SQLAlchemy 2.0 - Major Migration Guide¶
Note for Readers
SQLAlchemy 2.0’s transition documents are separated into two documents - one which details major API shifts from the 1.x to 2.x series, and the other which details new features and behaviors relative to SQLAlchemy 1.4:
SQLAlchemy 2.0 - Major Migration Guide - this document, 1.x to 2.x API shifts
What’s New in SQLAlchemy 2.0? - new features and behaviors for SQLAlchemy 2.0
Readers who have already updated their 1.4 application to follow SQLAlchemy 2.0 engine and ORM conventions may navigate to What’s New in SQLAlchemy 2.0? for an overview of new features and capabilities.
About this document
This document describes changes between SQLAlchemy version 1.4 and SQLAlchemy version 2.0.
SQLAlchemy 2.0 presents a major shift for a wide variety of key SQLAlchemy usage patterns in both the Core and ORM components. The goal of this release is to make a slight readjustment in some of the most fundamental assumptions of SQLAlchemy since its early beginnings, and to deliver a newly streamlined usage model that is hoped to be significantly more minimalist and consistent between the Core and ORM components, as well as more capable. The move of Python to be Python 3 only as well as the emergence of gradual typing systems for Python 3 are the initial inspirations for this shift, as is the changing nature of the Python community which now includes not just hardcore database programmers but a vast new community of data scientists and students of many different disciplines.
SQLAlchemy started with Python 2.3 which had no context managers, no
function decorators, Unicode as a second class feature, and a variety of
other shortcomings that would be unknown today. The biggest changes in
SQLAlchemy 2.0 are targeting the residual assumptions left over from this
early period in SQLAlchemy’s development as well as the leftover artifacts
resulting from the incremental introduction of key API features such as
Query
and Declarative. It also hopes standardize some
newer capabilities that have proven to be very effective.
The 1.4->2.0 Migration Path¶
The most prominent architectural features and API changes that are considered to be “SQLAlchemy 2.0” were in fact released as fully available within the 1.4 series, to provide for a clean upgrade path from the 1.x to the 2.x series as well as to serve as a beta platform for the features themselves. These changes include:
The above bullets link to the description of these new paradigms as introduced in SQLAlchemy 1.4. in the What’s New in SQLAlchemy 1.4? document.
For SQLAlchemy 2.0, all API features and behaviors that were marked as deprecated for 2.0 are now finalized; in particular, major APIs that are no longer present include:
Python 2 support
The above bullets refer to the most prominent fully backwards-incompatible changes that are finalized in the 2.0 release. The migration path for applications to accommodate for these changes as well as others is framed as a transition path first into the 1.4 series of SQLAlchemy where the “future” APIs are available to provide for the “2.0” way of working, and then to the 2.0 series where the no-longer-used APIs above and others are removed.
The complete steps for this migration path are later in this document at 1.x -> 2.x Migration Overview.
1.x -> 2.x Migration Overview¶
The SQLAlchemy 2.0 transition presents itself in the SQLAlchemy 1.4 release as a series of steps that allow an application of any size or complexity to be migrated to SQLAlchemy 2.0 using a gradual, iterative process. Lessons learned from the Python 2 to Python 3 transition have inspired a system that intends to as great a degree as possible to not require any “breaking” changes, or any change that would need to be made universally or not at all.
As a means of both proving the 2.0 architecture as well as allowing a fully iterative transition environment, the entire scope of 2.0’s new APIs and features are present and available within the 1.4 series; this includes major new areas of functionality such as the SQL caching system, the new ORM statement execution model, new transactional paradigms for both ORM and Core, a new ORM declarative system that unifies classical and declarative mapping, support for Python dataclasses, and asyncio support for Core and ORM.
The steps to achieve 2.0 migration are in the following subsections; overall, the general strategy is that once an application runs on 1.4 with all warning flags turned on and does not emit any 2.0-deprecation warnings, it is now mostly cross-compatible with SQLAlchemy 2.0. Please note there may be additional API and behavioral changes that may behave differently when running against SQLAlchemy 2.0; always test code against an actual SQLAlchemy 2.0 release as the final step in migrating.
First Prerequisite, step one - A Working 1.3 Application¶
The first step is getting an existing application onto 1.4, in the case of
a typical non trivial application, is to ensure it runs on SQLAlchemy 1.3 with
no deprecation warnings. Release 1.4 does have a few changes linked to
conditions that warn in previous version, including some warnings that were
introduced in 1.3, in particular some changes to the behavior of the
relationship.viewonly
and
relationship.sync_backref
flags.
For best results, the application should be able to run, or pass all of its
tests, with the latest SQLAlchemy 1.3 release with no SQLAlchemy deprecation
warnings; these are warnings emitted for the SADeprecationWarning
class.
First Prerequisite, step two - A Working 1.4 Application¶
Once the application is good to go on SQLAlchemy 1.3, the next step is to get it running on SQLAlchemy 1.4. In the vast majority of cases, applications should run without problems from SQLAlchemy 1.3 to 1.4. However, it’s always the case between any 1.x and 1.y release, APIs and behaviors have changed either subtly or in some cases a little less subtly, and the SQLAlchemy project always gets a good deal of regression reports for the first few months.
The 1.x->1.y release process usually has a few changes around the margins that are a little bit more dramatic and are based around use cases that are expected to be very seldom if at all used. For 1.4, the changes identified as being in this realm are as follows:
The URL object is now immutable - this impacts code that would be manipulating the
URL
object and may impact code that makes use of theCreateEnginePlugin
extension point. This is an uncommon case but may affect in particular some test suites that are making use of special database provisioning logic. A github search for code that uses the relatively new and little-knownCreateEnginePlugin
class found two projects that were unaffected by the change.A SELECT statement is no longer implicitly considered to be a FROM clause - this change may impact code that was somehow relying upon behavior that was mostly unusable in the
Select
construct, where it would create unnamed subqueries that were usually confusing and non-working. These subqueries would be rejected by most databases in any case as a name is usually required except on SQLite, however it is possible some applications will need to adjust some queries that are inadvertently relying upon this.select().join() and outerjoin() add JOIN criteria to the current query, rather than creating a subquery - somewhat related, the
Select
class featured.join()
and.outerjoin()
methods that implicitly created a subquery and then returned aJoin
construct, which again would be mostly useless and produced lots of confusion. The decision was made to move forward with the vastly more useful 2.0-style join-building approach where these methods now work the same way as the ORMQuery.join()
method.Many Core and ORM statement objects now perform much of their construction and validation in the compile phase - some error messages related to construction of a
Query
orSelect
may not be emitted until compilation / execution, rather than at construction time. This might impact some test suites that are testing against failure modes.
For the full overview of SQLAlchemy 1.4 changes, see the What’s New in SQLAlchemy 1.4? document.
Migration to 2.0 Step One - Python 3 only (Python 3.7 minimum for 2.0 compatibility)¶
SQLAlchemy 2.0 was first inspired by the fact that Python 2’s EOL was in 2020. SQLAlchemy is taking a longer period of time than other major projects to drop Python 2.7 support. However, in order to use SQLAlchemy 2.0, the application will need to be runnable on at least Python 3.7. SQLAlchemy 1.4 supports Python 3.6 or newer within the Python 3 series; throughout the 1.4 series, the application can remain running on Python 2.7 or on at least Python 3.6. Version 2.0 however starts at Python 3.7.
Migration to 2.0 Step Two - Turn on RemovedIn20Warnings¶
SQLAlchemy 1.4 features a conditional deprecation warning system inspired
by the Python “-3” flag that would indicate legacy patterns in a running
application. For SQLAlchemy 1.4, the RemovedIn20Warning
deprecation class is emitted only when an environment variable
SQLALCHEMY_WARN_20
is set to either of true
or 1
.
Given the example program below:
from sqlalchemy import column
from sqlalchemy import create_engine
from sqlalchemy import select
from sqlalchemy import table
engine = create_engine("sqlite://")
engine.execute("CREATE TABLE foo (id integer)")
engine.execute("INSERT INTO foo (id) VALUES (1)")
foo = table("foo", column("id"))
result = engine.execute(select([foo.c.id]))
print(result.fetchall())
The above program uses several patterns that many users will already identify
as “legacy”, namely the use of the Engine.execute()
method
that’s part of the “connectionless execution” API. When we run the above
program against 1.4, it returns a single line:
$ python test3.py
[(1,)]
To enable “2.0 deprecations mode”, we enable the SQLALCHEMY_WARN_20=1
variable, and additionally ensure that a warnings filter that will not
suppress any warnings is selected:
SQLALCHEMY_WARN_20=1 python -W always::DeprecationWarning test3.py
Since the reported warning location is not always in the correct place, locating
the offending code may be difficult without the full stacktrace. This can be achieved
by transforming the warnings to exceptions by specifying the error
warning filter,
using Python option -W error::DeprecationWarning
.
