Working with ORM Related Objects¶
In this section, we will cover one more essential ORM concept, which is
how the ORM interacts with mapped classes that refer to other objects. In the
section Declaring Mapped Classes, the mapped class examples
made use of a construct called relationship(). This construct
defines a linkage between two different mapped classes, or from a mapped class
to itself, the latter of which is called a self-referential relationship.
To describe the basic idea of relationship(), first we’ll review
the mapping in short form, omitting the mapped_column() mappings
and other directives:
from sqlalchemy.orm import Mapped
from sqlalchemy.orm import relationship
class User(Base):
__tablename__ = "user_account"
# ... mapped_column() mappings
addresses: Mapped[List["Address"]] = relationship(back_populates="user")
class Address(Base):
__tablename__ = "address"
# ... mapped_column() mappings
user: Mapped["User"] = relationship(back_populates="addresses")Above, the User class now has an attribute User.addresses and the
Address class has an attribute Address.user. The
relationship() construct, in conjunction with the
Mapped construct to indicate typing behavior, will be used to
inspect the table relationships between the Table objects that
are mapped to the User and Address classes. As the
Table object representing the address table has a
ForeignKeyConstraint which refers to the user_account
table, the relationship() can determine unambiguously that there is
a one to many relationship from the User class to the Address
class, along the User.addresses relationship; one particular row in the
user_account table may be referenced by many rows in the address
table.
All one-to-many relationships naturally correspond to a many to one
relationship in the other direction, in this case the one noted by
Address.user. The relationship.back_populates parameter,
seen above configured on both relationship() objects referring to
the other name, establishes that each of these two relationship()
constructs should be considered to be complimentary to each other; we will see
how this plays out in the next section.
Persisting and Loading Relationships¶
We can start by illustrating what relationship() does to instances
of objects. If we make a new User object, we can note that there is a
Python list when we access the .addresses element:
>>> u1 = User(name="pkrabs", fullname="Pearl Krabs")
>>> u1.addresses
[]This object is a SQLAlchemy-specific version of Python list which
has the ability to track and respond to changes made to it. The collection
also appeared automatically when we accessed the attribute, even though we never assigned it to the object.
This is similar to the behavior noted at Inserting Rows using the ORM Unit of Work pattern where
it was observed that column-based attributes to which we don’t explicitly
assign a value also display as None automatically, rather than raising
an AttributeError as would be Python’s usual behavior.
As the u1 object is still transient and the list that we got
from u1.addresses has not been mutated (i.e. appended or extended), it’s
not actually associated with the object yet, but as we make changes to it,
it will become part of the state of the User object.
The collection is specific to the Address class which is the only type
of Python object that may be persisted within it. Using the list.append()
method we may add an Address object:
>>> a1 = Address(email_address="pearl.krabs@gmail.com")
>>> u1.addresses.append(a1)At this point, the u1.addresses collection as expected contains the
new Address object:
>>> u1.addresses
[Address(id=None, email_address='pearl.krabs@gmail.com')]As we associated the Address object with the User.addresses collection
of the u1 instance, another behavior also occurred, which is that the
User.addresses relationship synchronized itself with the Address.user
relationship, such that we can navigate not only from the User object
to the Address object, we can also navigate from the Address object
back to the “parent” User object:
>>> a1.user
User(id=None, name='pkrabs', fullname='Pearl Krabs')This synchronization occurred as a result of our use of the
relationship.back_populates parameter between the two
relationship() objects. This parameter names another
relationship() for which complementary attribute assignment / list
mutation should occur. It will work equally well in the other
direction, which is that if we create another Address object and assign
to its Address.user attribute, that Address becomes part of the
User.addresses collection on that User object:
>>> a2 = Address(email_address="pearl@aol.com", user=u1)
>>> u1.addresses
[Address(id=None, email_address='pearl.krabs@gmail.com'), Address(id=None, email_address='pearl@aol.com')]We actually made use of the user parameter as a keyword argument in the
Address constructor, which is accepted just like any other mapped attribute
that was declared on the Address class. It is equivalent to assignment
of the Address.user attribute after the fact:
# equivalent effect as a2 = Address(user=u1)
>>> a2.user = u1Cascading Objects into the Session¶
We now have a User and two Address objects that are associated in a
bidirectional structure
in memory, but as noted previously in Inserting Rows using the ORM Unit of Work pattern ,
these objects are said to be in the transient state until they
are associated with a Session object.
