Singleton objects within an OM context do no scale across multiple machines/VMs. Its pipe dream material. I've saw a demo of this kind of stuff from an OODB vendor in the late nineties and it was impressive, but look how far OODBs have progressed (answer: not very).
Real-life ORM, typically provides different ways of handling stale, transient, and persistent objects. HIbernate, probably the most well-known ORM tool of today, supports some versioning features by adding "system columns" to tables to prevent old data clobbering fresh data.
What you're looking for is for the RDB to push change events to an ORM-maintained cache in the application server layer, e.g. Django. Microsoft SQL Server 2005 is, AFAIK, the only RDBMS which dispatches these "cache update events" to the application layer. I don't know how successful this new technology is but its sounds expensive in terms of resources.
The problem you describe is a classic ORM, and typically you have to assume worst-case scenario and code around the problem by using transactions and refreshing your objects.
Have you considered Python's SQLObject library ? You might get better mileage.
Hope it helps,
Ijonas
On 2/28/06, ZebZiggle <[EMAIL PROTECTED]> wrote:
Here is a blog article about what core functionality an ORM should
support:
http://pythonnotes.blogspot.com/2004/09/python-orm-tools.html
Specifically, take note of the paragraph that reads:
To implement true object persistence, each object needs a unique
identifier. Each ORM will use a different approach here. However, one
issue that is common to all ORMs is how to handle multiple copies of
the same object. For the sake of consistence, objects should be
singletons. If the ORM tool doesn't do it automatically, then it's up
to the programmer to make sure that no multiple copies of the same
object exist at the same time, avoiding concurrency and consistency
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