SQLAlchemy 2.0 Documentation
SQLAlchemy ORM
- ORM Quick Start¶
- ORM Mapped Class Configuration
- Relationship Configuration
- ORM Querying Guide
- Using the Session
- Events and Internals
- ORM Extensions
- ORM Examples
Project Versions
ORM Quick Start¶
For new users who want to quickly see what basic ORM use looks like, here’s an abbreviated form of the mappings and examples used in the SQLAlchemy Unified Tutorial. The code here is fully runnable from a clean command line.
As the descriptions in this section are intentionally very short, please proceed to the full SQLAlchemy Unified Tutorial for a much more in-depth description of each of the concepts being illustrated here.
Changed in version 2.0: The ORM Quickstart is updated for the latest
PEP 484-aware features using new constructs including
mapped_column()
. See the section
ORM Declarative Models for migration information.
Declare Models¶
Here, we define module-level constructs that will form the structures which we will be querying from the database. This structure, known as a Declarative Mapping, defines at once both a Python object model, as well as database metadata that describes real SQL tables that exist, or will exist, in a particular database:
>>> from typing import List
>>> from typing import Optional
>>> from sqlalchemy import ForeignKey
>>> from sqlalchemy import String
>>> from sqlalchemy.orm import DeclarativeBase
>>> from sqlalchemy.orm import Mapped
>>> from sqlalchemy.orm import mapped_column
>>> from sqlalchemy.orm import relationship
>>> class Base(DeclarativeBase):
... pass
>>> class User(Base):
... __tablename__ = "user_account"
...
... id: Mapped[int] = mapped_column(primary_key=True)
... name: Mapped[str] = mapped_column(String(30))
... fullname: Mapped[Optional[str]]
...
... addresses: Mapped[List["Address"]] = relationship(
... back_populates="user", cascade="all, delete-orphan"
... )
...
... def __repr__(self) -> str:
... return f"User(id={self.id!r}, name={self.name!r}, fullname={self.fullname!r})"
>>> class Address(Base):
... __tablename__ = "address"
...
... id: Mapped[int] = mapped_column(primary_key=True)
... email_address: Mapped[str]
... user_id: Mapped[int] = mapped_column(ForeignKey("user_account.id"))
...
... user: Mapped["User"] = relationship(back_populates="addresses")
...
... def __repr__(self) -> str:
... return f"Address(id={self.id!r}, email_address={self.email_address!r})"
The mapping starts with a base class, which above is called Base
, and is
created by making a simple subclass against the DeclarativeBase
class.
Individual mapped classes are then created by making subclasses of Base
.
A mapped class typically refers to a single particular database table,
the name of which is indicated by using the __tablename__
class-level
attribute.
Next, columns that are part of the table are declared, by adding attributes
that include a special typing annotation called Mapped
. The name
of each attribute corresponds to the column that is to be part of the database
table. The datatype of each column is taken first from the Python datatype
that’s associated with each Mapped
annotation; int
for
INTEGER
, str
for VARCHAR
, etc. Nullability derives from whether or
not the Optional[]
type modifier is used. More specific typing information
may be indicated using SQLAlchemy type objects in the right side
mapped_column()
directive, such as the String
datatype
used above in the User.name
column. The association between Python types
and SQL types can be customized using the
type annotation map.
The mapped_column()
directive is used for all column-based
attributes that require more specific customization. Besides typing
information, this directive accepts a wide variety of arguments that indicate
specific details about a database column, including server defaults and
constraint information, such as membership within the primary key and foreign
keys. The mapped_column()
directive accepts a superset of arguments
that are accepted by the SQLAlchemy Column
class, which is
used by SQLAlchemy Core to represent database columns.
All ORM mapped classes require at least one column be declared as part of the
primary key, typically by using the Column.primary_key
parameter on those mapped_column()
objects that should be part
of the key. In the above example, the User.id
and Address.id
columns are marked as primary key.
Taken together, the combination of a string table name as well as a list of column declarations is known in SQLAlchemy as table metadata. Setting up table metadata using both Core and ORM approaches is introduced in the SQLAlchemy Unified Tutorial at Working with Database Metadata. The above mapping is an example of what’s known as Annotated Declarative Table configuration.
Other variants of Mapped
are available, most commonly
the relationship()
construct indicated above. In contrast
to the column-based attributes, relationship()
denotes a linkage
between two ORM classes. In the above example, User.addresses
links
User
to Address
, and Address.user
links Address
to User
.
The relationship()
construct is introduced in the
SQLAlchemy Unified Tutorial at Working with ORM Related Objects.
Finally, the above example classes include a __repr__()
method, which is
not required but is useful for debugging. Mapped classes can be created with
methods such as __repr__()
generated automatically, using dataclasses. More
on dataclass mapping at Declarative Dataclass Mapping.
