What’s New in SQLAlchemy 1.2?

About this Document

This document describes changes between SQLAlchemy version 1.1 and SQLAlchemy version 1.2.


This guide introduces what’s new in SQLAlchemy version 1.2, and also documents changes which affect users migrating their applications from the 1.1 series of SQLAlchemy to 1.2.

Please carefully review the sections on behavioral changes for potentially backwards-incompatible changes in behavior.

Platform Support

Targeting Python 2.7 and Up

SQLAlchemy 1.2 now moves the minimum Python version to 2.7, no longer supporting 2.6. New language features are expected to be merged into the 1.2 series that were not supported in Python 2.6. For Python 3 support, SQLAlchemy is currently tested on versions 3.5 and 3.6.

New Features and Improvements - ORM

“Baked” loading now the default for lazy loads

The sqlalchemy.ext.baked extension, first introduced in the 1.0 series, allows for the construction of a so-called BakedQuery object, which is an object that generates a Query object in conjunction with a cache key representing the structure of the query; this cache key is then linked to the resulting string SQL statement so that subsequent use of another BakedQuery with the same structure will bypass all the overhead of building the Query object, building the core select() object within, as well as the compilation of the select() into a string, cutting out well the majority of function call overhead normally associated with constructing and emitting an ORM Query object.

The BakedQuery is now used by default by the ORM when it generates a “lazy” query for the lazy load of a relationship() construct, e.g. that of the default lazy="select" relationship loader strategy. This will allow for a significant reduction in function calls within the scope of an application’s use of lazy load queries to load collections and related objects. Previously, this feature was available in 1.0 and 1.1 through the use of a global API method or by using the baked_select strategy, it’s now the only implementation for this behavior. The feature has also been improved such that the caching can still take place for objects that have additional loader options in effect subsequent to the lazy load.

The caching behavior can be disabled on a per-relationship basis using the relationship.bake_queries flag, which is available for very unusual cases, such as a relationship that uses a custom Query implementation that’s not compatible with caching.


New “selectin” eager loading, loads all collections at once using IN

A new eager loader called “selectin” loading is added, which in many ways is similar to “subquery” loading, however produces a simpler SQL statement that is cacheable as well as more efficient.

Given a query as below:

q = (

The SQL produced would be the query against User followed by the subqueryload for User.addresses (note the parameters are also listed):

SELECT users.id AS users_id, users.name AS users_name
FROM users
WHERE users.name LIKE ?

SELECT addresses.id AS addresses_id,
       addresses.user_id AS addresses_user_id,
       addresses.email_address AS addresses_email_address,
       anon_1.users_id AS anon_1_users_id
FROM (SELECT users.id AS users_id
FROM users
WHERE users.name LIKE ?) AS anon_1
JOIN addresses ON anon_1.users_id = addresses.user_id
ORDER BY anon_1.users_id

With “selectin” loading, we instead get a SELECT that refers to the actual primary key values loaded in the parent query:

q = (


SELECT users.id AS users_id, users.name AS users_name
FROM users
WHERE users.name LIKE ?

SELECT users_1.id AS users_1_id,
       addresses.id AS addresses_id,
       addresses.user_id AS addresses_user_id,
       addresses.email_address AS addresses_email_address
FROM users AS users_1
JOIN addresses ON users_1.id = addresses.user_id
WHERE users_1.id IN (?, ?)
ORDER BY users_1.id
(1, 3)

The above SELECT statement includes these advantages:

  • It doesn’t use a subquery, just an INNER JOIN, meaning it will perform much better on a database like MySQL that doesn’t like subqueries

  • Its structure is independent of the original query; in conjunction with the new expanding IN parameter system we can in most cases use the “baked” query to cache the string SQL, reducing per-query overhead significantly

  • Because the query only fetches for a given list of primary key identifiers, “selectin” loading is potentially compatible with Query.yield_per() to operate on chunks of a SELECT result at a time, provided that the database driver allows for multiple, simultaneous cursors (SQLite, PostgreSQL; not MySQL drivers or SQL Server ODBC drivers). Neither joined eager loading nor subquery eager loading are compatible with Query.yield_per().

The disadvantages of selectin eager loading are potentially large SQL queries, with large lists of IN parameters. The list of IN parameters themselves are chunked in groups of 500, so a result set of more than 500 lead objects will have more additional “SELECT IN” queries following. Also, support for composite primary keys depends on the database’s ability to use tuples with IN, e.g. (table.column_one, table_column_two) IN ((?, ?), (?, ?) (?, ?)). Currently, PostgreSQL and MySQL are known to be compatible with this syntax, SQLite is not.


“selectin” polymorphic loading, loads subclasses using separate IN queries

Along similar lines as the “selectin” relationship loading feature just described at New “selectin” eager loading, loads all collections at once using IN is “selectin” polymorphic loading. This is a polymorphic loading feature tailored primarily towards joined eager loading that allows the loading of the base entity to proceed with a simple SELECT statement, but then the attributes of the additional subclasses are loaded with additional SELECT statements:

>>> from sqlalchemy.orm import selectin_polymorphic

>>> query = session.query(Employee).options(
...     selectin_polymorphic(Employee, [Manager, Engineer])
... )

>>> query.all()
SELECT employee.id AS employee_id, employee.name AS employee_name, employee.type AS employee_type FROM employee () SELECT engineer.id AS engineer_id, employee.id AS employee_id, employee.type AS employee_type, engineer.engineer_name AS engineer_engineer_name FROM employee JOIN engineer ON employee.id = engineer.id WHERE employee.id IN (?, ?) ORDER BY employee.id (1, 2) SELECT manager.id AS manager_id, employee.id AS employee_id, employee.type AS employee_type, manager.manager_name AS manager_manager_name FROM employee JOIN manager ON employee.id = manager.id WHERE employee.id IN (?) ORDER BY employee.id (3,)


ORM attributes that can receive ad-hoc SQL expressions

A new ORM attribute type query_expression() is added which is similar to deferred(), except its SQL expression is determined at query time using a new option with_expression(); if not specified, the attribute defaults to None:

from sqlalchemy.orm import query_expression
from sqlalchemy.orm import with_expression

class A(Base):
    __tablename__ = "a"
    id = Column(Integer, primary_key=True)
    x = Column(Integer)
    y = Column(Integer)

    # will be None normally...
    expr = query_expression()

# but let's give it x + y
a1 = session.query(A).options(with_expression(A.expr, A.x + A.y)).first()


ORM Support of multiple-table deletes

The ORM Query.delete() method supports multiple-table criteria for DELETE, as introduced in Multiple-table criteria support for DELETE. The feature works in the same manner as multiple-table criteria for UPDATE, first introduced in 0.8 and described at Query.update() supports UPDATE..FROM.

