SQLAlchemy 0.9 Documentation

Release: 0.9.4 | Release Date: March 28, 2014 | Download PDF
SQLAlchemy 0.9 Documentation » Frequently Asked Questions

Frequently Asked Questions

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Frequently Asked Questions

Connections / Engines

How do I configure logging?

See Configuring Logging.

How do I pool database connections? Are my connections pooled?

SQLAlchemy performs application-level connection pooling automatically in most cases. With the exception of SQLite, a Engine object refers to a QueuePool as a source of connectivity.

For more detail, see Engine Configuration and Connection Pooling.

How do I pass custom connect arguments to my database API?

The create_engine() call accepts additional arguments either directly via the connect_args keyword argument:

e = create_engine("mysql://scott:tiger@localhost/test",
                                        connect_args={"encoding": "utf8"})

Or for basic string and integer arguments, they can usually be specified in the query string of the URL:

e = create_engine("mysql://scott:tiger@localhost/test?encoding=utf8")

“MySQL Server has gone away”

There are two major causes for this error:

1. The MySQL client closes connections which have been idle for a set period of time, defaulting to eight hours. This can be avoided by using the pool_recycle setting with create_engine(), described at Connection Timeouts.

2. Usage of the MySQLdb DBAPI, or a similar DBAPI, in a non-threadsafe manner, or in an otherwise inappropriate way. The MySQLdb connection object is not threadsafe - this expands out to any SQLAlchemy system that links to a single connection, which includes the ORM Session. For background on how Session should be used in a multithreaded environment, see Is the session thread-safe?.

Why does SQLAlchemy issue so many ROLLBACKs?

SQLAlchemy currently assumes DBAPI connections are in “non-autocommit” mode - this is the default behavior of the Python database API, meaning it must be assumed that a transaction is always in progress. The connection pool issues connection.rollback() when a connection is returned. This is so that any transactional resources remaining on the connection are released. On a database like Postgresql or MSSQL where table resources are aggressively locked, this is critical so that rows and tables don’t remain locked within connections that are no longer in use. An application can otherwise hang. It’s not just for locks, however, and is equally critical on any database that has any kind of transaction isolation, including MySQL with InnoDB. Any connection that is still inside an old transaction will return stale data, if that data was already queried on that connection within isolation. For background on why you might see stale data even on MySQL, see http://dev.mysql.com/doc/refman/5.1/en/innodb-transaction-model.html

I’m on MyISAM - how do I turn it off?

The behavior of the connection pool’s connection return behavior can be configured using reset_on_return:

from sqlalchemy import create_engine
from sqlalchemy.pool import QueuePool

engine = create_engine('mysql://scott:tiger@localhost/myisam_database', pool=QueuePool(reset_on_return=False))

I’m on SQL Server - how do I turn those ROLLBACKs into COMMITs?

reset_on_return accepts the values commit, rollback in addition to True, False, and None. Setting to commit will cause a COMMIT as any connection is returned to the pool:

engine = create_engine('mssql://scott:tiger@mydsn', pool=QueuePool(reset_on_return='commit'))

I am using multiple connections with a SQLite database (typically to test transaction operation), and my test program is not working!

If using a SQLite :memory: database, or a version of SQLAlchemy prior to version 0.7, the default connection pool is the SingletonThreadPool, which maintains exactly one SQLite connection per thread. So two connections in use in the same thread will actually be the same SQLite connection. Make sure you’re not using a :memory: database and use NullPool, which is the default for non-memory databases in current SQLAlchemy versions.

See also

Threading/Pooling Behavior - info on PySQLite’s behavior.

How do I get at the raw DBAPI connection when using an Engine?

With a regular SA engine-level Connection, you can get at a pool-proxied version of the DBAPI connection via the Connection.connection attribute on Connection, and for the really-real DBAPI connection you can call the ConnectionFairy.connection attribute on that - but there should never be any need to access the non-pool-proxied DBAPI connection, as all methods are proxied through:

engine = create_engine(...)
conn = engine.connect()
conn.connection.<do DBAPI things>
cursor = conn.connection.cursor(<DBAPI specific arguments..>)

You must ensure that you revert any isolation level settings or other operation-specific settings on the connection back to normal before returning it to the pool.

