Release: 2.0.42 | Release Date: July 29, 2025

# SQLAlchemy 2.0 Documentation

# Working with Large Collections

The default behavior of `relationship()` is to fully load the contents of collections into memory, based on a configured loader strategy that controls when and how these contents are loaded from the database. Related collections may be loaded into memory not just when they are accessed, or eagerly loaded, but in most cases will require population when the collection itself is mutated, as well as in cases where the owning object is to be deleted by the unit of work system.

When a related collection is potentially very large, it may not be feasible for such a collection to be populated into memory under any circumstances, as the operation may be overly consuming of time, network and memory resources.

This section includes API features intended to allow `relationship()` to be used with large collections while maintaining adequate performance.

## Write Only Relationships

The write only loader strategy is the primary means of configuring a `relationship()` that will remain writeable, but will not load its contents into memory. A write-only ORM configuration in modern type-annotated Declarative form is illustrated below:

    >>> from decimal import Decimal
    >>> from datetime import datetime

    >>> from sqlalchemy import ForeignKey
    >>> from sqlalchemy import func
    >>> from sqlalchemy.orm import DeclarativeBase
    >>> from sqlalchemy.orm import Mapped
    >>> from sqlalchemy.orm import mapped_column
    >>> from sqlalchemy.orm import relationship
    >>> from sqlalchemy.orm import Session
    >>> from sqlalchemy.orm import WriteOnlyMapped

    >>> class Base(DeclarativeBase):
    ...     pass

    >>> class Account(Base):
    ...     __tablename__ = "account"
    ...     id: Mapped[int] = mapped_column(primary_key=True)
    ...     identifier: Mapped[str]
    ...
    ...     account_transactions: WriteOnlyMapped["AccountTransaction"] = relationship(
    ...         cascade="all, delete-orphan",
    ...         passive_deletes=True,
    ...         order_by="AccountTransaction.timestamp",
    ...     )
    ...
    ...     def __repr__(self):
    ...         return f"Account(identifier={self.identifier!r})"

    >>> class AccountTransaction(Base):
    ...     __tablename__ = "account_transaction"
    ...     id: Mapped[int] = mapped_column(primary_key=True)
    ...     account_id: Mapped[int] = mapped_column(
    ...         ForeignKey("account.id", ondelete="cascade")
    ...     )
    ...     description: Mapped[str]
    ...     amount: Mapped[Decimal]
    ...     timestamp: Mapped[datetime] = mapped_column(default=func.now())
    ...
    ...     def __repr__(self):
    ...         return (
    ...             f"AccountTransaction(amount={self.amount:.2f}, "
    ...             f"timestamp={self.timestamp.isoformat()!r})"
    ...         )
    ...
    ...     __mapper_args__ = {"eager_defaults": True}

Above, the `account_transactions` relationship is configured not using the ordinary `Mapped` annotation, but instead using the `WriteOnlyMapped` type annotation, which at runtime will assign the loader strategy of `lazy="write_only"` to the target `relationship()`. The `WriteOnlyMapped` annotation is an alternative form of the `Mapped` annotation which indicate the use of the `WriteOnlyCollection` collection type on instances of the object.

The above `relationship()` configuration also includes several elements that are specific to what action to take when `Account` objects are deleted, as well as when `AccountTransaction` objects are removed from the `account_transactions` collection. These elements are:

- `passive_deletes=True` - allows the unit of work to forego having to load the collection when `Account` is deleted; see Using foreign key ON DELETE cascade with ORM relationships.

- `ondelete="cascade"` configured on the `ForeignKey` constraint. This is also detailed at Using foreign key ON DELETE cascade with ORM relationships.

- `cascade="all, delete-orphan"` - instructs the unit of work to delete `AccountTransaction` objects when they are removed from the collection. See delete-orphan in the Cascades document.

Added in version 2.0: Added “Write only” relationship loaders.

### Creating and Persisting New Write Only Collections

The write-only collection allows for direct assignment of the collection as a whole only for transient or pending objects. With our above mapping, this indicates we can create a new `Account` object with a sequence of `AccountTransaction` objects to be added to a `Session`. Any Python iterable may be used as the source of objects to start, where below we use a Python `list`:

    >>> new_account = Account(
    ...     identifier="account_01",
    ...     account_transactions=[
    ...         AccountTransaction(description="initial deposit", amount=Decimal("500.00")),
    ...         AccountTransaction(description="transfer", amount=Decimal("1000.00")),
    ...         AccountTransaction(description="withdrawal", amount=Decimal("-29.50")),
    ...     ],
    ... )