With warnings turned on, our program now has a lot to say:
$ SQLALCHEMY_WARN_20=1 python2 -W always::DeprecationWarning test3.py
test3.py:9: RemovedIn20Warning: The Engine.execute() function/method is considered legacy as of the 1.x series of SQLAlchemy and will be removed in 2.0. All statement execution in SQLAlchemy 2.0 is performed by the Connection.execute() method of Connection, or in the ORM by the Session.execute() method of Session. (Background on SQLAlchemy 2.0 at: https://sqlalche.me/e/b8d9) (Background on SQLAlchemy 2.0 at: https://sqlalche.me/e/b8d9)
engine.execute("CREATE TABLE foo (id integer)")
/home/classic/dev/sqlalchemy/lib/sqlalchemy/engine/base.py:2856: RemovedIn20Warning: Passing a string to Connection.execute() is deprecated and will be removed in version 2.0. Use the text() construct, or the Connection.exec_driver_sql() method to invoke a driver-level SQL string. (Background on SQLAlchemy 2.0 at: https://sqlalche.me/e/b8d9)
return connection.execute(statement, *multiparams, **params)
/home/classic/dev/sqlalchemy/lib/sqlalchemy/engine/base.py:1639: RemovedIn20Warning: The current statement is being autocommitted using implicit autocommit.Implicit autocommit will be removed in SQLAlchemy 2.0. Use the .begin() method of Engine or Connection in order to use an explicit transaction for DML and DDL statements. (Background on SQLAlchemy 2.0 at: https://sqlalche.me/e/b8d9)
self._commit_impl(autocommit=True)
test3.py:10: RemovedIn20Warning: The Engine.execute() function/method is considered legacy as of the 1.x series of SQLAlchemy and will be removed in 2.0. All statement execution in SQLAlchemy 2.0 is performed by the Connection.execute() method of Connection, or in the ORM by the Session.execute() method of Session. (Background on SQLAlchemy 2.0 at: https://sqlalche.me/e/b8d9) (Background on SQLAlchemy 2.0 at: https://sqlalche.me/e/b8d9)
engine.execute("INSERT INTO foo (id) VALUES (1)")
/home/classic/dev/sqlalchemy/lib/sqlalchemy/engine/base.py:2856: RemovedIn20Warning: Passing a string to Connection.execute() is deprecated and will be removed in version 2.0. Use the text() construct, or the Connection.exec_driver_sql() method to invoke a driver-level SQL string. (Background on SQLAlchemy 2.0 at: https://sqlalche.me/e/b8d9)
return connection.execute(statement, *multiparams, **params)
/home/classic/dev/sqlalchemy/lib/sqlalchemy/engine/base.py:1639: RemovedIn20Warning: The current statement is being autocommitted using implicit autocommit.Implicit autocommit will be removed in SQLAlchemy 2.0. Use the .begin() method of Engine or Connection in order to use an explicit transaction for DML and DDL statements. (Background on SQLAlchemy 2.0 at: https://sqlalche.me/e/b8d9)
self._commit_impl(autocommit=True)
/home/classic/dev/sqlalchemy/lib/sqlalchemy/sql/selectable.py:4271: RemovedIn20Warning: The legacy calling style of select() is deprecated and will be removed in SQLAlchemy 2.0. Please use the new calling style described at select(). (Background on SQLAlchemy 2.0 at: https://sqlalche.me/e/b8d9) (Background on SQLAlchemy 2.0 at: https://sqlalche.me/e/b8d9)
return cls.create_legacy_select(*args, **kw)
test3.py:14: RemovedIn20Warning: The Engine.execute() function/method is considered legacy as of the 1.x series of SQLAlchemy and will be removed in 2.0. All statement execution in SQLAlchemy 2.0 is performed by the Connection.execute() method of Connection, or in the ORM by the Session.execute() method of Session. (Background on SQLAlchemy 2.0 at: https://sqlalche.me/e/b8d9) (Background on SQLAlchemy 2.0 at: https://sqlalche.me/e/b8d9)
result = engine.execute(select([foo.c.id]))
[(1,)]
With the above guidance, we can migrate our program to use 2.0 styles, and as a bonus our program is much clearer:
from sqlalchemy import column
from sqlalchemy import create_engine
from sqlalchemy import select
from sqlalchemy import table
from sqlalchemy import text
engine = create_engine("sqlite://")
# don't rely on autocommit for DML and DDL
with engine.begin() as connection:
# use connection.execute(), not engine.execute()
# use the text() construct to execute textual SQL
connection.execute(text("CREATE TABLE foo (id integer)"))
connection.execute(text("INSERT INTO foo (id) VALUES (1)"))
foo = table("foo", column("id"))
with engine.connect() as connection:
# use connection.execute(), not engine.execute()
# select() now accepts column / table expressions positionally
result = connection.execute(select(foo.c.id))
print(result.fetchall())
The goal of “2.0 deprecations mode” is that a program which runs with no
RemovedIn20Warning
warnings with “2.0 deprecations mode” turned
on is then ready to run in SQLAlchemy 2.0.
Migration to 2.0 Step Three - Resolve all RemovedIn20Warnings¶
Code can be developed iteratively to resolve these warnings. Within the SQLAlchemy project itself, the approach taken is as follows:
enable the
SQLALCHEMY_WARN_20=1
environment variable in the test suite, for SQLAlchemy this is in the tox.ini fileWithin the setup for the test suite, set up a series of warnings filters that will select for particular subsets of warnings to either raise an exception, or to be ignored (or logged). Work with just one subgroup of warnings at a time. Below, a warnings filter is configured for an application where the change to the Core level
.execute()
calls will be needed in order for all tests to pass, but all other 2.0-style warnings will be suppressed:import warnings from sqlalchemy import exc # for warnings not included in regex-based filter below, just log warnings.filterwarnings("always", category=exc.RemovedIn20Warning) # for warnings related to execute() / scalar(), raise for msg in [ r"The (?:Executable|Engine)\.(?:execute|scalar)\(\) function", r"The current statement is being autocommitted using implicit autocommit,", r"The connection.execute\(\) method in SQLAlchemy 2.0 will accept " "parameters as a single dictionary or a single sequence of " "dictionaries only.", r"The Connection.connect\(\) function/method is considered legacy", r".*DefaultGenerator.execute\(\)", ]: warnings.filterwarnings( "error", message=msg, category=exc.RemovedIn20Warning, )
As each sub-category of warnings are resolved in the application, new warnings that are caught by the “always” filter can be added to the list of “errors” to be resolved.
Once no more warnings are emitted, the filter can be removed.
Migration to 2.0 Step Four - Use the future
flag on Engine¶
The Engine
object features an updated
transaction-level API in version 2.0. In 1.4, this new API is available
by passing the flag future=True
to the create_engine()
function.
When the create_engine.future
flag is used, the Engine
and Connection
objects support the 2.0 API fully and not at all
any legacy features, including the new argument format for Connection.execute()
,
the removal of “implicit autocommit”, string statements require the
text()
construct unless the Connection.exec_driver_sql()
method is used, and connectionless execution from the Engine
is removed.
If all RemovedIn20Warning
warnings have been resolved regarding
use of the Engine
and Connection
, then the
create_engine.future
flag may be enabled and there should be
no errors raised.
The new engine is described at Engine
which delivers a new
Connection
object. In addition to the above changes, the,
Connection
object features
Connection.commit()
and
Connection.rollback()
methods, to support the new
“commit-as-you-go” mode of operation:
from sqlalchemy import create_engine
engine = create_engine("postgresql+psycopg2:///")
with engine.connect() as conn:
conn.execute(text("insert into table (x) values (:some_x)"), {"some_x": 10})
conn.commit() # commit as you go
Migration to 2.0 Step Five - Use the future
flag on Session¶
The Session
object also features an updated transaction/connection
level API in version 2.0. This API is available in 1.4 using the
Session.future
flag on Session
or on
sessionmaker
.
The Session
object supports “future” mode in place, and involves
these changes:
The
Session
no longer supports “bound metadata” when it resolves the engine to be used for connectivity. This means that anEngine
object must be passed to the constructor (this may be either a legacy or future style object).The
Session.begin.subtransactions
flag is no longer supported.The
Session.commit()
method always emits a COMMIT to the database, rather than attempting to reconcile “subtransactions”.The
Session.rollback()
method always rolls back the full stack of transactions at once, rather than attempting to keep “subtransactions” in place.
