Object Relational Tutorial (1.x API)

About this document

This tutorial covers the well known SQLAlchemy ORM API that has been in use for many years. As of SQLAlchemy 1.4, there are two distinct styles of ORM use known as 1.x style and 2.0 style, the latter of which makes a wide range of changes most prominently around how ORM queries are constructed and executed.

The plan is that in SQLAlchemy 2.0, the 1.x style of ORM use will be considered legacy and no longer featured in documentation and many aspects of it will be removed. However, the most central element of 1.x style ORM use, the Query object, will still remain available for long-term legacy use cases.

This tutorial is applicable to users who want to learn how SQLAlchemy has been used for many years, particularly those users working with existing applications or related learning material that is in 1.x style.

For an introduction to SQLAlchemy from the new 1.4/2.0 perspective, see SQLAlchemy 1.4 / 2.0 Tutorial.

The SQLAlchemy Object Relational Mapper presents a method of associating user-defined Python classes with database tables, and instances of those classes (objects) with rows in their corresponding tables. It includes a system that transparently synchronizes all changes in state between objects and their related rows, called a unit of work, as well as a system for expressing database queries in terms of the user defined classes and their defined relationships between each other.

The ORM is in contrast to the SQLAlchemy Expression Language, upon which the ORM is constructed. Whereas the SQL Expression Language, introduced in SQL Expression Language Tutorial (1.x API), presents a system of representing the primitive constructs of the relational database directly without opinion, the ORM presents a high level and abstracted pattern of usage, which itself is an example of applied usage of the Expression Language.

While there is overlap among the usage patterns of the ORM and the Expression Language, the similarities are more superficial than they may at first appear. One approaches the structure and content of data from the perspective of a user-defined domain model which is transparently persisted and refreshed from its underlying storage model. The other approaches it from the perspective of literal schema and SQL expression representations which are explicitly composed into messages consumed individually by the database.

A successful application may be constructed using the Object Relational Mapper exclusively. In advanced situations, an application constructed with the ORM may make occasional usage of the Expression Language directly in certain areas where specific database interactions are required.

The following tutorial is in doctest format, meaning each >>> line represents something you can type at a Python command prompt, and the following text represents the expected return value.

Version Check

A quick check to verify that we are on at least version 1.4 of SQLAlchemy:

>>> import sqlalchemy
>>> sqlalchemy.__version__  


For this tutorial we will use an in-memory-only SQLite database. To connect we use create_engine():

>>> from sqlalchemy import create_engine
>>> engine = create_engine("sqlite:///:memory:", echo=True)

The echo flag is a shortcut to setting up SQLAlchemy logging, which is accomplished via Python’s standard logging module. With it enabled, we’ll see all the generated SQL produced. If you are working through this tutorial and want less output generated, set it to False. This tutorial will format the SQL behind a popup window so it doesn’t get in our way; just click the “SQL” links to see what’s being generated.

The return value of create_engine() is an instance of Engine, and it represents the core interface to the database, adapted through a dialect that handles the details of the database and DBAPI in use. In this case the SQLite dialect will interpret instructions to the Python built-in sqlite3 module.

The first time a method like Engine.execute() or Engine.connect() is called, the Engine establishes a real DBAPI connection to the database, which is then used to emit the SQL. When using the ORM, we typically don’t use the Engine directly once created; instead, it’s used behind the scenes by the ORM as we’ll see shortly.

See also

Database URLs - includes examples of create_engine() connecting to several kinds of databases with links to more information.

Declare a Mapping

When using the ORM, the configurational process starts by describing the database tables we’ll be dealing with, and then by defining our own classes which will be mapped to those tables. In modern SQLAlchemy, these two tasks are usually performed together, using a system known as Declarative Extensions, which allows us to create classes that include directives to describe the actual database table they will be mapped to.

Classes mapped using the Declarative system are defined in terms of a base class which maintains a catalog of classes and tables relative to that base - this is known as the declarative base class. Our application will usually have just one instance of this base in a commonly imported module. We create the base class using the declarative_base() function, as follows:

>>> from sqlalchemy.orm import declarative_base

>>> Base = declarative_base()

Now that we have a “base”, we can define any number of mapped classes in terms of it. We will start with just a single table called users, which will store records for the end-users using our application. A new class called User will be the class to which we map this table. Within the class, we define details about the table to which we’ll be mapping, primarily the table name, and names and datatypes of columns:

>>> from sqlalchemy import Column, Integer, String
>>> class User(Base):
...     __tablename__ = "users"
...     id = Column(Integer, primary_key=True)
...     name = Column(String)
...     fullname = Column(String)
...     nickname = Column(String)
...     def __repr__(self):
...         return "<User(name='%s', fullname='%s', nickname='%s')>" % (
...             self.name,
...             self.fullname,
...             self.nickname,
...         )

A class using Declarative at a minimum needs a __tablename__ attribute, and at least one Column which is part of a primary key [1]. SQLAlchemy never makes any assumptions by itself about the table to which a class refers, including that it has no built-in conventions for names, datatypes, or constraints. But this doesn’t mean boilerplate is required; instead, you’re encouraged to create your own automated conventions using helper functions and mixin classes, which is described in detail at Mixin and Custom Base Classes.

When our class is constructed, Declarative replaces all the Column objects with special Python accessors known as descriptors; this is a process known as instrumentation. The “instrumented” mapped class will provide us with the means to refer to our table in a SQL context as well as to persist and load the values of columns from the database.

Outside of what the mapping process does to our class, the class remains otherwise mostly a normal Python class, to which we can define any number of ordinary attributes and methods needed by our application.

