Drools expert-docs

Drools Expert User Guide
Version 5.4.0.Final

by The JBoss Drools team [http://www.jboss.org/drools/team.html]

....................................................................................................................................... vii
1. Introduction ................................................................................................................. 1
1.1. Artificial Intelligence ............................................................................................ 1
1.1.1. A Little History ......................................................................................... 1
1.1.2. Knowledge Representation and Reasoning ................................................ 2
1.1.3. Rule Engines and Production Rule Systems (PRS) .................................... 3
1.1.4. Hybrid Reasoning Systems (HRS) ............................................................ 5
1.1.5. Expert Systems ........................................................................................ 8
1.1.6. Recommended Reading ........................................................................... 9
1.2. Why use a Rule Engine? .................................................................................. 12
1.2.1. Advantages of a Rule Engine ................................................................. 13
1.2.2. When should you use a Rule Engine? ..................................................... 14
1.2.3. When not to use a Rule Engine .............................................................. 15
1.2.4. Scripting or Process Engines .................................................................. 15
1.2.5. Strong and Loose Coupling .................................................................... 16
1.3. Rete Algorithm .................................................................................................. 16
2. User Guide ................................................................................................................. 23
2.1. The Basics ....................................................................................................... 23
2.1.1. Stateless Knowledge Session ................................................................. 23
2.1.2. Stateful Knowledge Session .................................................................... 26
2.1.3. Methods versus Rules ............................................................................ 31
2.1.4. Cross Products ...................................................................................... 32
2.2. Inference .......................................................................................................... 33
2.2.1. Buss Pass Example ............................................................................... 33
2.3. Truth Maintenance with Logical Objects ............................................................. 35
2.3.1. Overview ............................................................................................... 35
2.4. Decision Tables in Spreadsheets ....................................................................... 40
2.4.1. When to Use Decision Tables ................................................................. 41
2.4.2. Overview ............................................................................................... 41
2.4.3. How Decision Tables Work ..................................................................... 43
2.4.4. Spreadsheet Syntax ............................................................................... 46
2.4.5. Creating and integrating Spreadsheet based Decision Tables .................... 55
2.4.6. Managing Business Rules in Decision Tables .......................................... 56
2.4.7. Rule Templates ...................................................................................... 57
2.5. Templates ........................................................................................................ 59
2.5.1. The Rule Template File .......................................................................... 60
2.5.2. Expanding a Template ............................................................................ 61
2.5.3. Example ................................................................................................ 63
2.6. More on building and deploying ......................................................................... 66
2.6.1. Knowledge Base by Configuration Using Changesets ............................... 66
2.6.2. Knowledge Agent ................................................................................... 67
3. Api Reference ............................................................................................................ 71
3.1. Building ............................................................................................................ 71
3.1.1. Building using Code ............................................................................... 71

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3.1.2. Building using Configuration and the ChangeSet XML .............................. 76
3.1.3. Changing default building result severity .................................................. 79
3.2. Deploying ......................................................................................................... 80
3.2.1. KnowledgePackage and Knowledge Definitions ........................................ 80
3.2.2. KnowledgeBase ..................................................................................... 81
3.2.3. In-Process Building and Deployment ....................................................... 83
3.2.4. Building and Deployment in Separate Processes ...................................... 84
3.2.5. StatefulknowledgeSessions and KnowledgeBase Modifications ................. 85
3.2.6. KnowledgeAgent .................................................................................... 85
3.3. Running ............................................................................................................ 88
3.3.1. KnowledgeBase ..................................................................................... 88
3.3.2. StatefulKnowledgeSession ...................................................................... 89
3.3.3. KnowledgeRuntime ................................................................................. 89
3.3.4. Agenda .................................................................................................. 97
3.3.5. Event Model ......................................................................................... 101
3.3.6. KnowledgeRuntimeLogger .................................................................... 104
3.3.7. StatelessKnowledgeSession .................................................................. 104
3.3.8. Commands and the CommandExecutor ................................................. 108
3.3.9. Marshalling .......................................................................................... 116
3.3.10. Persistence and Transactions .............................................................. 118
3.3.11. Drools Clips ....................................................................................... 120
4. Rule Language Reference ........................................................................................ 123
4.1. Overview ........................................................................................................ 123
4.1.1. A rule file ............................................................................................. 123
4.1.2. What makes a rule ............................................................................... 124
4.2. Keywords ........................................................................................................ 124
4.3. Comments ...................................................................................................... 126
4.3.1. Single line comment ............................................................................. 126
4.3.2. Multi-line comment ............................................................................... 127
4.4. Error Messages .............................................................................................. 127
4.4.1. Message format ................................................................................... 127
4.4.2. Error Messages Description .................................................................. 128
4.4.3. Other Messages ................................................................................... 132
4.5. Package ......................................................................................................... 132
4.5.1. import .................................................................................................. 133
4.5.2. global .................................................................................................. 134
4.6. Function ......................................................................................................... 135
4.7. Type Declaration ............................................................................................. 136
4.7.1. Declaring New Types ........................................................................... 137
4.7.2. Declaring Metadata .............................................................................. 139
4.7.3. Declaring Metadata for Existing Types ................................................... 143
4.7.4. Parametrized constructors for declared types ......................................... 144
4.7.5. Non Typesafe Classes .......................................................................... 144
4.7.6. Accessing Declared Types from the Application Code ............................. 144

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4.7.7. Type Declaration 'extends' .................................................................... 146
4.7.8. Traits ................................................................................................... 147
4.8. Rule ............................................................................................................... 150
4.8.1. Rule Attributes ..................................................................................... 151
4.8.2. Timers and Calendars .......................................................................... 155
4.8.3. Left Hand Side (when) syntax ............................................................... 156
4.8.4. The Right Hand Side (then) .................................................................. 207
4.8.5. A Note on Auto-boxing and Primitive Types ........................................... 210
4.9. Query ............................................................................................................. 210
4.10. Domain Specific Languages ........................................................................... 213
4.10.1. When to Use a DSL ........................................................................... 213
4.10.2. DSL Basics ........................................................................................ 214
4.10.3. Adding Constraints to Facts ................................................................ 216
4.10.4. Developing a DSL .............................................................................. 218
4.10.5. DSL and DSLR Reference .................................................................. 218
4.11. XML Rule Language ...................................................................................... 222
4.11.1. Legacy Drools 2.x XML rule format ...................................................... 222
4.11.2. When to use XML .............................................................................. 222
4.11.3. The XML format ................................................................................. 223
4.11.4. Legacy Drools 2.x XML rule format ...................................................... 228
4.11.5. Automatic transforming between formats (XML and DRL) ...................... 228
5. The Java Rule Engine API (JSR94) .......................................................................... 229
5.1. Introduction ..................................................................................................... 229
5.2. How To Use ................................................................................................... 229
5.2.1. Building and Registering RuleExecutionSets .......................................... 229
5.2.2. Using Stateful and Stateless RuleSessions ............................................ 231
5.3. References ..................................................................................................... 233
6. The Rule IDE (Eclipse) ............................................................................................. 235
6.1. Features Outline ............................................................................................. 236
6.2. Drools Runtimes ............................................................................................. 237
6.2.1. Defining a Drools Runtime .................................................................... 237
6.2.2. Selecting a runtime for your Drools project ............................................. 241
6.3. Creating a Rule Project ................................................................................... 243
6.4. Creating a New Rule and Wizards ................................................................... 246
6.5. Textual Rule Editor ......................................................................................... 247
6.6. Drools Views .................................................................................................. 249
6.6.1. The Working Memory View ................................................................... 249
6.6.2. The Agenda View ................................................................................. 250
6.6.3. The Global Data View .......................................................................... 250
6.6.4. The Audit View .................................................................................... 251
6.7. Domain Specific Languages ............................................................................ 253
6.7.1. Editing languages ................................................................................. 253
6.8. The Rete View ................................................................................................ 254
6.9. Large DRL Files .............................................................................................. 255

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6.10. Debugging Rules ........................................................................................... 256
6.10.1. Creating Breakpoints .......................................................................... 257
6.10.2. Debugging Rules ................................................................................ 257
6.11. Preferences .................................................................................................. 260
7. Examples ................................................................................................................. 263
7.1. Getting the Examples ...................................................................................... 263
7.2. Hello World ..................................................................................................... 263
7.3. State Example ................................................................................................ 269
7.3.1. Understanding the State Example ......................................................... 269
7.4. Fibonacci Example .......................................................................................... 276
7.5. Banking Tutorial .............................................................................................. 283
7.6. Pricing Rule Decision Table Example ............................................................... 297
7.6.1. Executing the example ......................................................................... 297
7.6.2. The decision table ................................................................................ 298
7.7. Pet Store Example .......................................................................................... 300
7.8. Honest Politician Example ............................................................................... 312
7.9. Sudoku Example ............................................................................................. 316
7.9.1. Sudoku Overview ................................................................................. 317
7.9.2. Running the Example ........................................................................... 317
7.9.3. Java Source and Rules Overview .......................................................... 321
7.9.4. Sudoku Validator Rules (validate.drl) ..................................................... 322
7.9.5. Sudoku Solving Rules (sudoku.drl) ........................................................ 323
7.10. Number Guess .............................................................................................. 324
7.11. Conway's Game Of Life ................................................................................. 331
7.12. Pong ............................................................................................................ 338
7.13. Adventures with Drools .................................................................................. 339
7.14. Wumpus World ............................................................................................. 340
7.15. Miss Manners and Benchmarking ................................................................... 343
7.15.1. Introduction ........................................................................................ 344
7.15.2. In depth Discussion ............................................................................ 347
7.15.3. Output Summary ................................................................................ 353

vi

Chapter 1.

Chapter 1. Introduction
1.1. Artificial Intelligence
1.1.1. A Little History
Over the last few decades artificial intelligence (AI) became an unpopular term, with the well know
"AI Winter" [http://en.wikipedia.org/wiki/AI_winter]. There were large boasts from scientists and
engineers looking for funding, that never lived up to expectations along with many failed projects.
Thinking Machines Corporation [http://en.wikipedia.org/wiki/Thinking_Machines_Corporation]
and the 5th Generation Computer [http://en.wikipedia.org/wiki/Fifth-generation_computer] (5GP)
project probably exemplify best the problems at the time.

Thinking Machines Corporation was one of the leading AI firms in 1990, it had sales of nearly $65
million. Here is quote from it's brochure:

“Some day we will build a thinking machine. It will be a truly intelligent machine. One that can see
and hear and speak. A machine that will be proud of us.”

Yet 5 years later it filed for Chapter 11. inc.com has a fascinating article titled "The Rise and Fall of
Thinking Machines" [http://www.inc.com/magazine/19950915/2622.html]. The article covers the
growth of the industry and how a cosy relationship with Thinking Machines and DARPA [http://
en.wikipedia.org/wiki/DARPA] over heated the market, to the point of collapse. It explains how
and why commerce moved away from AI and towards more practical number crunching super
computers.

