SQLAlchemy 1.4 Documentation
SQLAlchemy ORM
- ORM Quick Start
- Object Relational Tutorial (1.x API)
- ORM Mapped Class Configuration
- Relationship Configuration
- Querying Data, Loading Objects
- ORM Querying Guide¶
- SELECT statements
- ORM Update / Delete with Arbitrary WHERE clause
- Inspecting entities and columns from ORM-enabled SELECT and DML statements
- Loading Columns
- Relationship Loading Techniques
- Loading Inheritance Hierarchies
- Query API
- ORM Querying Guide¶
- Using the Session
- Events and Internals
- ORM Extensions
- ORM Examples
Project Versions
- Previous: Querying Data, Loading Objects
- Next: Loading Columns
- Up: Home
- On this page:
- ORM Querying Guide
- SELECT statements
- ORM Update / Delete with Arbitrary WHERE clause
- Inspecting entities and columns from ORM-enabled SELECT and DML statements
ORM Querying Guide¶
This section provides an overview of emitting queries with the SQLAlchemy ORM using 2.0 style usage.
Readers of this section should be familiar with the SQLAlchemy overview at SQLAlchemy 1.4 / 2.0 Tutorial, and in particular most of the content here expands upon the content at Selecting Rows with Core or ORM.
SELECT statements¶
SELECT statements are produced by the select()
function which
returns a Select
object:
>>> from sqlalchemy import select
>>> stmt = select(User).where(User.name == "spongebob")
To invoke a Select
with the ORM, it is passed to
Session.execute()
:
sql>>> 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.
Below we select from the User
entity, producing a Select
that selects from the mapped Table
to which User
is mapped:
sql>>> 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.fetchone()
(User(id=1, name='spongebob', fullname='Spongebob Squarepants'),)
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, which is achieved using the
Result.scalars()
method:
>>> result.scalars().all()
[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')]
ORM Entities are named in the result row based on their class name,
such as below where we SELECT from both User
and Address
at the
same time:
>>> stmt = select(User, Address).join(User.addresses).order_by(User.id, Address.id)
sql>>> 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
Selecting Individual Attributes¶
The attributes on a mapped class, such as User.name
and Address.email_address
,
have a similar behavior as that of the entity class itself such as User
in that they are automatically converted into ORM-annotated Core objects
when passed to select()
. They may be used in the same way
as table columns are used:
sql>>> 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
[...] ()
ORM attributes, themselves known as
InstrumentedAttribute
objects, can be used in the same way as any ColumnElement
,
and are delivered in result rows just the same way, such as below
where we refer to their values by column name within each row:
>>> 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)
sql>>> 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 as well as custom column groupings such as mappings.
See also
Column Bundles - in the ORM loading documentation.
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)
sql>>> 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
The aliased
construct is also central to making use of subqueries
with the ORM; the sections Selecting Entities from Subqueries and
Joining to Subqueries discusses this further.
Getting ORM Results from Textual and Core 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. The
text()
construct, once constructed, 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:
>>> # using from_statement()
>>> 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:
>>> # using aliased() to select from a subquery
>>> 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.
See also
Using INSERT, UPDATE and ON CONFLICT (i.e. upsert) to return ORM Objects - The Select.from_statement()
method also works with DML statements that support RETURNING.
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 and Core 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 1.4 / 2.0 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 1.4 / 2.0 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.
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 or Selectable¶
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
linakges 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 the relationship-bound
attribute; this form in fact states the target of Address
twice, however
this is accepted:
>>> 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 syntax has more functionality if we use it in terms of aliased
entities. The default target for User.addresses
is the Address
class, however if we pass aliased forms using aliased()
, the
aliased()
form will be used as the target, as in the example
below:
>>> a1 = aliased(Address)
>>> a2 = aliased(Address)
>>> stmt = (
... select(User)
... .join(a1, User.addresses)
... .join(a2, User.addresses)
... .where(a1.email_address == "ed@foo.com")
... .where(a2.email_address == "ed@bar.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
When using relationship-bound attributes, the target entity can also be
substituted with an aliased entity by using the
PropComparator.of_type()
method. The same example using
this method would be:
>>> stmt = (
... select(User)
... .join(User.addresses.of_type(a1))
... .join(User.addresses.of_type(a2))
... .where(a1.email_address == "ed@foo.com")
... .where(a2.email_address == "ed@bar.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
Augmenting Built-in ON Clauses¶
As a substitute for providing a full custom ON condition for an
existing relationship, the PropComparator.and_()
function
may be applied to a relationship attribute to augment additional
criteria into the ON clause; the additional criteria will be combined
with the default criteria using AND. Below, the ON criteria between
user_account
and address
contains two separate elements joined
by AND
, the first one being the natural join along the foreign key,
and the second being a custom limiting criteria:
>>> stmt = select(User).join(User.addresses.and_(Address.email_address != "foo@bar.com"))
>>> print(stmt)
SELECT user_account.id, user_account.name, user_account.fullname
FROM user_account
JOIN address ON user_account.id = address.user_id
AND address.email_address != :email_address_1
See also
The PropComparator.and_()
method also works with loader
strategies. See the section Adding Criteria to loader options for an example.