Create a Schema

With our User class constructed via the Declarative system, we have defined information about our table, known as table metadata. The object used by SQLAlchemy to represent this information for a specific table is called the Table object, and here Declarative has made one for us. We can see this object by inspecting the __table__ attribute:

>>> User.__table__  
Table('users', MetaData(),
            Column('id', Integer(), table=<users>, primary_key=True, nullable=False),
            Column('name', String(), table=<users>),
            Column('fullname', String(), table=<users>),
            Column('nickname', String(), table=<users>), schema=None)

When we declared our class, Declarative used a Python metaclass in order to perform additional activities once the class declaration was complete; within this phase, it then created a Table object according to our specifications, and associated it with the class by constructing a Mapper object. This object is a behind-the-scenes object we normally don’t need to deal with directly (though it can provide plenty of information about our mapping when we need it).

The Table object is a member of a larger collection known as MetaData. When using Declarative, this object is available using the .metadata attribute of our declarative base class.

The MetaData is a registry which includes the ability to emit a limited set of schema generation commands to the database. As our SQLite database does not actually have a users table present, we can use MetaData to issue CREATE TABLE statements to the database for all tables that don’t yet exist. Below, we call the MetaData.create_all() method, passing in our Engine as a source of database connectivity. We will see that special commands are first emitted to check for the presence of the users table, and following that the actual CREATE TABLE statement:

>>> Base.metadata.create_all(engine)
    name VARCHAR,
    fullname VARCHAR,
    nickname VARCHAR,
    PRIMARY KEY (id)
[...] ()

Create an Instance of the Mapped Class

With mappings complete, let’s now create and inspect a User object:

>>> ed_user = User(name="ed", fullname="Ed Jones", nickname="edsnickname")
>>> ed_user.name
>>> ed_user.nickname
>>> str(ed_user.id)

Even though we didn’t specify it in the constructor, the id attribute still produces a value of None when we access it (as opposed to Python’s usual behavior of raising AttributeError for an undefined attribute). SQLAlchemy’s instrumentation normally produces this default value for column-mapped attributes when first accessed. For those attributes where we’ve actually assigned a value, the instrumentation system is tracking those assignments for use within an eventual INSERT statement to be emitted to the database.

Creating a Session

We’re now ready to start talking to the database. The ORM’s “handle” to the database is the Session. When we first set up the application, at the same level as our create_engine() statement, we define a Session class which will serve as a factory for new Session objects:

>>> from sqlalchemy.orm import sessionmaker
>>> Session = sessionmaker(bind=engine)

In the case where your application does not yet have an Engine when you define your module-level objects, just set it up like this:

>>> Session = sessionmaker()

Later, when you create your engine with create_engine(), connect it to the Session using sessionmaker.configure():

>>> Session.configure(bind=engine)  # once engine is available

This custom-made Session class will create new Session objects which are bound to our database. Other transactional characteristics may be defined when calling sessionmaker as well; these are described in a later chapter. Then, whenever you need to have a conversation with the database, you instantiate a Session:

>>> session = Session()

The above Session is associated with our SQLite-enabled Engine, but it hasn’t opened any connections yet. When it’s first used, it retrieves a connection from a pool of connections maintained by the Engine, and holds onto it until we commit all changes and/or close the session object.

Adding and Updating Objects

To persist our User object, we Session.add() it to our Session:

>>> ed_user = User(name="ed", fullname="Ed Jones", nickname="edsnickname")
>>> session.add(ed_user)

At this point, we say that the instance is pending; no SQL has yet been issued and the object is not yet represented by a row in the database. The Session will issue the SQL to persist Ed Jones as soon as is needed, using a process known as a flush. If we query the database for Ed Jones, all pending information will first be flushed, and the query is issued immediately thereafter.

For example, below we create a new Query object which loads instances of User. We “filter by” the name attribute of ed, and indicate that we’d like only the first result in the full list of rows. A User instance is returned which is equivalent to that which we’ve added:

sql>>> our_user = (
...     session.query(User).filter_by(name="ed").first()
... )  
>>> our_user
<User(name='ed', fullname='Ed Jones', nickname='edsnickname')>

In fact, the Session has identified that the row returned is the same row as one already represented within its internal map of objects, so we actually got back the identical instance as that which we just added:

>>> ed_user is our_user

The ORM concept at work here is known as an identity map and ensures that all operations upon a particular row within a Session operate upon the same set of data. Once an object with a particular primary key is present in the Session, all SQL queries on that Session will always return the same Python object for that particular primary key; it also will raise an error if an attempt is made to place a second, already-persisted object with the same primary key within the session.

We can add more User objects at once using add_all():

>>> session.add_all(
...     [
...         User(name="wendy", fullname="Wendy Williams", nickname="windy"),
...         User(name="mary", fullname="Mary Contrary", nickname="mary"),
...         User(name="fred", fullname="Fred Flintstone", nickname="freddy"),
...     ]
... )

Also, we’ve decided Ed’s nickname isn’t that great, so lets change it:

>>> ed_user.nickname = "eddie"

The Session is paying attention. It knows, for example, that Ed Jones has been modified:

>>> session.dirty
IdentitySet([<User(name='ed', fullname='Ed Jones', nickname='eddie')>])

and that three new User objects are pending:

>>> session.new  
IdentitySet([<User(name='wendy', fullname='Wendy Williams', nickname='windy')>,
<User(name='mary', fullname='Mary Contrary', nickname='mary')>,
<User(name='fred', fullname='Fred Flintstone', nickname='freddy')>])

We tell the Session that we’d like to issue all remaining changes to the database and commit the transaction, which has been in progress throughout. We do this via Session.commit(). The Session emits the UPDATE statement for the nickname change on “ed”, as well as INSERT statements for the three new User objects we’ve added:

sql>>> session.commit()

Session.commit() flushes the remaining changes to the database, and commits the transaction. The connection resources referenced by the session are now returned to the connection pool. Subsequent operations with this session will occur in a new transaction, which will again re-acquire connection resources when first needed.

