SQLAlchemy 1.3 Documentation
SQLAlchemy ORM
- Object Relational Tutorial
- Mapper Configuration
- Relationship Configuration
- Loading Objects
- Using the Session
- Session Basics¶
- State Management
- Cascades
- Transactions and Connection Management
- Additional Persistence Techniques
- Contextual/Thread-local Sessions
- Tracking Object and Session Changes with Events
- Session API
- Events and Internals
- ORM Extensions
- ORM Examples
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Session Basics¶
What does the Session do ?¶
In the most general sense, the Session
establishes all
conversations with the database and represents a “holding zone” for all the
objects which you’ve loaded or associated with it during its lifespan. It
provides the entrypoint to acquire a Query
object, which sends
queries to the database using the Session
object’s current database
connection, populating result rows into objects that are then stored in the
Session
, inside a structure called the Identity Map - a data structure
that maintains unique copies of each object, where “unique” means “only one
object with a particular primary key”.
The Session
begins in an essentially stateless form. Once queries
are issued or other objects are persisted with it, it requests a connection
resource from an Engine
that is associated either with the
Session
itself or with the mapped Table
objects being
operated upon. This connection represents an ongoing transaction, which
remains in effect until the Session
is instructed to commit or roll
back its pending state.
All changes to objects maintained by a Session
are tracked - before
the database is queried again or before the current transaction is committed,
it flushes all pending changes to the database. This is known as the Unit
of Work pattern.
When using a Session
, it’s important to note that the objects
which are associated with it are proxy objects to the transaction being
held by the Session
- there are a variety of events that will cause
objects to re-access the database in order to keep synchronized. It is
possible to “detach” objects from a Session
, and to continue using
them, though this practice has its caveats. It’s intended that
usually, you’d re-associate detached objects with another Session
when you
want to work with them again, so that they can resume their normal task of
representing database state.
Getting a Session¶
Session
is a regular Python class which can
be directly instantiated. However, to standardize how sessions are configured
and acquired, the sessionmaker
class is normally
used to create a top level Session
configuration which can then be used throughout an application without the
need to repeat the configurational arguments.
The usage of sessionmaker
is illustrated below:
from sqlalchemy import create_engine
from sqlalchemy.orm import sessionmaker
# an Engine, which the Session will use for connection
# resources
some_engine = create_engine('postgresql://scott:tiger@localhost/')
# create a configured "Session" class
Session = sessionmaker(bind=some_engine)
# create a Session
session = Session()
# work with sess
myobject = MyObject('foo', 'bar')
session.add(myobject)
session.commit()
Above, the sessionmaker
call creates a factory for us,
which we assign to the name Session
. This factory, when
called, will create a new Session
object using the configurational
arguments we’ve given the factory. In this case, as is typical,
we’ve configured the factory to specify a particular Engine
for
connection resources.
A typical setup will associate the sessionmaker
with an Engine
,
so that each Session
generated will use this Engine
to acquire connection resources. This association can
be set up as in the example above, using the bind
argument.
When you write your application, place the
sessionmaker
factory at the global level. This
factory can then
be used by the rest of the application as the source of new Session
instances, keeping the configuration for how Session
objects
are constructed in one place.
The sessionmaker
factory can also be used in conjunction with
other helpers, which are passed a user-defined sessionmaker
that
is then maintained by the helper. Some of these helpers are discussed in the
section When do I construct a Session, when do I commit it, and when do I close it?.
Adding Additional Configuration to an Existing sessionmaker()¶
A common scenario is where the sessionmaker
is invoked
at module import time, however the generation of one or more Engine
instances to be associated with the sessionmaker
has not yet proceeded.
For this use case, the sessionmaker
construct offers the
sessionmaker.configure()
method, which will place additional configuration
directives into an existing sessionmaker
that will take place
when the construct is invoked:
from sqlalchemy.orm import sessionmaker
from sqlalchemy import create_engine
# configure Session class with desired options
Session = sessionmaker()
# later, we create the engine
engine = create_engine('postgresql://...')