The 5th Generation Computer project was a 400mill USD project in Japan to build a next
generation computer. Valves was first, transistors was second, integrated circuits was third and
finally microprocessors was fourth. This project spurred an "arms" race with the UK and USA,
that caused much of the AI bubble. The 5GP would provide massive multi-cpu parallel processing
hardware along with powerful knowledge representation and reasoning software via Prolog; a
type of expert system. By 1992 the project was considered a failure and cancelled. It was the
largest and most visible commercial venture for Prolog, and many of the failures are pinned on the
problems trying to run a logic based programming language concurrently on multi cpu hardware
with effective results. Some believe that the failure of the 5GP project tainted Prolog and resigned it
academia, see "Whatever Happened to Prolog" [http://www.dvorak.org/blog/whatever-happened-
to-prolog/] by John C. Dvorak.

However while research funding dried up and the term AI became less used, many green shoots
where planted and continued more quietly under discipline specific names: cognitive systems,
machine learning, intelligent systems, knowledge representation and reasoning. Offshoots of
these then made their way into commercial systems, such as expert systems in the Business
Rules Management System (BRMS) market.

Imperative, system based languages, languages such as C, C++, Java and .Net have dominated
the last 20 years. Enabled by the practicality of the languages and ability to run with good

1


performance on commodity hardware. However many believe there is renaissance undergoing in
the field of AI, spurred by advances in hardware capabilities and AI research. In 2005 Heather
Havenstein authored "Spring comes to AI winter" [http://www.computerworld.com/s/article/99691/
Spring_comes_to_AI_winter] which outlines a case for this resurgence, which she refers to as a
spring. Norvig and Russel dedicate several pages to what factors allowed the industry to over
come it's problems and the research that came about as a result:

“Recent years have seen a revolution in both the content and the methodology of work in artificial
intelligence. It is now more common to build on existing theories than to propose brand-new ones,
to base claims on rigorous theorems or hard experimental evidence rather than on intuition, and
to show relevance to real-world applications rather than toy examples.” (Artificial Intelligence : A
Modern Approach.)

Computer vision, neural networks, machine learning and knowledge representation and reasoning
(KRR) have made great strides in become practical in commercial environments. For example
vision based systems can now fully map out and navigate their environments with strong
recognition skills, as a result we now have self driving cars about to enter the commercial
market. Ontological research, based around description logic, has provided very rich semantics to
represent our world. Algorithms such as the tableaux algorithm have made it possible to effectively
use those rich semantics in large complex ontologies. Early KRR systems, like Prolog in 5GP,
were dogged by the limited semantic capabilities and memory restrictions on the size of those
ontologies.

1.1.2. Knowledge Representation and Reasoning
In A Little History talks about AI as a broader subject and touches on Knowledge Representation
and Reasoning (KRR) and also Expert Systems, I'll come back to Expert Systems later.

KRR is about how we represent our knowledge in symbolic form, i.e. how we describe something.
Reasoning is about how we go about the act of thinking using this knowledge. System based
languages, like Java or C+, have classification systems, called Classes, to be able to describe
things, in Java we calls these things beans or instances. However those classification systems
are limited to ensure computational efficiency. Over the years researchers have developed
increasingly sophisticated ways to represent our world, many of you may already have heard
of OWL (Web Ontology Language). Although there is always a gap between what we can be
theoretically represented and what can be used computationally in practically timely manner,
which is why OWL has different sub languages from Lite to Full. It is not believed that any
reasoning system can support OWL Full. Although Each year algorithmic advances try and narrow
that gap and improve expressiveness available to reasoning engines.

There are also many approaches to how these systems go about thinking. You may have heard
of discussions comparing the merits of forward chaining, which is reactive and data driven, or
backward chaining, which is passive and query driven. Many other types of reasoning techniques
exists, each of which enlarges the scope of the problems we can tackle declaratively. To list just a
few: imperfect reasoning (fuzzy logic, certainty factors), defeasible logic, belief systems, temporal
reasoning and correlation. Don't worry if some of those words look alien to you, they aren't needed

2

Rule Engines and Production Rule Systems (PRS)

to understand Drools and are just there to give an idea of the range of scope of research topics;
which is actually far more extensive than this small list and continues to grow as researches push
new boundaries.

KRR is often refereed as the core of Artificial Intelligence Even when using biological approaches
like neural networks, which model the brain and are more about pattern recognition than thinking,
they still build on KRR theory. My first endeavours with Drools were engineering oriented, as I
had no formal training or understanding of KRR. Learning KRR has allowed me to get a much
wider theoretical background. Allowing me to better understand both what I've done and where
I'm going, as it underpins nearly all of the theoretical side to our Drools R&D. It really is a vast and
fascinating subject that will pay dividends for those that take the time learn, I know it did and still
does for me. Bracham and Levesque have written a seminal piece of work, called "Knowledge
Representation and Reasoning" that for anyone wanting to build strong foundations is a must read.
I would also recommend the Russel and Norvig book "Artificial Intelligence, a modern approach"
which also covers KRR.

1.1.3. Rule Engines and Production Rule Systems (PRS)

We've now covered a brief history of AI and learnt that the core of AI is formed around KRR.
We've shown than KRR is vast and fascinating subject which forms the bulk of the theory driving
Drools R&D.

The rule engine is the computer program that delivers KRR functionality to the developer. At a
high level it has three components:

• Ontology

• Rules

• Data

As previous mentioned the ontology is the representation model we use for our "things". It could be
a simple records or Java classes or full blown OWL based ontologies. The Rules do the reasoning
and facilitate thinking. The distinction between rules and ontologies blurs a little with OWL based
ontologies, who's richness is rule based.

The term rule engine is quite ambiguous in that it can be any system that uses rules, in any form,
that can be applied to data to produce outcomes. This includes simple systems like form validation
and dynamic expression engines. The book "How to Build a Business Rules Engine (2004)" by
Malcolm Chisholm exemplifies this ambiguity. The book is actually about how to build and alter a
database schema to hold validation rules. The book then shows how to generate VB code from
those validation rules to validate data entry. Which while very valid, it is very different to what we
talking about so far.

Drools started life as a specific type of rule engine called a production rule system (PRS) and
was based around the Rete algorithm. The Rete algorithm, developed by Charles Forgey in 1979,

3


forms the brain of a Production Rules System and is able to scale to a large number of rules
and facts. A Production Rule is a two-part structure: the engine matches facts and data against
Production Rules - also called Productions or just Rules - to infer conclusions which result in
actions.

when
<conditions>
then
<actions>;

The process of matching the new or existing facts against Production Rules is called pattern
matching, which is performed by the inference engine. Actions execute in response to changes
in data, like a database trigger; we say this is a data driven approach to reasoning. The actions
themselves can change data, which in turn could match against other rules causing them to fire;
this is referred to asforward chaining

Drools implements and extends the Rete algorithm;. The Drools Rete implementation is called
ReteOO, signifying that Drools has an enhanced and optimized implementation of the Rete
algorithm for object oriented systems. Our more recent work goes well beyond Rete. Other
Rete based engines also have marketing terms for their proprietary enhancements to Rete, like
RetePlus and Rete III. Th e most common enhancements are covered in "Production Matching for
Large Learning Systems (Rete/UL)" (1995) by Robert B. Doorenbos. Leaps used to be provided
but was retired as it became unmaintained, the good news is our research is close to producing
an algorithm that merges the benefits of Leaps with Rete.

The Rules are stored in the Production Memory and the facts that the Inference Engine matches
against are kept in the Working Memory. Facts are asserted into the Working Memory where they
may then be modified or retracted. A system with a large number of rules and facts may result in
many rules being true for the same fact assertion; these rules are said to be in conflict. The Agenda
manages the execution order of these conflicting rules using a Conflict Resolution strategy.

4

Hybrid Reasoning Systems (HRS)

Figure 1.1. High-level View of a Production Rule System

1.1.4. Hybrid Reasoning Systems (HRS)

You may have read discussions comparing the merits of forward chaining (reactive and data
driven) or backward chaining(passive query). Here is a quick explanation of these two main types
of reasoning.

Forward chaining is "data-driven" and thus reactionary, with facts being asserted into working
memory, which results in one or more rules being concurrently true and scheduled for execution
by the Agenda. In short, we start with a fact, it propagates and we end in a conclusion.

5


Figure 1.2. Forward Chaining

Backward chaining is "goal-driven", meaning that we start with a conclusion which the engine tries
to satisfy. If it can't it then searches for conclusions that it can satisfy; these are known as subgoals,
that will help satisfy some unknown part of the current goal. It continues this process until either
the initial conclusion is proven or there are no more subgoals. Prolog is an example of a Backward
Chaining engine. Drools can also do backward chaining, which we refer to as derivation queries.

6

Hybrid Reasoning Systems (HRS)

Figure 1.3. Backward Chaining

7


Historically you would have to make a choice between systems like OPS5 (forward) or Prolog
(backward). Now many modern systems provide both types of reasoning capabilities. There are
also many other types of reasoning techniques, each of which enlarges the scope of the problems
we can tackle declaratively. To list just a few: imperfect reasoning (fuzzy logic, certainty factors),
defeasible logic, belief systems, temporal reasoning and correlation. Modern systems are merging
these capabilities, and others not listed, to create hybrid reasoning systems (HRS).

While Drools started out as a PRS, 5.x introduced Prolog style backward chaining reasoning as
well as some functional programming styles. For this reason HRS is now the preferred term when
referring to Drools, and what it is.

Drools current provides crisp reasoning, but imperfect reasoning is almost ready. Initially this will
be imperfect reasoning with fuzzy logic, later we'll add support for other types of uncertainty. Work
is also under way to bring OWL based ontological reasoning, which will integrate with our traits
system. We also continue to improve our functional programming capabilities.

1.1.5. Expert Systems

You will often hear the terms expert systems used to refer to production rule systems or Prolog like
systems. While this is normally acceptable, it's technically wrong as these are frameworks to build
expert systems with, and not actually expert systems themselves. It becomes an expert system
once there is an ontological model to represent the domain and there are facilities for knowledge
acquisition and explanation.

Mycin is the most famous expert system, built during the 70s. It is still heavily covered in academic
literature, such as the recommended book "Expert Systems" by Peter Jackson.

8

Recommended Reading

Figure 1.4. Early History of Expert Systems

1.1.6. Recommended Reading

General AI, KRR and Expert System Books

For those wanting to get a strong theoretical background in KRR and expert systems, I'd strongly
recommend the following books. "Artificial Intelligence: A Modern Approach" is must have, for
anyone's bookshelf.

• Introduction to Expert Systems

• Peter Jackson

• Expert Systems: Principles and Programming

9


• Joseph C. Giarratano, Gary D. Riley

• Knowledge Representation and Reasoning

• Ronald J. Brachman, Hector J. Levesque

• Artificial Intelligence : A Modern Approach.

• Stuart Russell and Peter Norvig

Figure 1.5. Recommended Reading

10

Recommended Reading

Papers

Here are some recommended papers that cover some interesting areas in rule engine research.