If we look at Ed’s id attribute, which earlier was None, it now has a value:

sql>>> ed_user.id  

After the Session inserts new rows in the database, all newly generated identifiers and database-generated defaults become available on the instance, either immediately or via load-on-first-access. In this case, the entire row was re-loaded on access because a new transaction was begun after we issued Session.commit(). SQLAlchemy by default refreshes data from a previous transaction the first time it’s accessed within a new transaction, so that the most recent state is available. The level of reloading is configurable as is described in Using the Session.

Rolling Back

Since the Session works within a transaction, we can roll back changes made too. Let’s make two changes that we’ll revert; ed_user’s user name gets set to Edwardo:

>>> ed_user.name = "Edwardo"

and we’ll add another erroneous user, fake_user:

>>> fake_user = User(name="fakeuser", fullname="Invalid", nickname="12345")
>>> session.add(fake_user)

Querying the session, we can see that they’re flushed into the current transaction:

sql>>> session.query(User).filter(User.name.in_(["Edwardo", "fakeuser"])).all()
[<User(name='Edwardo', fullname='Ed Jones', nickname='eddie')>, <User(name='fakeuser', fullname='Invalid', nickname='12345')>]

Rolling back, we can see that ed_user’s name is back to ed, and fake_user has been kicked out of the session:

sql>>> session.rollback()

sql>>> ed_user.name
>>> fake_user in session

issuing a SELECT illustrates the changes made to the database:

sql>>> session.query(User).filter(User.name.in_(["ed", "fakeuser"])).all()
[<User(name='ed', fullname='Ed Jones', nickname='eddie')>]


A Query object is created using the query() method on Session. This function takes a variable number of arguments, which can be any combination of classes and class-instrumented descriptors. Below, we indicate a Query which loads User instances. When evaluated in an iterative context, the list of User objects present is returned:

sql>>> for instance in session.query(User).order_by(User.id):
...     print(instance.name, instance.fullname)
ed Ed Jones
wendy Wendy Williams
mary Mary Contrary
fred Fred Flintstone

The Query also accepts ORM-instrumented descriptors as arguments. Any time multiple class entities or column-based entities are expressed as arguments to the query() function, the return result is expressed as tuples:

sql>>> for name, fullname in session.query(User.name, User.fullname):
...     print(name, fullname)
ed Ed Jones
wendy Wendy Williams
mary Mary Contrary
fred Fred Flintstone

The tuples returned by Query are named tuples, supplied by the Row class, and can be treated much like an ordinary Python object. The names are the same as the attribute’s name for an attribute, and the class name for a class:

sql>>> for row in session.query(User, User.name).all():
...     print(row.User, row.name)
<User(name='ed', fullname='Ed Jones', nickname='eddie')> ed
<User(name='wendy', fullname='Wendy Williams', nickname='windy')> wendy
<User(name='mary', fullname='Mary Contrary', nickname='mary')> mary
<User(name='fred', fullname='Fred Flintstone', nickname='freddy')> fred

You can control the names of individual column expressions using the ColumnElement.label() construct, which is available from any ColumnElement-derived object, as well as any class attribute which is mapped to one (such as User.name):

sql>>> for row in session.query(User.name.label("name_label")).all():
...     print(row.name_label)

The name given to a full entity such as User, assuming that multiple entities are present in the call to Session.query(), can be controlled using aliased() :

>>> from sqlalchemy.orm import aliased
>>> user_alias = aliased(User, name="user_alias")

sql>>> for row in session.query(user_alias, user_alias.name).all():
...     print(row.user_alias)
<User(name='ed', fullname='Ed Jones', nickname='eddie')>
<User(name='wendy', fullname='Wendy Williams', nickname='windy')>
<User(name='mary', fullname='Mary Contrary', nickname='mary')>
<User(name='fred', fullname='Fred Flintstone', nickname='freddy')>

Basic operations with Query include issuing LIMIT and OFFSET, most conveniently using Python array slices and typically in conjunction with ORDER BY:

sql>>> for u in session.query(User).order_by(User.id)[1:3]:
...     print(u)
<User(name='wendy', fullname='Wendy Williams', nickname='windy')>
<User(name='mary', fullname='Mary Contrary', nickname='mary')>

and filtering results, which is accomplished either with filter_by(), which uses keyword arguments:

sql>>> for (name,) in session.query(User.name).filter_by(fullname="Ed Jones"):
...     print(name)

…or filter(), which uses more flexible SQL expression language constructs. These allow you to use regular Python operators with the class-level attributes on your mapped class:

sql>>> for (name,) in session.query(User.name).filter(User.fullname == "Ed Jones"):
...     print(name)

The Query object is fully generative, meaning that most method calls return a new Query object upon which further criteria may be added. For example, to query for users named “ed” with a full name of “Ed Jones”, you can call filter() twice, which joins criteria using AND:

sql>>> for user in (
...     session.query(User).filter(User.name == "ed").filter(User.fullname == "Ed Jones")
... ):
...     print(user)
<User(name='ed', fullname='Ed Jones', nickname='eddie')>

Common Filter Operators

Here’s a rundown of some of the most common operators used in filter():


ColumnOperators.like() renders the LIKE operator, which is case insensitive on some backends, and case sensitive on others. For guaranteed case-insensitive comparisons, use ColumnOperators.ilike().


most backends don’t support ILIKE directly. For those, the ColumnOperators.ilike() operator renders an expression combining LIKE with the LOWER SQL function applied to each operand.