Create an Engine¶
The Engine
is a factory that can create new
database connections for us, which also holds onto connections inside
of a Connection Pool for fast reuse. For learning
purposes, we normally use a SQLite memory-only database
for convenience:
>>> from sqlalchemy import create_engine
>>> engine = create_engine("sqlite://", echo=True)
Tip
The echo=True
parameter indicates that SQL emitted by connections will
be logged to standard out.
A full intro to the Engine
starts at Establishing Connectivity - the Engine.
Emit CREATE TABLE DDL¶
Using our table metadata and our engine, we can generate our schema at once
in our target SQLite database, using a method called MetaData.create_all()
:
>>> Base.metadata.create_all(engine)
BEGIN (implicit)
PRAGMA main.table_...info("user_account")
...
PRAGMA main.table_...info("address")
...
CREATE TABLE user_account (
id INTEGER NOT NULL,
name VARCHAR(30) NOT NULL,
fullname VARCHAR,
PRIMARY KEY (id)
)
...
CREATE TABLE address (
id INTEGER NOT NULL,
email_address VARCHAR NOT NULL,
user_id INTEGER NOT NULL,
PRIMARY KEY (id),
FOREIGN KEY(user_id) REFERENCES user_account (id)
)
...
COMMIT
A lot just happened from that bit of Python code we wrote. For a complete overview of what’s going on on with Table metadata, proceed in the Tutorial at Working with Database Metadata.
Create Objects and Persist¶
We are now ready to insert data in the database. We accomplish this by
creating instances of User
and Address
classes, which have
an __init__()
method already as established automatically by the
declarative mapping process. We then pass them
to the database using an object called a Session,
which makes use of the Engine
to interact with the
database. The Session.add_all()
method is used here to add
multiple objects at once, and the Session.commit()
method
will be used to flush any pending changes to the
database and then commit the current database
transaction, which is always in progress whenever the Session
is used:
>>> from sqlalchemy.orm import Session
>>> with Session(engine) as session:
... spongebob = User(
... name="spongebob",
... fullname="Spongebob Squarepants",
... addresses=[Address(email_address="spongebob@sqlalchemy.org")],
... )
... sandy = User(
... name="sandy",
... fullname="Sandy Cheeks",
... addresses=[
... Address(email_address="sandy@sqlalchemy.org"),
... Address(email_address="sandy@squirrelpower.org"),
... ],
... )
... patrick = User(name="patrick", fullname="Patrick Star")
...
... session.add_all([spongebob, sandy, patrick])
...
... session.commit()
BEGIN (implicit)
INSERT INTO user_account (name, fullname) VALUES (?, ?) RETURNING id
[...] ('spongebob', 'Spongebob Squarepants')
INSERT INTO user_account (name, fullname) VALUES (?, ?) RETURNING id
[...] ('sandy', 'Sandy Cheeks')
INSERT INTO user_account (name, fullname) VALUES (?, ?) RETURNING id
[...] ('patrick', 'Patrick Star')
INSERT INTO address (email_address, user_id) VALUES (?, ?) RETURNING id
[...] ('spongebob@sqlalchemy.org', 1)
INSERT INTO address (email_address, user_id) VALUES (?, ?) RETURNING id
[...] ('sandy@sqlalchemy.org', 2)
INSERT INTO address (email_address, user_id) VALUES (?, ?) RETURNING id
[...] ('sandy@squirrelpower.org', 2)
COMMIT
Tip
It’s recommended that the Session
be used in context
manager style as above, that is, using the Python with:
statement.
The Session
object represents active database resources
so it’s good to make sure it’s closed out when a series of operations
are completed. In the next section, we’ll keep a Session
opened just for illustration purposes.
Basics on creating a Session
are at
Executing with an ORM Session and more at Basics of Using a Session.
Then, some varieties of basic persistence operations are introduced at Inserting Rows using the ORM Unit of Work pattern.
Simple SELECT¶
With some rows in the database, here’s the simplest form of emitting a SELECT
statement to load some objects. To create SELECT statements, we use the
select()
function to create a new Select
object, which
we then invoke using a Session
. The method that is often useful
when querying for ORM objects is the Session.scalars()
method, which
will return a ScalarResult
object that will iterate through
the ORM objects we’ve selected:
>>> from sqlalchemy import select
>>> session = Session(engine)
>>> stmt = select(User).where(User.name.in_(["spongebob", "sandy"]))
>>> for user in session.scalars(stmt):
... print(user)
BEGIN (implicit)
SELECT user_account.id, user_account.name, user_account.fullname
FROM user_account
WHERE user_account.name IN (?, ?)
[...] ('spongebob', 'sandy')
User(id=1, name='spongebob', fullname='Spongebob Squarepants')
User(id=2, name='sandy', fullname='Sandy Cheeks')
The above query also made use of the Select.where()
method
to add WHERE criteria, and also used the ColumnOperators.in_()
method that’s part of all SQLAlchemy column-like constructs to use the
SQL IN operator.
More detail on how to select objects and individual columns is at Selecting ORM Entities and Columns.