Below, we emit a DELETE against SomeEntity, adding a FROM clause (or equivalent, depending on backend) against SomeOtherEntity:

query(SomeEntity).filter(SomeEntity.id == SomeOtherEntity.id).filter(
    SomeOtherEntity.foo == "bar"


Support for bulk updates of hybrids, composites

Both hybrid attributes (e.g. sqlalchemy.ext.hybrid) as well as composite attributes (Composite Column Types) now support being used in the SET clause of an UPDATE statement when using Query.update().

For hybrids, simple expressions can be used directly, or the new decorator hybrid_property.update_expression() can be used to break a value into multiple columns/expressions:

class Person(Base):
    # ...

    first_name = Column(String(10))
    last_name = Column(String(10))

    def name(self):
        return self.first_name + " " + self.last_name

    def name(cls):
        return func.concat(cls.first_name, " ", cls.last_name)

    def name(cls, value):
        f, l = value.split(" ", 1)
        return [(cls.first_name, f), (cls.last_name, l)]

Above, an UPDATE can be rendered using:

session.query(Person).filter(Person.id == 5).update({Person.name: "Dr. No"})

Similar functionality is available for composites, where composite values will be broken out into their individual columns for bulk UPDATE:

session.query(Vertex).update({Edge.start: Point(3, 4)})

Hybrid attributes support reuse among subclasses, redefinition of @getter

The sqlalchemy.ext.hybrid.hybrid_property class now supports calling mutators like @setter, @expression etc. multiple times across subclasses, and now provides a @getter mutator, so that a particular hybrid can be repurposed across subclasses or other classes. This now is similar to the behavior of @property in standard Python:

class FirstNameOnly(Base):
    # ...

    first_name = Column(String)

    def name(self):
        return self.first_name

    def name(self, value):
        self.first_name = value

class FirstNameLastName(FirstNameOnly):
    # ...

    last_name = Column(String)

    def name(self):
        return self.first_name + " " + self.last_name

    def name(self, value):
        self.first_name, self.last_name = value.split(" ", maxsplit=1)

    def name(cls):
        return func.concat(cls.first_name, " ", cls.last_name)

Above, the FirstNameOnly.name hybrid is referenced by the FirstNameLastName subclass in order to repurpose it specifically to the new subclass. This is achieved by copying the hybrid object to a new one within each call to @getter, @setter, as well as in all other mutator methods like @expression, leaving the previous hybrid’s definition intact. Previously, methods like @setter would modify the existing hybrid in-place, interfering with the definition on the superclass.


Be sure to read the documentation at Reusing Hybrid Properties across Subclasses for important notes regarding how to override hybrid_property.expression() and hybrid_property.comparator(), as a special qualifier hybrid_property.overrides may be necessary to avoid name conflicts with QueryableAttribute in some cases.


This change in @hybrid_property implies that when adding setters and other state to a @hybrid_property, the methods must retain the name of the original hybrid, else the new hybrid with the additional state will be present on the class as the non-matching name. This is the same behavior as that of the @property construct that is part of standard Python:

class FirstNameOnly(Base):
    def name(self):
        return self.first_name

    # WRONG - will raise AttributeError: can't set attribute when
    # assigning to .name
    def _set_name(self, value):
        self.first_name = value

class FirstNameOnly(Base):
    def name(self):
        return self.first_name

    # CORRECT - note regular Python @property works the same way
    def name(self, value):
        self.first_name = value



New bulk_replace event

To suit the validation use case described in A @validates method receives all values on bulk-collection set before comparison, a new AttributeEvents.bulk_replace() method is added, which is called in conjunction with the AttributeEvents.append() and AttributeEvents.remove() events. “bulk_replace” is called before “append” and “remove” so that the collection can be modified ahead of comparison to the existing collection. After that, individual items are appended to a new target collection, firing off the “append” event for items new to the collection, as was the previous behavior. Below illustrates both “bulk_replace” and “append” at the same time, including that “append” will receive an object already handled by “bulk_replace” if collection assignment is used. A new symbol attributes.OP_BULK_REPLACE may be used to determine if this “append” event is the second part of a bulk replace:

from sqlalchemy.orm.attributes import OP_BULK_REPLACE

@event.listens_for(SomeObject.collection, "bulk_replace")
def process_collection(target, values, initiator):
    values[:] = [_make_value(value) for value in values]

@event.listens_for(SomeObject.collection, "append", retval=True)
def process_collection(target, value, initiator):
    # make sure bulk_replace didn't already do it
    if initiator is None or initiator.op is not OP_BULK_REPLACE:
        return _make_value(value)
        return value


New “modified” event handler for sqlalchemy.ext.mutable

A new event handler AttributeEvents.modified() is added, which is triggered corresponding to calls to the flag_modified() method, which is normally called from the sqlalchemy.ext.mutable extension:

from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.ext.mutable import MutableDict
from sqlalchemy import event

Base = declarative_base()

class MyDataClass(Base):
    __tablename__ = "my_data"
    id = Column(Integer, primary_key=True)
    data = Column(MutableDict.as_mutable(JSONEncodedDict))

@event.listens_for(MyDataClass.data, "modified")
def modified_json(instance):
    print("json value modified:", instance.data)

Above, the event handler will be triggered when an in-place change to the .data dictionary occurs.