The Session
also supports more flexible creational patterns
in 1.4 which are now closely matched to the patterns used by the
Connection
object. Highlights include that the
Session
may be used as a context manager:
from sqlalchemy.orm import Session
with Session(engine) as session:
session.add(MyObject())
session.commit()
In addition, the sessionmaker
object supports a
sessionmaker.begin()
context manager that will create a
Session
and begin /commit a transaction in one block:
from sqlalchemy.orm import sessionmaker
Session = sessionmaker(engine)
with Session.begin() as session:
session.add(MyObject())
See the section Session-level vs. Engine level transaction control for a comparison of
Session
creational patterns compared to those of
Connection
.
Once the application passes all tests/ runs with SQLALCHEMY_WARN_20=1
and all exc.RemovedIn20Warning
occurrences set to raise an error,
the application is ready!.
The sections that follow will detail the specific changes to make for all major API modifications.
Migration to 2.0 Step Six - Add __allow_unmapped__
to explicitly typed ORM models¶
SQLAlchemy 2.0 has new support for runtime interpretation of PEP 484 typing annotations
on ORM models. A requirement of these annotations is that they must make use
of the Mapped
generic container. Annotations which don’t use
Mapped
which link to constructs such as relationship()
will raise errors in Python, as they suggest mis-configurations.
SQLAlchemy applications that use the Mypy plugin with
explicit annotations that don’t use Mapped
in their annotations
are subject to these errors, as would occur in the example below:
Base = declarative_base()
class Foo(Base):
__tablename__ = "foo"
id: int = Column(Integer, primary_key=True)
# will raise
bars: List["Bar"] = relationship("Bar", back_populates="foo")
class Bar(Base):
__tablename__ = "bar"
id: int = Column(Integer, primary_key=True)
foo_id = Column(ForeignKey("foo.id"))
# will raise
foo: Foo = relationship(Foo, back_populates="bars", cascade="all")
Above, the Foo.bars
and Bar.foo
relationship()
declarations
will raise an error at class construction time because they don’t use
Mapped
(by contrast, the annotations that use
Column
are ignored by 2.0, as these are able to be
recognized as a legacy configuration style). To allow all annotations that
don’t use Mapped
to pass without error,
the __allow_unmapped__
attribute may be used on the class or any
subclasses, which will cause the annotations in these cases to be
ignored completely by the new Declarative system.
Note
The __allow_unmapped__
directive applies only to the
runtime behavior of the ORM. It does not affect the behavior of
Mypy, and the above mapping as written still requires that the Mypy
plugin be installed. For fully 2.0 style ORM models that will type
correctly under Mypy without a plugin, follow the migration steps
at Migrating an Existing Mapping.
The example below illustrates the application of __allow_unmapped__
to the Declarative Base
class, where it will take effect for all classes
that descend from Base
:
# qualify the base with __allow_unmapped__. Can also be
# applied to classes directly if preferred
class Base:
__allow_unmapped__ = True
Base = declarative_base(cls=Base)
# existing mapping proceeds, Declarative will ignore any annotations
# which don't include ``Mapped[]``
class Foo(Base):
__tablename__ = "foo"
id: int = Column(Integer, primary_key=True)
bars: List["Bar"] = relationship("Bar", back_populates="foo")
class Bar(Base):
__tablename__ = "bar"
id: int = Column(Integer, primary_key=True)
foo_id = Column(ForeignKey("foo.id"))
foo: Foo = relationship(Foo, back_populates="bars", cascade="all")
Changed in version 2.0.0beta3: - improved the __allow_unmapped__
attribute support to allow for 1.4-style explicit annotated relationships
that don’t use Mapped
to remain usable.
Migration to 2.0 Step Seven - Test against a SQLAlchemy 2.0 Release¶
As mentioned previously, SQLAlchemy 2.0 has additional API and behavioral changes that are intended to be backwards compatible, however may introduce some incompatibilities nonetheless. Therefore after the overall porting process is complete, the final step is to test against the most recent release of SQLAlchemy 2.0 to correct for any remaining issues that might be present.
The guide at What’s New in SQLAlchemy 2.0? provides an overview of new features and behaviors for SQLAlchemy 2.0 which extend beyond the base set of 1.4->2.0 API changes.
2.0 Migration - Core Connection / Transaction¶
Library-level (but not driver level) “Autocommit” removed from both Core and ORM¶
Synopsis
In SQLAlchemy 1.x, the following statements will automatically commit the underlying DBAPI transaction, but in SQLAlchemy 2.0 this will not occur:
conn = engine.connect()
# won't autocommit in 2.0
conn.execute(some_table.insert().values(foo="bar"))
Nor will this autocommit:
conn = engine.connect()
# won't autocommit in 2.0
conn.execute(text("INSERT INTO table (foo) VALUES ('bar')"))
The common workaround for custom DML that requires commit, the “autocommit” execution option, will be removed:
conn = engine.connect()
# won't autocommit in 2.0
conn.execute(text("EXEC my_procedural_thing()").execution_options(autocommit=True))
Migration to 2.0
The method that is cross-compatible with 1.x style and 2.0
style execution is to make use of the Connection.begin()
method,
or the Engine.begin()
context manager:
with engine.begin() as conn:
conn.execute(some_table.insert().values(foo="bar"))
conn.execute(some_other_table.insert().values(bat="hoho"))
with engine.connect() as conn:
with conn.begin():
conn.execute(some_table.insert().values(foo="bar"))
conn.execute(some_other_table.insert().values(bat="hoho"))
with engine.begin() as conn:
conn.execute(text("EXEC my_procedural_thing()"))
When using 2.0 style with the create_engine.future
flag, “commit as you go” style may also be used, as the
Connection
features autobegin behavior, which takes place
when a statement is first invoked in the absence of an explicit call to
Connection.begin()
:
with engine.connect() as conn:
conn.execute(some_table.insert().values(foo="bar"))
conn.execute(some_other_table.insert().values(bat="hoho"))
conn.commit()
When 2.0 deprecations mode is enabled, a warning will emit when the deprecated “autocommit” feature takes place, indicating those places where an explicit transaction should be noted.
Discussion
SQLAlchemy’s first releases were at odds with the spirit of the Python DBAPI (PEP 249) in that it tried to hide PEP 249’s emphasis on “implicit begin” and “explicit commit” of transactions. Fifteen years later we now see this was essentially a mistake, as SQLAlchemy’s many patterns that attempt to “hide” the presence of a transaction make for a more complex API which works inconsistently and is extremely confusing to especially those users who are new to relational databases and ACID transactions in general. SQLAlchemy 2.0 will do away with all attempts to implicitly commit transactions, and usage patterns will always require that the user demarcate the “beginning” and the “end” of a transaction in some way, in the same way as reading or writing to a file in Python has a “beginning” and an “end”.
In the case of autocommit for a pure textual statement, there is actually a regular expression that parses every statement in order to detect autocommit! Not surprisingly, this regex is continuously failing to accommodate for various kinds of statements and stored procedures that imply a “write” to the database, leading to ongoing confusion as some statements produce results in the database and others don’t. By preventing the user from being aware of the transactional concept, we get a lot of bug reports on this one because users don’t understand that databases always use a transaction, whether or not some layer is autocommitting it.
SQLAlchemy 2.0 will require that all database actions at every level be explicit as to how the transaction should be used. For the vast majority of Core use cases, it’s the pattern that is already recommended:
with engine.begin() as conn:
conn.execute(some_table.insert().values(foo="bar"))
For “commit as you go, or rollback instead” usage, which resembles how the
Session
is normally used today, the “future” version of
Connection
, which is the one that is returned from an
Engine
that was created using the
create_engine.future
flag, includes new
Connection.commit()
and Connection.rollback()
methods, which act upon a transaction that is now begun automatically when
a statement is first invoked:
# 1.4 / 2.0 code
from sqlalchemy import create_engine
engine = create_engine(..., future=True)
with engine.connect() as conn:
conn.execute(some_table.insert().values(foo="bar"))
conn.commit()
conn.execute(text("some other SQL"))
conn.rollback()
Above, the engine.connect()
method will return a Connection
that
features autobegin, meaning the begin()
event is emitted when the
execute method is first used (note however that there is no actual “BEGIN” in
the Python DBAPI). “autobegin” is a new pattern in SQLAlchemy 1.4 that
is featured both by Connection
as well as the ORM
Session
object; autobegin allows that the Connection.begin()
method may be called explicitly when the object is first acquired, for schemes
that wish to demarcate the beginning of the transaction, but if the method
is not called, then it occurs implicitly when work is first done on the object.
The removal of “autocommit” is closely related to the removal of “connectionless” execution discussed at “Implicit” and “Connectionless” execution, “bound metadata” removed. All of these legacy patterns built up from the fact that Python did not have context managers or decorators when SQLAlchemy was first created, so there were no convenient idiomatic patterns for demarcating the use of a resource.
