Version Check¶

A quick check to verify that we are on at least version 0.6 of SQLAlchemy:

>>> import sqlalchemy
>>> sqlalchemy.__version__ 
0.6.0

Connecting¶

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.

Define and Create a Table¶

Next we want to tell SQLAlchemy about our tables. We will start with just a single table called users, which will store records for the end-users using our application (lets assume it’s a website). We define our tables within a catalog called MetaData, using the Table construct, which is used in a manner similar to SQL’s CREATE TABLE syntax:

>>> from sqlalchemy import Table, Column, Integer, String, MetaData, ForeignKey
>>> metadata = MetaData()
>>> users_table = Table('users', metadata,
...     Column('id', Integer, primary_key=True),
...     Column('name', String),
...     Column('fullname', String),
...     Column('password', String)
... )

Schema Definition Language covers all about how to define Table objects, as well as how to load their definition from an existing database (known as reflection).

Next, we can issue CREATE TABLE statements derived from our table metadata, by calling create_all() and passing it the engine instance which points to our database. This will check for the presence of a table first before creating, so it’s safe to call multiple times:

sql>>> metadata.create_all(engine) 
PRAGMA table_info("users")
()
CREATE TABLE users (
    id INTEGER NOT NULL,
    name VARCHAR,
    fullname VARCHAR,
    password VARCHAR,
    PRIMARY KEY (id)
)
()
COMMIT

Column('name', String(50))

from sqlalchemy import Sequence
Column('id', Integer, Sequence('user_id_seq'), primary_key=True)

users_table = Table('users', metadata,
   Column('id', Integer, Sequence('user_id_seq'), primary_key=True),
   Column('name', String(50)),
   Column('fullname', String(50)),
   Column('password', String(12))
)

Define a Python Class to be Mapped¶

While the Table object defines information about our database, it does not say anything about the definition or behavior of the business objects used by our application; SQLAlchemy views this as a separate concern. To correspond to our users table, let’s create a rudimentary User class. It only need subclass Python’s built-in object class (i.e. it’s a new style class):

>>> class User(object):
...     def __init__(self, name, fullname, password):
...         self.name = name
...         self.fullname = fullname
...         self.password = password
...
...     def __repr__(self):
...        return "<User('%s','%s', '%s')>" % (self.name, self.fullname, self.password)

The class has an __init__() and a __repr__() method for convenience. These methods are both entirely optional, and can be of any form. SQLAlchemy never calls __init__() directly.

Setting up the Mapping¶

With our users_table and User class, we now want to map the two together. That’s where the SQLAlchemy ORM package comes in. We’ll use the mapper() function to create a mapping between users_table and User:

>>> from sqlalchemy.orm import mapper
>>> mapper(User, users_table) 
<Mapper at 0x...; User>

The mapper() function creates a new Mapper object and stores it away for future reference, associated with our class. Let’s now create and inspect a User object:

>>> ed_user = User('ed', 'Ed Jones', 'edspassword')
>>> ed_user.name
'ed'
>>> ed_user.password
'edspassword'
>>> str(ed_user.id)
'None'

The id attribute, which while not defined by our __init__() method, exists due to the id column present within the users_table object. By default, the mapper() creates class attributes for all columns present within the Table. These class attributes exist as Python descriptors, and define instrumentation for the mapped class. The functionality of this instrumentation is very rich and includes the ability to track modifications and automatically load new data from the database when needed.

Since we have not yet told SQLAlchemy to persist Ed Jones within the database, its id is None. When we persist the object later, this attribute will be populated with a newly generated value.

Creating Table, Class and Mapper All at Once Declaratively¶

The preceding approach to configuration involved a Table, a user-defined class, and a call to mapper(). This illustrates classical SQLAlchemy usage, which values the highest separation of concerns possible. A large number of applications don’t require this degree of separation, and for those SQLAlchemy offers an alternate “shorthand” configurational style called declarative. For many applications, this is the only style of configuration needed. Our above example using this style is as follows:

>>> from sqlalchemy.ext.declarative import declarative_base

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

Above, the declarative_base() function defines a new class which we name Base, from which all of our ORM-enabled classes will derive. Note that we define Column objects with no “name” field, since it’s inferred from the given attribute name.

The underlying Table object created by our declarative_base() version of User is accessible via the __table__ attribute:

>>> users_table = User.__table__

The owning MetaData object is available as well:

>>> metadata = Base.metadata

declarative is covered at Declarative as well as throughout Mapper Configuration.

Yet another “declarative” method is available for SQLAlchemy as a third party library called Elixir. This is a full-featured configurational product which also includes many higher level mapping configurations built in. Like declarative, once classes and mappings are defined, ORM usage is the same as with a classical SQLAlchemy configuration.

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 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 new Objects¶

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

>>> ed_user = User('ed', 'Ed Jones', 'edspassword')
>>> 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() 
BEGIN (implicit)
INSERT INTO users (name, fullname, password) VALUES (?, ?, ?)
('ed', 'Ed Jones', 'edspassword')
SELECT users.id AS users_id, users.name AS users_name, users.fullname AS users_fullname, users.password AS users_password
FROM users
WHERE users.name = ?
 LIMIT 1 OFFSET 0
('ed',)
>>> our_user
<User('ed','Ed Jones', 'edspassword')>

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
True

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('wendy', 'Wendy Williams', 'foobar'),
...     User('mary', 'Mary Contrary', 'xxg527'),
...     User('fred', 'Fred Flinstone', 'blah')])

Also, Ed has already decided his password isn’t too secure, so lets change it:

>>> ed_user.password = 'f8s7ccs'

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

>>> session.dirty
IdentitySet([<User('ed','Ed Jones', 'f8s7ccs')>])

and that three new User objects are pending:

>>> session.new  
IdentitySet([<User('wendy','Wendy Williams', 'foobar')>,
<User('mary','Mary Contrary', 'xxg527')>,
<User('fred','Fred Flinstone', 'blah')>])

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 commit():

sql>>> session.commit()
UPDATE users SET password=? WHERE users.id = ?
('f8s7ccs', 1)
INSERT INTO users (name, fullname, password) VALUES (?, ?, ?)
('wendy', 'Wendy Williams', 'foobar')
INSERT INTO users (name, fullname, password) VALUES (?, ?, ?)
('mary', 'Mary Contrary', 'xxg527')
INSERT INTO users (name, fullname, password) VALUES (?, ?, ?)
('fred', 'Fred Flinstone', 'blah')
COMMIT

commit() flushes whatever remaining changes remain 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 
BEGIN (implicit)
SELECT users.id AS users_id, users.name AS users_name, users.fullname AS users_fullname, users.password AS users_password
FROM users
WHERE users.id = ?
(1,)
1

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 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 the chapter on Sessions.

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('fakeuser', 'Invalid', '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() 
UPDATE users SET name=? WHERE users.id = ?
('Edwardo', 1)
INSERT INTO users (name, fullname, password) VALUES (?, ?, ?)
('fakeuser', 'Invalid', '12345')
SELECT users.id AS users_id, users.name AS users_name, users.fullname AS users_fullname, users.password AS users_password
FROM users
WHERE users.name IN (?, ?)
('Edwardo', 'fakeuser')
[<User('Edwardo','Ed Jones', 'f8s7ccs')>, <User('fakeuser','Invalid', '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()
ROLLBACK
sql>>> ed_user.name 
BEGIN (implicit)
SELECT users.id AS users_id, users.name AS users_name, users.fullname AS users_fullname, users.password AS users_password
FROM users
WHERE users.id = ?
(1,)
u'ed'
>>> fake_user in session
False

issuing a SELECT illustrates the changes made to the database:

sql>>> session.query(User).filter(User.name.in_(['ed', 'fakeuser'])).all() 
SELECT users.id AS users_id, users.name AS users_name, users.fullname AS users_fullname, users.password AS users_password
FROM users
WHERE users.name IN (?, ?)
('ed', 'fakeuser')
[<User('ed','Ed Jones', 'f8s7ccs')>]

Querying¶

A Query is created using the query() function 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
SELECT users.id AS users_id, users.name AS users_name,
users.fullname AS users_fullname, users.password AS users_password
FROM users ORDER BY users.id
()
ed Ed Jones
wendy Wendy Williams
mary Mary Contrary
fred Fred Flinstone