# associate it with our custom Session class
Session.configure(bind=engine)
# work with the session
session = Session()
Creating Ad-Hoc Session Objects with Alternate Arguments¶
For the use case where an application needs to create a new Session
with
special arguments that deviate from what is normally used throughout the application,
such as a Session
that binds to an alternate
source of connectivity, or a Session
that should
have other arguments such as expire_on_commit
established differently from
what most of the application wants, specific arguments can be passed to the
sessionmaker
factory’s sessionmaker.__call__()
method.
These arguments will override whatever
configurations have already been placed, such as below, where a new Session
is constructed against a specific Connection
:
# at the module level, the global sessionmaker,
# bound to a specific Engine
Session = sessionmaker(bind=engine)
# later, some unit of code wants to create a
# Session that is bound to a specific Connection
conn = engine.connect()
session = Session(bind=conn)
The typical rationale for the association of a Session
with a specific
Connection
is that of a test fixture that maintains an external
transaction - see Joining a Session into an External Transaction (such as for test suites) for an example of this.
Session Frequently Asked Questions¶
By this point, many users already have questions about sessions.
This section presents a mini-FAQ (note that we have also a real FAQ)
of the most basic issues one is presented with when using a Session
.
When do I make a sessionmaker
?¶
Just one time, somewhere in your application’s global scope. It should be
looked upon as part of your application’s configuration. If your
application has three .py files in a package, you could, for example,
place the sessionmaker
line in your __init__.py
file; from
that point on your other modules say “from mypackage import Session”. That
way, everyone else just uses Session()
,
and the configuration of that session is controlled by that central point.
If your application starts up, does imports, but does not know what
database it’s going to be connecting to, you can bind the
Session
at the “class” level to the
engine later on, using sessionmaker.configure()
.
In the examples in this section, we will frequently show the
sessionmaker
being created right above the line where we actually
invoke Session
. But that’s just for
example’s sake! In reality, the sessionmaker
would be somewhere
at the module level. The calls to instantiate Session
would then be placed at the point in the application where database
conversations begin.
When do I construct a Session
, when do I commit it, and when do I close it?¶
A Session
is typically constructed at the beginning of a logical
operation where database access is potentially anticipated.
The Session
, whenever it is used to talk to the database,
begins a database transaction as soon as it starts communicating.
Assuming the autocommit
flag is left at its recommended default
of False
, this transaction remains in progress until the Session
is rolled back, committed, or closed. The Session
will
begin a new transaction if it is used again, subsequent to the previous
transaction ending; from this it follows that the Session
is capable of having a lifespan across many transactions, though only
one at a time. We refer to these two concepts as transaction scope
and session scope.
The implication here is that the SQLAlchemy ORM is encouraging the
developer to establish these two scopes in their application,
including not only when the scopes begin and end, but also the
expanse of those scopes, for example should a single
Session
instance be local to the execution flow within a
function or method, should it be a global object used by the
entire application, or somewhere in between these two.
The burden placed on the developer to determine this scope is one area where the SQLAlchemy ORM necessarily has a strong opinion about how the database should be used. The unit of work pattern is specifically one of accumulating changes over time and flushing them periodically, keeping in-memory state in sync with what’s known to be present in a local transaction. This pattern is only effective when meaningful transaction scopes are in place.
It’s usually not very hard to determine the best points at which
to begin and end the scope of a Session
, though the wide
variety of application architectures possible can introduce
challenging situations.
A common choice is to tear down the Session
at the same
time the transaction ends, meaning the transaction and session scopes
are the same. This is a great choice to start out with as it
removes the need to consider session scope as separate from transaction
scope.
While there’s no one-size-fits-all recommendation for how transaction scope should be determined, there are common patterns. Especially if one is writing a web application, the choice is pretty much established.
