Release: 1.2.0b1 | Release Date: unreleased

SQLAlchemy 1.2 Documentation

State Management

Quickie Intro to Object States

It’s helpful to know the states which an instance can have within a session:

  • Transient - an instance that’s not in a session, and is not saved to the database; i.e. it has no database identity. The only relationship such an object has to the ORM is that its class has a mapper() associated with it.

  • Pending - when you add() a transient instance, it becomes pending. It still wasn’t actually flushed to the database yet, but it will be when the next flush occurs.

  • Persistent - An instance which is present in the session and has a record in the database. You get persistent instances by either flushing so that the pending instances become persistent, or by querying the database for existing instances (or moving persistent instances from other sessions into your local session).

  • Deleted - An instance which has been deleted within a flush, but the transaction has not yet completed. Objects in this state are essentially in the opposite of “pending” state; when the session’s transaction is committed, the object will move to the detached state. Alternatively, when the session’s transaction is rolled back, a deleted object moves back to the persistent state.

    Changed in version 1.1: The ‘deleted’ state is a newly added session object state distinct from the ‘persistent’ state.

  • Detached - an instance which corresponds, or previously corresponded, to a record in the database, but is not currently in any session. The detached object will contain a database identity marker, however because it is not associated with a session, it is unknown whether or not this database identity actually exists in a target database. Detached objects are safe to use normally, except that they have no ability to load unloaded attributes or attributes that were previously marked as “expired”.

For a deeper dive into all possible state transitions, see the section Object Lifecycle Events which describes each transition as well as how to programmatically track each one.

Getting the Current State of an Object

The actual state of any mapped object can be viewed at any time using the inspect() system:

>>> from sqlalchemy import inspect
>>> insp = inspect(my_object)
>>> insp.persistent
True

Session Attributes

The Session itself acts somewhat like a set-like collection. All items present may be accessed using the iterator interface:

for obj in session:
    print(obj)

And presence may be tested for using regular “contains” semantics:

if obj in session:
    print("Object is present")

The session is also keeping track of all newly created (i.e. pending) objects, all objects which have had changes since they were last loaded or saved (i.e. “dirty”), and everything that’s been marked as deleted:

# pending objects recently added to the Session
session.new

# persistent objects which currently have changes detected
# (this collection is now created on the fly each time the property is called)
session.dirty

# persistent objects that have been marked as deleted via session.delete(obj)
session.deleted

# dictionary of all persistent objects, keyed on their
# identity key
session.identity_map

(Documentation: Session.new, Session.dirty, Session.deleted, Session.identity_map).

Session Referencing Behavior

Objects within the session are weakly referenced. This means that when they are dereferenced in the outside application, they fall out of scope from within the Session as well and are subject to garbage collection by the Python interpreter. The exceptions to this include objects which are pending, objects which are marked as deleted, or persistent objects which have pending changes on them. After a full flush, these collections are all empty, and all objects are again weakly referenced.

To cause objects in the Session to remain strongly referenced, usually a simple approach is all that’s needed. Examples of externally managed strong-referencing behavior include loading objects into a local dictionary keyed to their primary key, or into lists or sets for the span of time that they need to remain referenced. These collections can be associated with a Session, if desired, by placing them into the Session.info dictionary.

An event based approach is also feasible. A simple recipe that provides “strong referencing” behavior for all objects as they remain within the persistent state is as follows:

from sqlalchemy import event

def strong_reference_session(session):
    @event.listens_for(session, "pending_to_persistent")
    @event.listens_for(session, "deleted_to_persistent")
    @event.listens_for(session, "detached_to_persistent")
    @event.listens_for(session, "loaded_as_persistent")
    def strong_ref_object(sess, instance):
        if 'refs' not in sess.info:
            sess.info['refs'] = refs = set()
        else:
            refs = sess.info['refs']

        refs.add(instance)


    @event.listens_for(session, "persistent_to_detached")
    @event.listens_for(session, "persistent_to_deleted")
    @event.listens_for(session, "persistent_to_transient")
    def deref_object(sess, instance):
        sess.info['refs'].discard(instance)

Above, we intercept the SessionEvents.pending_to_persistent(), SessionEvents.detached_to_persistent(), SessionEvents.deleted_to_persistent() and SessionEvents.loaded_as_persistent() event hooks in order to intercept objects as they enter the persistent transition, and the SessionEvents.persistent_to_detached() and SessionEvents.persistent_to_deleted() hooks to intercept objects as they leave the persistent state.