Added “for update” arguments to Session.refresh

Added new argument Session.refresh.with_for_update to the Session.refresh() method. When the Query.with_lockmode() method were deprecated in favor of Query.with_for_update(), the Session.refresh() method was never updated to reflect the new option:

session.refresh(some_object, with_for_update=True)

The Session.refresh.with_for_update argument accepts a dictionary of options that will be passed as the same arguments which are sent to Query.with_for_update():

session.refresh(some_objects, with_for_update={"read": True})

The new parameter supersedes the Session.refresh.lockmode parameter.


In-place mutation operators work for MutableSet, MutableList

Implemented the in-place mutation operators __ior__, __iand__, __ixor__ and __isub__ for MutableSet and __iadd__ for MutableList. While these methods would successfully update the collection previously, they would not correctly fire off change events. The operators mutate the collection as before but additionally emit the correct change event so that the change becomes part of the next flush process:

model = session.query(MyModel).first()
model.json_set &= {1, 3}


AssociationProxy any(), has(), contains() work with chained association proxies

The AssociationProxy.any(), AssociationProxy.has() and AssociationProxy.contains() comparison methods now support linkage to an attribute that is itself also an AssociationProxy, recursively. Below, A.b_values is an association proxy that links to AtoB.bvalue, which is itself an association proxy onto B:

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

    b_values = association_proxy("atob", "b_value")
    c_values = association_proxy("atob", "c_value")

class B(Base):
    __tablename__ = "b"
    id = Column(Integer, primary_key=True)
    a_id = Column(ForeignKey("a.id"))
    value = Column(String)

    c = relationship("C")

class C(Base):
    __tablename__ = "c"
    id = Column(Integer, primary_key=True)
    b_id = Column(ForeignKey("b.id"))
    value = Column(String)

class AtoB(Base):
    __tablename__ = "atob"

    a_id = Column(ForeignKey("a.id"), primary_key=True)
    b_id = Column(ForeignKey("b.id"), primary_key=True)

    a = relationship("A", backref="atob")
    b = relationship("B", backref="atob")

    b_value = association_proxy("b", "value")
    c_value = association_proxy("b", "c")

We can query on A.b_values using AssociationProxy.contains() to query across the two proxies A.b_values, AtoB.b_value:

>>> s.query(A).filter(A.b_values.contains("hi")).all()
SELECT a.id AS a_id FROM a WHERE EXISTS (SELECT 1 FROM atob WHERE a.id = atob.a_id AND (EXISTS (SELECT 1 FROM b WHERE b.id = atob.b_id AND b.value = :value_1)))

Similarly, we can query on A.c_values using AssociationProxy.any() to query across the two proxies A.c_values, AtoB.c_value:

>>> s.query(A).filter(A.c_values.any(value="x")).all()
SELECT a.id AS a_id FROM a WHERE EXISTS (SELECT 1 FROM atob WHERE a.id = atob.a_id AND (EXISTS (SELECT 1 FROM b WHERE b.id = atob.b_id AND (EXISTS (SELECT 1 FROM c WHERE b.id = c.b_id AND c.value = :value_1)))))


Identity key enhancements to support sharding

The identity key structure used by the ORM now contains an additional member, so that two identical primary keys that originate from different contexts can co-exist within the same identity map.

The example at Horizontal Sharding has been updated to illustrate this behavior. The example shows a sharded class WeatherLocation that refers to a dependent WeatherReport object, where the WeatherReport class is mapped to a table that stores a simple integer primary key. Two WeatherReport objects from different databases may have the same primary key value. The example now illustrates that a new identity_token field tracks this difference so that the two objects can co-exist in the same identity map:

tokyo = WeatherLocation("Asia", "Tokyo")
newyork = WeatherLocation("North America", "New York")


sess = create_session()

sess.add_all([tokyo, newyork, quito])


# the Report class uses a simple integer primary key.  So across two
# databases, a primary key will be repeated.  The "identity_token" tracks
# in memory that these two identical primary keys are local to different
# databases.

newyork_report = newyork.reports[0]
tokyo_report = tokyo.reports[0]

assert inspect(newyork_report).identity_key == (Report, (1,), "north_america")
assert inspect(tokyo_report).identity_key == (Report, (1,), "asia")

# the token representing the originating shard is also available directly

assert inspect(newyork_report).identity_token == "north_america"
assert inspect(tokyo_report).identity_token == "asia"


New Features and Improvements - Core

Boolean datatype now enforces strict True/False/None values

In version 1.1, the change described in Non-native boolean integer values coerced to zero/one/None in all cases produced an unintended side effect of altering the way Boolean behaves when presented with a non-integer value, such as a string. In particular, the string value "0", which would previously result in the value False being generated, would now produce True. Making matters worse, the change in behavior was only for some backends and not others, meaning code that sends string "0" values to Boolean would break inconsistently across backends.

The ultimate solution to this problem is that string values are not supported with Boolean, so in 1.2 a hard TypeError is raised if a non-integer / True/False/None value is passed. Additionally, only the integer values 0 and 1 are accepted.

To accommodate for applications that wish to have more liberal interpretation of boolean values, the TypeDecorator should be used. Below illustrates a recipe that will allow for the “liberal” behavior of the pre-1.1 Boolean datatype:

from sqlalchemy import Boolean
from sqlalchemy import TypeDecorator

class LiberalBoolean(TypeDecorator):
    impl = Boolean

    def process_bind_param(self, value, dialect):
        if value is not None:
            value = bool(int(value))
        return value


Pessimistic disconnection detection added to the connection pool

The connection pool documentation has long featured a recipe for using the ConnectionEvents.engine_connect() engine event to emit a simple statement on a checked-out connection to test it for liveness. The functionality of this recipe has now been added into the connection pool itself, when used in conjunction with an appropriate dialect. Using the new parameter create_engine.pool_pre_ping, each connection checked out will be tested for freshness before being returned:

engine = create_engine("mysql+pymysql://", pool_pre_ping=True)

While the “pre-ping” approach adds a small amount of latency to the connection pool checkout, for a typical application that is transactionally-oriented (which includes most ORM applications), this overhead is minimal, and eliminates the problem of acquiring a stale connection that will raise an error, requiring that the application either abandon or retry the operation.