As an alternative to reverting settings, you can call the Connection.detach() method on either Connection or the proxied connection, which will de-associate the connection from the pool such that it will be closed and discarded when Connection.close() is called:

conn = engine.connect()
conn.detach()  # detaches the DBAPI connection from the connection pool
conn.connection.<go nuts>
conn.close()  # connection is closed for real, the pool replaces it with a new connection

MetaData / Schema

My program is hanging when I say table.drop() / metadata.drop_all()

This usually corresponds to two conditions: 1. using PostgreSQL, which is really strict about table locks, and 2. you have a connection still open which contains locks on the table and is distinct from the connection being used for the DROP statement. Heres the most minimal version of the pattern:

connection = engine.connect()
result = connection.execute(mytable.select())


Above, a connection pool connection is still checked out; furthermore, the result object above also maintains a link to this connection. If “implicit execution” is used, the result will hold this connection opened until the result object is closed or all rows are exhausted.

The call to mytable.drop(engine) attempts to emit DROP TABLE on a second connection procured from the Engine which will lock.

The solution is to close out all connections before emitting DROP TABLE:

connection = engine.connect()
result = connection.execute(mytable.select())

# fully read result sets

# close connections

# now locks are removed

Does SQLAlchemy support ALTER TABLE, CREATE VIEW, CREATE TRIGGER, Schema Upgrade Functionality?

General ALTER support isn’t present in SQLAlchemy directly. For special DDL on an ad-hoc basis, the DDL and related constructs can be used. See Customizing DDL for a discussion on this subject.

A more comprehensive option is to use schema migration tools, such as Alembic or SQLAlchemy-Migrate; see Altering Schemas through Migrations for discussion on this.

How can I sort Table objects in order of their dependency?

This is available via the MetaData.sorted_tables function:

metadata = MetaData()
# ... add Table objects to metadata
ti = metadata.sorted_tables:
for t in ti:
    print t

How can I get the CREATE TABLE/ DROP TABLE output as a string?

Modern SQLAlchemy has clause constructs which represent DDL operations. These can be rendered to strings like any other SQL expression:

from sqlalchemy.schema import CreateTable

print CreateTable(mytable)

To get the string specific to a certain engine:

print CreateTable(mytable).compile(engine)

There’s also a special form of Engine that can let you dump an entire metadata creation sequence, using this recipe:

def dump(sql, *multiparams, **params):
    print sql.compile(dialect=engine.dialect)
engine = create_engine('postgresql://', strategy='mock', executor=dump)
metadata.create_all(engine, checkfirst=False)

The Alembic tool also supports an “offline” SQL generation mode that renders database migrations as SQL scripts.

How can I subclass Table/Column to provide certain behaviors/configurations?

Table and Column are not good targets for direct subclassing. However, there are simple ways to get on-construction behaviors using creation functions, and behaviors related to the linkages between schema objects such as constraint conventions or naming conventions using attachment events. An example of many of these techniques can be seen at Naming Conventions.

SQL Expressions

Why does .col.in_([]) Produce col != col? Why not 1=0?

A little introduction to the issue. The IN operator in SQL, given a list of elements to compare against a column, generally does not accept an empty list, that is while it is valid to say:

column IN (1, 2, 3)

it’s not valid to say:

column IN ()

SQLAlchemy’s Operators.in_() operator, when given an empty list, produces this expression:

column != column

As of version 0.6, it also produces a warning stating that a less efficient comparison operation will be rendered. This expression is the only one that is both database agnostic and produces correct results.

