Migrating to SQLAlchemy 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 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 in with all warning flags turned on and does not emit any 2.0-deprecation warnings, it is now cross-compatible with SQLAlchemy 2.0.

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 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 object and may impact code that makes use of the CreateEnginePlugin 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-known 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 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 class featured .join() and .outerjoin() methods that implicitly created a subquery and then returned a Join 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 ORM 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 or Select 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 document.

Migration to 2.0 Step One - Python 3 only (Python 3.6 minimum)

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, since it is not too much in the way of things for the moment. However, version 2.0 hopes to start embracing and other new features to a great degree, so it is likely that release 1.4 will be the last Python 2 supporting version, even if there is a SQLAlchemy 1.5 (which is also unlikely at the moment).

In order to use SQLAlchemy 2.0, the application will need to be runnable on at least Python 3.6 as of this writing. SQLAlchemy 1.4 now 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.

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 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:

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 system. 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

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: http://sqlalche.me/e/b8d9) (Background on SQLAlchemy 2.0 at: http://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: http://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: http://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: http://sqlalche.me/e/b8d9) (Background on SQLAlchemy 2.0 at: http://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: http://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: http://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: http://sqlalche.me/e/b8d9) (Background on SQLAlchemy 2.0 at: http://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: http://sqlalche.me/e/b8d9) (Background on SQLAlchemy 2.0 at: http://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 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:

1. enable the SQLALCHEMY_WARN_20=1 environment variable in the test suite, for SQLAlchemy this is in the tox.ini file

2. Within 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 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=sa_exc.RemovedIn20Warning,
     )
   # for all other warnings, just log
   warnings.filterwarnings(
     "always", category=exc.RemovedIn20Warning
   )

3. 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.

4. 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 function.

When the create_engine.future flag is used, the and Connection objects support the 2.0 API fully and not at all any legacy features, including the new argument format for , 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 is removed.

If all RemovedIn20Warning warnings have been resolved regarding use of the and Connection, then the flag may be enabled and there should be no errors raised.

The new engine is described at Engine which delivers a new object. In addition to the above changes, the, Connection object features and Connection.rollback() methods, to support the new “commit-as-you-go” mode of operation:

from sqlalchemy import create_engine
engine = create_engine("postgresql:///")
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 Four - 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 flag on Session or on .

The Session object supports “future” mode in place, and involves these changes:

1. The no longer supports “bound metadata” when it resolves the engine to be used for connectivity. This means that an Engine object must be passed to the constructor (this may be either a legacy or future style object).

2. The flag is no longer supported.

3. The Session.commit() method always emits a COMMIT to the database, rather than attempting to reconcile “subtransactions”.

4. The 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 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 object supports a sessionmaker.begin() context manager that will create a 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 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.

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 and 2.0 style execution is to make use of the 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 with the create_engine.future flag, “commit as you go” style may also be used, as the 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 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 ’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 , which is the one that is returned from an Engine that was created using the flag, includes new Connection.commit() and 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 as well as the ORM Session object; autobegin allows that the 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 . 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 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 with a MetaData object, which then makes available a range of so-called “connectionless” execution patterns, is removed:

from sqlalchemy import MetaData
metadata = MetaData(bind=engine)  # no longer supported
metadata.create_all()   # requires Engine or Connection
metadata.reflect()  # requires Engine or Connection
t = Table('t', metadata, 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 or Connection is required. The may still be used directly as the source of connectivity for a MetaData.create_all() operation or autoload operation. For executing statements, only the object has an Connection.execute() method (in addition to the ORM-level method):

from sqlalchemy import MetaData
metadata = MetaData()
# engine level:
# create tables
metadata.create_all(engine)
# reflect all tables
metadata.reflect(engine)
# reflect individual table
t = Table('t', metadata, autoload_with=engine)
# connection level:
with engine.connect() as connection:
    # create tables, requires explicit begin and/or commit:
    with connection.begin():
        metadata.create_all(connection)
    # reflect all tables
    metadata.reflect(connection)
    # reflect individual table
    t = Table('t', metadata, autoload_with=connection)
    # execute SQL statements
    result = conn.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 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 between engine.execute() and connection.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 that Engine.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 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 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”:

# bound metadata?
metadata = MetaData(engine)
# or not?
metadata = 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():