• Production Matching for Large Learning Systems : Rete/UL (1993)

• Robert B. Doorenbos

• Advances In Rete Pattern Matching

• Marshall Schor, Timothy P. Daly, Ho Soo Lee, Beth R. Tibbitts (AAAI 1986)

• Collection-Oriented Match

• Anurag Acharya and Milind Tambe (1993)

• The Leaps Algorithm (1990)

• Don Battery

• Gator: An Optimized Discrimination Network for Active Database Rule Condition Testing (1993)

• Eric Hanson , Mohammed S. Hasan

Drools Books

There are currently three Drools books, all from Packt Publishing.

• JBoss Drools Business Rules

• Paul Brown

• Drools JBoss Rules 5.0 Developers Guide

• Michali Bali

• Drools Developer's Cookbook

• Lucas Amador

11


Figure 1.6. Recommended Reading

1.2. Why use a Rule Engine?
Some frequently asked questions:

1. When should you use a rule engine?

2. What advantage does a rule engine have over hand coded "if...then" approaches?

12

Advantages of a Rule Engine

3. Why should you use a rule engine instead of a scripting framework, like BeanShell?

We will attempt to address these questions below.

1.2.1. Advantages of a Rule Engine

• Declarative Programming

Rule engines allow you to say "What to do", not "How to do it".

The key advantage of this point is that using rules can make it easy to express solutions to
difficult problems and consequently have those solutions verified. Rules are much easier to
read than code.

Rule systems are capable of solving very, very hard problems, providing an explanation of how
the solution was arrived at and why each "decision" along the way was made (not so easy with
other of AI systems like neural networks or the human brain - I have no idea why I scratched
the side of the car).

• Logic and Data Separation

Your data is in your domain objects, the logic is in the rules. This is fundamentally breaking the
OO coupling of data and logic, which can be an advantage or a disadvantage depending on
your point of view. The upshot is that the logic can be much easier to maintain as there are
changes in the future, as the logic is all laid out in rules. This can be especially true if the logic
is cross-domain or multi-domain logic. Instead of the logic being spread across many domain
objects or controllers, it can all be organized in one or more very distinct rules files.

• Speed and Scalability

The Rete algorithm,the Leaps algorithm, and their descendants such as Drools' ReteOO,
provide very efficient ways of matching rule patterns to your domain object data. These are
especially efficient when you have datasets that change in small portions as the rule engine
can remember past matches. These algorithms are battle proven.

• Centralization of Knowledge

By using rules, you create a repository of knowledge (a knowledge base) which is executable.
This means it's a single point of truth, for business policy, for instance. Ideally rules are so
readable that they can also serve as documentation.

• Tool Integration

Tools such as Eclipse (and in future, Web based user interfaces) provide ways to edit and
manage rules and get immediate feedback, validation and content assistance. Auditing and
debugging tools are also available.

• Explanation Facility

13


Rule systems effectively provide an "explanation facility" by being able to log the decisions made
by the rule engine along with why the decisions were made.

• Understandable Rules

By creating object models and, optionally, Domain Specific Languages that model your problem
domain you can set yourself up to write rules that are very close to natural language. They lend
themselves to logic that is understandable to, possibly nontechnical, domain experts as they
are expressed in their language, with all the program plumbing, the technical know-how being
hidden away in the usual code.

1.2.2. When should you use a Rule Engine?

The shortest answer to this is "when there is no satisfactory traditional programming approach to
solve the problem.". Given that short answer, some more explanation is required. The reason why
there is no "traditional" approach is possibly one of the following:

• The problem is just too fiddle for traditional code.

The problem may not be complex, but you can't see a non-fragile way of building a solution for it.

• The problem is beyond any obvious algorithmic solution.

It is a complex problem to solve, there are no obvious traditional solutions, or basically the
problem isn't fully understood.

• The logic changes often

The logic itself may even be simple but the rules change quite often. In many organizations
software releases are few and far between and pluggable rules can help provide the "agility"
that is needed and expected in a reasonably safe way.

• Domain experts (or business analysts) are readily available, but are nontechnical.

Domain experts often possess a wealth of knowledge about business rules and processes. They
typically are nontechnical, but can be very logical. Rules can allow them to express the logic in
their own terms. Of course, they still have to think critically and be capable of logical thinking.
Many people in nontechnical positions do not have training in formal logic, so be careful and
work with them, as by codifying business knowledge in rules, you will often expose holes in the
way the business rules and processes are currently understood.

If rules are a new technology for your project teams, the overhead in getting going must be factored
in. It is not a trivial technology, but this document tries to make it easier to understand.

Typically in a modern OO application you would use a rule engine to contain key parts of your
business logic, especially the really messy parts. This is an inversion of the OO concept of

14

When not to use a Rule Engine

encapsulating all the logic inside your objects. This is not to say that you throw out OO practices,
on the contrary in any real world application, business logic is just one part of the application.
If you ever notice lots of conditional statements such as "if" and "switch", an overabundance of
strategy patterns and other messy logic in your code that just doesn't feel right: that would be a
place for rules. If there is some such logic and you keep coming back to fix it, either because you
got it wrong, or the logic or your understanding changes: think about using rules. If you are faced
with tough problems for which there are no algorithms or patterns: consider using rules.

Rules could be used embedded in your application or perhaps as a service. Often a rule engine
works best as "stateful" component, being an integral part of an application. However, there have
been successful cases of creating reusable rule services which are stateless.

For your organization it is important to decide about the process you will use for updating rules in
systems that are in production. The options are many, but different organizations have different
requirements. Frequently, rules maintenance is out of the control of the application vendors or
project developers.

1.2.3. When not to use a Rule Engine

To quote a Drools mailing list regular:

It seems to me that in the excitement of working with rules engines, that people
forget that a rules engine is only one piece of a complex application or solution.
Rules engines are not really intended to handle workflow or process executions
nor are workflow engines or process management tools designed to do rules. Use
the right tool for the job. Sure, a pair of pliers can be used as a hammering tool
in a pinch, but that's not what it's designed for.
—Dave Hamu

As rule engines are dynamic (dynamic in the sense that the rules can be stored and managed
and updated as data), they are often looked at as a solution to the problem of deploying software.
(Most IT departments seem to exist for the purpose of preventing software being rolled out.) If this
is the reason you wish to use a rule engine, be aware that rule engines work best when you are
able to write declarative rules. As an alternative, you can consider data-driven designs (lookup
tables), or script processing engines where the scripts are managed in a database and are able
to be updated on the fly.

1.2.4. Scripting or Process Engines

Hopefully the preceding sections have explained when you may want to use a rule engine.

Alternatives are script-based engines that provide the drive for "changes on the fly", and there
are many such solutions.

Alternatively Process Engines (also capable of workflow) such as jBPM allow you to graphically
(or programmatically) describe steps in a process. Those steps can also involve decision points

15


which are in themselves a simple rule. Process engines and rules often can work nicely together,
so they are not mutually exclusive.

One key point to note with rule engines is that some rule engines are really scripting engines.
The downside of scripting engines is that you are tightly coupling your application to the scripts. If
they are rules, you are effectively calling rules directly and this may cause more difficulty in future
maintenance, as they tend to grow in complexity over time. The upside of scripting engines is that
they can be easier to implement initially, producing results quickly, and are conceptually simpler
for imperative programmers.

Many people have also implemented data-driven systems successfully in the past (where there
are control tables that store meta-data that changes your applications behavior) - these can work
well when the control can remain very limited. However, they can quickly grow out of control if
extended too much (such that only the original creators can change the applications behavior) or
they cause the application to stagnate as they are too inflexible.

1.2.5. Strong and Loose Coupling

No doubt you have heard terms like "tight coupling" and "loose coupling" in systems design.
Generally people assert that "loose" or "weak" coupling is preferable in design terms, due to the
added flexibility it affords. Similarly, you can have "strongly coupled" and "weakly coupled" rules.
Strongly coupled in this sense means that one rule "firing" will clearly result in another rule firing,
and so on; in other words, there is a clear (probably obvious) chain of logic. If your rules are all
strongly coupled, the chances are that the will turn out to be inflexible, and, more significantly, that
a rule engine is an overkill. A clear chain can be hard coded, or implemented using a Decision
Tree. This is not to say that strong coupling is inherently bad, but it is a point to keep in mind
when considering a rule engine and the way you capture the rules. "Loosely" coupled rules should
result in a system that allows rules to be changed, removed and added without requiring changes
to other, unrelated rules.

1.3. Rete Algorithm
The Rete algorithm was invented by Dr. Charles Forgy and documented in his PhD thesis in
1978-79. A simplified version of the paper was published in 1982 (http://citeseer.ist.psu.edu/
context/505087/0). The latin word "rete" means "net" or "network". The Rete algorithm can be
broken into 2 parts: rule compilation and runtime execution.

The compilation algorithm describes how the Rules in the Production Memory are processed to
generate an efficient discrimination network. In non-technical terms, a discrimination network is
used to filter data as it propagates through the network. The nodes at the top of the network would
have many matches, and as we go down the network, there would be fewer matches. At the very
bottom of the network are the terminal nodes. In Dr. Forgy's 1982 paper, he described 4 basic
nodes: root, 1-input, 2-input and terminal.

16

Rete Algorithm

Figure 1.7. Rete Nodes

The root node is where all objects enter the network. From there, it immediately goes to the
ObjectTypeNode. The purpose of the ObjectTypeNode is to make sure the engine doesn't do
more work than it needs to. For example, say we have 2 objects: Account and Order. If the rule
engine tried to evaluate every single node against every object, it would waste a lot of cycles. To
make things efficient, the engine should only pass the object to the nodes that match the object
type. The easiest way to do this is to create an ObjectTypeNode and have all 1-input and 2-input
nodes descend from it. This way, if an application asserts a new Account, it won't propagate to
the nodes for the Order object. In Drools when an object is asserted it retrieves a list of valid
ObjectTypesNodes via a lookup in a HashMap from the object's Class; if this list doesn't exist
it scans all the ObjectTypeNodes finding valid matches which it caches in the list. This enables
Drools to match against any Class type that matches with an instanceof check.

17


Figure 1.8. ObjectTypeNodes

ObjectTypeNodes can propagate to AlphaNodes, LeftInputAdapterNodes and BetaNodes.
AlphaNodes are used to evaluate literal conditions. Although the 1982 paper only covers equality
conditions, many RETE implementations support other operations. For example, Account.name
== "Mr Trout" is a literal condition. When a rule has multiple literal conditions for a single object
type, they are linked together. This means that if an application asserts an Account object, it must
first satisfy the first literal condition before it can proceed to the next AlphaNode. In Dr. Forgy's
paper, he refers to these as IntraElement conditions. The following diagram shows the AlphaNode
combinations for Cheese( name == "cheddar", strength == "strong" ):

Figure 1.9. AlphaNodes

18

Rete Algorithm

Drools extends Rete by optimizing the propagation from ObjectTypeNode to AlphaNode using
hashing. Each time an AlphaNode is added to an ObjectTypeNode it adds the literal value as a key
to the HashMap with the AlphaNode as the value. When a new instance enters the ObjectType
node, rather than propagating to each AlphaNode, it can instead retrieve the correct AlphaNode
from the HashMap,thereby avoiding unnecessary literal checks.