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
SELECT users.name AS users_name, users.fullname AS users_fullname
FROM users
()
ed Ed Jones
wendy Wendy Williams
mary Mary Contrary
fred Fred Flinstone

The tuples returned by Query are named tuples, 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
SELECT users.id AS users_id, users.name AS users_name, users.fullname AS users_fullname, users.password AS users_password
FROM users
()
<User('ed','Ed Jones', 'f8s7ccs')> ed
<User('wendy','Wendy Williams', 'foobar')> wendy
<User('mary','Mary Contrary', 'xxg527')> mary
<User('fred','Fred Flinstone', 'blah')> fred

You can control the names using the label() construct for scalar attributes and aliased for class constructs:

>>> from sqlalchemy.orm import aliased
>>> user_alias = aliased(User, name='user_alias')
sql>>> for row in session.query(user_alias, user_alias.name.label('name_label')).all(): 
...    print row.user_alias, row.name_label
SELECT users_1.id AS users_1_id, users_1.name AS users_1_name, users_1.fullname AS users_1_fullname, users_1.password AS users_1_password, users_1.name AS name_label
FROM users AS users_1
()
<User('ed','Ed Jones', 'f8s7ccs')> ed
<User('wendy','Wendy Williams', 'foobar')> wendy
<User('mary','Mary Contrary', 'xxg527')> mary
<User('fred','Fred Flinstone', 'blah')> fred

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
SELECT users.id AS users_id, users.name AS users_name, users.fullname AS users_fullname, users.password AS users_password
FROM users ORDER BY users.id
LIMIT 2 OFFSET 1
()
<User('wendy','Wendy Williams', 'foobar')>
<User('mary','Mary Contrary', 'xxg527')>

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
SELECT users.name AS users_name FROM users
WHERE users.fullname = ?
('Ed Jones',)
ed

...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
SELECT users.name AS users_name FROM users
WHERE users.fullname = ?
('Ed Jones',)
ed

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
SELECT users.id AS users_id, users.name AS users_name, users.fullname AS users_fullname, users.password AS users_password
FROM users
WHERE users.name = ? AND users.fullname = ?
('ed', 'Ed Jones')
<User('ed','Ed Jones', 'f8s7ccs')>

>>> query = session.query(User).filter(User.name.like('%ed')).order_by(User.id)
sql>>> query.all() 
SELECT users.id AS users_id, users.name AS users_name, users.fullname AS users_fullname, users.password AS users_password
FROM users
WHERE users.name LIKE ? ORDER BY users.id
('%ed',)
[<User('ed','Ed Jones', 'f8s7ccs')>, <User('fred','Fred Flinstone', 'blah')>]

sql>>> query.first() 
SELECT users.id AS users_id, users.name AS users_name, users.fullname AS users_fullname, users.password AS users_password
FROM users
WHERE users.name LIKE ? ORDER BY users.id
 LIMIT 1 OFFSET 0
('%ed',)
<User('ed','Ed Jones', 'f8s7ccs')>

sql>>> from sqlalchemy.orm.exc import MultipleResultsFound
>>> try: 
...     user = query.one()
... except MultipleResultsFound, e:
...     print e
SELECT users.id AS users_id, users.name AS users_name, users.fullname AS users_fullname, users.password AS users_password
FROM users
WHERE users.name LIKE ? ORDER BY users.id
('%ed',)
Multiple rows were found for one()

sql>>> from sqlalchemy.orm.exc import NoResultFound
>>> try: 
...     user = query.filter(User.id == 99).one()
... except NoResultFound, e:
...     print e
SELECT users.id AS users_id, users.name AS users_name, users.fullname AS users_fullname, users.password AS users_password
FROM users
WHERE users.name LIKE ? AND users.id = ? ORDER BY users.id
('%ed', 99)
No row was found for one()

sql>>> for user in session.query(User).filter("id<224").order_by("id").all(): 
...     print user.name
SELECT users.id AS users_id, users.name AS users_name, users.fullname AS users_fullname, users.password AS users_password
FROM users
WHERE id<224 ORDER BY id
()
ed
wendy
mary
fred

sql>>> session.query(User).filter("id<:value and name=:name").\
...     params(value=224, name='fred').order_by(User.id).one() 
SELECT users.id AS users_id, users.name AS users_name, users.fullname AS users_fullname, users.password AS users_password
FROM users
WHERE id<User('fred','Fred Flinstone', 'blah')>

