Release: 2.0.48 | Release Date: March 2, 2026

# SQLAlchemy 2.0 Documentation

# Sessions / Queries

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

The main issue regarding this behavior is that the session acts as though the transaction is in the serializable isolation state, even if it’s not (and it usually is not). In practical terms, this means that the session does not alter any data that it’s already read within the scope of a transaction.

If the term “isolation level” is unfamiliar, then you first need to read this link:

Isolation Level

In short, serializable isolation level generally means that once you SELECT a series of rows in a transaction, you will get the identical data back each time you re-emit that SELECT. If you are in the next-lower isolation level, “repeatable read”, you’ll see newly added rows (and no longer see deleted rows), but for rows that you’ve already loaded, you won’t see any change. Only if you are in a lower isolation level, e.g. “read committed”, does it become possible to see a row of data change its value.

For information on controlling the isolation level when using the SQLAlchemy ORM, see Setting Transaction Isolation Levels / DBAPI AUTOCOMMIT.

To simplify things dramatically, the `Session` itself works in terms of a completely isolated transaction, and doesn’t overwrite any mapped attributes it’s already read unless you tell it to. The use case of trying to re-read data you’ve already loaded in an ongoing transaction is an uncommon use case that in many cases has no effect, so this is considered to be the exception, not the norm; to work within this exception, several methods are provided to allow specific data to be reloaded within the context of an ongoing transaction.

To understand what we mean by “the transaction” when we talk about the `Session`, your `Session` is intended to only work within a transaction. An overview of this is at Managing Transactions.

Once we’ve figured out what our isolation level is, and we think that our isolation level is set at a low enough level so that if we re-SELECT a row, we should see new data in our `Session`, how do we see it?

Three ways, from most common to least:

1. We simply end our transaction and start a new one on next access with our `Session` by calling `Session.commit()` (note that if the `Session` is in the lesser-used “autocommit” mode, there would be a call to `Session.begin()` as well). The vast majority of applications and use cases do not have any issues with not being able to “see” data in other transactions because they stick to this pattern, which is at the core of the best practice of short lived transactions. See When do I construct a Session, when do I commit it, and when do I close it? for some thoughts on this.

2. We tell our `Session` to re-read rows that it has already read, either when we next query for them using `Session.expire_all()` or `Session.expire()`, or immediately on an object using `refresh`. See Refreshing / Expiring for detail on this.

3. We can run whole queries while setting them to definitely overwrite already-loaded objects as they read rows by using “populate existing”. This is an execution option described at Populate Existing.

But remember, the ORM cannot see changes in rows if our isolation level is repeatable read or higher, unless we start a new transaction.

## “This Session’s transaction has been rolled back due to a previous exception during flush.” (or similar)

This is an error that occurs when a `Session.flush()` raises an exception, rolls back the transaction, but further commands upon the `Session` are called without an explicit call to `Session.rollback()` or `Session.close()`.

It usually corresponds to an application that catches an exception upon `Session.flush()` or `Session.commit()` and does not properly handle the exception. For example:

    from sqlalchemy import create_engine, Column, Integer
    from sqlalchemy.orm import sessionmaker
    from sqlalchemy.ext.declarative import declarative_base

    Base = declarative_base(create_engine("sqlite://"))

    class Foo(Base):
        __tablename__ = "foo"
        id = Column(Integer, primary_key=True)

    Base.metadata.create_all()

    session = sessionmaker()()

    # constraint violation
    session.add_all([Foo(id=1), Foo(id=1)])

    try:
        session.commit()
    except:
        # ignore error
        pass

    # continue using session without rolling back
    session.commit()

The usage of the `Session` should fit within a structure similar to this:

    try:
        # <use session>
        session.commit()
    except:
        session.rollback()
        raise
    finally:
        session.close()  # optional, depends on use case

Many things can cause a failure within the try/except besides flushes. Applications should ensure some system of “framing” is applied to ORM-oriented processes so that connection and transaction resources have a definitive boundary, and so that transactions can be explicitly rolled back if any failure conditions occur.