There are two two-input nodes, JoinNode and NotNode, and both are types of BetaNodes.
BetaNodes are used to compare 2 objects, and their fields, to each other. The objects may be the
same or different types. By convention we refer to the two inputs as left and right. The left input for
a BetaNode is generally a list of objects; in Drools this is a Tuple. The right input is a single object.
Two Nodes can be used to implement 'exists' checks. BetaNodes also have memory. The left
input is called the Beta Memory and remembers all incoming tuples. The right input is called the
Alpha Memory and remembers all incoming objects. Drools extends Rete by performing indexing
on the BetaNodes. For instance, if we know that a BetaNode is performing a check on a String
field, as each object enters we can do a hash lookup on that String value. This means when facts
enter from the opposite side, instead of iterating over all the facts to find valid joins, we do a lookup
returning potentially valid candidates. At any point a valid join is found the Tuple is joined with the
Object; which is referred to as a partial match; and then propagated to the next node.

19


Figure 1.10. JoinNode

To enable the first Object, in the above case Cheese, to enter the network we use a
LeftInputNodeAdapter - this takes an Object as an input and propagates a single Object Tuple.

Terminal nodes are used to indicate a single rule having matched all its conditions; at this point we
say the rule has a full match. A rule with an 'or' conditional disjunctive connective results in subrule
generation for each possible logically branch; thus one rule can have multiple terminal nodes.

Drools also performs node sharing. Many rules repeat the same patterns, and node sharing allows
us to collapse those patterns so that they don't have to be re-evaluated for every single instance.
The following two rules share the first pattern, but not the last:

rule
when
Cheese( $cheddar : name == "cheddar" )
$person : Person( favouriteCheese == $cheddar )

20

Rete Algorithm

then
System.out.println( $person.getName() + " likes cheddar" );
end

rule
when
Cheese( $cheddar : name == "cheddar" )
$person : Person( favouriteCheese != $cheddar )
then
System.out.println( $person.getName() + " does not like cheddar" );
end

As you can see below, the compiled Rete network shows that the alpha node is shared, but the
beta nodes are not. Each beta node has its own TerminalNode. Had the second pattern been the
same it would have also been shared.

21


Figure 1.11. Node Sharing

22

Chapter 2.

Chapter 2. User Guide
2.1. The Basics

2.1.1. Stateless Knowledge Session
So where do we get started, there are so many use cases and so much functionality in a rule engine
such as Drools that it becomes beguiling. Have no fear my intrepid adventurer, the complexity is
layered and you can ease yourself into with simple use cases.

Stateless session, not utilising inference, forms the simplest use case. A stateless session can be
called like a function passing it some data and then receiving some results back. Some common
use cases for stateless sessions are, but not limited to:

• Validation

• Is this person eligible for a mortgage?

• Calculation

• Compute a mortgage premium.

• Routing and Filtering

• Filter incoming messages, such as emails, into folders.

• Send incoming messages to a destination.

So let's start with a very simple example using a driving license application.

public class Applicant {
private String name;
private int age;
private boolean valid;
// getter and setter methods here
}

Now that we have our data model we can write our first rule. We assume that the application uses
rules to refute invalid applications. As this is a simple validation use case we will add a single rule
to disqualify any applicant younger than 18.

package com.company.license

rule "Is of valid age"

23


when
$a : Applicant( age < 18 )
then
$a.setValid( false );
end

To make the engine aware of data, so it can be processed against the rules, we have to insert
the data, much like with a database. When the Applicant instance is inserted into the engine it
is evaluated against the constraints of the rules, in this case just two constraints for one rule.
We say two because the type Applicant is the first object type constraint, and age < 18 is the
second field constraint. An object type constraint plus its zero or more field constraints is referred
to as a pattern. When an inserted instance satisfies both the object type constraint and all the field
constraints, it is said to be matched. The $a is a binding variable which permits us to reference the
matched object in the consequence. There its properties can be updated. The dollar character ('$')
is optional, but it helps to differentiate variable names from field names. The process of matching
patterns against the inserted data is, not surprisingly, often referred to as pattern matching.

Let's assume that the rules are in the same folder as the classes, so we can use the classpath
resource loader to build our first KnowledgeBase. A Knowledge Base is what we call our
collection of compiled definitions, such as rules and processes, which are compiled using the
KnowledgeBuilder. Both the KnowledgeBuilder and KnowledgeBase can be created from the
factories KnowledgeBuilderFactory and KnowledgeBaseFactory.

KnowledgeBuilder kbuilder = KnowledgeBuilderFactory.newKnowledgeBuilder();
kbuilder.add( ResourceFactory.newClassPathResource( "licenseApplication.drl", getClass() ),
ResourceType.DRL );
if ( kbuilder.hasErrors() ) {
System.err.println( kbuilder.getErrors().toString() );
}
KnowledgeBase kbase = KnowledgeBaseFactory.newKnowledgeBase();
kbase.addKnowledgePackages( kbuilder.getKnowledgePackages() );

The above code snippet looks on the classpath for the licenseApplication.drl file, using the
method newClassPathResource(). The resource type is DRL, short for "Drools Rule Language".
Once the DRL file has been added we can check the Knowledge Builder object for any errors.
If there are no errors, we can add the resulting packages to our Knowledge Base. Now we are
ready to build our session and execute against some data:

StatelessKnowledgeSession ksession = kbase.newStatelessKnowledgeSession();
Applicant applicant = new Applicant( "Mr John Smith", 16 );
assertTrue( applicant.isValid() );
ksession.execute( applicant );
assertFalse( applicant.isValid() );

24

Stateless Knowledge Session

The preceding code executes the data against the rules. Since the applicant is under the age of
18, the application is marked as invalid.

So far we've only used a single instance, but what if we want to use more than one? We can
execute against any object implementing Iterable, such as a collection. Let's add another class
called Application, which has the date of the application, and we'll also move the boolean valid
field to the Application class.

public class Applicant {
private int age;
}

public class Application {
private Date dateApplied;
private boolean valid;
}

We can also add another rule to validate that the application was made within a period of time.

package com.company.license

rule "Is of valid age"
when
Applicant( age < 18 )
$a : Application()
then
end

rule "Application was made this year"
when
$a : Application( dateApplied > "01-jan-2009" )
then
end

Unfortunately a Java array does not implement the Iterable interface, so we have to use the JDK
converter method Arrays.asList(...). The code shown below executes against an iterable list,
where all collection elements are inserted before any matched rules are fired.


25


Applicant applicant = new Applicant( "Mr John Smith", 16 );
Application application = new Application();
assertTrue( application.isValid() );
ksession.execute( Arrays.asList( new Object[] { application, applicant } ) );
assertFalse( application.isValid() );

The two execute methods execute(Object object) and execute(Iterable objects) are
actually convenience methods for the interface BatchExecutor's method execute(Command
command).

A CommandFactory is used to create commands, so that the following is equivalent to
execute(Iterable it):

ksession.execute( CommandFactory.newInsertIterable( new Object[] { application, applicant } ) )

Batch Executor and Command Factory are particularly useful when working with multiple
Commands and with output identifiers for obtaining results.

List<Command> cmds = new ArrayList<Command>();
cmds.add( CommandFactory.newInsert( new Person( "Mr John Smith" ), "mrSmith" );
cmds.add( CommandFactory.newInsert( new Person( "Mr John Doe" ), "mrDoe" );
BatchExecutionResults results = ksession.execute( CommandFactory.newBatchExecution( cmds ) );
assertEquals( new Person( "Mr John Smith" ), results.getValue( "mrSmith" ) );

CommandFactory supports many other Commands that can be used in the BatchExecutor like
StartProcess, Query, and SetGlobal.

2.1.2. Stateful Knowledge Session
Stateful Sessions are longer lived and allow iterative changes over time. Some common use cases
for Stateful Sessions are, but not limited to:

• Monitoring

• Stock market monitoring and analysis for semi-automatic buying.

• Diagnostics

• Fault finding, medical diagnostics

• Logistics

• Parcel tracking and delivery provisioning

• Compliance

26

Stateful Knowledge Session

• Validation of legality for market trades.

In contrast to a Stateless Session, the dispose() method must be called afterwards to ensure
there are no memory leaks, as the Knowledge Base contains references to Stateful Knowledge
Sessions when they are created. StatefulKnowledgeSession also supports the BatchExecutor
interface, like StatelessKnowledgeSession, the only difference being that the FireAllRules
command is not automatically called at the end for a Stateful Session.

We illustrate the monitoring use case with an example for raising a fire alarm. Using just four
classes, we represent rooms in a house, each of which has one sprinkler. If a fire starts in a room,
we represent that with a single Fire instance.

public class Room {
private String name
}
public class Sprinkler {
private Room room;
private boolean on;
}
public class Fire {
private Room room;
}
public class Alarm {
}

In the previous section on Stateless Sessions the concepts of inserting and matching against data
were introduced. That example assumed that only a single instance of each object type was ever
inserted and thus only used literal constraints. However, a house has many rooms, so rules must
express relationships between objects, such as a sprinkler being in a certain room. This is best
done by using a binding variable as a constraint in a pattern. This "join" process results in what
is called cross products, which are covered in the next section.

When a fire occurs an instance of the Fire class is created, for that room, and inserted into the
session. The rule uses a binding on the room field of the Fire object to constrain matching to
the sprinkler for that room, which is currently off. When this rule fires and the consequence is
executed the sprinkler is turned on.

rule "When there is a fire turn on the sprinkler"
when
Fire($room : room)
$sprinkler : Sprinkler( room == $room, on == false )
then

27


modify( $sprinkler ) { setOn( true ) };
System.out.println( "Turn on the sprinkler for room " + $room.getName() );
end

Whereas the Stateless Session uses standard Java syntax to modify a field, in the above rule
we use the modify statement, which acts as a sort of "with" statement. It may contain a series
of comma separated Java expressions, i.e., calls to setters of the object selected by the modify
statement's control expression. This modifies the data, and makes the engine aware of those
changes so it can reason over them once more. This process is called inference, and it's essential
for the working of a Stateful Session. Stateless Sessions typically do not use inference, so the
engine does not need to be aware of changes to data. Inference can also be turned off explicitly
by using the sequential mode.

So far we have rules that tell us when matching data exists, but what about when it does not exist?
How do we determine that a fire has been extinguished, i.e., that there isn't a Fire object any
more? Previously the constraints have been sentences according to Propositional Logic, where
the engine is constraining against individual instances. Drools also has support for First Order
Logic that allows you to look at sets of data. A pattern under the keyword not matches when
something does not exist. The rule given below turns the sprinkler off as soon as the fire in that
room has disappeared.

rule "When the fire is gone turn off the sprinkler"
when
$room : Room( )
$sprinkler : Sprinkler( room == $room, on == true )
not Fire( room == $room )
then
modify( $sprinkler ) { setOn( false ) };
System.out.println( "Turn off the sprinkler for room " + $room.getName() );
end

While there is one sprinkler per room, there is just a single alarm for the building. An Alarm object
is created when a fire occurs, but only one Alarm is needed for the entire building, no matter how
many fires occur. Previously not was introduced to match the absence of a fact; now we use its
complement exists which matches for one or more instances of some category.

rule "Raise the alarm when we have one or more fires"
when
exists Fire()
then
insert( new Alarm() );
System.out.println( "Raise the alarm" );
end

28

Stateful Knowledge Session

Likewise, when there are no fires we want to remove the alarm, so the not keyword can be used
again.

rule "Cancel the alarm when all the fires have gone"
when
not Fire()
$alarm : Alarm()
then
retract( $alarm );
System.out.println( "Cancel the alarm" );
end

Finally there is a general health status message that is printed when the application first starts
and after the alarm is removed and all sprinklers have been turned off.

rule "Status output when things are ok"
when
not Alarm()
not Sprinkler( on == true )
then
System.out.println( "Everything is ok" );
end

The above rules should be placed in a single DRL file and saved to some directory on the classpath
and using the file name fireAlarm.drl, as in the Stateless Session example. We can then build
a Knowledge Base, as before, just using the new name fireAlarm.drl. The difference is that
this time we create a Stateful Session from the Knowledge Base, whereas before we created a
Stateless Session.

kbuilder.add( ResourceFactory.newClassPathResource( "fireAlarm.drl", getClass() ),
ResourceType.DRL );
System.err.println( kbuilder.getErrors().toString() );
}
StatefulKnowledgeSession ksession = kbase.newStatefulKnowledgeSession();

With the session created it is now possible to iteratively work with it over time. Four Room objects
are created and inserted, as well as one Sprinkler object for each room. At this point the engine
has done all of its matching, but no rules have fired yet. Calling ksession.fireAllRules() allows
the matched rules to fire, but without a fire that will just produce the health message.

29


String[] names = new String[]{"kitchen", "bedroom", "office", "livingroom"};
Map<String,Room> name2room = new HashMap<String,Room>();
for( String name: names ){
Room room = new Room( name );
name2room.put( name, room );
ksession.insert( room );
Sprinkler sprinkler = new Sprinkler( room );
ksession.insert( sprinkler );
}

ksession.fireAllRules();

> Everything is ok

We now create two fires and insert them; this time a reference is kept for the returned FactHandle.
A Fact Handle is an internal engine reference to the inserted instance and allows instances to be
retracted or modified at a later point in time. With the fires now in the engine, once fireAllRules()
is called, the alarm is raised and the respective sprinklers are turned on.

Fire kitchenFire = new Fire( name2room.get( "kitchen" ) );
Fire officeFire = new Fire( name2room.get( "office" ) );

FactHandle kitchenFireHandle = ksession.insert( kitchenFire );
FactHandle officeFireHandle = ksession.insert( officeFire );


> Raise the alarm
> Turn on the sprinkler for room kitchen
> Turn on the sprinkler for room office

After a while the fires will be put out and the Fire instances are retracted. This results in the
sprinklers being turned off, the alarm being cancelled, and eventually the health message is printed
again.

ksession.retract( kitchenFireHandle );
ksession.retract( officeFireHandle );


30

Methods versus Rules

> Cancel the alarm
> Turn off the sprinkler for room office
> Turn off the sprinkler for room kitchen
> Everything is ok

Everyone still with me? That wasn't so hard and already I'm hoping you can start to see the value
and power of a declarative rule system.

2.1.3. Methods versus Rules

People often confuse methods and rules, and new rule users regular ask, "How do I call a rule?"
After the last section, you are now feeling like a rule expert and the answer to that is obvious, but
let's summarize the differences nonetheless.

public void helloWorld(Person person) {
if ( person.getName().equals( "Chuck" ) ) {
System.out.println( "Hello Chuck" );
}
}

• Methods are called directly.

• Specific instances are passed.

• One call results in a single execution.

rule "Hello World"
when
Person( name == "Chuck" )
then
System.out.println( "Hello Chuck" );
end

• Rules execute by matching against any data as long it is inserted into the engine.

• Rules can never be called directly.

• Specific instances cannot be passed to a rule.

• Depending on the matches, a rule may fire once or several times, or not at all.

31


2.1.4. Cross Products

Earlier the term "cross product" was mentioned, which is the result of a join. Imagine for a moment
that the data from the fire alarm example were used in combination with the following rule where
there ar no field constraints:

rule
when
$room : Room()
$sprinkler : Sprinkler()
then
System.out.println( "room:" + $room.getName() +
" sprinkler:" + $sprinkler.getRoom().getName() );
end

In SQL terms this would be like doing select * from Room, Sprinkler and every row in the
Room table would be joined with every row in the Sprinkler table resulting in the following output:

room:office sprinkler:office
room:office sprinkler:kitchen
room:office sprinkler:livingroom
room:office sprinkler:bedroom
room:kitchen sprinkler:office
room:kitchen sprinkler:kitchen
room:kitchen sprinkler:livingroom
room:kitchen sprinkler:bedroom
room:livingroom sprinkler:office
room:livingroom sprinkler:kitchen
room:livingroom sprinkler:livingroom
room:livingroom sprinkler:bedroom
room:bedroom sprinkler:office
room:bedroom sprinkler:kitchen
room:bedroom sprinkler:livingroom
room:bedroom sprinkler:bedroom

These cross products can obviously become huge, and they may very well contain spurious data.
The size of cross products is often the source of performance problems for new rule authors. From
this it can be seen that it's always desirable to constrain the cross products, which is done with
the variable constraint.

rule
when
$room : Room()
$sprinkler : Sprinkler( room == $room )

32

Inference

then
System.out.println( "room:" + $room.getName() +
" sprinkler:" + $sprinkler.getRoom().getName() );
end

This results in just four rows of data, with the correct Sprinkler for each Room. In SQL (actually
HQL) the corresponding query would be select * from Room, Sprinkler where Room ==
Sprinkler.room.

room:office sprinkler:office
room:kitchen sprinkler:kitchen
room:livingroom sprinkler:livingroom
room:bedroom sprinkler:bedroom

2.2. Inference

2.2.1. Buss Pass Example

Inference has a bad names these days, as something not relevant to business use cases and
just too complicated to be useful. It is true that contrived and complicated examples occur with
inference, but that should not detract from the fact that simple and useful ones exist too. But more
than this, correct use of inference can crate more agile and less error prone businesses with easier
to maintain software.

So what is inference? Something is inferred when we gain knowledge of something from using
previous knowledge. For example given a Person fact with an age field and a rule that provides
age policy control, we can infer whether a Person is an adult or a child and act on this.

rule "Infer Adult"
when
$p : Person( age >= 18 )
then
insert( new IsAdult( $p ) )
end

So in the above every Person who is 18 or over will have an instance of IsAdult inserted for them.
This fact is special in that it is known as a relation. We can use this inferred relation in any rule:

$p : Person()
IsAdult( person == $p )

33


So now we know what inference is, and have a basic example, how does this facilitate good rule
design and maintenance?

Let's take a government department that are responsible for issuing ID cards when children
become adults, hence forth referred to as ID department. They might have a decision table that
includes logic like this, which says when an adult living in london is 18 or over, issue the card:

However the ID department does not set the policy on who an adult is. That's done at a central
government level. If the central government where to change that age to 21 there is a change
management process. Someone has to liaise with the ID department and make sure their systems
are updated, in time for the law going live.

This change management process and communication between departments is not ideal for
an agile environment and change become costly and error prone. Also the card department is
managing more information than it needs to be aware of with its "monolithic" approach to rules
management which is "leaking" information better placed else where. By this I mean that it doesn't
care what explicit "age >= 18" information determines whether someone is an adult, only that they
are an adult.

Instead what if we were to split (de-couple) the authoring responsibility, so the central government
maintains its rules and the ID department maintains its.

So its the central governments job to determine who is an adult and if they change the law they
just update their central repository with the new rules, which others use:

The IsAdult fact, as discussed previously, is inferred from the policy rules. It encapsulates the
seemingly arbitrary piece of logic "age >= 18" and provides semantic abstractions for it's meaning.
Now if anyone uses the above rules, they no longer need to be aware of explicit information that
determines whether someone is an adult or not. They can just use the inferred fact:

34

Truth Maintenance with Logical Objects

While the example is very minimal and trivial it illustrates some important points. We started with
a monolithic and leaky approach to our knowledge engineering. We create a single decision table
that had all possible information in it that leaks information from central government that the ID
department did not care about and did not want to manage.

We first de-coupled the knowledge process so each department was responsible for only what it
needed to know. We then encapsulated this leaky knowledge using an inferred fact IsAdult. The
use of the term IsAdult also gave a semantic abstraction to the previously arbitrary logic "age >=
18".

So a general rule or thumb when doing your knowledge engineering is:

• Bad

• Monolithic

• Leaky

• Good

• De-couple knowledge responsibilities

• Encapsulate knowledge

• Provide semantic abstractions for those encapsulations

2.3. Truth Maintenance with Logical Objects
2.3.1. Overview
After regular inserts you have to retract facts explicitly. With logical assertions, the fact that was
asserted will be automatically retracted when the conditions that asserted it in the first place are
no longer true. Actually, it's even cleverer then that, because it will be retracted only if there isn't
any single condition that supports the logical assertion.

Normal insertions are said to be stated, i.e., just like the intuitive meaning of "stating a fact" implies.
Using a HashMap and a counter, we track how many times a particular equality is stated; this
means we count how many different instances are equal.

35


When we logically insert an object during a RHS execution we are said to justify it, and it is
considered to be justified by the firing rule. For each logical insertion there can only be one equal
object, and each subsequent equal logical insertion increases the justification counter for this
logical assertion. A justification is removed by the LHS of the creating rule becoming untrue, and
the counter is decreased accordingly. As soon as we have no more justifications the logical object
is automatically retracted.

If we try to logically insert an object when there is an equal stated object, this will fail and return
null. If we state an object that has an existing equal object that is justified we override the Fact;
how this override works depends on the configuration setting WM_BEHAVIOR_PRESERVE. When the
property is set to discard we use the existing handle and replace the existing instance with the
new Object, which is the default behavior; otherwise we override it to stated but we create an
new FactHandle.

This can be confusing on a first read, so hopefully the flow charts below help. When it says that it
returns a new FactHandle, this also indicates the Object was propagated through the network.

36

Overview

Figure 2.1. Stated Insertion

37


Figure 2.2. Logical Insertion

2.3.1.1. Bus Pass Example With Inference and TMS

The previous example was issuing ID cards to over 18s, in this example we now issue bus passes,
either a child or adult pass.

rule "Issue Child Bus Pass" when
$p : Person( age < 16 )
then
insert(new ChildBusPass( $p ) );
end

rule "Issue Adult Bus Pass" when
then
insert(new AdultBusPass( $p ) );

38

Overview

end

As before the above example is considered monolithic, leaky and providing poor separation of
concerns.