A web application is the easiest case because such an application is already
constructed around a single, consistent scope - this is the request,
which represents an incoming request from a browser, the processing
of that request to formulate a response, and finally the delivery of that
response back to the client. Integrating web applications with the
Session
is then the straightforward task of linking the
scope of the Session
to that of the request. The Session
can be established as the request begins, or using a lazy initialization
pattern which establishes one as soon as it is needed. The request
then proceeds, with some system in place where application logic can access
the current Session
in a manner associated with how the actual
request object is accessed. As the request ends, the Session
is torn down as well, usually through the usage of event hooks provided
by the web framework. The transaction used by the Session
may also be committed at this point, or alternatively the application may
opt for an explicit commit pattern, only committing for those requests
where one is warranted, but still always tearing down the Session
unconditionally at the end.
Some web frameworks include infrastructure to assist in the task
of aligning the lifespan of a Session
with that of a web request.
This includes products such as Flask-SQLAlchemy,
for usage in conjunction with the Flask web framework,
and Zope-SQLAlchemy,
typically used with the Pyramid framework.
SQLAlchemy recommends that these products be used as available.
In those situations where the integration libraries are not
provided or are insufficient, SQLAlchemy includes its own “helper” class known as
scoped_session
. A tutorial on the usage of this object
is at Contextual/Thread-local Sessions. It provides both a quick way
to associate a Session
with the current thread, as well as
patterns to associate Session
objects with other kinds of
scopes.
As mentioned before, for non-web applications there is no one clear pattern, as applications themselves don’t have just one pattern of architecture. The best strategy is to attempt to demarcate “operations”, points at which a particular thread begins to perform a series of operations for some period of time, which can be committed at the end. Some examples:
A background daemon which spawns off child forks would want to create a
Session
local to each child process, work with thatSession
through the life of the “job” that the fork is handling, then tear it down when the job is completed.For a command-line script, the application would create a single, global
Session
that is established when the program begins to do its work, and commits it right as the program is completing its task.For a GUI interface-driven application, the scope of the
Session
may best be within the scope of a user-generated event, such as a button push. Or, the scope may correspond to explicit user interaction, such as the user “opening” a series of records, then “saving” them.
As a general rule, the application should manage the lifecycle of the session externally to functions that deal with specific data. This is a fundamental separation of concerns which keeps data-specific operations agnostic of the context in which they access and manipulate that data.
E.g. don’t do this:
### this is the **wrong way to do it** ###
class ThingOne(object):
def go(self):
session = Session()
try:
session.query(FooBar).update({"x": 5})
session.commit()
except:
session.rollback()
raise
class ThingTwo(object):
def go(self):
session = Session()
try:
session.query(Widget).update({"q": 18})
session.commit()
except:
session.rollback()
raise
def run_my_program():
ThingOne().go()
ThingTwo().go()
Keep the lifecycle of the session (and usually the transaction) separate and external:
### this is a **better** (but not the only) way to do it ###
class ThingOne(object):
def go(self, session):
session.query(FooBar).update({"x": 5})
class ThingTwo(object):
def go(self, session):
session.query(Widget).update({"q": 18})
def run_my_program():
session = Session()
try:
ThingOne().go(session)
ThingTwo().go(session)
session.commit()
except:
session.rollback()
raise
finally:
session.close()
The most comprehensive approach, recommended for more substantial applications, will try to keep the details of session, transaction and exception management as far as possible from the details of the program doing its work. For example, we can further separate concerns using a context manager:
### another way (but again *not the only way*) to do it ###
from contextlib import contextmanager
@contextmanager
def session_scope():
"""Provide a transactional scope around a series of operations."""
session = Session()
try:
yield session
session.commit()
except:
session.rollback()
raise
finally:
session.close()
def run_my_program():
with session_scope() as session:
ThingOne().go(session)
ThingTwo().go(session)
Is the Session a cache?¶
Yeee…no. It’s somewhat used as a cache, in that it implements the
identity map pattern, and stores objects keyed to their primary key.