This does not mean there should be try/except blocks throughout an application, which would not be a scalable architecture. Instead, a typical approach is that when ORM-oriented methods and functions are first called, the process that’s calling the functions from the very top would be within a block that commits transactions at the successful completion of a series of operations, as well as rolls transactions back if operations fail for any reason, including failed flushes. There are also approaches using function decorators or context managers to achieve similar results. The kind of approach taken depends very much on the kind of application being written.

For a detailed discussion on how to organize usage of the `Session`, please see When do I construct a Session, when do I commit it, and when do I close it?.

### But why does flush() insist on issuing a ROLLBACK?

It would be great if `Session.flush()` could partially complete and then not roll back, however this is beyond its current capabilities since its internal bookkeeping would have to be modified such that it can be halted at any time and be exactly consistent with what’s been flushed to the database. While this is theoretically possible, the usefulness of the enhancement is greatly decreased by the fact that many database operations require a ROLLBACK in any case. Postgres in particular has operations which, once failed, the transaction is not allowed to continue:

    test=> create table foo(id integer primary key);
    NOTICE:  CREATE TABLE / PRIMARY KEY will create implicit index "foo_pkey" for table "foo"
    CREATE TABLE
    test=> begin;
    BEGIN
    test=> insert into foo values(1);
    INSERT 0 1
    test=> commit;
    COMMIT
    test=> begin;
    BEGIN
    test=> insert into foo values(1);
    ERROR:  duplicate key value violates unique constraint "foo_pkey"
    test=> insert into foo values(2);
    ERROR:  current transaction is aborted, commands ignored until end of transaction block

What SQLAlchemy offers that solves both issues is support of SAVEPOINT, via `Session.begin_nested()`. Using `Session.begin_nested()`, you can frame an operation that may potentially fail within a transaction, and then “roll back” to the point before its failure while maintaining the enclosing transaction.

### But why isn’t the one automatic call to ROLLBACK enough? Why must I ROLLBACK again?

The rollback that’s caused by the flush() is not the end of the complete transaction block; while it ends the database transaction in play, from the `Session` point of view there is still a transaction that is now in an inactive state.

Given a block such as:

    sess = Session()  # begins a logical transaction
    try:
        sess.flush()

        sess.commit()
    except:
        sess.rollback()

Above, when a `Session` is first created, assuming “autocommit mode” isn’t used, a logical transaction is established within the `Session`. This transaction is “logical” in that it does not actually use any database resources until a SQL statement is invoked, at which point a connection-level and DBAPI-level transaction is started. However, whether or not database-level transactions are part of its state, the logical transaction will stay in place until it is ended using `Session.commit()`, `Session.rollback()`, or `Session.close()`.

When the `flush()` above fails, the code is still within the transaction framed by the try/commit/except/rollback block. If `flush()` were to fully roll back the logical transaction, it would mean that when we then reach the `except:` block the `Session` would be in a clean state, ready to emit new SQL on an all new transaction, and the call to `Session.rollback()` would be out of sequence. In particular, the `Session` would have begun a new transaction by this point, which the `Session.rollback()` would be acting upon erroneously. Rather than allowing SQL operations to proceed on a new transaction in this place where normal usage dictates a rollback is about to take place, the `Session` instead refuses to continue until the explicit rollback actually occurs.

In other words, it is expected that the calling code will always call `Session.commit()`, `Session.rollback()`, or `Session.close()` to correspond to the current transaction block. `flush()` keeps the `Session` within this transaction block so that the behavior of the above code is predictable and consistent.

## How do I make a Query that always adds a certain filter to every query?

See the recipe at FilteredQuery.