Joining to Subqueries¶
The target of a join may be any “selectable” entity which usefully 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 add Address
entities to the set of entities that would be
returned in result sets, we construct an aliased()
object against
the Address
entity and the custom subquery. Note we also apply a name
"address"
to the aliased()
construct so that we may
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')
The same subquery may be referred towards by multiple entities as well,
for a subquery that represents more than one entity. The subquery itself
will remain unique within the statement, while the entities that are linked
to it using aliased
refer to distinct sets of columns:
>>> user_address_subq = (
... select(User.id, User.name, Address.id, Address.email_address)
... .join_from(User, Address)
... .where(Address.email_address.in_(["pat999@aol.com", "squirrel@squirrelpower.org"]))
... .subquery()
... )
>>> user_alias = aliased(User, user_address_subq, name="user")
>>> address_alias = aliased(Address, user_address_subq, name="address")
>>> 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.id_1, anon_1.email_address
FROM (SELECT user_account.id AS id, user_account.name AS name, 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')
Controlling what to Join From¶
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:
>>> user_table = User.__table__
>>> address_table = Address.__table__
>>> from sqlalchemy.sql import join
>>>
>>> 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.
Special Relationship Operators¶
As detailed in the SQLAlchemy 1.4 / 2.0 Tutorial at
Using Relationships in Queries, ORM attributes mapped by
relationship()
may be used in a variety of ways as SQL construction
helpers. In addition to the above documentation on
Joins, relationships may produce criteria to be used in
the WHERE clause as well. See the linked sections below.
See also
Sections in the Working with ORM Related Objects section of the SQLAlchemy 1.4 / 2.0 Tutorial:
EXISTS forms: has() / any() - helpers to generate EXISTS clauses using
relationship()
Common Relationship Operators - helpers to create comparisons in terms of a
relationship()
in reference to a specific object instance
ORM Loader Options¶
Loader options are objects that are passed to the Select.options()
method which affect the loading of both column and relationship-oriented
attributes. The majority of loader options descend from the Load
hierarchy. For a complete overview of using loader options, see the linked
sections below.
See also
Loading Columns - details mapper and loading options that affect how column and SQL-expression mapped attributes are loaded
Relationship Loading Techniques - details relationship and loading options that affect how
relationship()
mapped attributes are loaded
ORM Execution Options¶
Execution options are keyword arguments that are passed to an
“execution_options” method, which take place at the level of statement
execution. The primary “execution option” method is in Core at
Connection.execution_options()
. In the ORM, execution options may
also be passed to Session.execute()
using the
Session.execute.execution_options
parameter. Perhaps more
succinctly, most execution options, including those specific to the ORM, can be
assigned to a statement directly, using the
Executable.execution_options()
method, so that the options may be
associated directly with the statement instead of being configured separately.
The examples below will use this form.
Populate Existing¶
The populate_existing
execution option ensures that for all rows
loaded, the corresponding instances in the Session
will
be fully refreshed, erasing any existing data within the objects
(including pending changes) and replacing with the data loaded from the
result.
Example use looks like:
>>> stmt = select(User).execution_options(populate_existing=True)
sql>>> result = session.execute(stmt)
SELECT user_account.id, user_account.name, user_account.fullname
FROM user_account
...
Normally, ORM objects are only loaded once, and if they are matched up
to the primary key in a subsequent result row, the row is not applied to the
object. This is both to preserve pending, unflushed changes on the object
as well as to avoid the overhead and complexity of refreshing data which
is already there. The Session
assumes a default working
model of a highly isolated transaction, and to the degree that data is
expected to change within the transaction outside of the local changes being
made, those use cases would be handled using explicit steps such as this method.
Using populate_existing
, any set of objects that matches a query
can be refreshed, and it also allows control over relationship loader options.