The feature does not accommodate for connections dropped within an ongoing transaction or SQL operation. If an application must recover from these as well, it would need to employ its own operation retry logic to anticipate these errors.


The IN / NOT IN operator’s empty collection behavior is now configurable; default expression simplified

An expression such as column.in_([]), which is assumed to be false, now produces the expression 1 != 1 by default, instead of column != column. This will change the result of a query that is comparing a SQL expression or column that evaluates to NULL when compared to an empty set, producing a boolean value false or true (for NOT IN) rather than NULL. The warning that would emit under this condition is also removed. The old behavior is available using the create_engine.empty_in_strategy parameter to create_engine().

In SQL, the IN and NOT IN operators do not support comparison to a collection of values that is explicitly empty; meaning, this syntax is illegal:

mycolumn IN ()

To work around this, SQLAlchemy and other database libraries detect this condition and render an alternative expression that evaluates to false, or in the case of NOT IN, to true, based on the theory that “col IN ()” is always false since nothing is in “the empty set”. Typically, in order to produce a false/true constant that is portable across databases and works in the context of the WHERE clause, a simple tautology such as 1 != 1 is used to evaluate to false and 1 = 1 to evaluate to true (a simple constant “0” or “1” often does not work as the target of a WHERE clause).

SQLAlchemy in its early days began with this approach as well, but soon it was theorized that the SQL expression column IN () would not evaluate to false if the “column” were NULL; instead, the expression would produce NULL, since “NULL” means “unknown”, and comparisons to NULL in SQL usually produce NULL.

To simulate this result, SQLAlchemy changed from using 1 != 1 to instead use th expression expr != expr for empty “IN” and expr = expr for empty “NOT IN”; that is, instead of using a fixed value we use the actual left-hand side of the expression. If the left-hand side of the expression passed evaluates to NULL, then the comparison overall also gets the NULL result instead of false or true.

Unfortunately, users eventually complained that this expression had a very severe performance impact on some query planners. At that point, a warning was added when an empty IN expression was encountered, favoring that SQLAlchemy continues to be “correct” and urging users to avoid code that generates empty IN predicates in general, since typically they can be safely omitted. However, this is of course burdensome in the case of queries that are built up dynamically from input variables, where an incoming set of values might be empty.

In recent months, the original assumptions of this decision have been questioned. The notion that the expression “NULL IN ()” should return NULL was only theoretical, and could not be tested since databases don’t support that syntax. However, as it turns out, you can in fact ask a relational database what value it would return for “NULL IN ()” by simulating the empty set as follows:


With the above test, we see that the databases themselves can’t agree on the answer. PostgreSQL, considered by most to be the most “correct” database, returns False; because even though “NULL” represents “unknown”, the “empty set” means nothing is present, including all unknown values. On the other hand, MySQL and MariaDB return NULL for the above expression, defaulting to the more common behavior of “all comparisons to NULL return NULL”.

SQLAlchemy’s SQL architecture is more sophisticated than it was when this design decision was first made, so we can now allow either behavior to be invoked at SQL string compilation time. Previously, the conversion to a comparison expression were done at construction time, that is, the moment the ColumnOperators.in_() or ColumnOperators.notin_() operators were invoked. With the compilation-time behavior, the dialect itself can be instructed to invoke either approach, that is, the “static” 1 != 1 comparison or the “dynamic” expr != expr comparison. The default has been changed to be the “static” comparison, since this agrees with the behavior that PostgreSQL would have in any case and this is also what the vast majority of users prefer. This will change the result of a query that is comparing a null expression to the empty set, particularly one that is querying for the negation where(~null_expr.in_([])), since this now evaluates to true and not NULL.

The behavior can now be controlled using the flag create_engine.empty_in_strategy, which defaults to the "static" setting, but may also be set to "dynamic" or "dynamic_warn", where the "dynamic_warn" setting is equivalent to the previous behavior of emitting expr != expr as well as a performance warning. However, it is anticipated that most users will appreciate the “static” default.


Late-expanded IN parameter sets allow IN expressions with cached statements

Added a new kind of bindparam() called “expanding”. This is for use in IN expressions where the list of elements is rendered into individual bound parameters at statement execution time, rather than at statement compilation time. This allows both a single bound parameter name to be linked to an IN expression of multiple elements, as well as allows query caching to be used with IN expressions. The new feature allows the related features of “select in” loading and “polymorphic in” loading to make use of the baked query extension to reduce call overhead:

stmt = select([table]).where(table.c.col.in_(bindparam("foo", expanding=True)))
conn.execute(stmt, {"foo": [1, 2, 3]})

The feature should be regarded as experimental within the 1.2 series.


Flattened operator precedence for comparison operators

The operator precedence for operators like IN, LIKE, equals, IS, MATCH, and other comparison operators has been flattened into one level. This will have the effect of more parenthesization being generated when comparison operators are combined together, such as:

(column("q") == null()) != (column("y") == null())

Will now generate (q IS NULL) != (y IS NULL) rather than q IS NULL != y IS NULL.


Support for SQL Comments on Table, Column, includes DDL, reflection

The Core receives support for string comments associated with tables and columns. These are specified via the Table.comment and Column.comment arguments:

    Column("q", Integer, comment="the Q value"),
    comment="my Q table",

Above, DDL will be rendered appropriately upon table create to associate the above comments with the table/ column within the schema. When the above table is autoloaded or inspected with Inspector.get_columns(), the comments are included. The table comment is also available independently using the Inspector.get_table_comment() method.

Current backend support includes MySQL, PostgreSQL, and Oracle.


Multiple-table criteria support for DELETE

The Delete construct now supports multiple-table criteria, implemented for those backends which support it, currently these are PostgreSQL, MySQL and Microsoft SQL Server (support is also added to the currently non-working Sybase dialect). The feature works in the same was as that of multiple-table criteria for UPDATE, first introduced in the 0.7 and 0.8 series.

