About this Document
This document describes changes between SQLAlchemy version 0.7, undergoing maintenance releases as of October, 2012, and SQLAlchemy version 0.8, which is expected for release in early 2013.
Document date: October 25, 2012 Updated: March 9, 2013
This guide introduces what's new in SQLAlchemy version 0.8, and also documents changes which affect users migrating their applications from the 0.7 series of SQLAlchemy to 0.8.
SQLAlchemy releases are closing in on 1.0, and each new version since 0.5 features fewer major usage changes. Most applications that are settled into modern 0.7 patterns should be movable to 0.8 with no changes. Applications that use 0.6 and even 0.5 patterns should be directly migratable to 0.8 as well, though larger applications may want to test with each interim version.
SQLAlchemy 0.8 will target Python 2.5 and forward; compatibility for Python 2.4 is being dropped.
The internals will be able to make usage of Python ternaries
(that is, x if y else z) which will improve things
versus the usage of y and x or z, which naturally has
been the source of some bugs, as well as context managers
(that is, with:) and perhaps in some cases
try:/except:/else: blocks which will help with code
readability.
SQLAlchemy will eventually drop 2.5 support as well - when
2.6 is reached as the baseline, SQLAlchemy will move to use
2.6/3.3 in-place compatibility, removing the usage of the
2to3 tool and maintaining a source base that works with
Python 2 and 3 at the same time.
Rewritten :func:`_orm.relationship` mechanics
0.8 features a much improved and capable system regarding how :func:`_orm.relationship` determines how to join between two entities. The new system includes these features:
The
primaryjoinargument is no longer needed when constructing a :func:`_orm.relationship` against a class that has multiple foreign key paths to the target. Only theforeign_keysargument is needed to specify those columns which should be included:class Parent(Base): __tablename__ = "parent" id = Column(Integer, primary_key=True) child_id_one = Column(Integer, ForeignKey("child.id")) child_id_two = Column(Integer, ForeignKey("child.id")) child_one = relationship("Child", foreign_keys=child_id_one) child_two = relationship("Child", foreign_keys=child_id_two) class Child(Base): __tablename__ = "child" id = Column(Integer, primary_key=True)relationships against self-referential, composite foreign keys where a column points to itself are now supported. The canonical case is as follows:
class Folder(Base): __tablename__ = "folder" __table_args__ = ( ForeignKeyConstraint( ["account_id", "parent_id"], ["folder.account_id", "folder.folder_id"] ), ) account_id = Column(Integer, primary_key=True) folder_id = Column(Integer, primary_key=True) parent_id = Column(Integer) name = Column(String) parent_folder = relationship( "Folder", backref="child_folders", remote_side=[account_id, folder_id] )Above, the
Folderrefers to its parentFolderjoining fromaccount_idto itself, andparent_idtofolder_id. When SQLAlchemy constructs an auto- join, no longer can it assume all columns on the "remote" side are aliased, and all columns on the "local" side are not - theaccount_idcolumn is on both sides. So the internal relationship mechanics were totally rewritten to support an entirely different system whereby two copies ofaccount_idare generated, each containing different annotations to determine their role within the statement. Note the join condition within a basic eager load:SELECT folder.account_id AS folder_account_id, folder.folder_id AS folder_folder_id, folder.parent_id AS folder_parent_id, folder.name AS folder_name, folder_1.account_id AS folder_1_account_id, folder_1.folder_id AS folder_1_folder_id, folder_1.parent_id AS folder_1_parent_id, folder_1.name AS folder_1_name FROM folder LEFT OUTER JOIN folder AS folder_1 ON folder_1.account_id = folder.account_id AND folder.folder_id = folder_1.parent_id WHERE folder.folder_id = ? AND folder.account_id = ?
Previously difficult custom join conditions, like those involving functions and/or CASTing of types, will now function as expected in most cases:
class HostEntry(Base): __tablename__ = "host_entry" id = Column(Integer, primary_key=True) ip_address = Column(INET) content = Column(String(50)) # relationship() using explicit foreign_keys, remote_side parent_host = relationship( "HostEntry", primaryjoin=ip_address == cast(content, INET), foreign_keys=content, remote_side=ip_address, )The new :func:`_orm.relationship` mechanics make use of a SQLAlchemy concept known as :term:`annotations`. These annotations are also available to application code explicitly via the :func:`.foreign` and :func:`.remote` functions, either as a means to improve readability for advanced configurations or to directly inject an exact configuration, bypassing the usual join-inspection heuristics:
from sqlalchemy.orm import foreign, remote class HostEntry(Base): __tablename__ = "host_entry" id = Column(Integer, primary_key=True) ip_address = Column(INET) content = Column(String(50)) # relationship() using explicit foreign() and remote() annotations # in lieu of separate arguments parent_host = relationship( "HostEntry", primaryjoin=remote(ip_address) == cast(foreign(content), INET), )
.. seealso::
:ref:`relationship_configure_joins` - a newly revised section on :func:`_orm.relationship`
detailing the latest techniques for customizing related attributes and collection
access.