## My Query does not return the same number of objects as query.count() tells me - why?

The `Query` object, when asked to return a list of ORM-mapped objects, will deduplicate the objects based on primary key. That is, if we for example use the `User` mapping described at Using ORM Declarative Forms to Define Table Metadata, and we had a SQL query like the following:

    q = session.query(User).outerjoin(User.addresses).filter(User.name == "jack")

Above, the sample data used in the tutorial has two rows in the `addresses` table for the `users` row with the name `'jack'`, primary key value 5. If we ask the above query for a `Query.count()`, we will get the answer 2:

    >>> q.count()
    2

However, if we run `Query.all()` or iterate over the query, we get back one element:

    >>> q.all()
    [User(id=5, name='jack', ...)]

This is because when the `Query` object returns full entities, they are deduplicated. This does not occur if we instead request individual columns back:

    >>> session.query(User.id, User.name).outerjoin(User.addresses).filter(
    ...     User.name == "jack"
    ... ).all()
    [(5, 'jack'), (5, 'jack')]

There are two main reasons the `Query` will deduplicate:

- To allow joined eager loading to work correctly - Joined Eager Loading works by querying rows using joins against related tables, where it then routes rows from those joins into collections upon the lead objects. In order to do this, it has to fetch rows where the lead object primary key is repeated for each sub-entry. This pattern can then continue into further sub-collections such that a multiple of rows may be processed for a single lead object, such as `User(id=5)`. The dedpulication allows us to receive objects in the way they were queried, e.g. all the `User()` objects whose name is `'jack'` which for us is one object, with the `User.addresses` collection eagerly loaded as was indicated either by `lazy='joined'` on the `relationship()` or via the `joinedload()` option. For consistency, the deduplication is still applied whether or not the joinedload is established, as the key philosophy behind eager loading is that these options never affect the result.

- To eliminate confusion regarding the identity map - this is admittedly the less critical reason. As the `Session` makes use of an identity map, even though our SQL result set has two rows with primary key 5, there is only one `User(id=5)` object inside the `Session` which must be maintained uniquely on its identity, that is, its primary key / class combination. It doesn’t actually make much sense, if one is querying for `User()` objects, to get the same object multiple times in the list. An ordered set would potentially be a better representation of what `Query` seeks to return when it returns full objects.

The issue of `Query` deduplication remains problematic, mostly for the single reason that the `Query.count()` method is inconsistent, and the current status is that joined eager loading has in recent releases been superseded first by the “subquery eager loading” strategy and more recently the “select IN eager loading” strategy, both of which are generally more appropriate for collection eager loading. As this evolution continues, SQLAlchemy may alter this behavior on `Query`, which may also involve new APIs in order to more directly control this behavior, and may also alter the behavior of joined eager loading in order to create a more consistent usage pattern.

## I’ve created a mapping against an Outer Join, and while the query returns rows, no objects are returned. Why not?

Rows returned by an outer join may contain NULL for part of the primary key, as the primary key is the composite of both tables. The `Query` object ignores incoming rows that don’t have an acceptable primary key. Based on the setting of the `allow_partial_pks` flag on `Mapper`, a primary key is accepted if the value has at least one non-NULL value, or alternatively if the value has no NULL values. See `allow_partial_pks` at `Mapper`.

## I’m using `joinedload()` or `lazy=False` to create a JOIN/OUTER JOIN and SQLAlchemy is not constructing the correct query when I try to add a WHERE, ORDER BY, LIMIT, etc. (which relies upon the (OUTER) JOIN)

The joins generated by joined eager loading are only used to fully load related collections, and are designed to have no impact on the primary results of the query. Since they are anonymously aliased, they cannot be referenced directly.

For detail on this behavior, see The Zen of Joined Eager Loading.