The above function may be called for any Session in order to provide strong-referencing behavior on a per-Session basis:

from sqlalchemy.orm import Session

my_session = Session()
strong_reference_session(my_session)

It may also be called for any sessionmaker:

from sqlalchemy.orm import sessionmaker

maker = sessionmaker()
strong_reference_session(maker)

Merging

merge() transfers state from an outside object into a new or already existing instance within a session. It also reconciles the incoming data against the state of the database, producing a history stream which will be applied towards the next flush, or alternatively can be made to produce a simple “transfer” of state without producing change history or accessing the database. Usage is as follows:

merged_object = session.merge(existing_object)

When given an instance, it follows these steps:

  • It examines the primary key of the instance. If it’s present, it attempts to locate that instance in the local identity map. If the load=True flag is left at its default, it also checks the database for this primary key if not located locally.

  • If the given instance has no primary key, or if no instance can be found with the primary key given, a new instance is created.

  • The state of the given instance is then copied onto the located/newly created instance. For attributes which are present on the source instance, the value is transferred to the target instance. For mapped attributes which aren’t present on the source, the attribute is expired on the target instance, discarding its existing value.

    If the load=True flag is left at its default, this copy process emits events and will load the target object’s unloaded collections for each attribute present on the source object, so that the incoming state can be reconciled against what’s present in the database. If load is passed as False, the incoming data is “stamped” directly without producing any history.

  • The operation is cascaded to related objects and collections, as indicated by the merge cascade (see Cascades).

  • The new instance is returned.

With merge(), the given “source” instance is not modified nor is it associated with the target Session, and remains available to be merged with any number of other Session objects. merge() is useful for taking the state of any kind of object structure without regard for its origins or current session associations and copying its state into a new session. Here’s some examples:

  • An application which reads an object structure from a file and wishes to save it to the database might parse the file, build up the structure, and then use merge() to save it to the database, ensuring that the data within the file is used to formulate the primary key of each element of the structure. Later, when the file has changed, the same process can be re-run, producing a slightly different object structure, which can then be merged in again, and the Session will automatically update the database to reflect those changes, loading each object from the database by primary key and then updating its state with the new state given.

  • An application is storing objects in an in-memory cache, shared by many Session objects simultaneously. merge() is used each time an object is retrieved from the cache to create a local copy of it in each Session which requests it. The cached object remains detached; only its state is moved into copies of itself that are local to individual Session objects.

    In the caching use case, it’s common to use the load=False flag to remove the overhead of reconciling the object’s state with the database. There’s also a “bulk” version of merge() called merge_result() that was designed to work with cache-extended Query objects - see the section Dogpile Caching.

  • An application wants to transfer the state of a series of objects into a Session maintained by a worker thread or other concurrent system. merge() makes a copy of each object to be placed into this new Session. At the end of the operation, the parent thread/process maintains the objects it started with, and the thread/worker can proceed with local copies of those objects.

    In the “transfer between threads/processes” use case, the application may want to use the load=False flag as well to avoid overhead and redundant SQL queries as the data is transferred.

Merge Tips

merge() is an extremely useful method for many purposes. However, it deals with the intricate border between objects that are transient/detached and those that are persistent, as well as the automated transference of state. The wide variety of scenarios that can present themselves here often require a more careful approach to the state of objects. Common problems with merge usually involve some unexpected state regarding the object being passed to merge().

Lets use the canonical example of the User and Address objects:

class User(Base):
    __tablename__ = 'user'

    id = Column(Integer, primary_key=True)
    name = Column(String(50), nullable=False)
    addresses = relationship("Address", backref="user")

class Address(Base):
    __tablename__ = 'address'

    id = Column(Integer, primary_key=True)
    email_address = Column(String(50), nullable=False)
    user_id = Column(Integer, ForeignKey('user.id'), nullable=False)

Assume a User object with one Address, already persistent:

>>> u1 = User(name='ed', addresses=[Address(email_address='ed@ed.com')])
>>> session.add(u1)
>>> session.commit()

We now create a1, an object outside the session, which we’d like to merge on top of the existing Address:

>>> existing_a1 = u1.addresses[0]
>>> a1 = Address(id=existing_a1.id)

A surprise would occur if we said this:

>>> a1.user = u1
>>> a1 = session.merge(a1)
>>> session.commit()
sqlalchemy.orm.exc.FlushError: New instance <Address at 0x1298f50>
with identity key (<class '__main__.Address'>, (1,)) conflicts with
persistent instance <Address at 0x12a25d0>

Why is that ? We weren’t careful with our cascades. The assignment of a1.user to a persistent object cascaded to the backref of User.addresses and made our a1 object pending, as though we had added it. Now we have two Address objects in the session:

>>> a1 = Address()
>>> a1.user = u1
>>> a1 in session
True
>>> existing_a1 in session
True
>>> a1 is existing_a1
False

Above, our a1 is already pending in the session. The subsequent merge() operation essentially does nothing. Cascade can be configured via the cascade option on relationship(), although in this case it would mean removing the save-update cascade from the User.addresses relationship - and usually, that behavior is extremely convenient. The solution here would usually be to not assign a1.user to an object already persistent in the target session.

