.. highlight:: pycon+sql .. |prev| replace:: :doc:`relationships` .. |next| replace:: :doc:`query` .. include:: queryguide_nav_include.rst ============================= ORM API Features for Querying ============================= ORM Loader Options ------------------- Loader options are objects which, when passed to the :meth:`_sql.Select.options` method of a :class:`.Select` object or similar SQL construct, affect the loading of both column and relationship-oriented attributes. The majority of loader options descend from the :class:`_orm.Load` hierarchy. For a complete overview of using loader options, see the linked sections below. .. seealso:: * :ref:`loading_columns` - details mapper and loading options that affect how column and SQL-expression mapped attributes are loaded * :ref:`loading_toplevel` - details relationship and loading options that affect how :func:`_orm.relationship` mapped attributes are loaded .. _orm_queryguide_execution_options: ORM Execution Options --------------------- ORM-level execution options are keyword options that may be associated with a statement execution using either the :paramref:`_orm.Session.execute.execution_options` parameter, which is a dictionary argument accepted by :class:`_orm.Session` methods such as :meth:`_orm.Session.execute` and :meth:`_orm.Session.scalars`, or by associating them directly with the statement to be invoked itself using the :meth:`_sql.Executable.execution_options` method, which accepts them as arbitrary keyword arguments. ORM-level options are distinct from the Core level execution options documented at :meth:`_engine.Connection.execution_options`. It's important to note that the ORM options discussed below are **not** compatible with Core level methods :meth:`_engine.Connection.execution_options` or :meth:`_engine.Engine.execution_options`; the options are ignored at this level, even if the :class:`.Engine` or :class:`.Connection` is associated with the :class:`_orm.Session` in use. Within this section, the :meth:`_sql.Executable.execution_options` method style will be illustrated for examples. .. _orm_queryguide_populate_existing: Populate Existing ^^^^^^^^^^^^^^^^^^ The ``populate_existing`` execution option ensures that, for all rows loaded, the corresponding instances in the :class:`_orm.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) {execsql}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 :class:`_orm.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: .. sourcecode:: python 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 :func:`_orm.with_expression` option * The :meth:`_orm.PropComparator.and_` method that can modify what a loader strategy loads * The :func:`_orm.contains_eager` option * The :func:`_orm.with_loader_criteria` option The ``populate_existing`` execution option is equvialent to the :meth:`_orm.Query.populate_existing` method in :term:`1.x style` ORM queries. .. seealso:: :ref:`faq_session_identity` - in :doc:`/faq/index` :ref:`session_expire` - in the ORM :class:`_orm.Session` documentation .. _orm_queryguide_autoflush: Autoflush ^^^^^^^^^ This option, when passed as ``False``, will cause the :class:`_orm.Session` to not invoke the "autoflush" step. It is equivalent to using the :attr:`_orm.Session.no_autoflush` context manager to disable autoflush:: >>> stmt = select(User).execution_options(autoflush=False) >>> session.execute(stmt) {execsql}SELECT user_account.id, user_account.name, user_account.fullname FROM user_account ... This option will also work on ORM-enabled :class:`_sql.Update` and :class:`_sql.Delete` queries. The ``autoflush`` execution option is equvialent to the :meth:`_orm.Query.autoflush` method in :term:`1.x style` ORM queries. .. seealso:: :ref:`session_flushing` .. _orm_queryguide_yield_per: Fetching Large Result Sets with Yield Per ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ The ``yield_per`` execution option is an integer value which will cause the :class:`_engine.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 :class:`_engine.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 :class:`_engine.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 :class:`_engine.Result`. .. tip:: From the above, it follows that the :class:`_engine.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 :meth:`_engine.Result.fetchmany` or :meth:`_engine.Result.partitions`. Calling :meth:`_engine.Result.all` will defeat the purpose of using ``yield_per``. Using ``yield_per`` is equivalent to making use of both the :paramref:`_engine.Connection.execution_options.stream_results` execution option, which selects for server side cursors to be used by the backend if supported, and the :meth:`_engine.Result.yield_per` method on the returned :class:`_engine.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 :ref:`engine_stream_results`. This section details the use of ``yield_per`` as an execution option with an ORM :class:`_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 :meth:`.Executable.execution_options` method on the given statement or by passing it to the :paramref:`_orm.Session.execute.execution_options` parameter of :meth:`_orm.Session.execute` or other similar :class:`_orm.Session` method such as :meth:`_orm.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) {execsql}SELECT user_account.id, user_account.name, user_account.fullname FROM user_account [...] () {stop}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 :paramref:`_engine.Connection.execution_options.stream_results` and :paramref:`_engine.Connection.execution_options.max_row_buffer` Core-level execution options in conjunction with the :meth:`_engine.Result.yield_per` method of :class:`_engine.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) {execsql}SELECT user_account.id, user_account.name, user_account.fullname FROM user_account [...] () {stop}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 :meth:`_engine.