As before we can provide a more robust application with a separation of concerns using inference.
Notice this time we don't just insert the inferred object, we use "insertLogical":

rule "Infer Child" when
$p : Person( age < 16 )
then
insertLogical( new IsChild( $p ) )
end
rule "Infer Adult" when
then
insertLogical( new IsAdult( $p ) )
end

A "insertLogical" is part of the Drools Truth Maintenance System (TMS). Here the fact is logically
inserted, this fact is dependant on the truth of the "when" clause. It means that when the rule
becomes false the fact is automatically retracted. This works particularly well as the two rules are
mutually exclusive. So in the above rules if the person is under 16 it inserts an IsChild fact, once
the person is 16 or over the IsChild fact is automatically retracted and the IsAdult fact inserted.

We can now bring back in the code to issue the passes, these two can also be logically inserted,
as the TMS supports chaining of logical insertions for a cascading set of retracts.

rule "Issue Child Bus Pass" when
$p : Person( )
IsChild( person == $p )
then
insertLogical(new ChildBusPass( $p ) );
end

rule "Issue Adult Bus Pass" when
IsAdult( person =$p )
then
insertLogical(new AdultBusPass( $p ) );
end

Now when the person changes from being 15 to 16, not only is the IsChild fact automatically
retracted, so is the person's ChildBusPass fact. For bonus points we can combine this with the

39


'not' conditional element to handle notifications, in this situation a request for the returning of the
pass. So when the TMS automatically retracts the ChildBusPass object, this rule triggers and
sends a request to the person:

rule "Return ChildBusPass Request "when
$p : Person( )
not( ChildBusPass( person == $p ) )
then
requestChildBusPass( $p );
end

2.3.1.2. Lazy Truth Maintenance

You no longer need to enable or disable truth maintenance, via the kbase configuration. It is
now handled automatically and turned on only when needed. This was done along with the code
changes so that all entry points use the same code, previous to this the default entry point and
named entry points used different code, to avoid TMS overhead for event processing.

2.3.1.3. Important note: Equality for Java objects

It is important to note that for Truth Maintenance (and logical assertions) to work at all, your
Fact objects (which may be JavaBeans) must override equals and hashCode methods (from
java.lang.Object) correctly. As the truth maintenance system needs to know when two different
physical objects are equal in value, both equals and hashCode must be overridden correctly, as
per the Java standard.

Two objects are equal if and only if their equals methods return true for each other and if their
hashCode methods return the same values. See the Java API for more details (but do keep in
mind you MUST override both equals and hashCode).

TMS behaviour is not affected by theruntime configuration of Identity vs Equality, TMS is always
equality.

2.4. Decision Tables in Spreadsheets
Decision tables are a "precise yet compact" (ref. Wikipedia) way of representing conditional logic,
and are well suited to business level rules.

Drools supports managing rules in a spreadsheet format. Supported formats are Excel (XLS),
and CSV, which means that a variety of spreadsheet programs (such as Microsoft Excel,
OpenOffice.org Calc amongst others) can be utilized. It is expected that web based decision table
editors will be included in a near future release.

Decision tables are an old concept (in software terms) but have proven useful over the years. Very
briefly speaking, in Drools decision tables are a way to generate rules driven from the data entered

40

When to Use Decision Tables

into a spreadsheet. All the usual features of a spreadsheet for data capture and manipulation can
be taken advantage of.

2.4.1. When to Use Decision Tables

Consider decision tables as a course of action if rules exist that can be expressed as rule templates
and data: each row of a decision table provides data that is combined with a template to generate
a rule.

Many businesses already use spreadsheets for managing data, calculations, etc. If you are happy
to continue this way, you can also manage your business rules this way. This also assumes you are
happy to manage packages of rules in .xls or .csv files. Decision tables are not recommended
for rules that do not follow a set of templates, or where there are a small number of rules (or if there
is a dislike towards software like Excel or OpenOffice.org). They are ideal in the sense that there
can be control over what parameters of rules can be edited, without exposing the rules directly.

Decision tables also provide a degree of insulation from the underlying object model.

2.4.2. Overview

Here are some examples of real world decision tables (slightly edited to protect the innocent).

41


In the above examples, the technical aspects of the decision table have been collapsed away
(using a standard spreadsheet feature).

The rules start from row 17, with each row resulting in a rule. The conditions are in columns C, D,
E, etc., the actions being off-screen. The values in the cells are quite simple, and their meaning
is indicated by the headers in Row 16. Column B is just a description. It is customary to use color
to make it obvious what the different areas of the table mean.

Note
Note that although the decision tables look like they process top down, this is not
necessarily the case. Ideally, rules are authored without regard for the order of
rows, simply because this makes maintenance easier, as rows will not need to be
shifted around all the time.

As each row is a rule, the same principles apply. As the rule engine processes the facts, any rules
that match may fire. (Some people are confused by this. It is possible to clear the agenda when a

42

How Decision Tables Work

rule fires and simulate a very simple decision table where only the first match effects an action.)
Also note that you can have multiple tables on one spreadsheet. This way, rules can be grouped
where they share common templates, yet at the end of the day they are all combined into one rule
package. Decision tables are essentially a tool to generate DRL rules automatically.

2.4.3. How Decision Tables Work

The key point to keep in mind is that in a decision table each row is a rule, and each column in
that row is either a condition or action for that rule.

The spreadsheet looks for the RuleTable keyword to indicate the start of a rule table (both the
starting row and column). Other keywords are also used to define other package level attributes
(covered later). It is important to keep the keywords in one column. By convention the second
column ("B") is used for this, but it can be any column (convention is to leave a margin on the
left for notes). In the following diagram, C is actually the column where it starts. Everything to the
left of this is ignored.

43


If we expand the hidden sections, it starts to make more sense how it works; note the keywords
in column C.

Now the hidden magic which makes it work can be seen. The RuleSet keyword indicates the name
to be used in the rule package that will encompass all the rules. This name is optional, using a
default, but it must have the RuleSet keyword in the cell immediately to the right.

The other keywords visible in Column C are Import and Sequential which will be covered later.
The RuleTable keyword is important as it indicates that a chunk of rules will follow, based on some
rule templates. After the RuleTable keyword there is a name, used to prefix the names of the
generated rules. The row numbers are appended to guarantee unique rule names. The column
of RuleTable indicates the column in which the rules start; columns to the left are ignored.

Note
In general the keywords make up name-value pairs.

Referring to row 14 (the row immediately after RuleTable), the keywords CONDITION and
ACTION indicate that the data in the columns below are for either the LHS or the RHS parts of a
rule. There are other attributes on the rule which can also be optionally set this way.

Row 15 contains declarations of ObjectTypes. The content in this row is optional, but if this option
is not in use, the row must be left blank; however this option is usually found to be quite useful.

44

How Decision Tables Work

When using this row, the values in the cells below (row 16) become constraints on that object type.
In the above case, it generates Person(age=="42") and Cheese(type=="stilton"), where 42
and "stilton" come from row 18. In the above example, the "==" is implicit; if just a field name is
given the translator assumes that it is to generate an exact match.

Note

An ObjectType declaration can span columns (via merged cells), meaning that all
columns below the merged range are to be combined into one set of constraints
within a single pattern matching a single fact at a time, as opposed to non-merged
cells containing the same ObjectType, but resulting in different patterns, potentially
matching different or identical facts.

Row 16 contains the rule templates themselves. They can use the "$param" placeholder to
indicate where data from the cells below should be interpolated. (For multiple insertions, use "$1",
"$2", etc., indicating parameters from a comma-separated list in a cell below.) Row 17 is ignored;
it may contain textual descriptions of the column's purpose.

Rows 18 and 19 show data, which will be combined (interpolated) with the templates in row 15, to
generate rules. If a cell contains no data, then its template is ignored. (This would mean that some
condition or action does not apply for that rule row.) Rule rows are read until there is a blank row.
Multiple RuleTables can exist in a sheet. Row 20 contains another keyword, and a value. The row
positions of keywords like this do not matter (most people put them at the top) but their column
should be the same one where the RuleTable or RuleSet keywords should appear. In our case
column C has been chosen to be significant, but any other column could be used instead.

In the above example, rules would be rendered like the following (as it uses the "ObjectType" row):

//row 18
rule "Cheese_fans_18"
when
Person(age=="42")
Cheese(type=="stilton")
then
list.add("Old man stilton");
end

Note

The constraints age=="42" and type=="stilton" are interpreted as single
constraints, to be added to the respective ObjectType in the cell above. If the cells
above were spanned, then there could be multiple constraints on one "column".

45


2.4.4. Spreadsheet Syntax

2.4.4.1. Spreadsheet Structure

There are two types of rectangular areas defining data that is used for generating a DRL file. One,
marked by a cell labelled RuleSet, defines all DRL items except rules. The other one may occur
repeatedly and is to the right and below a cell whose contents begin with RuleTable. These areas
represent the actual decision tables, each area resulting in a set of rules of similar structure.

A Rule Set area may contain cell pairs, one below the RuleSet cell and containing a keyword
designating the kind of value contained in the other one that follows in the same row.

The columns of a Rule Table area define patterns and constraints for the left hand sides of the
rules derived from it, actions for the consequences of the rules, and the values of individual rule
attributes. Thus, a Rule Table area should contain one or more columns, both for conditions and
actions, and an arbitrary selection of columns for rule attributes, at most one column for each of
these. The first four rows following the row with the cell marked with RuleTable are earmarked
as header area, mostly used for the definition of code to construct the rules. It is any additional
row below these four header rows that spawns another rule, with its data providing for variations
in the code defined in the Rule Table header.

All keywords are case insensitive.

Only the first worksheet is examined for decision tables.

2.4.4.2. Rule Set Entries

Entries in a Rule Set area may define DRL constructs (except rules), and specify rule attributes.
While entries for constructs may be used repeatedly, each rule attribute may be given at most
once, and it applies to all rules unless it is overruled by the same attribute being defined within
the Rule Table area.

Entries must be given in a vertically stacked sequence of cell pairs. The first one contains a
keyword and the one to its right the value, as shown in the table below. This sequence of cell
pairs may be interrupted by blank rows or even a Rule Table, as long as the column marked by
RuleSet is upheld as the one containing the keyword.

Table 2.1. Entries in the Rule Set area

Keyword Value Usage
RuleSet The package name for the Must be First entry.
generated DRL file. Optional,
the default is rule_table.
Sequential "true" or "false". If "true", then Optional, at most once. If
salience is used to ensure that omitted, no firing order is
rules fire from the top down. imposed.

46

Spreadsheet Syntax

Keyword Value Usage
EscapeQuotes "true" or "false". If "true", then Optional, at most once. If
quotation marks are escaped omitted, quotation marks are
so that they appear literally in escaped.
the DRL.
Import A comma-separated list of Optional, may be used
Java classes to import. repeatedly.
Variables Declarations of DRL globals, Optional, may be used
i.e., a type followed by a repeatedly.
variable name. Multiple global
definitions must be separated
with a comma.
Functions One or more function Optional, may be used
definitions, according to DRL repeatedly.
syntax.
Queries One or more query definitions, Optional, may be used
according to DRL syntax. repeatedly.
Declare One or more declarative types, Optional, may be used
according to DRL syntax. repeatedly.

For defining rule attributes that apply to all rules in the generated DRL file you can use any of the
entries in the following table. Notice, however, that the proper keyword must be used. Also, each
of these attributes may be used only once.