    >>> with Session(engine) as session:
    ...     session.add(new_account)
    ...     session.commit()
    BEGIN (implicit)
    INSERT INTO account (identifier) VALUES (?)
    [...] ('account_01',)
    INSERT INTO account_transaction (account_id, description, amount, timestamp)
    VALUES (?, ?, ?, CURRENT_TIMESTAMP) RETURNING id, timestamp
    [... (insertmanyvalues) 1/3 (ordered; batch not supported)] (1, 'initial deposit', 500.0)
    INSERT INTO account_transaction (account_id, description, amount, timestamp)
    VALUES (?, ?, ?, CURRENT_TIMESTAMP) RETURNING id, timestamp
    [insertmanyvalues 2/3 (ordered; batch not supported)] (1, 'transfer', 1000.0)
    INSERT INTO account_transaction (account_id, description, amount, timestamp)
    VALUES (?, ?, ?, CURRENT_TIMESTAMP) RETURNING id, timestamp
    [insertmanyvalues 3/3 (ordered; batch not supported)] (1, 'withdrawal', -29.5)
    COMMIT

Once an object is database-persisted (i.e. in the persistent or detached state), the collection has the ability to be extended with new items as well as the ability for individual items to be removed. However, the collection may no longer be re-assigned with a full replacement collection, as such an operation requires that the previous collection is fully loaded into memory in order to reconcile the old entries with the new ones:

    >>> new_account.account_transactions = [
    ...     AccountTransaction(description="some transaction", amount=Decimal("10.00"))
    ... ]
    Traceback (most recent call last):
    ...
    sqlalchemy.exc.InvalidRequestError: Collection "Account.account_transactions" does not
    support implicit iteration; collection replacement operations can't be used

### Adding New Items to an Existing Collection

For write-only collections of persistent objects, modifications to the collection using unit of work processes may proceed only by using the `WriteOnlyCollection.add()`, `WriteOnlyCollection.add_all()` and `WriteOnlyCollection.remove()` methods:

    >>> from sqlalchemy import select
    >>> session = Session(engine, expire_on_commit=False)
    >>> existing_account = session.scalar(select(Account).filter_by(identifier="account_01"))
    BEGIN (implicit)
    SELECT account.id, account.identifier
    FROM account
    WHERE account.identifier = ?
    [...] ('account_01',)
    >>> existing_account.account_transactions.add_all(
    ...     [
    ...         AccountTransaction(description="paycheck", amount=Decimal("2000.00")),
    ...         AccountTransaction(description="rent", amount=Decimal("-800.00")),
    ...     ]
    ... )
    >>> session.commit()
    INSERT INTO account_transaction (account_id, description, amount, timestamp)
    VALUES (?, ?, ?, CURRENT_TIMESTAMP) RETURNING id, timestamp
    [... (insertmanyvalues) 1/2 (ordered; batch not supported)] (1, 'paycheck', 2000.0)
    INSERT INTO account_transaction (account_id, description, amount, timestamp)
    VALUES (?, ?, ?, CURRENT_TIMESTAMP) RETURNING id, timestamp
    [insertmanyvalues 2/2 (ordered; batch not supported)] (1, 'rent', -800.0)
    COMMIT

The items added above are held in a pending queue within the `Session` until the next flush, at which point they are INSERTed into the database, assuming the added objects were previously transient.

### Querying Items

The `WriteOnlyCollection` does not at any point store a reference to the current contents of the collection, nor does it have any behavior where it would directly emit a SELECT to the database in order to load them; the overriding assumption is that the collection may contain many thousands or millions of rows, and should never be fully loaded into memory as a side effect of any other operation.

Instead, the `WriteOnlyCollection` includes SQL-generating helpers such as `WriteOnlyCollection.select()`, which will generate a `Select` construct pre-configured with the correct WHERE / FROM criteria for the current parent row, which can then be further modified in order to SELECT any range of rows desired, as well as invoked using features like server side cursors for processes that wish to iterate through the full collection in a memory-efficient manner.

The statement generated is illustrated below. Note it also includes ORDER BY criteria, indicated in the example mapping by the `relationship.order_by` parameter of `relationship()`; this criteria would be omitted if the parameter were not configured:

    >>> print(existing_account.account_transactions.select())
    SELECT account_transaction.id, account_transaction.account_id, account_transaction.description,
    account_transaction.amount, account_transaction.timestamp
    FROM account_transaction
    WHERE :param_1 = account_transaction.account_id ORDER BY account_transaction.timestamp

We may use this `Select` construct along with the `Session` in order to query for `AccountTransaction` objects, most easily using the `Session.scalars()` method that will return a `Result` that yields ORM objects directly. It’s typical, though not required, that the `Select` would be modified further to limit the records returned; in the example below, additional WHERE criteria to load only “debit” account transactions is added, along with “LIMIT 10” to retrieve only the first ten rows:

    >>> account_transactions = session.scalars(
    ...     existing_account.account_transactions.select()
    ...     .where(AccountTransaction.amount < 0)
    ...     .limit(10)
    ... ).all()
    BEGIN (implicit)
    SELECT account_transaction.id, account_transaction.account_id, account_transaction.description,
    account_transaction.amount, account_transaction.timestamp
    FROM account_transaction
    WHERE ? = account_transaction.account_id AND account_transaction.amount < ?
    ORDER BY account_transaction.timestamp  LIMIT ? OFFSET ?
    [...] (1, 0, 10, 0)
    >>> print(account_transactions)
    [AccountTransaction(amount=-29.50, timestamp='...'), AccountTransaction(amount=-800.00, timestamp='...')]

### Removing Items

Individual items that are loaded in the persistent state against the current `Session` may be marked for removal from the collection using the `WriteOnlyCollection.remove()` method. The flush process will implicitly consider the object to be already part of the collection when the operation proceeds. The example below illustrates removal of an individual `AccountTransaction` item, which per cascade settings results in a DELETE of that row:

    >>> existing_transaction = account_transactions[0]
    >>> existing_account.account_transactions.remove(existing_transaction)
    >>> session.commit()
    DELETE FROM account_transaction WHERE account_transaction.id = ?
    [...] (3,)
    COMMIT

As with any ORM-mapped collection, object removal may proceed either to de-associate the object from the collection while leaving the object present in the database, or may issue a DELETE for its row, based on the delete-orphan configuration of the `relationship()`.

Collection removal without deletion involves setting foreign key columns to NULL for a one-to-many relationship, or deleting the corresponding association row for a many-to-many relationship.

### Bulk INSERT of New Items

The `WriteOnlyCollection` can generate DML constructs such as `Insert` objects, which may be used in an ORM context to produce bulk insert behavior. See the section ORM Bulk INSERT Statements for an overview of ORM bulk inserts.

#### One to Many Collections

For a regular one to many collection only, the `WriteOnlyCollection.insert()` method will produce an `Insert` construct which is pre-established with VALUES criteria corresponding to the parent object. As this VALUES criteria is entirely against the related table, the statement can be used to INSERT new rows that will at the same time become new records in the related collection:

    >>> session.execute(
    ...     existing_account.account_transactions.insert(),
    ...     [
    ...         {"description": "transaction 1", "amount": Decimal("47.50")},
    ...         {"description": "transaction 2", "amount": Decimal("-501.25")},
    ...         {"description": "transaction 3", "amount": Decimal("1800.00")},
    ...         {"description": "transaction 4", "amount": Decimal("-300.00")},
    ...     ],
    ... )
    BEGIN (implicit)
    INSERT INTO account_transaction (account_id, description, amount, timestamp) VALUES (?, ?, ?, CURRENT_TIMESTAMP)
    [...] [(1, 'transaction 1', 47.5), (1, 'transaction 2', -501.25), (1, 'transaction 3', 1800.0), (1, 'transaction 4', -300.0)]
    <...>
    >>> session.commit()
    COMMIT

See also

ORM Bulk INSERT Statements - in the ORM Querying Guide

One To Many - at Basic Relationship Patterns

#### Many to Many Collections

For a many to many collection, the relationship between two classes involves a third table that is configured using the `relationship.secondary` parameter of `relationship`. To bulk insert rows into a collection of this type using `WriteOnlyCollection`, the new records may be bulk-inserted separately first, retrieved using RETURNING, and those records then passed to the `WriteOnlyCollection.add_all()` method where the unit of work process will proceed to persist them as part of the collection.

Supposing a class `BankAudit` referred to many `AccountTransaction` records using a many-to-many table:

    >>> from sqlalchemy import Table, Column
    >>> audit_to_transaction = Table(
    ...     "audit_transaction",
    ...     Base.metadata,
    ...     Column("audit_id", ForeignKey("audit.id", ondelete="CASCADE"), primary_key=True),
    ...     Column(
    ...         "transaction_id",
    ...         ForeignKey("account_transaction.id", ondelete="CASCADE"),
    ...         primary_key=True,
    ...     ),
    ... )
    >>> class BankAudit(Base):
    ...     __tablename__ = "audit"
    ...     id: Mapped[int] = mapped_column(primary_key=True)
    ...     account_transactions: WriteOnlyMapped["AccountTransaction"] = relationship(
    ...         secondary=audit_to_transaction, passive_deletes=True
    ...     )

