Session Basics — SQLAlchemy 1.4 Documentation

Release: 1.4.51 | Release Date: January 2, 2024

# SQLAlchemy 1.4 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 interface where SELECT and other queries are made that will return and modify ORM-mapped objects. The ORM objects themselves are maintained inside 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 a mostly stateless form. Once queries are issued or other objects are persisted with it, it requests a connection resource from an `Engine` that is associated with the `Session`, and then establishes a transaction on that connection. This transaction remains in effect until the `Session` is instructed to commit or roll back the transaction.

The ORM objects maintained by a `Session` are instrumented such that whenever an attribute or a collection is modified in the Python program, a change event is generated which is recorded by the `Session`. Whenever the database is about to be queried, or when the transaction is about to be committed, the `Session` first flushes all pending changes stored in memory to the database. This is known as the unit of work pattern.

When using a `Session`, it’s useful to consider the ORM mapped objects that it maintains as proxy objects to database rows, which are local to the transaction being held by the `Session`. In order to maintain the state on the objects as matching what’s actually in the database, 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.

## Basics of Using a Session

The most basic `Session` use patterns are presented here.

### Opening and Closing a Session

The `Session` may be constructed on its own or by using the `sessionmaker` class. It typically is passed a single `Engine` as a source of connectivity up front. A typical use may look like:

    from sqlalchemy import create_engine
    from sqlalchemy.orm import Session

    # an Engine, which the Session will use for connection
    # resources
    engine = create_engine("postgresql://scott:tiger@localhost/")

    # create session and add objects
    with Session(engine) as session:
        session.add(some_object)
        session.add(some_other_object)
        session.commit()

Above, the `Session` is instantiated with an `Engine` associated with a particular database URL. It is then used in a Python context manager (i.e. `with:` statement) so that it is automatically closed at the end of the block; this is equivalent to calling the `Session.close()` method.

The call to `Session.commit()` is optional, and is only needed if the work we’ve done with the `Session` includes new data to be persisted to the database. If we were only issuing SELECT calls and did not need to write any changes, then the call to `Session.commit()` would be unnecessary.

Note

Note that after `Session.commit()` is called, either explicitly or when using a context manager, all objects associated with the `Session` are expired, meaning their contents are erased to be re-loaded within the next transaction. If these objects are instead detached, they will be non-functional until re-associated with a new `Session`, unless the `Session.expire_on_commit` parameter is used to disable this behavior. See the section Committing for more detail.

### Framing out a begin / commit / rollback block

We may also enclose the `Session.commit()` call and the overall “framing” of the transaction within a context manager for those cases where we will be committing data to the database. By “framing” we mean that if all operations succeed, the `Session.commit()` method will be called, but if any exceptions are raised, the `Session.rollback()` method will be called so that the transaction is rolled back immediately, before propagating the exception outward. In Python this is most fundamentally expressed using a `try: / except: / else:` block such as:

    # verbose version of what a context manager will do
    with Session(engine) as session:
        session.begin()
        try:
            session.add(some_object)
            session.add(some_other_object)
        except:
            session.rollback()
            raise
        else:
            session.commit()

The long-form sequence of operations illustrated above can be achieved more succinctly by making use of the `SessionTransaction` object returned by the `Session.begin()` method, which provides a context manager interface for the same sequence of operations:

    # create session and add objects
    with Session(engine) as session:
        with session.begin():
            session.add(some_object)
            session.add(some_other_object)
        # inner context calls session.commit(), if there were no exceptions
    # outer context calls session.close()

More succinctly, the two contexts may be combined:

    # create session and add objects
    with Session(engine) as session, session.begin():
        session.add(some_object)
        session.add(some_other_object)
    # inner context calls session.commit(), if there were no exceptions
    # outer context calls session.close()

### Using a sessionmaker

The purpose of `sessionmaker` is to provide a factory for `Session` objects with a fixed configuration. As it is typical that an application will have an `Engine` object in module scope, the `sessionmaker` can provide a factory for `Session` objects that are against this engine:

    from sqlalchemy import create_engine
    from sqlalchemy.orm import sessionmaker

    # an Engine, which the Session will use for connection
    # resources, typically in module scope
    engine = create_engine("postgresql://scott:tiger@localhost/")