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 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)

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 . 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 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 removal of direct string SQL is to resolve an inconsistency between Connection.execute() and , 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 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 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 class, which is the base of the same Row that comes back with Core statement results when using the flag with Engine (when the flag is not set, Core result sets use the LegacyRow subclass, which maintains backwards-compatible behaviors for the __contains__() method; ORM exclusively uses the 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 class as used by the ORM also supports access via entity or attribute:

See also

RowProxy is no longer a “proxy”; is now called Row and behaves like an enhanced named tuple

select() no longer accepts varied constructor arguments, columns are passed positionally

synopsis

The select() construct as well as the related method 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 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 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 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, 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]))

See also

select() construct created in “legacy” mode; keyword arguments, etc.

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 , update() and 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 and declared_attr() functions are present without any behavioral changes. A new super-implementation of 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 .

See also

Mapping Python Classes - all new unified documentation for Declarative, classical mapping, dataclasses, attrs, etc.

The original “mapper()” function now a core element of Declarative, renamed

Synopsis

The 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 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 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 object and is available as registry.map_imperatively().

In addition, the primary rationale used for “classical mapping” is that of keeping the 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 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

- all new unified documentation for Declarative, classical mapping, dataclasses, attrs, etc.

The biggest visible change in SQLAlchemy 2.0 is the use of Session.execute() in conjunction with to run ORM queries, instead of using Session.query(). As mentioned elsewhere, there is no plan to actually remove the 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.

ORM Query Unified with Core Select

Synopsis

The object (as well as the BakedQuery and extensions) become long term legacy objects, replaced by the direct usage of the select() construct in conjunction with the method. Results that are returned from Query in the form of lists of objects or tuples, or as scalar ORM objects are returned from 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).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 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 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 construct adds many new methods for compatibility with Query, including Select.filter_by(), newly reworked and Select.outerjoin() methods, , etc. Other more supplemental methods of Query such as 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 , including Result.one(), , Result.first(), , 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 does, the Result.scalars() modifier must be called first. In order to return uniqued objects, as is necessary when using joined eager loading, the modifier must be called first.

Documentation for all new features of select() including execution options, etc. are at .

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()
# 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 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 object didn’t exist at all. The original idea was that the Mapper construct itself would be able to select rows, and that 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 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.

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 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 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.

See also

ORM Query is internally unified with select, update, delete; 2.0 style execution available

ORM Query - get() method moves to Session

Synopsis

The Query.get() method remains for legacy purposes, but the primary interface is now the 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 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 object. As the Query.get() method defines a special interaction with the 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 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 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 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("addresess"))
# 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.addresess))
q = session.query(Address).filter(with_parent(u1, User.addresses))

The same techniques apply to 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.addresess))
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.

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 use, Select has the same behavior of , 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 .

ORM Query - join(…, aliased=True), from_joinpoint removed

Synopsis

The aliased=True option on 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 that 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 object didn’t yet have good capabilities regarding joining along relationship attributes, functions like PropComparator.of_type() did not exist, and the construct itself didn’t exist early on.

Using DISTINCT with additional columns, but only select the entity

Synopsis

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).scalars(User).all()

Discussion

This case is an example of the limited flexibility of 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 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 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 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 :

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 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 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 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 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.id == 5).subquery()
ua = aliased(User, subquery)
user = session.query(ua).first()

Using :

from sqlalchemy.orm import aliased
subquery = select(User).where(User.id == 5).subquery()
ua = aliased(User, subquery)
user = session.execute(select(ua)).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 , 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 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 strategy presents a collection-oriented eager loader that is superior in most respects to joinedload() and should be preferred.

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
sess.flush()

Migration to 2.0

The main reason a 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 now features autobegin behavior which resolves this issue; the method may now be called:

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

TODO: more detail

See the section Migrating from the “subtransaction” pattern for background on this change.

2.0 Migration - ORM Extension and Recipe Changes

Dogpile cache recipe and Horizontal Sharding uses new Session API

TODO: more detail

As the object becomes legacy, these two recipes which previously relied upon subclassing of the Query object now make use of the hook. See the section Re-Executing Statements for an example.

TODO: more detail

The baked query extension is superseded by the built in caching system and is no longer used by the ORM internals.

See for full background on the new caching system.

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.