To illustrate the two operations, we add more `AccountTransaction` objects using bulk insert, which we retrieve using RETURNING by adding `returning(AccountTransaction)` to the bulk INSERT statement (note that we could just as easily use existing `AccountTransaction` objects as well):

    >>> new_transactions = session.scalars(
    ...     existing_account.account_transactions.insert().returning(AccountTransaction),
    ...     [
    ...         {"description": "odd trans 1", "amount": Decimal("50000.00")},
    ...         {"description": "odd trans 2", "amount": Decimal("25000.00")},
    ...         {"description": "odd trans 3", "amount": Decimal("45.00")},
    ...     ],
    ... ).all()
    BEGIN (implicit)
    INSERT INTO account_transaction (account_id, description, amount, timestamp) VALUES
    (?, ?, ?, CURRENT_TIMESTAMP), (?, ?, ?, CURRENT_TIMESTAMP), (?, ?, ?, CURRENT_TIMESTAMP)
    RETURNING id, account_id, description, amount, timestamp
    [...] (1, 'odd trans 1', 50000.0, 1, 'odd trans 2', 25000.0, 1, 'odd trans 3', 45.0)

With a list of `AccountTransaction` objects ready, the `WriteOnlyCollection.add_all()` method is used to associate many rows at once with a new `BankAudit` object:

    >>> bank_audit = BankAudit()
    >>> session.add(bank_audit)
    >>> bank_audit.account_transactions.add_all(new_transactions)
    >>> session.commit()
    INSERT INTO audit DEFAULT VALUES
    [...] ()
    INSERT INTO audit_transaction (audit_id, transaction_id) VALUES (?, ?)
    [...] [(1, 10), (1, 11), (1, 12)]
    COMMIT

See also

ORM Bulk INSERT Statements - in the ORM Querying Guide

Many To Many - at Basic Relationship Patterns

### Bulk UPDATE and DELETE of Items

In a similar way in which `WriteOnlyCollection` can generate `Select` constructs with WHERE criteria pre-established, it can also generate `Update` and `Delete` constructs with that same WHERE criteria, to allow criteria-oriented UPDATE and DELETE statements against the elements in a large collection.

#### One To Many Collections

As is the case with INSERT, this feature is most straightforward with one to many collections.

In the example below, the `WriteOnlyCollection.update()` method is used to generate an UPDATE statement is emitted against the elements in the collection, locating rows where the “amount” is equal to `-800` and adding the amount of `200` to them:

    >>> session.execute(
    ...     existing_account.account_transactions.update()
    ...     .values(amount=AccountTransaction.amount + 200)
    ...     .where(AccountTransaction.amount == -800),
    ... )
    BEGIN (implicit)
    UPDATE account_transaction SET amount=(account_transaction.amount + ?)
    WHERE ? = account_transaction.account_id AND account_transaction.amount = ?
    [...] (200, 1, -800)
    <...>

In a similar way, `WriteOnlyCollection.delete()` will produce a DELETE statement that is invoked in the same way:

    >>> session.execute(
    ...     existing_account.account_transactions.delete().where(
    ...         AccountTransaction.amount.between(0, 30)
    ...     ),
    ... )
    DELETE FROM account_transaction WHERE ? = account_transaction.account_id
    AND account_transaction.amount BETWEEN ? AND ? RETURNING id
    [...] (1, 0, 30)
    <...>

#### Many to Many Collections

Tip

The techniques here involve multi-table UPDATE expressions, which are slightly more advanced.

For bulk UPDATE and DELETE of many to many collections, in order for an UPDATE or DELETE statement to relate to the primary key of the parent object, the association table must be explicitly part of the UPDATE/DELETE statement, which requires either that the backend includes supports for non-standard SQL syntaxes, or extra explicit steps when constructing the UPDATE or DELETE statement.

For backends that support multi-table versions of UPDATE, the `WriteOnlyCollection.update()` method should work without extra steps for a many-to-many collection, as in the example below where an UPDATE is emitted against `AccountTransaction` objects in terms of the many-to-many `BankAudit.account_transactions` collection:

    >>> session.execute(
    ...     bank_audit.account_transactions.update().values(
    ...         description=AccountTransaction.description + " (audited)"
    ...     )
    ... )
    UPDATE account_transaction SET description=(account_transaction.description || ?)
    FROM audit_transaction WHERE ? = audit_transaction.audit_id
    AND account_transaction.id = audit_transaction.transaction_id RETURNING id
    [...] (' (audited)', 1)
    <...>

The above statement automatically makes use of “UPDATE..FROM” syntax, supported by SQLite and others, to name the additional `audit_transaction` table in the WHERE clause.

