Release: 1.1.0b1 | Release Date: not released

SQLAlchemy 1.1 Documentation


Mappers support the concept of configurable cascade behavior on relationship() constructs. This refers to how operations performed on a “parent” object relative to a particular Session should be propagated to items referred to by that relationship (e.g. “child” objects), and is affected by the relationship.cascade option.

The default behavior of cascade is limited to cascades of the so-called save-update and merge settings. The typical “alternative” setting for cascade is to add the delete and delete-orphan options; these settings are appropriate for related objects which only exist as long as they are attached to their parent, and are otherwise deleted.

Cascade behavior is configured using the cascade option on relationship():

class Order(Base):
    __tablename__ = 'order'

    items = relationship("Item", cascade="all, delete-orphan")
    customer = relationship("User", cascade="save-update")

To set cascades on a backref, the same flag can be used with the backref() function, which ultimately feeds its arguments back into relationship():

class Item(Base):
    __tablename__ = 'item'

    order = relationship("Order",
                    backref=backref("items", cascade="all, delete-orphan")

The default value of cascade is save-update, merge. The typical alternative setting for this parameter is either all or more commonly all, delete-orphan. The all symbol is a synonym for save-update, merge, refresh-expire, expunge, delete, and using it in conjunction with delete-orphan indicates that the child object should follow along with its parent in all cases, and be deleted once it is no longer associated with that parent.

The list of available values which can be specified for the cascade parameter are described in the following subsections.


save-update cascade indicates that when an object is placed into a Session via Session.add(), all the objects associated with it via this relationship() should also be added to that same Session. Suppose we have an object user1 with two related objects address1, address2:

>>> user1 = User()
>>> address1, address2 = Address(), Address()
>>> user1.addresses = [address1, address2]

If we add user1 to a Session, it will also add address1, address2 implicitly:

>>> sess = Session()
>>> sess.add(user1)
>>> address1 in sess

save-update cascade also affects attribute operations for objects that are already present in a Session. If we add a third object, address3 to the user1.addresses collection, it becomes part of the state of that Session:

>>> address3 = Address()
>>> user1.append(address3)
>>> address3 in sess
>>> True

save-update has the possibly surprising behavior which is that persistent objects which were removed from a collection or in some cases a scalar attribute may also be pulled into the Session of a parent object; this is so that the flush process may handle that related object appropriately. This case can usually only arise if an object is removed from one Session and added to another:

>>> user1 = sess1.query(User).filter_by(id=1).first()
>>> address1 = user1.addresses[0]
>>> sess1.close()   # user1, address1 no longer associated with sess1
>>> user1.addresses.remove(address1)  # address1 no longer associated with user1
>>> sess2 = Session()
>>> sess2.add(user1)   # ... but it still gets added to the new session,
>>> address1 in sess2  # because it's still "pending" for flush

The save-update cascade is on by default, and is typically taken for granted; it simplifies code by allowing a single call to Session.add() to register an entire structure of objects within that Session at once. While it can be disabled, there is usually not a need to do so.

One case where save-update cascade does sometimes get in the way is in that it takes place in both directions for bi-directional relationships, e.g. backrefs, meaning that the association of a child object with a particular parent can have the effect of the parent object being implicitly associated with that child object’s Session; this pattern, as well as how to modify its behavior using the cascade_backrefs flag, is discussed in the section Controlling Cascade on Backrefs.


The delete cascade indicates that when a “parent” object is marked for deletion, its related “child” objects should also be marked for deletion. If for example we we have a relationship User.addresses with delete cascade configured:

class User(Base):
    # ...

    addresses = relationship("Address", cascade="save-update, merge, delete")

If using the above mapping, we have a User object and two related Address objects:

>>> user1 = sess.query(User).filter_by(id=1).first()
>>> address1, address2 = user1.addresses

If we mark user1 for deletion, after the flush operation proceeds, address1 and address2 will also be deleted:

>>> sess.delete(user1)
>>> sess.commit()
DELETE FROM address WHERE = ? ((1,), (2,)) DELETE FROM user WHERE = ? (1,) COMMIT

Alternatively, if our User.addresses relationship does not have delete cascade, SQLAlchemy’s default behavior is to instead de-associate address1 and address2 from user1 by setting their foreign key reference to NULL. Using a mapping as follows:

class User(Base):
    # ...

    addresses = relationship("Address")

Upon deletion of a parent User object, the rows in address are not deleted, but are instead de-associated:

>>> sess.delete(user1)
>>> sess.commit()
UPDATE address SET user_id=? WHERE = ? (None, 1) UPDATE address SET user_id=? WHERE = ? (None, 2) DELETE FROM user WHERE = ? (1,) COMMIT

delete cascade is more often than not used in conjunction with delete-orphan cascade, which will emit a DELETE for the related row if the “child” object is deassociated from the parent. The combination of delete and delete-orphan cascade covers both situations where SQLAlchemy has to decide between setting a foreign key column to NULL versus deleting the row entirely.

ORM-level “delete” cascade vs. FOREIGN KEY level “ON DELETE” cascade

The behavior of SQLAlchemy’s “delete” cascade has a lot of overlap with the ON DELETE CASCADE feature of a database foreign key, as well as with that of the ON DELETE SET NULL foreign key setting when “delete” cascade is not specified. Database level “ON DELETE” cascades are specific to the “FOREIGN KEY” construct of the relational database; SQLAlchemy allows configuration of these schema-level constructs at the DDL level using options on ForeignKeyConstraint which are described at ON UPDATE and ON DELETE.

