SQLAlchemy 2.1 Documentation
SQLAlchemy ORM
- ORM Quick Start
- ORM Mapped Class Configuration
- Relationship Configuration
- ORM Querying Guide
- Writing SELECT statements for ORM Mapped Classes
- 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
- Using the Session
- Events and Internals
- ORM Extensions
- ORM Examples
Project Versions
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ORM API Features for Querying¶
ORM Loader Options¶
Loader options are objects which, when passed to the
Select.options()
method of a Select
object or similar SQL
construct, 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
Column Loading Options - 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¶
ORM-level execution options are keyword options that may be associated with a
statement execution using either the
Session.execute.execution_options
parameter, which is a
dictionary argument accepted by Session
methods such as
Session.execute()
and Session.scalars()
, or by
associating them directly with the statement to be invoked itself using the
Executable.execution_options()
method, which accepts them as
arbitrary keyword arguments.
ORM-level options are distinct from the Core level execution options
documented at Connection.execution_options()
.
It’s important to note that the ORM options
discussed below are not compatible with Core level methods
Connection.execution_options()
or
Engine.execution_options()
; the options are ignored at this
level, even if the Engine
or Connection
is associated
with the Session
in use.
Within this section, the Executable.execution_options()
method
style will be illustrated for examples.
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)
>>> 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()
optionThe
load_only()
option to select what attributes to refresh
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 is equivalent to using the
Session.no_autoflush
context manager to disable autoflush:
>>> stmt = select(User).execution_options(autoflush=False)
>>> 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 a limited number of rows and/or ORM
objects at a time, before making data available to the client.
Normally, the ORM will fetch all rows immediately, constructing ORM objects
for each and assembling those objects 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 (e.g.
> 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.
When used with the ORM, yield_per
must be 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 such as Session.scalars()
. Typical use for fetching
ORM objects is illustrated below:
>>> stmt = select(User).execution_options(yield_per=10)
>>> for user_obj in session.scalars(stmt):
... print(user_obj)
SELECT user_account.id, user_account.name, user_account.fullname
FROM user_account
[...] ()
User(id=1, name='spongebob', fullname='Spongebob Squarepants')
User(id=2, name='sandy', fullname='Sandy Cheeks')
...
>>> # ... rows continue ...
The above code is equivalent to the example below, which uses
Connection.execution_options.stream_results
and Connection.execution_options.max_row_buffer
Core-level
execution options in conjunction with the Result.yield_per()
method of Result
:
# equivalent code
>>> stmt = select(User).execution_options(stream_results=True, max_row_buffer=10)
>>> for user_obj in session.scalars(stmt).yield_per(10):
... print(user_obj)
SELECT user_account.id, user_account.name, user_account.fullname
FROM user_account
[...] ()
User(id=1, name='spongebob', fullname='Spongebob Squarepants')
User(id=2, name='sandy', fullname='Sandy Cheeks')
...
>>> # ... rows continue ...
yield_per
is also commonly used in combination with the
Result.partitions()
method, which 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)
>>> for partition in session.scalars(stmt).partitions():
... for user_obj in partition:
... print(user_obj)
SELECT user_account.id, user_account.name, user_account.fullname
FROM user_account
[...] ()
User(id=1, name='spongebob', fullname='Spongebob Squarepants')
User(id=2, name='sandy', fullname='Sandy Cheeks')
...
>>> # ... rows continue ...
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.
Identity Token¶
Deep Alchemy
This option is an advanced-use feature mostly intended
to be used with the Horizontal Sharding extension. For
typical cases of loading objects with identical primary keys from different
“shards” or partitions, consider using individual Session
objects per shard first.
The “identity token” is an arbitrary value that can be associated within the identity key of newly loaded objects. This element exists first and foremost to support extensions which perform per-row “sharding”, where objects may be loaded from any number of replicas of a particular database table that nonetheless have overlapping primary key values. The primary consumer of “identity token” is the Horizontal Sharding extension, which supplies a general framework for persisting objects among multiple “shards” of a particular database table.
