchemy.org
sandy sandy@sqlalchemy.org
sandy squirrel@squirrelpower.org
patrick pat999@aol.com
squidward stentcl@sqlalchemy.org
Grouping Selected Attributes with Bundles¶
The Bundle
construct is an extensible ORM-only construct that
allows sets of column expressions to be grouped in result rows:
>>> from sqlalchemy.orm import Bundle
>>> stmt = select(
... Bundle("user", User.name, User.fullname),
... Bundle("email", Address.email_address),
... ).join_from(User, Address)
>>> for row in session.execute(stmt):
... print(f"{row.user.name} {row.user.fullname} {row.email.email_address}")
SELECT user_account.name, user_account.fullname, address.email_address
FROM user_account JOIN address ON user_account.id = address.user_id
[...] ()
spongebob Spongebob Squarepants spongebob@sqlalchemy.org
sandy Sandy Cheeks sandy@sqlalchemy.org
sandy Sandy Cheeks squirrel@squirrelpower.org
patrick Patrick Star pat999@aol.com
squidward Squidward Tentacles stentcl@sqlalchemy.org
The Bundle
is potentially useful for creating lightweight views
and custom column groupings. Bundle
may also be subclassed in
order to return alternate data structures; see
Bundle.create_row_processor()
for an example.
Selecting ORM Aliases¶
As discussed in the tutorial at Using Aliases, to create a
SQL alias of an ORM entity is achieved using the aliased()
construct against a mapped class:
>>> from sqlalchemy.orm import aliased
>>> u1 = aliased(User)
>>> print(select(u1).order_by(u1.id))
SELECT user_account_1.id, user_account_1.name, user_account_1.fullname
FROM user_account AS user_account_1 ORDER BY user_account_1.id
As is the case when using Table.alias()
, the SQL alias
is anonymously named. For the case of selecting the entity from a row
with an explicit name, the aliased.name
parameter may be
passed as well:
>>> from sqlalchemy.orm import aliased
>>> u1 = aliased(User, name="u1")
>>> stmt = select(u1).order_by(u1.id)
>>> row = session.execute(stmt).first()
SELECT u1.id, u1.name, u1.fullname
FROM user_account AS u1 ORDER BY u1.id
[...] ()
>>> print(f"{row.u1.name}")
spongebob
See also
The aliased
construct is central for several use cases,
including:
making use of subqueries with the ORM; the sections Selecting Entities from Subqueries and Joining to Subqueries discuss this further.
Controlling the name of an entity in a result set; see Selecting Multiple ORM Entities Simultaneously for an example
Joining to the same ORM entity multiple times; see Using Relationship to join between aliased targets for an example.
Getting ORM Results from Textual Statements¶
The ORM supports loading of entities from SELECT statements that come from
other sources. The typical use case is that of a textual SELECT statement,
which in SQLAlchemy is represented using the text()
construct. A
text()
construct can be augmented with information about the
ORM-mapped columns that the statement would load; this can then be associated
with the ORM entity itself so that ORM objects can be loaded based on this
statement.
Given a textual SQL statement we’d like to load from:
>>> from sqlalchemy import text
>>> textual_sql = text("SELECT id, name, fullname FROM user_account ORDER BY id")
We can add column information to the statement by using the
TextClause.columns()
method; when this method is invoked, the
TextClause
object is converted into a TextualSelect
object, which takes on a role that is comparable to the Select
construct. The TextClause.columns()
method
is typically passed Column
objects or equivalent, and in this
case we can make use of the ORM-mapped attributes on the User
class
directly:
>>> textual_sql = textual_sql.columns(User.id, User.name, User.fullname)
We now have an ORM-configured SQL construct that as given, can load the “id”,
“name” and “fullname” columns separately. To use this SELECT statement as a
source of complete User
entities instead, we can link these columns to a
regular ORM-enabled
Select
construct using the Select.from_statement()
method:
>>> orm_sql = select(User).from_statement(textual_sql)
>>> for user_obj in session.execute(orm_sql).scalars():
... print(user_obj)
SELECT id, name, fullname FROM user_account ORDER BY id
[...] ()
User(id=1, name='spongebob', fullname='Spongebob Squarepants')
User(id=2, name='sandy', fullname='Sandy Cheeks')
User(id=3, name='patrick', fullname='Patrick Star')
User(id=4, name='squidward', fullname='Squidward Tentacles')
User(id=5, name='ehkrabs', fullname='Eugene H. Krabs')
The same TextualSelect
object can also be converted into
a subquery using the TextualSelect.subquery()
method,
and linked to the User
entity to it using the aliased()
construct, in a similar manner as discussed below in Selecting Entities from Subqueries:
>>> orm_subquery = aliased(User, textual_sql.subquery())
>>> stmt = select(orm_subquery)
>>> for user_obj in session.execute(stmt).scalars():
... print(user_obj)
SELECT anon_1.id, anon_1.name, anon_1.fullname
FROM (SELECT id, name, fullname FROM user_account ORDER BY id) AS anon_1
[...] ()
User(id=1, name='spongebob', fullname='Spongebob Squarepants')
User(id=2, name='sandy', fullname='Sandy Cheeks')
User(id=3, name='patrick', fullname='Patrick Star')
User(id=4, name='squidward', fullname='Squidward Tentacles')
User(id=5, name='ehkrabs', fullname='Eugene H. Krabs')
The difference between using the TextualSelect
directly with
Select.from_statement()
versus making use of aliased()
is that in the former case, no subquery is produced in the resulting SQL.