  • ColumnOperators.in_():

    query.filter(User.name.in_(["ed", "wendy", "jack"]))
    # works with query objects too:
    # use tuple_() for composite (multi-column) queries
    from sqlalchemy import tuple_
        tuple_(User.name, User.nickname).in_([("ed", "edsnickname"), ("wendy", "windy")])
  • ColumnOperators.not_in():

    query.filter(~User.name.in_(["ed", "wendy", "jack"]))
  • ColumnOperators.is_():

    query.filter(User.name == None)
    # alternatively, if pep8/linters are a concern
  • ColumnOperators.is_not():

    query.filter(User.name != None)
    # alternatively, if pep8/linters are a concern
  • AND:

    # use and_()
    from sqlalchemy import and_
    query.filter(and_(User.name == 'ed', User.fullname == 'Ed Jones'))
    # or send multiple expressions to .filter()
    query.filter(User.name == 'ed', User.fullname == 'Ed Jones')
    # or chain multiple filter()/filter_by() calls
    query.filter(User.name == 'ed').filter(User.fullname == 'Ed Jones')


Make sure you use and_() and not the Python and operator!

  • OR:

    from sqlalchemy import or_
    query.filter(or_(User.name == 'ed', User.name == 'wendy'))


Make sure you use or_() and not the Python or operator!


ColumnOperators.match() uses a database-specific MATCH or CONTAINS function; its behavior will vary by backend and is not available on some backends such as SQLite.

Returning Lists and Scalars

A number of methods on Query immediately issue SQL and return a value containing loaded database results. Here’s a brief tour:

  • Query.all() returns a list:

    >>> query = session.query(User).filter(User.name.like("%ed")).order_by(User.id)
    sql>>> query.all()
    [<User(name='ed', fullname='Ed Jones', nickname='eddie')>,
          <User(name='fred', fullname='Fred Flintstone', nickname='freddy')>]


    When the Query object returns lists of ORM-mapped objects such as the User object above, the entries are deduplicated based on primary key, as the results are interpreted from the SQL result set. That is, if SQL query returns a row with id=7 twice, you would only get a single User(id=7) object back in the result list. This does not apply to the case when individual columns are queried.

  • Query.first() applies a limit of one and returns the first result as a scalar:

    sql>>> query.first()
    <User(name='ed', fullname='Ed Jones', nickname='eddie')>
  • Query.one() fully fetches all rows, and if not exactly one object identity or composite row is present in the result, raises an error. With multiple rows found:

    >>> user = query.one()
    Traceback (most recent call last):
    MultipleResultsFound: Multiple rows were found for one()

    With no rows found:

    >>> user = query.filter(User.id == 99).one()
    Traceback (most recent call last):
    NoResultFound: No row was found for one()

    The Query.one() method is great for systems that expect to handle “no items found” versus “multiple items found” differently; such as a RESTful web service, which may want to raise a “404 not found” when no results are found, but raise an application error when multiple results are found.

  • Query.one_or_none() is like Query.one(), except that if no results are found, it doesn’t raise an error; it just returns None. Like Query.one(), however, it does raise an error if multiple results are found.

  • Query.scalar() invokes the Query.one() method, and upon success returns the first column of the row:

    >>> query = session.query(User.id).filter(User.name == "ed").order_by(User.id)
    sql>>> query.scalar()

Using Textual SQL

Literal strings can be used flexibly with Query, by specifying their use with the text() construct, which is accepted by most applicable methods. For example, Query.filter() and Query.order_by():

>>> from sqlalchemy import text
sql>>> for user in session.query(User).filter(text("id<224")).order_by(text("id")).all():
...     print(user.name)

Bind parameters can be specified with string-based SQL, using a colon. To specify the values, use the Query.params() method:

sql>>> session.query(User).filter(text("id<:value and name=:name")).params(
...     value=224, name="fred"
... ).order_by(User.id).one()
<User(name='fred', fullname='Fred Flintstone', nickname='freddy')>

To use an entirely string-based statement, a text() construct representing a complete statement can be passed to Query.from_statement(). Without further specification, the ORM will match columns in the ORM mapping to the result returned by the SQL statement based on column name:

sql>>> session.query(User).from_statement(text("SELECT * FROM users where name=:name")).params(
...     name="ed"
... ).all()
[<User(name='ed', fullname='Ed Jones', nickname='eddie')>]

For better targeting of mapped columns to a textual SELECT, as well as to match on a specific subset of columns in arbitrary order, individual mapped columns are passed in the desired order to TextClause.columns():

>>> stmt = text("SELECT name, id, fullname, nickname " "FROM users where name=:name")
>>> stmt = stmt.columns(User.name, User.id, User.fullname, User.nickname)
sql>>> session.query(User).from_statement(stmt).params(name="ed").all()
[<User(name='ed', fullname='Ed Jones', nickname='eddie')>]

When selecting from a text() construct, the Query may still specify what columns and entities are to be returned; instead of query(User) we can also ask for the columns individually, as in any other case:

>>> stmt = text("SELECT name, id FROM users where name=:name")
>>> stmt = stmt.columns(User.name, User.id)
sql>>> session.query(User.id, User.name).from_statement(stmt).params(name="ed").all()
[(1, u'ed')]

See also

Using Textual SQL - The text() construct explained from the perspective of Core-only queries.