Driver-level autocommit remains available¶
True “autocommit” behavior is now widely available with most DBAPI
implementations, and is supported by SQLAlchemy via the
Connection.execution_options.isolation_level
parameter as
discussed at Setting Transaction Isolation Levels including DBAPI Autocommit. True autocommit is treated as an “isolation level”
so that the structure of application code does not change when autocommit is
used; the Connection.begin()
context manager as well as
methods like Connection.commit()
may still be used, they are
simply no-ops at the database driver level when DBAPI-level autocommit
is turned on.
“Implicit” and “Connectionless” execution, “bound metadata” removed¶
Synopsis
The ability to associate an Engine
with a MetaData
object, which then makes available a range of so-called “connectionless”
execution patterns, is removed:
from sqlalchemy import MetaData
metadata_obj = MetaData(bind=engine) # no longer supported
metadata_obj.create_all() # requires Engine or Connection
metadata_obj.reflect() # requires Engine or Connection
t = Table("t", metadata_obj, autoload=True) # use autoload_with=engine
result = engine.execute(t.select()) # no longer supported
result = t.select().execute() # no longer supported
Migration to 2.0
For schema level patterns, explicit use of an Engine
or Connection
is required. The Engine
may still be used directly as the source of connectivity for a
MetaData.create_all()
operation or autoload operation.
For executing statements, only the Connection
object
has a Connection.execute()
method (in addition to
the ORM-level Session.execute()
method):
from sqlalchemy import MetaData
metadata_obj = MetaData()
# engine level:
# create tables
metadata_obj.create_all(engine)
# reflect all tables
metadata_obj.reflect(engine)
# reflect individual table
t = Table("t", metadata_obj, autoload_with=engine)
# connection level:
with engine.connect() as connection:
# create tables, requires explicit begin and/or commit:
with connection.begin():
metadata_obj.create_all(connection)
# reflect all tables
metadata_obj.reflect(connection)
# reflect individual table
t = Table("t", metadata_obj, autoload_with=connection)
# execute SQL statements
result = connection.execute(t.select())
Discussion
The Core documentation has already standardized on the desired pattern here,
so it is likely that most modern applications would not have to change
much in any case, however there are likely many applications that still
rely upon engine.execute()
calls that will need to be adjusted.
“Connectionless” execution refers to the still fairly popular pattern of
invoking .execute()
from the Engine
:
result = engine.execute(some_statement)
The above operation implicitly procures a Connection
object,
and runs the .execute()
method on it. While this appears to be a simple
convenience feature, it has been shown to give rise to several issues:
Programs that feature extended strings of
engine.execute()
calls have become prevalent, overusing a feature that was intended to be seldom used and leading to inefficient non-transactional applications. New users are confused as to the difference betweenengine.execute()
andconnection.execute()
and the nuance between these two approaches is often not understood.The feature relies upon the “application level autocommit” feature in order to make sense, which itself is also being removed as it is also inefficient and misleading.
In order to handle result sets,
Engine.execute
returns a result object with unconsumed cursor results. This cursor result necessarily still links to the DBAPI connection which remains in an open transaction, all of which is released once the result set has fully consumed the rows waiting within the cursor. This means thatEngine.execute
does not actually close out the connection resources that it claims to be managing when the call is complete. SQLAlchemy’s “autoclose” behavior is well-tuned enough that users don’t generally report any negative effects from this system, however it remains an overly implicit and inefficient system left over from SQLAlchemy’s earliest releases.
The removal of “connectionless” execution then leads to the removal of an even more legacy pattern, that of “implicit, connectionless” execution:
result = some_statement.execute()
The above pattern has all the issues of “connectionless” execution, plus it
relies upon the “bound metadata” pattern, which SQLAlchemy has tried to
de-emphasize for many years. This was SQLAlchemy’s very first advertised
usage model in version 0.1, which became obsolete almost immediately when
the Connection
object was introduced and later Python
context managers provided a better pattern for using resources within a
fixed scope.
With implicit execution removed, “bound metadata” itself also no longer has
a purpose within this system. In modern use “bound metadata” tends to still
be somewhat convenient for working within MetaData.create_all()
calls as well as with Session
objects, however having these
functions receive an Engine
explicitly provides for clearer
application design.
Many Choices becomes One Choice¶
Overall, the above executional patterns were introduced in SQLAlchemy’s
very first 0.1 release before the Connection
object even existed.
After many years of de-emphasizing these patterns, “implicit, connectionless”
execution and “bound metadata” are no longer as widely used so in 2.0 we seek
to finally reduce the number of choices for how to execute a statement in
Core from “many choices”:
# many choices
# bound metadata?
metadata_obj = MetaData(engine)
# or not?
metadata_obj = MetaData()
# execute from engine?
result = engine.execute(stmt)
# or execute the statement itself (but only if you did
# "bound metadata" above, which means you can't get rid of "bound" if any
# part of your program uses this form)
result = stmt.execute()
# execute from connection, but it autocommits?
conn = engine.connect()
conn.execute(stmt)
# execute from connection, but autocommit isn't working, so use the special
# option?
conn.execution_options(autocommit=True).execute(stmt)
# or on the statement ?!
conn.execute(stmt.execution_options(autocommit=True))
# or execute from connection, and we use explicit transaction?
with conn.begin():
conn.execute(stmt)
to “one choice”, where by “one choice” we mean “explicit connection with
explicit transaction”; there are still a few ways to demarcate
transaction blocks depending on need. The “one choice” is to procure a
Connection
and then to explicitly demarcate the transaction,
in the case that the operation is a write operation:
# one choice - work with explicit connection, explicit transaction
# (there remain a few variants on how to demarcate the transaction)
# "begin once" - one transaction only per checkout
with engine.begin() as conn:
result = conn.execute(stmt)
# "commit as you go" - zero or more commits per checkout
with engine.connect() as conn:
result = conn.execute(stmt)
conn.commit()
# "commit as you go" but with a transaction block instead of autobegin
with engine.connect() as conn:
with conn.begin():
result = conn.execute(stmt)
execute() method more strict, execution options are more prominent¶
Synopsis
The argument patterns that may be used with the sqlalchemy.engine.Connection()
execute method in SQLAlchemy 2.0 are highly simplified, removing many previously
available argument patterns. The new API in the 1.4 series is described at
sqlalchemy.engine.Connection()
. The examples below illustrate the patterns that
require modification:
connection = engine.connect()
# direct string SQL not supported; use text() or exec_driver_sql() method
result = connection.execute("select * from table")
# positional parameters no longer supported, only named
# unless using exec_driver_sql()
result = connection.execute(table.insert(), ("x", "y", "z"))
# **kwargs no longer accepted, pass a single dictionary
result = connection.execute(table.insert(), x=10, y=5)
# multiple *args no longer accepted, pass a list
result = connection.execute(
table.insert(), {"x": 10, "y": 5}, {"x": 15, "y": 12}, {"x": 9, "y": 8}
)
Migration to 2.0
The new Connection.execute()
method now accepts a subset of the
argument styles that are accepted by the 1.x Connection.execute()
method, so the following code is cross-compatible between 1.x and 2.0:
connection = engine.connect()
from sqlalchemy import text
result = connection.execute(text("select * from table"))
# pass a single dictionary for single statement execution
result = connection.execute(table.insert(), {"x": 10, "y": 5})
# pass a list of dictionaries for executemany
result = connection.execute(
table.insert(), [{"x": 10, "y": 5}, {"x": 15, "y": 12}, {"x": 9, "y": 8}]
)
Discussion
The use of *args
and **kwargs
has been removed both to remove the
complexity of guessing what kind of arguments were passed to the method, as
well as to make room for other options, namely the
Connection.execute.execution_options
dictionary that is now
available to provide options on a per statement basis. The method is also
modified so that its use pattern matches that of the
Session.execute()
method, which is a much more prominent API in 2.0
style.
The removal of direct string SQL is to resolve an inconsistency between
Connection.execute()
and Session.execute()
,
where in the former case the string is passed to the driver raw, and in the
latter case it is first converted to a text()
construct. By
allowing only text()
this also limits the accepted parameter
format to “named” and not “positional”. Finally, the string SQL use case
is becoming more subject to scrutiny from a security perspective, and
the text()
construct has come to represent an explicit boundary
into the textual SQL realm where attention to untrusted user input must be
given.
Result rows act like named tuples¶
Synopsis
Version 1.4 introduces an all new Result object
that in turn returns Row
objects, which behave like named
tuples when using “future” mode:
engine = create_engine(..., future=True) # using future mode
with engine.connect() as conn:
result = conn.execute(text("select x, y from table"))
row = result.first() # suppose the row is (1, 2)
"x" in row # evaluates to False, in 1.x / future=False, this would be True
1 in row # evaluates to True, in 1.x / future=False, this would be False
Migration to 2.0
Application code or test suites that are testing for a particular key
being present in a row would need to test the row.keys()
collection
instead. This is however an unusual use case as a result row is typically
used by code that already knows what columns are present within it.