Table 2.2. Rule attribute entries in the Rule Set area

Keyword Initial Value
PRIORITY P An integer defining the
"salience" value for the
rule. Overridden by the
"Sequential" flag.
DURATION D A long integer value defining
the "duration" value for the
rule.
TIMER T A timer definition. See "Timers
and Calendars".
CALENDARS E A calendars definition. See
"Timers and Calendars".
NO-LOOP U A Boolean value. "true"
inhibits looping of rules due
to changes made by its
consequence.

47


Keyword Initial Value
LOCK-ON-ACTIVE L A Boolean value. "true" inhibits
additional activations of all
rules with this flag set within
the same ruleflow or agenda
group.
AUTO-FOCUS F A Boolean value. "true" for a
rule within an agenda group
causes activations of the rule
to automatically give the focus
to the group.
ACTIVATION-GROUP X A string identifying an
activation (or XOR) group.
Only one rule within an
activation group will fire, i.e.,
the first one to fire cancels any
existing activations of other
rules within the same group.
AGENDA-GROUP G A string identifying an agenda
group, which has to be
activated by giving it the
"focus", which is one way of
controlling the flow between
groups of rules.
RULEFLOW-GROUP R A string identifying a rule-flow
group.

2.4.4.3. Rule Tables

All Rule Tables begin with a cell containing "RuleTable", optionally followed by a string within the
same cell. The string is used as the initial part of the name for all rules derived from this Rule
Table, with the row number appended for distinction. (This automatic naming can be overridden
by using a NAME column.) All other cells defining rules of this Rule Table are below and to the
right of this cell.

The next row defines the column type, with each column resulting in a part of the condition or
the consequence, or providing some rule attribute, the rule name or a comment. The table below
shows which column headers are available; additional columns may be used according to the table
showing rule attribute entries given in the preceding section. Note that each attribute column may
be used at most once. For a column header, either use the keyword or any other word beginning
with the letter given in the "Initial" column of these tables.

48

Spreadsheet Syntax

Table 2.3. Column Headers in the Rule Table
Keyword Initial Value Usage
NAME N Provides the name At most one column
for the rule generated
from that row. The
default is constructed
from the text following
the RuleTable tag and
the row number.
DESCRIPTION I A text, resulting in a At most one column
comment within the
generated rule.
CONDITION C Code snippet and At least one per rule
interpolated values table
for constructing a
constraint within a
pattern in a condition.
ACTION A Code snippet and At least one per rule
interpolated values for table
constructing an action
for the consequence
of the rule.
METADATA @ Code snippet and Optional, any number
interpolated values of columns
for constructing a
metadata entry for the
rule.

Given a column headed CONDITION, the cells in successive lines result in a conditional element.

• Text in the first cell below CONDITION develops into a pattern for the rule condition, with
the snippet in the next line becoming a constraint. If the cell is merged with one or more
neighbours, a single pattern with multiple constraints is formed: all constraints are combined
into a parenthesized list and appended to the text in this cell. The cell may be left blank, which
means that the code snippet in the next row must result in a valid conditional element on its own.

To include a pattern without constraints, you can write the pattern in front of the text for another
pattern.

The pattern may be written with or without an empty pair of parentheses. A "from" clause may
be appended to the pattern.

If the pattern ends with "eval", code snippets are supposed to produce boolean expressions for
inclusion into a pair of parentheses after "eval".

49


• Text in the second cell below CONDITION is processed in two steps.

1. The code snippet in this cell is modified by interpolating values from cells farther down in
the column. If you want to create a constraint consisting of a comparison using "==" with
the value from the cells below, the field selector alone is sufficient. Any other comparison
operator must be specified as the last item within the snippet, and the value from the cells
below is appended. For all other constraint forms, you must mark the position for including
the contents of a cell with the symbol $param. Multiple insertions are possible by using the
symbols $1, $2, etc., and a comma-separated list of values in the cells below.

A text according to the pattern forall(delimiter){snippet} is expanded by repeating the
snippet once for each of the values of the comma-separated list of values in each of the cells
below, inserting the value in place of the symbol $ and by joining these expansions by the
given delimiter. Note that the forall construct may be surrounded by other text.

2. If the cell in the preceding row is not empty, the completed code snippet is added to the
conditional element from that cell. A pair of parentheses is provided automatically, as well as
a separating comma if multiple constraints are added to a pattern in a merged cell.

If the cell above is empty, the interpolated result is used as is.

• Text in the third cell below CONDITION is for documentation only. It should be used to indicate
the column's purpose to a human reader.

• From the fourth row on, non-blank entries provide data for interpolation as described above. A
blank cell results in the omission of the conditional element or constraint for this rule.

Given a column headed ACTION, the cells in successive lines result in an action statement.

• Text in the first cell below ACTION is optional. If present, it is interpreted as an object reference.

• Text in the second cell below ACTION is processed in two steps.

1. The code snippet in this cell is modified by interpolating values from cells farther down in the
column. For a singular insertion, mark the position for including the contents of a cell with
the symbol $param. Multiple insertions are possible by using the symbols $1, $2, etc., and a
comma-separated list of values in the cells below.

A method call without interpolation can be achieved by a text without any marker symbols.
In this case, use any non-blank entry in a row below to include the statement.

The forall construct is available here, too.

2. If the first cell is not empty, its text, followed by a period, the text in the second cell and a
terminating semicolon are stringed together, resulting in a method call which is added as an
action statement for the consequence.

If the cell above is empty, the interpolated result is used as is.

50

Spreadsheet Syntax

• Text in the third cell below ACTION is for documentation only. It should be used to indicate the
column's purpose to a human reader.

blank cell results in the omission of the action statement for this rule.

Note

Using $1 instead of $param works in most cases, but it will fail if the replacement
text contains a comma: then, only the part preceding the first comma is inserted.
Use this "abbreviation" judiciously.

Given a column headed METADATA, the cells in successive lines result in a metadata annotation
for the generated rules.

• Text in the first cell below METADATA is ignored.

• Text in the second cell below METADATA is subject to interpolation, as described above, using
values from the cells in the rule rows. The metadata marker character @ is prefixed automatically,
and thus it should not be included in the text for this cell.

• Text in the third cell below METADATA is for documentation only. It should be used to indicate
the column's purpose to a human reader.

blank cell results in the omission of the metadata annotation for this rule.

2.4.4.4. Examples

The various interpolations are illustrated in the following example.

Example 2.1. Interpolating cell data

If the template is Foo(bar == $param) and the cell is 42, then the result is Foo(bar == 42).

If the template is Foo(bar < $1, baz == $2) and the cell contains 42,43, the result will be
Foo(bar < 42, baz ==43).

The template forall(&&){bar != $} with a cell containing 42,43 results in bar != 42 &&
bar != 43.

The next example demonstrates the joint effect of a cell defining the pattern type and the code
snippet below it.

51


This spreadsheet section shows how the Person type declaration spans 2 columns, and thus both
constraints will appear as Person(age == ..., type == ...). Since only the field names are
present in the snippet, they imply an equality test.

In the following example the marker symbol $param is used.

The result of this column is the pattern Person(age == "42")). You may have noticed that the
marker and the operator "==" are redundant.

The next example illustrates that a trailing insertion marker can be omitted.

52

Spreadsheet Syntax

Here, appending the value from the cell is implied, resulting in Person(age < "42")).

You can provide the definition of a binding variable, as in the example below. .

Here, the result is c: Cheese(type == "stilton"). Note that the quotes are provided
automatically. Actually, anything can be placed in the object type row. Apart from the definition of
a binding variable, it could also be an additional pattern that is to be inserted literally.

A simple construction of an action statement with the insertion of a single value is shown below.

53


The cell below the ACTION header is left blank. Using this style, anything can be placed in
the consequence, not just a single method call. (The same technique is applicable within a
CONDITION column as well.)

Below is a comprehensive example, showing the use of various column headers. It is not an error
to have no value below a column header (as in the NO-LOOP column): here, the attribute will not
be applied in any of the rules.

And, finally, here is an example of Import, Variables and Functions.

54

Creating and integrating Spreadsheet based Decision Tables

Multiple package names within the same cell must be separated by a comma. Also, the pairs of
type and variable names must be comma-separated. Functions, however, must be written as they
appear in a DRL file. This should appear in the same column as the "RuleSet" keyword; it could
be above, between or below all the rule rows.

Note

It may be more convenient to use Import, Variables, Functions and Queries
repeatedly rather than packing several definitions into a single cell.

2.4.5. Creating and integrating Spreadsheet based Decision
Tables

The API to use spreadsheet based decision tables is in the drools-decisiontables module. There
is really only one class to look at: SpreadsheetCompiler. This class will take spreadsheets in
various formats, and generate rules in DRL (which you can then use in the normal way). The
SpreadsheetCompiler can just be used to generate partial rule files if it is wished, and assemble
it into a complete rule package after the fact (this allows the separation of technical and non-
technical aspects of the rules if needed).

To get started, a sample spreadsheet can be used as a base. Alternatively, if the plug-in is being
used (Rule Workbench IDE), the wizard can generate a spreadsheet from a template (to edit it an
xls compatible spreadsheet editor will need to be used).

55


2.4.6. Managing Business Rules in Decision Tables

2.4.6.1. Workflow and Collaboration

Spreadsheets are well established business tools (in use for over 25 years). Decision tables lend
themselves to close collaboration between IT and domain experts, while making the business
rules clear to business analysts, it is an ideal separation of concerns.

Typically, the whole process of authoring rules (coming up with a new decision table) would be
something like:

1. Business analyst takes a template decision table (from a repository, or from IT)

2. Decision table business language descriptions are entered in the table(s)

3. Decision table rules (rows) are entered (roughly)

4. Decision table is handed to a technical resource, who maps the business language
(descriptions) to scripts (this may involve software development of course, if it is a new
application or data model)

5. Technical person hands back and reviews the modifications with the business analyst.

6. The business analyst can continue editing the rule rows as needed (moving columns around
is also fine etc).

7. In parallel, the technical person can develop test cases for the rules (liaising with business
analysts) as these test cases can be used to verify rules and rule changes once the system
is running.

2.4.6.2. Using spreadsheet features

Features of applications like Excel can be used to provide assistance in entering data into
spreadsheets, such as validating fields. Lists that are stored in other worksheets can be used to
provide valid lists of values for cells, like in the following diagram.

56

Rule Templates

Some applications provide a limited ability to keep a history of changes, but it is recommended to
use an alternative means of revision control. When changes are being made to rules over time,
older versions are archived (many open source solutions exist for this, such as Subversion or Git).

2.4.7. Rule Templates
Related to decision tables (but not necessarily requiring a spreadsheet) are "Rule Templates" (in
the drools-templates module). These use any tabular data source as a source of rule data -
populating a template to generate many rules. This can allow both for more flexible spreadsheets,
but also rules in existing databases for instance (at the cost of developing the template up front
to generate the rules).