Given a statement as:

stmt = (
    .where(users.c.id == addresses.c.id)

The resulting SQL from the above statement on a PostgreSQL backend would render as:

DELETE FROM users USING addresses
WHERE users.id = addresses.id
AND (addresses.email_address LIKE %(email_address_1)s || '%%')


New “autoescape” option for startswith(), endswith()

The “autoescape” parameter is added to ColumnOperators.startswith(), ColumnOperators.endswith(), ColumnOperators.contains(). This parameter when set to True will automatically escape all occurrences of %, _ with an escape character, which defaults to a forwards slash /; occurrences of the escape character itself are also escaped. The forwards slash is used to avoid conflicts with settings like PostgreSQL’s standard_confirming_strings, whose default value changed as of PostgreSQL 9.1, and MySQL’s NO_BACKSLASH_ESCAPES settings. The existing “escape” parameter can now be used to change the autoescape character, if desired.


This feature has been changed as of 1.2.0 from its initial implementation in 1.2.0b2 such that autoescape is now passed as a boolean value, rather than a specific character to use as the escape character.

An expression such as:

>>> column("x").startswith("total%score", autoescape=True)

Renders as:

x LIKE :x_1 || '%' ESCAPE '/'

Where the value of the parameter “x_1” is 'total/%score'.

Similarly, an expression that has backslashes:

>>> column("x").startswith("total/score", autoescape=True)

Will render the same way, with the value of the parameter “x_1” as 'total//score'.


Stronger typing added to “float” datatypes

A series of changes allow for use of the Float datatype to more strongly link itself to Python floating point values, instead of the more generic Numeric. The changes are mostly related to ensuring that Python floating point values are not erroneously coerced to Decimal(), and are coerced to float if needed, on the result side, if the application is working with plain floats.

  • A plain Python “float” value passed to a SQL expression will now be pulled into a literal parameter with the type Float; previously, the type was Numeric, with the default “asdecimal=True” flag, which meant the result type would coerce to Decimal(). In particular, this would emit a confusing warning on SQLite:

    float_value = connection.scalar(
        select([literal(4.56)])  # the "BindParameter" will now be
        # Float, not Numeric(asdecimal=True)
  • Math operations between Numeric, Float, and Integer will now preserve the Numeric or Float type in the resulting expression’s type, including the asdecimal flag as well as if the type should be Float:

    # asdecimal flag is maintained
    expr = column("a", Integer) * column("b", Numeric(asdecimal=False))
    assert expr.type.asdecimal == False
    # Float subclass of Numeric is maintained
    expr = column("a", Integer) * column("b", Float())
    assert isinstance(expr.type, Float)
  • The Float datatype will apply the float() processor to result values unconditionally if the DBAPI is known to support native Decimal() mode. Some backends do not always guarantee that a floating point number comes back as plain float and not precision numeric such as MySQL.





All three of GROUPING SETS, CUBE, ROLLUP are available via the func namespace. In the case of CUBE and ROLLUP, these functions already work in previous versions, however for GROUPING SETS, a placeholder is added to the compiler to allow for the space. All three functions are named in the documentation now:

>>> from sqlalchemy import select, table, column, func, tuple_
>>> t = table("t", column("value"), column("x"), column("y"), column("z"), column("q"))
>>> stmt = select([func.sum(t.c.value)]).group_by(
...     func.grouping_sets(
...         tuple_(t.c.x, t.c.y),
...         tuple_(t.c.z, t.c.q),
...     )
... )
>>> print(stmt)
SELECT sum(t.value) AS sum_1 FROM t GROUP BY GROUPING SETS((t.x, t.y), (t.z, t.q))


Parameter helper for multi-valued INSERT with contextual default generator

A default generation function, e.g. that described at Context-Sensitive Default Functions, can look at the current parameters relevant to the statement via the DefaultExecutionContext.current_parameters attribute. However, in the case of a Insert construct that specifies multiple VALUES clauses via the Insert.values() method, the user-defined function is called multiple times, once for each parameter set, however there was no way to know which subset of keys in DefaultExecutionContext.current_parameters apply to that column. A new function DefaultExecutionContext.get_current_parameters() is added, which includes a keyword argument DefaultExecutionContext.get_current_parameters.isolate_multiinsert_groups defaulting to True, which performs the extra work of delivering a sub-dictionary of DefaultExecutionContext.current_parameters which has the names localized to the current VALUES clause being processed:

def mydefault(context):
    return context.get_current_parameters()["counter"] + 12

mytable = Table(
    Column("counter", Integer),
    Column("counter_plus_twelve", Integer, default=mydefault, onupdate=mydefault),

stmt = mytable.insert().values([{"counter": 5}, {"counter": 18}, {"counter": 20}])



Key Behavioral Changes - ORM

The after_rollback() Session event now emits before the expiration of objects

The SessionEvents.after_rollback() event now has access to the attribute state of objects before their state has been expired (e.g. the “snapshot removal”). This allows the event to be consistent with the behavior of the SessionEvents.after_commit() event which also emits before the “snapshot” has been removed:

sess = Session()

user = sess.query(User).filter_by(name="x").first()

@event.listens_for(sess, "after_rollback")
def after_rollback(session):
    # 'user.name' is now present, assuming it was already
    # loaded.  previously this would raise upon trying
    # to emit a lazy load.
    print("user name: %s" % user.name)

@event.listens_for(sess, "after_commit")
def after_commit(session):
    # 'user.name' is present, assuming it was already
    # loaded.  this is the existing behavior.
    print("user name: %s" % user.name)

if should_rollback:

Note that the Session will still disallow SQL from being emitted within this event; meaning that unloaded attributes will still not be able to load within the scope of the event.


Fixed issue involving single-table inheritance with select_from()

The Query.select_from() method now honors the single-table inheritance column discriminator when generating SQL; previously, only the expressions in the query column list would be taken into account.