Discussion
Already part of 1.4, the previous KeyedTuple
class that was used when
selecting rows from the Query
object has been replaced by the
Row
class, which is the base of the same Row
that comes
back with Core statement results when using the
create_engine.future
flag with Engine
(when
the create_engine.future
flag is not set, Core result sets use
the LegacyRow
subclass, which maintains backwards-compatible
behaviors for the __contains__()
method; ORM exclusively uses the
Row
class directly).
This Row
behaves like a named tuple, in that it acts as a sequence
but also supports attribute name access, e.g. row.some_column
. However,
it also provides the previous “mapping” behavior via the special attribute
row._mapping
, which produces a Python mapping such that keyed access
such as row["some_column"]
can be used.
In order to receive results as mappings up front, the mappings()
modifier
on the result can be used:
from sqlalchemy.future.orm import Session
session = Session(some_engine)
result = session.execute(stmt)
for row in result.mappings():
print("the user is: %s" % row["User"])
The Row
class as used by the ORM also supports access via entity
or attribute:
from sqlalchemy.future import select
stmt = select(User, Address).join(User.addresses)
for row in session.execute(stmt).mappings():
print("the user is: %s the address is: %s" % (row[User], row[Address]))
2.0 Migration - Core Usage¶
select() no longer accepts varied constructor arguments, columns are passed positionally¶
synopsis
The select()
construct as well as the related method FromClause.select()
will no longer accept keyword arguments to build up elements such as the
WHERE clause, FROM list and ORDER BY. The list of columns may now be
sent positionally, rather than as a list. Additionally, the case()
construct
now accepts its WHEN criteria positionally, rather than as a list:
# select_from / order_by keywords no longer supported
stmt = select([1], select_from=table, order_by=table.c.id)
# whereclause parameter no longer supported
stmt = select([table.c.x], table.c.id == 5)
# whereclause parameter no longer supported
stmt = table.select(table.c.id == 5)
# list emits a deprecation warning
stmt = select([table.c.x, table.c.y])
# list emits a deprecation warning
case_clause = case(
[(table.c.x == 5, "five"), (table.c.x == 7, "seven")],
else_="neither five nor seven",
)
Migration to 2.0
Only the “generative” style of select()
will be supported. The list
of columns / tables to SELECT from should be passed positionally. The
select()
construct in SQLAlchemy 1.4 accepts both the legacy
styles and the new styles using an auto-detection scheme, so the code below
is cross-compatible with 1.4 and 2.0:
# use generative methods
stmt = select(1).select_from(table).order_by(table.c.id)
# use generative methods
stmt = select(table).where(table.c.id == 5)
# use generative methods
stmt = table.select().where(table.c.id == 5)
# pass columns clause expressions positionally
stmt = select(table.c.x, table.c.y)
# case conditions passed positionally
case_clause = case(
(table.c.x == 5, "five"), (table.c.x == 7, "seven"), else_="neither five nor seven"
)
Discussion
SQLAlchemy has for many years developed a convention for SQL constructs
accepting an argument either as a list or as positional arguments. This
convention states that structural elements, those that form the structure
of a SQL statement, should be passed positionally. Conversely,
data elements, those that form the parameterized data of a SQL statement,
should be passed as lists. For many years, the select()
construct could not participate in this convention smoothly because of the
very legacy calling pattern where the “WHERE” clause would be passed positionally.
SQLAlchemy 2.0 finally resolves this by changing the select()
construct
to only accept the “generative” style that has for many years been the only
documented style in the Core tutorial.
Examples of “structural” vs. “data” elements are as follows:
# table columns for CREATE TABLE - structural
table = Table("table", metadata_obj, Column("x", Integer), Column("y", Integer))
# columns in a SELECT statement - structural
stmt = select(table.c.x, table.c.y)
# literal elements in an IN clause - data
stmt = stmt.where(table.c.y.in_([1, 2, 3]))
insert/update/delete DML no longer accept keyword constructor arguments¶
Synopsis
In a similar way as to the previous change to select()
, the
constructor arguments to insert()
, update()
and
delete()
other than the table argument are essentially removed:
# no longer supported
stmt = insert(table, values={"x": 10, "y": 15}, inline=True)
# no longer supported
stmt = insert(table, values={"x": 10, "y": 15}, returning=[table.c.x])
# no longer supported
stmt = table.delete(table.c.x > 15)
# no longer supported
stmt = table.update(table.c.x < 15, preserve_parameter_order=True).values(
[(table.c.y, 20), (table.c.x, table.c.y + 10)]
)
Migration to 2.0
The following examples illustrate generative method use for the above examples:
# use generative methods, **kwargs OK for values()
stmt = insert(table).values(x=10, y=15).inline()
# use generative methods, dictionary also still OK for values()
stmt = insert(table).values({"x": 10, "y": 15}).returning(table.c.x)
# use generative methods
stmt = table.delete().where(table.c.x > 15)
# use generative methods, ordered_values() replaces preserve_parameter_order
stmt = (
table.update()
.where(
table.c.x < 15,
)
.ordered_values((table.c.y, 20), (table.c.x, table.c.y + 10))
)
Discussion
The API and internals is being simplified for the DML constructs in a similar
manner as that of the select()
construct.
2.0 Migration - ORM Configuration¶
Declarative becomes a first class API¶
Synopsis
The sqlalchemy.ext.declarative
package is mostly, with some exceptions,
moved to the sqlalchemy.orm
package. The declarative_base()
and declared_attr()
functions are present without any behavioral
changes. A new super-implementation of declarative_base()
known as registry
now serves as the top-level ORM configurational
construct, which also provides for decorator-based declarative and new
support for classical mappings that integrate with the declarative registry.
Migration to 2.0
Change imports:
from sqlalchemy.ext import declarative_base, declared_attr
To:
from sqlalchemy.orm import declarative_base, declared_attr
Discussion
After ten years or so of popularity, the sqlalchemy.ext.declarative
package is now integrated into the sqlalchemy.orm
namespace, with the
exception of the declarative “extension” classes which remain as Declarative
extensions. The change is detailed further in the 1.4 migration guide
at Declarative is now integrated into the ORM with new features.
See also
ORM Mapped Class Overview - all new unified documentation for Declarative, classical mapping, dataclasses, attrs, etc.
Declarative is now integrated into the ORM with new features
The original “mapper()” function now a core element of Declarative, renamed¶
Synopsis
The sqlalchemy.orm.mapper()
standalone function moves behind the scenes to
be invoked by higher level APIs. The new version of this function is the method
registry.map_imperatively()
taken from a registry
object.
Migration to 2.0
Code that works with classical mappings should change imports and code from:
from sqlalchemy.orm import mapper
mapper(SomeClass, some_table, properties={"related": relationship(SomeRelatedClass)})
To work from a central registry
object:
from sqlalchemy.orm import registry
mapper_reg = registry()
mapper_reg.map_imperatively(
SomeClass, some_table, properties={"related": relationship(SomeRelatedClass)}
)
The above registry
is also the source for declarative mappings,
and classical mappings now have access to this registry including string-based
configuration on relationship()
:
from sqlalchemy.orm import registry
mapper_reg = registry()
Base = mapper_reg.generate_base()
class SomeRelatedClass(Base):
__tablename__ = "related"
# ...
mapper_reg.map_imperatively(
SomeClass,
some_table,
properties={
"related": relationship(
"SomeRelatedClass",
primaryjoin="SomeRelatedClass.related_id == SomeClass.id",
)
},
)
Discussion
By popular demand, “classical mapping” is staying around, however the new
form of it is based off of the registry
object and is available
as registry.map_imperatively()
.
In addition, the primary rationale used for “classical mapping” is that of
keeping the Table
setup distinct from the class. Declarative
has always allowed this style using so-called
hybrid declarative. However, to
remove the base class requirement, a first class decorator form has been added.
As yet another separate but related enhancement, support for Python dataclasses is added as well to both declarative decorator and classical mapping forms.
See also
ORM Mapped Class Overview - all new unified documentation for Declarative, classical mapping, dataclasses, attrs, etc.
2.0 Migration - ORM Usage¶
The biggest visible change in SQLAlchemy 2.0 is the use of
Session.execute()
in conjunction with select()
to run ORM
queries, instead of using Session.query()
. As mentioned elsewhere,
there is no plan to actually remove the Session.query()
API itself,
as it is now implemented by using the new API internally it will remain as a
legacy API, and both APIs can be used freely.