To UPDATE or DELETE a many-to-many collection where multi-table syntax is not available, the many-to-many criteria may be moved into SELECT that for example may be combined with IN to match rows. The `WriteOnlyCollection` still helps us here, as we use the `WriteOnlyCollection.select()` method to generate this SELECT for us, making use of the `Select.with_only_columns()` method to produce a scalar subquery:

    >>> from sqlalchemy import update
    >>> subq = bank_audit.account_transactions.select().with_only_columns(AccountTransaction.id)
    >>> session.execute(
    ...     update(AccountTransaction)
    ...     .values(description=AccountTransaction.description + " (audited)")
    ...     .where(AccountTransaction.id.in_(subq))
    ... )
    UPDATE account_transaction SET description=(account_transaction.description || ?)
    WHERE account_transaction.id IN (SELECT account_transaction.id
    FROM audit_transaction
    WHERE ? = audit_transaction.audit_id AND account_transaction.id = audit_transaction.transaction_id)
    RETURNING id
    [...] (' (audited)', 1)
    <...>

### Write Only Collections - API Documentation

Object Name | Description
--- | ---
WriteOnlyCollection | Write-only collection which can synchronize changes into the attribute event system.
WriteOnlyMapped | Represent the ORM mapped attribute type for a “write only” relationship.

class sqlalchemy.orm.WriteOnlyCollection

  Write-only collection which can synchronize changes into the attribute event system.

  The `WriteOnlyCollection` is used in a mapping by using the `"write_only"` lazy loading strategy with `relationship()`. For background on this configuration, see Write Only Relationships.

  Added in version 2.0.

  See also

  Write Only Relationships

  Members

  add(), add_all(), delete(), insert(), remove(), select(), update()

  Class signature

  class `sqlalchemy.orm.WriteOnlyCollection` (`sqlalchemy.orm.writeonly.AbstractCollectionWriter`)

  method `sqlalchemy.orm.WriteOnlyCollection.`add(item: _T) → None

    Add an item to this `WriteOnlyCollection`.

    The given item will be persisted to the database in terms of the parent instance’s collection on the next flush.

  method `sqlalchemy.orm.WriteOnlyCollection.`add_all(iterator: Iterable[_T]) → None

    Add an iterable of items to this `WriteOnlyCollection`.

    The given items will be persisted to the database in terms of the parent instance’s collection on the next flush.

  method `sqlalchemy.orm.WriteOnlyCollection.`delete() → Delete

    Produce a `Delete` which will refer to rows in terms of this instance-local `WriteOnlyCollection`.

  method `sqlalchemy.orm.WriteOnlyCollection.`insert() → Insert

    For one-to-many collections, produce a `Insert` which will insert new rows in terms of this this instance-local `WriteOnlyCollection`.

    This construct is only supported for a `Relationship` that does not include the `relationship.secondary` parameter. For relationships that refer to a many-to-many table, use ordinary bulk insert techniques to produce new objects, then use `AbstractCollectionWriter.add_all()` to associate them with the collection.

  method `sqlalchemy.orm.WriteOnlyCollection.`remove(item: _T) → None

    Remove an item from this `WriteOnlyCollection`.

    The given item will be removed from the parent instance’s collection on the next flush.

  method `sqlalchemy.orm.WriteOnlyCollection.`select() → Select[Tuple[_T]]

    Produce a `Select` construct that represents the rows within this instance-local `WriteOnlyCollection`.

  method `sqlalchemy.orm.WriteOnlyCollection.`update() → Update

    Produce a `Update` which will refer to rows in terms of this instance-local `WriteOnlyCollection`.

class sqlalchemy.orm.WriteOnlyMapped

  Represent the ORM mapped attribute type for a “write only” relationship.

  The `WriteOnlyMapped` type annotation may be used in an Annotated Declarative Table mapping to indicate that the `lazy="write_only"` loader strategy should be used for a particular `relationship()`.

  E.g.:

      class User(Base):
          __tablename__ = "user"
          id: Mapped[int] = mapped_column(primary_key=True)
          addresses: WriteOnlyMapped[Address] = relationship(
              cascade="all,delete-orphan"
          )

  See the section Write Only Relationships for background.

  Added in version 2.0.

  See also

  Write Only Relationships - complete background

  `DynamicMapped` - includes legacy `Query` support

  Class signature

  class `sqlalchemy.orm.WriteOnlyMapped` (`sqlalchemy.orm.base._MappedAnnotationBase`)

## Dynamic Relationship Loaders

Legacy Feature

The “dynamic” lazy loader strategy is the legacy form of what is now the “write_only” strategy described in the section Write Only Relationships.