Supposing Manager is a subclass of Employee. A query like the following:


Would generate SQL as:

SELECT employee.id FROM employee WHERE employee.type IN ('manager')

However, if Manager were only specified by Query.select_from() and not in the columns list, the discriminator would not be added:


would generate:

SELECT count(1) FROM employee

With the fix, Query.select_from() now works correctly and we get:

SELECT count(1) FROM employee WHERE employee.type IN ('manager')

Applications that may have been working around this by supplying the WHERE clause manually may need to be adjusted.


Previous collection is no longer mutated upon replacement

The ORM emits events whenever the members of a mapped collection change. In the case of assigning a collection to an attribute that would replace the previous collection, a side effect of this was that the collection being replaced would also be mutated, which is misleading and unnecessary:

>>> a1, a2, a3 = Address("a1"), Address("a2"), Address("a3")
>>> user.addresses = [a1, a2]

>>> previous_collection = user.addresses

# replace the collection with a new one
>>> user.addresses = [a2, a3]

>>> previous_collection
[Address('a1'), Address('a2')]

Above, prior to the change, the previous_collection would have had the “a1” member removed, corresponding to the member that’s no longer in the new collection.


A @validates method receives all values on bulk-collection set before comparison

A method that uses @validates will now receive all members of a collection during a “bulk set” operation, before comparison is applied against the existing collection.

Given a mapping as:

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

    def convert_dict_to_b(self, key, value):
        return B(data=value["data"])

class B(Base):
    __tablename__ = "b"
    id = Column(Integer, primary_key=True)
    a_id = Column(ForeignKey("a.id"))
    data = Column(String)

Above, we could use the validator as follows, to convert from an incoming dictionary to an instance of B upon collection append:

a1 = A()
a1.bs.append({"data": "b1"})

However, a collection assignment would fail, since the ORM would assume incoming objects are already instances of B as it attempts to compare them to the existing members of the collection, before doing collection appends which actually invoke the validator. This would make it impossible for bulk set operations to accommodate non-ORM objects like dictionaries that needed up-front modification:

a1 = A()
a1.bs = [{"data": "b1"}]

The new logic uses the new AttributeEvents.bulk_replace() event to ensure that all values are sent to the @validates function up front.

As part of this change, this means that validators will now receive all members of a collection upon bulk set, not just the members that are new. Supposing a simple validator such as:

class A(Base):
    # ...

    def validate_b(self, key, value):
        assert value.data is not None
        return value

Above, if we began with a collection as:

a1 = A()

b1, b2 = B(data="one"), B(data="two")
a1.bs = [b1, b2]

And then, replaced the collection with one that overlaps the first:

b3 = B(data="three")
a1.bs = [b2, b3]

Previously, the second assignment would trigger the A.validate_b method only once, for the b3 object. The b2 object would be seen as being already present in the collection and not validated. With the new behavior, both b2 and b3 are passed to A.validate_b before passing onto the collection. It is thus important that validation methods employ idempotent behavior to suit such a case.


Use flag_dirty() to mark an object as “dirty” without any attribute changing

An exception is now raised if the flag_modified() function is used to mark an attribute as modified that isn’t actually loaded:

a1 = A(data="adf")


# expire, similarly as though we said s.commit()
s.expire(a1, "data")

# will raise InvalidRequestError
attributes.flag_modified(a1, "data")

This because the flush process will most likely fail in any case if the attribute remains un-present by the time flush occurs. To mark an object as “modified” without referring to any attribute specifically, so that it is considered within the flush process for the purpose of custom event handlers such as SessionEvents.before_flush(), use the new flag_dirty() function:

from sqlalchemy.orm import attributes



“scope” keyword removed from scoped_session

A very old and undocumented keyword argument scope has been removed:

from sqlalchemy.orm import scoped_session

Session = scoped_session(sessionmaker())

session = Session(scope=None)

The purpose of this keyword was an attempt to allow for variable “scopes”, where None indicated “no scope” and would therefore return a new Session. The keyword has never been documented and will now raise TypeError if encountered. It is not anticipated that this keyword is in use, however if users report issues related to this during beta testing, it can be restored with a deprecation.


Refinements to post_update in conjunction with onupdate

A relationship that uses the relationship.post_update feature will now interact better with a column that has an Column.onupdate value set. If an object is inserted with an explicit value for the column, it is re-stated during the UPDATE so that the “onupdate” rule does not overwrite it:

class A(Base):
    __tablename__ = "a"
    id = Column(Integer, primary_key=True)
    favorite_b_id = Column(ForeignKey("b.id", name="favorite_b_fk"))
    bs = relationship("B", primaryjoin="A.id == B.a_id")
    favorite_b = relationship(
        "B", primaryjoin="A.favorite_b_id == B.id", post_update=True
    updated = Column(Integer, onupdate=my_onupdate_function)

class B(Base):
    __tablename__ = "b"
    id = Column(Integer, primary_key=True)
    a_id = Column(ForeignKey("a.id", name="a_fk"))

a1 = A()
b1 = B()

a1.favorite_b = b1
a1.updated = 5

Above, the previous behavior would be that an UPDATE would emit after the INSERT, thus triggering the “onupdate” and overwriting the value “5”. The SQL now looks like:

INSERT INTO a (favorite_b_id, updated) VALUES (?, ?)
(None, 5)
UPDATE a SET favorite_b_id=?, updated=? WHERE a.id = ?
(1, 5, 1)

Additionally, if the value of “updated” is not set, then we correctly get back the newly generated value on a1.updated; previously, the logic that refreshes or expires the attribute to allow the generated value to be present would not fire off for a post-update. The InstanceEvents.refresh_flush() event is also emitted when a refresh within flush occurs in this case.



post_update integrates with ORM versioning

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. This UPDATE statement now participates in the versioning feature, documented at Configuring a Version Counter.