## Query has no `__len__()`, why not?

The Python `__len__()` magic method applied to an object allows the `len()` builtin to be used to determine the length of the collection. It’s intuitive that a SQL query object would link `__len__()` to the `Query.count()` method, which emits a SELECT COUNT. The reason this is not possible is because evaluating the query as a list would incur two SQL calls instead of one:

    class Iterates:
        def __len__(self):
            print("LEN!")
            return 5

        def __iter__(self):
            print("ITER!")
            return iter([1, 2, 3, 4, 5])

    list(Iterates())

output:

    ITER!
    LEN!

## How Do I use Textual SQL with ORM Queries?

See:

- Getting ORM Results from Textual Statements - Ad-hoc textual blocks with `Query`

- Using SQL Expressions with Sessions - Using `Session` with textual SQL directly.

## I’m calling `Session.delete(myobject)` and it isn’t removed from the parent collection!

See Notes on Delete - Deleting Objects Referenced from Collections and Scalar Relationships for a description of this behavior.

## why isn’t my `__init__()` called when I load objects?

See Maintaining Non-Mapped State Across Loads for a description of this behavior.

## how do I use ON DELETE CASCADE with SA’s ORM?

SQLAlchemy will always issue UPDATE or DELETE statements for dependent rows which are currently loaded in the `Session`. For rows which are not loaded, it will by default issue SELECT statements to load those rows and update/delete those as well; in other words it assumes there is no ON DELETE CASCADE configured. To configure SQLAlchemy to cooperate with ON DELETE CASCADE, see Using foreign key ON DELETE cascade with ORM relationships.

## I set the “foo_id” attribute on my instance to “7”, but the “foo” attribute is still `None` - shouldn’t it have loaded Foo with id #7?

The ORM is not constructed in such a way as to support immediate population of relationships driven from foreign key attribute changes - instead, it is designed to work the other way around - foreign key attributes are handled by the ORM behind the scenes, the end user sets up object relationships naturally. Therefore, the recommended way to set `o.foo` is to do just that - set it!:

    foo = session.get(Foo, 7)
    o.foo = foo
    Session.commit()

Manipulation of foreign key attributes is of course entirely legal. However, setting a foreign-key attribute to a new value currently does not trigger an “expire” event of the `relationship()` in which it’s involved. This means that for the following sequence:

    o = session.scalars(select(SomeClass).limit(1)).first()

    # assume the existing o.foo_id value is None;
    # accessing o.foo will reconcile this as ``None``, but will effectively
    # "load" the value of None
    assert o.foo is None

    # now set foo_id to something.  o.foo will not be immediately affected
    o.foo_id = 7

`o.foo` is loaded with its effective database value of `None` when it is first accessed. Setting `o.foo_id = 7` will have the value of “7” as a pending change, but no flush has occurred - so `o.foo` is still `None`:

    # attribute is already "loaded" as None, has not been
    # reconciled with o.foo_id = 7 yet
    assert o.foo is None

For `o.foo` to load based on the foreign key mutation is usually achieved naturally after the commit, which both flushes the new foreign key value and expires all state:

    session.commit()  # expires all attributes

    foo_7 = session.get(Foo, 7)

    # o.foo will lazyload again, this time getting the new object
    assert o.foo is foo_7

A more minimal operation is to expire the attribute individually - this can be performed for any persistent object using `Session.expire()`:

    o = session.scalars(select(SomeClass).limit(1)).first()
    o.foo_id = 7
    Session.expire(o, ["foo"])  # object must be persistent for this

    foo_7 = session.get(Foo, 7)

    assert o.foo is foo_7  # o.foo lazyloads on access

Note that if the object is not persistent but present in the `Session`, it’s known as pending. This means the row for the object has not been INSERTed into the database yet. For such an object, setting `foo_id` does not have meaning until the row is inserted; otherwise there is no row yet:

    new_obj = SomeClass()
    new_obj.foo_id = 7

    Session.add(new_obj)

    # returns None but this is not a "lazyload", as the object is not
    # persistent in the DB yet, and the None value is not part of the
    # object's state
    assert new_obj.foo is None

    Session.flush()  # emits INSERT

    assert new_obj.foo is foo_7  # now it loads

Attribute loading for non-persistent objects

One variant on the “pending” behavior above is if we use the flag `load_on_pending` on `relationship()`. When this flag is set, the lazy loader will emit for `new_obj.foo` before the INSERT proceeds; another variant of this is to use the `Session.enable_relationship_loading()` method, which can “attach” an object to a `Session` in such a way that many-to-one relationships load as according to foreign key attributes regardless of the object being in any particular state. Both techniques are not recommended for general use; they were added to suit specific programming scenarios encountered by users which involve the repurposing of the ORM’s usual object states.