Query includes a convenience method for counting called Query.count():

sql>>> session.query(User).filter(User.name.like("%ed")).count()

The Query.count() method is used to determine how many rows the SQL statement would return. Looking at the generated SQL above, SQLAlchemy always places whatever it is we are querying into a subquery, then counts the rows from that. In some cases this can be reduced to a simpler SELECT count(*) FROM table, however modern versions of SQLAlchemy don’t try to guess when this is appropriate, as the exact SQL can be emitted using more explicit means.

For situations where the “thing to be counted” needs to be indicated specifically, we can specify the “count” function directly using the expression func.count(), available from the expression.func construct. Below we use it to return the count of each distinct user name:

>>> from sqlalchemy import func
sql>>> session.query(func.count(User.name), User.name).group_by(User.name).all()
[(1, u'ed'), (1, u'fred'), (1, u'mary'), (1, u'wendy')]

To achieve our simple SELECT count(*) FROM table, we can apply it as:

sql>>> session.query(func.count("*")).select_from(User).scalar()

The usage of Query.select_from() can be removed if we express the count in terms of the User primary key directly:

sql>>> session.query(func.count(User.id)).scalar()

Building a Relationship

Let’s consider how a second table, related to User, can be mapped and queried. Users in our system can store any number of email addresses associated with their username. This implies a basic one to many association from the users to a new table which stores email addresses, which we will call addresses. Using declarative, we define this table along with its mapped class, Address:

>>> from sqlalchemy import ForeignKey
>>> from sqlalchemy.orm import relationship

>>> class Address(Base):
...     __tablename__ = "addresses"
...     id = Column(Integer, primary_key=True)
...     email_address = Column(String, nullable=False)
...     user_id = Column(Integer, ForeignKey("users.id"))
...     user = relationship("User", back_populates="addresses")
...     def __repr__(self):
...         return "<Address(email_address='%s')>" % self.email_address

>>> User.addresses = relationship("Address", order_by=Address.id, back_populates="user")

The above class introduces the ForeignKey construct, which is a directive applied to Column that indicates that values in this column should be constrained to be values present in the named remote column. This is a core feature of relational databases, and is the “glue” that transforms an otherwise unconnected collection of tables to have rich overlapping relationships. The ForeignKey above expresses that values in the addresses.user_id column should be constrained to those values in the users.id column, i.e. its primary key.

A second directive, known as relationship(), tells the ORM that the Address class itself should be linked to the User class, using the attribute Address.user. relationship() uses the foreign key relationships between the two tables to determine the nature of this linkage, determining that Address.user will be many to one. An additional relationship() directive is placed on the User mapped class under the attribute User.addresses. In both relationship() directives, the parameter relationship.back_populates is assigned to refer to the complementary attribute names; by doing so, each relationship() can make intelligent decision about the same relationship as expressed in reverse; on one side, Address.user refers to a User instance, and on the other side, User.addresses refers to a list of Address instances.


The relationship.back_populates parameter is a newer version of a very common SQLAlchemy feature called relationship.backref. The relationship.backref parameter hasn’t gone anywhere and will always remain available! The relationship.back_populates is the same thing, except a little more verbose and easier to manipulate. For an overview of the entire topic, see the section Using the legacy ‘backref’ relationship parameter.

The reverse side of a many-to-one relationship is always one to many. A full catalog of available relationship() configurations is at Basic Relationship Patterns.

The two complementing relationships Address.user and User.addresses are referred to as a bidirectional relationship, and is a key feature of the SQLAlchemy ORM. The section Using the legacy ‘backref’ relationship parameter discusses the “backref” feature in detail.

Arguments to relationship() which concern the remote class can be specified using strings, assuming the Declarative system is in use. Once all mappings are complete, these strings are evaluated as Python expressions in order to produce the actual argument, in the above case the User class. The names which are allowed during this evaluation include, among other things, the names of all classes which have been created in terms of the declared base.

See the docstring for relationship() for more detail on argument style.

We’ll need to create the addresses table in the database, so we will issue another CREATE from our metadata, which will skip over tables which have already been created:

sql>>> Base.metadata.create_all(engine)

Querying with Joins

Now that we have two tables, we can show some more features of Query, specifically how to create queries that deal with both tables at the same time. The Wikipedia page on SQL JOIN offers a good introduction to join techniques, several of which we’ll illustrate here.

To construct a simple implicit join between User and Address, we can use Query.filter() to equate their related columns together. Below we load the User and Address entities at once using this method:

sql>>> for u, a in (
...     session.query(User, Address)
...     .filter(User.id == Address.user_id)
...     .filter(Address.email_address == "jack@google.com")
...     .all()
... ):
...     print(u)
...     print(a)
<User(name='jack', fullname='Jack Bean', nickname='gjffdd')>

The actual SQL JOIN syntax, on the other hand, is most easily achieved using the Query.join() method:

sql>>> session.query(User).join(Address).filter(
...     Address.email_address == "jack@google.com"
... ).all()
[<User(name='jack', fullname='Jack Bean', nickname='gjffdd')>]

Query.join() knows how to join between User and Address because there’s only one foreign key between them. If there were no foreign keys, or several, Query.join() works better when one of the following forms are used:

query.join(Address, User.id == Address.user_id)  # explicit condition
query.join(User.addresses)  # specify relationship from left to right
query.join(Address, User.addresses)  # same, with explicit target
    User.addresses.and_(Address.name != "foo")
)  # use relationship + additional ON criteria

As you would expect, the same idea is used for “outer” joins, using the Query.outerjoin() function:

query.outerjoin(User.addresses)  # LEFT OUTER JOIN

The reference documentation for Query.join() contains detailed information and examples of the calling styles accepted by this method; Query.join() is an important method at the center of usage for any SQL-fluent application.

