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Last week I presented at Agile 2009 a workshop for those new to Agile entitled: The Agile Game: An Experiential Workshop. I love this workshop because it allows those new to Agile to experience the rhythm of an agile project in action and learn first hand many of the practices in a non-threatening, non-technical, and fun way. I have used this workshop for clients a number of times and it works. The feedback from this session was overwhelmingly positive too. Comments such as, “Fun game, good to demonstrate how teams do and don’t work together” and “Very, very, practical way to get concept through” are always great to see.

Recently I had been wondering if Project Management was a questionable career choice. I have spent the last couple of years surrounded by talented individuals who seemed to be able to work fine without me. The test is always when the project manager (me) goes on vacation. Has the team fallen apart? Are they forgetting all of the practices they were doing? Is stuff getting delivered to clients? I was finding that I had a backlog of chores when I came back, but overall things were still humming along.

I’ve used the ILog Business Rules products on a number of projects. Reasonable software. Does the job. Not exactly cheap. I’m not sure why they went for the aquisition by IBM — it was announced back at the end of July, so likely nothing to do with hard economic times.

What does this mean for software developers and system integrators? Back when the aquisition plans were first announced, I joked that we would soon be using WebSphere rules. And wouldn’t you know it, the aquisition announcement mentions ILog and WebSphere in the same sentence several times.

JBoss Rules (the former “Drools,” though it’s sad to see that cringe-inducing name making a comeback) is a Business Rules Engine (BRE). Those are the logic engines that allow you to execute a large set of “if-then” rules against a large set of facts (the most common algorithm to achieve this kind of performance is called RETE, from the ancient Greek for “net”). For more on BRE’s and how to use them, have a look at our Business Rules blog.

One of the things that has differentiated commercial BRE’s like Blaze Advisor and ILOG JRules from the Open Source JBoss Rules is the tools. The commercial BRE’s were part of a suite of development and management tools — IDE’s, “natural” language support, Business Rules Management Systems (BRMS) — that made their use in a corporate IT “ecosystem” much easier.

With version 4 of JBoss Rules, you now get a powerful workbench plugin for Eclipse, support for Domain Specific Languages (DSL) that mimick natural language, and a web-based BRMS. With it you can deploy, roll back, and report on your rules.

The BRMS has a nice Ajax interface and is in fact written in GWT. This is a natural for a Java-based system like JBoss Rules. If the developers who use and develop JBoss Rules can also tinker with the BRMS interface (GWT==Java in the browser), the odds are better that the BRMS will evolve apace with the core system.

Compare this with a framework like Echo2/3 where there is only a small cadre of developers who develop the JavaScript piece of what is essentially a Java centric framework, and consequently the development has lagged relative to other frameworks. So, good decision for JBoss Rules and probably a good decision if you are adding a web interface to your own Java system.

As Rails (and Ruby) climbs the hype curve, more and more systems are getting implemented that could benefit from a BRE. There are two (Rools and SIE) that don’t use RETE. But Ruleby does, though it is in version 0.1 now. I’ll post a couple of examples in it in the next couple of weeks.

Technorati Tags: bre, ruby, rails, rete, rools, SIE, Ruleby

In explaining this pattern, I wanted to take a step back and explain where and how OO and BRE’s intersect. To start with, this excellent post over at Mark Proctor’s blog makes the following statement:

Assert all your objects as facts: Rule engines usually know how to handle a fairly heavy load of facts, so let the engine do its magic. If you want to reason over a nested set of objects, assert all of them (not only the top level ones) into the engine and make sure your business model models the relationship between them. After that, writing your rules will be easier and the engine will be able to optimize execution (when compared to using eval and other constructs to access deeply nested structures). Example: if you have an Order fact that contains a list of OrderItems, assert both Order and OrdemItems as facts (if you need to reason over OrderItems too, obviously).

Why, by all that is object oriented, do you have to turn your domain model inside out, exposing what was hidden, unencapsulating what was encapsulated? Isn’t this defeating the purpose of object orientation? The answer is, in short, yes. You are defeating the purpose of OO when you expose the innards of your model to the BRE. But it is a tradeoff. You get something for your trouble. Specifically, you get the super efficient rule matching of RETE and it’s decendents.