The identity_token
execution option may be used on a per-query basis
to directly affect this token. Using it directly, one can populate a
Session
with multiple instances of an object that have the
same primary key and source table, but different “identities”.
One such example is to populate a Session
with objects that
come from same-named tables in different schemas, using the
Translation of Schema Names feature which can affect the choice of schema
within the scope of queries. Given a mapping as:
from sqlalchemy.orm import DeclarativeBase
from sqlalchemy.orm import Mapped
from sqlalchemy.orm import mapped_column
class Base(DeclarativeBase):
pass
class MyTable(Base):
__tablename__ = "my_table"
id: Mapped[int] = mapped_column(primary_key=True)
name: Mapped[str]
The default “schema” name for the class above is None
, meaning, no
schema qualification will be written into SQL statements. However,
if we make use of Connection.execution_options.schema_translate_map
,
mapping None
to an alternate schema, we can place instances of
MyTable
into two different schemas:
engine = create_engine(
"postgresql+psycopg://scott:tiger@localhost/test",
)
with Session(
engine.execution_options(schema_translate_map={None: "test_schema"})
) as sess:
sess.add(MyTable(name="this is schema one"))
sess.commit()
with Session(
engine.execution_options(schema_translate_map={None: "test_schema_2"})
) as sess:
sess.add(MyTable(name="this is schema two"))
sess.commit()
The above two blocks create a Session
object linked to a different
schema translate map each time, and an instance of MyTable
is persisted
into both test_schema.my_table
as well as test_schema_2.my_table
.
The Session
objects above are independent. If we wanted to
persist both objects in one transaction, we would need to use the
Horizontal Sharding extension to do this.
However, we can illustrate querying for these objects in one session as follows:
with Session(engine) as sess:
obj1 = sess.scalar(
select(MyTable)
.where(MyTable.id == 1)
.execution_options(
schema_translate_map={None: "test_schema"},
identity_token="test_schema",
)
)
obj2 = sess.scalar(
select(MyTable)
.where(MyTable.id == 1)
.execution_options(
schema_translate_map={None: "test_schema_2"},
identity_token="test_schema_2",
)
)
Both obj1
and obj2
are distinct from each other. However, they both
refer to primary key id 1 for the MyTable
class, yet are distinct.
This is how the identity_token
comes into play, which we can see in the
inspection of each object, where we look at InstanceState.key
to view the two distinct identity tokens:
>>> from sqlalchemy import inspect
>>> inspect(obj1).key
(<class '__main__.MyTable'>, (1,), 'test_schema')
>>> inspect(obj2).key
(<class '__main__.MyTable'>, (1,), 'test_schema_2')
The above logic takes place automatically when using the Horizontal Sharding extension.
New in version 2.0.0rc1: - added the identity_token
ORM level execution
option.
See also
Horizontal Sharding - in the ORM Examples section.
See the script separate_schema_translates.py
for a demonstration of
the above use case using the full sharding API.
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(), table=<user_account>, nullable=False),
'name': 'name',
'type': String()},
{'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.
Additional ORM API Constructs¶
Object Name | Description |
---|---|
aliased(element[, alias, name, flat, ...]) |
Produce an alias of the given element, usually an |
Represents an “aliased” form of a mapped class for usage with Query. |
|
Provide an inspection interface for an
|
|
A grouping of SQL expressions that are returned by a |
|
join(left, right[, onclause, isouter, ...]) |
Produce an inner join between left and right clauses. |
outerjoin(left, right[, onclause, full]) |
Produce a left outer join between left and right clauses. |
with_loader_criteria(entity_or_base, where_criteria[, loader_only, include_aliases, ...]) |
Add additional WHERE criteria to the load for all occurrences of a particular entity. |
with_parent(instance, prop[, from_entity]) |
Create filtering criterion that relates this query’s primary entity
to the given related instance, using established
|
- function sqlalchemy.orm.aliased(element: _EntityType[_O] | FromClause, alias: FromClause | None = None, name: str | None = None, flat: bool = False, adapt_on_names: bool = False) → AliasedClass[_O] | FromClause | AliasedType[_O]¶
Produce an alias of the given element, usually an
AliasedClass
instance.E.g.:
my_alias = aliased(MyClass) stmt = select(MyClass, my_alias).filter(MyClass.id > my_alias.id) result = session.execute(stmt)
The
aliased()
function is used to create an ad-hoc mapping of a mapped class to a new selectable. By default, a selectable is generated from the normally mapped selectable (typically aTable
) using theFromClause.alias()
method. However,aliased()
can also be used to link the class to a newselect()
statement. Also, thewith_polymorphic()
function is a variant ofaliased()
that is intended to specify a so-called “polymorphic selectable”, that corresponds to the union of several joined-inheritance subclasses at once.For convenience, the
aliased()
function also accepts plainFromClause
constructs, such as aTable
orselect()
construct. In those cases, theFromClause.alias()
method is called on the object and the newAlias
object returned. The returnedAlias
is not ORM-mapped in this case.See also
ORM Entity Aliases - in the SQLAlchemy Unified Tutorial
Selecting ORM Aliases - in the ORM Querying Guide
- Parameters:
element¶ – element to be aliased. Is normally a mapped class, but for convenience can also be a
FromClause
element.alias¶ – Optional selectable unit to map the element to. This is usually used to link the object to a subquery, and should be an aliased select construct as one would produce from the
Query.subquery()
method or theSelect.subquery()
orSelect.alias()
methods of theselect()
construct.name¶ – optional string name to use for the alias, if not specified by the
alias
parameter. The name, among other things, forms the attribute name that will be accessible via tuples returned by aQuery
object. Not supported when creating aliases ofJoin
objects.flat¶ –
Boolean, will be passed through to the
FromClause.alias()
call so that aliases ofJoin
objects will alias the individual tables inside the join, rather than creating a subquery. This is generally supported by all modern databases with regards to right-nested joins and generally produces more efficient queries.When
aliased.flat
is combined withaliased.name
, the resulting joins will alias individual tables using a naming scheme similar to<prefix>_<tablename>
. This naming scheme is for visibility / debugging purposes only and the specific scheme is subject to change without notice.New in version 2.0.32: added support for combining
aliased.name
withaliased.flat
. Previously, this would raiseNotImplementedError
.adapt_on_names¶ –
if True, more liberal “matching” will be used when mapping the mapped columns of the ORM entity to those of the given selectable - a name-based match will be performed if the given selectable doesn’t otherwise have a column that corresponds to one on the entity. The use case for this is when associating an entity with some derived selectable such as one that uses aggregate functions:
class UnitPrice(Base): __tablename__ = 'unit_price' ... unit_id = Column(Integer) price = Column(Numeric) aggregated_unit_price = Session.query( func.sum(UnitPrice.price).label('price') ).group_by(UnitPrice.unit_id).subquery() aggregated_unit_price = aliased(UnitPrice, alias=aggregated_unit_price, adapt_on_names=True)
Above, functions on
aggregated_unit_price
which refer to.price
will return thefunc.sum(UnitPrice.price).label('price')
column, as it is matched on the name “price”. Ordinarily, the “price” function wouldn’t have any “column correspondence” to the actualUnitPrice.price
column as it is not a proxy of the original.
- class sqlalchemy.orm.util.AliasedClass¶
Represents an “aliased” form of a mapped class for usage with Query.