Using Aliases

When querying across multiple tables, if the same table needs to be referenced more than once, SQL typically requires that the table be aliased with another name, so that it can be distinguished against other occurrences of that table. This is supported using the aliased() construct. When joining to relationships using using aliased(), the special attribute method PropComparator.of_type() may be used to alter the target of a relationship join to refer to a given aliased() object. Below we join to the Address entity twice, to locate a user who has two distinct email addresses at the same time:

>>> from sqlalchemy.orm import aliased
>>> adalias1 = aliased(Address)
>>> adalias2 = aliased(Address)
sql>>> for username, email1, email2 in (
...     session.query(User.name, adalias1.email_address, adalias2.email_address)
...     .join(User.addresses.of_type(adalias1))
...     .join(User.addresses.of_type(adalias2))
...     .filter(adalias1.email_address == "jack@google.com")
...     .filter(adalias2.email_address == "j25@yahoo.com")
... ):
...     print(username, email1, email2)
jack jack@google.com j25@yahoo.com

In addition to using the PropComparator.of_type() method, it is common to see the Query.join() method joining to a specific target by indicating it separately:

# equivalent to query.join(User.addresses.of_type(adalias1))
q = query.join(adalias1, User.addresses)

Using Subqueries

The Query is suitable for generating statements which can be used as subqueries. Suppose we wanted to load User objects along with a count of how many Address records each user has. The best way to generate SQL like this is to get the count of addresses grouped by user ids, and JOIN to the parent. In this case we use a LEFT OUTER JOIN so that we get rows back for those users who don’t have any addresses, e.g.:

SELECT users.*, adr_count.address_count FROM users LEFT OUTER JOIN
    (SELECT user_id, count(*) AS address_count
        FROM addresses GROUP BY user_id) AS adr_count
    ON users.id=adr_count.user_id

Using the Query, we build a statement like this from the inside out. The statement accessor returns a SQL expression representing the statement generated by a particular Query - this is an instance of a select() construct, which are described in SQL Expression Language Tutorial (1.x API):

>>> from sqlalchemy.sql import func
>>> stmt = (
...     session.query(Address.user_id, func.count("*").label("address_count"))
...     .group_by(Address.user_id)
...     .subquery()
... )

The func keyword generates SQL functions, and the subquery() method on Query produces a SQL expression construct representing a SELECT statement embedded within an alias (it’s actually shorthand for query.statement.alias()).

Once we have our statement, it behaves like a Table construct, such as the one we created for users at the start of this tutorial. The columns on the statement are accessible through an attribute called c:

sql>>> for u, count in (
...     session.query(User, stmt.c.address_count)
...     .outerjoin(stmt, User.id == stmt.c.user_id)
...     .order_by(User.id)
... ):
...     print(u, count)
<User(name='ed', fullname='Ed Jones', nickname='eddie')> None
<User(name='wendy', fullname='Wendy Williams', nickname='windy')> None
<User(name='mary', fullname='Mary Contrary', nickname='mary')> None
<User(name='fred', fullname='Fred Flintstone', nickname='freddy')> None
<User(name='jack', fullname='Jack Bean', nickname='gjffdd')> 2

Selecting Entities from Subqueries

Above, we just selected a result that included a column from a subquery. What if we wanted our subquery to map to an entity ? For this we use aliased() to associate an “alias” of a mapped class to a subquery:

sql>>> stmt = (
...     session.query(Address).filter(Address.email_address != "j25@yahoo.com").subquery()
... )
>>> addr_alias = aliased(Address, stmt)
>>> for user, address in session.query(User, addr_alias).join(addr_alias, User.addresses):
...     print(user)
...     print(address)
<User(name='jack', fullname='Jack Bean', nickname='gjffdd')>


The EXISTS keyword in SQL is a boolean operator which returns True if the given expression contains any rows. It may be used in many scenarios in place of joins, and is also useful for locating rows which do not have a corresponding row in a related table.

There is an explicit EXISTS construct, which looks like this:

>>> from sqlalchemy.sql import exists
>>> stmt = exists().where(Address.user_id == User.id)
sql>>> for (name,) in session.query(User.name).filter(stmt):
...     print(name)

The Query features several operators which make usage of EXISTS automatically. Above, the statement can be expressed along the User.addresses relationship using Comparator.any():

sql>>> for (name,) in session.query(User.name).filter(User.addresses.any()):
...     print(name)

Comparator.any() takes criterion as well, to limit the rows matched:

sql>>> for (name,) in session.query(User.name).filter(
...     User.addresses.any(Address.email_address.like("%google%"))
... ):
...     print(name)

Comparator.has() is the same operator as Comparator.any() for many-to-one relationships (note the ~ operator here too, which means “NOT”):

sql>>> session.query(Address).filter(~Address.user.has(User.name == "jack")).all()

Common Relationship Operators

Here’s all the operators which build on relationships - each one is linked to its API documentation which includes full details on usage and behavior:

  • Comparator.__eq__() (many-to-one “equals” comparison):

    query.filter(Address.user == someuser)
  • Comparator.__ne__() (many-to-one “not equals” comparison):

    query.filter(Address.user != someuser)
  • IS NULL (many-to-one comparison, also uses Comparator.__eq__()):

    query.filter(Address.user == None)
  • Comparator.contains() (used for one-to-many collections):

  • Comparator.any() (used for collections):

    query.filter(User.addresses.any(Address.email_address == "bar"))
    # also takes keyword arguments:
  • Comparator.has() (used for scalar references):

  • Query.with_parent() (used for any relationship):

    session.query(Address).with_parent(someuser, "addresses")

Eager Loading

Recall earlier that we illustrated a lazy loading operation, when we accessed the User.addresses collection of a User and SQL was emitted. If you want to reduce the number of queries (dramatically, in many cases), we can apply an eager load to the query operation. SQLAlchemy offers three types of eager loading, two of which are automatic, and a third which involves custom criterion. All three are usually invoked via functions known as query options which give additional instructions to the Query on how we would like various attributes to be loaded, via the Query.options() method.