Lots of SQLAlchemy users are writing systems that require the ability to inspect the attributes of a mapped class, including being able to get at the primary key columns, object relationships, plain attributes, and so forth, typically for the purpose of building data-marshalling systems, like JSON/XML conversion schemes and of course form libraries galore.
Originally, the :class:`_schema.Table` and :class:`_schema.Column` model were the original inspection points, which have a well-documented system. While SQLAlchemy ORM models are also fully introspectable, this has never been a fully stable and supported feature, and users tended to not have a clear idea how to get at this information.
0.8 now provides a consistent, stable and fully documented API for this purpose, including an inspection system which works on mapped classes, instances, attributes, and other Core and ORM constructs. The entrypoint to this system is the core-level :func:`_sa.inspect` function. In most cases, the object being inspected is one already part of SQLAlchemy's system, such as :class:`_orm.Mapper`, :class:`.InstanceState`, :class:`_reflection.Inspector`. In some cases, new objects have been added with the job of providing the inspection API in certain contexts, such as :class:`.AliasedInsp` and :class:`.AttributeState`.
A walkthrough of some key capabilities follows:
>>> class User(Base):
... __tablename__ = "user"
... id = Column(Integer, primary_key=True)
... name = Column(String)
... name_syn = synonym(name)
... addresses = relationship("Address")
>>> # universal entry point is inspect()
>>> b = inspect(User)
>>> # b in this case is the Mapper
>>> b
<Mapper at 0x101521950; User>
>>> # Column namespace
>>> b.columns.id
Column('id', Integer(), table=<user>, primary_key=True, nullable=False)
>>> # mapper's perspective of the primary key
>>> b.primary_key
(Column('id', Integer(), table=<user>, primary_key=True, nullable=False),)
>>> # MapperProperties available from .attrs
>>> b.attrs.keys()
['name_syn', 'addresses', 'id', 'name']
>>> # .column_attrs, .relationships, etc. filter this collection
>>> b.column_attrs.keys()
['id', 'name']
>>> list(b.relationships)
[<sqlalchemy.orm.properties.RelationshipProperty object at 0x1015212d0>]
>>> # they are also namespaces
>>> b.column_attrs.id
<sqlalchemy.orm.properties.ColumnProperty object at 0x101525090>
>>> b.relationships.addresses
<sqlalchemy.orm.properties.RelationshipProperty object at 0x1015212d0>
>>> # point inspect() at a mapped, class level attribute,
>>> # returns the attribute itself
>>> b = inspect(User.addresses)
>>> b
<sqlalchemy.orm.attributes.InstrumentedAttribute object at 0x101521fd0>
>>> # From here we can get the mapper:
>>> b.mapper
<Mapper at 0x101525810; Address>
>>> # the parent inspector, in this case a mapper
>>> b.parent
<Mapper at 0x101521950; User>
>>> # an expression
>>> print(b.expression)
{printsql}"user".id = address.user_id{stop}
>>> # inspect works on instances
>>> u1 = User(id=3, name="x")
>>> b = inspect(u1)
>>> # it returns the InstanceState
>>> b
<sqlalchemy.orm.state.InstanceState object at 0x10152bed0>
>>> # similar attrs accessor refers to the
>>> b.attrs.keys()
['id', 'name_syn', 'addresses', 'name']
>>> # attribute interface - from attrs, you get a state object
>>> b.attrs.id
<sqlalchemy.orm.state.AttributeState object at 0x10152bf90>
>>> # this object can give you, current value...
>>> b.attrs.id.value
3
>>> # ... current history
>>> b.attrs.id.history
History(added=[3], unchanged=(), deleted=())
>>> # InstanceState can also provide session state information
>>> # lets assume the object is persistent
>>> s = Session()
>>> s.add(u1)
>>> s.commit()
>>> # now we can get primary key identity, always
>>> # works in query.get()
>>> b.identity
(3,)
>>> # the mapper level key
>>> b.identity_key
(<class '__main__.User'>, (3,))
>>> # state within the session
>>> b.persistent, b.transient, b.deleted, b.detached
(True, False, False, False)
>>> # owning session
>>> b.session
<sqlalchemy.orm.session.Session object at 0x101701150>
.. seealso::
:ref:`core_inspection_toplevel`
The :meth:`_query.Query.with_polymorphic` method allows the user to specify which tables should be present when querying against a joined-table entity. Unfortunately the method is awkward and only applies to the first entity in the list, and otherwise has awkward behaviors both in usage as well as within the internals. A new enhancement to the :func:`.aliased` construct has been added called :func:`.with_polymorphic` which allows any entity to be "aliased" into a "polymorphic" version of itself, freely usable anywhere:
from sqlalchemy.orm import with_polymorphic
palias = with_polymorphic(Person, [Engineer, Manager])
session.query(Company).join(palias, Company.employees).filter(
or_(Engineer.language == "java", Manager.hair == "pointy")
)
.. seealso::
:ref:`with_polymorphic` - newly updated documentation for polymorphic
loading control.