The “dynamic” strategy produces a legacy `Query` object from the related collection. However, a major drawback of “dynamic” relationships is that there are several cases where the collection will fully iterate, some of which are non-obvious, which can only be prevented with careful programming and testing on a case-by-case basis. Therefore, for truly large collection management, the `WriteOnlyCollection` should be preferred.

The dynamic loader is also not compatible with the Asynchronous I/O (asyncio) extension. It can be used with some limitations, as indicated in Asyncio dynamic guidelines, but again the `WriteOnlyCollection`, which is fully compatible with asyncio, should be preferred.

The dynamic relationship strategy allows configuration of a `relationship()` which when accessed on an instance will return a legacy `Query` object in place of the collection. The `Query` can then be modified further so that the database collection may be iterated based on filtering criteria. The returned `Query` object is an instance of `AppenderQuery`, which combines the loading and iteration behavior of `Query` along with rudimentary collection mutation methods such as `AppenderQuery.append()` and `AppenderQuery.remove()`.

The “dynamic” loader strategy may be configured with type-annotated Declarative form using the `DynamicMapped` annotation class:

    from sqlalchemy.orm import DynamicMapped

    class User(Base):
        __tablename__ = "user"

        id: Mapped[int] = mapped_column(primary_key=True)
        posts: DynamicMapped[Post] = relationship()

Above, the `User.posts` collection on an individual `User` object will return the `AppenderQuery` object, which is a subclass of `Query` that also supports basic collection mutation operations:

    jack = session.get(User, id)

    # filter Jack's blog posts
    posts = jack.posts.filter(Post.headline == "this is a post")

    # apply array slices
    posts = jack.posts[5:20]

The dynamic relationship supports limited write operations, via the `AppenderQuery.append()` and `AppenderQuery.remove()` methods:

    oldpost = jack.posts.filter(Post.headline == "old post").one()
    jack.posts.remove(oldpost)

    jack.posts.append(Post("new post"))

Since the read side of the dynamic relationship always queries the database, changes to the underlying collection will not be visible until the data has been flushed. However, as long as “autoflush” is enabled on the `Session` in use, this will occur automatically each time the collection is about to emit a query.

### Dynamic Relationship Loaders - API

Object Name | Description
--- | ---
AppenderQuery | A dynamic query that supports basic collection storage operations.
DynamicMapped | Represent the ORM mapped attribute type for a “dynamic” relationship.

class sqlalchemy.orm.AppenderQuery

  A dynamic query that supports basic collection storage operations.

  Methods on `AppenderQuery` include all methods of `Query`, plus additional methods used for collection persistence.

  Members

  add(), add_all(), append(), count(), extend(), get_children(), prefix_with(), remove(), suffix_with(), with_hint(), with_statement_hint()

  Class signature

  class `sqlalchemy.orm.AppenderQuery` (`sqlalchemy.orm.dynamic.AppenderMixin`, `sqlalchemy.orm.Query`)

  method `sqlalchemy.orm.AppenderQuery.`add(item: _T) → None

    inherited from the `AppenderMixin.add()` method of `AppenderMixin`

    Add an item to this `AppenderQuery`.

    The given item will be persisted to the database in terms of the parent instance’s collection on the next flush.

    This method is provided to assist in delivering forwards-compatibility with the `WriteOnlyCollection` collection class.

    Added in version 2.0.

  method `sqlalchemy.orm.AppenderQuery.`add_all(iterator: Iterable[_T]) → None

    inherited from the `AppenderMixin.add_all()` method of `AppenderMixin`

    Add an iterable of items to this `AppenderQuery`.

    The given items will be persisted to the database in terms of the parent instance’s collection on the next flush.

    This method is provided to assist in delivering forwards-compatibility with the `WriteOnlyCollection` collection class.

    Added in version 2.0.

  method `sqlalchemy.orm.AppenderQuery.`append(item: _T) → None

    inherited from the `AppenderMixin.append()` method of `AppenderMixin`

    Append an item to this `AppenderQuery`.

    The given item will be persisted to the database in terms of the parent instance’s collection on the next flush.

  method `sqlalchemy.orm.AppenderQuery.`count() → int

    inherited from the `AppenderMixin.count()` method of `AppenderMixin`

    Return a count of rows this the SQL formed by this `Query` would return.

    This generates the SQL for this Query as follows:

        SELECT count(1) AS count_1 FROM (
            SELECT <rest of query follows...>
        ) AS anon_1

    The above SQL returns a single row, which is the aggregate value of the count function; the `Query.count()` method then returns that single integer value.

    Warning

    It is important to note that the value returned by count() is not the same as the number of ORM objects that this Query would return from a method such as the .all() method. The `Query` object, when asked to return full entities, will deduplicate entries based on primary key, meaning if the same primary key value would appear in the results more than once, only one object of that primary key would be present. This does not apply to a query that is against individual columns.