For example, the naive approach of “just evaluate to false, by comparing 1=0 or 1!=1”, does not handle nulls properly. An expression like:

NOT column != column

will not return a row when “column” is null, but an expression which does not take the column into account:

NOT 1=0


Closer to the mark is the following CASE expression:


We don’t use this expression due to its verbosity, and its also not typically accepted by Oracle within a WHERE clause - depending on how you phrase it, you’ll either get “ORA-00905: missing keyword” or “ORA-00920: invalid relational operator”. It’s also still less efficient than just rendering SQL without the clause altogether (or not issuing the SQL at all, if the statement is just a simple search).

The best approach therefore is to avoid the usage of IN given an argument list of zero length. Instead, don’t emit the Query in the first place, if no rows should be returned. The warning is best promoted to a full error condition using the Python warnings filter (see http://docs.python.org/library/warnings.html).

ORM Configuration

How do I map a table that has no primary key?

The SQLAlchemy ORM, in order to map to a particular table, needs there to be at least one column denoted as a primary key column; multiple-column, i.e. composite, primary keys are of course entirely feasible as well. These columns do not need to be actually known to the database as primary key columns, though it’s a good idea that they are. It’s only necessary that the columns behave as a primary key does, e.g. as a unique and not nullable identifier for a row.

Most ORMs require that objects have some kind of primary key defined because the object in memory must correspond to a uniquely identifiable row in the database table; at the very least, this allows the object can be targeted for UPDATE and DELETE statements which will affect only that object’s row and no other. However, the importance of the primary key goes far beyond that. In SQLAlchemy, all ORM-mapped objects are at all times linked uniquely within a Session to their specific database row using a pattern called the identity map, a pattern that’s central to the unit of work system employed by SQLAlchemy, and is also key to the most common (and not-so-common) patterns of ORM usage.


It’s important to note that we’re only talking about the SQLAlchemy ORM; an application which builds on Core and deals only with Table objects, select() constructs and the like, does not need any primary key to be present on or associated with a table in any way (though again, in SQL, all tables should really have some kind of primary key, lest you need to actually update or delete specific rows).

In almost all cases, a table does have a so-called candidate key, which is a column or series of columns that uniquely identify a row. If a table truly doesn’t have this, and has actual fully duplicate rows, the table is not corresponding to first normal form and cannot be mapped. Otherwise, whatever columns comprise the best candidate key can be applied directly to the mapper:

class SomeClass(Base):
        __table__ = some_table_with_no_pk
        __mapper_args__ = {
                'primary_key':[some_table_with_no_pk.c.uid, some_table_with_no_pk.c.bar]

Better yet is when using fully declared table metadata, use the primary_key=True flag on those columns:

class SomeClass(Base):
        __tablename__ = "some_table_with_no_pk"

        uid = Column(Integer, primary_key=True)
        bar = Column(String, primary_key=True)

All tables in a relational database should have primary keys. Even a many-to-many association table - the primary key would be the composite of the two association columns:

CREATE TABLE my_association (
  user_id INTEGER REFERENCES user(id),
  account_id INTEGER REFERENCES account(id),
  PRIMARY KEY (user_id, account_id)

How do I configure a Column that is a Python reserved word or similar?

Column-based attributes can be given any name desired in the mapping. See Naming Columns Distinctly from Attribute Names.

How do I get a list of all columns, relationships, mapped attributes, etc. given a mapped class?

This information is all available from the Mapper object.

To get at the Mapper for a particular mapped class, call the inspect() function on it:

from sqlalchemy import inspect

mapper = inspect(MyClass)

From there, all information about the class can be acquired using such methods as:

I’m using Declarative and setting primaryjoin/secondaryjoin using an and_() or or_(), and I am getting an error message about foreign keys.