This sort of exploding of the object model happens all the time in systems, and it isn’t necessarily always bad. Alen Hollub (author of Why getter and setter methods are evil and other Edsger Dijkstra-like articles) has identified the “procedural boundary layer” and explains it nicely in his book Holub on Patterns: Learning Design Patterns by Looking at Code:

One big exception exists to the no-getter-or-setter rule. I think of all OO systems as having a “procedural boundary layer.” The vast majority of OO programs run on procedural operating systems and talk to procedural databases, for example. The interfaces to these external procedural subsystems are by their nature generic. The designer of JDBC hasn’t a clue about what you’ll be doing with the database, so the class design has to be unfocused and highly flexible. UI-builder classes such as Java’s Swing library are another example of a “boundary-layer” library. The designers of Swing can’t have any idea about how their classes will be used; they’re too generic. Normally, it would be a bad thing to build lots of flexibility that you didn’t use because it increases development time. Nonetheless, the extra flexibility is unavoidable in these boundary APIs, so the boundary-layer classes are loaded with accessor and mutator methods. The designers really have no choice.

BRE’s are not OO. Yes, the facts can be viewed as objects and the BRE’s can be implemented using and OO language and design, but really the internal working of the BRE are most definitely not OO, the more the rule engine knows about each and every object and its innards, the more efficient it can be. Thus the PBL (procedural boundary layer) applies.

As an example why you need to expose all object, consider an order that is made up of line items.

public interface Order {
[...]
void addItem(OrderLineItem item);
boolean removeItem(OrderLineItem item);
[...]
}

Now normally you would hide the details of all the line items inside the class that implements the Order interface. Let’s suppose now that we haven’t added any of the encapsulated line items to the working memory of the BRE’s, just the order. Now let’s say we update one of the line items through an operation. The only way we have to telling the BRE that something has changes is by flagging the enclosing Order object. That will trigger a reevaluation of every rule in which the Order plays a part, even ones where the line items play no part.

There are all sorts of ways of dealing with exposing the internals of your domain model to the BRE without breaking encapsulation. One way is to implement both the business interface and a bean interface (getters, setters, etc.) in the implementation (see diagram). The business interface is used within the domain model, the bean interface in the BRE.

Another approach is to wrap the domain objects in bean containers that access the private instance variables through reflection.

Of course lots of this depends on the specific BRE you are using. Some require you to expose your instance variables directly and don’t deal well with method calls. I’d suggest not using those.

Next time I’ll go through a simple example using JBoss Rules showing how to convert a domain model to a Collaboration Cop rule base.

Technorati : bre, business rules, holub, ooad, patterns, procedural boundary

While JBoss Rules 3.0.x has been a great step from its predecessor (the unfortunately named Drools) — providing a rule editing environment, universal and existential quantifiers (the “not” A is the same as “for all” ~A), and Domain Specific Languages (DSL’s) — there were still a few things missing. I’ve watched the progress on 3.1 with barely contained excitement and impatience, but the result has been worth the wait. The 3.1 M1 drop contains operations on collections:

rule "Find interesting crash events, with 10 or more port scans within a 1 day window"
when
$crash : OutageEvent( eventType == OutageEvent.SERVER_CRASH)
ArrayList( size > 9) from collect ( HackEvent(hackType == HackEvent.PORT_SCAN, $date : eventDate -> (DateHelper.withinDayWindow($crash.getEventDate(), $date, 1))))
then
suspicious.add($crash);
end

There is also the accumulate operator, which is even more powerful than the collect operator. The documentation has lagged a little behind (it isn’t a full on release, after all), but you can see some examples of it’s use here. I find that I use these sorts of collection operations quite a bit. That being said, the above rule isn’t terribly efficient. If you have about 50 of those rules and lots of events of both categories, you will run into some cross-product hell (sort of like a poorly thought out database join). I’ll show some ways of making this sort of rule more efficient in an upcoming entry.

Finally, the system now in theory allows better debugging of rulesets. The various views into the working memory have been around for a while. See below for some of my issues.

