Release: 1.0.14 | Release Date: July 6, 2016

# SQLAlchemy 1.0 Documentation

# 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 applcation 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?

tl;dr;

1. As a general rule, keep the lifecycle of the session separate and external from functions and objects that access and/or manipulate database data. This will greatly help with achieving a predictable and consistent transactional scope.
2. Make sure you have a clear notion of where transactions begin and end, and keep transactions short, meaning, they end at the series of a sequence of operations, instead of being held open indefinitely.

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 that `Session` 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 advanced developer 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 `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 `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_toplevel.

### Adding New or Existing Items

`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 `add_all()`:

    session.add_all([item1, item2, item3])

The `add()` operation cascades along the `save-update` cascade. For more details see the section Cascades.

### Deleting

The `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 from Collections

A common confusion that arises regarding `delete()` is when objects which are members of a collection are being deleted. While the collection member is marked for deletion from the database, this does not impact the collection itself in memory until the collection is expired. Below, we illustrate that even 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

The usual practice of deleting items within collections is to forego the usage of `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:

    mapper(User, users_table, properties={
        'addresses':relationship(Address, cascade="all, delete, 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 `delete()`.

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 `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 `commit()` call before the transaction is committed. It also occurs before a SAVEPOINT is issued when `begin_nested()` is used.

Regardless of the autoflush setting, a flush can always be forced by issuing `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

Some autoflush-disable recipes are available at DisableAutoFlush.

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, `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 `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.

### Committing

`commit()` is used to commit the current transaction. It always issues `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 `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 `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

`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 `flush()` fails, typically for reasons like primary key, foreign key, or “not nullable” constraint violations, a `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 `rollback()` to fully reset the state of the `Session`.

### Closing

The `close()` method issues a `expunge_all()`, and releases any transactional/connection resources. When connections are returned to the connection pool, transactional state is rolled back as well.