    # a sessionmaker(), also in the same scope as the engine
    Session = sessionmaker(engine)

    # we can now construct a Session() without needing to pass the
    # engine each time
    with Session() as session:
        session.add(some_object)
        session.add(some_other_object)
        session.commit()
    # closes the session

The `sessionmaker` is analogous to the `Engine` as a module-level factory for function-level sessions / connections. As such it also has its own `sessionmaker.begin()` method, analogous to `Engine.begin()`, which returns a `Session` object and also maintains a begin/commit/rollback block:

    from sqlalchemy import create_engine
    from sqlalchemy.orm import sessionmaker

    # an Engine, which the Session will use for connection
    # resources
    engine = create_engine("postgresql://scott:tiger@localhost/")

    # a sessionmaker(), also in the same scope as the engine
    Session = sessionmaker(engine)

    # we can now construct a Session() and include begin()/commit()/rollback()
    # at once
    with Session.begin() as session:
        session.add(some_object)
        session.add(some_other_object)
    # commits the transaction, closes the session

Where above, the `Session` will both have its transaction committed as well as that the `Session` will be closed, when the above `with:` block ends.

When you write your application, the `sessionmaker` factory should be scoped the same as the `Engine` object created by `create_engine()`, which is typically at module-level or global scope. As these objects are both factories, they can be used by any number of functions and threads simultaneously.

See also

`sessionmaker`

`Session`

### Querying (1.x Style)

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. By “entity” we refer to a mapped class, an attribute of a mapped class, or other ORM constructs such as an `aliased()` construct:

    # query from a class
    results = session.query(User).filter_by(name="ed").all()

    # query with multiple classes, returns tuples
    results = session.query(User, Address).join("addresses").filter_by(name="ed").all()

    # query using orm-columns, also returns tuples
    results = session.query(User.name, User.fullname).all()

When ORM objects are returned in results, they are also stored in the identity map. When an incoming database row has a primary key that matches an object which is already present, the same object is returned, and those attributes of the object which already have a value are not re-populated.

The `Session` automatically expires all instances along transaction boundaries (i.e. when the current transaction is committed or rolled back) so that with a normally isolated transaction, data will refresh itself when a new transaction begins.

The `Query` object is introduced in great detail in Object Relational Tutorial (1.x API), and further documented in Query API.

See also

Object Relational Tutorial (1.x API)

`Session.query()`

Query API

### Querying (2.0 style)

New in version 1.4.

SQLAlchemy 2.0 will standardize the production of SELECT statements across both Core and ORM by making direct use of the `Select` object within the ORM, removing the need for there to be a separate `Query` object. This mode of operation is available in SQLAlchemy 1.4 right now to support applications that will be migrating to 2.0. The `Session` must be instantiated with the `Session.future` flag set to `True`; from that point on the `Session.execute()` method will return ORM results via the standard `Result` object when invoking `select()` statements that use ORM entities:

    from sqlalchemy import select
    from sqlalchemy.orm import Session

    session = Session(engine, future=True)

    # query from a class
    statement = select(User).filter_by(name="ed")

    # list of first element of each row (i.e. User objects)
    result = session.execute(statement).scalars().all()

    # query with multiple classes
    statement = select(User, Address).join("addresses").filter_by(name="ed")

    # list of tuples
    result = session.execute(statement).all()

    # query with ORM columns
    statement = select(User.name, User.fullname)

    # list of tuples
    result = session.execute(statement).all()

It’s important to note that while methods of `Query` such as `Query.all()` and `Query.one()` will return instances of ORM mapped objects directly in the case that only a single complete entity were requested, the `Result` object returned by `Session.execute()` will always deliver rows (named tuples) by default; this is so that results against single or multiple ORM objects, columns, tables, etc. may all be handled identically.

If only one ORM entity was queried, the rows returned will have exactly one column, consisting of the ORM-mapped object instance for each row. To convert these rows into object instances without the tuples, the `Result.scalars()` method is used to first apply a “scalars” filter to the result; then the `Result` can be iterated or deliver rows via standard methods such as `Result.all()`, `Result.first()`, etc.