A few nits that I’m sure the JBoss Rules team will work out:

• The system desperately needs better syntax error handling in rule files. Right now the stack traces you get out of the parser/builder are pretty cryptic.
• While you can now set breakpoints in the rule files, it doesn’t seem that they are recognized by the debugging environment. As of now, you can only set breakpoints on a WorkingMemory fireAllRules call. Not that useful for debugging rule issues. I’m not sure if this mod is supposed to address that problem, but regardless, it would be handy to have a fireSingleRule call so one can control the execution of the rule engine more closely.
• A real-time audit view to inspect engine events would be handy.
• The graphical RETE display would be more useful if the nodes were labeled. A display showing the facts in each node during execution would be too much to hope for, but one can dream.

The best thing about this latest version of JBoss Rules is, of course, that I can now start developing non-trivial examples and publishing them without having to constantly caveat that “this feature is present in Blaze and ILog, but not here.” An even more interesting question is this: since JBoss Rules is getting close on being a reasonable alternative to some of the commercial products, will what happened in the database arena with mySQL also happen in the BRE arena? Will costs come down, interoperability increase? Will the commercial vendors concentrate on service, support and high end features? Will these same vendors become more open and share more information with the public? I don’t think BRE’s will ever be as widespread as RDBMS’s, but I think the same dynamics might apply.

Some things to look forward to that I hope the JBoss Rules team is thinking about: rule management, specifically rule repositories and deployment management, e.g. revision control for rule sets and deployment of rule modifications to distributed, live production engines; Rule analytics, e.g. analysis of a rule set to see if it contains likely problems (infinite loops, possibly bad logic, etc.), and stats on actual rule execution, performance, etc.

Technorati : bre, business rules, drools, jboss rules

The pattern I’m writing about today — Collaboration Cop — is much meatier than the two previous ones, in my opinion. This one is all about transplanting the implicit and explicit business rules of an OO system from its objects to an external BRE.

This is a somewhat involved pattern, and I’ll provide some more examples and references in part II. Also in part II, I’ll run down the differences between the rule classifications that come out this pattern and the ones you commonly see in works by Ross.

Pattern Name: Collaboration Cop

Synopsis
Extract the business logic that is in an OO domain model — collaboration rules, property rules and controller logic — and express it as a set of business rules. Activity in the system is triggered by the UI inserting events into the working memory and firing the rules. Objects can be persisted by something like and ORM framework.

Slightly longer version: if you strip away the user interface and the persistence mechamism, a typical OO application is designed around a domain model and controller or service classes that set object collaborations within the domain model in motion in response to external events. The methods of the controller/service classes often correspond to steps in a Use Case. Embedded in the various domain classes is logic about how object can be modified and accessed, what collaborations can be created or desolved, etc. With this pattern, you extract all the service/controller and validation and collaboration logic into business rules. You populate the working memory with the objects that comprise the context for a Use Case. Finally, in response to an external event, you inject an event object into the working memory and fire the rules that modify the state of the object model (and persist it).

Context
Take the somewhat simplified Use Case where a customer is trying to rent a tape (he’s a sucker for punishment and doesn’t like DVD’s). In a OO system, we would try to create a collaboration as follows (yes, very simplified):

customer.addRental(new Rental(title.getTape()));

The customer object knows how many active rentals a customer has and knows hat he cannot have more than three. This attempted collaboration would throw an exception to indicate that it is not permitted.

In the Collaboration Cop version we would create an event and insert it into the working memory:

VideoStoreEvent event = new VideoStoreEvent();
event.type = VideoStoreEvent.RENT_TAPE;
event.value = "Gone With the Wind";


IF event.type == VideoStoreEvent.RENT_TAPE AND title.getTitle() == event.value AND title.isInStock()
AND customer.activeRentals() < 3
THEN
customer.addRental(new Rental(title.getTape()));
externalObjectSet.add(customer);
externalResponse.message = "Rented tape.";
externalResponse.success = true;
ELSE
externalResponse.message = "Unable to rent tape.";
externalResponse.success = false;

You can wrap a transaction around the sequence of event injections, then save (using an ORM) all of the modified objects in the externalObjectSet.

———–

This pattern is complex and powerful, and it’s implementation is very much dependent on the capabilities of the chosen BRE, how you choose to deal with concurrency, etc.. In part II, I’ll also construct a simple example using JBoss Rules.

