1. Mike Bayer
  2. SQLSoup


SQLSoup / docs / build / tutorial.rst


SQLSoup provides a convenient way to access existing database tables without having to declare table or mapper classes ahead of time. It is built on top of the SQLAlchemy ORM and provides a super-minimalistic interface to an existing database.

SQLSoup effectively provides a coarse grained, alternative interface to working with the SQLAlchemy ORM, providing a "self configuring" interface for extremely rudimental operations. It's somewhat akin to a "super novice mode" version of the ORM. While you can do a lot more with the SQLAlchemy ORM directly, SQLSoup will have you querying an existing database in just two lines of code.

Getting Ready to Connect

Suppose we have a database with users, books, and loans tables (corresponding to the PyWebOff dataset, if you're curious).

Creating a SQLSoup gateway is just like creating a SQLAlchemy engine:

>>> import sqlsoup
>>> db = sqlsoup.SQLSoup('sqlite:///:memory:')

or, you can re-use an existing engine:

>>> db = sqlsoup.SQLSoup(engine)

You can optionally specify a schema within the database for your SQLSoup:

>>> db.schema = "myschemaname"

Note that the :class:`.SQLSoup` object doesn't actually connect to the database until it's first asked to do something. If the connection string is incorrect, the error will be raised when SQLSoup first tries to connect.

Loading objects

Loading objects is as easy as this:

>>> users = db.users.all()
>>> users.sort()
>>> users
    MappedUsers(name=u'Joe Student',email=u'student@example.edu',
    MappedUsers(name=u'Bhargan Basepair',email=u'basepair@example.edu',

Of course, letting the database do the sort is better:

>>> db.users.order_by(db.users.name).all()
    MappedUsers(name=u'Bhargan Basepair',email=u'basepair@example.edu',
    MappedUsers(name=u'Joe Student',email=u'student@example.edu',

Field access is intuitive:

>>> users[0].email

Of course, you don't want to load all users very often. Let's add a WHERE clause. Let's also switch the order_by to DESC while we're at it:

>>> from sqlalchemy import or_, and_, desc
>>> where = or_(db.users.name=='Bhargan Basepair', db.users.email=='student@example.edu')
>>> db.users.filter(where).order_by(desc(db.users.name)).all()
    MappedUsers(name=u'Joe Student',email=u'student@example.edu',
    MappedUsers(name=u'Bhargan Basepair',email=u'basepair@example.edu',

You can also use .first() (to retrieve only the first object from a query) or .one() (like .first when you expect exactly one user -- it will raise an exception if more were returned):

>>> db.users.filter(db.users.name=='Bhargan Basepair').one()
MappedUsers(name=u'Bhargan Basepair',email=u'basepair@example.edu',

Since name is the primary key, this is equivalent to

>>> db.users.get('Bhargan Basepair')
MappedUsers(name=u'Bhargan Basepair',email=u'basepair@example.edu',

This is also equivalent to

>>> db.users.filter_by(name='Bhargan Basepair').one()
MappedUsers(name=u'Bhargan Basepair',email=u'basepair@example.edu',

filter_by is like filter, but takes kwargs instead of full clause expressions. This makes it more concise for simple queries like this, but you can't do complex queries like the or_ above or non-equality based comparisons this way.

Full query documentation

Get, filter, filter_by, order_by, limit, and the rest of the query methods are explained in detail in :ref:`ormtutorial_querying`.

Modifying objects

Modifying objects is intuitive:

>>> user = _
>>> user.email = 'basepair+nospam@example.edu'
>>> db.commit()

(SQLSoup leverages the sophisticated SQLAlchemy unit-of-work code, so multiple updates to a single object will be turned into a single UPDATE statement when you commit.)