See also

Migrating to SQLAlchemy 2.0

### 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()

`Session.delete()` marks an object for deletion, which will result in a DELETE statement emitted for each primary key affected. Before the pending deletes are flushed, objects marked by “delete” are present in the `Session.deleted` collection. After the DELETE, they are expunged from the `Session`, which becomes permanent after the transaction is committed.

There are various important behaviors related to the `Session.delete()` operation, particularly in how relationships to other objects and collections are handled. There’s more information on how this works in the section Cascades, but in general the rules are:

- Rows that correspond to mapped objects that are related to a deleted object via the `relationship()` directive are not deleted by default. If those objects have a foreign key constraint back to the row being deleted, those columns are set to NULL. This will cause a constraint violation if the columns are non-nullable.

- To change the “SET NULL” into a DELETE of a related object’s row, use the delete cascade on the `relationship()`.

- Rows that are in tables linked as “many-to-many” tables, via the `relationship.secondary` parameter, are deleted in all cases when the object they refer to is deleted.

- When related objects include a foreign key constraint back to the object being deleted, and the related collections to which they belong are not currently loaded into memory, the unit of work will emit a SELECT to fetch all related rows, so that their primary key values can be used to emit either UPDATE or DELETE statements on those related rows. In this way, the ORM without further instruction will perform the function of ON DELETE CASCADE, even if this is configured on Core `ForeignKeyConstraint` objects.

- The `relationship.passive_deletes` parameter can be used to tune this behavior and rely upon “ON DELETE CASCADE” more naturally; when set to True, this SELECT operation will no longer take place, however rows that are locally present will still be subject to explicit SET NULL or DELETE. Setting `relationship.passive_deletes` to the string `"all"` will disable all related object update/delete.

- When the DELETE occurs for an object marked for deletion, the object is not automatically removed from collections or object references that refer to it. When the `Session` is expired, these collections may be loaded again so that the object is no longer present. However, it is preferable that instead of using `Session.delete()` for these objects, the object should instead be removed from its collection and then delete-orphan should be used so that it is deleted as a secondary effect of that collection removal. See the section Notes on Delete - Deleting Objects Referenced from Collections and Scalar Relationships for an example of this.

See also

delete - describes “delete cascade”, which marks related objects for deletion when a lead object is deleted.

delete-orphan - describes “delete orphan cascade”, which marks related objects for deletion when they are de-associated from their lead object.

Notes on Delete - Deleting Objects Referenced from Collections and Scalar Relationships - important background on `Session.delete()` as involves relationships being refreshed in memory.

### 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 SQL statement is issued as a result of a `Query` or a 2.0-style `Session.execute()` call, 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.

A `Session` flush can be forced at any time by calling the `Session.flush()` method:

    session.flush()

The flush which occurs automatically within the scope of certain methods is known as autoflush. Autoflush is defined as a configurable, automatic flush call which occurs at the beginning of methods including:

- `Session.execute()` and other SQL-executing methods, when used against ORM-enabled SQL constructs, such as `select()` objects that refer to ORM entities and/or ORM-mapped attributes

- When a `Query` is invoked to send SQL to the database

- Within the `Session.merge()` method before querying the database

- When objects are refreshed

- When ORM lazy load operations occur against unloaded object attributes.

There are also points at which flushes occur unconditionally; these points are within key transactional boundaries which include:

- Within the process of the `Session.commit()` method

- When `Session.begin_nested()` is called

- When the `Session.prepare()` 2PC method is used.

The autoflush behavior, as applied to the previous list of items, can be disabled by constructing a `Session` or `sessionmaker` passing the `Session.autoflush` parameter as `False`:

    Session = sessionmaker(autoflush=False)

Additionally, autoflush can be temporarily disabled within the flow of using a `Session` using the `Session.no_autoflush` context manager:

    with mysession.no_autoflush:
        mysession.add(some_object)
        mysession.flush()

To reiterate: The flush process always occurs when transactional methods such as `Session.commit()` and `Session.begin_nested()` are called, regardless of any “autoflush” settings, when the `Session` has remaining pending changes to process.

The flush process always occurs within a transaction, (subject to the isolation level of the database transaction), provided that the DBAPI is not in driver level autocommit mode. This includes even if the `Session` has been configured with the deprecated `Session.autocommit` setting, which disables the session’s persistent transactional state. If no transaction is present, `Session.flush()` creates its own transaction and commits it. This means that assuming the database connection is providing for atomicity within its transactional settings, if any individual DML statement inside the flush fails, the entire operation will be rolled back.

Outside of using `Session.autocommit`, when a failure occurs within a flush, in order to continue using that same `Session`, an explicit call to `Session.rollback()` is required after a flush fails, even though the underlying transaction will have been rolled back already (even if the database driver is technically in driver-level autocommit mode). This is so that the overall nesting pattern of so-called “subtransactions” is consistently maintained. The FAQ section “This Session’s transaction has been rolled back due to a previous exception during flush.” (or similar) contains a more detailed description of this behavior.

### Get by Primary Key

As the `Session` makes use of an identity map which refers to current in-memory objects by primary key, the `Session.get()` method is provided as a means of locating objects by primary key, first looking within the current identity map and then querying the database for non present values. Such as, to locate a `User` entity with primary key identity `(5, )`:

    my_user = session.get(User, 5)

The `Session.get()` also includes calling forms for composite primary key values, which may be passed as tuples or dictionaries, as well as additional parameters which allow for specific loader and execution options. See `Session.get()` for the complete parameter list.

See also

`Session.get()`

### 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 the `Session.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 or execution option - This is now an execution option documented at Populate Existing; in legacy form it’s found on the `Query` object as the `Query.populate_existing()` method. This operation in either form indicates that objects being returned from a query should be 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.

See also

Refreshing / Expiring

I’m re-loading data with my Session but it isn’t seeing changes that I committed elsewhere

### UPDATE and DELETE with arbitrary WHERE clause

The sections above on `Session.flush()` and `Session.delete()` detail how rows can be inserted, updated and deleted in the database, based on primary key identities that are referred towards by mapped Python objects in the application. The `Session` can also emit UPDATE and DELETE statements with arbitrary WHERE clauses as well, and at the same time refresh locally present objects which match those rows.

To emit an ORM-enabled UPDATE in 1.x style, the `Query.update()` method may be used:

    session.query(User).filter(User.name == "squidward").update(
        {"name": "spongebob"}, synchronize_session="fetch"
    )

Above, an UPDATE will be emitted against all rows that match the name “squidward” and be updated to the name “spongebob”. The `Query.update.synchronize_session` parameter referring to “fetch” indicates the list of affected primary keys should be fetched either via a separate SELECT statement or via RETURNING if the backend database supports it; objects locally present in memory will be updated in memory based on these primary key identities.

For ORM-enabled UPDATEs in 2.0 style, `Session.execute()` is used with the Core `Update` construct:

    from sqlalchemy import update

    stmt = (
        update(User)
        .where(User.name == "squidward")
        .values(name="spongebob")
        .execution_options(synchronize_session="fetch")
    )

    result = session.execute(stmt)

Above, the `Update.execution_options()` method may be used to establish execution-time options such as “synchronize_session”.

The result object returned is an instance of `CursorResult`; to retrieve the number of rows matched by any UPDATE or DELETE statement, use `CursorResult.rowcount`:

    num_rows_matched = result.rowcount

DELETEs work in the same way as UPDATE except there is no “values / set” clause established. When synchronize_session is used, matching objects within the `Session` will be marked as deleted and expunged.

ORM-enabled delete, 1.x style:

    session.query(User).filter(User.name == "squidward").delete(synchronize_session="fetch")

ORM-enabled delete, 2.0 style:

    from sqlalchemy import delete

    stmt = (
        delete(User)
        .where(User.name == "squidward")
        .execution_options(synchronize_session="fetch")
    )

    session.execute(stmt)

#### Selecting a Synchronization Strategy

With both the 1.x and 2.0 form of ORM-enabled updates and deletes, the following values for `synchronize_session` are supported:

- `False` - don’t synchronize the session. This option is the most efficient and is reliable once the session is expired, which typically occurs after a commit(), or explicitly using expire_all(). Before the expiration, objects that were updated or deleted in the database may still remain in the session with stale values, which can lead to confusing results.