of_type() works with alias(), with_polymorphic(), any(), has(), joinedload(), subqueryload(), contains_eager()
The :meth:`.PropComparator.of_type` method is used to specify a specific subtype to use when constructing SQL expressions along a :func:`_orm.relationship` that has a :term:`polymorphic` mapping as its target. This method can now be used to target any number of target subtypes, by combining it with the new :func:`.with_polymorphic` function:
# use eager loading in conjunction with with_polymorphic targets
Job_P = with_polymorphic(Job, [SubJob, ExtraJob], aliased=True)
q = (
s.query(DataContainer)
.join(DataContainer.jobs.of_type(Job_P))
.options(contains_eager(DataContainer.jobs.of_type(Job_P)))
)
The method now works equally well in most places a regular relationship attribute is accepted, including with loader functions like :func:`_orm.joinedload`, :func:`.subqueryload`, :func:`.contains_eager`, and comparison methods like :meth:`.PropComparator.any` and :meth:`.PropComparator.has`:
# use eager loading in conjunction with with_polymorphic targets
Job_P = with_polymorphic(Job, [SubJob, ExtraJob], aliased=True)
q = (
s.query(DataContainer)
.join(DataContainer.jobs.of_type(Job_P))
.options(contains_eager(DataContainer.jobs.of_type(Job_P)))
)
# pass subclasses to eager loads (implicitly applies with_polymorphic)
q = s.query(ParentThing).options(
joinedload_all(ParentThing.container, DataContainer.jobs.of_type(SubJob))
)
# control self-referential aliasing with any()/has()
Job_A = aliased(Job)
q = (
s.query(Job)
.join(DataContainer.jobs)
.filter(
DataContainer.jobs.of_type(Job_A).any(
and_(Job_A.id < Job.id, Job_A.type == "fred")
)
)
)
.. seealso::
:ref:`inheritance_of_type`
Mapper and instance events can now be associated with an unmapped
superclass, where those events will be propagated to subclasses
as those subclasses are mapped. The propagate=True flag
should be used. This feature allows events to be associated
with a declarative base class:
from sqlalchemy.ext.declarative import declarative_base
Base = declarative_base()
@event.listens_for("load", Base, propagate=True)
def on_load(target, context):
print("New instance loaded:", target)
# on_load() will be applied to SomeClass
class SomeClass(Base):
__tablename__ = "sometable"
# ...
A key feature of Declarative is the ability to refer to other mapped classes using their string name. The registry of class names is now sensitive to the owning module and package of a given class. The classes can be referred to via dotted name in expressions:
class Snack(Base):
# ...
peanuts = relationship(
"nuts.Peanut", primaryjoin="nuts.Peanut.snack_id == Snack.id"
)
The resolution allows that any full or partial disambiguating package name can be used. If the path to a particular class is still ambiguous, an error is raised.
The "deferred reflection" example has been moved to a
supported feature within Declarative. This feature allows
the construction of declarative mapped classes with only
placeholder Table metadata, until a prepare() step
is called, given an Engine with which to reflect fully
all tables and establish actual mappings. The system
supports overriding of columns, single and joined
inheritance, as well as distinct bases-per-engine. A full
declarative configuration can now be created against an
existing table that is assembled upon engine creation time
in one step:
class ReflectedOne(DeferredReflection, Base):
__abstract__ = True
class ReflectedTwo(DeferredReflection, Base):
__abstract__ = True
class MyClass(ReflectedOne):
__tablename__ = "mytable"
class MyOtherClass(ReflectedOne):
__tablename__ = "myothertable"
class YetAnotherClass(ReflectedTwo):
__tablename__ = "yetanothertable"
ReflectedOne.prepare(engine_one)
ReflectedTwo.prepare(engine_two)
.. seealso::
:class:`.DeferredReflection`
While the SQL expressions used with :meth:`_query.Query.filter`,
such as User.id == 5, have always been compatible for
use with core constructs such as :func:`_expression.select`, the mapped
class itself would not be recognized when passed to :func:`_expression.select`,
:meth:`_expression.Select.select_from`, or :meth:`_expression.Select.correlate`.
A new SQL registration system allows a mapped class to be
accepted as a FROM clause within the core:
from sqlalchemy import select stmt = select([User]).where(User.id == 5)
Above, the mapped User class will expand into
the :class:`_schema.Table` to which User is mapped.
The new UPDATE..FROM mechanics work in query.update().
Below, we emit an UPDATE against SomeEntity, adding
a FROM clause (or equivalent, depending on backend)
against SomeOtherEntity:
query(SomeEntity).filter(SomeEntity.id == SomeOtherEntity.id).filter(
SomeOtherEntity.foo == "bar"
).update({"data": "x"})
In particular, updates to joined-inheritance
entities are supported, provided the target of the UPDATE is local to the
table being filtered on, or if the parent and child tables
are mixed, they are joined explicitly in the query. Below,
given Engineer as a joined subclass of Person:
query(Engineer).filter(Person.id == Engineer.id).filter(
Person.name == "dilbert"
).update({"engineer_data": "java"})
would produce:
UPDATE engineer SET engineer_data='java' FROM person
WHERE person.id=engineer.id AND person.name='dilbert'A behavioral change that should improve efficiency for those
users using SAVEPOINT via Session.begin_nested() - upon
rollback(), only those objects that were made dirty
since the last flush will be expired, the rest of the
Session remains intact. This because a ROLLBACK to a
SAVEPOINT does not terminate the containing transaction's
isolation, so no expiry is needed except for those changes
that were not flushed in the current transaction.
The caching example now uses dogpile.cache. Dogpile.cache is a rewrite of the caching portion of Beaker, featuring vastly simpler and faster operation, as well as support for distributed locking.
Note that the SQLAlchemy APIs used by the Dogpile example as well as the previous Beaker example have changed slightly, in particular this change is needed as illustrated in the Beaker example:
--- examples/beaker_caching/caching_query.py
+++ examples/beaker_caching/caching_query.py
@@ -222,7 +222,8 @@
"""
if query._current_path:
- mapper, key = query._current_path[-2:]
+ mapper, prop = query._current_path[-2:]
+ key = prop.key
for cls in mapper.class_.__mro__:
if (cls, key) in self._relationship_options:.. seealso::
:ref:`examples_caching`
The Core has to date never had any system of adding support
for new SQL operators to Column and other expression
constructs, other than the :meth:`.ColumnOperators.op` method
which is "just enough" to make things work. There has also
never been any system in place for Core which allows the
behavior of existing operators to be overridden. Up until
now, the only way operators could be flexibly redefined was
in the ORM layer, using :func:`.column_property` given a
comparator_factory argument. Third party libraries
like GeoAlchemy therefore were forced to be ORM-centric and
rely upon an array of hacks to apply new operations as well
as to get them to propagate correctly.
The new operator system in Core adds the one hook that's been missing all along, which is to associate new and overridden operators with types. Since after all, it's not really a column, CAST operator, or SQL function that really drives what kinds of operations are present, it's the type of the expression. The implementation details are minimal - only a few extra methods are added to the core :class:`_expression.ColumnElement` type so that it consults its :class:`.TypeEngine` object for an optional set of operators. New or revised operations can be associated with any type, either via subclassing of an existing type, by using :class:`.TypeDecorator`, or "globally across-the-board" by attaching a new :class:`.TypeEngine.Comparator` object to an existing type class.
For example, to add logarithm support to :class:`.Numeric` types:
from sqlalchemy.types import Numeric
from sqlalchemy.sql import func
class CustomNumeric(Numeric):
class comparator_factory(Numeric.Comparator):
def log(self, other):
return func.log(self.expr, other)
The new type is usable like any other type:
data = Table(
"data",
metadata,
Column("id", Integer, primary_key=True),
Column("x", CustomNumeric(10, 5)),
Column("y", CustomNumeric(10, 5)),
)
stmt = select([data.c.x.log(data.c.y)]).where(data.c.x.log(2) < value)
print(conn.execute(stmt).fetchall())
New features which have come from this immediately include support for PostgreSQL's HSTORE type, as well as new operations associated with PostgreSQL's ARRAY type. It also paves the way for existing types to acquire lots more operators that are specific to those types, such as more string, integer and date operators.
.. seealso::
:ref:`types_operators`
:class:`.HSTORE`
The :meth:`_expression.Insert.values` method now supports a list of dictionaries,
which will render a multi-VALUES statement such as
VALUES (<row1>), (<row2>), .... This is only relevant to backends which
support this syntax, including PostgreSQL, SQLite, and MySQL. It is
not the same thing as the usual executemany() style of INSERT which
remains unchanged:
users.insert().values(
[
{"name": "some name"},
{"name": "some other name"},
{"name": "yet another name"},
]
)
.. seealso::
:meth:`_expression.Insert.values`
SQL expressions can now be associated with types. Historically, :class:`.TypeEngine` has always allowed Python-side functions which receive both bound parameters as well as result row values, passing them through a Python side conversion function on the way to/back from the database. The new feature allows similar functionality, except on the database side:
from sqlalchemy.types import String
from sqlalchemy import func, Table, Column, MetaData
class LowerString(String):
def bind_expression(self, bindvalue):
return func.lower(bindvalue)
def column_expression(self, col):
return func.lower(col)
metadata = MetaData()
test_table = Table("test_table", metadata, Column("data", LowerString))
Above, the LowerString type defines a SQL expression that will be emitted
whenever the test_table.c.data column is rendered in the columns
clause of a SELECT statement:
>>> print(select([test_table]).where(test_table.c.data == "HI"))
{printsql}SELECT lower(test_table.data) AS data
FROM test_table
WHERE test_table.data = lower(:data_1)
This feature is also used heavily by the new release of GeoAlchemy, to embed PostGIS expressions inline in SQL based on type rules.