Given a mapping:

class Node(Base):
    __tablename__ = "node"
    id = Column(Integer, primary_key=True)
    version_id = Column(Integer, default=0)
    parent_id = Column(ForeignKey("node.id"))
    favorite_node_id = Column(ForeignKey("node.id"))

    nodes = relationship("Node", primaryjoin=remote(parent_id) == id)
    favorite_node = relationship(
        "Node", primaryjoin=favorite_node_id == remote(id), post_update=True

    __mapper_args__ = {"version_id_col": version_id}

An UPDATE of a node that associates another node as “favorite” will now increment the version counter as well as match the current version:

node = Node()
session.commit()  # node is now version #1

node = session.query(Node).get(node.id)
node.favorite_node = Node()
session.commit()  # node is now version #2

Note that this means an object that receives an UPDATE in response to other attributes changing, and a second UPDATE due to a post_update relationship change, will now receive two version counter updates for one flush. However, if the object is subject to an INSERT within the current flush, the version counter will not be incremented an additional time, unless a server-side versioning scheme is in place.

The reason post_update emits an UPDATE even for an UPDATE is now discussed at Why does post_update emit UPDATE in addition to the first UPDATE?.


Key Behavioral Changes - Core

The typing behavior of custom operators has been made consistent

User defined operators can be made on the fly using the Operators.op() function. Previously, the typing behavior of an expression against such an operator was inconsistent and also not controllable.

Whereas in 1.1, an expression such as the following would produce a result with no return type (assume -%> is some special operator supported by the database):

>>> column("x", types.DateTime).op("-%>")(None).type

Other types would use the default behavior of using the left-hand type as the return type:

>>> column("x", types.String(50)).op("-%>")(None).type

These behaviors were mostly by accident, so the behavior has been made consistent with the second form, that is the default return type is the same as the left-hand expression:

>>> column("x", types.DateTime).op("-%>")(None).type

As most user-defined operators tend to be “comparison” operators, often one of the many special operators defined by PostgreSQL, the Operators.op.is_comparison flag has been repaired to follow its documented behavior of allowing the return type to be Boolean in all cases, including for ARRAY and JSON:

>>> column("x", types.String(50)).op("-%>", is_comparison=True)(None).type
>>> column("x", types.ARRAY(types.Integer)).op("-%>", is_comparison=True)(None).type
>>> column("x", types.JSON()).op("-%>", is_comparison=True)(None).type

To assist with boolean comparison operators, a new shorthand method Operators.bool_op() has been added. This method should be preferred for on-the-fly boolean operators:

>>> print(column("x", types.Integer).bool_op("-%>")(5))
x -%> :x_1

Percent signs in literal_column() now conditionally escaped

The literal_column construct now escapes percent sign characters conditionally, based on whether or not the DBAPI in use makes use of a percent-sign-sensitive paramstyle or not (e.g. ‘format’ or ‘pyformat’).

Previously, it was not possible to produce a literal_column construct that stated a single percent sign:

>>> from sqlalchemy import literal_column
>>> print(literal_column("some%symbol"))

The percent sign is now unaffected for dialects that are not set to use the ‘format’ or ‘pyformat’ paramstyles; dialects such most MySQL dialects which do state one of these paramstyles will continue to escape as is appropriate:

>>> from sqlalchemy import literal_column
>>> print(literal_column("some%symbol"))
>>> from sqlalchemy.dialects import mysql >>> print(literal_column("some%symbol").compile(dialect=mysql.dialect()))

As part of this change, the doubling that has been present when using operators like ColumnOperators.contains(), ColumnOperators.startswith() and ColumnOperators.endswith() is also refined to only occur when appropriate.


The column-level COLLATE keyword now quotes the collation name

A bug in the collate() and ColumnOperators.collate() functions, used to supply ad-hoc column collations at the statement level, is fixed, where a case sensitive name would not be quoted:

stmt = select([mytable.c.x, mytable.c.y]).order_by(

now renders:

SELECT mytable.x, mytable.y,
FROM mytable ORDER BY mytable.somecolumn COLLATE "fr_FR"

Previously, the case sensitive name “fr_FR” would not be quoted. Currently, manual quoting of the “fr_FR” name is not detected, so applications that are manually quoting the identifier should be adjusted. Note that this change does not impact the use of collations at the type level (e.g. specified on the datatype like String at the table level), where quoting is already applied.


Dialect Improvements and Changes - PostgreSQL

Support for Batch Mode / Fast Execution Helpers

The psycopg2 cursor.executemany() method has been identified as performing poorly, particularly with INSERT statements. To alleviate this, psycopg2 has added Fast Execution Helpers which rework statements into fewer server round trips by sending multiple DML statements in batch. SQLAlchemy 1.2 now includes support for these helpers to be used transparently whenever the Engine makes use of cursor.executemany() to invoke a statement against multiple parameter sets. The feature is off by default and can be enabled using the use_batch_mode argument on create_engine():

engine = create_engine(
    "postgresql+psycopg2://scott:tiger@host/dbname", use_batch_mode=True

The feature is considered to be experimental for the moment but may become on by default in a future release.


Support for fields specification in INTERVAL, including full reflection

The “fields” specifier in PostgreSQL’s INTERVAL datatype allows specification of which fields of the interval to store, including such values as “YEAR”, “MONTH”, “YEAR TO MONTH”, etc. The INTERVAL datatype now allows these values to be specified:

from sqlalchemy.dialects.postgresql import INTERVAL

Table("my_table", metadata, Column("some_interval", INTERVAL(fields="DAY TO SECOND")))

Additionally, all INTERVAL datatypes can now be reflected independently of the “fields” specifier present; the “fields” parameter in the datatype itself will also be present:

>>> inspect(engine).get_columns("my_table")
[{'comment': None,
  'name': u'some_interval', 'nullable': True,
  'default': None, 'autoincrement': False,
  'type': INTERVAL(fields=u'day to second')}]


Dialect Improvements and Changes - MySQL


The ON DUPLICATE KEY UPDATE clause of INSERT supported by MySQL is now supported using a MySQL-specific version of the Insert object, via sqlalchemy.dialects.mysql.dml.insert(). This Insert subclass adds a new method Insert.on_duplicate_key_update() that implements MySQL’s syntax:

from sqlalchemy.dialects.mysql import insert

insert_stmt = insert(my_table).values(id="some_id", data="some data to insert")

on_conflict_stmt = insert_stmt.on_duplicate_key_update(
    data=insert_stmt.inserted.data, status="U"


The above will render:

INSERT INTO my_table (id, data)
VALUES (:id, :data)
ON DUPLICATE KEY UPDATE data=VALUES(data), status=:status_1


Dialect Improvements and Changes - Oracle

Major Refactor to cx_Oracle Dialect, Typing System

With the introduction of the 6.x series of the cx_Oracle DBAPI, SQLAlchemy’s cx_Oracle dialect has been reworked and simplified to take advantage of recent improvements in cx_Oracle as well as dropping support for patterns that were more relevant before the 5.x series of cx_Oracle.