The table below provides an introduction to the general change in calling form with links to documentation for each technique presented. The individual migration notes are in the embedded sections following the table, and may include additional notes not summarized here.
1.x style form |
2.0 style form |
See Also |
---|---|---|
session.query(User).get(42) |
session.get(User, 42) |
|
session.query(User).all() |
session.execute(
select(User)
).scalars().all()
# or
session.scalars(
select(User)
).all() |
|
session.query(User).\
filter_by(name="some user").\
one() |
session.execute(
select(User).
filter_by(name="some user")
).scalar_one() |
|
session.query(User).\
filter_by(name="some user").\
first() |
session.scalars(
select(User).
filter_by(name="some user").
limit(1)
).first() |
|
session.query(User).options(
joinedload(User.addresses)
).all() |
session.scalars(
select(User).
options(
joinedload(User.addresses)
)
).unique().all() |
|
session.query(User).\
join(Address).\
filter(
Address.email == "e@sa.us"
).\
all() |
session.execute(
select(User).
join(Address).
where(
Address.email == "e@sa.us"
)
).scalars().all() |
|
session.query(User).\
from_statement(
text("select * from users")
).\
all() |
session.scalars(
select(User).
from_statement(
text("select * from users")
)
).all() |
|
session.query(User).\
join(User.addresses).\
options(
contains_eager(User.addresses)
).\
populate_existing().all() |
session.execute(
select(User)
.join(User.addresses)
.options(
contains_eager(User.addresses)
)
.execution_options(
populate_existing=True
)
).scalars().all() |
|
session.query(User).\
filter(User.name == "foo").\
update(
{"fullname": "Foo Bar"},
synchronize_session="evaluate"
) |
session.execute(
update(User)
.where(User.name == "foo")
.values(fullname="Foo Bar")
.execution_options(
synchronize_session="evaluate"
)
) |
|
session.query(User).count() |
session.scalar(
select(func.count()).
select_from(User)
)
# or
session.scalar(
select(func.count(User.id))
) |
ORM Query Unified with Core Select¶
Synopsis
The Query
object (as well as the BakedQuery
and
ShardedQuery
extensions) become long term legacy objects,
replaced by the direct usage of the select()
construct in conjunction
with the Session.execute()
method. Results
that are returned from Query
in the form of lists of objects
or tuples, or as scalar ORM objects are returned from Session.execute()
uniformly as Result
objects, which feature an interface
consistent with that of Core execution.
Legacy code examples are illustrated below:
session = Session(engine)
# becomes legacy use case
user = session.query(User).filter_by(name="some user").one()
# becomes legacy use case
user = session.query(User).filter_by(name="some user").first()
# becomes legacy use case
user = session.query(User).get(5)
# becomes legacy use case
for user in (
session.query(User).join(User.addresses).filter(Address.email == "some@email.com")
):
...
# becomes legacy use case
users = session.query(User).options(joinedload(User.addresses)).order_by(User.id).all()
# becomes legacy use case
users = session.query(User).from_statement(text("select * from users")).all()
# etc
Migration to 2.0
Because the vast majority of an ORM application is expected to make use of
Query
objects as well as that the Query
interface
being available does not impact the new interface, the object will stay
around in 2.0 but will no longer be part of documentation nor will it be
supported for the most part. The select()
construct now suits
both the Core and ORM use cases, which when invoked via the Session.execute()
method will return ORM-oriented results, that is, ORM objects if that’s what
was requested.
The Select()
construct adds many new methods for
compatibility with Query
, including Select.filter()
Select.filter_by()
, newly reworked Select.join()
and Select.outerjoin()
methods, Select.options()
,
etc. Other more supplemental methods of Query
such as
Query.populate_existing()
are implemented via execution options.
Return results are in terms of a
Result
object, the new version of the SQLAlchemy
ResultProxy
object, which also adds many new methods for compatibility
with Query
, including Result.one()
, Result.all()
,
Result.first()
, Result.one_or_none()
, etc.
The Result
object however does require some different calling
patterns, in that when first returned it will always return tuples
and it will not deduplicate results in memory. In order to return
single ORM objects the way Query
does, the Result.scalars()
modifier must be called first. In order to return uniqued objects, as is
necessary when using joined eager loading, the Result.unique()
modifier must be called first.
Documentation for all new features of select()
including execution
options, etc. are at <no title>.
Below are some examples of how to migrate to select()
:
session = Session(engine)
user = session.execute(select(User).filter_by(name="some user")).scalar_one()
# for first(), no LIMIT is applied automatically; add limit(1) if LIMIT
# is desired on the query
user = (
session.execute(select(User).filter_by(name="some user").limit(1)).scalars().first()
)
# get() moves to the Session directly
user = session.get(User, 5)
for user in session.execute(
select(User).join(User.addresses).filter(Address.email == "some@email.case")
).scalars():
...
# when using joinedload() against collections, use unique() on the result
users = (
session.execute(select(User).options(joinedload(User.addresses)).order_by(User.id))
.unique()
.all()
)
# select() has ORM-ish methods like from_statement() that only work
# if the statement is against ORM entities
users = (
session.execute(select(User).from_statement(text("select * from users")))
.scalars()
.all()
)
Discussion
The fact that SQLAlchemy has both a select()
construct
as well as a separate Query
object that features an extremely
similar, but fundamentally incompatible interface is likely the greatest
inconsistency in SQLAlchemy, one that arose as a result of small incremental
additions over time that added up to two major APIs that are divergent.
In SQLAlchemy’s first releases, the Query
object didn’t exist
at all. The original idea was that the Mapper
construct itself would
be able to select rows, and that Table
objects, not classes,
would be used to create the various criteria in a Core-style approach. The
Query
came along some months / years into SQLAlchemy’s history
as a user proposal for a new, “buildable” querying object originally called SelectResults
was accepted.
Concepts like a .where()
method, which SelectResults
called .filter()
,
were not present in SQLAlchemy previously, and the select()
construct
used only the “all-at-once” construction style that’s now deprecated
at select() no longer accepts varied constructor arguments, columns are passed positionally.
As the new approach took off, the object evolved into the Query
object as new features such as being able to select individual columns,
being able to select multiple entities at once, being able to build subqueries
from a Query
object rather than from a select
object were added. The goal became that Query
should have the
full functionality of select
in that it could be composed to
build SELECT statements fully with no explicit use of select()
needed. At the same time, select()
had also evolved “generative”
methods like Select.where()
and Select.order_by()
.
In modern SQLAlchemy, this goal has been achieved and the two objects are now
completely overlapping in functionality. The major challenge to unifying these
objects was that the select()
object needed to remain completely
agnostic of the ORM. To achieve this, the vast majority of logic from
Query
has been moved into the SQL compile phase, where
ORM-specific compiler plugins receive the
Select
construct and interpret its contents in terms of an
ORM-style query, before passing off to the core-level compiler in order to
create a SQL string. With the advent of the new
SQL compilation caching system <change_4639>,
the majority of this ORM logic is also cached.
ORM Query - get() method moves to Session¶
Synopsis
The Query.get()
method remains for legacy purposes, but the
primary interface is now the Session.get()
method:
# legacy usage
user_obj = session.query(User).get(5)
Migration to 2.0
In 1.4 / 2.0, the Session
object adds a new
Session.get()
method:
# 1.4 / 2.0 cross-compatible use
user_obj = session.get(User, 5)
Discussion
The Query
object is to be a legacy object in 2.0, as ORM
queries are now available using the select()
object. As the
Query.get()
method defines a special interaction with the
Session
and does not necessarily even emit a query, it’s more
appropriate that it be part of Session
, where it is similar
to other “identity” methods such as refresh
and
merge
.
SQLAlchemy originally included “get()” to resemble the Hibernate
Session.load()
method. As is so often the case, we got it slightly
wrong as this method is really more about the Session
than
with writing a SQL query.
ORM Query - Joining / loading on relationships uses attributes, not strings¶
Synopsis
This refers to patterns such as that of Query.join()
as well as
query options like joinedload()
which currently accept a mixture of
string attribute names or actual class attributes. The string forms
will all be removed in 2.0:
# string use removed
q = session.query(User).join("addresses")
# string use removed
q = session.query(User).options(joinedload("addresses"))
# string use removed
q = session.query(Address).filter(with_parent(u1, "addresses"))
Migration to 2.0
Modern SQLAlchemy 1.x versions support the recommended technique which is to use mapped attributes:
# compatible with all modern SQLAlchemy versions
q = session.query(User).join(User.addresses)
q = session.query(User).options(joinedload(User.addresses))
q = session.query(Address).filter(with_parent(u1, User.addresses))
The same techniques apply to 2.0-style style use:
# SQLAlchemy 1.4 / 2.0 cross compatible use
stmt = select(User).join(User.addresses)
result = session.execute(stmt)
stmt = select(User).options(joinedload(User.addresses))
result = session.execute(stmt)
stmt = select(Address).where(with_parent(u1, User.addresses))
result = session.execute(stmt)
Discussion
The string calling form is ambiguous and requires that the internals do extra work to determine the appropriate path and retrieve the correct mapped property. By passing the ORM mapped attribute directly, not only is the necessary information passed up front, the attribute is also typed and is more potentially compatible with IDEs and pep-484 integrations.