.. seealso::
:ref:`types_sql_value_processing`
The :func:`_sa.inspect` function introduced in :ref:`feature_orminspection_08` also applies to the core. Applied to an :class:`_engine.Engine` it produces an :class:`_reflection.Inspector` object:
from sqlalchemy import inspect
from sqlalchemy import create_engine
engine = create_engine("postgresql://scott:tiger@localhost/test")
insp = inspect(engine)
print(insp.get_table_names())
It can also be applied to any :class:`_expression.ClauseElement`, which returns the :class:`_expression.ClauseElement` itself, such as :class:`_schema.Table`, :class:`_schema.Column`, :class:`_expression.Select`, etc. This allows it to work fluently between Core and ORM constructs.
New Method :meth:`_expression.Select.correlate_except`
:func:`_expression.select` now has a method :meth:`_expression.Select.correlate_except` which specifies "correlate on all FROM clauses except those specified". It can be used for mapping scenarios where a related subquery should correlate normally, except against a particular target selectable:
class SnortEvent(Base):
__tablename__ = "event"
id = Column(Integer, primary_key=True)
signature = Column(Integer, ForeignKey("signature.id"))
signatures = relationship("Signature", lazy=False)
class Signature(Base):
__tablename__ = "signature"
id = Column(Integer, primary_key=True)
sig_count = column_property(
select([func.count("*")])
.where(SnortEvent.signature == id)
.correlate_except(SnortEvent)
)
.. seealso::
:meth:`_expression.Select.correlate_except`
Support for PostgreSQL's HSTORE type is now available as
:class:`_postgresql.HSTORE`. This type makes great usage
of the new operator system to provide a full range of operators
for HSTORE types, including index access, concatenation,
and containment methods such as
:meth:`~.HSTORE.comparator_factory.has_key`,
:meth:`~.HSTORE.comparator_factory.has_any`, and
:meth:`~.HSTORE.comparator_factory.matrix`:
from sqlalchemy.dialects.postgresql import HSTORE
data = Table(
"data_table",
metadata,
Column("id", Integer, primary_key=True),
Column("hstore_data", HSTORE),
)
engine.execute(select([data.c.hstore_data["some_key"]])).scalar()
engine.execute(select([data.c.hstore_data.matrix()])).scalar()
.. seealso::
:class:`_postgresql.HSTORE`
:class:`_postgresql.hstore`
The :class:`_postgresql.ARRAY` type will accept an optional "dimension" argument, pinning it to a fixed number of dimensions and greatly improving efficiency when retrieving results:
# old way, still works since PG supports N-dimensions per row:
Column("my_array", postgresql.ARRAY(Integer))
# new way, will render ARRAY with correct number of [] in DDL,
# will process binds and results more efficiently as we don't need
# to guess how many levels deep to go
Column("my_array", postgresql.ARRAY(Integer, dimensions=2))
The type also introduces new operators, using the new type-specific operator framework. New operations include indexed access:
result = conn.execute(select([mytable.c.arraycol[2]]))
slice access in SELECT:
result = conn.execute(select([mytable.c.arraycol[2:4]]))
slice updates in UPDATE:
conn.execute(mytable.update().values({mytable.c.arraycol[2:3]: [7, 8]}))
freestanding array literals:
>>> from sqlalchemy.dialects import postgresql >>> conn.scalar(select([postgresql.array([1, 2]) + postgresql.array([3, 4, 5])])) [1, 2, 3, 4, 5]
array concatenation, where below, the right side [4, 5, 6] is coerced into an array literal:
select([mytable.c.arraycol + [4, 5, 6]])
.. seealso::
:class:`_postgresql.ARRAY`
:class:`_postgresql.array`
SQLite has no built-in DATE, TIME, or DATETIME types, and instead provides some support for storage of date and time values either as strings or integers. The date and time types for SQLite are enhanced in 0.8 to be much more configurable as to the specific format, including that the "microseconds" portion is optional, as well as pretty much everything else.
Column("sometimestamp", sqlite.DATETIME(truncate_microseconds=True))
Column(
"sometimestamp",
sqlite.DATETIME(
storage_format=(
"%(year)04d%(month)02d%(day)02d"
"%(hour)02d%(minute)02d%(second)02d%(microsecond)06d"
),
regexp="(\d{4})(\d{2})(\d{2})(\d{2})(\d{2})(\d{2})(\d{6})",
),
)
Column(
"somedate",
sqlite.DATE(
storage_format="%(month)02d/%(day)02d/%(year)04d",
regexp="(?P<month>\d+)/(?P<day>\d+)/(?P<year>\d+)",
),
)
Huge thanks to Nate Dub for the sprinting on this at Pycon 2012.
.. seealso::
:class:`_sqlite.DATETIME`
:class:`_sqlite.DATE`
:class:`_sqlite.TIME`
The "collate" keyword, long accepted by the MySQL dialect, is now established on all :class:`.String` types and will render on any backend, including when features such as :meth:`_schema.MetaData.create_all` and :func:`.cast` is used:
>>> stmt = select([cast(sometable.c.somechar, String(20, collation="utf8"))])
>>> print(stmt)
{printsql}SELECT CAST(sometable.somechar AS VARCHAR(20) COLLATE "utf8") AS anon_1
FROM sometable
.. seealso::
:class:`.String`
"Prefixes" now supported for :func:`_expression.update`, :func:`_expression.delete`
Geared towards MySQL, a "prefix" can be rendered within any of these constructs. E.g.:
stmt = table.delete().prefix_with("LOW_PRIORITY", dialect="mysql")
stmt = table.update().prefix_with("LOW_PRIORITY", dialect="mysql")
The method is new in addition to those which already existed on :func:`_expression.insert`, :func:`_expression.select` and :class:`_query.Query`.
