you can either make the "R" table the "secondary" table between A and
B, as well as B and C, which would have the effect of always having a
null value in at least one column of "R", or you can use the existing
technique and use python property tricks to make the task easier:
class A(object):
def __init__(self):
self.relation = ABCRelation()
def _get_b(self):
return self.relation.b
def _set_b(self, b):
self.relation.b = b
related_b = property(_get_b, _set_b)
def _get_c(self):
return self.relation.c
def _set_b(self, c):
self.relation.c = c
related_c = property(_get_c, _set_c)
On Sep 27, 2006, at 2:05 PM, Martin Kaffanke wrote:
> Am Mittwoch, den 27.09.2006, 13:39 -0400 schrieb Michael Bayer:
>> first off, note that this style of relationship is severely limited
>> with regards to the arrangements of A->B->C. the C's attached to B's
>> are intertwined with the B's that are attached to A's and it will be
>> very confusing to manage those relationships independently of each
>> other. if you are looking for select efficiency, you would probably
>> be better off just doing normal joins from A->B->C including
>> association tables if they are many-to-many relationships; SA can
>> eager load such a relationship in one query.
>
> Ok, I hope that helps out. because now I found out, that there is -
> based on given data - not really a need to have an entry in B, an A
> can
> have a C without an B, thats because People may not want to tell us B
> but only C.
>
>> anyway, the closest SA can get to this is to represent your "R" table
>> as a relationship object with its own mapper:
>>
>> class ABCRelation(object):pass
>>
>> mapper(A, Atable)
>> mapper(B,Btable)
>> mapper(C,Ctable)
>> mapper(ABCRelation, Rtable, primary_key=[R.c.a_id, R.c.b_id,
>> R.c.c_id], properties={
>> 'as':relation(A, backref='relations'),
>> 'bs':relation(B, backref='relations'),
>> 'cs':relation(C, backref='relations')
>> }, allow_null_pks=True)
>>
>> a = A()
>> b = B()
>> c = C()
>> r = ABCRelation()
>> r.a = a
>> r.b = b
>> r.c = c
>> [session.save(x) for x in [a,b,c,r]]
>> session.flush()
>
> Ok, I see. But so I have to update many objects. It is sure, that
> all
> the objects depend somehow on A. So I thought on an other Structure
> like doing:
>
> A.B.C # normal relation
> A.C # if we have no B, but if we have a B A.C and A.B.C are refer to
> the same object.
>
> Can this be done?
>
> Martin
>
>> etc
>>
>> the good news is that the latest trunk of SQLAlchemy (i.e. release
>> 0.2.9) will even keep it straight if you delete an ABCRelation and
>> create a new one with the same relationships in one session; it will
>> turn the DELETE/INSERT into an UPDATE.
>>
>>
>>
>> On Sep 27, 2006, at 1:10 PM, Martin Kaffanke wrote:
>>
>>> Hi there!
>>>
>>> I don't know if my english is good enaugh to tell you what I want.
>>>
>>> I have 3 Tables, lets tell them A, B and C. The relations:
>>>
>>> An A kann have some B's and a B can have some C's. But the main
>>> Select
>>> will be:
>>>
>>> and_(A.c.a_id==B.c.a_id, B.c.b_id==C.c.b_id, C.c.c_id=some_integer)
>>>
>>> So now there are two joins.
>>> As you see, we have two joins. We would have only one by doing
>>>
>>> A B C and a R table, where R has all three ID's:
>>>
>>> R.c.a_id
>>> R.c.b_id
>>> R.c.c_id
>>>
>>> A relation table...
>>>
>>> and_(A.c.a_id==R.c.a_id, C.c.c_id==some_integer)
>>>
>>> would be enaught.
>>>
>>> But how can I save data this way with a mapper?
>>>
>>> Martin
>>>
>>>
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