The ORM equivalent of a
alias()
construct, this object mimics the mapped class using a__getattr__
scheme and maintains a reference to a realAlias
object.A primary purpose of
AliasedClass
is to serve as an alternate within a SQL statement generated by the ORM, such that an existing mapped entity can be used in multiple contexts. A simple example:# find all pairs of users with the same name user_alias = aliased(User) session.query(User, user_alias).\ join((user_alias, User.id > user_alias.id)).\ filter(User.name == user_alias.name)
AliasedClass
is also capable of mapping an existing mapped class to an entirely new selectable, provided this selectable is column- compatible with the existing mapped selectable, and it can also be configured in a mapping as the target of arelationship()
. See the links below for examples.The
AliasedClass
object is constructed typically using thealiased()
function. It also is produced with additional configuration when using thewith_polymorphic()
function.The resulting object is an instance of
AliasedClass
. This object implements an attribute scheme which produces the same attribute and method interface as the original mapped class, allowingAliasedClass
to be compatible with any attribute technique which works on the original class, including hybrid attributes (see Hybrid Attributes).The
AliasedClass
can be inspected for its underlyingMapper
, aliased selectable, and other information usinginspect()
:from sqlalchemy import inspect my_alias = aliased(MyClass) insp = inspect(my_alias)
The resulting inspection object is an instance of
AliasedInsp
.See also
Class signature
class
sqlalchemy.orm.AliasedClass
(sqlalchemy.inspection.Inspectable
,sqlalchemy.orm.ORMColumnsClauseRole
)
- class sqlalchemy.orm.util.AliasedInsp¶
Provide an inspection interface for an
AliasedClass
object.The
AliasedInsp
object is returned given anAliasedClass
using theinspect()
function:from sqlalchemy import inspect from sqlalchemy.orm import aliased my_alias = aliased(MyMappedClass) insp = inspect(my_alias)
Attributes on
AliasedInsp
include:entity
- theAliasedClass
represented.mapper
- theMapper
mapping the underlying class.selectable
- theAlias
construct which ultimately represents an aliasedTable
orSelect
construct.name
- the name of the alias. Also is used as the attribute name when returned in a result tuple fromQuery
.with_polymorphic_mappers
- collection ofMapper
objects indicating all those mappers expressed in the select construct for theAliasedClass
.polymorphic_on
- an alternate column or SQL expression which will be used as the “discriminator” for a polymorphic load.
See also
Class signature
class
sqlalchemy.orm.AliasedInsp
(sqlalchemy.orm.ORMEntityColumnsClauseRole
,sqlalchemy.orm.ORMFromClauseRole
,sqlalchemy.sql.cache_key.HasCacheKey
,sqlalchemy.orm.base.InspectionAttr
,sqlalchemy.util.langhelpers.MemoizedSlots
,sqlalchemy.inspection.Inspectable
,typing.Generic
)
- class sqlalchemy.orm.Bundle¶
A grouping of SQL expressions that are returned by a
Query
under one namespace.The
Bundle
essentially allows nesting of the tuple-based results returned by a column-orientedQuery
object. It also is extensible via simple subclassing, where the primary capability to override is that of how the set of expressions should be returned, allowing post-processing as well as custom return types, without involving ORM identity-mapped classes.Members
__init__(), c, columns, create_row_processor(), is_aliased_class, is_bundle, is_clause_element, is_mapper, label(), single_entity
Class signature
class
sqlalchemy.orm.Bundle
(sqlalchemy.orm.ORMColumnsClauseRole
,sqlalchemy.sql.annotation.SupportsCloneAnnotations
,sqlalchemy.sql.cache_key.MemoizedHasCacheKey
,sqlalchemy.inspection.Inspectable
,sqlalchemy.orm.base.InspectionAttr
)-
method
sqlalchemy.orm.Bundle.
__init__(name: str, *exprs: _ColumnExpressionArgument[Any], **kw: Any)¶ Construct a new
Bundle
.e.g.:
bn = Bundle("mybundle", MyClass.x, MyClass.y) for row in session.query(bn).filter( bn.c.x == 5).filter(bn.c.y == 4): print(row.mybundle.x, row.mybundle.y)
-
attribute
sqlalchemy.orm.Bundle.
c: ReadOnlyColumnCollection[str, KeyedColumnElement[Any]]¶ An alias for
Bundle.columns
.