The recipe ExpireRelationshipOnFKChange features an example using SQLAlchemy events in order to coordinate the setting of foreign key attributes with many-to-one relationships.

## How do I walk all objects that are related to a given object?

An object that has other objects related to it will correspond to the `relationship()` constructs set up between mappers. This code fragment will iterate all the objects, correcting for cycles as well:

    from sqlalchemy import inspect

    def walk(obj):
        deque = [obj]

        seen = set()

        while deque:
            obj = deque.pop(0)
            if obj in seen:
                continue
            else:
                seen.add(obj)
                yield obj
            insp = inspect(obj)
            for relationship in insp.mapper.relationships:
                related = getattr(obj, relationship.key)
                if relationship.uselist:
                    deque.extend(related)
                elif related is not None:
                    deque.append(related)

The function can be demonstrated as follows:

    Base = declarative_base()

    class A(Base):
        __tablename__ = "a"
        id = Column(Integer, primary_key=True)
        bs = relationship("B", backref="a")

    class B(Base):
        __tablename__ = "b"
        id = Column(Integer, primary_key=True)
        a_id = Column(ForeignKey("a.id"))
        c_id = Column(ForeignKey("c.id"))
        c = relationship("C", backref="bs")

    class C(Base):
        __tablename__ = "c"
        id = Column(Integer, primary_key=True)

    a1 = A(bs=[B(), B(c=C())])

    for obj in walk(a1):
        print(obj)

Output:

    <__main__.A object at 0x10303b190>
    <__main__.B object at 0x103025210>
    <__main__.B object at 0x10303b0d0>
    <__main__.C object at 0x103025490>

## Is there a way to automagically have only unique keywords (or other kinds of objects) without doing a query for the keyword and getting a reference to the row containing that keyword?

When people read the many-to-many example in the docs, they get hit with the fact that if you create the same `Keyword` twice, it gets put in the DB twice. Which is somewhat inconvenient.

This UniqueObject recipe was created to address this issue.

## Why does post_update emit UPDATE in addition to the first UPDATE?

The post_update feature, documented at Rows that point to themselves / Mutually Dependent Rows, involves that an UPDATE statement is emitted in response to changes to a particular relationship-bound foreign key, in addition to the INSERT/UPDATE/DELETE that would normally be emitted for the target row. While the primary purpose of this UPDATE statement is that it pairs up with an INSERT or DELETE of that row, so that it can post-set or pre-unset a foreign key reference in order to break a cycle with a mutually dependent foreign key, it currently is also bundled as a second UPDATE that emits when the target row itself is subject to an UPDATE. In this case, the UPDATE emitted by post_update is usually unnecessary and will often appear wasteful.

However, some research into trying to remove this “UPDATE / UPDATE” behavior reveals that major changes to the unit of work process would need to occur not just throughout the post_update implementation, but also in areas that aren’t related to post_update for this to work, in that the order of operations would need to be reversed on the non-post_update side in some cases, which in turn can impact other cases, such as correctly handling an UPDATE of a referenced primary key value (see #1063 for a proof of concept).

The answer is that “post_update” is used to break a cycle between two mutually dependent foreign keys, and to have this cycle breaking be limited to just INSERT/DELETE of the target table implies that the ordering of UPDATE statements elsewhere would need to be liberalized, leading to breakage in other edge cases.