Yep, that all makes sense for the more elaborated context. Sounds like you're working with a model wherein you needn't concern yourself with relational logic between instances, so I think the value of splitting sessions over threads with a multi-consumer queueing setup could allow you the opportunity to async your process with better throughput. What you and I propose differs only in high-availability and scalability of the input stream (potentially arising from throttling to a single instance responsible for maintaining task scheduling and executor lifecycle), offering an ability to recover should you lose your application containing pooled tasks and allow for smaller pool sizes to maintain (pull only as needed/desired from queueing and adjust that capacity on the fly for high-usage times such as first thing in the morning). 

Semi-related I just found a video about a large-scale operation that Alexandre Porcelli created that might be of some interest to you. http://vimeo.com/27209589

My only other thoughts going into it is consider some different approaches for the scheduling mechanism given that, as I've had the unpleasure of discovering before, callbacks from runnables can be fun to keep up with of you're dependent on them, so fire-and-forget vs. results synchronicity can make a difference in the mechanism you choose to maintain your tasks and pools.

Regards,
Jeremy


On Thu, Feb 7, 2013 at 10:47 AM, Cotton, Ben <Ben.Cotton@morganstanley.com> wrote:

Thanks for your response, Ary.

 

It is much more about accommodating high-frequency and throughput.  The rules  are ZERO sensitive to time and order – they are rendered 1x at start of day.  They are exceedingly complicated, and there are lots of them … but once they are bound to a KB nothing changes about them for the whole day.   When we put a fact on a KS.fireAllRules() task the rendered decision is idempotent wrt to rules’ firing(s) order. 

 

Also, all arriving facts are immutable and all sessions are stateless, so we kind of have ignored CEP (seeing it as more appropriate for a long-living ecosystem of continuously mutating facts).   

 

Effectively, we want a “small, simple, safe, speedy” body of operations on “complex, cumbersome, concurrently-arriving, constant” facts.

 

 

 

From: rules-users-bounces@lists.jboss.org [mailto:rules-users-bounces@lists.jboss.org] On Behalf Of Jeremy Ary
Sent: Thursday, February 07, 2013 11:32 AM
To: Rules Users List
Subject: Re: [rules-users] ambition = ThreadPoolExecutor delegating to KBPool(s) & KSPools(s)

 

Are you in a place where your rules have become sensitive to time and order? If so, have you considered CEP? If it's less about that and more about getting the work done ASAP, you could also investigate a messaging integration pattern to assist with all the pooling/throttling/queueing needs you've mentioned.

 

On Thu, Feb 7, 2013 at 10:04 AM, Cotton, Ben <Ben.Cotton@morganstanley.com> wrote:

Let’s say that a start-of-day, every day, we generate a giant 2,000+  rule .DRL, that we then use to construct into a single run-time KnowledgeBase reference.  We then construct a single run-time KnowledgeSession reference (also at start of day).  Throughout the day, all day, facts “arrive” asynchronously into our expert system.  When a fact “arrives”, we synchronously place the fact onto our single KS and call .fireAllRules(), which in turn synchronously outputs answers that satisfy our “what’s the next step?” decision requirements. 

 

We have this working very well, but we have the ambition to achieve more.   

 

We want  to attempt to scale this solution to accommodate the high-frequency simultaneous “arrival” of many facts.  We have at our disposal a 24xCPU 128 gb Linux-based compute resource (nice, right?) … so, ideally, we have the ambition to potentially accommodate the simultaneous arrival of 24 facts into our expert system.

 

Assuming that all of our 2,000+ rules are completely isolated (i.e. no rule i ever depends on any rule j, for all i,j) we want to consider building (at start of day) a KSPool (size 24) , KBPool (size 24), and a ThreadPoolExecutor (size 24, backed by BlockingQueue).   As facts arrive throughout the day, those that arrive simultaneously are Queue’d to the TPE, that then delegates the fact’s need for service to a task Runnable,  which in turn calls a KSPool[i].fireAllRules() (with isolation to KBPool[i]).  In such a scheme, we would potentially be able to render decisions concurrently when facts arrive simultaneously ( capacity 24).

 

Is this design ambition common w/in current DROOLs use cases?  Does the current (or future) DROOLS offering include any in-place capability to Pool KS or Pool KB?  If not, are there any potential DROOLs concerns or “gotchas” wrt to our pursuing this ambition (in a “let’s build this now!” prototype)? 

 

As always, tremendous thanks to all in this community forum.

 

 

Ben D Cotton III
Morgan Stanley & Co.
OTC Derivatives Clearing Technology
1221 AOTA Rockefeller Ctr - Flr 27
New York, NY 10020
(212)762.9094
ben.cotton@ms.com

 

 

 


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