-
attribute
sqlalchemy.orm.Bundle.
columns: ReadOnlyColumnCollection[str, KeyedColumnElement[Any]]¶ A namespace of SQL expressions referred to by this
Bundle
.e.g.:
bn = Bundle("mybundle", MyClass.x, MyClass.y) q = sess.query(bn).filter(bn.c.x == 5)
Nesting of bundles is also supported:
b1 = Bundle("b1", Bundle('b2', MyClass.a, MyClass.b), Bundle('b3', MyClass.x, MyClass.y) ) q = sess.query(b1).filter( b1.c.b2.c.a == 5).filter(b1.c.b3.c.y == 9)
See also
-
method
sqlalchemy.orm.Bundle.
create_row_processor(query: Select[Unpack[TupleAny]], procs: Sequence[Callable[[Row[Unpack[TupleAny]]], Any]], labels: Sequence[str]) → Callable[[Row[Unpack[TupleAny]]], Any]¶ Produce the “row processing” function for this
Bundle
.May be overridden by subclasses to provide custom behaviors when results are fetched. The method is passed the statement object and a set of “row processor” functions at query execution time; these processor functions when given a result row will return the individual attribute value, which can then be adapted into any kind of return data structure.
The example below illustrates replacing the usual
Row
return structure with a straight Python dictionary:from sqlalchemy.orm import Bundle class DictBundle(Bundle): def create_row_processor(self, query, procs, labels): 'Override create_row_processor to return values as dictionaries' def proc(row): return dict( zip(labels, (proc(row) for proc in procs)) ) return proc
A result from the above
Bundle
will return dictionary values:bn = DictBundle('mybundle', MyClass.data1, MyClass.data2) for row in session.execute(select(bn)).where(bn.c.data1 == 'd1'): print(row.mybundle['data1'], row.mybundle['data2'])
-
attribute
sqlalchemy.orm.Bundle.
is_aliased_class = False¶ True if this object is an instance of
AliasedClass
.
-
attribute
sqlalchemy.orm.Bundle.
is_bundle = True¶ True if this object is an instance of
Bundle
.
-
attribute
sqlalchemy.orm.Bundle.
is_clause_element = False¶ True if this object is an instance of
ClauseElement
.
-
attribute
sqlalchemy.orm.Bundle.
is_mapper = False¶ True if this object is an instance of
Mapper
.
-
method
sqlalchemy.orm.Bundle.
label(name)¶ Provide a copy of this
Bundle
passing a new label.
-
attribute
sqlalchemy.orm.Bundle.
single_entity = False¶ If True, queries for a single Bundle will be returned as a single entity, rather than an element within a keyed tuple.
-
method
- function sqlalchemy.orm.with_loader_criteria(entity_or_base: _EntityType[Any], where_criteria: _ColumnExpressionArgument[bool] | Callable[[Any], _ColumnExpressionArgument[bool]], loader_only: bool = False, include_aliases: bool = False, propagate_to_loaders: bool = True, track_closure_variables: bool = True) → LoaderCriteriaOption¶
Add additional WHERE criteria to the load for all occurrences of a particular entity.
New in version 1.4.
