On Wed, 2006-09-20 at 20:27 +0200, Alexandre CONRAD wrote:
> First, I created a "tables" file where I can hold all my SA tables.
>
> models/my_tables.py:
>
> =============
> from sqlalchemy import *
>
> # Clients
> clients_table = Table('clients', metadata,
> Column('id_client', Integer, primary_key=True),
> Column('name', String(40), nullable=False),
> )
>
> # Sites
> sites_table = Table('sites', metadata,
> Column('id_site', Integer, primary_key=True),
> Column('id_client', Integer,
> ForeignKey('clients.id_client')),
> Column('name', String(40), nullable=False),
> )
> =============
>
> Then my python objects stored into a "models" file.
>
> models/my_models.py:
>
> =============
> from sqlalchemy import *
>
> class Client(object):
> def __init__(self, name):
> self.name = name
>
> class Site(object):
> def __init__(self, name, client_id):
> self.name = name
> self.name = client_id
> =============
>
>
> Then I'd like to map tables and my python objects together.
>
> models/__init__.py:
>
> =============
> from sqlalchemy import *
>
> from my_tables import clients_table, sites_table
> from my_models import Client, Site
>
> metadata = DynamicMetaData() # Gotta do something with that somewhere...
>
> client_mapper = mapper(clients_table, Client)
> site_mapper = mapper(sites_table, Site)
> =============
>From an encapsulation standpoint, I would tend to put tables and classes
together by "subject", e.g.,
--- models/clients.py ---
from sqlalchemy import * # probably better to restrict this
clients_table = Table('clients', metadata,
Column('id_client', Integer, primary_key=True),
Column('name', String(40), nullable=False),
)
class Client(object):
def __init__(self, name):
self.name = name
client_mapper = mapper(clients_table, Client)
--- models/sites.py ---
from sqlalchemy import *
sites_table = Table('sites', metadata,
Column('id_site', Integer, primary_key=True),
Column('id_client', Integer,
ForeignKey('clients.id_client')),
Column('name', String(40), nullable=False),
)
class Site(object):
def __init__(self, name, client_id):
self.name = name
self.name = client_id
site_mapper = mapper(sites_table, Site)
--- models/__init__.py ---
from sqlalchemy import *
from clients import Client
from sites import Site
metadata = DynamicMetaData()
---
The point is that if I add or change a column to the clients table, I'll
probably have to change the Client class. If one class depends on
another, I could import the latter in the former module. Granted that
can get tricky with circular dependencies, but those are very rare. Now
this is all from my C/C++ "upbringing" so I must admit I have little
practical experience with this approach in Python.
> But basicly, that's how I see things best. I don't want everything
> inside the models/__init__.py file because bigger projects might take up
> some pages and I don't want to have it all-in-one-file-long-file. It
> might not be the right way to do it, but I hope it is. :)
The one-big-model-file is an issue that I had raised in an earlier post.
I don't understand the internals of Python and Pylons well enough, but
it seems like a waste of cycles to load a ten-table/class model for say,
a contact page which will only trigger a mail send and probably not even
need to access the database.
I was also discussing with Mike Bayer the issue of not repeating the
database schema. The autoload=True option allows you to eliminate the
repetition but there is a cost involved: you have to be connected to
the database (which means you can't use DynamicMetaData) because SA has
to query the db's catalogs (or data dictionary) to do the autoload.
OTOH, I wonder how does Django's ORM and SLQObject (which emphasize DRY)
manage this. They presumably implement autoload and not give you a
choice. Even with connection pooling and session and database caches,
it seems to me there's a performance impact, unless there's some
selective mechanism that only queries that which is needed.
I'm still learning so please forgive my ignorance.
Joe
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