We make use of the Session that’s still ongoing, and note that
when we apply the Session.add() method to the lead User object,
the related Address object also gets added to that same Session:
>>> session.add(u1)
>>> u1 in session
True
>>> a1 in session
True
>>> a2 in session
TrueThe above behavior, where the Session received a User object,
and followed along the User.addresses relationship to locate a related
Address object, is known as the save-update cascade and is discussed
in detail in the ORM reference documentation at Cascades.
The three objects are now in the pending state; this means they are
ready to be the subject of an INSERT operation but this has not yet proceeded;
all three objects have no primary key assigned yet, and in addition, the a1
and a2 objects have an attribute called user_id which refers to the
Column that has a ForeignKeyConstraint
referring to the user_account.id column; these are also None as the
objects are not yet associated with a real database row:
>>> print(u1.id)
None
>>> print(a1.user_id)
NoneIt’s at this stage that we can see the very great utility that the unit of
work process provides; recall in the section INSERT usually generates the “values” clause automatically,
rows were inserted into the user_account and
address tables using some elaborate syntaxes in order to automatically
associate the address.user_id columns with those of the user_account
rows. Additionally, it was necessary that we emit INSERT for user_account
rows first, before those of address, since rows in address are
dependent on their parent row in user_account for a value in their
user_id column.
When using the Session, all this tedium is handled for us and
even the most die-hard SQL purist can benefit from automation of INSERT,
UPDATE and DELETE statements. When we Session.commit() the
transaction all steps invoke in the correct order, and furthermore the
newly generated primary key of the user_account row is applied to the
address.user_id column appropriately:
>>> session.commit()
INSERT INTO user_account (name, fullname) VALUES (?, ?)
[...] ('pkrabs', 'Pearl Krabs')
INSERT INTO address (email_address, user_id) VALUES (?, ?) RETURNING id
[... (insertmanyvalues) 1/2 (ordered; batch not supported)] ('pearl.krabs@gmail.com', 6)
INSERT INTO address (email_address, user_id) VALUES (?, ?) RETURNING id
[insertmanyvalues 2/2 (ordered; batch not supported)] ('pearl@aol.com', 6)
COMMIT
Loading Relationships¶
In the last step, we called Session.commit() which emitted a COMMIT
for the transaction, and then per
Session.commit.expire_on_commit expired all objects so that
they refresh for the next transaction.
When we next access an attribute on these objects, we’ll see the SELECT
emitted for the primary attributes of the row, such as when we view the
newly generated primary key for the u1 object:
>>> u1.id
BEGIN (implicit)
SELECT user_account.id AS user_account_id, user_account.name AS user_account_name,
user_account.fullname AS user_account_fullname
FROM user_account
WHERE user_account.id = ?
[...] (6,)
6The u1 User object now has a persistent collection User.addresses
that we may also access. As this collection consists of an additional set
of rows from the address table, when we access this collection as well
we again see a lazy load emitted in order to retrieve the objects:
>>> u1.addresses
SELECT address.id AS address_id, address.email_address AS address_email_address,
address.user_id AS address_user_id
FROM address
WHERE ? = address.user_id
[...] (6,)
[Address(id=4, email_address='pearl.krabs@gmail.com'), Address(id=5, email_address='pearl@aol.com')]Collections and related attributes in the SQLAlchemy ORM are persistent in
memory; once the collection or attribute is populated, SQL is no longer emitted
until that collection or attribute is expired. We may access
u1.addresses again as well as add or remove items and this will not
incur any new SQL calls:
>>> u1.addresses
[Address(id=4, email_address='pearl.krabs@gmail.com'), Address(id=5, email_address='pearl@aol.com')]While the loading emitted by lazy loading can quickly become expensive if
we don’t take explicit steps to optimize it, the network of lazy loading
at least is fairly well optimized to not perform redundant work; as the
u1.addresses collection was refreshed, per the identity map
these are in fact the same
Address instances as the a1 and a2 objects we’ve been dealing with
already, so we’re done loading all attributes in this particular object
graph:
>>> a1
Address(id=4, email_address='pearl.krabs@gmail.com')
>>> a2
Address(id=5, email_address='pearl@aol.com')The issue of how relationships load, or not, is an entire subject onto itself. Some additional introduction to these concepts is later in this section at Loader Strategies.