The
with_loader_criteria()
option is intended to add limiting criteria to a particular kind of entity in a query, globally, meaning it will apply to the entity as it appears in the SELECT query as well as within any subqueries, join conditions, and relationship loads, including both eager and lazy loaders, without the need for it to be specified in any particular part of the query. The rendering logic uses the same system used by single table inheritance to ensure a certain discriminator is applied to a table.E.g., using 2.0-style queries, we can limit the way the
User.addresses
collection is loaded, regardless of the kind of loading used:from sqlalchemy.orm import with_loader_criteria stmt = select(User).options( selectinload(User.addresses), with_loader_criteria(Address, Address.email_address != 'foo')) )
Above, the “selectinload” for
User.addresses
will apply the given filtering criteria to the WHERE clause.Another example, where the filtering will be applied to the ON clause of the join, in this example using 1.x style queries:
q = session.query(User).outerjoin(User.addresses).options( with_loader_criteria(Address, Address.email_address != 'foo')) )
The primary purpose of
with_loader_criteria()
is to use it in theSessionEvents.do_orm_execute()
event handler to ensure that all occurrences of a particular entity are filtered in a certain way, such as filtering for access control roles. It also can be used to apply criteria to relationship loads. In the example below, we can apply a certain set of rules to all queries emitted by a particularSession
:session = Session(bind=engine) @event.listens_for("do_orm_execute", session) def _add_filtering_criteria(execute_state): if ( execute_state.is_select and not execute_state.is_column_load and not execute_state.is_relationship_load ): execute_state.statement = execute_state.statement.options( with_loader_criteria( SecurityRole, lambda cls: cls.role.in_(['some_role']), include_aliases=True ) )
In the above example, the
SessionEvents.do_orm_execute()
event will intercept all queries emitted using theSession
. For those queries which are SELECT statements and are not attribute or relationship loads a customwith_loader_criteria()
option is added to the query. Thewith_loader_criteria()
option will be used in the given statement and will also be automatically propagated to all relationship loads that descend from this query.The criteria argument given is a
lambda
that accepts acls
argument. The given class will expand to include all mapped subclass and need not itself be a mapped class.Tip
When using
with_loader_criteria()
option in conjunction with thecontains_eager()
loader option, it’s important to note thatwith_loader_criteria()
only affects the part of the query that determines what SQL is rendered in terms of the WHERE and FROM clauses. Thecontains_eager()
option does not affect the rendering of the SELECT statement outside of the columns clause, so does not have any interaction with thewith_loader_criteria()
option. However, the way things “work” is thatcontains_eager()
is meant to be used with a query that is already selecting from the additional entities in some way, wherewith_loader_criteria()
can apply it’s additional criteria.In the example below, assuming a mapping relationship as
A -> A.bs -> B
, the givenwith_loader_criteria()
option will affect the way in which the JOIN is rendered:stmt = select(A).join(A.bs).options( contains_eager(A.bs), with_loader_criteria(B, B.flag == 1) )
Above, the given
with_loader_criteria()
option will affect the ON clause of the JOIN that is specified by.join(A.bs)
, so is applied as expected. Thecontains_eager()
option has the effect that columns fromB
are added to the columns clause:SELECT b.id, b.a_id, b.data, b.flag, a.id AS id_1, a.data AS data_1 FROM a JOIN b ON a.id = b.a_id AND b.flag = :flag_1
The use of the
contains_eager()
option within the above statement has no effect on the behavior of thewith_loader_criteria()
option. If thecontains_eager()
option were omitted, the SQL would be the same as regards the FROM and WHERE clauses, wherewith_loader_criteria()
continues to add its criteria to the ON clause of the JOIN. The addition ofcontains_eager()
only affects the columns clause, in that additional columns againstb
are added which are then consumed by the ORM to produceB
instances.Warning
The use of a lambda inside of the call to
with_loader_criteria()
is only invoked once per unique class. Custom functions should not be invoked within this lambda. See Using Lambdas to add significant speed gains to statement production for an overview of the “lambda SQL” feature, which is for advanced use only.- Parameters:
entity_or_base¶ – a mapped class, or a class that is a super class of a particular set of mapped classes, to which the rule will apply.
where_criteria¶ –
a Core SQL expression that applies limiting criteria. This may also be a “lambda:” or Python function that accepts a target class as an argument, when the given class is a base with many different mapped subclasses.