- `'fetch'` - Retrieves the primary key identity of affected rows by either performing a SELECT before the UPDATE or DELETE, or by using RETURNING if the database supports it, so that in-memory objects which are affected by the operation can be refreshed with new values (updates) or expunged from the `Session` (deletes). Note that this synchronization strategy is not available if the given `update()` or `delete()` construct specifies columns for `UpdateBase.returning()` explicitly.

- `'evaluate'` - Evaluate the WHERE criteria given in the UPDATE or DELETE statement in Python, to locate matching objects within the `Session`. This approach does not add any round trips and in the absence of RETURNING support is more efficient. For UPDATE or DELETE statements with complex criteria, the `'evaluate'` strategy may not be able to evaluate the expression in Python and will raise an error. If this occurs, use the `'fetch'` strategy for the operation instead.

  Warning

  The `"evaluate"` strategy should be avoided if an UPDATE operation is to run on a `Session` that has many objects which have been expired, because it will necessarily need to refresh those objects as they are located which will emit a SELECT for each one. The `Session` may have expired objects if it is being used across multiple `Session.commit()` calls and the `Session.expire_on_commit` flag is at its default value of `True`.

Warning

Additional Caveats for ORM-enabled updates and deletes

The ORM-enabled UPDATE and DELETE features bypass ORM unit-of-work automation in favor being able to emit a single UPDATE or DELETE statement that matches multiple rows at once without complexity.

- The operations do not offer in-Python cascading of relationships - it is assumed that ON UPDATE CASCADE and/or ON DELETE CASCADE is configured for any foreign key references which require it, otherwise the database may emit an integrity violation if foreign key references are being enforced.

- After the UPDATE or DELETE, dependent objects in the `Session` which were impacted by an ON UPDATE CASCADE or ON DELETE CASCADE on related tables may not contain the current state; this issue is resolved once the `Session` is expired, which normally occurs upon `Session.commit()` or can be forced by using `Session.expire_all()`.

- The `'fetch'` strategy, when run on a database that does not support RETURNING such as MySQL or SQLite, results in an additional SELECT statement emitted which may reduce performance. Use SQL echoing when developing to evaluate the impact of SQL emitted.

- ORM-enabled UPDATEs and DELETEs do not handle joined table inheritance automatically. If the operation is against multiple tables, typically individual UPDATE / DELETE statements against the individual tables should be used. Some databases support multiple table UPDATEs. Similar guidelines as those detailed at UPDATE..FROM may be applied.

- The WHERE criteria needed in order to limit the polymorphic identity to specific subclasses for single-table-inheritance mappings is included automatically . This only applies to a subclass mapper that has no table of its own.

  Changed in version 1.4: ORM updates/deletes now automatically accommodate for the WHERE criteria added for single-inheritance mappings.

- The `with_loader_criteria()` option is supported by ORM update and delete operations; criteria here will be added to that of the UPDATE or DELETE statement being emitted, as well as taken into account during the “synchronize” process.

- In order to intercept ORM-enabled UPDATE and DELETE operations with event handlers, use the `SessionEvents.do_orm_execute()` event.

#### Selecting ORM Objects Inline with UPDATE.. RETURNING or INSERT..RETURNING

This section has moved. See Using INSERT, UPDATE and ON CONFLICT (i.e. upsert) to return ORM Objects.

### Auto Begin

New in version 1.4: This section describes a behavior that is new in SQLAlchemy 1.4 and does not apply to previous versions. Further details on the “autobegin” change are at Session features new “autobegin” behavior.

The `Session` object features a behavior known as autobegin. This indicates that the `Session` will internally consider itself to be in a “transactional” state as soon as any work is performed with the `Session`, either involving modifications to the internal state of the `Session` with regards to object state changes, or with operations that require database connectivity.

When the `Session` is first constructed, there’s no transactional state present. The transactional state is begun automatically, when a method such as `Session.add()` or `Session.execute()` is invoked, or similarly if a `Query` is executed to return results (which ultimately uses `Session.execute()`), or if an attribute is modified on a persistent object.