.. seealso::
:meth:`_expression.Update.prefix_with`
:meth:`_expression.Delete.prefix_with`
:meth:`_expression.Insert.prefix_with`
:meth:`_expression.Select.prefix_with`
:meth:`_query.Query.prefix_with`
This is a late add to the 0.8 series, however it is hoped that the new behavior
is generally more consistent and intuitive in a wider variety of
situations. The ORM has since at least version 0.4 included behavior
such that an object that's "pending", meaning that it's
associated with a :class:`.Session` but hasn't been inserted into the database
yet, is automatically expunged from the :class:`.Session` when it becomes an "orphan",
which means it has been de-associated with a parent object that refers to it
with delete-orphan cascade on the configured :func:`_orm.relationship`. This
behavior is intended to approximately mirror the behavior of a persistent
(that is, already inserted) object, where the ORM will emit a DELETE for such
objects that become orphans based on the interception of detachment events.
The behavioral change comes into play for objects that
are referred to by multiple kinds of parents that each specify delete-orphan; the
typical example is an :ref:`association object <association_pattern>` that bridges two other kinds of objects
in a many-to-many pattern. Previously, the behavior was such that the
pending object would be expunged only when de-associated with all of its parents.
With the behavioral change, the pending object
is expunged as soon as it is de-associated from any of the parents that it was
previously associated with. This behavior is intended to more closely
match that of persistent objects, which are deleted as soon
as they are de-associated from any parent.
The rationale for the older behavior dates back at least to version 0.4, and was basically a defensive decision to try to alleviate confusion when an object was still being constructed for INSERT. But the reality is that the object is re-associated with the :class:`.Session` as soon as it is attached to any new parent in any case.
It's still possible to flush an object that is not associated with all of its required parents, if the object was either not associated with those parents in the first place, or if it was expunged, but then re-associated with a :class:`.Session` via a subsequent attachment event but still not fully associated. In this situation, it is expected that the database would emit an integrity error, as there are likely NOT NULL foreign key columns that are unpopulated. The ORM makes the decision to let these INSERT attempts occur, based on the judgment that an object that is only partially associated with its required parents but has been actively associated with some of them, is more often than not a user error, rather than an intentional omission which should be silently skipped - silently skipping the INSERT here would make user errors of this nature very hard to debug.
The old behavior, for applications that might have been relying upon it, can be re-enabled for
any :class:`_orm.Mapper` by specifying the flag legacy_is_orphan as a mapper
option.
The new behavior allows the following test case to work:
from sqlalchemy import Column, Integer, String, ForeignKey
from sqlalchemy.orm import relationship, backref
from sqlalchemy.ext.declarative import declarative_base
Base = declarative_base()
class User(Base):
__tablename__ = "user"
id = Column(Integer, primary_key=True)
name = Column(String(64))
class UserKeyword(Base):
__tablename__ = "user_keyword"
user_id = Column(Integer, ForeignKey("user.id"), primary_key=True)
keyword_id = Column(Integer, ForeignKey("keyword.id"), primary_key=True)
user = relationship(
User, backref=backref("user_keywords", cascade="all, delete-orphan")
)
keyword = relationship(
"Keyword", backref=backref("user_keywords", cascade="all, delete-orphan")
)
# uncomment this to enable the old behavior
# __mapper_args__ = {"legacy_is_orphan": True}
class Keyword(Base):
__tablename__ = "keyword"
id = Column(Integer, primary_key=True)
keyword = Column("keyword", String(64))
from sqlalchemy import create_engine
from sqlalchemy.orm import Session
# note we're using PostgreSQL to ensure that referential integrity
# is enforced, for demonstration purposes.
e = create_engine("postgresql://scott:tiger@localhost/test", echo=True)
Base.metadata.drop_all(e)
Base.metadata.create_all(e)
session = Session(e)
u1 = User(name="u1")
k1 = Keyword(keyword="k1")
session.add_all([u1, k1])
uk1 = UserKeyword(keyword=k1, user=u1)
# previously, if session.flush() were called here,
# this operation would succeed, but if session.flush()
# were not called here, the operation fails with an
# integrity error.
# session.flush()
del u1.user_keywords[0]
session.commit()
The after_attach event fires after the item is associated with the Session instead of before; before_attach added
Event handlers which use after_attach can now assume the given instance is associated with the given session:
@event.listens_for(Session, "after_attach")
def after_attach(session, instance):
assert instance in session
Some use cases require that it work this way. However, other use cases require that the item is not yet part of the session, such as when a query, intended to load some state required for an instance, emits autoflush first and would otherwise prematurely flush the target object. Those use cases should use the new "before_attach" event:
@event.listens_for(Session, "before_attach")
def before_attach(session, instance):
instance.some_necessary_attribute = (
session.query(Widget).filter_by(instance.widget_name).first()
)
Previously it was necessary to call :meth:`_query.Query.correlate` in order to have a column- or WHERE-subquery correlate to the parent:
subq = (
session.query(Entity.value)
.filter(Entity.id == Parent.entity_id)
.correlate(Parent)
.as_scalar()
)
session.query(Parent).filter(subq == "some value")
This was the opposite behavior of a plain select()
construct which would assume auto-correlation by default.