Are you doing this?:

class MyClass(Base):
    # ....

    foo = relationship("Dest", primaryjoin=and_("MyClass.id==Dest.foo_id", "MyClass.foo==Dest.bar"))

That’s an and_() of two string expressions, which SQLAlchemy cannot apply any mapping towards. Declarative allows relationship() arguments to be specified as strings, which are converted into expression objects using eval(). But this doesn’t occur inside of an and_() expression - it’s a special operation declarative applies only to the entirety of what’s passed to primaryjoin or other arguments as a string:

class MyClass(Base):
    # ....

    foo = relationship("Dest", primaryjoin="and_(MyClass.id==Dest.foo_id, MyClass.foo==Dest.bar)")

Or if the objects you need are already available, skip the strings:

class MyClass(Base):
    # ....

    foo = relationship(Dest, primaryjoin=and_(MyClass.id==Dest.foo_id, MyClass.foo==Dest.bar))

The same idea applies to all the other arguments, such as foreign_keys:

# wrong !
foo = relationship(Dest, foreign_keys=["Dest.foo_id", "Dest.bar_id"])

# correct !
foo = relationship(Dest, foreign_keys="[Dest.foo_id, Dest.bar_id]")

# also correct !
foo = relationship(Dest, foreign_keys=[Dest.foo_id, Dest.bar_id])

# if you're using columns from the class that you're inside of, just use the column objects !
class MyClass(Base):
    foo_id = Column(...)
    bar_id = Column(...)
    # ...

    foo = relationship(Dest, foreign_keys=[foo_id, bar_id])

Sessions / Queries

“This Session’s transaction has been rolled back due to a previous exception during flush.” (or similar)

This is an error that occurs when a Session.flush() raises an exception, rolls back the transaction, but further commands upon the Session are called without an explicit call to Session.rollback() or Session.close().

It usually corresponds to an application that catches an exception upon Session.flush() or Session.commit() and does not properly handle the exception. For example:

from sqlalchemy import create_engine, Column, Integer
from sqlalchemy.orm import sessionmaker
from sqlalchemy.ext.declarative import declarative_base

Base = declarative_base(create_engine('sqlite://'))

class Foo(Base):
    __tablename__ = 'foo'
    id = Column(Integer, primary_key=True)


session = sessionmaker()()

# constraint violation
session.add_all([Foo(id=1), Foo(id=1)])

    # ignore error

# continue using session without rolling back

The usage of the Session should fit within a structure similar to this:

    <use session>
   session.close()  # optional, depends on use case

Many things can cause a failure within the try/except besides flushes. You should always have some kind of “framing” of your session operations so that connection and transaction resources have a definitive boundary, otherwise your application doesn’t really have its usage of resources under control. This is not to say that you need to put try/except blocks all throughout your application - on the contrary, this would be a terrible idea. You should architect your application such that there is one (or few) point(s) of “framing” around session operations.

For a detailed discussion on how to organize usage of the Session, please see When do I construct a Session, when do I commit it, and when do I close it?.

But why does flush() insist on issuing a ROLLBACK?

It would be great if Session.flush() could partially complete and then not roll back, however this is beyond its current capabilities since its internal bookkeeping would have to be modified such that it can be halted at any time and be exactly consistent with what’s been flushed to the database. While this is theoretically possible, the usefulness of the enhancement is greatly decreased by the fact that many database operations require a ROLLBACK in any case. Postgres in particular has operations which, once failed, the transaction is not allowed to continue:

test=> create table foo(id integer primary key);
NOTICE:  CREATE TABLE / PRIMARY KEY will create implicit index "foo_pkey" for table "foo"
test=> begin;
test=> insert into foo values(1);
test=> commit;
test=> begin;
test=> insert into foo values(1);
ERROR:  duplicate key value violates unique constraint "foo_pkey"
test=> insert into foo values(2);
ERROR:  current transaction is aborted, commands ignored until end of transaction block

What SQLAlchemy offers that solves both issues is support of SAVEPOINT, via Session.begin_nested(). Using Session.begin_nested(), you can frame an operation that may potentially fail within a transaction, and then “roll back” to the point before its failure while maintaining the enclosing transaction.

But why isn’t the one automatic call to ROLLBACK enough? Why must I ROLLBACK again?