Using Relationships in Queries¶
The previous section introduced the behavior of the relationship()
construct when working with instances of a mapped class, above, the
u1, a1 and a2 instances of the User and Address classes.
In this section, we introduce the behavior of relationship() as it
applies to class level behavior of a mapped class, where it serves in
several ways to help automate the construction of SQL queries.
Using Relationships to Join¶
The sections Explicit FROM clauses and JOINs and
Setting the ON Clause introduced the usage of the
Select.join() and Select.join_from() methods to compose
SQL JOIN clauses. In order to describe how to join between tables, these
methods either infer the ON clause based on the presence of a single
unambiguous ForeignKeyConstraint object within the table
metadata structure that links the two tables, or otherwise we may provide an
explicit SQL Expression construct that indicates a specific ON clause.
When using ORM entities, an additional mechanism is available to help us set up
the ON clause of a join, which is to make use of the relationship()
objects that we set up in our user mapping, as was demonstrated at
Declaring Mapped Classes. The class-bound attribute
corresponding to the relationship() may be passed as the single
argument to Select.join(), where it serves to indicate both the
right side of the join as well as the ON clause at once:
>>> print(select(Address.email_address).select_from(User).join(User.addresses))
SELECT address.email_address
FROM user_account JOIN address ON user_account.id = address.user_id
The presence of an ORM relationship() on a mapping is not used
by Select.join() or Select.join_from()
to infer the ON clause if we don’t
specify it. This means, if we join from User to Address without an
ON clause, it works because of the ForeignKeyConstraint
between the two mapped Table objects, not because of the
relationship() objects on the User and Address classes:
>>> print(select(Address.email_address).join_from(User, Address))
SELECT address.email_address
FROM user_account JOIN address ON user_account.id = address.user_id
See the section Joins in the ORM Querying Guide
for many more examples of how to use Select.join() and Select.join_from()
with relationship() constructs.
See also
Joins in the ORM Querying Guide
Relationship WHERE Operators¶
There are some additional varieties of SQL generation helpers that come with
relationship() which are typically useful when building up the
WHERE clause of a statement. See the section
Relationship WHERE Operators in the ORM Querying Guide.
See also
Loader Strategies¶
In the section Loading Relationships we introduced the concept
that when we work with instances of mapped objects, accessing the attributes
that are mapped using relationship() in the default case will emit
a lazy load when the collection is not populated in order to load
the objects that should be present in this collection.
Lazy loading is one of the most famous ORM patterns, and is also the one that is most controversial. When several dozen ORM objects in memory each refer to a handful of unloaded attributes, routine manipulation of these objects can spin off many additional queries that can add up (otherwise known as the N plus one problem), and to make matters worse they are emitted implicitly. These implicit queries may not be noticed, may cause errors when they are attempted after there’s no longer a database transaction available, or when using alternative concurrency patterns such as asyncio, they actually won’t work at all.
At the same time, lazy loading is a vastly popular and useful pattern when it is compatible with the concurrency approach in use and isn’t otherwise causing problems. For these reasons, SQLAlchemy’s ORM places a lot of emphasis on being able to control and optimize this loading behavior.
Above all, the first step in using ORM lazy loading effectively is to test
the application, turn on SQL echoing, and watch the SQL that is emitted. If
there seem to be lots of redundant SELECT statements that look very much like
they could be rolled into one much more efficiently, if there are loads
occurring inappropriately for objects that have been detached from
their Session, that’s when to look into using loader
strategies.
Loader strategies are represented as objects that may be associated with a
SELECT statement using the Select.options() method, e.g.:
for user_obj in session.execute(
select(User).options(selectinload(User.addresses))
).scalars():
user_obj.addresses # access addresses collection already loadedThey may be also configured as defaults for a relationship() using
the relationship.lazy option, e.g.:
from sqlalchemy.orm import Mapped
from sqlalchemy.orm import relationship
class User(Base):
__tablename__ = "user_account"
addresses: Mapped[List["Address"]] = relationship(
back_populates="user", lazy="selectin"
)Each loader strategy object adds some kind of information to the statement that
will be used later by the Session when it is deciding how various
attributes should be loaded and/or behave when they are accessed.
The sections below will introduce a few of the most prominently used loader strategies.