This can in some scenarios be advantageous from a performance or complexity
perspective.
Selecting Entities from Subqueries¶
The aliased()
construct discussed in the previous section
can be used with any Subquery
construct that comes from a
method such as Select.subquery()
to link ORM entities to the
columns returned by that subquery; there must be a column correspondence
relationship between the columns delivered by the subquery and the columns
to which the entity is mapped, meaning, the subquery needs to be ultimately
derived from those entities, such as in the example below:
>>> inner_stmt = select(User).where(User.id < 7).order_by(User.id)
>>> subq = inner_stmt.subquery()
>>> aliased_user = aliased(User, subq)
>>> stmt = select(aliased_user)
>>> for user_obj in session.execute(stmt).scalars():
... print(user_obj)
SELECT anon_1.id, anon_1.name, anon_1.fullname
FROM (SELECT user_account.id AS id, user_account.name AS name, user_account.fullname AS fullname
FROM user_account
WHERE user_account.id < ? ORDER BY user_account.id) AS anon_1
[generated in ...] (7,)
User(id=1, name='spongebob', fullname='Spongebob Squarepants')
User(id=2, name='sandy', fullname='Sandy Cheeks')
User(id=3, name='patrick', fullname='Patrick Star')
User(id=4, name='squidward', fullname='Squidward Tentacles')
User(id=5, name='ehkrabs', fullname='Eugene H. Krabs')
Selecting Entities from UNIONs and other set operations¶
The union()
and union_all()
functions are the most
common set operations, which along with other set operations such as
except_()
, intersect()
and others deliver an object known as
a CompoundSelect
, which is composed of multiple
Select
constructs joined by a set-operation keyword. ORM entities may
be selected from simple compound selects using the Select.from_statement()
method illustrated previously at Getting ORM Results from Textual Statements. In
this method, the UNION statement is the complete statement that will be
rendered, no additional criteria can be added after Select.from_statement()
is used:
>>> from sqlalchemy import union_all
>>> u = union_all(
... select(User).where(User.id < 2), select(User).where(User.id == 3)
... ).order_by(User.id)
>>> stmt = select(User).from_statement(u)
>>> for user_obj in session.execute(stmt).scalars():
... print(user_obj)
SELECT user_account.id, user_account.name, user_account.fullname
FROM user_account
WHERE user_account.id < ? UNION ALL SELECT user_account.id, user_account.name, user_account.fullname
FROM user_account
WHERE user_account.id = ? ORDER BY id
[generated in ...] (2, 3)
User(id=1, name='spongebob', fullname='Spongebob Squarepants')
User(id=3, name='patrick', fullname='Patrick Star')
A CompoundSelect
construct can be more flexibly used within
a query that can be further modified by organizing it into a subquery
and linking it to an ORM entity using aliased()
,
as illustrated previously at Selecting Entities from Subqueries. In the
example below, we first use CompoundSelect.subquery()
to create
a subquery of the UNION ALL statement, we then package that into the
aliased()
construct where it can be used like any other mapped
entity in a select()
construct, including that we can add filtering
and order by criteria based on its exported columns:
>>> subq = union_all(
... select(User).where(User.id < 2), select(User).where(User.id == 3)
... ).subquery()
>>> user_alias = aliased(User, subq)
>>> stmt = select(user_alias).order_by(user_alias.id)
>>> for user_obj in session.execute(stmt).scalars():
... print(user_obj)
SELECT anon_1.id, anon_1.name, anon_1.fullname
FROM (SELECT user_account.id AS id, user_account.name AS name, user_account.fullname AS fullname
FROM user_account
WHERE user_account.id < ? UNION ALL SELECT user_account.id AS id, user_account.name AS name, user_account.fullname AS fullname
FROM user_account
WHERE user_account.id = ?) AS anon_1 ORDER BY anon_1.id
[generated in ...] (2, 3)
User(id=1, name='spongebob', fullname='Spongebob Squarepants')
User(id=3, name='patrick', fullname='Patrick Star')
See also
Selecting ORM Entities from Unions - in the SQLAlchemy Unified Tutorial
Joins¶
The Select.join()
and Select.join_from()
methods
are used to construct SQL JOINs against a SELECT statement.