The transactional state can be checked by accessing the `Session.in_transaction()` method, which returns `True` or `False` indicating if the “autobegin” step has proceeded. While not normally needed, the `Session.get_transaction()` method will return the actual `SessionTransaction` object that represents this transactional state.

The transactional state of the `Session` may also be started explicitly, by invoking the `Session.begin()` method. When this method is called, the `Session` is placed into the “transactional” state unconditionally. `Session.begin()` may be used as a context manager as described at Framing out a begin / commit / rollback block.

Changed in version 1.4.12: - autobegin now correctly occurs if object attributes are modified; previously this was not occurring.

### Committing

`Session.commit()` is used to commit the current transaction. At its core this indicates that it emits `COMMIT` on all current database connections that have a transaction in progress; from a DBAPI perspective this means the `connection.commit()` DBAPI method is invoked on each DBAPI connection.

When there is no transaction in place for the `Session`, indicating that no operations were invoked on this `Session` since the previous call to `Session.commit()`, the method will begin and commit an internal-only “logical” transaction, that does not normally affect the database unless pending flush changes were detected, but will still invoke event handlers and object expiration rules.

The `Session.commit()` operation unconditionally issues `Session.flush()` before emitting COMMIT on relevant database connections. If no pending changes are detected, then no SQL is emitted to the database. This behavior is not configurable and is not affected by the `Session.autoflush` parameter.

Subsequent to that, `Session.commit()` will then COMMIT the actual database transaction or transactions, if any, that are in place.

Finally, all objects within the `Session` are expired as the transaction is closed out. This is so that when the instances are next accessed, either through attribute access or by them being present in the result of a SELECT, they receive the most recent state. This behavior may be controlled by the `Session.expire_on_commit` flag, which may be set to `False` when this behavior is undesirable.

See also

Auto Begin

### Rolling Back

`Session.rollback()` rolls back the current transaction, if any. When there is no transaction in place, the method passes silently.

With a default configured session, the post-rollback state of the session, subsequent to a transaction having been begun either via autobegin or by calling the `Session.begin()` method explicitly, 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 - this is regardless of the `Session.expire_on_commit` setting.

With that state understood, the `Session` may safely continue usage after a rollback occurs.

Changed in version 1.4: The `Session` object now features deferred “begin” behavior, as described in autobegin. If no transaction is begun, methods like `Session.commit()` and `Session.rollback()` have no effect. This behavior would not have been observed prior to 1.4 as under non-autocommit mode, a transaction would always be implicitly present.

When a `Session.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 `Session` goes into a state known as “inactive” at this point, and the calling application must always call the `Session.rollback()` method explicitly so that the `Session` can go back into a usable state (it can also be simply closed and discarded). See the FAQ entry at “This Session’s transaction has been rolled back due to a previous exception during flush.” (or similar) for further discussion.

See also

Auto Begin

### 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.

It’s recommended that the scope of a `Session` be limited by a call to `Session.close()` at the end, especially if the `Session.commit()` or `Session.rollback()` methods are not used. The `Session` may be used as a context manager to ensure that `Session.close()` is called:

    with Session(engine) as session:
        result = session.execute(select(User))

    # closes session automatically

Changed in version 1.4: The `Session` object features deferred “begin” behavior, as described in autobegin. no longer immediately begins a new transaction after the `Session.close()` method is called.

## 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. 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.

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.

Some sample scenarios include:

- Web applications. In this case, it’s best to make use of the SQLAlchemy integrations provided by the web framework in use. Or otherwise, the basic pattern is create a `Session` at the start of a web request, call the `Session.commit()` method at the end of web requests that do POST, PUT, or DELETE, and then close the session at the end of web request. It’s also usually a good idea to set `Session.expire_on_commit` to False so that subsequent access to objects that came from a `Session` within the view layer do not need to emit new SQL queries to refresh the objects, if the transaction has been committed already.

- 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. The example below illustrates how this might look, and additionally makes use of a Python context manager (i.e. the `with:` keyword) in order to manage the scope of the `Session` and its transaction automatically:

    ### 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():
        with Session() as session:
            with session.begin():
                ThingOne().go(session)
                ThingTwo().go(session)

Changed in version 1.4: The `Session` may be used as a context manager without the use of external helper functions.

### 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`.