However, it doesn’t do any kind of query caching. This means, if you say
session.query(Foo).filter_by(name='bar')
, even if Foo(name='bar')
is right there, in the identity map, the session has no idea about that.
It has to issue SQL to the database, get the rows back, and then when it
sees the primary key in the row, then it can look in the local identity
map and see that the object is already there. It’s only when you say
query.get({some primary key})
that the
Session
doesn’t have to issue a query.
Additionally, the Session stores object instances using a weak reference by default. This also defeats the purpose of using the Session as a cache.
The Session
is not designed to be a
global object from which everyone consults as a “registry” of objects.
That’s more the job of a second level cache. SQLAlchemy provides
a pattern for implementing second level caching using dogpile.cache,
via the Dogpile Caching example.
How can I get the Session
for a certain object?¶
Use the Session.object_session()
classmethod
available on Session
:
session = Session.object_session(someobject)
The newer Runtime Inspection API system can also be used:
from sqlalchemy import inspect
session = inspect(someobject).session
Is the session thread-safe?¶
The Session
is very much intended to be used in a
non-concurrent fashion, which usually means in only one thread at a
time.
The Session
should be used in such a way that one
instance exists for a single series of operations within a single
transaction. One expedient way to get this effect is by associating
a Session
with the current thread (see Contextual/Thread-local Sessions
for background). Another is to use a pattern
where the Session
is passed between functions and is otherwise
not shared with other threads.
The bigger point is that you should not want to use the session with multiple concurrent threads. That would be like having everyone at a restaurant all eat from the same plate. The session is a local “workspace” that you use for a specific set of tasks; you don’t want to, or need to, share that session with other threads who are doing some other task.
Making sure the Session
is only used in a single concurrent thread at a time
is called a “share nothing” approach to concurrency. But actually, not
sharing the Session
implies a more significant pattern; it
means not just the Session
object itself, but
also all objects that are associated with that Session, must be kept within
the scope of a single concurrent thread. The set of mapped
objects associated with a Session
are essentially proxies for data
within database rows accessed over a database connection, and so just like
the Session
itself, the whole
set of objects is really just a large-scale proxy for a database connection
(or connections). Ultimately, it’s mostly the DBAPI connection itself that
we’re keeping away from concurrent access; but since the Session
and all the objects associated with it are all proxies for that DBAPI connection,
the entire graph is essentially not safe for concurrent access.
If there are in fact multiple threads participating
in the same task, then you may consider sharing the session and its objects between
those threads; however, in this extremely unusual scenario the application would
need to ensure that a proper locking scheme is implemented so that there isn’t
concurrent access to the Session
or its state. A more common approach
to this situation is to maintain a single Session
per concurrent thread,
but to instead copy objects from one Session
to another, often
using the Session.merge()
method to copy the state of an object into
a new object local to a different Session
.
Basics of Using a Session¶
The most basic Session
use patterns are presented here.
Querying¶
The Session.query()
function takes one or more
entities and returns a new Query
object which
will issue mapper queries within the context of this Session. An entity is
defined as a mapped class, a Mapper
object, an
orm-enabled descriptor, or an AliasedClass
object:
# query from a class
session.query(User).filter_by(name='ed').all()
# query with multiple classes, returns tuples
session.query(User, Address).join('addresses').filter_by(name='ed').all()
# query using orm-enabled descriptors
session.query(User.name, User.fullname).all()
# query from a mapper
user_mapper = class_mapper(User)
session.query(user_mapper)
When Query
returns results, each object
instantiated is stored within the identity map. When a row matches an object
which is already present, the same object is returned. In the latter case,
whether or not the row is populated onto an existing object depends upon
whether the attributes of the instance have been expired or not. A
default-configured Session
automatically
expires all instances along transaction boundaries, so that with a normally
isolated transaction, there shouldn’t be any issue of instances representing
data which is stale with regards to the current transaction.