The above statement in 0.8 will correlate automatically:
subq = session.query(Entity.value).filter(Entity.id == Parent.entity_id).as_scalar() session.query(Parent).filter(subq == "some value")
like in select(), correlation can be disabled by calling
query.correlate(None) or manually set by passing an
entity, query.correlate(someentity).
To allow a wider variety of correlation scenarios, the behavior of :meth:`_expression.Select.correlate` and :meth:`_query.Query.correlate` has changed slightly such that the SELECT statement will omit the "correlated" target from the FROM clause only if the statement is actually used in that context. Additionally, it's no longer possible for a SELECT statement that's placed as a FROM in an enclosing SELECT statement to "correlate" (i.e. omit) a FROM clause.
This change only makes things better as far as rendering SQL, in that it's no longer possible to render illegal SQL where there are insufficient FROM objects relative to what's being selected:
from sqlalchemy.sql import table, column, select
t1 = table("t1", column("x"))
t2 = table("t2", column("y"))
s = select([t1, t2]).correlate(t1)
print(s)
Prior to this change, the above would return:
SELECT t1.x, t2.y FROM t2which is invalid SQL as "t1" is not referred to in any FROM clause.
Now, in the absence of an enclosing SELECT, it returns:
SELECT t1.x, t2.y FROM t1, t2Within a SELECT, the correlation takes effect as expected:
s2 = select([t1, t2]).where(t1.c.x == t2.c.y).where(t1.c.x == s)
print(s2)SELECT t1.x, t2.y FROM t1, t2
WHERE t1.x = t2.y AND t1.x =
(SELECT t1.x, t2.y FROM t2)This change is not expected to impact any existing applications, as the correlation behavior remains identical for properly constructed expressions. Only an application that relies, most likely within a testing scenario, on the invalid string output of a correlated SELECT used in a non-correlating context would see any change.
The methods :meth:`_schema.MetaData.create_all` and :meth:`_schema.MetaData.drop_all`
will now accept a list of :class:`_schema.Table` objects that is empty,
and will not emit any CREATE or DROP statements. Previously,
an empty list was interpreted the same as passing None
for a collection, and CREATE/DROP would be emitted for all
items unconditionally.
This is a bug fix but some applications may have been relying upon the previous behavior.
Repaired the Event Targeting of :class:`.InstrumentationEvents`
The :class:`.InstrumentationEvents` series of event targets have
documented that the events will only be fired off according to
the actual class passed as a target. Through 0.7, this wasn't the
case, and any event listener applied to :class:`.InstrumentationEvents`
would be invoked for all classes mapped. In 0.8, additional
logic has been added so that the events will only invoke for those
classes sent in. The propagate flag here is set to True
by default as class instrumentation events are typically used to
intercept classes that aren't yet created.
We found a very old behavior in the MSSQL dialect which would attempt to rescue users from themselves when doing something like this:
scalar_subq = select([someothertable.c.id]).where(someothertable.c.data == "foo") select([sometable]).where(sometable.c.id == scalar_subq)
SQL Server doesn't allow an equality comparison to a scalar SELECT, that is, "x = (SELECT something)". The MSSQL dialect would convert this to an IN. The same thing would happen however upon a comparison like "(SELECT something) = x", and overall this level of guessing is outside of SQLAlchemy's usual scope so the behavior is removed.
Fixed the behavior of :meth:`.Session.is_modified`
The :meth:`.Session.is_modified` method accepts an argument
passive which basically should not be necessary, the
argument in all cases should be the value True - when
left at its default of False it would have the effect of
hitting the database, and often triggering autoflush which
would itself change the results. In 0.8 the passive
argument will have no effect, and unloaded attributes will
never be checked for history since by definition there can
be no pending state change on an unloaded attribute.
