SQLAlchemy 2.0 Documentation
Frequently Asked Questions
- Installation
- Connections / Engines
- MetaData / Schema
- SQL Expressions
- ORM Configuration¶
- How do I map a table that has no primary key?
- How do I configure a Column that is a Python reserved word or similar?
- How do I get a list of all columns, relationships, mapped attributes, etc. given a mapped class?
- I’m getting a warning or error about “Implicitly combining column X under attribute Y”
- I’m using Declarative and setting primaryjoin/secondaryjoin using an
and_()
oror_()
, and I am getting an error message about foreign keys. - Why is
ORDER BY
recommended withLIMIT
(especially withsubqueryload()
)? - What are
default
,default_factory
andinsert_default
and what should I use?
- Performance
- Sessions / Queries
- Third Party Integration Issues
Project Versions
- Previous: SQL Expressions
- Next: Performance
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- On this page:
- ORM Configuration
- How do I map a table that has no primary key?
- How do I configure a Column that is a Python reserved word or similar?
- How do I get a list of all columns, relationships, mapped attributes, etc. given a mapped class?
- I’m getting a warning or error about “Implicitly combining column X under attribute Y”
- I’m using Declarative and setting primaryjoin/secondaryjoin using an
and_()
oror_()
, and I am getting an error message about foreign keys. - Why is
ORDER BY
recommended withLIMIT
(especially withsubqueryload()
)? - What are
default
,default_factory
andinsert_default
and what should I use?
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.
Note
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 Declarative Mapped Columns Explicitly.
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:
Mapper.attrs
- a namespace of all mapped attributes. The attributes themselves are instances ofMapperProperty
, which contain additional attributes that can lead to the mapped SQL expression or column, if applicable.Mapper.column_attrs
- the mapped attribute namespace limited to column and SQL expression attributes. You might want to useMapper.columns
to get at theColumn
objects directly.Mapper.relationships
- namespace of allRelationshipProperty
attributes.Mapper.all_orm_descriptors
- namespace of all mapped attributes, plus user-defined attributes defined using systems such ashybrid_property
,AssociationProxy
and others.Mapper.columns
- A namespace ofColumn
objects and other named SQL expressions associated with the mapping.Mapper.mapped_table
- TheTable
or other selectable to which this mapper is mapped.Mapper.local_table
- TheTable
that is “local” to this mapper; this differs fromMapper.mapped_table
in the case of a mapper mapped using inheritance to a composed selectable.
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("a.id"))
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("a.id"))
Suppose we did want A.id
and B.id
to be mirrors of each other, despite
the fact that B.a_id
is where A.id
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)
a_id = Column(Integer, ForeignKey("a.id"))
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])
Why is ORDER BY
recommended with LIMIT
(especially with subqueryload()
)?¶
When ORDER BY is not used for a SELECT statement that returns rows, the
relational database is free to returned matched rows in any arbitrary
order. 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
, or which merely selects the first row of the result,
discarding the rest, will not be deterministic in terms of what result row is
returned, assuming there’s more than one row that matches the query’s criteria.
While we may not notice this for simple queries on databases that usually
returns rows in their natural order, it becomes more of an issue if we
also use subqueryload()
to load related collections, and we may not
be loading the collections as intended.
SQLAlchemy implements 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.scalars(select(User).options(subqueryload(User.addresses))).all()
-- the "main" query
SELECT users.id AS users_id
FROM users
-- the "load" query issued by subqueryload
SELECT addresses.id AS addresses_id,
addresses.user_id AS addresses_user_id,
anon_1.users_id AS anon_1_users_id
FROM (SELECT users.id 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.scalars(
... select(User).options(subqueryload(User.addresses)).limit(1)
... ).first()
-- the "main" query
SELECT users.id AS users_id
FROM users
LIMIT 1
-- the "load" query issued by subqueryload
SELECT addresses.id AS addresses_id,
addresses.user_id AS addresses_user_id,
anon_1.users_id AS anon_1_users_id
FROM (SELECT users.id 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
+--------+
|users_id|
+--------+
| 1|
+--------+
-- query #2
+------------+-----------------+---------------+
|addresses_id|addresses_user_id|anon_1_users_id|
+------------+-----------------+---------------+
| 3| 2| 2|
+------------+-----------------+---------------+
| 4| 2| 2|
+------------+-----------------+---------------+
Above, we receive two addresses
rows for user.id
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 Select.order_by()
on a unique column on the table.
The primary key is a good choice for this:
session.scalars(
select(User).options(subqueryload(User.addresses)).order_by(User.id).limit(1)
).first()
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.
See also
What are default
, default_factory
and insert_default
and what should I use?¶
There’s a bit of a clash in SQLAlchemy’s API here due to the addition of PEP-681
dataclass transforms, which is strict about its naming conventions. PEP-681 comes
into play if you are using MappedAsDataclass
as shown in Declarative Dataclass Mapping.
If you are not using MappedAsDataclass, then it does not apply.
Part One - Classic SQLAlchemy that is not using dataclasses¶
When not using MappedAsDataclass
, as has been the case for many years
in SQLAlchemy, the mapped_column()
(and Column
)
construct supports a parameter mapped_column.default
.
This indicates a Python-side default (as opposed to a server side default that
would be part of your database’s schema definition) that will take place when
an INSERT
statement is emitted. This default can be any of a static Python value
like a string, or a Python callable function, or a SQLAlchemy SQL construct.
Full documentation for mapped_column.default
is at
Client-Invoked SQL Expressions.
When using mapped_column.default
with an ORM mapping that is not
using MappedAsDataclass
, this default value /callable does not show
up on your object when you first construct it. It only takes place when SQLAlchemy
works up an INSERT
statement for your object.
A very important thing to note is that when using mapped_column()
(and Column
), the classic mapped_column.default
parameter is also available under a new name, called
mapped_column.insert_default
. If you build a
mapped_column()
and you are not using MappedAsDataclass
, the
mapped_column.default
and mapped_column.insert_default
parameters are synonymous.
Part Two - Using Dataclasses support with MappedAsDataclass¶
When you are using MappedAsDataclass
, that is, the specific form
of mapping used at Declarative Dataclass Mapping, the meaning of the
mapped_column.default
keyword changes. We recognize that it’s not
ideal that this name changes its behavior, however there was no alternative as
PEP-681 requires mapped_column.default
to take on this meaning.
When dataclasses are used, the mapped_column.default
parameter must
be used the way it’s described at
Python Dataclasses - it refers
to a constant value like a string or a number, and is applied to your object
immediately when constructed. It is also at the moment also applied to the
mapped_column.default
parameter of Column
where
it would be used in an INSERT
statement automatically even if not present
on the object. If you instead want to use a callable for your dataclass,
which will be applied to the object when constructed, you would use
mapped_column.default_factory
.
To get access to the INSERT
-only behavior of mapped_column.default
that is described in part one above, you would use the
mapped_column.insert_default
parameter instead.
mapped_column.insert_default
when dataclasses are used continues
to be a direct route to the Core-level “default” process where the parameter can
be a static value or callable.
Construct |
Works with dataclasses? |
Works without dataclasses? |
Accepts scalar? |
Accepts callable? |
Populates object immediately? |
---|---|---|---|---|---|
✔ |
✔ |
✔ |
Only if no dataclasses |
Only if dataclasses |
|
✔ |
✔ |
✔ |
✔ |
✖ |
|
✔ |
✖ |
✖ |
✔ |
Only if dataclasses |
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