sql>>> session.query(User).from_statement(
...                     "SELECT * FROM users where name=:name").\
...                     params(name='ed').all()
SELECT * FROM users where name=?
('ed',)
[<User('ed','Ed Jones', 'f8s7ccs')>]

sql>>> session.query("id", "name", "thenumber12").\
...         from_statement("SELECT id, name, 12 as "
...                 "thenumber12 FROM users where name=:name").\
...                 params(name='ed').all()
SELECT id, name, 12 as thenumber12 FROM users where name=?
('ed',)
[(1, u'ed', 12)]

sql>>> session.query(User).filter(User.name.like('%ed')).count() 
SELECT count(1) AS count_1
FROM users
WHERE users.name LIKE ?
('%ed',)
2

sql>>> session.query(User.id, User.name).filter(User.name.like('%ed')).count() 
SELECT count(1) AS count_1
FROM (SELECT users.id AS users_id, users.name AS users_name
FROM users
WHERE users.name LIKE ?) AS anon_1
('%ed',)
2

sql>>> session.query(User.name).group_by(User.name).count()  
SELECT count(1) AS count_1
FROM (SELECT users.name AS users_name
FROM users GROUP BY users.name) AS anon_1
()
4

>>> from sqlalchemy import func
sql>>> session.query(func.count(User.name), User.name).group_by(User.name).all()  
SELECT count(users.name) AS count_1, users.name AS users_name
FROM users GROUP BY users.name
()
[(1, u'ed'), (1, u'fred'), (1, u'mary'), (1, u'wendy')]

Building a Relationship¶

Now let’s consider a second table to be dealt with. Users in our system also can store any number of email addresses associated with their username. This implies a basic one to many association from the users_table 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, backref
>>> 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, backref=backref('addresses', order_by=id))
...
...     def __init__(self, email_address):
...         self.email_address = email_address
...
...     def __repr__(self):
...         return "<Address('%s')>" % self.email_address

The above class introduces a foreign key constraint which references the users table. This defines for SQLAlchemy the relationship between the two tables at the database level. The relationship between the User and Address classes is defined separately using the relationship() function, which defines an attribute user to be placed on the Address class, as well as an addresses collection to be placed on the User class. Such a relationship is known as a bidirectional relationship. Because of the placement of the foreign key, from Address to User it is many to one, and from User to Address it is one to many. SQLAlchemy is automatically aware of many-to-one/one-to-many based on foreign keys.

The relationship() function is extremely flexible, and could just have easily been defined on the User class:

class User(Base):
    # ....
    addresses = relationship(Address, order_by=Address.id, backref="user")

We are also free to not define a backref, and to define the relationship() only on one class and not the other. It is also possible to define two separate relationship() constructs for either direction, which is generally safe for many-to-one and one-to-many relationships, but not for many-to-many relationships.

When using the declarative extension, relationship() gives us the option to use strings for most arguments that concern the target class, in the case that the target class has not yet been defined. This only works in conjunction with declarative:

class User(Base):
    ....
    addresses = relationship("Address", order_by="Address.id", backref="user")

When declarative is not in use, you typically define your mapper() well after the target classes and Table objects have been defined, so string expressions are not needed.

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>>> metadata.create_all(engine) 
PRAGMA table_info("users")
()
PRAGMA table_info("addresses")
()
CREATE TABLE addresses (
    id INTEGER NOT NULL,
    email_address VARCHAR NOT NULL,
    user_id INTEGER,
    PRIMARY KEY (id),
     FOREIGN KEY(user_id) REFERENCES users (id)
)
()
COMMIT

Working with Related Objects¶

Now when we create a User, a blank addresses collection will be present. Various collection types, such as sets and dictionaries, are possible here (see Customizing Collection Access for details), but by default, the collection is a Python list.

>>> jack = User('jack', 'Jack Bean', 'gjffdd')
>>> jack.addresses
[]

We are free to add Address objects on our User object. In this case we just assign a full list directly:

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

When using a bidirectional relationship, elements added in one direction automatically become visible in the other direction. This is the basic behavior of the backref keyword, which maintains the relationship purely in memory, without using any SQL:

>>> jack.addresses[1]
<Address('j25@yahoo.com')>

>>> jack.addresses[1].user
<User('jack','Jack Bean', 'gjffdd')>

Let’s add and commit Jack Bean to the database. jack as well as the two Address members in his addresses collection are both added to the session at once, using a process known as cascading:

>>> session.add(jack)
sql>>> session.commit()
INSERT INTO users (name, fullname, password) VALUES (?, ?, ?)
('jack', 'Jack Bean', 'gjffdd')
INSERT INTO addresses (email_address, user_id) VALUES (?, ?)
('jack@google.com', 5)
INSERT INTO addresses (email_address, user_id) VALUES (?, ?)
('j25@yahoo.com', 5)
COMMIT

Querying for Jack, we get just Jack back. No SQL is yet issued for Jack’s addresses:

sql>>> jack = session.query(User).\
... filter_by(name='jack').one() 
BEGIN (implicit)
SELECT users.id AS users_id, users.name AS users_name,
users.fullname AS users_fullname, users.password AS users_password
FROM users
WHERE users.name = ?
('jack',)
>>> jack
<User('jack','Jack Bean', 'gjffdd')>

Let’s look at the addresses collection. Watch the SQL:

sql>>> jack.addresses 
SELECT addresses.id AS addresses_id, addresses.email_address AS
addresses_email_address, addresses.user_id AS addresses_user_id
FROM addresses
WHERE ? = addresses.user_id ORDER BY addresses.id
(5,)
[<Address('jack@google.com')>, <Address('j25@yahoo.com')>]

When we accessed the addresses collection, SQL was suddenly issued. This is an example of a lazy loading relationship. The addresses collection is now loaded and behaves just like an ordinary list.

If you want to reduce the number of queries (dramatically, in many cases), we can apply an eager load to the query operation, using the joinedload() function. This function is a query option that gives additional instructions to the query on how we would like it to load, in this case we’d like to indicate that we’d like addresses to load “eagerly”. SQLAlchemy then constructs an outer join between the users and addresses tables, and loads them at once, populating the addresses collection on each User object if it’s not already populated:

>>> from sqlalchemy.orm import joinedload

sql>>> jack = session.query(User).\
...                        options(joinedload('addresses')).\
...                        filter_by(name='jack').one() 
SELECT users.id AS users_id, users.name AS users_name, users.fullname AS users_fullname,
users.password AS users_password, addresses_1.id AS addresses_1_id, addresses_1.email_address
AS addresses_1_email_address, addresses_1.user_id AS addresses_1_user_id
FROM users LEFT OUTER JOIN addresses AS addresses_1 ON users.id = addresses_1.user_id
WHERE users.name = ? ORDER BY addresses_1.id
('jack',)
>>> jack
<User('jack','Jack Bean', 'gjffdd')>

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

See Relationship Loading Techniques for information on joinedload() and its new brother, subqueryload(). We’ll also see another way to “eagerly” load in the next section.

Querying with Joins¶

While joinedload() created an anonymous, non-accessible JOIN specifically to populate a collection, we can also work explicitly with joins in many ways. For example, to construct a simple inner join between User and Address, we can just filter() 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, a
SELECT users.id AS users_id, users.name AS users_name, users.fullname AS users_fullname,
users.password AS users_password, addresses.id AS addresses_id,
addresses.email_address AS addresses_email_address, addresses.user_id AS addresses_user_id
FROM users, addresses
WHERE users.id = addresses.user_id AND addresses.email_address = ?
('jack@google.com',)
<User('jack','Jack Bean', 'gjffdd')> <Address('jack@google.com')>

Or we can make a real JOIN construct; the most common way is to use join():

sql>>> session.query(User).join(Address).\
...         filter(Address.email_address=='jack@google.com').all() 
SELECT users.id AS users_id, users.name AS users_name, users.fullname AS users_fullname, users.password AS users_password
FROM users JOIN addresses ON users.id = addresses.user_id
WHERE addresses.email_address = ?
('jack@google.com',)
[<User('jack','Jack Bean', 'gjffdd')>]

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, join() works better when one of the following forms are used:

query.join((Address, User.id==Address.user_id))  # explicit condition (note the tuple)
query.join(User.addresses)                       # specify relationship from left to right
query.join((Address, User.addresses))            # same, with explicit target
query.join('addresses')                          # same, using a string

Note that when join() is called with an explicit target as well as an ON clause, we use a tuple as the argument. This is so that multiple joins can be chained together, as in:

session.query(Foo).join(
                        Foo.bars,
                        (Bat, bar.bats),
                        (Widget, Bat.widget_id==Widget.id)
                        )

The above would produce SQL something like foo JOIN bars ON <onclause> JOIN bats ON <onclause> JOIN widgets ON <onclause>.