Technorati : bre, business rules, patterns

I have to apologize in advance. I’m in the middle of squeezing out a book chapter and haven’t had time to tackle any of the BRE articles on my writing TODO list. So today I’m going to have to do what I usually hate to do — become a blog echo chamber. The post I want to alert you to is one posted almost a year ago by Charles Young, entitled Microsoft’s Rule Engine Scalability Results – A comparison with Jess and Drools. The article claims to show that Microsoft’s BRE cannot be criticized about not implementing RETE properly based on MS’s published test results. That’s a long way to go for such an ephemeral point.

I don’t recommend the article because of the claims it makes, but rather because it both demonstrates the use of three different BRE’s through actual code samples, and it provides an approach to benchmarking and comparing BRE’s for scalability and performance. I’m a firm believer in benchmarking BRE’s based on your actual use cases rather than on abstract worst-case scenarios like Miss Manners. Read through this blog entry. Even if his approach is flawed, as I believe it is, he deserves full credit for publishing the full code to his test and opening it up to public scrutiny.

Technorati : drools, jboss rules, jess, microsoft, performance, scalability

Mark Proctor of the JBoss Rules team gave me the heads up about an interesting debate (here and here) about First Order Logic (FOL) and whether various BRE’s do or should support it. One question that has come up is whether BRE’s can still be said to be Turing Complete? Actually, the two questions — whether a BRE supports FOL and whether it is Turing Complete — are quite seperate.

Turing Complete

First the easy question: when is a system Turing Complete? From the Wikipedia:

In computability theory, an abstract machine or programming language is called Turing complete, Turing equivalent, or (computationally) universal if it has a computational power equivalent to (i.e., capable of emulating) a simplified model of a programmable computer known as the universal Turing machine. Being equivalent to the universal Turing machine essentially means being able to perform any computational task – though it does not mean being able to perform such tasks efficiently, quickly, or easily.

So, what is a Turing machine? Again you can look at the Wikipedia, but in a nutshell, it’s a tape with an infinite number of cells, a read/write head, a finite alphabet that the head can write to a cell, a finite number of ‘states’ that the machine can be in, and a finite number of rules that say things like “if you are in state 3 and the symbol on the tape is ’0′, the write a ’1′, move the head one cell to the right and change your state to 8.”

While this may seem like a silly sort of machine (just try to write a word processor with it), it can do anything, given the right number of states and the right set of rules, that a von Neuman style of computer — essentiall the sort of computer with which you are reading this blog — can do. It’s handy for proving certain theorems about computability and complexity. If you can write any Turing machine with your device, then it is said to be a Universal Turing Machine, i.e. if you can compute it with an algorithm, you can do it with your machine. It should be obvious that a BRE, even without First Order Logic, is a UTM. We can have a fact that tells us what our state is, another fact that tells us where our read/write head is, a set of facts that represent all of the cells we’ve seen so far, and our rules that govern how our state, head and cells are modified. Therefore a BRE is Turing Complete.

Of course there’s a small fly in the ointment. Up above we said that a turing machine had an “infinite” number of cells. Of course your computer can’t accomodate an infinite number of cells, since it has a finite amount of memory and storage. This is a rather important point, as it turns out. Anything program running on a modern computer with finite memory and storage is at best a Finit State Machine, basically the most primite of the interesting algorithms. But in principle, a BRE with infinite memory is Turing Complete.

First Order Logic

I’ll try to stay a little non-technical here, mostly because all of my math text books are in storage. First Order Logic (or First Order Predicate Logic) is an extension of regular propositional logic with the addition of the universal and existential quantifiers. That means we can write conditions in our rules that are true for all facts of a particular type in our fact base or which are true for at least one fact. I’ve written before why having First Order Logic built into your BRE is a good thing. In short, you can work around your BRE not having it, but you’ll lose the performance advantage of your rule execution algorithm in the process, such as RETE. And there are plenty of scenarios where you are looking at an event history of a customer or client that call for FOL.

You can see, however, that our simulation of a Turing machine above never called for a quantifier. Thus even a BRE without FOL is Turing Complete (if it has infinit memory, yada yada). I hope this explanation I hope this entry has been useful and not too geeky. Behind all of the hype behind BRE’s, it is sometimes useful to understand what exactly they are and what they can do.