To finish covering the basics, let's insert a new loan, then delete it:

>>> book_id = db.books.filter_by(title='Regional Variation in Moss').first().id
>>> db.loans.insert(book_id=book_id, user_name=user.name)
MappedLoans(book_id=2,user_name=u'Bhargan Basepair',loan_date=None)

>>> loan = db.loans.filter_by(book_id=2, user_name='Bhargan Basepair').one()
>>> db.delete(loan)
>>> db.commit()

You can also delete rows that have not been loaded as objects. Let's do our insert/delete cycle once more, this time using the loans table's delete method. (For SQLAlchemy experts: note that no flush() call is required since this delete acts at the SQL level, not at the Mapper level.) The same where-clause construction rules apply here as to the select methods:

>>> db.loans.insert(book_id=book_id, user_name=user.name)
MappedLoans(book_id=2,user_name=u'Bhargan Basepair',loan_date=None)
>>> db.loans.delete(db.loans.book_id==2)

You can similarly update multiple rows at once. This will change the book_id to 1 in all loans whose book_id is 2:

>>> db.loans.filter_by(db.loans.book_id==2).update({'book_id':1})
>>> db.loans.filter_by(book_id=1).all()
[MappedLoans(book_id=1,user_name=u'Joe Student',
    loan_date=datetime.datetime(2006, 7, 12, 0, 0))]


Occasionally, you will want to pull out a lot of data from related tables all at once. In this situation, it is far more efficient to have the database perform the necessary join. (Here we do not have a lot of data but hopefully the concept is still clear.) SQLAlchemy is smart enough to recognize that loans has a foreign key to users, and uses that as the join condition automatically:

>>> join1 = db.join(db.users, db.loans, isouter=True)
>>> join1.filter_by(name='Joe Student').all()
    MappedJoin(name=u'Joe Student',email=u'student@example.edu',
        user_name=u'Joe Student',loan_date=datetime.datetime(2006, 7, 12, 0, 0))

If you're unfortunate enough to be using MySQL with the default MyISAM storage engine, you'll have to specify the join condition manually, since MyISAM does not store foreign keys. Here's the same join again, with the join condition explicitly specified:

>>> db.join(db.users, db.loans, db.users.name==db.loans.user_name, isouter=True)
<class 'sqlsoup.MappedJoin'>

You can compose arbitrarily complex joins by combining Join objects with tables or other joins. Here we combine our first join with the books table:

>>> join2 = db.join(join1, db.books)
>>> join2.all()
    MappedJoin(name=u'Joe Student',email=u'student@example.edu',
        user_name=u'Joe Student',loan_date=datetime.datetime(2006, 7, 12, 0, 0),
        id=1,title=u'Mustards I Have Known',published_year=u'1989',

If you join tables that have an identical column name, wrap your join with with_labels, to disambiguate columns with their table name (.c is short for .columns):

>>> db.with_labels(join1).c.keys()
[u'users_name', u'users_email', u'users_password',
    u'users_classname', u'users_admin', u'loans_book_id',
    u'loans_user_name', u'loans_loan_date']

You can also join directly to a labeled object:

>>> labeled_loans = db.with_labels(db.loans)
>>> db.join(db.users, labeled_loans, isouter=True).c.keys()
[u'name', u'email', u'password', u'classname',
    u'admin', u'loans_book_id', u'loans_user_name', u'loans_loan_date']


You can define relationships between classes using the :meth:`.TableClassType.relate` method from any mapped table:

>>> db.users.relate('loans', db.loans)

These can then be used like a normal SA property:

>>> db.users.get('Joe Student').loans
[MappedLoans(book_id=1,user_name=u'Joe Student',
                loan_date=datetime.datetime(2006, 7, 12, 0, 0))]
>>> db.users.filter(~db.users.loans.any()).all()
[MappedUsers(name=u'Bhargan Basepair',

relate can take any options that the relationship function accepts in normal mapper definition:

>>> del db._cache['users']
>>> db.users.relate('loans', db.loans, order_by=db.loans.loan_date, cascade='all, delete-orphan')

Advanced Use

Sessions, Transations and Application Integration


Please read and understand this section thoroughly before using SQLSoup in any web application.