The general functionality of join() is also available as a standalone function join(), which is an ORM-enabled version of the same function present in the SQL expression language. This function accepts two or three arguments (left side, right side, optional ON clause) and can be used in conjunction with the select_from() method to set an explicit FROM clause:

>>> from sqlalchemy.orm import join
sql>>> session.query(User).\
...                select_from(join(User, Address, User.addresses)).\
...                filter(Address.email_address=='jack@google.com').all() 
SELECT users.id AS users_id, users.name AS users_name, users.fullname AS users_fullname, users.password AS users_password
FROM users JOIN addresses ON users.id = addresses.user_id
WHERE addresses.email_address = ?
('jack@google.com',)
[<User('jack','Jack Bean', 'gjffdd')>]

>>> from sqlalchemy.orm import contains_eager
sql>>> for address in session.query(Address).\
...                join(Address.user).\
...                filter(User.name=='jack').\
...                options(contains_eager(Address.user)): 
...         print address, address.user
SELECT users.id AS users_id, users.name AS users_name, users.fullname AS users_fullname,
 users.password AS users_password, addresses.id AS addresses_id,
 addresses.email_address AS addresses_email_address, addresses.user_id AS addresses_user_id
FROM addresses JOIN users ON users.id = addresses.user_id
WHERE users.name = ?
('jack',)
<Address('jack@google.com')> <User('jack','Jack Bean', 'gjffdd')>
<Address('j25@yahoo.com')> <User('jack','Jack Bean', 'gjffdd')>

>>> 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((adalias1, User.addresses), (adalias2, User.addresses)).\
...     filter(adalias1.email_address=='jack@google.com').\
...     filter(adalias2.email_address=='j25@yahoo.com'):
...     print username, email1, email2      
SELECT users.name AS users_name, addresses_1.email_address AS addresses_1_email_address,
addresses_2.email_address AS addresses_2_email_address
FROM users JOIN addresses AS addresses_1 ON users.id = addresses_1.user_id
JOIN addresses AS addresses_2 ON users.id = addresses_2.user_id
WHERE addresses_1.email_address = ? AND addresses_2.email_address = ?
('jack@google.com', 'j25@yahoo.com')
jack jack@google.com j25@yahoo.com

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

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

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
SELECT users.id AS users_id, users.name AS users_name,
users.fullname AS users_fullname, users.password AS users_password,
anon_1.address_count AS anon_1_address_count
FROM users LEFT OUTER JOIN (SELECT addresses.user_id AS user_id, count(?) AS address_count
FROM addresses GROUP BY addresses.user_id) AS anon_1 ON users.id = anon_1.user_id
ORDER BY users.id
('*',)
<User('ed','Ed Jones', 'f8s7ccs')> None
<User('wendy','Wendy Williams', 'foobar')> None
<User('mary','Mary Contrary', 'xxg527')> None
<User('fred','Fred Flinstone', 'blah')> None
<User('jack','Jack Bean', 'gjffdd')> 2

sql>>> stmt = session.query(Address).filter(Address.email_address != 'j25@yahoo.com').subquery()
>>> adalias = aliased(Address, stmt)
>>> for user, address in session.query(User, adalias).join((adalias, User.addresses)): 
...     print user, address
SELECT users.id AS users_id, users.name AS users_name, users.fullname AS users_fullname,
users.password AS users_password, anon_1.id AS anon_1_id,
anon_1.email_address AS anon_1_email_address, anon_1.user_id AS anon_1_user_id
FROM users JOIN (SELECT addresses.id AS id, addresses.email_address AS email_address, addresses.user_id AS user_id
FROM addresses
WHERE addresses.email_address != ?) AS anon_1 ON users.id = anon_1.user_id
('j25@yahoo.com',)
<User('jack','Jack Bean', 'gjffdd')> <Address('jack@google.com')>