ORM Query - Chaining using lists of attributes, rather than individual calls, removed¶
Synopsis
“Chained” forms of joining and loader options which accept multiple mapped attributes in a list will be removed:
# chaining removed
q = session.query(User).join("orders", "items", "keywords")
Migration to 2.0
Use individual calls to Query.join()
for 1.x /2.0 cross compatible
use:
q = session.query(User).join(User.orders).join(Order.items).join(Item.keywords)
For 2.0-style use, Select
has the same behavior of
Select.join()
, and also features a new Select.join_from()
method that allows an explicit left side:
# 1.4 / 2.0 cross compatible
stmt = select(User).join(User.orders).join(Order.items).join(Item.keywords)
result = session.execute(stmt)
# join_from can also be helpful
stmt = select(User).join_from(User, Order).join_from(Order, Item, Order.items)
result = session.execute(stmt)
Discussion
Removing the chaining of attributes is in line with simplifying the calling
interface of methods such as Select.join()
.
ORM Query - join(…, aliased=True), from_joinpoint removed¶
Synopsis
The aliased=True
option on Query.join()
is removed, as is
the from_joinpoint
flag:
# no longer supported
q = (
session.query(Node)
.join("children", aliased=True)
.filter(Node.name == "some sub child")
.join("children", from_joinpoint=True, aliased=True)
.filter(Node.name == "some sub sub child")
)
Migration to 2.0
Use explicit aliases instead:
n1 = aliased(Node)
n2 = aliased(Node)
q = (
select(Node)
.join(Node.children.of_type(n1))
.where(n1.name == "some sub child")
.join(n1.children.of_type(n2))
.where(n2.name == "some sub child")
)
Discussion
The aliased=True
option on Query.join()
is another feature that
seems to be almost never used, based on extensive code searches to find
actual use of this feature. The internal complexity that the aliased=True
flag requires is enormous, and will be going away in 2.0.
Most users aren’t familiar with this flag, however it allows for automatic
aliasing of elements along a join, which then applies automatic aliasing
to filter conditions. The original use case was to assist in long chains
of self-referential joins, as in the example shown above. However,
the automatic adaption of the filter criteria is enormously
complicated internally and almost never used in real world applications. The
pattern also leads to issues such as if filter criteria need to be added
at each link in the chain; the pattern then must use the from_joinpoint
flag which SQLAlchemy developers could absolutely find no occurrence of this
parameter ever being used in real world applications.
The aliased=True
and from_joinpoint
parameters were developed at a time
when the Query
object didn’t yet have good capabilities regarding
joining along relationship attributes, functions like
PropComparator.of_type()
did not exist, and the aliased()
construct itself didn’t exist early on.
Using DISTINCT with additional columns, but only select the entity¶
Synopsis
Query
will automatically add columns in the ORDER BY when
distinct is used. The following query will select from all User columns
as well as “address.email_address” but only return User objects:
# 1.xx code
result = (
session.query(User)
.join(User.addresses)
.distinct()
.order_by(Address.email_address)
.all()
)
In version 2.0, the “email_address” column will not be automatically added to the columns clause, and the above query will fail, since relational databases won’t allow you to ORDER BY “address.email_address” when using DISTINCT if it isn’t also in the columns clause.
Migration to 2.0
In 2.0, the column must be added explicitly. To resolve the issue of only
returning the main entity object, and not the extra column, use the
Result.columns()
method:
# 1.4 / 2.0 code
stmt = (
select(User, Address.email_address)
.join(User.addresses)
.distinct()
.order_by(Address.email_address)
)
result = session.execute(stmt).columns(User).all()
Discussion
This case is an example of the limited flexibility of Query
leading to the case where implicit, “magical” behavior needed to be added;
the “email_address” column is implicitly added to the columns clause, then
additional internal logic would omit that column from the actual results
returned.
The new approach simplifies the interaction and makes what’s going on explicit, while still making it possible to fulfill the original use case without inconvenience.
Selecting from the query itself as a subquery, e.g. “from_self()”¶
Synopsis
The Query.from_self()
method will be removed from Query
:
# from_self is removed
q = (
session.query(User, Address.email_address)
.join(User.addresses)
.from_self(User)
.order_by(Address.email_address)
)
Migration to 2.0
The aliased()
construct may be used to emit ORM queries against
an entity that is in terms of any arbitrary selectable. It has been enhanced
in version 1.4 to smoothly accommodate being used multiple times against
the same subquery for different entities as well. This can be
used in 1.x style with Query
as below; note that
since the final query wants to query in terms of both the User
and
Address
entities, two separate aliased()
constructs are created:
from sqlalchemy.orm import aliased
subq = session.query(User, Address.email_address).join(User.addresses).subquery()
ua = aliased(User, subq)
aa = aliased(Address, subq)
q = session.query(ua, aa).order_by(aa.email_address)
The same form may be used in 2.0 style:
from sqlalchemy.orm import aliased
subq = select(User, Address.email_address).join(User.addresses).subquery()
ua = aliased(User, subq)
aa = aliased(Address, subq)
stmt = select(ua, aa).order_by(aa.email_address)
result = session.execute(stmt)
Discussion
The Query.from_self()
method is a very complicated method that is rarely
used. The purpose of this method is to convert a Query
into a
subquery, then return a new Query
which SELECTs from that subquery.
The elaborate aspect of this method is that the returned query applies
automatic translation of ORM entities and columns to be stated in the SELECT in
terms of the subquery, as well as that it allows the entities and columns to be
SELECTed from to be modified.
Because Query.from_self()
packs an intense amount of implicit
translation into the SQL it produces, while it does allow a certain kind of
pattern to be executed very succinctly, real world use of this method is
infrequent as it is not simple to understand.
The new approach makes use of the aliased()
construct so that the
ORM internals don’t need to guess which entities and columns should be adapted
and in what way; in the example above, the ua
and aa
objects, both
of which are AliasedClass
instances, provide to the internals
an unambiguous marker as to where the subquery should be referred towards
as well as what entity column or relationship is being considered for a given
component of the query.
SQLAlchemy 1.4 also features an improved labeling style that no longer requires
the use of long labels that include the table name in order to disambiguate
columns of same names from different tables. In the above examples, even if
our User
and Address
entities have overlapping column names, we can
select from both entities at once without having to specify any particular
labeling:
# 1.4 / 2.0 code
subq = select(User, Address).join(User.addresses).subquery()
ua = aliased(User, subq)
aa = aliased(Address, subq)
stmt = select(ua, aa).order_by(aa.email_address)
result = session.execute(stmt)
The above query will disambiguate the .id
column of User
and
Address
, where Address.id
is rendered and tracked as id_1
:
SELECT anon_1.id AS anon_1_id, anon_1.id_1 AS anon_1_id_1,
anon_1.user_id AS anon_1_user_id,
anon_1.email_address AS anon_1_email_address
FROM (
SELECT "user".id AS id, address.id AS id_1,
address.user_id AS user_id, address.email_address AS email_address
FROM "user" JOIN address ON "user".id = address.user_id
) AS anon_1 ORDER BY anon_1.email_address
Selecting entities from alternative selectables; Query.select_entity_from()¶
Synopsis
The Query.select_entity_from()
method will be removed in 2.0:
subquery = session.query(User).filter(User.id == 5).subquery()
user = session.query(User).select_entity_from(subquery).first()
Migration to 2.0
As is the case described at Selecting from the query itself as a subquery, e.g. “from_self()”, the
aliased()
object provides a single place that operations like
“select entity from a subquery” may be achieved. Using 1.x style:
from sqlalchemy.orm import aliased
subquery = session.query(User).filter(User.name.like("%somename%")).subquery()
ua = aliased(User, subquery)
user = session.query(ua).order_by(ua.id).first()
Using 2.0 style:
from sqlalchemy.orm import aliased
subquery = select(User).where(User.name.like("%somename%")).subquery()
ua = aliased(User, subquery)
# note that LIMIT 1 is not automatically supplied, if needed
user = session.execute(select(ua).order_by(ua.id).limit(1)).scalars().first()
Discussion
The points here are basically the same as those discussed at
Selecting from the query itself as a subquery, e.g. “from_self()”. The Query.select_from_entity()
method was another way to instruct the query to load rows for a particular
ORM mapped entity from an alternate selectable, which involved having the
ORM apply automatic aliasing to that entity wherever it was used in the
query later on, such as in the WHERE clause or ORDER BY. This intensely
complex feature is seldom used in this way, where as was the case with
Query.from_self()
, it’s much easier to follow what’s going on
when using an explicit aliased()
object, both from a user point
of view as well as how the internals of the SQLAlchemy ORM must handle it.
ORM Rows not uniquified by default¶
Synopsis
ORM rows returned by session.execute(stmt)
are no longer automatically
“uniqued”. This will normally be a welcome change, except in the case
where the “joined eager loading” loader strategy is used with collections:
# In the legacy API, many rows each have the same User primary key, but
# only one User per primary key is returned
users = session.query(User).options(joinedload(User.addresses))
# In the new API, uniquing is available but not implicitly
# enabled
result = session.execute(select(User).options(joinedload(User.addresses)))
# this actually will raise an error to let the user know that
# uniquing should be applied
rows = result.all()
Migrating to 2.0
When using a joined load of a collection, it’s required that the
Result.unique()
method is called. The ORM will actually set
a default row handler that will raise an error if this is not done, to
ensure that a joined eager load collection does not return duplicate rows
while still maintaining explicitness:
# 1.4 / 2.0 code
stmt = select(User).options(joinedload(User.addresses))
# statement will raise if unique() is not used, due to joinedload()
# of a collection. in all other cases, unique() is not needed.
# By stating unique() explicitly, confusion over discrepancies between
# number of objects/ rows returned vs. "SELECT COUNT(*)" is resolved
rows = session.execute(stmt).unique().all()
Discussion
The situation here is a little bit unusual, in that SQLAlchemy is requiring
that a method be invoked that it is in fact entirely capable of doing
automatically. The reason for requiring that the method be called is to
ensure the developer is “opting in” to the use of the
Result.unique()
method, such that they will not be confused when
a straight count of rows does not conflict with the count of
records in the actual result set, which has been a long running source of
user confusion and bug reports for many years. That the uniquifying is
not happening in any other case by default will improve performance and
also improve clarity in those cases where automatic uniquing was causing
confusing results.
To the degree that having to call Result.unique()
when joined
eager load collections are used is inconvenient, in modern SQLAlchemy
the selectinload()
strategy presents a collection-oriented
eager loader that is superior in most respects to joinedload()
and should be preferred.
“Dynamic” relationship loaders superseded by “Write Only”¶
Synopsis
The lazy="dynamic"
relationship loader strategy, discussed at
Dynamic Relationship Loaders, makes use of the Query
object
which is legacy in 2.0. The “dynamic” relationship is not directly compatible
with asyncio without workarounds, and additionally it does not fulfill its
original purpose of preventing iteration of large collections as it has several
behaviors where this iteration occurs implicitly.
A new loader strategy known as lazy="write_only"
is introduced, which
through the WriteOnlyCollection
collection class
provides a very strict “no implicit iteration” API and additionally integrates
with 2.0 style statement execution, supporting asyncio as well as
direct integrations with the new ORM-enabled Bulk DML
featureset.
At the same time, lazy="dynamic"
remains fully supported in version
2.0; applications can delay migrating this particular pattern until they
are fully on the 2.0 series.
Migration to 2.0
The new “write only” feature is only available in SQLAlchemy 2.0, and is
not part of 1.4. At the same time, the lazy="dynamic"
loader strategy
remains fully supported in version 2.0, and even includes new pep-484
and annotated mapping support.
Therefore the best strategy for migrating from “dynamic” is to wait until
the application is fully running on 2.0, then migrate directly from
AppenderQuery
, which is the collection type used by the “dynamic”
strategy, to WriteOnlyCollection
, which is the collection type
used by hte “write_only” strategy.
Some techniques are available to use lazy="dynamic"
under 1.4 in a more
“2.0” style however. There are two ways to achieve 2.0 style querying that’s in
terms of a specific relationship:
Make use of the
Query.statement
attribute on an existinglazy="dynamic"
relationship. We can use methods likeSession.scalars()
with the dynamic loader straight away as follows:class User(Base): __tablename__ = "user" posts = relationship(Post, lazy="dynamic") jack = session.get(User, 5) # filter Jack's blog posts posts = session.scalars(jack.posts.statement.where(Post.headline == "this is a post"))
Use the
with_parent()
function to construct aselect()
construct directly:from sqlalchemy.orm import with_parent jack = session.get(User, 5) posts = session.scalars( select(Post) .where(with_parent(jack, User.posts)) .where(Post.headline == "this is a post") )
Discussion
The original idea was that the with_parent()
function should be
sufficient, however continuing to make use of special attributes on the
relationship itself remains appealing, and there’s no reason a 2.0 style
construct can’t be made to work here as well.
The new “write_only” loader strategy provides a new kind of collection which
does not support implicit iteration or item access. Instead, reading the
contents of the collection is performed by calling upon its .select()
method to help construct an appropriate SELECT statement. The collection
also includes methods .insert()
, .update()
, .delete()
which may be used to emit bulk DML statements for the items in the collection.
In a manner similar to that of the “dynamic” feature, there are also methods
.add()
, .add_all()
and .remove()
which queue individual members
for addition or removal using the unit of work process. An introduction to the
new feature is as New “Write Only” relationship strategy supersedes “dynamic”.
Autocommit mode removed from Session; autobegin support added¶
Synopsis
The Session
will no longer support “autocommit” mode, that
is, this pattern:
from sqlalchemy.orm import Session
sess = Session(engine, autocommit=True)
# no transaction begun, but emits SQL, won't be supported
obj = sess.query(Class).first()
# session flushes in a transaction that it begins and
# commits, won't be supported
sess.flush()
Migration to 2.0
The main reason a Session
is used in “autocommit” mode
is so that the Session.begin()
method is available, so that framework
integrations and event hooks can control when this event happens. In 1.4,
the Session
now features autobegin behavior
which resolves this issue; the Session.begin()
method may now
be called:
from sqlalchemy.orm import Session
sess = Session(engine)
sess.begin() # begin explicitly; if not called, will autobegin
# when database access is needed
sess.add(obj)
sess.commit()
Discussion
The “autocommit” mode is another holdover from the first versions
of SQLAlchemy. The flag has stayed around mostly in support of allowing
explicit use of Session.begin()
, which is now solved by 1.4,
as well as to allow the use of “subtransactions”, which are also removed in
2.0.
Session “subtransaction” behavior removed¶
Synopsis
The “subtransaction” pattern that was often used with autocommit mode is
also deprecated in 1.4. This pattern allowed the use of the
Session.begin()
method when a transaction were already begun,
resulting in a construct called a “subtransaction”, which was essentially
a block that would prevent the Session.commit()
method from actually
committing.
Migration to 2.0
To provide backwards compatibility for applications that make use of this pattern, the following context manager or a similar implementation based on a decorator may be used:
import contextlib
@contextlib.contextmanager
def transaction(session):
if not session.in_transaction():
with session.begin():
yield
else:
yield
The above context manager may be used in the same way the “subtransaction” flag works, such as in the following example:
# method_a starts a transaction and calls method_b
def method_a(session):
with transaction(session):
method_b(session)
# method_b also starts a transaction, but when
# called from method_a participates in the ongoing
# transaction.
def method_b(session):
with transaction(session):
session.add(SomeObject("bat", "lala"))
Session = sessionmaker(engine)
# create a Session and call method_a
with Session() as session:
method_a(session)
To compare towards the preferred idiomatic pattern, the begin block should be at the outermost level. This removes the need for individual functions or methods to be concerned with the details of transaction demarcation:
def method_a(session):
method_b(session)
def method_b(session):
session.add(SomeObject("bat", "lala"))
Session = sessionmaker(engine)
# create a Session and call method_a
with Session() as session:
with session.begin():
method_a(session)
Discussion
This pattern has been shown to be confusing in real world applications, and it is preferable for an application to ensure that the top-most level of database operations are performed with a single begin/commit pair.
2.0 Migration - ORM Extension and Recipe Changes¶
Dogpile cache recipe and Horizontal Sharding uses new Session API¶
As the Query
object becomes legacy, these two recipes
which previously relied upon subclassing of the Query
object now make use of the SessionEvents.do_orm_execute()
hook. See the section Re-Executing Statements for
an example.
Baked Query Extension Superseded by built-in caching¶
The baked query extension is superseded by the built in caching system and is no longer used by the ORM internals.
See SQL Compilation Caching for full background on the new caching system.
Asyncio Support¶
SQLAlchemy 1.4 includes asyncio support for both Core and ORM. The new API exclusively makes use of the “future” patterns noted above. See Asynchronous IO Support for Core and ORM for background.