The partitioned relationship actually referred to the tertiary table in
both the primary and secondary join - the problem for me was that in the
primaryjoin
primaryjoin="and_(First.first_id==Second.first_id,
First.partition_key==Partitioned.partition_key)"
only First.first_id will be interpolated with the actual value first_id of
the instance in question whereas First.partition_key on the other hand will
be interpolated as column object. The problem is that in this case
First.partition_key has to be interpolated with the actual value to get the
constraint-exclusion to work. In a normal many-to-many relationship this
would not be necessary and maybe that is why it only interpolates the
values for the join on the secondary table.
The partitioned relationship emits a query like this if the attribute is
accessed:
SELECT partitioned.*
FROM partitioned, second, first
WHERE %(param_1)s = second.first_id
AND first.partition_key = partitioned.partition_key
AND second.other_id = partitioned.other_id
But I would need first.partitioned_key to be %(param_2)s.
So far I used a @property around a query function to add the partition_key
to query.filter() manually.
On Thu, Dec 5, 2013 at 4:37 PM, Michael Bayer <mike...@zzzcomputing.com>wrote:
> oh, you want to refer to the tertiary table in both the primary and
> secondary join. so right this pattern does not correspond to the
> A->secondary->B pattern and isn’t really a classic many-to-many.
>
> a quick way to map these are to use non primary mappers (was going to just
> paraphrase, but let me just try it out b.c. these are fun anyway, and I
> want to see the new joining behavior we have in 0.9…):
>
> from sqlalchemy import *
> from sqlalchemy.orm import *
> from sqlalchemy.ext.declarative import declarative_base
>
> Base = declarative_base()
>
> class First(Base):
> __tablename__ = 'first'
>
> first_id = Column(Integer, primary_key=True)
> partition_key = Column(String)
>
> def __repr__(self):
> return ("First(%s, %s)" % (self.first_id, self.partition_key))
>
> class Second(Base):
> __tablename__ = 'second'
>
> id = Column(Integer, primary_key=True)
> first_id = Column(Integer)
> other_id = Column(Integer)
>
> class Partitioned(Base):
> __tablename__ = 'partitioned'
>
> id = Column(Integer, primary_key=True)
> partition_key = Column(String)
> other_id = Column(Integer)
>
> def __repr__(self):
> return ("Partitioned(%s, %s)" % (self.partition_key,
> self.other_id))
>
>
> j = join(Partitioned, Second, Partitioned.other_id == Second.other_id)
>
> partitioned_second = mapper(Partitioned, j, non_primary=True, properties={
> # note we need to disambiguate columns here - the join()
> # will provide them as j.c.<tablename>_<colname> for access,
> # but they retain their real names in the mapping
> "id": j.c.partitioned_id,
> "other_id": [j.c.partitioned_other_id, j.c.second_other_id],
> "secondary_id": j.c.second_id
> })
>
> First.partitioned = relationship(
> partitioned_second,
> primaryjoin=and_(
> First.partition_key ==
> partitioned_second.c.partition_key,
> First.first_id ==
> foreign(partitioned_second.c.first_id)
> ), innerjoin=True)
>
> e = create_engine("postgresql://scott:tiger@localhost/test", echo=True)
> Base.metadata.drop_all(e)
> Base.metadata.create_all(e)
> s = Session(e)
> s.add_all([
> First(first_id=1, partition_key='p1'),
> First(first_id=2, partition_key='p1'),
> First(first_id=3, partition_key='p2'),
> Second(first_id=1, other_id=1),
> Second(first_id=2, other_id=1),
> Second(first_id=3, other_id=2),
> Partitioned(partition_key='p1', other_id=1),
> Partitioned(partition_key='p1', other_id=2),
> Partitioned(partition_key='p2', other_id=2),
> ])
> s.commit()
>
> for row in s.query(First, Partitioned).join(First.partitioned):
> print(row)
>
> for f in s.query(First):
> for p in f.partitioned:
> print(f.partition_key, p.partition_key)
>
>
> I mapped to a join directly, and not a select, so as long as we aren’t
> using SQLite (and are using 0.9) we get nested join behavior like this:
>
> SELECT first.first_id AS first_first_id, first.partition_key AS
> first_partition_key, partitioned.id AS partitioned_id,
> partitioned.partition_key AS partitioned_partition_key,
> partitioned.other_id AS partitioned_other_id
> FROM first JOIN (partitioned JOIN second ON partitioned.other_id =
> second.other_id) ON first.partition_key = partitioned.partition_key AND
> first.first_id = second.first_id
> 2013-12-05 11:27:18,347 INFO sqlalchemy.engine.base.Engine {}
> (First(1, p1), Partitioned(p1, 1))
> (First(2, p1), Partitioned(p1, 1))
> (First(3, p2), Partitioned(p2, 2))
>
>
> the load of f.partitioned will load the Partitioned objects in terms of
> the “partitioned_second” mapper, so those objects will have those extra
> cols from “second” on them. You can screw around with this using
> exclude_properties for those cols you don’t need to refer to on the
> mapping, and perhaps primary_key if the mapper complains, such as:
>
> partitioned_second = mapper(Partitioned, j, non_primary=True, properties={
> "id": j.c.partitioned_id,
> "other_id": [j.c.partitioned_other_id, j.c.second_other_id],
> }, exclude_properties=[j.c.second_id],
> primary_key=[j.c.partitioned_id, j.c.second_other_id])
>
> or you can just ignore those extra attributes on some of your Partitioned
> objects.
>
>
>
>
>
>
>
>
> On Dec 5, 2013, at 11:03 AM, Adrian Schreyer <adrian.schre...@gmail.com>
> wrote:
>
> Given the three mappings *First*, *Second* and *Partitioned*, I want to
> declare a relationship between *First* and *Partitioned*. The problem is
> that *Partitioned* is partitioned by partition_key which is a column in
> *First* but not in *Second*. *Second* however contains the identifier
> that actually links *First* to specific rows in the partitioned table.
>
> So far the mapping looks like this mock example:
>
> partitioned = relationship("Partitioned",
> secondary=Base.metadata.tables['schema.seconds'],
> primaryjoin="and_(First.first_id==Second.first_id,
> First.partition_key==Partitioned.partition_key)",
> secondaryjoin="Second.other_id==Partitioned.other_id",
> foreign_keys="[Second.first_id, Partitioned.partition_key,
> Partitioned.other_id]",
> uselist=True, innerjoin=True, lazy='dynamic')
>
> It works, but it only interpolates the First.first_id with the actual
> value which normally makes sense but to make the PostgreSQL
> constraint-exclusion work the First.partition_key would need to be
> interpolated with the proper value as well. Right now it is only given as
> First.partition_key==Partitioned.partition_key.
>
> Does that make sense? I am not sure if my relationship configuration is
> wrong or if this kind of mapping is simply not supported.
>
>
> On Thu, Dec 5, 2013 at 3:31 PM, Michael Bayer <mike...@zzzcomputing.com>wrote:
>
>>
>> On Dec 5, 2013, at 6:57 AM, Adrian Schreyer <adrian.schre...@gmail.com>
>> wrote:
>>
>> Actually that was a bit too early but I tracked the problem down to the
>> many-to-many relationship. Parameters are only interpolated (e.g.
>> %(param_1)s) for the primaryjoin to the secondary table. Is there a
>> technique to force relationship() to interpolate a parameter between the
>> 1st and 3rd table instead of using only table.column=table.column?
>>
>>
>> there’s no reason why that would be the case can you provide more
>> specifics?
>>
>>
>>
>>
>>
>>
>> On Thu, Dec 5, 2013 at 10:58 AM, Adrian Schreyer <
>> adrian.schre...@gmail.com> wrote:
>>
>>> Never mind,
>>>
>>> the problem was that I specified the clause in a secondaryjoin and not
>>> in the primaryjoin of the relationship().
>>>
>>>
>>> On Thu, Dec 5, 2013 at 10:44 AM, Adrian <adrian.schre...@gmail.com>wrote:
>>>
>>>> Hi All,
>>>>
>>>> I have a few partitioned tables in my PostgreSQL database but I do not
>>>> know yet how to make the ORM relationship() with partition
>>>> constraint-exclusion<http://www.postgresql.org/docs/9.3/static/ddl-partitioning.html#DDL-PARTITIONING-CONSTRAINT-EXCLUSION>
>>>> on
>>>> the instance level. Constraint-exclusion does not work with joins and
>>>> requires scalar values - the problem is that I would need to add an
>>>> additional WHERE clause to the primaryjoin (which adds the partition key)
>>>> if the relationship is accessed from the* instance level*, e.g.
>>>> user.addresses. Is there a mechanism in relationship() to distinguish
>>>> between class-based joins (User.addresses) and instance-level access?
>>>>
>>>> --
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>>>
>>>
>>
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