  • The minimum cx_Oracle version supported is now 5.1.3; 5.3 or the most recent 6.x series are recommended.

  • The handling of datatypes has been refactored. The cursor.setinputsizes() method is no longer used for any datatype except LOB types, per advice from cx_Oracle’s developers. As a result, the parameters auto_setinputsizes and exclude_setinputsizes are deprecated and no longer have any effect.

  • The coerce_to_decimal flag, when set to False to indicate that coercion of numeric types with precision and scale to Decimal should not occur, only impacts untyped (e.g. plain string with no TypeEngine objects) statements. A Core expression that includes a Numeric type or subtype will now follow the decimal coercion rules of that type.

  • The “two phase” transaction support in the dialect, already dropped for the 6.x series of cx_Oracle, has now been removed entirely as this feature has never worked correctly and is unlikely to have been in production use. As a result, the allow_twophase dialect flag is deprecated and also has no effect.

  • Fixed a bug involving the column keys present with RETURNING. Given a statement as follows:

    result = conn.execute(table.insert().values(x=5).returning(table.c.a, table.c.b))

    Previously, the keys in each row of the result would be ret_0 and ret_1, which are identifiers internal to the cx_Oracle RETURNING implementation. The keys will now be a and b as is expected for other dialects.

  • cx_Oracle’s LOB datatype represents return values as a cx_Oracle.LOB object, which is a cursor-associated proxy that returns the ultimate data value via a .read() method. Historically, if more rows were read before these LOB objects were consumed (specifically, more rows than the value of cursor.arraysize which causes a new batch of rows to be read), these LOB objects would raise the error “LOB variable no longer valid after subsequent fetch”. SQLAlchemy worked around this by both automatically calling .read() upon these LOBs within its typing system, as well as using a special BufferedColumnResultSet which would ensure this data was buffered in case a call like cursor.fetchmany() or cursor.fetchall() were used.

    The dialect now makes use of a cx_Oracle outputtypehandler to handle these .read() calls, so that they are always called up front regardless of how many rows are being fetched, so that this error can no longer occur. As a result, the use of the BufferedColumnResultSet, as well as some other internals to the Core ResultSet that were specific to this use case, have been removed. The type objects are also simplified as they no longer need to process a binary column result.

    Additionally, cx_Oracle 6.x has removed the conditions under which this error occurs in any case, so the error is no longer possible. The error can occur on SQLAlchemy in the case that the seldom (if ever) used auto_convert_lobs=False option is in use, in conjunction with the previous 5.x series of cx_Oracle, and more rows are read before the LOB objects can be consumed. Upgrading to cx_Oracle 6.x will resolve that issue.

Oracle Unique, Check constraints now reflected

UNIQUE and CHECK constraints now reflect via Inspector.get_unique_constraints() and Inspector.get_check_constraints(). A Table object that’s reflected will now include CheckConstraint objects as well. See the notes at Constraint Reflection for information on behavioral quirks here, including that most Table objects will still not include any UniqueConstraint objects as these usually represent via Index.


Oracle foreign key constraint names are now “name normalized”

The names of foreign key constraints as delivered to a ForeignKeyConstraint object during table reflection as well as within the Inspector.get_foreign_keys() method will now be “name normalized”, that is, expressed as lower case for a case insensitive name, rather than the raw UPPERCASE format that Oracle uses:

>>> insp.get_indexes("addresses")
[{'unique': False, 'column_names': [u'user_id'],
  'name': u'address_idx', 'dialect_options': {}}]

>>> insp.get_pk_constraint("addresses")
{'name': u'pk_cons', 'constrained_columns': [u'id']}

>>> insp.get_foreign_keys("addresses")
[{'referred_table': u'users', 'referred_columns': [u'id'],
  'referred_schema': None, 'name': u'user_id_fk',
  'constrained_columns': [u'user_id']}]

Previously, the foreign keys result would look like:

        "referred_table": "users",
        "referred_columns": ["id"],
        "referred_schema": None,
        "name": "USER_ID_FK",
        "constrained_columns": ["user_id"],

Where the above could create problems particularly with Alembic autogenerate.


Dialect Improvements and Changes - SQL Server

SQL Server schema names with embedded dots supported

The SQL Server dialect has a behavior such that a schema name with a dot inside of it is assumed to be a “database”.”owner” identifier pair, which is necessarily split up into these separate components during table and component reflection operations, as well as when rendering quoting for the schema name so that the two symbols are quoted separately. The schema argument can now be passed using brackets to manually specify where this split occurs, allowing database and/or owner names that themselves contain one or more dots:

Table("some_table", metadata, Column("q", String(50)), schema="[MyDataBase.dbo]")

The above table will consider the “owner” to be MyDataBase.dbo, which will also be quoted upon render, and the “database” as None. To individually refer to database name and owner, use two pairs of brackets:

    Column("q", String(50)),

Additionally, the quoted_name construct is now honored when passed to “schema” by the SQL Server dialect; the given symbol will not be split on the dot if the quote flag is True and will be interpreted as the “owner”.


AUTOCOMMIT isolation level support

Both the PyODBC and pymssql dialects now support the “AUTOCOMMIT” isolation level as set by Connection.execution_options() which will establish the correct flags on the DBAPI connection object.