.. seealso::
:meth:`.Session.is_modified`
:attr:`_schema.Column.key` is honored in the :attr:`_expression.Select.c` attribute of :func:`_expression.select` with :meth:`_expression.Select.apply_labels`
Users of the expression system know that :meth:`_expression.Select.apply_labels` prepends the table name to each column name, affecting the names that are available from :attr:`_expression.Select.c`:
s = select([table1]).apply_labels() s.c.table1_col1 s.c.table1_col2
Before 0.8, if the :class:`_schema.Column` had a different :attr:`_schema.Column.key`, this key would be ignored, inconsistently versus when :meth:`_expression.Select.apply_labels` were not used:
# before 0.8
table1 = Table("t1", metadata, Column("col1", Integer, key="column_one"))
s = select([table1])
s.c.column_one # would be accessible like this
s.c.col1 # would raise AttributeError
s = select([table1]).apply_labels()
s.c.table1_column_one # would raise AttributeError
s.c.table1_col1 # would be accessible like this
In 0.8, :attr:`_schema.Column.key` is honored in both cases:
# with 0.8
table1 = Table("t1", metadata, Column("col1", Integer, key="column_one"))
s = select([table1])
s.c.column_one # works
s.c.col1 # AttributeError
s = select([table1]).apply_labels()
s.c.table1_column_one # works
s.c.table1_col1 # AttributeError
All other behavior regarding "name" and "key" are the same,
including that the rendered SQL will still use the form
<tablename>_<colname> - the emphasis here was on
preventing the :attr:`_schema.Column.key` contents from being rendered into the
SELECT statement so that there are no issues with
special/ non-ascii characters used in the :attr:`_schema.Column.key`.
A :func:`_orm.relationship` that is many-to-one or many-to-many and
specifies "cascade='all, delete-orphan'", which is an
awkward but nonetheless supported use case (with
restrictions) will now raise an error if the relationship
does not specify the single_parent=True option.
Previously it would only emit a warning, but a failure would
follow almost immediately within the attribute system in any
case.
0.7 added a new event called column_reflect, provided so
that the reflection of columns could be augmented as each
one were reflected. We got this event slightly wrong in
that the event gave no way to get at the current
Inspector and Connection being used for the
reflection, in the case that additional information from the
database is needed. As this is a new event not widely used
yet, we'll be adding the inspector argument into it
directly:
@event.listens_for(Table, "column_reflect")
def listen_for_col(inspector, table, column_info):
...
The MySQL dialect does two calls, one very expensive, to
load all possible collations from the database as well as
information on casing, the first time an Engine
connects. Neither of these collections are used for any
SQLAlchemy functions, so these calls will be changed to no
longer be emitted automatically. Applications that might
have relied on these collections being present on
engine.dialect will need to call upon
_detect_collations() and _detect_casing() directly.
Referring to a non-existent column in an insert() or
update() construct will raise an error instead of a
warning:
t1 = table("t1", column("x"))
t1.insert().values(x=5, z=5) # raises "Unconsumed column names: z"
These two methods on Inspector were redundant, where
get_primary_keys() would return the same information as
get_pk_constraint() minus the name of the constraint:
>>> insp.get_primary_keys()
["a", "b"]
>>> insp.get_pk_constraint()
{"name":"pk_constraint", "constrained_columns":["a", "b"]}
A very old behavior, the column names in RowProxy were
always compared case-insensitively:
>>> row = result.fetchone() >>> row["foo"] == row["FOO"] == row["Foo"] True
This was for the benefit of a few dialects which in the early days needed this, like Oracle and Firebird, but in modern usage we have more accurate ways of dealing with the case-insensitive behavior of these two platforms.
Going forward, this behavior will be available only
optionally, by passing the flag `case_sensitive=False`
to `create_engine()`, but otherwise column names
requested from the row must match as far as casing.
The sqlalchemy.orm.interfaces.InstrumentationManager
class is moved to
sqlalchemy.ext.instrumentation.InstrumentationManager.
The "alternate instrumentation" system was built for the
benefit of a very small number of installations that needed
to work with existing or unusual class instrumentation
systems, and generally is very seldom used. The complexity
of this system has been exported to an ext. module. It
remains unused until once imported, typically when a third
party library imports InstrumentationManager, at which
point it is injected back into sqlalchemy.orm by
replacing the default InstrumentationFactory with
ExtendedInstrumentationRegistry.
SQLSoup is a handy package that presents an alternative interface on top of the SQLAlchemy ORM. SQLSoup is now moved into its own project and documented/released separately; see https://bitbucket.org/zzzeek/sqlsoup.
SQLSoup is a very simple tool that could also benefit from contributors who are interested in its style of usage.
The older "mutable" system within the SQLAlchemy ORM has
been removed. This refers to the MutableType interface
which was applied to types such as PickleType and
conditionally to TypeDecorator, and since very early
SQLAlchemy versions has provided a way for the ORM to detect
changes in so-called "mutable" data structures such as JSON
structures and pickled objects. However, the
implementation was never reasonable and forced a very
inefficient mode of usage on the unit-of-work which caused
an expensive scan of all objects to take place during flush.
In 0.7, the sqlalchemy.ext.mutable extension was
introduced so that user-defined datatypes can appropriately
send events to the unit of work as changes occur.
Today, usage of MutableType is expected to be low, as
warnings have been in place for some years now regarding its
inefficiency.
We had left in an alias sqlalchemy.exceptions to attempt
to make it slightly easier for some very old libraries that
hadn't yet been upgraded to use sqlalchemy.exc. Some
users are still being confused by it however so in 0.8 we're
taking it out entirely to eliminate any of that confusion.