This is again a matter of the Session providing a consistent interface and refusing to guess about what context its being used. For example, the Session supports “framing” above within multiple levels. Such as, suppose you had a decorator @with_session(), which did this:

def with_session(fn):
   def go(*args, **kw):
           ret = fn(*args, **kw)
           return ret
   return go

The above decorator begins a transaction if one does not exist already, and then commits it, if it were the creator. The “subtransactions” flag means that if Session.begin() were already called by an enclosing function, nothing happens except a counter is incremented - this counter is decremented when Session.commit() is called and only when it goes back to zero does the actual COMMIT happen. It allows this usage pattern:

def one():
   # do stuff

def two():
   # etc.



one() can call two(), or two() can be called by itself, and the @with_session decorator ensures the appropriate “framing” - the transaction boundaries stay on the outermost call level. As you can see, if two() calls flush() which throws an exception and then issues a rollback(), there will always be a second rollback() performed by the decorator, and possibly a third corresponding to two levels of decorator. If the flush() pushed the rollback() all the way out to the top of the stack, and then we said that all remaining rollback() calls are moot, there is some silent behavior going on there. A poorly written enclosing method might suppress the exception, and then call commit() assuming nothing is wrong, and then you have a silent failure condition. The main reason people get this error in fact is because they didn’t write clean “framing” code and they would have had other problems down the road.

If you think the above use case is a little exotic, the same kind of thing comes into play if you want to SAVEPOINT- you might call begin_nested() several times, and the commit()/rollback() calls each resolve the most recent begin_nested(). The meaning of rollback() or commit() is dependent upon which enclosing block it is called, and you might have any sequence of rollback()/commit() in any order, and its the level of nesting that determines their behavior.

In both of the above cases, if flush() broke the nesting of transaction blocks, the behavior is, depending on scenario, anywhere from “magic” to silent failure to blatant interruption of code flow.

flush() makes its own “subtransaction”, so that a transaction is started up regardless of the external transactional state, and when complete it calls commit(), or rollback() upon failure - but that rollback() corresponds to its own subtransaction - it doesn’t want to guess how you’d like to handle the external “framing” of the transaction, which could be nested many levels with any combination of subtransactions and real SAVEPOINTs. The job of starting/ending the “frame” is kept consistently with the code external to the flush(), and we made a decision that this was the most consistent approach.

I’m inserting 400,000 rows with the ORM and it’s really slow!

The SQLAlchemy ORM uses the unit of work pattern when synchronizing changes to the database. This pattern goes far beyond simple “inserts” of data. It includes that attributes which are assigned on objects are received using an attribute instrumentation system which tracks changes on objects as they are made, includes that all rows inserted are tracked in an identity map which has the effect that for each row SQLAlchemy must retrieve its “last inserted id” if not already given, and also involves that rows to be inserted are scanned and sorted for dependencies as needed. Objects are also subject to a fair degree of bookkeeping in order to keep all of this running, which for a very large number of rows at once can create an inordinate amount of time spent with large data structures, hence it’s best to chunk these.

Basically, unit of work is a large degree of automation in order to automate the task of persisting a complex object graph into a relational database with no explicit persistence code, and this automation has a price.

ORMs are basically not intended for high-performance bulk inserts - this is the whole reason SQLAlchemy offers the Core in addition to the ORM as a first-class component.

For the use case of fast bulk inserts, the SQL generation and execution system that the ORM builds on top of is part of the Core. Using this system directly, we can produce an INSERT that is competitive with using the raw database API directly.

The example below illustrates time-based tests for four different methods of inserting rows, going from the most automated to the least. With cPython 2.7, runtimes observed:

classics-MacBook-Pro:sqlalchemy classic$ python test.py
SQLAlchemy ORM: Total time for 100000 records 14.3528850079 secs
SQLAlchemy ORM pk given: Total time for 100000 records 10.0164160728 secs
SQLAlchemy Core: Total time for 100000 records 0.775382995605 secs
sqlite3: Total time for 100000 records 0.676795005798 sec