This section will detail ORM use cases for these methods. For a general overview of their use from a Core perspective, see Explicit FROM clauses and JOINs in the SQLAlchemy Unified Tutorial.
The usage of Select.join()
in an ORM context for 2.0 style
queries is mostly equivalent, minus legacy use cases, to the usage of the
Query.join()
method in 1.x style queries.
Simple Relationship Joins¶
Consider a mapping between two classes User
and Address
,
with a relationship User.addresses
representing a collection
of Address
objects associated with each User
. The most
common usage of Select.join()
is to create a JOIN along this
relationship, using the User.addresses
attribute as an indicator
for how this should occur:
>>> stmt = select(User).join(User.addresses)
Where above, the call to Select.join()
along
User.addresses
will result in SQL approximately equivalent to:
>>> print(stmt)
SELECT user_account.id, user_account.name, user_account.fullname
FROM user_account JOIN address ON user_account.id = address.user_id
In the above example we refer to User.addresses
as passed to
Select.join()
as the “on clause”, that is, it indicates
how the “ON” portion of the JOIN should be constructed.
Tip
Note that using Select.join()
to JOIN from one entity to another
affects the FROM clause of the SELECT statement, but not the columns clause;
the SELECT statement in this example will continue to return rows from only
the User
entity. To SELECT
columns / entities from both User
and Address
at the same time,
the Address
entity must also be named in the select()
function,
or added to the Select
construct afterwards using the
Select.add_columns()
method. See the section
Selecting Multiple ORM Entities Simultaneously for examples of both
of these forms.
Chaining Multiple Joins¶
To construct a chain of joins, multiple Select.join()
calls may be
used. The relationship-bound attribute implies both the left and right side of
the join at once. Consider additional entities Order
and Item
, where
the User.orders
relationship refers to the Order
entity, and the
Order.items
relationship refers to the Item
entity, via an association
table order_items
. Two Select.join()
calls will result in
a JOIN first from User
to Order
, and a second from Order
to
Item
. However, since Order.items
is a many to many
relationship, it results in two separate JOIN elements, for a total of three
JOIN elements in the resulting SQL:
>>> stmt = select(User).join(User.orders).join(Order.items)
>>> print(stmt)
SELECT user_account.id, user_account.name, user_account.fullname
FROM user_account
JOIN user_order ON user_account.id = user_order.user_id
JOIN order_items AS order_items_1 ON user_order.id = order_items_1.order_id
JOIN item ON item.id = order_items_1.item_id
The order in which each call to the Select.join()
method
is significant only to the degree that the “left” side of what we would like
to join from needs to be present in the list of FROMs before we indicate a
new target. Select.join()
would not, for example, know how to
join correctly if we were to specify
select(User).join(Order.items).join(User.orders)
, and would raise an
error. In correct practice, the Select.join()
method is invoked
in such a way that lines up with how we would want the JOIN clauses in SQL
to be rendered, and each call should represent a clear link from what
precedes it.
