>>> users = db.users.all()
>>> users.sort()
>>> users
- [MappedUsers(name='Joe Student',email='student@example.edu',password='student',classname=None,admin=0), MappedUsers(name='Bhargan Basepair',email='basepair@example.edu',password='basepair',classname=None,admin=1)]
+ [MappedUsers(name=u'Joe Student',email=u'student@example.edu',password=u'student',classname=None,admin=0), MappedUsers(name=u'Bhargan Basepair',email=u'basepair@example.edu',password=u'basepair',classname=None,admin=1)]
Of course, letting the database do the sort is better::
>>> db.users.order_by(db.users.name).all()
- [MappedUsers(name='Bhargan Basepair',email='basepair@example.edu',password='basepair',classname=None,admin=1), MappedUsers(name='Joe Student',email='student@example.edu',password='student',classname=None,admin=0)]
+ [MappedUsers(name=u'Bhargan Basepair',email=u'basepair@example.edu',password=u'basepair',classname=None,admin=1), MappedUsers(name=u'Joe Student',email=u'student@example.edu',password=u'student',classname=None,admin=0)]
Field access is intuitive::
>>> 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='Joe Student',email='student@example.edu',password='student',classname=None,admin=0), MappedUsers(name='Bhargan Basepair',email='basepair@example.edu',password='basepair',classname=None,admin=1)]
+ [MappedUsers(name=u'Joe Student',email=u'student@example.edu',password=u'student',classname=None,admin=0), MappedUsers(name=u'Bhargan Basepair',email=u'basepair@example.edu',password=u'basepair',classname=None,admin=1)]
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='Bhargan Basepair',email='basepair@example.edu',password='basepair',classname=None,admin=1)
+ MappedUsers(name=u'Bhargan Basepair',email=u'basepair@example.edu',password=u'basepair',classname=None,admin=1)
Since name is the primary key, this is equivalent to
>>> db.users.get('Bhargan Basepair')
- MappedUsers(name='Bhargan Basepair',email='basepair@example.edu',password='basepair',classname=None,admin=1)
+ MappedUsers(name=u'Bhargan Basepair',email=u'basepair@example.edu',password=u'basepair',classname=None,admin=1)
This is also equivalent to
>>> db.users.filter_by(name='Bhargan Basepair').one()
- MappedUsers(name='Bhargan Basepair',email='basepair@example.edu',password='basepair',classname=None,admin=1)
+ MappedUsers(name=u'Bhargan Basepair',email=u'basepair@example.edu',password=u'basepair',classname=None,admin=1)
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
>>> 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='Bhargan Basepair',loan_date=None)
+ MappedLoans(book_id=2,user_name=u'Bhargan Basepair',loan_date=None)
>>> db.flush()
>>> loan = db.loans.filter_by(book_id=2, user_name='Bhargan Basepair').one()
::
>>> db.loans.insert(book_id=book_id, user_name=user.name)
- MappedLoans(book_id=2,user_name='Bhargan Basepair',loan_date=None)
+ MappedLoans(book_id=2,user_name=u'Bhargan Basepair',loan_date=None)
>>> db.flush()
>>> db.loans.delete(db.loans.book_id==2)
>>> db.loans.update(db.loans.book_id==2, book_id=1)
>>> db.loans.filter_by(book_id=1).all()
- [MappedLoans(book_id=1,user_name='Joe Student',loan_date=datetime.datetime(2006, 7, 12, 0, 0))]
+ [MappedLoans(book_id=1,user_name=u'Joe Student',loan_date=datetime.datetime(2006, 7, 12, 0, 0))]
Joins
>>> join1 = db.join(db.users, db.loans, isouter=True)
>>> join1.filter_by(name='Joe Student').all()
- [MappedJoin(name='Joe Student',email='student@example.edu',password='student',classname=None,admin=0,book_id=1,user_name='Joe Student',loan_date=datetime.datetime(2006, 7, 12, 0, 0))]
+ [MappedJoin(name=u'Joe Student',email=u'student@example.edu',password=u'student',classname=None,admin=0,book_id=1,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,
>>> join2 = db.join(join1, db.books)
>>> join2.all()
- [MappedJoin(name='Joe Student',email='student@example.edu',password='student',classname=None,admin=0,book_id=1,user_name='Joe Student',loan_date=datetime.datetime(2006, 7, 12, 0, 0),id=1,title='Mustards I Have Known',published_year='1989',authors='Jones')]
+ [MappedJoin(name=u'Joe Student',email=u'student@example.edu',password=u'student',classname=None,admin=0,book_id=1,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',authors=u'Jones')]
If you join tables that have an identical column name, wrap your join
with `with_labels`, to disambiguate columns with their table name
These can then be used like a normal SA property:
>>> db.users.get('Joe Student').loans
- [MappedLoans(book_id=1,user_name='Joe Student',loan_date=datetime.datetime(2006, 7, 12, 0, 0))]
+ [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='Bhargan Basepair',email='basepair+nospam@example.edu',password='basepair',classname=None,admin=1)]
+ [MappedUsers(name=u'Bhargan Basepair',email='basepair+nospam@example.edu',password=u'basepair',classname=None,admin=1)]
relate can take any options that the relation function accepts in normal mapper definition:
>>> 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()
- [MappedBooks(published_year='1989',n=1)]
+ [MappedBooks(published_year=u'1989',n=1)]
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
::
>>> db.users.filter_by(classname=None).order_by(db.users.name).all()
- [MappedUsers(name='Bhargan Basepair',email='basepair+nospam@example.edu',password='basepair',classname=None,admin=1), MappedUsers(name='Joe Student',email='student@example.edu',password='student',classname=None,admin=0)]
+ [MappedUsers(name=u'Bhargan Basepair',email=u'basepair+nospam@example.edu',password=u'basepair',classname=None,admin=1), MappedUsers(name=u'Joe Student',email=u'student@example.edu',password=u'student',classname=None,admin=0)]
>>> db.nopk
Traceback (most recent call last):
return cmp(t1, t2)
def __repr__(self):
- import locale
- encoding = locale.getdefaultlocale()[1] or 'ascii'
- L = []
- for k in self.__class__.c.keys():
- value = getattr(self, k, '')
- if isinstance(value, unicode):
- value = value.encode(encoding)
- L.append("%s=%r" % (k, value))
+ L = ["%s=%r" % (key, getattr(self, key, ''))
+ for key in self.__class__.c.keys()]
return '%s(%s)' % (self.__class__.__name__, ','.join(L))
for m in ['__cmp__', '__repr__']: