Release: 1.4.0b1 pre release | Release Date: unreleased

SQLAlchemy 1.4 Documentation

What’s New in SQLAlchemy 1.4?

About this Document

This document describes changes between SQLAlchemy version 1.3 and SQLAlchemy version 1.4.

Version 1.4 is taking on a different focus than other SQLAlchemy releases in that it is in many ways attempting to serve as a potential migration point for a more dramatic series of API changes currently planned for release 2.0 of SQLAlchemy. The focus of SQLAlchemy 2.0 is a modernized and slimmed down API that removes lots of usage patterns that have long been discouraged, as well as mainstreams the best ideas in SQLAlchemy as first class API features, with the goal being that there is much less ambiguity in how the API is to be used, as well as that a series of implicit behaviors and rarely-used API flags that complicate the internals and hinder performance will be removed.

API Changes - Core

A SELECT statement is no longer implicitly considered to be a FROM clause

This change is one of the larger conceptual changes in SQLAlchemy in many years, however it is hoped that the end user impact is relatively small, as the change more closely matches what databases like MySQL and PostgreSQL require in any case.

The most immediate noticeable impact is that a select() can no longer be embedded inside of another select() directly, without explicitly turning the inner select() into a subquery first. This is historically performed by using the SelectBase.alias() method, which remains, however is more explicitly suited by using a new method SelectBase.subquery(); both methods do the same thing. The object returned is now Subquery, which is very similar to the Alias object and shares a common base AliasedReturnsRows.

That is, this will now raise:

stmt1 = select([,])
stmt2 = select([addresses, stmt1]).select_from(addresses.join(stmt1))


sqlalchemy.exc.ArgumentError: Column expression or FROM clause expected,
got <...Select object ...>. To create a FROM clause from a <class
'sqlalchemy.sql.selectable.Select'> object, use the .subquery() method.

The correct calling form is instead:

sq1 = select([,]).subquery()
stmt2 = select([addresses, sq1]).select_from(addresses.join(sq1))

Noting above that the SelectBase.subquery() method is essentially equivalent to using the SelectBase.alias() method.

The above calling form is typically required in any case as the call to SelectBase.subquery() or SelectBase.alias() is needed to ensure the subquery has a name. The MySQL and PostgreSQL databases do not accept unnamed subqueries in the FROM clause and they are of limited use on other platforms; this is described further below.

Along with the above change, the general capability of select() and related constructs to create unnamed subqueries, which means a FROM subquery that renders without any name i.e. “AS somename”, has been removed, and the ability of the select() construct to implicitly create subqueries without explicit calling code to do so is mostly deprecated. In the above example, as has always been the case, using the SelectBase.alias() method as well as the new SelectBase.subquery() method without passing a name will generate a so-called “anonymous” name, which is the familiar anon_1 name we see in SQLAlchemy queries:

SELECT,, addresses.user_id,,
addresses JOIN
(SELECT AS id, AS name FROM users) AS anon_1
ON addresses.user_id =

Unnamed subqueries in the FROM clause (which note are different from so-called “scalar subqueries” which take the place of a column expression in the columns clause or WHERE clause) are of extremely limited use in SQL, and their production in SQLAlchemy has mostly presented itself as an undesirable behavior that needs to be worked around. For example, both the MySQL and PostgreSQL outright reject the usage of unnamed subqueries:

# MySQL / MariaDB:

MariaDB [(none)]> select * from (select 1);

# PostgreSQL:

test=> select * from (select 1);
ERROR:  subquery in FROM must have an alias
LINE 1: select * from (select 1);

A database like SQLite accepts them, however it is still often the case that the names produced from such a subquery are too ambiguous to be useful:

sqlite> CREATE TABLE a(id integer);
sqlite> CREATE TABLE b(id integer);
Error: ambiguous column name: id
Error: no such column:

# use a name
sqlite> SELECT * FROM a JOIN (SELECT * FROM b) AS anon_1 ON;

Due to the above limitations, there are very few places in SQLAlchemy where such a query form was valid; the one exception was within the Oracle dialect where they were used to create OFFSET / LIMIT subqueries as Oracle does not support these keywords directly; this implementation has been replaced by one which uses anonymous subqueries. Throughout the ORM, exception cases that detect where a SELECT statement would be SELECTed from either encourage the user to, or implicitly create, an anonymously named subquery; it is hoped by moving to an all-explicit subquery much of the complexity incurred by these areas can be removed.

As SelectBase objects are no longer FromClause objects, attributes like the .c attribute as well as methods like .select(), .join(), and .outerjoin() upon SelectBase are now deprecated, as these methods all imply implicit production of a subquery. Instead, as is already what the vast majority of applications have to do in any case, invoking SelectBase.alias() or SelectBase.subquery() will provide for a Subquery object that provides all these attributes, as it is part of the FromClause hierarchy. In the interim, these methods are still available, however they now produce an anonymously named subquery rather than an unnamed one, and this subquery is distinct from the SelectBase construct itself.

In place of the .c attribute, a new attribute SelectBase.selected_columns is added. This attribute resolves to a column collection that is what most people hope that .c does (but does not), which is to reference the columns that are in the columns clause of the SELECT statement. A common beginner mistake is code such as the following:

stmt = select([users])
stmt = stmt.where( == 'foo')

The above code appears intuitive and that it would generate “SELECT * FROM users WHERE name=’foo’”, however veteran SQLAlchemy users will recognize that it in fact generates a useless subquery resembling “SELECT * FROM (SELECT * FROM users) WHERE name=’foo’”.

The new SelectBase.selected_columns attribute however does suit the use case above, as in a case like the above it links directly to the columns present in the users.c collection:

stmt = select([users])
stmt = stmt.where( == 'foo')

There is of course the notion that perhaps .c on SelectBase could simply act the way SelectBase.selected_columns does above, however in light of the fact that .c is strongly associated with the FromClause hierarchy, meaning that it is a set of columns that can be directly in the FROM clause of another SELECT, it’s better that a column collection that serves an entirely different purpose have a new name.

In the bigger picture, the reason this change is being made now is towards the goal of unifying the ORM Query object into the SelectBase hierarchy in SQLAlchemy 2.0, so that the ORM will have a “select()” construct that extends directly from the existing select() object, having the same methods and behaviors except that it will have additional ORM functionality. All statement objects in Core will also be fully cacheable using a new system that resembles “baked queries” except that it will work transparently for all statements across Core and ORM. In order to achieve this, the Core class hierarchy needs to be refined to behave in such a way that is more easily compatible with the ORM, and the ORM class hierarchy needs to be refined so that it is more compatible with Core.


Behavioral Changes - ORM

Eager loaders emit during unexpire operations

A long sought behavior was that when an expired object is accessed, configured eager loaders will run in order to eagerly load relationships on the expired object when the object is refreshed or otherwise unexpired. This behavior has now been added, so that joinedloaders will add inline JOINs as usual, and selectin/subquery loaders will run an “immediateload” operation for a given relationship, when an expired object is unexpired or an object is refreshed:

>>> a1 = session.query(A).options(joinedload(
>>> = 'new data'
>>> session.commit()

Above, the A object was loaded with a joinedload() option associated with it in order to eagerly load the bs collection. After the session.commit(), the state of the object is expired. Upon accessing the .data column attribute, the object is refreshed and this will now include the joinedload operation as well:

SELECT AS a_id, AS a_data, AS b_1_id, b_1.a_id AS b_1_a_id
FROM a LEFT OUTER JOIN b AS b_1 ON = b_1.a_id

The behavior applies both to loader strategies applied to the relationship() directly, as well as with options used with Query.options(), provided that the object was originally loaded by that query.

For the “secondary” eager loaders “selectinload” and “subqueryload”, the SQL strategy for these loaders is not necessary in order to eagerly load attributes on a single object; so they will instead invoke the “immediateload” strategy in a refresh scenario, which resembles the query emitted by “lazyload”, emitted as an additional query:

>>> a1 = session.query(A).options(selectinload(
>>> = 'new data'
>>> session.commit()
SELECT AS a_id, AS a_data
SELECT AS b_id, b.a_id AS b_a_id
WHERE ? = b.a_id

Note that a loader option does not apply to an object that was introduced into the Session in a different way. That is, if the a1 object were just persisted in this Session, or was loaded with a different query before the eager option had been applied, then the object doesn’t have an eager load option associated with it. This is not a new concept, however users who are looking for the eagerload on refresh behavior may find this to be more noticeable.


Accessing an uninitialized collection attribute on a transient object no longer mutates __dict__

It has always been SQLAlchemy’s behavior that accessing mapped attributes on a newly created object returns an implicitly generated value, rather than raising AttributeError, such as None for scalar attributes or [] for a list-holding relationship:

>>> u1 = User()
>>> u1.addresses

The rationale for the above behavior was originally to make ORM objects easier to work with. Since an ORM object represents an empty row when first created without any state, it is intuitive that its un-accessed attributes would resolve to None (or SQL NULL) for scalars and to empty collections for relationships. In particular, it makes possible an extremely common pattern of being able to mutate the new collection without manually creating and assigning an empty collection first:

>>> u1 = User()
>>> u1.addresses.append(Address())  # no need to assign u1.addresses = []

Up until version 1.0 of SQLAlchemy, the behavior of this initialization system for both scalar attributes as well as collections would be that the None or empty collection would be populated into the object’s state, e.g. __dict__. This meant that the following two operations were equivalent:

>>> u1 = User()
>>> = None  # explicit assignment

>>> u2 = User()
>>>  # implicit assignment just by accessing it

Where above, both u1 and u2 would have the value None populated in the value of the name attribute. Since this is a SQL NULL, the ORM would skip including these values within an INSERT so that SQL-level defaults take place, if any, else the value defaults to NULL on the database side.

In version 1.0 as part of Changes to attribute events and other operations regarding attributes that have no pre-existing value, this behavior was refined so that the None value was no longer populated into __dict__, only returned. Besides removing the mutating side effect of a getter operation, this change also made it possible to set columns that did have server defaults to the value NULL by actually assigning None, which was now distinguished from just reading it.

The change however did not accommodate for collections, where returning an empty collection that is not assigned meant that this mutable collection would be different each time and also would not be able to correctly accommodate for mutating operations (e.g. append, add, etc.) called upon it. While the behavior continued to generally not get in anyone’s way, an edge case was eventually identified in #4519 where this empty collection could be harmful, which is when the object is merged into a session:

>>> u1 = User(id=1)  # create an empty User to merge with id=1 in the database
>>> merged1 = session.merge(u1)  # value of merged1.addresses is unchanged from that of the DB

>>> u2 = User(id=2) # create an empty User to merge with id=2 in the database
>>> u2.addresses
>>> merged2 = session.merge(u2)  # value of merged2.addresses has been emptied in the DB

Above, the .addresses collection on merged1 will contain all the Address() objects that were already in the database. merged2 will not; because it has an empty list implicitly assigned, the .addresses collection will be erased. This is an example of where this mutating side effect can actually mutate the database itself.

While it was considered that perhaps the attribute system should begin using strict “plain Python” behavior, raising AttributeError in all cases for non-existent attributes on non-persistent objects and requiring that all collections be explicitly assigned, such a change would likely be too extreme for the vast number of applications that have relied upon this behavior for many years, leading to a complex rollout / backwards compatibility problem as well as the likelihood that workarounds to restore the old behavior would become prevalent, thus rendering the whole change ineffective in any case.

The change then is to keep the default producing behavior, but to finally make the non-mutating behavior of scalars a reality for collections as well, via the addition of additional mechanics in the collection system. When accessing the empty attribute, the new collection is created and associated with the state, however is not added to __dict__ until it is actually mutated:

>>> u1 = User()
>>> l1 = u1.addresses  # new list is created, associated with the state
>>> assert u1.addresses is l1  # you get the same list each time you access it
>>> assert "addresses" not in u1.__dict__  # but it won't go into __dict__ until it's mutated
>>> from sqlalchemy import inspect
>>> inspect(u1).attrs.addresses.history
History(added=None, unchanged=None, deleted=None)