E.g. to refresh an instance while also refreshing a related set of objects:
stmt = (
select(User).
where(User.name.in_(names)).
execution_options(populate_existing=True).
options(selectinload(User.addresses)
)
# will refresh all matching User objects as well as the related
# Address objects
users = session.execute(stmt).scalars().all()
Another use case for populate_existing
is in support of various
attribute loading features that can change how an attribute is loaded on
a per-query basis. Options for which this apply include:
The
with_expression()
optionThe
PropComparator.and_()
method that can modify what a loader strategy loadsThe
contains_eager()
optionThe
with_loader_criteria()
option
The populate_existing
execution option is equvialent to the
Query.populate_existing()
method in 1.x style ORM queries.
See also
I’m re-loading data with my Session but it isn’t seeing changes that I committed elsewhere - in Frequently Asked Questions
Refreshing / Expiring - in the ORM Session
documentation
Autoflush¶
This option when passed as False
will cause the Session
to not invoke the “autoflush” step. It’s equivalent to using the
Session.no_autoflush
context manager to disable autoflush:
>>> stmt = select(User).execution_options(autoflush=False)
sql>>> session.execute(stmt)
SELECT user_account.id, user_account.name, user_account.fullname
FROM user_account
...
This option will also work on ORM-enabled Update
and
Delete
queries.
The autoflush
execution option is equvialent to the
Query.autoflush()
method in 1.x style ORM queries.
See also
Fetching Large Result Sets with Yield Per¶
The yield_per
execution option is an integer value which will cause the
Result
to buffer only limited number of rows and/or ORM
objects at a time, before making data available to the client.
Normally, the ORM will construct ORM objects for all rows up front,
assembling them into a single buffer, before passing this buffer to
the Result
object as a source of rows to be returned.
The rationale for this behavior is to allow correct behavior
for features such as joined eager loading, uniquifying of results, and the
general case of result handling logic that relies upon the identity map
maintaining a consistent state for every object in a result set as it is
fetched.
The purpose of the yield_per
option is to change this behavior so that the
ORM result set is optimized for iteration through very large result sets (> 10K
rows), where the user has determined that the above patterns don’t apply. When
yield_per
is used, the ORM will instead batch ORM results into
sub-collections and yield rows from each sub-collection individually as the
Result
object is iterated, so that the Python interpreter
doesn’t need to declare very large areas of memory which is both time consuming
and leads to excessive memory use. The option affects both the way the database
cursor is used as well as how the ORM constructs rows and objects to be
passed to the Result
.
Tip
From the above, it follows that the Result
must be
consumed in an iterable fashion, that is, using iteration such as
for row in result
or using partial row methods such as
Result.fetchmany()
or Result.partitions()
.
Calling Result.all()
will defeat the purpose of using
yield_per
.
Using yield_per
is equivalent to making use
of both the Connection.execution_options.stream_results
execution option, which selects for server side cursors to be used
by the backend if supported, and the Result.yield_per()
method
on the returned Result
object,
which establishes a fixed size of rows to be fetched as well as a
corresponding limit to how many ORM objects will be constructed at once.
Tip
yield_per
is now available as a Core execution option as well,
described in detail at Using Server Side Cursors (a.k.a. stream results). This section details
the use of yield_per
as an execution option with an ORM
Session
. The option behaves as similarly as possible
in both contexts.
yield_per
when used with the ORM is typically established either
via the Executable.execution_options()
method on the given statement
or by passing it to the Session.execute.execution_options
parameter of Session.execute()
or other similar Session
method. In the example below its invoked upon a statement:
>>> stmt = select(User).execution_options(yield_per=10)
sql>>> for row in session.execute(stmt):
... print(row)
SELECT user_account.id, user_account.name, user_account.fullname
FROM user_account
[...] ()
(User(id=1, name='spongebob', fullname='Spongebob Squarepants'),)
...
The above code is mostly equivalent as making use of the
Connection.execution_options.stream_results
execution
option, setting the Connection.execution_options.max_row_buffer
to the given integer size, and then using the Result.yield_per()
method on the Result
returned by the
Session
, as in the following example:
# equivalent code
>>> stmt = select(User).execution_options(stream_results=True, max_row_buffer=10)
sql>>> for row in session.execute(stmt).yield_per(10):
... print(row)
SELECT user_account.id, user_account.name, user_account.fullname
FROM user_account
[...] ()
(User(id=1, name='spongebob', fullname='Spongebob Squarepants'),)
...
yield_per
is also commonly used in combination with the
Result.partitions()
method, that will iterate rows in grouped
partitions. The size of each partition defaults to the integer value passed to
yield_per
, as in the below example:
>>> stmt = select(User).execution_options(yield_per=10)
sql>>> for partition in session.execute(stmt).partitions():
... for row in partition:
... print(row)
SELECT user_account.id, user_account.name, user_account.fullname
FROM user_account
[...] ()
(User(id=1, name='spongebob', fullname='Spongebob Squarepants'),)
...
