I've been thinking about a feature like this for some time as well, but
haven't found the right way to describe it. Seems like it's time to take a
stab at it. This is likely to be half-baked at best:
It's something in between a full eager load and a lazy load. I call it
"vector load", but it's essentially an in-memory join. Here's how it could
work:
Suppose you have a list result of a simple query:
vector = S.query(MappedObj).filter(foo).all()
and assume "MappedObj" has a simple relation "children", each of those in
turn having a simple relation "grandchildren". It would be nice to say
something like
S.vectorload(vector, 'children.grandchildren')
and have *all* the children + grandchildren loaded for each item in
'vector'.
SA already has most of the join aliasing machinery to issue such a query and
do the in-memory join. But......the problem is restricting the set of
'children' to match those of 'vector' -- the seemingly easiest thing would
be to use a IN clause list of the children's foreign key, like so:
WHERE children.foreign_key IN ([obj.primary_key for obj in vector])
the issue is, the list of keys for the IN clause could be huge, and many
database engines perform poorly with huge lists of literals in an IN clause,
notably Postgresql, which at least until recently had quadratic behavior
with large IN lists.
Still for small sets, it might be a interesting feature.
BTW it's kind of harder than it would seem to "roll your own" for this one:
If you take the naive approach to just issue the query for children with an
eager load of grandchildren and do the in-memory join in a loop:
keys = [item.id for item in vector]
all_children =
S.query(ChildObj).options(eagerload('grandchildren')).filter(ChildObj.foreign_key.in_(keys))
dvec = dict((v.id, v) for v in vector)
for child in all_children:
dvec[child.foreign_key].children.append(child)
you'll find you trigger an unwanted lazy-load by issuing the children.append
operation.
--~--~---------~--~----~------------~-------~--~----~
You received this message because you are subscribed to the Google Groups
"sqlalchemy" group.
To post to this group, send email to sqlalchemy@googlegroups.com
To unsubscribe from this group, send email to [EMAIL PROTECTED]
For more options, visit this group at
http://groups.google.com/group/sqlalchemy?hl=en
-~----------~----~----~----~------~----~------~--~---
