Hi Mark,
Thanks for the response. Comforting we seem to be on the right track,
pity it's just an imaginary one :-)
Will see what happens, but thanks again for now.
- Scott.
Mark Proctor wrote:
I've had ideas on how to do this, but no timelines or concrete plans.
The basic idea is as rules are evaluated, based on the constraints pulls
in the data from the database - this is basically about building special
join nodes. A caching system is used to cache by data partition
segments, with a time based eviction system, to avoid continous db hits.
This way people could declare their rules, but never need to insert
data, they just assign it a db source. Patches welcome :)
Mark
Scott Finnie wrote:
Hi,
As a drools newbie, I'm grappling with the above question; any help
much appreciated. To elaborate:
- We have a largish dataset (~50GB+) stored in an rdbms (oracle).
- we're considering using drools to implement business rules (e.g.
for data validation constraints and derivations).
- the issue is how to give the rulebase efficient & scalable access
to the db. It would potentially need to access the whole dataset,
since business rules can potentially affect all tables.
We've done an artificial pilot with a much smaller dataset, simply by
syncing the entire db into the rulebase. Users like it because they
can read the rules directly (using a DSL). Before we go any further
however we need to find a scaling strategy before we go any further.
We were thinking about some kind of caching strategy: conceptually the
rulebase would have a cache hit failure causing data to be loaded from
the db. However we've no idea if that's a practical option, or if
there's something better.
Hope that makes sense; any help much appreciated. Oh, and btw, thanks
for a great piece of software!
- Scoot.
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