    See also

    My Query does not return the same number of objects as query.count() tells me - why?

    For fine grained control over specific columns to count, to skip the usage of a subquery or otherwise control of the FROM clause, or to use other aggregate functions, use `expression.func` expressions in conjunction with `Session.query()`, i.e.:

        from sqlalchemy import func

        # count User records, without
        # using a subquery.
        session.query(func.count(User.id))

        # return count of user "id" grouped
        # by "name"
        session.query(func.count(User.id)).group_by(User.name)

        from sqlalchemy import distinct

        # count distinct "name" values
        session.query(func.count(distinct(User.name)))

    See also

    2.0 Migration - ORM Usage

  method `sqlalchemy.orm.AppenderQuery.`extend(iterator: Iterable[_T]) → None

    inherited from the `AppenderMixin.extend()` method of `AppenderMixin`

    Add an iterable of items to this `AppenderQuery`.

    The given items will be persisted to the database in terms of the parent instance’s collection on the next flush.

  method `sqlalchemy.orm.AppenderQuery.`get_children(*, omit_attrs: Tuple[str, ...] = (), **kw: Any) → Iterable[HasTraverseInternals]

    inherited from the `HasTraverseInternals.get_children()` method of `HasTraverseInternals`

    Return immediate child `HasTraverseInternals` elements of this `HasTraverseInternals`.

    This is used for visit traversal.

    **kw may contain flags that change the collection that is returned, for example to return a subset of items in order to cut down on larger traversals, or to return child items from a different context (such as schema-level collections instead of clause-level).

  method `sqlalchemy.orm.AppenderQuery.`prefix_with(*prefixes: _TextCoercedExpressionArgument[Any], dialect: str = '*') → Self

    inherited from the `HasPrefixes.prefix_with()` method of `HasPrefixes`

    Add one or more expressions following the statement keyword, i.e. SELECT, INSERT, UPDATE, or DELETE. Generative.

    This is used to support backend-specific prefix keywords such as those provided by MySQL.

    E.g.:

        stmt = table.insert().prefix_with("LOW_PRIORITY", dialect="mysql")

        # MySQL 5.7 optimizer hints
        stmt = select(table).prefix_with("/*+ BKA(t1) */", dialect="mysql")

    Multiple prefixes can be specified by multiple calls to `HasPrefixes.prefix_with()`.

    Parameters:

      - *prefixes – textual or `ClauseElement` construct which will be rendered following the INSERT, UPDATE, or DELETE keyword.

      - dialect – optional string dialect name which will limit rendering of this prefix to only that dialect.

  method `sqlalchemy.orm.AppenderQuery.`remove(item: _T) → None

    inherited from the `AppenderMixin.remove()` method of `AppenderMixin`

    Remove an item from this `AppenderQuery`.

    The given item will be removed from the parent instance’s collection on the next flush.

  method `sqlalchemy.orm.AppenderQuery.`suffix_with(*suffixes: _TextCoercedExpressionArgument[Any], dialect: str = '*') → Self

    inherited from the `HasSuffixes.suffix_with()` method of `HasSuffixes`

    Add one or more expressions following the statement as a whole.

    This is used to support backend-specific suffix keywords on certain constructs.

    E.g.:

        stmt = (
            select(col1, col2)
            .cte()
            .suffix_with(
                "cycle empno set y_cycle to 1 default 0", dialect="oracle"
            )
        )

    Multiple suffixes can be specified by multiple calls to `HasSuffixes.suffix_with()`.

    Parameters:

      - *suffixes – textual or `ClauseElement` construct which will be rendered following the target clause.

      - dialect – Optional string dialect name which will limit rendering of this suffix to only that dialect.

  method `sqlalchemy.orm.AppenderQuery.`with_hint(selectable: _FromClauseArgument, text: str, dialect_name: str = '*') → Self

    inherited from the `HasHints.with_hint()` method of `HasHints`

    Add an indexing or other executional context hint for the given selectable to this `Select` or other selectable object.

    Tip

    The `Select.with_hint()` method adds hints that are specific to a single table to a statement, in a location that is dialect-specific. To add generic optimizer hints to the beginning of a statement ahead of the SELECT keyword such as for MySQL or Oracle Database, use the `Select.prefix_with()` method. To add optimizer hints to the end of a statement such as for PostgreSQL, use the `Select.with_statement_hint()` method.