SELECT with JOIN¶
It’s very common to query amongst multiple tables at once, and in SQL
the JOIN keyword is the primary way this happens. The Select
construct creates joins using the Select.join()
method:
>>> stmt = (
... select(Address)
... .join(Address.user)
... .where(User.name == "sandy")
... .where(Address.email_address == "sandy@sqlalchemy.org")
... )
>>> sandy_address = session.scalars(stmt).one()
SELECT address.id, address.email_address, address.user_id
FROM address JOIN user_account ON user_account.id = address.user_id
WHERE user_account.name = ? AND address.email_address = ?
[...] ('sandy', 'sandy@sqlalchemy.org')
>>> sandy_address
Address(id=2, email_address='sandy@sqlalchemy.org')
The above query illustrates multiple WHERE criteria which are automatically
chained together using AND, as well as how to use SQLAlchemy column-like
objects to create “equality” comparisons, which uses the overridden Python
method ColumnOperators.__eq__()
to produce a SQL criteria object.
Some more background on the concepts above are at The WHERE clause and Explicit FROM clauses and JOINs.
Make Changes¶
The Session
object, in conjunction with our ORM-mapped classes
User
and Address
, automatically track changes to the objects as they
are made, which result in SQL statements that will be emitted the next
time the Session
flushes. Below, we change one email
address associated with “sandy”, and also add a new email address to
“patrick”, after emitting a SELECT to retrieve the row for “patrick”:
>>> stmt = select(User).where(User.name == "patrick")
>>> patrick = session.scalars(stmt).one()
SELECT user_account.id, user_account.name, user_account.fullname
FROM user_account
WHERE user_account.name = ?
[...] ('patrick',)
>>> patrick.addresses.append(Address(email_address="patrickstar@sqlalchemy.org"))
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
[...] (3,)
>>> sandy_address.email_address = "sandy_cheeks@sqlalchemy.org"
>>> session.commit()
UPDATE address SET email_address=? WHERE address.id = ?
[...] ('sandy_cheeks@sqlalchemy.org', 2)
INSERT INTO address (email_address, user_id) VALUES (?, ?)
[...] ('patrickstar@sqlalchemy.org', 3)
COMMIT
Notice when we accessed patrick.addresses
, a SELECT was emitted. This is
called a lazy load. Background on different ways to access related
items using more or less SQL is introduced at Loader Strategies.
A detailed walkthrough on ORM data manipulation starts at Data Manipulation with the ORM.
Some Deletes¶
All things must come to an end, as is the case for some of our database rows - here’s a quick demonstration of two different forms of deletion, both of which are important based on the specific use case.
First we will remove one of the Address
objects from the “sandy” user.
When the Session
next flushes, this will result in the
row being deleted. This behavior is something that we configured in our
mapping called the delete cascade. We can get a handle to the sandy
object by primary key using Session.get()
, then work with the object:
>>> sandy = session.get(User, 2)
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 = ?
[...] (2,)
>>> sandy.addresses.remove(sandy_address)
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
[...] (2,)
The last SELECT above was the lazy load operation proceeding so that
the sandy.addresses
collection could be loaded, so that we could remove the
sandy_address
member. There are other ways to go about this series
of operations that won’t emit as much SQL.
We can choose to emit the DELETE SQL for what’s set to be changed so far, without
committing the transaction, using the
Session.flush()
method:
>>> session.flush()
DELETE FROM address WHERE address.id = ?
[...] (2,)
Next, we will delete the “patrick” user entirely. For a top-level delete of
an object by itself, we use the Session.delete()
method; this
method doesn’t actually perform the deletion, but sets up the object
to be deleted on the next flush. The
operation will also cascade to related objects based on the cascade
options that we configured, in this case, onto the related Address
objects:
>>> session.delete(patrick)
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 = ?
[...] (3,)
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
[...] (3,)
The Session.delete()
method in this particular case emitted two
SELECT statements, even though it didn’t emit a DELETE, which might seem surprising.
This is because when the method went to inspect the object, it turns out the
patrick
object was expired, which happened when we last called upon
Session.commit()
, and the SQL emitted was to re-load the rows
from the new transaction. This expiration is optional, and in normal
use we will often be turning it off for situations where it doesn’t apply well.
To illustrate the rows being deleted, here’s the commit:
>>> session.commit()
DELETE FROM address WHERE address.id = ?
[...] (4,)
DELETE FROM user_account WHERE user_account.id = ?
[...] (3,)
COMMIT
The Tutorial discusses ORM deletion at Deleting ORM Objects using the Unit of Work pattern. Background on object expiration is at Expiring / Refreshing; cascades are discussed in depth at Cascades.
Learn the above concepts in depth¶
For a new user, the above sections were likely a whirlwind tour. There’s a lot of important concepts in each step above that weren’t covered. With a quick overview of what things look like, it’s recommended to work through the SQLAlchemy Unified Tutorial to gain a solid working knowledge of what’s really going on above. Good luck!
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