It is important to note the differences between the ORM and the relational database’s notion of “cascade” as well as how they integrate:

  • A database level ON DELETE cascade is configured effectively on the many-to-one side of the relationship; that is, we configure it relative to the FOREIGN KEY constraint that is the “many” side of a relationship. At the ORM level, this direction is reversed. SQLAlchemy handles the deletion of “child” objects relative to a “parent” from the “parent” side, which means that delete and delete-orphan cascade are configured on the one-to-many side.

  • Database level foreign keys with no ON DELETE setting are often used to prevent a parent row from being removed, as it would necessarily leave an unhandled related row present. If this behavior is desired in a one-to-many relationship, SQLAlchemy’s default behavior of setting a foreign key to NULL can be caught in one of two ways:

    • The easiest and most common is just to set the foreign-key-holding column to NOT NULL at the database schema level. An attempt by SQLAlchemy to set the column to NULL will fail with a simple NOT NULL constraint exception.
    • The other, more special case way is to set the passive_deletes flag to the string "all". This has the effect of entirely disabling SQLAlchemy’s behavior of setting the foreign key column to NULL, and a DELETE will be emitted for the parent row without any affect on the child row, even if the child row is present in memory. This may be desirable in the case when database-level foreign key triggers, either special ON DELETE settings or otherwise, need to be activated in all cases when a parent row is deleted.
  • Database level ON DELETE cascade is vastly more efficient than that of SQLAlchemy. The database can chain a series of cascade operations across many relationships at once; e.g. if row A is deleted, all the related rows in table B can be deleted, and all the C rows related to each of those B rows, and on and on, all within the scope of a single DELETE statement. SQLAlchemy on the other hand, in order to support the cascading delete operation fully, has to individually load each related collection in order to target all rows that then may have further related collections. That is, SQLAlchemy isn’t sophisticated enough to emit a DELETE for all those related rows at once within this context.

  • SQLAlchemy doesn’t need to be this sophisticated, as we instead provide smooth integration with the database’s own ON DELETE functionality, by using the passive_deletes option in conjunction with properly configured foreign key constraints. Under this behavior, SQLAlchemy only emits DELETE for those rows that are already locally present in the Session; for any collections that are unloaded, it leaves them to the database to handle, rather than emitting a SELECT for them. The section Using Passive Deletes provides an example of this use.

  • While database-level ON DELETE functionality works only on the “many” side of a relationship, SQLAlchemy’s “delete” cascade has limited ability to operate in the reverse direction as well, meaning it can be configured on the “many” side to delete an object on the “one” side when the reference on the “many” side is deleted. However this can easily result in constraint violations if there are other objects referring to this “one” side from the “many”, so it typically is only useful when a relationship is in fact a “one to one”. The single_parent flag should be used to establish an in-Python assertion for this case.

When using a relationship() that also includes a many-to-many table using the secondary option, SQLAlchemy’s delete cascade handles the rows in this many-to-many table automatically. Just like, as described in Deleting Rows from the Many to Many Table, the addition or removal of an object from a many-to-many collection results in the INSERT or DELETE of a row in the many-to-many table, the delete cascade, when activated as the result of a parent object delete operation, will DELETE not just the row in the “child” table but also in the many-to-many table.


delete-orphan cascade adds behavior to the delete cascade, such that a child object will be marked for deletion when it is de-associated from the parent, not just when the parent is marked for deletion. This is a common feature when dealing with a related object that is “owned” by its parent, with a NOT NULL foreign key, so that removal of the item from the parent collection results in its deletion.

delete-orphan cascade implies that each child object can only have one parent at a time, so is configured in the vast majority of cases on a one-to-many relationship. Setting it on a many-to-one or many-to-many relationship is more awkward; for this use case, SQLAlchemy requires that the relationship() be configured with the single_parent argument, establishes Python-side validation that ensures the object is associated with only one parent at a time.


merge cascade indicates that the Session.merge() operation should be propagated from a parent that’s the subject of the Session.merge() call down to referred objects. This cascade is also on by default.


refresh-expire is an uncommon option, indicating that the Session.expire() operation should be propagated from a parent down to referred objects. When using Session.refresh(), the referred objects are expired only, but not actually refreshed.


expunge cascade indicates that when the parent object is removed from the Session using Session.expunge(), the operation should be propagated down to referred objects.

Controlling Cascade on Backrefs

The save-update cascade by default takes place on attribute change events emitted from backrefs. This is probably a confusing statement more easily described through demonstration; it means that, given a mapping such as this:

mapper(Order, order_table, properties={
    'items' : relationship(Item, backref='order')

If an Order is already in the session, and is assigned to the order attribute of an Item, the backref appends the Item to the items collection of that Order, resulting in the save-update cascade taking place:

>>> o1 = Order()
>>> session.add(o1)
>>> o1 in session

>>> i1 = Item()
>>> i1.order = o1
>>> i1 in o1.items
>>> i1 in session

This behavior can be disabled using the cascade_backrefs flag:

mapper(Order, order_table, properties={
    'items' : relationship(Item, backref='order',

So above, the assignment of i1.order = o1 will append i1 to the items collection of o1, but will not add i1 to the session. You can, of course, add() i1 to the session at a later point. This option may be helpful for situations where an object needs to be kept out of a session until it’s construction is completed, but still needs to be given associations to objects which are already persistent in the target session.