Release: 1.4.54 | Release Date: September 5, 2024

# SQLAlchemy 1.4 Documentation

# 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 1.4 / 2.0 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 1.4 / 2.0 Tutorial for a much more in-depth description of each of the concepts being illustrated here.

## 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 sqlalchemy import Column
    >>> from sqlalchemy import ForeignKey
    >>> from sqlalchemy import Integer
    >>> from sqlalchemy import String
    >>> from sqlalchemy.orm import declarative_base
    >>> from sqlalchemy.orm import relationship

    >>> Base = declarative_base()

    >>> class User(Base):
    ...     __tablename__ = "user_account"
    ...
    ...     id = Column(Integer, primary_key=True)
    ...     name = Column(String(30))
    ...     fullname = Column(String)
    ...
    ...     addresses = relationship(
    ...         "Address", back_populates="user", cascade="all, delete-orphan"
    ...     )
    ...
    ...     def __repr__(self):
    ...         return f"User(id={self.id!r}, name={self.name!r}, fullname={self.fullname!r})"

    >>> class Address(Base):
    ...     __tablename__ = "address"
    ...
    ...     id = Column(Integer, primary_key=True)
    ...     email_address = Column(String, nullable=False)
    ...     user_id = Column(Integer, ForeignKey("user_account.id"), nullable=False)
    ...
    ...     user = relationship("User", back_populates="addresses")
    ...
    ...     def __repr__(self):
    ...         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 calling upon the `declarative_base()` function, which produces a new base 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 linked to the `Column` construct. `Column` describes all aspects of a database column, including typing information with type objects such as `Integer` and `String` as well as server defaults and constraint information, such as membership within the primary key and foreign keys.

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 `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 referred towards in SQLAlchemy as table metadata. Setting up table metadata using both Core and ORM approaches is introduced in the SQLAlchemy 1.4 / 2.0 Tutorial at Working with Database Metadata. The above mapping is an example of what’s referred towards as Declarative Table configuration.

Other Declarative directives 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 1.4 / 2.0 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.

## 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, future=True)

Tip

The `echo=True` parameter indicates that SQL emitted by connections will be logged to standard out. `future=True` is to ensure we are using the latest SQLAlchemy 2.0-style APIs.

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),
        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 (?, ?)
    [...] ('spongebob', 'Spongebob Squarepants')
    INSERT INTO user_account (name, fullname) VALUES (?, ?)
    [...] ('sandy', 'Sandy Cheeks')
    INSERT INTO user_account (name, fullname) VALUES (?, ?)
    [...] ('patrick', 'Patrick Star')
    INSERT INTO address (email_address, user_id) VALUES (?, ?)
    [...] ('spongebob@sqlalchemy.org', 1)
    INSERT INTO address (email_address, user_id) VALUES (?, ?)
    [...] ('sandy@sqlalchemy.org', 2)
    INSERT INTO address (email_address, user_id) VALUES (?, ?)
    [...] ('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 with the ORM.

## 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. 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 1.4 / 2.0 Tutorial to gain a solid working knowledge of what’s really going on above. Good luck!