The Query
object is introduced in great detail in
Object Relational Tutorial, and further documented in
Query API.
Adding New or Existing Items¶
Session.add()
is used to place instances in the
session. For transient (i.e. brand new) instances, this will have the effect
of an INSERT taking place for those instances upon the next flush. For
instances which are persistent (i.e. were loaded by this session), they are
already present and do not need to be added. Instances which are detached
(i.e. have been removed from a session) may be re-associated with a session
using this method:
user1 = User(name='user1')
user2 = User(name='user2')
session.add(user1)
session.add(user2)
session.commit() # write changes to the database
To add a list of items to the session at once, use
Session.add_all()
:
session.add_all([item1, item2, item3])
The Session.add()
operation cascades along
the save-update
cascade. For more details see the section
Cascades.
Deleting¶
The Session.delete()
method places an instance
into the Session’s list of objects to be marked as deleted:
# mark two objects to be deleted
session.delete(obj1)
session.delete(obj2)
# commit (or flush)
session.commit()
Deleting Objects Referenced from Collections and Scalar Relationships¶
The ORM in general never modifies the contents of a collection or scalar
relationship during the flush process. This means, if your class has a
relationship()
that refers to a collection of objects, or a reference
to a single object such as many-to-one, the contents of this attribute will
not be modified when the flush process occurs. Instead, if the Session
is expired afterwards, either through the expire-on-commit behavior of
Session.commit()
or through explicit use of Session.expire()
,
the referenced object or collection upon a given object associated with that
Session
will be cleared and will re-load itself upon next access.
This behavior is not to be confused with the flush process’ impact on column- bound attributes that refer to foreign key and primary key columns; these attributes are modified liberally within the flush, since these are the attributes that the flush process intends to manage. Nor should it be confused with the behavior of backreferences, as described at Linking Relationships with Backref; a backreference event will modify a collection or scalar attribute reference, however this behavior takes place during direct manipulation of related collections and object references, which is explicit within the calling application and is outside of the flush process.
A common confusion that arises regarding this behavior involves the use of the
Session.delete()
method. When Session.delete()
is invoked upon
an object and the Session
is flushed, the row is deleted from the
database. Rows that refer to the target row via foreign key, assuming they
are tracked using a relationship()
between the two mapped object types,
will also see their foreign key attributes UPDATED to null, or if delete
cascade is set up, the related rows will be deleted as well. However, even
though rows related to the deleted object might be themselves modified as well,
no changes occur to relationship-bound collections or object references on
the objects involved in the operation within the scope of the flush
itself. This means if the object was a
member of a related collection, it will still be present on the Python side
until that collection is expired. Similarly, if the object were
referenced via many-to-one or one-to-one from another object, that reference
will remain present on that object until the object is expired as well.
Below, we illustrate that after an Address
object is marked
for deletion, it’s still present in the collection associated with the
parent User
, even after a flush:
>>> address = user.addresses[1]
>>> session.delete(address)
>>> session.flush()
>>> address in user.addresses
True
When the above session is committed, all attributes are expired. The next
access of user.addresses
will re-load the collection, revealing the
desired state:
>>> session.commit()
>>> address in user.addresses
False
There is a recipe for intercepting Session.delete()
and invoking this
expiration automatically; see ExpireRelationshipOnFKChange for this. However, the usual practice of
deleting items within collections is to forego the usage of
Session.delete()
directly, and instead use cascade behavior to
automatically invoke the deletion as a result of removing the object from the
parent collection. The delete-orphan
cascade accomplishes this, as
illustrated in the example below:
class User(Base):
__tablename__ = 'user'
# ...
addresses = relationship(
"Address", cascade="all, delete-orphan")
# ...
del user.addresses[1]
session.flush()
Where above, upon removing the Address
object from the User.addresses
collection, the delete-orphan
cascade has the effect of marking the Address
object for deletion in the same way as passing it to Session.delete()
.