All of the elements that we target in the FROM clause remain available
as potential points to continue joining FROM. We can continue to add
other elements to join FROM the User
entity above, for example adding
on the User.addresses
relationship to our chain of joins:
>>> stmt = select(User).join(User.orders).join(Order.items).join(User.addresses)
>>> print(stmt)
SELECT user_account.id, user_account.name, user_account.fullname
FROM user_account
JOIN user_order ON user_account.id = user_order.user_id
JOIN order_items AS order_items_1 ON user_order.id = order_items_1.order_id
JOIN item ON item.id = order_items_1.item_id
JOIN address ON user_account.id = address.user_id
Joins to a Target Entity¶
A second form of Select.join()
allows any mapped entity or core
selectable construct as a target. In this usage, Select.join()
will attempt to infer the ON clause for the JOIN, using the natural foreign
key relationship between two entities:
>>> stmt = select(User).join(Address)
>>> print(stmt)
SELECT user_account.id, user_account.name, user_account.fullname
FROM user_account JOIN address ON user_account.id = address.user_id
In the above calling form, Select.join()
is called upon to infer
the “on clause” automatically. This calling form will ultimately raise
an error if either there are no ForeignKeyConstraint
setup
between the two mapped Table
constructs, or if there are multiple
ForeignKeyConstraint
linkages between them such that the
appropriate constraint to use is ambiguous.
Note
When making use of Select.join()
or Select.join_from()
without indicating an ON clause, ORM
configured relationship()
constructs are not taken into account.
Only the configured ForeignKeyConstraint
relationships between
the entities at the level of the mapped Table
objects are consulted
when an attempt is made to infer an ON clause for the JOIN.
Joins to a Target with an ON Clause¶
The third calling form allows both the target entity as well as the ON clause to be passed explicitly. A example that includes a SQL expression as the ON clause is as follows:
>>> stmt = select(User).join(Address, User.id == Address.user_id)
>>> print(stmt)
SELECT user_account.id, user_account.name, user_account.fullname
FROM user_account JOIN address ON user_account.id = address.user_id
The expression-based ON clause may also be a relationship()
-bound
attribute, in the same way it’s used in
Simple Relationship Joins:
>>> stmt = select(User).join(Address, User.addresses)
>>> print(stmt)
SELECT user_account.id, user_account.name, user_account.fullname
FROM user_account JOIN address ON user_account.id = address.user_id
The above example seems redundant in that it indicates the target of Address
in two different ways; however, the utility of this form becomes apparent
when joining to aliased entities; see the section
Using Relationship to join between aliased targets for an example.
Combining Relationship with Custom ON Criteria¶
The ON clause generated by the relationship()
construct may
be augmented with additional criteria. This is useful both for
quick ways to limit the scope of a particular join over a relationship path,
as well as for cases like configuring loader strategies such as
joinedload()
and selectinload()
.
The PropComparator.and_()
method accepts a series of SQL expressions positionally that will be joined
to the ON clause of the JOIN via AND. For example if we wanted to
JOIN from User
to Address
but also limit the ON criteria to only certain
email addresses:
>>> stmt = select(User.fullname).join(
... User.addresses.and_(Address.email_address == "squirrel@squirrelpower.org")
... )
>>> session.execute(stmt).all()
SELECT user_account.fullname
FROM user_account
JOIN address ON user_account.id = address.user_id AND address.email_address = ?
[...] ('squirrel@squirrelpower.org',)
[('Sandy Cheeks',)]
See also
The PropComparator.and_()
method also works with loader
strategies such as joinedload()
and selectinload()
.
See the section Adding Criteria to loader options.