The cascade_backrefs=False option of relationship() will also prevent the Address from being added to the session via the a1.user = u1 assignment.

Further detail on cascade operation is at Cascades.

Another example of unexpected state:

>>> a1 = Address(id=existing_a1.id, user_id=u1.id)
>>> assert a1.user is None
>>> True
>>> a1 = session.merge(a1)
>>> session.commit()
sqlalchemy.exc.IntegrityError: (IntegrityError) address.user_id
may not be NULL

Here, we accessed a1.user, which returned its default value of None, which as a result of this access, has been placed in the __dict__ of our object a1. Normally, this operation creates no change event, so the user_id attribute takes precedence during a flush. But when we merge the Address object into the session, the operation is equivalent to:

>>> existing_a1.id = existing_a1.id
>>> existing_a1.user_id = u1.id
>>> existing_a1.user = None

Where above, both user_id and user are assigned to, and change events are emitted for both. The user association takes precedence, and None is applied to user_id, causing a failure.

Most merge() issues can be examined by first checking - is the object prematurely in the session ?

>>> a1 = Address(id=existing_a1, user_id=user.id)
>>> assert a1 not in session
>>> a1 = session.merge(a1)

Or is there state on the object that we don’t want ? Examining __dict__ is a quick way to check:

>>> a1 = Address(id=existing_a1, user_id=user.id)
>>> a1.user
>>> a1.__dict__
{'_sa_instance_state': <sqlalchemy.orm.state.InstanceState object at 0x1298d10>,
    'user_id': 1,
    'id': 1,
    'user': None}
>>> # we don't want user=None merged, remove it
>>> del a1.user
>>> a1 = session.merge(a1)
>>> # success
>>> session.commit()

Expunging

Expunge removes an object from the Session, sending persistent instances to the detached state, and pending instances to the transient state:

session.expunge(obj1)

To remove all items, call expunge_all() (this method was formerly known as clear()).

Refreshing / Expiring

Expiring means that the database-persisted data held inside a series of object attributes is erased, in such a way that when those attributes are next accessed, a SQL query is emitted which will refresh that data from the database.

When we talk about expiration of data we are usually talking about an object that is in the persistent state. For example, if we load an object as follows:

user = session.query(User).filter_by(name='user1').first()

The above User object is persistent, and has a series of attributes present; if we were to look inside its __dict__, we’d see that state loaded:

>>> user.__dict__
{
  'id': 1, 'name': u'user1',
  '_sa_instance_state': <...>,
}

where id and name refer to those columns in the database. _sa_instance_state is a non-database-persisted value used by SQLAlchemy internally (it refers to the InstanceState for the instance. While not directly relevant to this section, if we want to get at it, we should use the inspect() function to access it).

At this point, the state in our User object matches that of the loaded database row. But upon expiring the object using a method such as Session.expire(), we see that the state is removed:

>>> session.expire(user)
>>> user.__dict__
{'_sa_instance_state': <...>}

We see that while the internal “state” still hangs around, the values which correspond to the id and name columns are gone. If we were to access one of these columns and are watching SQL, we’d see this:

>>> print(user.name)
SELECT user.id AS user_id, user.name AS user_name FROM user WHERE user.id = ? (1,)
user1

Above, upon accessing the expired attribute user.name, the ORM initiated a lazy load to retrieve the most recent state from the database, by emitting a SELECT for the user row to which this user refers. Afterwards, the __dict__ is again populated:

>>> user.__dict__
{
  'id': 1, 'name': u'user1',
  '_sa_instance_state': <...>,
}

Note

While we are peeking inside of __dict__ in order to see a bit of what SQLAlchemy does with object attributes, we should not modify the contents of __dict__ directly, at least as far as those attributes which the SQLAlchemy ORM is maintaining (other attributes outside of SQLA’s realm are fine). This is because SQLAlchemy uses descriptors in order to track the changes we make to an object, and when we modify __dict__ directly, the ORM won’t be able to track that we changed something.