We can reduce the time by a factor of three using recent versions of Pypy:

classics-MacBook-Pro:sqlalchemy classic$ /usr/local/src/pypy-2.1-beta2-osx64/bin/pypy test.py
SQLAlchemy ORM: Total time for 100000 records 5.88369488716 secs
SQLAlchemy ORM pk given: Total time for 100000 records 3.52294301987 secs
SQLAlchemy Core: Total time for 100000 records 0.613556146622 secs
sqlite3: Total time for 100000 records 0.442467927933 sec


import time
import sqlite3

from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy import Column, Integer, String,  create_engine
from sqlalchemy.orm import scoped_session, sessionmaker

Base = declarative_base()
DBSession = scoped_session(sessionmaker())
engine = None

class Customer(Base):
    __tablename__ = "customer"
    id = Column(Integer, primary_key=True)
    name = Column(String(255))

def init_sqlalchemy(dbname='sqlite:///sqlalchemy.db'):
    global engine
    engine = create_engine(dbname, echo=False)
    DBSession.configure(bind=engine, autoflush=False, expire_on_commit=False)

def test_sqlalchemy_orm(n=100000):
    t0 = time.time()
    for i in range(n):
        customer = Customer()
        customer.name = 'NAME ' + str(i)
        if i % 1000 == 0:
    print("SQLAlchemy ORM: Total time for " + str(n) +
                " records " + str(time.time() - t0) + " secs")

def test_sqlalchemy_orm_pk_given(n=100000):
    t0 = time.time()
    for i in range(n):
        customer = Customer(id=i+1, name="NAME " + str(i))
        if i % 1000 == 0:
    print("SQLAlchemy ORM pk given: Total time for " + str(n) +
        " records " + str(time.time() - t0) + " secs")

def test_sqlalchemy_core(n=100000):
    t0 = time.time()
        [{"name": 'NAME ' + str(i)} for i in range(n)]
    print("SQLAlchemy Core: Total time for " + str(n) +
        " records " + str(time.time() - t0) + " secs")

def init_sqlite3(dbname):
    conn = sqlite3.connect(dbname)
    c = conn.cursor()
    c.execute("DROP TABLE IF EXISTS customer")
    c.execute("CREATE TABLE customer (id INTEGER NOT NULL, "
                                "name VARCHAR(255), PRIMARY KEY(id))")
    return conn

def test_sqlite3(n=100000, dbname='sqlite3.db'):
    conn = init_sqlite3(dbname)
    c = conn.cursor()
    t0 = time.time()
    for i in range(n):
        row = ('NAME ' + str(i),)
        c.execute("INSERT INTO customer (name) VALUES (?)", row)
    print("sqlite3: Total time for " + str(n) +
        " records " + str(time.time() - t0) + " sec")

if __name__ == '__main__':

How do I make a Query that always adds a certain filter to every query?

See the recipe at PreFilteredQuery.

I’ve created a mapping against an Outer Join, and while the query returns rows, no objects are returned. Why not?

Rows returned by an outer join may contain NULL for part of the primary key, as the primary key is the composite of both tables. The Query object ignores incoming rows that don’t have an acceptable primary key. Based on the setting of the allow_partial_pks flag on mapper(), a primary key is accepted if the value has at least one non-NULL value, or alternatively if the value has no NULL values. See allow_partial_pks at mapper().

I’m using joinedload() or lazy=False to create a JOIN/OUTER JOIN and SQLAlchemy is not constructing the correct query when I try to add a WHERE, ORDER BY, LIMIT, etc. (which relies upon the (OUTER) JOIN)

The joins generated by joined eager loading are only used to fully load related collections, and are designed to have no impact on the primary results of the query. Since they are anonymously aliased, they cannot be referenced directly.

For detail on this beahvior, see Relationship Loading Techniques.

Query has no __len__(), why not?

The Python __len__() magic method applied to an object allows the len() builtin to be used to determine the length of the collection. It’s intuitive that a SQL query object would link __len__() to the Query.count() method, which emits a SELECT COUNT. The reason this is not possible is because evaluating the query as a list would incur two SQL calls instead of one:

class Iterates(object):
    def __len__(self):
        print "LEN!"
        return 5

    def __iter__(self):
        print "ITER!"
        return iter([1, 2, 3, 4, 5])




How Do I use Textual SQL with ORM Queries?