See also
Two sections in Relationship Loading Techniques:
Configuring Loader Strategies at Mapping Time - details on configuring the strategy on
relationship()Relationship Loading with Loader Options - details on using query-time loader strategies
Selectin Load¶
The most useful loader in modern SQLAlchemy is the
selectinload() loader option. This option solves the most common
form of the “N plus one” problem which is that of a set of objects that refer
to related collections. selectinload() will ensure that a particular
collection for a full series of objects are loaded up front using a single
query. It does this using a SELECT form that in most cases can be emitted
against the related table alone, without the introduction of JOINs or
subqueries, and only queries for those parent objects for which the
collection isn’t already loaded. Below we illustrate selectinload()
by loading all of the User objects and all of their related Address
objects; while we invoke Session.execute() only once, given a
select() construct, when the database is accessed, there are
in fact two SELECT statements emitted, the second one being to fetch the
related Address objects:
>>> from sqlalchemy.orm import selectinload
>>> stmt = select(User).options(selectinload(User.addresses)).order_by(User.id)
>>> for row in session.execute(stmt):
... print(
... f"{row.User.name} ({', '.join(a.email_address for a in row.User.addresses)})"
... )
SELECT user_account.id, user_account.name, user_account.fullname
FROM user_account ORDER BY user_account.id
[...] ()
SELECT address.user_id AS address_user_id, address.id AS address_id,
address.email_address AS address_email_address
FROM address
WHERE address.user_id IN (?, ?, ?, ?, ?, ?)
[...] (1, 2, 3, 4, 5, 6)
spongebob (spongebob@sqlalchemy.org)
sandy (sandy@sqlalchemy.org, sandy@squirrelpower.org)
patrick ()
squidward ()
ehkrabs ()
pkrabs (pearl.krabs@gmail.com, pearl@aol.com)See also
Joined Load¶
The joinedload() eager load strategy is the oldest eager loader in
SQLAlchemy, which augments the SELECT statement that’s being passed to the
database with a JOIN (which may be an outer or an inner join depending on options),
which can then load in related objects.
The joinedload() strategy is best suited towards loading
related many-to-one objects, as this only requires that additional columns
are added to a primary entity row that would be fetched in any case.
For greater efficiency, it also accepts an option joinedload.innerjoin
so that an inner join instead of an outer join may be used for a case such
as below where we know that all Address objects have an associated
User:
>>> from sqlalchemy.orm import joinedload
>>> stmt = (
... select(Address)
... .options(joinedload(Address.user, innerjoin=True))
... .order_by(Address.id)
... )
>>> for row in session.execute(stmt):
... print(f"{row.Address.email_address} {row.Address.user.name}")
SELECT address.id, address.email_address, address.user_id, user_account_1.id AS id_1,
user_account_1.name, user_account_1.fullname
FROM address
JOIN user_account AS user_account_1 ON user_account_1.id = address.user_id
ORDER BY address.id
[...] ()
spongebob@sqlalchemy.org spongebob
sandy@sqlalchemy.org sandy
sandy@squirrelpower.org sandy
pearl.krabs@gmail.com pkrabs
pearl@aol.com pkrabsjoinedload() also works for collections, meaning one-to-many relationships,
however it has the effect
of multiplying out primary rows per related item in a recursive way
that grows the amount of data sent for a result set by orders of magnitude for
nested collections and/or larger collections, so its use vs. another option
such as selectinload() should be evaluated on a per-case basis.
It’s important to note that the WHERE and ORDER BY criteria of the enclosing
Select statement do not target the table rendered by
joinedload(). Above, it can be seen in the SQL that an anonymous alias
is applied to the user_account table such that is not directly addressable
in the query. This concept is discussed in more detail in the section
The Zen of Joined Eager Loading.
Tip
It’s important to note that many-to-one eager loads are often not necessary,
as the “N plus one” problem is much less prevalent in the common case. When
many objects all refer to the same related object, such as many Address
objects that each refer to the same User, SQL will be emitted only once
for that User object using normal lazy loading. The lazy load routine
will look up the related object by primary key in the current
Session without emitting any SQL when possible.