When the list is changed, then it becomes part of the tracked changes to be persisted to the database:

>>> l1.append(Address())
>>> assert "addresses" in u1.__dict__
>>> inspect(u1).attrs.addresses.history
History(added=[<__main__.Address object at 0x7f49b725eda0>], unchanged=[], deleted=[])

This change is expected to have nearly no impact on existing applications in any way, except that it has been observed that some applications may be relying upon the implicit assignment of this collection, such as to assert that the object contains certain values based on its __dict__:

>>> u1 = User()
>>> u1.addresses
# this will now fail, would pass before
>>> assert {k: v for k, v in u1.__dict__.items() if not k.startswith("_")} == {"addresses": []}

or to ensure that the collection won’t require a lazy load to proceed, the (admittedly awkward) code below will now also fail:

>>> u1 = User()
>>> u1.addresses
>>> s.add(u1)
>>> s.flush()
>>> s.close()
>>> u1.addresses  # <-- will fail, .addresses is not loaded and object is detached

Applications that rely upon the implicit mutating behavior of collections will need to be changed so that they assign the desired collection explicitly:

>>> u1.addresses = []


The “New instance conflicts with existing identity” error is now a warning

SQLAlchemy has always had logic to detect when an object in the Session to be inserted has the same primary key as an object that is already present:

class Product(Base):
    __tablename__ = 'product'

    id = Column(Integer, primary_key=True)

session = Session(engine)

# add Product with primary key 1

# add another Product with same primary key
s.commit()  # <-- will raise FlushError

The change is that the FlushError is altered to be only a warning:

sqlalchemy/orm/ SAWarning: New instance <Product at 0x7f1ff65e0ba8> with identity key (<class '__main__.Product'>, (1,), None) conflicts with persistent instance <Product at 0x7f1ff60a4550>

Subsequent to that, the condition will attempt to insert the row into the database which will emit IntegrityError, which is the same error that would be raised if the primary key identity was not already present in the Session:

sqlalchemy.exc.IntegrityError: (sqlite3.IntegrityError) UNIQUE constraint failed:

The rationale is to allow code that is using IntegrityError to catch duplicates to function regardless of the existing state of the Session, as is often done using savepoints:

# add another Product with same primary key
    with session.begin_nested():
except exc.IntegrityError:
    print("row already exists")

The above logic was not fully feasible earlier, as in the case that the Product object with the existing identity were already in the Session, the code would also have to catch FlushError, which additionally is not filtered for the specific condition of integrity issues. With the change, the above block behaves consistently with the exception of the warning also being emitted.

Since the logic in question deals with the primary key, all databases emit an integrity error in the case of primary key conflicts on INSERT. The case where an error would not be raised, that would have earlier, is the extremely unusual scenario of a mapping that defines a primary key on the mapped selectable that is more restrictive than what is actually configured in the database schema, such as when mapping to joins of tables or when defining additional columns as part of a composite primary key that is not actually constrained in the database schema. However, these situations also work more consistently in that the INSERT would theoretically proceed whether or not the existing identity were still in the database. The warning can also be configured to raise an exception using the Python warnings filter.


Behavior Changes - Core

SELECT objects and derived FROM clauses allow for duplicate columns and column labels

This change allows that the select() construct now allows for duplicate column labels as well as duplicate column objects themselves, so that result tuples are organized and ordered in the identical way in that the columns were selected. The ORM Query already works this way, so this change allows for greater cross-compatibility between the two, which is a key goal of the 2.0 transition:

>>> from sqlalchemy import column, select
>>> c1, c2, c3, c4 = column('c1'), column('c2'), column('c3'), column('c4')
>>> stmt = select([c1, c2, c3.label('c2'), c2, c4])
>>> print(stmt)
SELECT c1, c2, c3 AS c2, c2, c4

To support this change, the ColumnCollection used by SelectBase as well as for derived FROM clauses such as subqueries also support duplicate columns; this includes the new SelectBase.selected_columns attribute, the deprecated SelectBase.c attribute, as well as the FromClause.c attribute seen on constructs such as Subquery and Alias:

>>> list(stmt.selected_columns)
    <sqlalchemy.sql.elements.ColumnClause at 0x7fa540bcca20; c1>,
    <sqlalchemy.sql.elements.ColumnClause at 0x7fa540bcc9e8; c2>,
    <sqlalchemy.sql.elements.Label object at 0x7fa540b3e2e8>,
    <sqlalchemy.sql.elements.ColumnClause at 0x7fa540bcc9e8; c2>,
    <sqlalchemy.sql.elements.ColumnClause at 0x7fa540897048; c4>

>>> print(stmt.subquery().select())
SELECT anon_1.c1, anon_1.c2, anon_1.c2, anon_1.c2, anon_1.c4
FROM (SELECT c1, c2, c3 AS c2, c2, c4) AS anon_1

ColumnCollection also allows access by integer index to support when the string “key” is ambiguous:

>>> stmt.selected_columns[2]
<sqlalchemy.sql.elements.Label object at 0x7fa540b3e2e8>

To suit the use of ColumnCollection in objects such as Table and PrimaryKeyConstraint, the old “deduplicating” behavior which is more critical for these objects is preserved in a new class DedupeColumnCollection.

The change includes that the familiar warning "Column %r on table %r being replaced by %r, which has the same key.  Consider use_labels for select() statements." is removed; the Select.apply_labels() is still available and is still used by the ORM for all SELECT operations, however it does not imply deduplication of column objects, although it does imply deduplication of implicitly generated labels:

>>> from sqlalchemy import table
>>> user = table('user', column('id'), column('name'))
>>> stmt = select([,,]).apply_labels()
>>> print(stmt)
SELECT "user".id AS user_id, "user".name AS user_name, "user".id AS id_1
FROM "user"

Finally, the change makes it easier to create UNION and other CompoundSelect objects, by ensuring that the number and position of columns in a SELECT statement mirrors what was given, in a use case such as:

>>> s1 = select([user,])
>>> s2 = select([c1, c2, c3])
>>> from sqlalchemy import union
>>> u = union(s1, s2)
>>> print(u)
SELECT "user".id, "user".name, "user".id
FROM "user" UNION SELECT c1, c2, c3


Improved column labeling for simple column expressions using CAST or similar

A user pointed out that the PostgreSQL database has a convenient behavior when using functions like CAST against a named column, in that the result column name is named the same as the inner expression:


(1 row)

This allows one to apply CAST to table columns while not losing the column name (above using the name "data") in the result row. Compare to databases such as MySQL/MariaDB, as well as most others, where the column name is taken from the full SQL expression and is not very portable:

MariaDB [test]> SELECT CAST(data AS CHAR) FROM foo;
| CAST(data AS CHAR) |
| 5                  |
1 row in set (0.003 sec)

In SQLAlchemy Core expressions, we never deal with a raw generated name like the above, as SQLAlchemy applies auto-labeling to expressions like these, which are up until now always a so-called “anonymous” expression:

>>> print(select([cast(, String)]))
SELECT CAST( AS VARCHAR) AS anon_1     # old behavior
FROM foo

These anonymous expressions were necessary as SQLAlchemy’s ResultProxy made heavy use of result column names in order to match up datatypes, such as the String datatype which used to have result-row-processing behavior, to the correct column, so most importantly the names had to be both easy to determine in a database-agnostic manner as well as unique in all cases. In SQLAlchemy 1.0 as part of #918, this reliance on named columns in result rows (specifically the cursor.description element of the PEP-249 cursor) was scaled back to not be necessary for most Core SELECT constructs; in release 1.4, the system overall is becoming more comfortable with SELECT statements that have duplicate column or label names such as in SELECT objects and derived FROM clauses allow for duplicate columns and column labels. So we now emulate PostgreSQL’s reasonable behavior for simple modifications to a single column, most prominently with CAST:

FROM foo

For CAST against expressions that don’t have a name, the previous logic is used to generate the usual “anonymous” labels:

>>> print(select([cast('hi there,' +, String)]))
SELECT CAST(:data_1 + AS VARCHAR) AS anon_1
FROM foo

A cast() against a Label, despite having to omit the label expression as these don’t render inside of a CAST, will nonetheless make use of the given name:

>>> print(select([cast(('hi there,' +'hello_data'), String)]))
SELECT CAST(:data_1 + AS VARCHAR) AS hello_data
FROM foo

And of course as was always the case, Label can be applied to the expression on the outside to apply an “AS <name>” label directly:

>>> print(select([cast(('hi there,' +, String).label('hello_data')]))
SELECT CAST(:data_1 + AS VARCHAR) AS hello_data
FROM foo


New “post compile” bound parameters used for LIMIT/OFFSET in Oracle, SQL Server

A major goal of the 1.4 series is to establish that all Core SQL constructs are completely cacheable, meaning that a particular Compiled structure will produce an identical SQL string regardless of any SQL parameters used with it, which notably includes those used to specify the LIMIT and OFFSET values, typically used for pagination and “top N” style results.

While SQLAlchemy has used bound parameters for LIMIT/OFFSET schemes for many years, a few outliers remained where such parameters were not allowed, including a SQL Server “TOP N” statement, such as:

SELECT TOP 5, FROM mytable

as well as with Oracle, where the FIRST_ROWS() hint (which SQLAlchemy will use if the optimize_limits=True parameter is passed to create_engine() with an Oracle URL) does not allow them, but also that using bound parameters with ROWNUM comparisons has been reported as producing slower query plans:

    SELECT /*+ FIRST_ROWS(5) */ AS id, AS data,
    ROWNUM AS ora_rn FROM (
        SELECT, FROM mytable
    ) anon_2
    WHERE ROWNUM <= :param_1
) anon_1 WHERE ora_rn > :param_2

In order to allow for all statements to be unconditionally cacheable at the compilation level, a new form of bound parameter called a “post compile” parameter has been added, which makes use of the same mechanism as that of “expanding IN parameters”. This is a bindparam() that behaves identically to any other bound parameter except that parameter value will be rendered literally into the SQL string before sending it to the DBAPI cursor.execute() method. The new parameter is used internally by the SQL Server and Oracle dialects, so that the drivers receive the literal rendered value but the rest of SQLAlchemy can still consider this as a bound parameter. The above two statements when stringified using str(statement.compile(dialect=<dialect>)) now look like:



    SELECT /*+ FIRST_ROWS([POSTCOMPILE__ora_frow_1]) */ AS id, AS data,
    ROWNUM AS ora_rn FROM (
        SELECT, FROM mytable
    ) anon_2
) anon_1 WHERE ora_rn > [POSTCOMPILE_param_2]

The [POSTCOMPILE_<param>] format is also what is seen when an “expanding IN” is used.

When viewing the SQL logging output, the final form of the statement will be seen:

    ROWNUM AS ora_rn FROM (
        SELECT AS id, AS data FROM mytable
    ) anon_2
) anon_1 WHERE ora_rn > 3

The “post compile parameter” feature is exposed as public API through the bindparam.literal_execute parameter, however is currently not intended for general use. The literal values are rendered using the TypeEngine.literal_processor() of the underlying datatype, which in SQLAlchemy has extremely limited scope, supporting only integers and simple string values.


Connection-level transactions can now be inactive based on subtransaction

A Connection now includes the behavior where a Transaction can be made inactive due to a rollback on an inner transaction, however the Transaction will not clear until it is itself rolled back.

This is essentially a new error condition which will disallow statement executions to proceed on a Connection if an inner “sub” transaction has been rolled back. The behavior works very similarly to that of the ORM Session, where if an outer transaction has been begun, it needs to be rolled back to clear the invalid transaction; this behavior is described in “This Session’s transaction has been rolled back due to a previous exception during flush.” (or similar)

While the Connection has had a less strict behavioral pattern than the Session, this change was made as it helps to identify when a subtransaction has rolled back the DBAPI transaction, however the external code isn’t aware of this and attempts to continue proceeding, which in fact runs operations on a new transaction. The “test harness” pattern described at Joining a Session into an External Transaction (such as for test suites) is the common place for this to occur.

The new behavior is described in the errors page at This connection is on an inactive transaction. Please rollback() fully before proceeding.

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