>>> 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
SELECT users.name AS users_name
FROM users
WHERE EXISTS (SELECT *
FROM addresses
WHERE addresses.user_id = users.id)
()
jack

sql>>> for name, in session.query(User.name).filter(User.addresses.any()):   
...     print name
SELECT users.name AS users_name
FROM users
WHERE EXISTS (SELECT 1
FROM addresses
WHERE users.id = addresses.user_id)
()
jack

sql>>> for name, in session.query(User.name).\
...     filter(User.addresses.any(Address.email_address.like('%google%'))):   
...     print name
SELECT users.name AS users_name
FROM users
WHERE EXISTS (SELECT 1
FROM addresses
WHERE users.id = addresses.user_id AND addresses.email_address LIKE ?)
('%google%',)
jack

sql>>> session.query(Address).filter(~Address.user.has(User.name=='jack')).all() 
SELECT addresses.id AS addresses_id, addresses.email_address AS addresses_email_address,
addresses.user_id AS addresses_user_id
FROM addresses
WHERE NOT (EXISTS (SELECT 1
FROM users
WHERE users.id = addresses.user_id AND users.name = ?))
('jack',)
[]

Deleting¶

Let’s try to delete jack and see how that goes. We’ll mark 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() 
UPDATE addresses SET user_id=? WHERE addresses.id = ?
(None, 1)
UPDATE addresses SET user_id=? WHERE addresses.id = ?
(None, 2)
DELETE FROM users WHERE users.id = ?
(5,)
SELECT count(1) AS count_1
FROM users
WHERE users.name = ?
('jack',)
0

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() 
SELECT count(1) AS count_1
FROM addresses
WHERE addresses.email_address IN (?, ?)
('jack@google.com', 'j25@yahoo.com')
2

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.

>>> session.close()  # roll back and close the transaction
>>> from sqlalchemy.orm import clear_mappers
>>> clear_mappers() # remove all class mappings

>>> users_table = User.__table__
>>> mapper(User, users_table, properties={    
...     'addresses':relationship(Address, backref='user', cascade="all, delete, delete-orphan")
... })
<Mapper at 0x...; User>

>>> addresses_table = Address.__table__
>>> mapper(Address, addresses_table) 
<Mapper at 0x...; Address>

# load Jack by primary key
sql>>> jack = session.query(User).get(5)    
BEGIN (implicit)
SELECT users.id AS users_id, users.name AS users_name, users.fullname AS users_fullname, users.password AS users_password
FROM users
WHERE users.id = ?
(5,)
# remove one Address (lazy load fires off)
sql>>> del jack.addresses[1] 
SELECT addresses.id AS addresses_id, addresses.email_address AS addresses_email_address, addresses.user_id AS addresses_user_id
FROM addresses
WHERE ? = addresses.user_id
(5,)
# only one address remains
sql>>> session.query(Address).filter(
...     Address.email_address.in_(['jack@google.com', 'j25@yahoo.com'])
... ).count() 
DELETE FROM addresses WHERE addresses.id = ?
(2,)
SELECT count(1) AS count_1
FROM addresses
WHERE addresses.email_address IN (?, ?)
('jack@google.com', 'j25@yahoo.com')
1

>>> session.delete(jack)

sql>>> session.query(User).filter_by(name='jack').count() 
DELETE FROM addresses WHERE addresses.id = ?
(1,)
DELETE FROM users WHERE users.id = ?
(5,)
SELECT count(1) AS count_1
FROM users
WHERE users.name = ?
('jack',)
0

sql>>> session.query(Address).filter(
...    Address.email_address.in_(['jack@google.com', 'j25@yahoo.com'])
... ).count() 
SELECT count(1) AS count_1
FROM addresses
WHERE addresses.email_address IN (?, ?)
('jack@google.com', 'j25@yahoo.com')
0

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.

The declarative setup is as follows:

>>> from sqlalchemy import Text

>>> # association table
>>> post_keywords = Table('post_keywords', metadata,
...     Column('post_id', Integer, ForeignKey('posts.id')),
...     Column('keyword_id', Integer, ForeignKey('keywords.id'))
... )

>>> 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, backref='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)
...
...     def __init__(self, keyword):
...         self.keyword = keyword

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.