    The text of the hint is rendered in the appropriate location for the database backend in use, relative to the given `Table` or `Alias` passed as the `selectable` argument. The dialect implementation typically uses Python string substitution syntax with the token `%(name)s` to render the name of the table or alias. E.g. when using Oracle Database, the following:

        select(mytable).with_hint(mytable, "index(%(name)s ix_mytable)")

    Would render SQL as:

        select /*+ index(mytable ix_mytable) */ ... from mytable

    The `dialect_name` option will limit the rendering of a particular hint to a particular backend. Such as, to add hints for both Oracle Database and MSSql simultaneously:

        select(mytable).with_hint(
            mytable, "index(%(name)s ix_mytable)", "oracle"
        ).with_hint(mytable, "WITH INDEX ix_mytable", "mssql")

    See also

    `Select.with_statement_hint()`

    `Select.prefix_with()` - generic SELECT prefixing which also can suit some database-specific HINT syntaxes such as MySQL or Oracle Database optimizer hints

  method `sqlalchemy.orm.AppenderQuery.`with_statement_hint(text: str, dialect_name: str = '*') → Self

    inherited from the `HasHints.with_statement_hint()` method of `HasHints`

    Add a statement hint to this `Select` or other selectable object.

    Tip

    `Select.with_statement_hint()` generally adds hints at the trailing end of a SELECT statement. To place dialect-specific hints such as optimizer hints at the front of the SELECT statement after the SELECT keyword, use the `Select.prefix_with()` method for an open-ended space, or for table-specific hints the `Select.with_hint()` may be used, which places hints in a dialect-specific location.

    This method is similar to `Select.with_hint()` except that it does not require an individual table, and instead applies to the statement as a whole.

    Hints here are specific to the backend database and may include directives such as isolation levels, file directives, fetch directives, etc.

    See also

    `Select.with_hint()`

    `Select.prefix_with()` - generic SELECT prefixing which also can suit some database-specific HINT syntaxes such as MySQL or Oracle Database optimizer hints

class sqlalchemy.orm.DynamicMapped

  Represent the ORM mapped attribute type for a “dynamic” relationship.

  The `DynamicMapped` type annotation may be used in an Annotated Declarative Table mapping to indicate that the `lazy="dynamic"` loader strategy should be used for a particular `relationship()`.

  Legacy Feature

  The “dynamic” lazy loader strategy is the legacy form of what is now the “write_only” strategy described in the section Write Only Relationships.

  E.g.:

      class User(Base):
          __tablename__ = "user"
          id: Mapped[int] = mapped_column(primary_key=True)
          addresses: DynamicMapped[Address] = relationship(
              cascade="all,delete-orphan"
          )

  See the section Dynamic Relationship Loaders for background.

  Added in version 2.0.

  See also

  Dynamic Relationship Loaders - complete background

  `WriteOnlyMapped` - fully 2.0 style version

  Class signature

  class `sqlalchemy.orm.DynamicMapped` (`sqlalchemy.orm.base._MappedAnnotationBase`)

## Setting RaiseLoad

A “raise”-loaded relationship will raise an `InvalidRequestError` where the attribute would normally emit a lazy load:

    class MyClass(Base):
        __tablename__ = "some_table"

        # ...

        children: Mapped[List[MyRelatedClass]] = relationship(lazy="raise")

Above, attribute access on the `children` collection will raise an exception if it was not previously populated. This includes read access but for collections will also affect write access, as collections can’t be mutated without first loading them. The rationale for this is to ensure that an application is not emitting any unexpected lazy loads within a certain context. Rather than having to read through SQL logs to determine that all necessary attributes were eager loaded, the “raise” strategy will cause unloaded attributes to raise immediately if accessed. The raise strategy is also available on a query option basis using the `raiseload()` loader option.

See also

Preventing unwanted lazy loads using raiseload

## Using Passive Deletes

An important aspect of collection management in SQLAlchemy is that when an object that refers to a collection is deleted, SQLAlchemy needs to consider the objects that are inside this collection. Those objects will need to be de-associated from the parent, which for a one-to-many collection would mean that foreign key columns are set to NULL, or based on cascade settings, may instead want to emit a DELETE for these rows.

The unit of work process only considers objects on a row-by-row basis, meaning a DELETE operation implies that all rows within a collection must be fully loaded into memory inside the flush process. This is not feasible for large collections, so we instead seek to rely upon the database’s own capability to update or delete the rows automatically using foreign key ON DELETE rules, instructing the unit of work to forego actually needing to load these rows in order to handle them. The unit of work can be instructed to work in this manner by configuring `relationship.passive_deletes` on the `relationship()` construct; the foreign key constraints in use must also be correctly configured.

For further detail on a complete “passive delete” configuration, see the section Using foreign key ON DELETE cascade with ORM relationships.