The yield_per
execution option is not compatible with
“subquery” eager loading loading or
“joined” eager loading when using collections. It
is potentially compatible with “select in” eager loading , provided the database driver supports multiple,
independent cursors.
Additionally, the yield_per
execution option is not compatible
with the Result.unique()
method; as this method relies upon
storing a complete set of identities for all rows, it would necessarily
defeat the purpose of using yield_per
which is to handle an arbitrarily
large number of rows.
Changed in version 1.4.6: An exception is raised when ORM rows are fetched
from a Result
object that makes use of the
Result.unique()
filter, at the same time as the yield_per
execution option is used.
When using the legacy Query
object with
1.x style ORM use, the Query.yield_per()
method
will have the same result as that of the yield_per
execution option.
ORM Update / Delete with Arbitrary WHERE clause¶
The Session.execute()
method, in addition to handling ORM-enabled
Select
objects, can also accommodate ORM-enabled
Update
and Delete
objects, which UPDATE or DELETE
any number of database rows while also being able to synchronize the state of
matching objects locally present in the Session
. See the section
UPDATE and DELETE with arbitrary WHERE clause for background on this feature.
Inspecting entities and columns from ORM-enabled SELECT and DML statements¶
The select()
construct, as well as the insert()
, update()
and delete()
constructs (for the latter DML constructs, as of SQLAlchemy
1.4.33), all support the ability to inspect the entities in which these
statements are created against, as well as the columns and datatypes that would
be returned in a result set.
For a Select
object, this information is available from the
Select.column_descriptions
attribute. This attribute operates in the
same way as the legacy Query.column_descriptions
attribute. The format
returned is a list of dictionaries:
>>> from pprint import pprint
>>> user_alias = aliased(User, name="user2")
>>> stmt = select(User, User.id, user_alias)
>>> pprint(stmt.column_descriptions)
[{'aliased': False,
'entity': <class 'User'>,
'expr': <class 'User'>,
'name': 'User',
'type': <class 'User'>},
{'aliased': False,
'entity': <class 'User'>,
'expr': <....InstrumentedAttribute object at ...>,
'name': 'id',
'type': Integer()},
{'aliased': True,
'entity': <AliasedClass ...; User>,
'expr': <AliasedClass ...; User>,
'name': 'user2',
'type': <class 'User'>}]
When Select.column_descriptions
is used with non-ORM objects
such as plain Table
or Column
objects, the entries
will contain basic information about individual columns returned in all
cases:
>>> stmt = select(user_table, address_table.c.id)
>>> pprint(stmt.column_descriptions)
[{'expr': Column('id', Integer(), table=<user_account>, primary_key=True, nullable=False),
'name': 'id',
'type': Integer()},
{'expr': Column('name', String(length=30), table=<user_account>),
'name': 'name',
'type': String(length=30)},
{'expr': Column('fullname', String(), table=<user_account>),
'name': 'fullname',
'type': String()},
{'expr': Column('id', Integer(), table=<address>, primary_key=True, nullable=False),
'name': 'id_1',
'type': Integer()}]
Changed in version 1.4.33: The Select.column_descriptions
attribute now returns
a value when used against a Select
that is not ORM-enabled. Previously,
this would raise NotImplementedError
.
For insert()
, update()
and delete()
constructs, there are
two separate attributes. One is UpdateBase.entity_description
which
returns information about the primary ORM entity and database table which the
DML construct would be affecting:
>>> from sqlalchemy import update
>>> stmt = update(User).values(name="somename").returning(User.id)
>>> pprint(stmt.entity_description)
{'entity': <class 'User'>,
'expr': <class 'User'>,
'name': 'User',
'table': Table('user_account', ...),
'type': <class 'User'>}
Tip
The UpdateBase.entity_description
includes an entry
"table"
which is actually the table to be inserted, updated or
deleted by the statement, which is not always the same as the SQL
“selectable” to which the class may be mapped. For example, in a
joined-table inheritance scenario, "table"
will refer to the local table
for the given entity.
The other is UpdateBase.returning_column_descriptions
which
delivers information about the columns present in the RETURNING collection
in a manner roughly similar to that of Select.column_descriptions
:
>>> pprint(stmt.returning_column_descriptions)
[{'aliased': False,
'entity': <class 'User'>,
'expr': <sqlalchemy.orm.attributes.InstrumentedAttribute ...>,
'name': 'id',
'type': Integer()}]
New in version 1.4.33: Added the UpdateBase.entity_description
and UpdateBase.returning_column_descriptions
attributes.
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