The many-to-many relationship is also bi-directional using the backref keyword. This is the one case where usage of backref is generally required, since if a separate posts relationship were added to the Keyword entity, both relationships would independently add and remove rows from the post_keywords table and produce conflicts.

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. To use it on the “reverse” side of a relationship(), we use the backref() function:

>>> from sqlalchemy.orm import backref
>>> # "dynamic" loading relationship to User
>>> BlogPost.author = relationship(User, backref=backref('posts', lazy='dynamic'))

Create new tables:

sql>>> metadata.create_all(engine) 
PRAGMA table_info("users")
()
PRAGMA table_info("addresses")
()
PRAGMA table_info("posts")
()
PRAGMA table_info("keywords")
()
PRAGMA table_info("post_keywords")
()
CREATE TABLE posts (
    id INTEGER NOT NULL,
    user_id INTEGER,
    headline VARCHAR(255) NOT NULL,
    body TEXT,
    PRIMARY KEY (id),
     FOREIGN KEY(user_id) REFERENCES users (id)
)
()
COMMIT
CREATE TABLE keywords (
    id INTEGER NOT NULL,
    keyword VARCHAR(50) NOT NULL,
    PRIMARY KEY (id),
     UNIQUE (keyword)
)
()
COMMIT
CREATE TABLE post_keywords (
    post_id INTEGER,
    keyword_id INTEGER,
     FOREIGN KEY(post_id) REFERENCES posts (id),
     FOREIGN KEY(keyword_id) REFERENCES keywords (id)
)
()
COMMIT

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() 
SELECT users.id AS users_id, users.name AS users_name, users.fullname AS users_fullname, users.password AS users_password
FROM users
WHERE users.name = ?
('wendy',)
>>> 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() 
INSERT INTO keywords (keyword) VALUES (?)
('wendy',)
INSERT INTO keywords (keyword) VALUES (?)
('firstpost',)
INSERT INTO posts (user_id, headline, body) VALUES (?, ?, ?)
(2, "Wendy's Blog Post", 'This is a test')
INSERT INTO post_keywords (post_id, keyword_id) VALUES (?, ?)
((1, 1), (1, 2))
SELECT posts.id AS posts_id, posts.user_id AS posts_user_id, posts.headline AS posts_headline, posts.body AS posts_body
FROM posts
WHERE EXISTS (SELECT 1
FROM post_keywords, keywords
WHERE posts.id = post_keywords.post_id AND keywords.id = post_keywords.keyword_id AND keywords.keyword = ?)
('firstpost',)
[BlogPost("Wendy's Blog Post", 'This is a test', <User('wendy','Wendy Williams', 'foobar')>)]

If we want to look up just Wendy’s posts, we can tell the query to narrow down to her as a parent:

sql>>> session.query(BlogPost).filter(BlogPost.author==wendy).\
... filter(BlogPost.keywords.any(keyword='firstpost')).all() 
SELECT posts.id AS posts_id, posts.user_id AS posts_user_id, posts.headline AS posts_headline, posts.body AS posts_body
FROM posts
WHERE ? = posts.user_id AND (EXISTS (SELECT 1
FROM post_keywords, keywords
WHERE posts.id = post_keywords.post_id AND keywords.id = post_keywords.keyword_id AND keywords.keyword = ?))
(2, 'firstpost')
[BlogPost("Wendy's Blog Post", 'This is a test', <User('wendy','Wendy Williams', 'foobar')>)]

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() 
SELECT posts.id AS posts_id, posts.user_id AS posts_user_id, posts.headline AS posts_headline, posts.body AS posts_body
FROM posts
WHERE ? = posts.user_id AND (EXISTS (SELECT 1
FROM post_keywords, keywords
WHERE posts.id = post_keywords.post_id AND keywords.id = post_keywords.keyword_id AND keywords.keyword = ?))
(2, 'firstpost')
[BlogPost("Wendy's Blog Post", 'This is a test', <User('wendy','Wendy Williams', 'foobar')>)]

Further Reference¶

Query Reference: Querying

Mapper Reference: Mapper Configuration

Relationship Reference: Relationship Configuration

Session Reference: Using the Session.