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) {execsql}SELECT user_account.id, user_account.name, user_account.fullname FROM user_account [...] () {stop}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 :ref:`"subquery" eager loading ` loading or :ref:`"joined" eager loading ` when using collections. It is potentially compatible with :ref:`"select in" eager loading ` , provided the database driver supports multiple, independent cursors. Additionally, the ``yield_per`` execution option is not compatible with the :meth:`_engine.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. .. versionchanged:: 1.4.6 An exception is raised when ORM rows are fetched from a :class:`_engine.Result` object that makes use of the :meth:`_engine.Result.unique` filter, at the same time as the ``yield_per`` execution option is used. When using the legacy :class:`_orm.Query` object with :term:`1.x style` ORM use, the :meth:`_orm.Query.yield_per` method will have the same result as that of the ``yield_per`` execution option. .. seealso:: :ref:`engine_stream_results` .. _queryguide_identity_token: Identity Token ^^^^^^^^^^^^^^ .. doctest-disable: .. deepalchemy:: This option is an advanced-use feature mostly intended to be used with the :ref:`horizontal_sharding_toplevel` extension. For typical cases of loading objects with identical primary keys from different "shards" or partitions, consider using individual :class:`_orm.Session` objects per shard first. The "identity token" is an arbitrary value that can be associated within the :term:`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 :ref:`horizontal_sharding_toplevel` 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 :class:`_orm.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 :class:`_orm.Session` with objects that come from same-named tables in different schemas, using the :ref:`schema_translating` feature which can affect the choice of schema within the scope of queries. Given a mapping as: .. sourcecode:: python 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 :paramref:`_engine.Connection.execution_options.schema_translate_map`, mapping ``None`` to an alternate schema, we can place instances of ``MyTable`` into two different schemas: .. sourcecode:: python 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 :class:`_orm.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 :class:`_orm.Session` objects above are independent. If we wanted to persist both objects in one transaction, we would need to use the :ref:`horizontal_sharding_toplevel` extension to do this. However, we can illustrate querying for these objects in one session as follows: .. sourcecode:: python 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 :attr:`_orm.InstanceState.key` to view the two distinct identity tokens:: >>> from sqlalchemy import inspect >>> inspect(obj1).key (, (1,), 'test_schema') >>> inspect(obj2).key (, (1,), 'test_schema_2') The above logic takes place automatically when using the :ref:`horizontal_sharding_toplevel` extension. .. versionadded:: 2.0.0rc1 - added the ``identity_token`` ORM level execution option. .. seealso:: :ref:`examples_sharding` - in the :ref:`examples_toplevel` section. See the script ``separate_schema_translates.py`` for a demonstration of the above use case using the full sharding API. .. doctest-enable: .. _queryguide_inspection: Inspecting entities and columns from ORM-enabled SELECT and DML statements ========================================================================== The :func:`_sql.select` construct, as well as the :func:`_sql.insert`, :func:`_sql.update` and :func:`_sql.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 :class:`.Select` object, this information is available from the :attr:`.Select.column_descriptions` attribute. This attribute operates in the same way as the legacy :attr:`.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': , 'expr': , 'name': 'User', 'type': }, {'aliased': False, 'entity': , 'expr': <....InstrumentedAttribute object at ...>, 'name': 'id', 'type': Integer()}, {'aliased': True, 'entity': , 'expr': , 'name': 'user2', 'type': }] When :attr:`.Select.column_descriptions` is used with non-ORM objects such as plain :class:`.Table` or :class:`.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=, primary_key=True, nullable=False), 'name': 'id', 'type': Integer()}, {'expr': Column('name', String(), table=, nullable=False), 'name': 'name', 'type': String()}, {'expr': Column('fullname', String(), table=), 'name': 'fullname', 'type': String()}, {'expr': Column('id', Integer(), table=
, primary_key=True, nullable=False), 'name': 'id_1', 'type': Integer()}] .. versionchanged:: 1.4.33 The :attr:`.Select.column_descriptions` attribute now returns a value when used against a :class:`.Select` that is not ORM-enabled. Previously, this would raise ``NotImplementedError``. For :func:`_sql.insert`, :func:`.update` and :func:`.delete` constructs, there are two separate attributes. One is :attr:`.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': , 'expr': , 'name': 'User', 'table': Table('user_account', ...), 'type': } .. tip:: The :attr:`.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 :attr:`.UpdateBase.returning_column_descriptions` which delivers information about the columns present in the RETURNING collection in a manner roughly similar to that of :attr:`.Select.column_descriptions`:: >>> pprint(stmt.returning_column_descriptions) [{'aliased': False, 'entity': , 'expr': , 'name': 'id', 'type': Integer()}] .. versionadded:: 1.4.33 Added the :attr:`.UpdateBase.entity_description` and :attr:`.UpdateBase.returning_column_descriptions` attributes. .. _queryguide_additional: Additional ORM API Constructs ============================= .. autofunction:: sqlalchemy.orm.aliased .. autoclass:: sqlalchemy.orm.util.AliasedClass .. autoclass:: sqlalchemy.orm.util.AliasedInsp .. autoclass:: sqlalchemy.orm.Bundle :members: .. autofunction:: sqlalchemy.orm.with_loader_criteria .. autofunction:: sqlalchemy.orm.join .. autofunction:: sqlalchemy.orm.outerjoin .. autofunction:: sqlalchemy.orm.with_parent .. Setup code, not for display >>> session.close() >>> conn.close() ROLLBACK