Selectin Load

In this case we’d like to indicate that User.addresses should load eagerly. A good choice for loading a set of objects as well as their related collections is the selectinload() option, which emits a second SELECT statement that fully loads the collections associated with the results just loaded. The name “selectin” originates from the fact that the SELECT statement uses an IN clause in order to locate related rows for multiple objects at once:

>>> from sqlalchemy.orm import selectinload
sql>>> jack = (
...     session.query(User)
...     .options(selectinload(User.addresses))
...     .filter_by(name="jack")
...     .one()
... )
>>> jack
<User(name='jack', fullname='Jack Bean', nickname='gjffdd')>

>>> jack.addresses
[<Address(email_address='jack@google.com')>, <Address(email_address='j25@yahoo.com')>]

Joined Load

The other automatic eager loading function is more well known and is called joinedload(). This style of loading emits a JOIN, by default a LEFT OUTER JOIN, so that the lead object as well as the related object or collection is loaded in one step. We illustrate loading the same addresses collection in this way - note that even though the User.addresses collection on jack is actually populated right now, the query will emit the extra join regardless:

>>> from sqlalchemy.orm import joinedload

sql>>> jack = (
...     session.query(User).options(joinedload(User.addresses)).filter_by(name="jack").one()
... )
>>> jack
<User(name='jack', fullname='Jack Bean', nickname='gjffdd')>

>>> jack.addresses
[<Address(email_address='jack@google.com')>, <Address(email_address='j25@yahoo.com')>]

Note that even though the OUTER JOIN resulted in two rows, we still only got one instance of User back. This is because Query applies a “uniquing” strategy, based on object identity, to the returned entities. This is specifically so that joined eager loading can be applied without affecting the query results.

While joinedload() has been around for a long time, selectinload() is a newer form of eager loading. selectinload() tends to be more appropriate for loading related collections while joinedload() tends to be better suited for many-to-one relationships, due to the fact that only one row is loaded for both the lead and the related object. Another form of loading, subqueryload(), also exists, which can be used in place of selectinload() when making use of composite primary keys on certain backends.

Explicit Join + Eagerload

A third style of eager loading is when we are constructing a JOIN explicitly in order to locate the primary rows, and would like to additionally apply the extra table to a related object or collection on the primary object. This feature is supplied via the contains_eager() function, and is most typically useful for pre-loading the many-to-one object on a query that needs to filter on that same object. Below we illustrate loading an Address row as well as the related User object, filtering on the User named “jack” and using contains_eager() to apply the “user” columns to the Address.user attribute:

>>> from sqlalchemy.orm import contains_eager
sql>>> jacks_addresses = (
...     session.query(Address)
...     .join(Address.user)
...     .filter(User.name == "jack")
...     .options(contains_eager(Address.user))
...     .all()
... )
>>> jacks_addresses
[<Address(email_address='jack@google.com')>, <Address(email_address='j25@yahoo.com')>]

>>> jacks_addresses[0].user
<User(name='jack', fullname='Jack Bean', nickname='gjffdd')>

For more information on eager loading, including how to configure various forms of loading by default, see the section Relationship Loading Techniques.


Let’s try to delete jack and see how that goes. We’ll mark the object as deleted in the session, then we’ll issue a count query to see that no rows remain:

>>> session.delete(jack)
sql>>> session.query(User).filter_by(name="jack").count()

So far, so good. How about Jack’s Address objects ?

sql>>> session.query(Address).filter(
...     Address.email_address.in_(["jack@google.com", "j25@yahoo.com"])
... ).count()

Uh oh, they’re still there ! Analyzing the flush SQL, we can see that the user_id column of each address was set to NULL, but the rows weren’t deleted. SQLAlchemy doesn’t assume that deletes cascade, you have to tell it to do so.

Configuring delete/delete-orphan Cascade

We will configure cascade options on the User.addresses relationship to change the behavior. While SQLAlchemy allows you to add new attributes and relationships to mappings at any point in time, in this case the existing relationship needs to be removed, so we need to tear down the mappings completely and start again - we’ll close the Session:

>>> session.close()

and use a new declarative_base():

>>> Base = declarative_base()

Next we’ll declare the User class, adding in the addresses relationship including the cascade configuration (we’ll leave the constructor out too):

>>> class User(Base):
...     __tablename__ = "users"
...     id = Column(Integer, primary_key=True)
...     name = Column(String)
...     fullname = Column(String)
...     nickname = Column(String)
...     addresses = relationship(
...         "Address", back_populates="user", cascade="all, delete, delete-orphan"
...     )
...     def __repr__(self):
...         return "<User(name='%s', fullname='%s', nickname='%s')>" % (
...             self.name,
...             self.fullname,
...             self.nickname,
...         )

Then we recreate Address, noting that in this case we’ve created the Address.user relationship via the User class already:

>>> class Address(Base):
...     __tablename__ = "addresses"
...     id = Column(Integer, primary_key=True)
...     email_address = Column(String, nullable=False)
...     user_id = Column(Integer, ForeignKey("users.id"))
...     user = relationship("User", back_populates="addresses")
...     def __repr__(self):
...         return "<Address(email_address='%s')>" % self.email_address