Note
To support pickling, use a module-level Python function to produce the SQL expression instead of a lambda or a fixed SQL expression, which tend to not be picklable.
include_aliases¶ – if True, apply the rule to
aliased()
constructs as well.propagate_to_loaders¶ –
defaults to True, apply to relationship loaders such as lazy loaders. This indicates that the option object itself including SQL expression is carried along with each loaded instance. Set to
False
to prevent the object from being assigned to individual instances.See also
ORM Query Events - includes examples of using
with_loader_criteria()
.Adding global WHERE / ON criteria - basic example on how to combine
with_loader_criteria()
with theSessionEvents.do_orm_execute()
event.track_closure_variables¶ –
when False, closure variables inside of a lambda expression will not be used as part of any cache key. This allows more complex expressions to be used inside of a lambda expression but requires that the lambda ensures it returns the identical SQL every time given a particular class.
New in version 1.4.0b2.
- function sqlalchemy.orm.join(left: _FromClauseArgument, right: _FromClauseArgument, onclause: _OnClauseArgument | None = None, isouter: bool = False, full: bool = False) → _ORMJoin¶
Produce an inner join between left and right clauses.
join()
is an extension to the core join interface provided byjoin()
, where the left and right selectable may be not only core selectable objects such asTable
, but also mapped classes orAliasedClass
instances. The “on” clause can be a SQL expression or an ORM mapped attribute referencing a configuredrelationship()
.join()
is not commonly needed in modern usage, as its functionality is encapsulated within that of theSelect.join()
andQuery.join()
methods. which feature a significant amount of automation beyondjoin()
by itself. Explicit use ofjoin()
with ORM-enabled SELECT statements involves use of theSelect.select_from()
method, as in:from sqlalchemy.orm import join stmt = select(User).\ select_from(join(User, Address, User.addresses)).\ filter(Address.email_address=='foo@bar.com')
In modern SQLAlchemy the above join can be written more succinctly as:
stmt = select(User).\ join(User.addresses).\ filter(Address.email_address=='foo@bar.com')
Warning
using
join()
directly may not work properly with modern ORM options such aswith_loader_criteria()
. It is strongly recommended to use the idiomatic join patterns provided by methods such asSelect.join()
andSelect.join_from()
when creating ORM joins.See also
Joins - in the ORM Querying Guide for background on idiomatic ORM join patterns
- function sqlalchemy.orm.outerjoin(left: _FromClauseArgument, right: _FromClauseArgument, onclause: _OnClauseArgument | None = None, full: bool = False) → _ORMJoin¶
Produce a left outer join between left and right clauses.
This is the “outer join” version of the
join()
function, featuring the same behavior except that an OUTER JOIN is generated. See that function’s documentation for other usage details.
- function sqlalchemy.orm.with_parent(instance: object, prop: attributes.QueryableAttribute[Any], from_entity: _EntityType[Any] | None = None) → ColumnElement[bool]¶
Create filtering criterion that relates this query’s primary entity to the given related instance, using established
relationship()
configuration.E.g.:
stmt = select(Address).where(with_parent(some_user, User.addresses))
The SQL rendered is the same as that rendered when a lazy loader would fire off from the given parent on that attribute, meaning that the appropriate state is taken from the parent object in Python without the need to render joins to the parent table in the rendered statement.
The given property may also make use of
PropComparator.of_type()
to indicate the left side of the criteria:a1 = aliased(Address) a2 = aliased(Address) stmt = select(a1, a2).where( with_parent(u1, User.addresses.of_type(a2)) )
The above use is equivalent to using the
from_entity()
argument:a1 = aliased(Address) a2 = aliased(Address) stmt = select(a1, a2).where( with_parent(u1, User.addresses, from_entity=a2) )
- Parameters:
instance¶ – An instance which has some
relationship()
.property¶ – Class-bound attribute, which indicates what relationship from the instance should be used to reconcile the parent/child relationship.
from_entity¶ –
Entity in which to consider as the left side. This defaults to the “zero” entity of the
Query
itself.New in version 1.2.
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