SQLSoup uses a :class:`sqlalchemy.orm.scoping.ScopedSession` to provide thread-local sessions. You can get a reference to the current one like this:

>>> session = db.session

The default session is available at the module level in SQLSoup, via:

>>> from sqlsoup import Session

The configuration of this session is autoflush=True, autocommit=False. This means when you work with the SQLSoup object, you need to call db.commit() in order to have changes persisted. You may also call db.rollback() to roll things back.

Since the SQLSoup object's Session automatically enters into a transaction as soon as it's used, it is essential that you call commit() or rollback() on it when the work within a thread completes. This means all the guidelines for web application integration at :ref:`session_lifespan` must be followed.

The SQLSoup object can have any session or scoped session configured onto it. This is of key importance when integrating with existing code or frameworks such as Pylons. If your application already has a Session configured, pass it to your SQLSoup object:

>>> from myapplication import Session
>>> db = SQLSoup(session=Session)

If the Session is configured with autocommit=True, use flush() instead of commit() to persist changes - in this case, the Session closes out its transaction immediately and no external management is needed. rollback() is also not available. Configuring a new SQLSoup object in "autocommit" mode looks like:

>>> from sqlalchemy.orm import scoped_session, sessionmaker
>>> db = SQLSoup('sqlite://', session=scoped_session(sessionmaker(autoflush=False, expire_on_commit=False, autocommit=True)))

Mapping arbitrary Selectables

SQLSoup can map any SQLAlchemy :class:`.Selectable` with the map method. Let's map an :func:`.expression.select` object that uses an aggregate function; we'll use the SQLAlchemy :class:`.Table` that SQLSoup introspected as the basis. (Since we're not mapping to a simple table or join, we need to tell SQLAlchemy how to find the primary key which just needs to be unique within the select, and not necessarily correspond to a real PK in the database.):

>>> from sqlalchemy import select, func
>>> b = db.books._table
>>> s = select([b.c.published_year, func.count('*').label('n')], from_obj=[b], group_by=[b.c.published_year])
>>> s = s.alias('years_with_count')
>>> years_with_count = db.map(s, primary_key=[s.c.published_year])
>>> years_with_count.filter_by(published_year='1989').all()

Obviously if we just wanted to get a list of counts associated with book years once, raw SQL is going to be less work. The advantage of mapping a Select is reusability, both standalone and in Joins. (And if you go to full SQLAlchemy, you can perform mappings like this directly to your object models.)

An easy way to save mapped selectables like this is to just hang them on your db object:

>>> db.years_with_count = years_with_count

Python is flexible like that!


SQLSoup works fine with SQLAlchemy's text construct, described in :ref:`sqlexpression_text`. You can also execute textual SQL directly using the :meth:`.SQLSoup.execute` method, which corresponds to the :meth:`sqlalchemy.orm.session.Session.execute` method on the underlying :class:`sqlalchemy.orm.session.Session`. Expressions here are expressed like :func:`sqlalchemy.sql.expression.text` constructs, using named parameters with colons:

>>> rp = db.execute('select name, email from users where name like :name order by name', name='%Bhargan%')
>>> for name, email in rp.fetchall(): print name, email
Bhargan Basepair basepair+nospam@example.edu

Or you can get at the current transaction's connection using :meth:`.SQLSoup.connection`. This is the raw connection object which can accept any sort of SQL expression or raw SQL string passed to the database:

>>> conn = db.connection()
>>> conn.execute("'select name, email from users where name like ? order by name'", '%Bhargan%')

Dynamic table names

You can load a table whose name is specified at runtime with the entity() method:

>>> tablename = 'loans'
>>> db.entity(tablename) == db.loans

entity() also takes an optional schema argument. If none is specified, the default schema is used.