Another key behavior of both expire() and refresh() is that all un-flushed changes on an object are discarded. That is, if we were to modify an attribute on our User:

>>> user.name = 'user2'

but then we call expire() without first calling flush(), our pending value of 'user2' is discarded:

>>> session.expire(user)
>>> user.name
'user1'

The expire() method can be used to mark as “expired” all ORM-mapped attributes for an instance:

# expire all ORM-mapped attributes on obj1
session.expire(obj1)

it can also be passed a list of string attribute names, referring to specific attributes to be marked as expired:

# expire only attributes obj1.attr1, obj1.attr2
session.expire(obj1, ['attr1', 'attr2'])

The refresh() method has a similar interface, but instead of expiring, it emits an immediate SELECT for the object’s row immediately:

# reload all attributes on obj1
session.refresh(obj1)

refresh() also accepts a list of string attribute names, but unlike expire(), expects at least one name to be that of a column-mapped attribute:

# reload obj1.attr1, obj1.attr2
session.refresh(obj1, ['attr1', 'attr2'])

The Session.expire_all() method allows us to essentially call Session.expire() on all objects contained within the Session at once:

session.expire_all()

What Actually Loads

The SELECT statement that’s emitted when an object marked with expire() or loaded with refresh() varies based on several factors, including:

  • The load of expired attributes is triggered from column-mapped attributes only. While any kind of attribute can be marked as expired, including a relationship() - mapped attribute, accessing an expired relationship() attribute will emit a load only for that attribute, using standard relationship-oriented lazy loading. Column-oriented attributes, even if expired, will not load as part of this operation, and instead will load when any column-oriented attribute is accessed.
  • relationship()- mapped attributes will not load in response to expired column-based attributes being accessed.
  • Regarding relationships, refresh() is more restrictive than expire() with regards to attributes that aren’t column-mapped. Calling refresh() and passing a list of names that only includes relationship-mapped attributes will actually raise an error. In any case, non-eager-loading relationship() attributes will not be included in any refresh operation.
  • relationship() attributes configured as “eager loading” via the lazy parameter will load in the case of refresh(), if either no attribute names are specified, or if their names are inclued in the list of attributes to be refreshed.
  • Attributes that are configured as deferred() will not normally load, during either the expired-attribute load or during a refresh. An unloaded attribute that’s deferred() instead loads on its own when directly accessed, or if part of a “group” of deferred attributes where an unloaded attribute in that group is accessed.
  • For expired attributes that are loaded on access, a joined-inheritance table mapping will emit a SELECT that typically only includes those tables for which unloaded attributes are present. The action here is sophisticated enough to load only the parent or child table, for example, if the subset of columns that were originally expired encompass only one or the other of those tables.
  • When refresh() is used on a joined-inheritance table mapping, the SELECT emitted will resemble that of when Session.query() is used on the target object’s class. This is typically all those tables that are set up as part of the mapping.

When to Expire or Refresh

The Session uses the expiration feature automatically whenever the transaction referred to by the session ends. Meaning, whenever Session.commit() or Session.rollback() is called, all objects within the Session are expired, using a feature equivalent to that of the Session.expire_all() method. The rationale is that the end of a transaction is a demarcating point at which there is no more context available in order to know what the current state of the database is, as any number of other transactions may be affecting it. Only when a new transaction starts can we again have access to the current state of the database, at which point any number of changes may have occurred.

The Session.expire() and Session.refresh() methods are used in those cases when one wants to force an object to re-load its data from the database, in those cases when it is known that the current state of data is possibly stale. Reasons for this might include:

  • some SQL has been emitted within the transaction outside of the scope of the ORM’s object handling, such as if a Table.update() construct were emitted using the Session.execute() method;
  • if the application is attempting to acquire data that is known to have been modified in a concurrent transaction, and it is also known that the isolation rules in effect allow this data to be visible.

The second bullet has the important caveat that “it is also known that the isolation rules in effect allow this data to be visible.” This means that it cannot be assumed that an UPDATE that happened on another database connection will yet be visible here locally; in many cases, it will not. This is why if one wishes to use expire() or refresh() in order to view data between ongoing transactions, an understanding of the isolation behavior in effect is essential.

See also

Session.expire()

Session.expire_all()

Session.refresh()

isolation - glossary explanation of isolation which includes links to Wikipedia.

The SQLAlchemy Session In-Depth - a video + slides with an in-depth discussion of the object lifecycle including the role of data expiration.

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