See also
Explicit Join + Eager load¶
If we were to load Address rows while joining to the user_account table
using a method such as Select.join() to render the JOIN, we could
also leverage that JOIN in order to eagerly load the contents of the
Address.user attribute on each Address object returned. This is
essentially that we are using “joined eager loading” but rendering the JOIN
ourselves. This common use case is achieved by using the
contains_eager() option. This option is very similar to
joinedload(), except that it assumes we have set up the JOIN
ourselves, and it instead only indicates that additional columns in the COLUMNS
clause should be loaded into related attributes on each returned object, for
example:
>>> from sqlalchemy.orm import contains_eager
>>> stmt = (
... select(Address)
... .join(Address.user)
... .where(User.name == "pkrabs")
... .options(contains_eager(Address.user))
... .order_by(Address.id)
... )
>>> for row in session.execute(stmt):
... print(f"{row.Address.email_address} {row.Address.user.name}")
SELECT user_account.id, user_account.name, user_account.fullname,
address.id AS id_1, address.email_address, address.user_id
FROM address JOIN user_account ON user_account.id = address.user_id
WHERE user_account.name = ? ORDER BY address.id
[...] ('pkrabs',)
pearl.krabs@gmail.com pkrabs
pearl@aol.com pkrabsAbove, we both filtered the rows on user_account.name and also loaded
rows from user_account into the Address.user attribute of the returned
rows. If we had applied joinedload() separately, we would get a
SQL query that unnecessarily joins twice:
>>> stmt = (
... select(Address)
... .join(Address.user)
... .where(User.name == "pkrabs")
... .options(joinedload(Address.user))
... .order_by(Address.id)
... )
>>> print(stmt) # SELECT has a JOIN and LEFT OUTER JOIN unnecessarily
SELECT address.id, address.email_address, address.user_id,
user_account_1.id AS id_1, user_account_1.name, user_account_1.fullname
FROM address JOIN user_account ON user_account.id = address.user_id
LEFT OUTER JOIN user_account AS user_account_1 ON user_account_1.id = address.user_id
WHERE user_account.name = :name_1 ORDER BY address.id
See also
Two sections in Relationship Loading Techniques:
The Zen of Joined Eager Loading - describes the above problem in detail
Routing Explicit Joins/Statements into Eagerly Loaded Collections - using
contains_eager()
Raiseload¶
One additional loader strategy worth mentioning is raiseload().
This option is used to completely block an application from having the
N plus one problem at all by causing what would normally be a lazy
load to raise an error instead. It has two variants that are controlled via
the raiseload.sql_only option to block either lazy loads
that require SQL, versus all “load” operations including those which
only need to consult the current Session.
One way to use raiseload() is to configure it on
relationship() itself, by setting relationship.lazy
to the value "raise_on_sql", so that for a particular mapping, a certain
relationship will never try to emit SQL:
>>> from sqlalchemy.orm import Mapped
>>> from sqlalchemy.orm import relationship
>>> class User(Base):
... __tablename__ = "user_account"
... id: Mapped[int] = mapped_column(primary_key=True)
... addresses: Mapped[List["Address"]] = relationship(
... back_populates="user", lazy="raise_on_sql"
... )
>>> class Address(Base):
... __tablename__ = "address"
... id: Mapped[int] = mapped_column(primary_key=True)
... user_id: Mapped[int] = mapped_column(ForeignKey("user_account.id"))
... user: Mapped["User"] = relationship(back_populates="addresses", lazy="raise_on_sql")Using such a mapping, the application is blocked from lazy loading, indicating that a particular query would need to specify a loader strategy:
>>> u1 = session.execute(select(User)).scalars().first()
SELECT user_account.id FROM user_account
[...] ()
>>> u1.addresses
Traceback (most recent call last):
...
sqlalchemy.exc.InvalidRequestError: 'User.addresses' is not available due to lazy='raise_on_sql'The exception would indicate that this collection should be loaded up front instead:
>>> u1 = (
... session.execute(select(User).options(selectinload(User.addresses)))
... .scalars()
... .first()
... )
SELECT user_account.id
FROM user_account
[...] ()
SELECT address.user_id AS address_user_id, address.id AS address_id
FROM address
WHERE address.user_id IN (?, ?, ?, ?, ?, ?)
[...] (1, 2, 3, 4, 5, 6)
The lazy="raise_on_sql" option tries to be smart about many-to-one
relationships as well; above, if the Address.user attribute of an
Address object were not loaded, but that User object were locally
present in the same Session, the “raiseload” strategy would not
raise an error.