Release: 1.2.1 current release | Release Date: January 15, 2018

SQLAlchemy 1.2 Documentation

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__ = {

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 accessed through properties such as:

I’m getting a warning or error about “Implicitly combining column X under attribute Y”

This condition refers to when a mapping contains two columns that are being mapped under the same attribute name due to their name, but there’s no indication that this is intentional. A mapped class needs to have explicit names for every attribute that is to store an independent value; when two columns have the same name and aren’t disambiguated, they fall under the same attribute and the effect is that the value from one column is copied into the other, based on which column was assigned to the attribute first.

This behavior is often desirable and is allowed without warning in the case where the two columns are linked together via a foreign key relationship within an inheritance mapping. When the warning or exception occurs, the issue can be resolved by either assigning the columns to differently-named attributes, or if combining them together is desired, by using column_property() to make this explicit.

Given the example as follows:

from sqlalchemy import Integer, Column, ForeignKey
from sqlalchemy.ext.declarative import declarative_base

Base = declarative_base()

class A(Base):
    __tablename__ = 'a'

    id = Column(Integer, primary_key=True)

class B(A):
    __tablename__ = 'b'

    id = Column(Integer, primary_key=True)
    a_id = Column(Integer, ForeignKey(''))

As of SQLAlchemy version 0.9.5, the above condition is detected, and will warn that the id column of A and B is being combined under the same-named attribute id, which above is a serious issue since it means that a B object’s primary key will always mirror that of its A.

A mapping which resolves this is as follows:

class A(Base):
    __tablename__ = 'a'

    id = Column(Integer, primary_key=True)

class B(A):
    __tablename__ = 'b'

    b_id = Column('id', Integer, primary_key=True)
    a_id = Column(Integer, ForeignKey(''))

Suppose we did want and to be mirrors of each other, despite the fact that B.a_id is where is related. We could combine them together using column_property():

class A(Base):
    __tablename__ = 'a'

    id = Column(Integer, primary_key=True)

class B(A):
    __tablename__ = 'b'

    # probably not what you want, but this is a demonstration
    id = column_property(Column(Integer, primary_key=True),
    a_id = Column(Integer, ForeignKey(''))

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_("", ""))

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_(,")

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

class MyClass(Base):
    # ....

    foo = relationship(Dest, primaryjoin=and_(,

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])

Why is ORDER BY required with LIMIT (especially with subqueryload())?

A relational database can return rows in any arbitrary order, when an explicit ordering is not set. While this ordering very often corresponds to the natural order of rows within a table, this is not the case for all databases and all queries. The consequence of this is that any query that limits rows using LIMIT or OFFSET should always specify an ORDER BY. Otherwise, it is not deterministic which rows will actually be returned.

When we use a SQLAlchemy method like Query.first(), we are in fact applying a LIMIT of one to the query, so without an explicit ordering it is not deterministic what row we actually get back. While we may not notice this for simple queries on databases that usually returns rows in their natural order, it becomes much more of an issue if we also use orm.subqueryload() to load related collections, and we may not be loading the collections as intended.

SQLAlchemy implements orm.subqueryload() by issuing a separate query, the results of which are matched up to the results from the first query. We see two queries emitted like this:

>>> session.query(User).options(subqueryload(User.addresses)).all()
-- the "main" query SELECT AS users_id FROM users
-- the "load" query issued by subqueryload SELECT AS addresses_id, addresses.user_id AS addresses_user_id, anon_1.users_id AS anon_1_users_id FROM (SELECT AS users_id FROM users) AS anon_1 JOIN addresses ON anon_1.users_id = addresses.user_id ORDER BY anon_1.users_id

The second query embeds the first query as a source of rows. When the inner query uses OFFSET and/or LIMIT without ordering, the two queries may not see the same results:

>>> user = session.query(User).options(subqueryload(User.addresses)).first()
-- the "main" query SELECT AS users_id FROM users LIMIT 1
-- the "load" query issued by subqueryload SELECT AS addresses_id, addresses.user_id AS addresses_user_id, anon_1.users_id AS anon_1_users_id FROM (SELECT AS users_id FROM users LIMIT 1) AS anon_1 JOIN addresses ON anon_1.users_id = addresses.user_id ORDER BY anon_1.users_id

Depending on database specifics, there is a chance we may get a result like the following for the two queries:

-- query #1
|       1|

-- query #2
|           3|                2|              2|
|           4|                2|              2|

Above, we receive two addresses rows for of 2, and none for 1. We’ve wasted two rows and failed to actually load the collection. This is an insidious error because without looking at the SQL and the results, the ORM will not show that there’s any issue; if we access the addresses for the User we have, it will emit a lazy load for the collection and we won’t see that anything actually went wrong.

The solution to this problem is to always specify a deterministic sort order, so that the main query always returns the same set of rows. This generally means that you should Query.order_by() on a unique column on the table. The primary key is a good choice for this:


Note that the joinedload() eager loader strategy does not suffer from the same problem because only one query is ever issued, so the load query cannot be different from the main query. Similarly, the selectinload() eager loader strategy also does not have this issue as it links its collection loads directly to primary key values just loaded.

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