Using Relationship to join between aliased targets¶
When constructing joins using relationship()
-bound attributes to indicate
the ON clause, the two-argument syntax illustrated in
Joins to a Target with an ON Clause can be expanded to work with the
aliased()
construct, to indicate a SQL alias as the target of a join
while still making use of the relationship()
-bound attribute
to indicate the ON clause, as in the example below, where the User
entity is joined twice to two different aliased()
constructs
against the Address
entity:
>>> address_alias_1 = aliased(Address)
>>> address_alias_2 = aliased(Address)
>>> stmt = (
... select(User)
... .join(address_alias_1, User.addresses)
... .where(address_alias_1.email_address == "patrick@aol.com")
... .join(address_alias_2, User.addresses)
... .where(address_alias_2.email_address == "patrick@gmail.com")
... )
>>> print(stmt)
SELECT user_account.id, user_account.name, user_account.fullname
FROM user_account
JOIN address AS address_1 ON user_account.id = address_1.user_id
JOIN address AS address_2 ON user_account.id = address_2.user_id
WHERE address_1.email_address = :email_address_1
AND address_2.email_address = :email_address_2
The same pattern may be expressed more succinctly using the
modifier PropComparator.of_type()
, which may be applied to the
relationship()
-bound attribute, passing along the target entity
in order to indicate the target
in one step. The example below uses PropComparator.of_type()
to produce the same SQL statement as the one just illustrated:
>>> print(
... select(User)
... .join(User.addresses.of_type(address_alias_1))
... .where(address_alias_1.email_address == "patrick@aol.com")
... .join(User.addresses.of_type(address_alias_2))
... .where(address_alias_2.email_address == "patrick@gmail.com")
... )
SELECT user_account.id, user_account.name, user_account.fullname
FROM user_account
JOIN address AS address_1 ON user_account.id = address_1.user_id
JOIN address AS address_2 ON user_account.id = address_2.user_id
WHERE address_1.email_address = :email_address_1
AND address_2.email_address = :email_address_2
To make use of a relationship()
to construct a join from an
aliased entity, the attribute is available from the aliased()
construct directly:
>>> user_alias_1 = aliased(User)
>>> print(select(user_alias_1.name).join(user_alias_1.addresses))
SELECT user_account_1.name
FROM user_account AS user_account_1
JOIN address ON user_account_1.id = address.user_id
Joining to Subqueries¶
The target of a join may be any “selectable” entity which includes
subqueries. When using the ORM, it is typical
that these targets are stated in terms of an
aliased()
construct, but this is not strictly required, particularly
if the joined entity is not being returned in the results. For example, to join from the
User
entity to the Address
entity, where the Address
entity
is represented as a row limited subquery, we first construct a Subquery
object using Select.subquery()
, which may then be used as the
target of the Select.join()
method:
>>> subq = select(Address).where(Address.email_address == "pat999@aol.com").subquery()
>>> stmt = select(User).join(subq, User.id == subq.c.user_id)
>>> print(stmt)
SELECT user_account.id, user_account.name, user_account.fullname
FROM user_account
JOIN (SELECT address.id AS id,
address.user_id AS user_id, address.email_address AS email_address
FROM address
WHERE address.email_address = :email_address_1) AS anon_1
ON user_account.id = anon_1.user_id
The above SELECT statement when invoked via Session.execute()
will
return rows that contain User
entities, but not Address
entities. In
order to include Address
entities to the set of entities that would be
returned in result sets, we construct an aliased()
object against
the Address
entity and Subquery
object. We also may wish to apply
a name to the aliased()
construct, such as "address"
used below,
so that we can refer to it by name in the result row:
>>> address_subq = aliased(Address, subq, name="address")
>>> stmt = select(User, address_subq).join(address_subq)
>>> for row in session.execute(stmt):
... print(f"{row.User} {row.address}")
SELECT user_account.id, user_account.name, user_account.fullname,
anon_1.id AS id_1, anon_1.user_id, anon_1.email_address
FROM user_account
JOIN (SELECT address.id AS id,
address.user_id AS user_id, address.email_address AS email_address
FROM address
WHERE address.email_address = ?) AS anon_1 ON user_account.id = anon_1.user_id
[...] ('pat999@aol.com',)
User(id=3, name='patrick', fullname='Patrick Star') Address(id=4, email_address='pat999@aol.com')
Joining to Subqueries along Relationship paths¶
The subquery form illustrated in the previous section
may be expressed with more specificity using a
relationship()
-bound attribute using one of the forms indicated at
Using Relationship to join between aliased targets. For example, to create the
same join while ensuring the join is along that of a particular
relationship()
, we may use the
PropComparator.of_type()
method, passing the aliased()
construct containing the Subquery
object that’s the target
of the join:
>>> address_subq = aliased(Address, subq, name="address")
>>> stmt = select(User, address_subq).join(User.addresses.of_type(address_subq))
>>> for row in session.execute(stmt):
... print(f"{row.User} {row.address}")
SELECT user_account.id, user_account.name, user_account.fullname,
anon_1.id AS id_1, anon_1.user_id, anon_1.email_address
FROM user_account
JOIN (SELECT address.id AS id,
address.user_id AS user_id, address.email_address AS email_address
FROM address
WHERE address.email_address = ?) AS anon_1 ON user_account.id = anon_1.user_id
[...] ('pat999@aol.com',)
User(id=3, name='patrick', fullname='Patrick Star') Address(id=4, email_address='pat999@aol.com')
Subqueries that Refer to Multiple Entities¶
A subquery that contains columns spanning more than one ORM entity may be
applied to more than one aliased()
construct at once, and
used in the same Select
construct in terms of each entity separately.