I’m calling Session.delete(myobject) and it isn’t removed from the parent collection!

See Deleting from Collections for a description of this behavior.

why isnt my __init__() called when I load objects?

See Constructors and Object Initialization for a description of this behavior.

how do I use ON DELETE CASCADE with SA’s ORM?

SQLAlchemy will always issue UPDATE or DELETE statements for dependent rows which are currently loaded in the Session. For rows which are not loaded, it will by default issue SELECT statements to load those rows and udpate/delete those as well; in other words it assumes there is no ON DELETE CASCADE configured. To configure SQLAlchemy to cooperate with ON DELETE CASCADE, see Using Passive Deletes.

I set the “foo_id” attribute on my instance to “7”, but the “foo” attribute is still None - shouldn’t it have loaded Foo with id #7?

The ORM is not constructed in such a way as to support immediate population of relationships driven from foreign key attribute changes - instead, it is designed to work the other way around - foreign key attributes are handled by the ORM behind the scenes, the end user sets up object relationships naturally. Therefore, the recommended way to set o.foo is to do just that - set it!:

foo = Session.query(Foo).get(7)
o.foo = foo

Manipulation of foreign key attributes is of course entirely legal. However, setting a foreign-key attribute to a new value currently does not trigger an “expire” event of the relationship() in which it’s involved. This means that for the following sequence:

o = Session.query(SomeClass).first()
assert o.foo is None  # accessing an un-set attribute sets it to None
o.foo_id = 7

o.foo is initialized to None when we first accessed it. Setting o.foo_id = 7 will have the value of “7” as pending, but no flush has occurred - so o.foo is still None:

# attribute is already set to None, has not been
# reconciled with o.foo_id = 7 yet
assert o.foo is None

For o.foo to load based on the foreign key mutation is usually achieved naturally after the commit, which both flushes the new foreign key value and expires all state:

Session.commit()  # expires all attributes

foo_7 = Session.query(Foo).get(7)

assert o.foo is foo_7  # o.foo lazyloads on access

A more minimal operation is to expire the attribute individually - this can be performed for any persistent object using Session.expire():

o = Session.query(SomeClass).first()
o.foo_id = 7
Session.expire(o, ['foo'])  # object must be persistent for this

foo_7 = Session.query(Foo).get(7)

assert o.foo is foo_7  # o.foo lazyloads on access

Note that if the object is not persistent but present in the Session, it’s known as pending. This means the row for the object has not been INSERTed into the database yet. For such an object, setting foo_id does not have meaning until the row is inserted; otherwise there is no row yet:

new_obj = SomeClass()
new_obj.foo_id = 7


# accessing an un-set attribute sets it to None
assert new_obj.foo is None

Session.flush()  # emits INSERT

# expire this because we already set .foo to None
Session.expire(o, ['foo'])

assert new_obj.foo is foo_7  # now it loads

Attribute loading for non-persistent objects

One variant on the “pending” behavior above is if we use the flag load_on_pending on relationship(). When this flag is set, the lazy loader will emit for new_obj.foo before the INSERT proceeds; another variant of this is to use the Session.enable_relationship_loading() method, which can “attach” an object to a Session in such a way that many-to-one relationships load as according to foreign key attributes regardless of the object being in any particular state. Both techniques are not recommended for general use; they were added to suit specfic programming scenarios encountered by users which involve the repurposing of the ORM’s usual object states.

The recipe ExpireRelationshipOnFKChange features an example using SQLAlchemy events in order to coordinate the setting of foreign key attributes with many-to-one relationships.

Is there a way to automagically have only unique keywords (or other kinds of objects) without doing a query for the keyword and getting a reference to the row containing that keyword?

When people read the many-to-many example in the docs, they get hit with the fact that if you create the same Keyword twice, it gets put in the DB twice. Which is somewhat inconvenient.

This UniqueObject recipe was created to address this issue.