In my discussions with James Taylor about using wizards to write rules, I was reminded of some cases where it was necessary to create a wizard for this purpose. They all shared two things in common: the need for justifications and non-shortcircuited OR’s. Let me give you an example.

Let’s suppose you are designing a system that decides whether or not to recommend prisoners for early release. Your facts inlcude the prisoners’ criminal records, the details of their behavior behind bars, recommendations by prison officials and the district attourney, enrollment in treatment and rehabilitation programs, and mitigating circumstances such as aged parent or infirm spouses or children. You formulate the state’s sentencing and release guidelines as business rules and implement them in your vendor platform of choice. When a feed of data comes from the department of corrections, you add it to your database and run it through your rule system which issues recommendations to a board charged with making the final decision. This board makes a judgement call based on the information you provide them.

When they look at your recommendation, they don’t just want to see a yes or no, they want to see an explanation or justification. Something like “Prisoner Joe Smith, #54-56, is not recommended for release because he had at least 3 incidents of misbehavior in the last 6 months and was convicted of at least a Class C felony.” Ideally, you’d want to spell out exactly what the issues were:

Prisoner: Joe Smith
Number: 54-56
Recommended for Early Release: No
Reasons: At least 3 incidents of misbehavior in the last 6 months (stabbing of guard, 4/5/2006; assault of fellow prisoner, 4/22/2006; contraband in cell, 5/17/2006) AND convicted of at least a Class C felony (Criminal Possession of Stolen Property, 6/11/2004)

Now as a board member, I can look at the prisoner and see that while he was convicted of a nonviolent crime, he looks to have been violent while in prison. If he had just been flagged for contraband, maybe I might consider releasing him.

OK, so justification is useful in this context, as it is in many other fields, like healthcare, insurance underwriting and claims, criminal science, etc. How do we produce a justification with our rule engine? The sad truth is that none of the commercial vendors of forward-chaining BRE’s will do that for you automatically. You have to roll your own. One approach is to use the Fact Harvest pattern to construct and build a ReleaseRecommendation object. This fact would tell us whether a particular prisoner was recommended for release (true or false) and all of the conditions that contributed to this recommendation. It might contain a list of all of the misbehavior’s over the last 6 months and the crime for which he was convicted.

Here we come to the first reason for using a wizard to write rules: if you are testing a particular condition in the ‘IF’ part of your rule and then updating your ReleaseRecommendation object with the details of that same condition in the ‘THEN’ part of the rule, you’re essentially recapitulating the condition in the action part of the rule. Aside from being a pain in the neck, it’s also a maintenance problem. Every time you update a condition, you have to make sure to update the corresponding action. If you don’t, Charles Manson gets released by accident — or, rather, because of your bug. It voilates the DRY principle (Don’t Repeat Yourself). One way of handling this is to have your wizard generate the rule for you, with both the condition and action parts. Now you make one change and the rule is generated in a way that is guaranteed to maintain consistency.

The second reason for using a wizard has to do with the short-circuited OR of the title. If our rule above actually tests for

has had at least 3 incidents of misbehavior in the last 6 months OR is a drug addict

then what happens with our rule? If you’ve programmed much, you know that most languages use what is known as “short-circuited OR,” i.e. if you are testing A OR B and A evaluates to true, then B is never tested. If you are depending on side effect of B (such as the building of a list of incidents, etc.), you can forget about it ever happening. The ‘is a drug addict’ part of the condition is never tested. To make things worse, if we are constructing a justification, we have to test in the action which parts of the OR clause in the condition actually evaluated to true. Imagine now a complex condition with lots of OR, NOT’s, AND’s and parentheses. Yuck.

A way around this mess is to break apart the OR clause into seperate rules and accumulating them in the ReleaseRecommendation object. The drawback, of course, is that now we have to create several rules where before we had only one. Again, generating these rules with a wizard is a more or less elegant way out.

If anyone has a niftier solution to justification or non-short-circuited OR, I’m more than happy to hear about it.

Update 1: Michael Chermside correctly points out in the comments that it would be more elegant to have a justification engine that worked directly with the BRE’s internals. I agree. Unfortunately, none of the forward-chaining BRE’s I’ve worked with have very good support for justification or supply hooks into their internals to support the building of a justification engine through AOP or something equivalent. If you work with an open source engine like JBoss Rules, you can build in your own justification logic.