The rendered SQL will continue to treat all such aliased()
constructs as the same subquery, however from the ORM / Python perspective
the different return values and object attributes can be referenced
by using the appropriate aliased()
construct.
Given for example a subquery that refers to both User
and Address
:
>>> user_address_subq = (
... select(User.id, User.name, User.fullname, Address.id, Address.email_address)
... .join_from(User, Address)
... .where(Address.email_address.in_(["pat999@aol.com", "squirrel@squirrelpower.org"]))
... .subquery()
... )
We can create aliased()
constructs against both User
and
Address
that each refer to the same object:
>>> user_alias = aliased(User, user_address_subq, name="user")
>>> address_alias = aliased(Address, user_address_subq, name="address")
A Select
construct selecting from both entities will render the
subquery once, but in a result-row context can return objects of both
User
and Address
classes at the same time:
>>> stmt = select(user_alias, address_alias).where(user_alias.name == "sandy")
>>> for row in session.execute(stmt):
... print(f"{row.user} {row.address}")
SELECT anon_1.id, anon_1.name, anon_1.fullname, anon_1.id_1, anon_1.email_address
FROM (SELECT user_account.id AS id, user_account.name AS name,
user_account.fullname AS fullname, address.id AS id_1,
address.email_address AS email_address
FROM user_account JOIN address ON user_account.id = address.user_id
WHERE address.email_address IN (?, ?)) AS anon_1
WHERE anon_1.name = ?
[...] ('pat999@aol.com', 'squirrel@squirrelpower.org', 'sandy')
User(id=2, name='sandy', fullname='Sandy Cheeks') Address(id=3, email_address='squirrel@squirrelpower.org')
Setting the leftmost FROM clause in a join¶
In cases where the left side of the current state of
Select
is not in line with what we want to join from,
the Select.join_from()
method may be used:
>>> stmt = select(Address).join_from(User, User.addresses).where(User.name == "sandy")
>>> print(stmt)
SELECT address.id, address.user_id, address.email_address
FROM user_account JOIN address ON user_account.id = address.user_id
WHERE user_account.name = :name_1
The Select.join_from()
method accepts two or three arguments, either
in the form (<join from>, <onclause>)
, or (<join from>, <join to>,
[<onclause>])
:
>>> stmt = select(Address).join_from(User, Address).where(User.name == "sandy")
>>> print(stmt)
SELECT address.id, address.user_id, address.email_address
FROM user_account JOIN address ON user_account.id = address.user_id
WHERE user_account.name = :name_1
To set up the initial FROM clause for a SELECT such that Select.join()
can be used subsequent, the Select.select_from()
method may also
be used:
>>> stmt = select(Address).select_from(User).join(Address).where(User.name == "sandy")
>>> print(stmt)
SELECT address.id, address.user_id, address.email_address
FROM user_account JOIN address ON user_account.id = address.user_id
WHERE user_account.name = :name_1
Tip
The Select.select_from()
method does not actually have the
final say on the order of tables in the FROM clause. If the statement
also refers to a Join
construct that refers to existing
tables in a different order, the Join
construct takes
precedence. When we use methods like Select.join()
and Select.join_from()
, these methods are ultimately creating
such a Join
object. Therefore we can see the contents
of Select.select_from()
being overridden in a case like this:
>>> stmt = select(Address).select_from(User).join(Address.user).where(User.name == "sandy")
>>> print(stmt)
SELECT address.id, address.user_id, address.email_address
FROM address JOIN user_account ON user_account.id = address.user_id
WHERE user_account.name = :name_1
Where above, we see that the FROM clause is address JOIN user_account
,
even though we stated select_from(User)
first. Because of the
.join(Address.user)
method call, the statement is ultimately equivalent
to the following:
>>> from sqlalchemy.sql import join
>>>
>>> user_table = User.__table__
>>> address_table = Address.__table__
>>>
>>> j = address_table.join(user_table, user_table.c.id == address_table.c.user_id)
>>> stmt = (
... select(address_table)
... .select_from(user_table)
... .select_from(j)
... .where(user_table.c.name == "sandy")
... )
>>> print(stmt)
SELECT address.id, address.user_id, address.email_address
FROM address JOIN user_account ON user_account.id = address.user_id
WHERE user_account.name = :name_1
The Join
construct above is added as another entry in the
Select.select_from()
list which supersedes the previous entry.