The delete-orphan
cascade can also be applied to a many-to-one
or one-to-one relationship, so that when an object is de-associated from its
parent, it is also automatically marked for deletion. Using delete-orphan
cascade on a many-to-one or one-to-one requires an additional flag
relationship.single_parent
which invokes an assertion
that this related object is not to shared with any other parent simultaneously:
class User(Base):
# ...
preference = relationship(
"Preference", cascade="all, delete-orphan",
single_parent=True)
Above, if a hypothetical Preference
object is removed from a User
,
it will be deleted on flush:
some_user.preference = None
session.flush() # will delete the Preference object
See also
Cascades for detail on cascades.
Deleting based on Filter Criterion¶
The caveat with Session.delete()
is that you need to have an object handy
already in order to delete. The Query includes a
delete()
method which deletes based on
filtering criteria:
session.query(User).filter(User.id==7).delete()
The Query.delete()
method includes functionality to “expire” objects
already in the session which match the criteria. However it does have some
caveats, including that “delete” and “delete-orphan” cascades won’t be fully
expressed for collections which are already loaded. See the API docs for
Query.delete()
for more details.
Flushing¶
When the Session
is used with its default
configuration, the flush step is nearly always done transparently.
Specifically, the flush occurs before any individual
Query
is issued, as well as within the
Session.commit()
call before the transaction is
committed. It also occurs before a SAVEPOINT is issued when
Session.begin_nested()
is used.
Regardless of the autoflush setting, a flush can always be forced by issuing
Session.flush()
:
session.flush()
The “flush-on-Query” aspect of the behavior can be disabled by constructing
sessionmaker
with the flag autoflush=False
:
Session = sessionmaker(autoflush=False)
Additionally, autoflush can be temporarily disabled by setting the
autoflush
flag at any time:
mysession = Session()
mysession.autoflush = False
More conveniently, it can be turned off within a context managed block using Session.no_autoflush
:
with mysession.no_autoflush:
mysession.add(some_object)
mysession.flush()
The flush process always occurs within a transaction, even if the
Session
has been configured with
autocommit=True
, a setting that disables the session’s persistent
transactional state. If no transaction is present,
Session.flush()
creates its own transaction and
commits it. Any failures during flush will always result in a rollback of
whatever transaction is present. If the Session is not in autocommit=True
mode, an explicit call to Session.rollback()
is
required after a flush fails, even though the underlying transaction will have
been rolled back already - this is so that the overall nesting pattern of
so-called “subtransactions” is consistently maintained.
Expiring / Refreshing¶
An important consideration that will often come up when using the
Session
is that of dealing with the state that is present on
objects that have been loaded from the database, in terms of keeping them
synchronized with the current state of the transaction. The SQLAlchemy
ORM is based around the concept of an identity map such that when
an object is “loaded” from a SQL query, there will be a unique Python
object instance maintained corresponding to a particular database identity.
This means if we emit two separate queries, each for the same row, and get
a mapped object back, the two queries will have returned the same Python
object:
>>> u1 = session.query(User).filter(id=5).first()
>>> u2 = session.query(User).filter(id=5).first()
>>> u1 is u2
True
Following from this, when the ORM gets rows back from a query, it will skip the population of attributes for an object that’s already loaded. The design assumption here is to assume a transaction that’s perfectly isolated, and then to the degree that the transaction isn’t isolated, the application can take steps on an as-needed basis to refresh objects from the database transaction. The FAQ entry at I’m re-loading data with my Session but it isn’t seeing changes that I committed elsewhere discusses this concept in more detail.