Now when we load the user jack (below using Query.get(), which loads by primary key), removing an address from the corresponding addresses collection will result in that Address being deleted:

# load Jack by primary key
sql>>> jack = session.get(User, 5)

# remove one Address (lazy load fires off)
sql>>> del jack.addresses[1]

# only one address remains
sql>>> session.query(Address).filter(
...     Address.email_address.in_(["jack@google.com", "j25@yahoo.com"])
... ).count()

Deleting Jack will delete both Jack and the remaining Address associated with the user:

>>> session.delete(jack)

sql>>> session.query(User).filter_by(name="jack").count()

sql>>> session.query(Address).filter(
...     Address.email_address.in_(["jack@google.com", "j25@yahoo.com"])
... ).count()

Building a Many To Many Relationship

We’re moving into the bonus round here, but lets show off a many-to-many relationship. We’ll sneak in some other features too, just to take a tour. We’ll make our application a blog application, where users can write BlogPost items, which have Keyword items associated with them.

For a plain many-to-many, we need to create an un-mapped Table construct to serve as the association table. This looks like the following:

>>> from sqlalchemy import Table, Text
>>> # association table
>>> post_keywords = Table(
...     "post_keywords",
...     Base.metadata,
...     Column("post_id", ForeignKey("posts.id"), primary_key=True),
...     Column("keyword_id", ForeignKey("keywords.id"), primary_key=True),
... )

Above, we can see declaring a Table directly is a little different than declaring a mapped class. Table is a constructor function, so each individual Column argument is separated by a comma. The Column object is also given its name explicitly, rather than it being taken from an assigned attribute name.

Next we define BlogPost and Keyword, using complementary relationship() constructs, each referring to the post_keywords table as an association table:

>>> class BlogPost(Base):
...     __tablename__ = "posts"
...     id = Column(Integer, primary_key=True)
...     user_id = Column(Integer, ForeignKey("users.id"))
...     headline = Column(String(255), nullable=False)
...     body = Column(Text)
...     # many to many BlogPost<->Keyword
...     keywords = relationship("Keyword", secondary=post_keywords, back_populates="posts")
...     def __init__(self, headline, body, author):
...         self.author = author
...         self.headline = headline
...         self.body = body
...     def __repr__(self):
...         return "BlogPost(%r, %r, %r)" % (self.headline, self.body, self.author)

>>> class Keyword(Base):
...     __tablename__ = "keywords"
...     id = Column(Integer, primary_key=True)
...     keyword = Column(String(50), nullable=False, unique=True)
...     posts = relationship("BlogPost", secondary=post_keywords, back_populates="keywords")
...     def __init__(self, keyword):
...         self.keyword = keyword


The above class declarations illustrate explicit __init__() methods. Remember, when using Declarative, it’s optional!

Above, the many-to-many relationship is BlogPost.keywords. The defining feature of a many-to-many relationship is the secondary keyword argument which references a Table object representing the association table. This table only contains columns which reference the two sides of the relationship; if it has any other columns, such as its own primary key, or foreign keys to other tables, SQLAlchemy requires a different usage pattern called the “association object”, described at Association Object.

We would also like our BlogPost class to have an author field. We will add this as another bidirectional relationship, except one issue we’ll have is that a single user might have lots of blog posts. When we access User.posts, we’d like to be able to filter results further so as not to load the entire collection. For this we use a setting accepted by relationship() called lazy='dynamic', which configures an alternate loader strategy on the attribute:

>>> BlogPost.author = relationship(User, back_populates="posts")
>>> User.posts = relationship(BlogPost, back_populates="author", lazy="dynamic")

Create new tables:

sql>>> Base.metadata.create_all(engine)

Usage is not too different from what we’ve been doing. Let’s give Wendy some blog posts:

sql>>> wendy = session.query(User).filter_by(name="wendy").one()
>>> post = BlogPost("Wendy's Blog Post", "This is a test", wendy)
>>> session.add(post)

We’re storing keywords uniquely in the database, but we know that we don’t have any yet, so we can just create them:

>>> post.keywords.append(Keyword("wendy"))
>>> post.keywords.append(Keyword("firstpost"))

We can now look up all blog posts with the keyword ‘firstpost’. We’ll use the any operator to locate “blog posts where any of its keywords has the keyword string ‘firstpost’”:

sql>>> session.query(BlogPost).filter(BlogPost.keywords.any(keyword="firstpost")).all()
[BlogPost("Wendy's Blog Post", 'This is a test', <User(name='wendy', fullname='Wendy Williams', nickname='windy')>)]

If we want to look up posts owned by the user wendy, we can tell the query to narrow down to that User object as a parent:

sql>>> session.query(BlogPost).filter(BlogPost.author == wendy).filter(
...     BlogPost.keywords.any(keyword="firstpost")
... ).all()
[BlogPost("Wendy's Blog Post", 'This is a test', <User(name='wendy', fullname='Wendy Williams', nickname='windy')>)]

Or we can use Wendy’s own posts relationship, which is a “dynamic” relationship, to query straight from there:

sql>>> wendy.posts.filter(BlogPost.keywords.any(keyword="firstpost")).all()
[BlogPost("Wendy's Blog Post", 'This is a test', <User(name='wendy', fullname='Wendy Williams', nickname='windy')>)]

Further Reference

Query Reference: Query API

Mapper Reference: ORM Mapped Class Configuration

Relationship Reference: Relationship Configuration

Session Reference: Using the Session