Relationship WHERE Operators¶
Besides the use of relationship()
constructs within the
Select.join()
and Select.join_from()
methods,
relationship()
also plays a role in helping to construct
SQL expressions that are typically for use in the WHERE clause, using
the Select.where()
method.
EXISTS forms: has() / any()¶
The Exists
construct was first introduced in the
SQLAlchemy Unified Tutorial in the section EXISTS subqueries. This object
is used to render the SQL EXISTS keyword in conjunction with a
scalar subquery. The relationship()
construct provides for some
helper methods that may be used to generate some common EXISTS styles
of queries in terms of the relationship.
For a one-to-many relationship such as User.addresses
, an EXISTS against
the address
table that correlates back to the user_account
table
can be produced using PropComparator.any()
. This method accepts
an optional WHERE criteria to limit the rows matched by the subquery:
>>> stmt = select(User.fullname).where(
... User.addresses.any(Address.email_address == "squirrel@squirrelpower.org")
... )
>>> session.execute(stmt).all()
SELECT user_account.fullname
FROM user_account
WHERE EXISTS (SELECT 1
FROM address
WHERE user_account.id = address.user_id AND address.email_address = ?)
[...] ('squirrel@squirrelpower.org',)
[('Sandy Cheeks',)]
As EXISTS tends to be more efficient for negative lookups, a common query
is to locate entities where there are no related entities present. This
is succinct using a phrase such as ~User.addresses.any()
, to select
for User
entities that have no related Address
rows:
>>> stmt = select(User.fullname).where(~User.addresses.any())
>>> session.execute(stmt).all()
SELECT user_account.fullname
FROM user_account
WHERE NOT (EXISTS (SELECT 1
FROM address
WHERE user_account.id = address.user_id))
[...] ()
[('Eugene H. Krabs',)]
The PropComparator.has()
method works in mostly the same way as
PropComparator.any()
, except that it’s used for many-to-one
relationships, such as if we wanted to locate all Address
objects
which belonged to “sandy”:
>>> stmt = select(Address.email_address).where(Address.user.has(User.name == "sandy"))
>>> session.execute(stmt).all()
SELECT address.email_address
FROM address
WHERE EXISTS (SELECT 1
FROM user_account
WHERE user_account.id = address.user_id AND user_account.name = ?)
[...] ('sandy',)
[('sandy@sqlalchemy.org',), ('squirrel@squirrelpower.org',)]
Relationship Instance Comparison Operators¶
The relationship()
-bound attribute also offers a few SQL construction
implementations that are geared towards filtering a relationship()
-bound
attribute in terms of a specific instance of a related object, which can unpack
the appropriate attribute values from a given persistent (or less
commonly a detached) object instance and construct WHERE criteria
in terms of the target relationship()
.
many to one equals comparison - a specific object instance can be compared to many-to-one relationship, to select rows where the foreign key of the target entity matches the primary key value of the object given:
>>> user_obj = session.get(User, 1)
SELECT ...>>> print(select(Address).where(Address.user == user_obj))SELECT address.id, address.user_id, address.email_address FROM address WHERE :param_1 = address.user_idmany to one not equals comparison - the not equals operator may also be used:
>>> print(select(Address).where(Address.user != user_obj))
SELECT address.id, address.user_id, address.email_address FROM address WHERE address.user_id != :user_id_1 OR address.user_id IS NULLobject is contained in a one-to-many collection - this is essentially the one-to-many version of the “equals” comparison, select rows where the primary key equals the value of the foreign key in a related object:
>>> address_obj = session.get(Address, 1)
SELECT ...>>> print(select(User).where(User.addresses.contains(address_obj)))SELECT user_account.id, user_account.name, user_account.fullname FROM user_account WHERE user_account.id = :param_1An object has a particular parent from a one-to-many perspective - the
with_parent()
function produces a comparison that returns rows which are referenced by a given parent, this is essentially the same as using the==
operator with the many-to-one side:>>> from sqlalchemy.orm import with_parent >>> print(select(Address).where(with_parent(user_obj, User.addresses)))
SELECT address.id, address.user_id, address.email_address FROM address WHERE :param_1 = address.user_id
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