When an ORM mapped object is loaded into memory, there are three general ways to refresh its contents with new data from the current transaction:
the expire() method - the
Session.expire()
method will erase the contents of selected or all attributes of an object, such that they will be loaded from the database when they are next accessed, e.g. using a lazy loading pattern:session.expire(u1) u1.some_attribute # <-- lazy loads from the transaction
the refresh() method - closely related is the
Session.refresh()
method, which does everything theSession.expire()
method does but also emits one or more SQL queries immediately to actually refresh the contents of the object:session.refresh(u1) # <-- emits a SQL query u1.some_attribute # <-- is refreshed from the transaction
the populate_existing() method - this method is actually on the
Query
object asQuery.populate_existing()
and indicates that it should return objects that are unconditionally re-populated from their contents in the database:u2 = session.query(User).populate_existing().filter(id=5).first()
Further discussion on the refresh / expire concept can be found at Refreshing / Expiring.
Committing¶
Session.commit()
is used to commit the current
transaction. It always issues Session.flush()
beforehand to flush any remaining state to the database; this is independent
of the “autoflush” setting. If no transaction is present, it raises an error.
Note that the default behavior of the Session
is that a “transaction” is always present; this behavior can be disabled by
setting autocommit=True
. In autocommit mode, a transaction can be
initiated by calling the Session.begin()
method.
Note
The term “transaction” here refers to a transactional
construct within the Session
itself which may be
maintaining zero or more actual database (DBAPI) transactions. An individual
DBAPI connection begins participation in the “transaction” as it is first
used to execute a SQL statement, then remains present until the session-level
“transaction” is completed. See Managing Transactions for
further detail.
Another behavior of Session.commit()
is that by
default it expires the state of all instances present after the commit is
complete. This is so that when the instances are next accessed, either through
attribute access or by them being present in a
Query
result set, they receive the most recent
state. To disable this behavior, configure
sessionmaker
with expire_on_commit=False
.
Normally, instances loaded into the Session
are never changed by subsequent queries; the assumption is that the current
transaction is isolated so the state most recently loaded is correct as long
as the transaction continues. Setting autocommit=True
works against this
model to some degree since the Session
behaves in exactly the same way with regard to attribute state, except no
transaction is present.
Rolling Back¶
Session.rollback()
rolls back the current
transaction. With a default configured session, the post-rollback state of the
session is as follows:
All transactions are rolled back and all connections returned to the connection pool, unless the Session was bound directly to a Connection, in which case the connection is still maintained (but still rolled back).
Objects which were initially in the pending state when they were added to the
Session
within the lifespan of the transaction are expunged, corresponding to their INSERT statement being rolled back. The state of their attributes remains unchanged.Objects which were marked as deleted within the lifespan of the transaction are promoted back to the persistent state, corresponding to their DELETE statement being rolled back. Note that if those objects were first pending within the transaction, that operation takes precedence instead.
All objects not expunged are fully expired.
With that state understood, the Session
may
safely continue usage after a rollback occurs.
When a Session.flush()
fails, typically for
reasons like primary key, foreign key, or “not nullable” constraint
violations, a Session.rollback()
is issued
automatically (it’s currently not possible for a flush to continue after a
partial failure). However, the flush process always uses its own transactional
demarcator called a subtransaction, which is described more fully in the
docstrings for Session
. What it means here is
that even though the database transaction has been rolled back, the end user
must still issue Session.rollback()
to fully
reset the state of the Session
.
Closing¶
The Session.close()
method issues a Session.expunge_all()
which
removes all ORM-mapped objects from the session, and releases any
transactional/connection resources from the Engine
object(s)
to which it is bound. When connections are returned to the connection pool,
transactional state is rolled back as well.
When the Session
is closed, it is essentially in the
original state as when it was first constructed, and may be used again.
In this sense, the Session.close()
method is more like a “reset”
back to the clean state and not as much like a “database close” method.
flambé! the dragon and The Alchemist image designs created and generously donated by Rotem Yaari.
Created using Sphinx 7.2.6. Documentation last generated: Sat 06 Jan 2024 12:16:23 PM