With Rule Templates the data is separated from the rule and there are no restrictions on which
part of the rule is data-driven. So whilst you can do everything you could do in decision tables
you can also do the following:

• store your data in a database (or any other format)

• conditionally generate rules based on the values in the data

• use data for any part of your rules (e.g. condition operator, class name, property name)

• run different templates over the same data

As an example, a more classic decision table is shown, but without any hidden rows for the rule
meta data (so the spreadsheet only contains the raw data to generate the rules).

57


See the ExampleCheese.xls in the examples download for the above spreadsheet.

If this was a regular decision table there would be hidden rows before row 1 and between rows
1 and 2 containing rule metadata. With rule templates the data is completely separate from the
rules. This has two handy consequences - you can apply multiple rule templates to the same data
and your data is not tied to your rules at all. So what does the template look like?

1 template header
2 age
3 type
4 log
5
6 package org.drools.examples.templates;
7
8 global java.util.List list;
9
10 template "cheesefans"
11
12 rule "Cheese fans_@{row.rowNumber}"
13 when
14 Person(age == @{age})
15 Cheese(type == "@{type}")
16 then
17 list.add("@{log}");
18 end
19
20 end template

Annotations to the preceding program listing:

• Line 1: All rule templates start with template header.

• Lines 2-4: Following the header is the list of columns in the order they appear in the data. In
this case we are calling the first column age, the second type and the third log.

• Line 5: An empty line signifies the end of the column definitions.

• Lines 6-9: Standard rule header text. This is standard rule DRL and will appear at the top of the
generated DRL. Put the package statement and any imports and global and function definitions
into this section.

• Line 10: The keyword template signals the start of a rule template. There can be more than
one template in a template file, but each template should have a unique name.

• Lines 11-18: The rule template - see below for details.

• Line 20: The keywords end template signify the end of the template.

58

Templates

The rule templates rely on MVEL to do substitution using the syntax @{token_name}. There is
currently one built-in expression, @{row.rowNumber} which gives a unique number for each row of
data and enables you to generate unique rule names. For each row of data a rule will be generated
with the values in the data substituted for the tokens in the template. With the example data above
the following rule file would be generated:

package org.drools.examples.templates;

global java.util.List list;

rule "Cheese fans_1"
when
Person(age == 42)
Cheese(type == "stilton")
then
list.add("Old man stilton");
end

rule "Cheese fans_2"
when
Person(age == 21)
Cheese(type == "cheddar")
then
list.add("Young man cheddar");
end

The code to run this is simple:

DecisionTableConfiguration dtableconfiguration =
KnowledgeBuilderFactory.newDecisionTableConfiguration();
dtableconfiguration.setInputType( DecisionTableInputType.XLS );


kbuilder.add( ResourceFactory.newClassPathResource( getSpreadsheetName(),
getClass() ),
ResourceType.DTABLE,
dtableconfiguration );

2.5. Templates
If you discover that you have a group of rules following the same arrangement of patterns,
constraints and actions on the RHS, differing only in constants or names for objects or fields, you

59


might think of employing Drools's rule template feature for generating the actual rules. You would
write a rule template file, containing the textual skeleton of your rule and use the Drools template
compiler in combination with a collection of objects providing the actual values for the "flesh" of
the rules for their instantiation.

The mechanism is very similar to what a macro processor does. The major advantage proffered by
template expansion is that it's nicely integrated in the overall handling of Knowledge Resources.

Caution

This is an experimental feature. In particular, the API is subject to change.

2.5.1. The Rule Template File
A rule template file begins with a header defining the placeholders, or formal template parameters
for the strings that are to be inserted during instantiation. After the first line, which invariably
contains template header, you should write a number of lines, each of which contains a single
parameter name.

Example 2.2. Rule template file: template header

template header
parameter-name-1
...
parameter-name-n
...

The template header is followed by the text that is to be replicated and interpolated with the actual
parameters. It may begin with a package statement, followed by some additional lines. These may
be sectioned into one or more templates, each of them between a pair of matching template and
end template statements. The template takes an argument, which puts a name to the template.
The name can be a simple unquoted name or an arbitrary string enclosed in double quotes. The
template text between these lines may contain one or more rules, constituting the "raw material"
for the expansion.

Example 2.3. Rule template file: templates

template header
parameter-name-1
...
parameter-name-n

60

Expanding a Template

package ... // optional
header text // optional
template template-name
...
// template text
...
end template
...

The resulting text will begin with the package line and the header text following it, if present. Then,
each template text will be expanded individually, yielding one set of rules for each of the actual
parameter sets. Therefore, the structure of the template sections affect the order of the generated
rules, since the generator iterates over the sections and then over the set of actual parameters.

Any interpolation takes place between a pair of template and end template statements, when
this template is expanded. The template text is scanned for occurrences of parameter expansions
written according to:

@{parameter-name}

The name between '@{' and '}' should be one of the parameter names defined in the template
header. The substitution is effected anywhere, even within string literals.

An important parameter is available without having to be included in the data source providing
the actual values. The parameter substitution @{row.rowNumber} expands to the integers 0, 1, 2,
etc., providing a unique distinction for the instantiation derived from a parameter set. You would
use this as part of each rule name, because, without this precaution, there would be duplicate rule
names. (You are, of course, free to use your own identification included as an extra parameter.)

2.5.2. Expanding a Template
To expand a template, you must prepare a data source. This can be a spreadsheet, as explained
in the previous section. Here, we'll concentrate on expansion driven by Java objects. There
are two straightforward ways of supplying values for a fixed set of names: Java objects, in the
JavaBeans style, and Maps. Both of them can be arranged in a Collection, whose elements will
be processed during the expansion, resulting in an instantiation for each element.

2.5.2.1. Instantiation from Java Objects
You may use a Java object that provides getter methods corresponding to all of the parameter
names of your template file. If, for instance, you have defined a header

template header

61


type
limit
word

the following Java class could be used:

public class ParamSet {
//...
public ParamSet( String t, int l, boolean w ) {
//...
}
public String getType(){...}
public int getLimit(){...}
public boolean isWord(){...}
}

Although interpolation is pure text manipulation, the actual values supplied may be of any type, just
as long as this type provides a reasonable toString() method. (For simple types, the eponymous
static method of the related class from java.lang is used.)

Assuming that we have created a Collection<ParamSet> for a template file template.drl, we
can now proceed to request its expansion.

Collection<ParamSet> paramSets = new ArrayList<ParamSet>();
// populate paramSets
paramSets.add( new ParamSet( "Foo", 42, true ) );
paramSets.add( new ParamSet( "Bar", 13, false ) );
ObjectDataCompiler converter = new ObjectDataCompiler();
InputStream templateStream =
this.getClass().getResourceAsStream( "template.drl" );
String drl = converter.compile( objs, templateStream );

The resulting string contains the expanded rules text. You could write it to a file and proceed as
usual, but it's also possible to feed this to a KnowledgeBuilder and continue with the resulting
Knowledge Packages.

KnowledgeBase kBase = KnowledgeBaseFactory.newKnowledgeBase();
KnowledgeBuilder kBuilder = KnowledgeBuilderFactory.newKnowledgeBuilder();
Reader rdr = new StringReader( drl );
kBuilder.add( ResourceFactory.newReaderResource( rdr ), ResourceType.DRL );
if( kBuilder.hasErrors() ){

62

Example

// ...
throw new IllegalStateException( "DRL errors" );
}
kBase.addKnowledgePackages( kBuilder.getKnowledgePackages() );

2.5.2.2. Instantiation from Maps

A Map that provides the values for substituting template parameters should have a (string) key
set matching all of the parameter names. Again, values could be from any class, as long as they
provide a good toString() method. The expansion would use the same approach, just differing
in the way the map collection is composed.

Collection<Map<String,Object>> paramMaps = new ArrayList<Map<String,Object>>();
// populate paramMaps
InputStream templateStream =
this.getClass().getResourceAsStream( "template.drl" );
String drl = converter.compile( objs, templateStream );

2.5.3. Example

The following example illustrates template expansion. It is based on simple objects of class Item
containing a couple of integer fields and an enum field of type ItemCode.

public class Item {
// ...
public Item( String n, int p, int w, ItemCode c ){...}

public String getName() {...}
public int getWeight() {...}
public int getPrice() {...}
public ItemCode getCode() {...}
}

public enum ItemCode {
LOCK,
STOCK,
BARREL;
}

The rule template contains a single rule. Notice that the field name for the range test is a parameter,
which enables us to instantiate the template for different fields.

63


template header
field
lower
upper
codes

package range;
template "inRange"
rule "is in range @{row.rowNumber}"
when
Item( $name : name, $v : @{field} >= @{lower} && <= @{upper}, $code :
code @{codes} )
then
System.out.println( "Item " + $name + " @{field} in range: " + $v + "
code: " + $code );
end
end template

The next code snippet is from the application, where several parameter sets have to be set up.
First, there is class ParamSet, for storing a set of actual parameters.

public class ParamSet {
//...
private EnumSet<ItemCode> codeSet;

public ParamSet( String f, int l, int u, EnumSet<ItemCode> cs ){...}

public String getField() { return field; }
public int getLower() { return lower; }
public int getUpper() { return upper; }

public String getCodes(){
StringBuilder sb = new StringBuilder();
String conn = "";
for( ItemCode ic: codeSet ){
sb.append( conn ).append( " == ItemCode." ).append( ic );
conn = " ||";
}
return sb.toString();
}
}

64

Example

Note that the method getCodes() does returns the EnumSet<ItemCode> field value as a String
value representing a multiple restriction, i.e., a test for one out of a list of values.

The task of expanding a template, passing the resulting DRL text to a Knowledge Builder and
adding the resulting Knowledge Packages to a Knowledge Base is generic. The utility class
Expander takes care of this, using a Knowledge Base, the InputStream with the rule template
and the collection of parameter sets.

public class Expander {

public void expand( KnowledgeBase kBase, InputStream is, Collection<?> act )
throws Exception {
String drl = converter.compile( act, is );

KnowledgeBuilder kBuilder = KnowledgeBuilderFactory.newKnowledgeBuilder();
Reader rdr = new StringReader( drl );
kBuilder.add( ResourceFactory.newReaderResource( rdr ), ResourceType.DRL );
if( kBuilder.hasErrors() ){
for( KnowledgeBuilderError err: kBuilder.getErrors() ){
System.err.println( err.toString() );
}
throw new IllegalStateException( "DRL errors" );
}
kBase.addKnowledgePackages( kBuilder.getKnowledgePackages() );
}
}

We are now all set to prepare the Knowledge Base with some generated rules. First, we define
several parameter sets, constructed as ParamSet objects, and add them to a List, which is
passed to the expand method shown above. Then we launch a stateful session, insert a few Item,
and watch what happens.

Collection<ParamSet> cfl = new ArrayList<ParamSet>();
cfl.add( new ParamSet( "weight", 10, 99, EnumSet.of( ItemCode.LOCK, ItemCode.STOCK ) ) );
cfl.add( new ParamSet( "price", 10, 50, EnumSet.of( ItemCode.BARREL ) ) );

KnowledgeBase kBase = KnowledgeBaseFactory.newKnowledgeBase();
Expander ex = new Expander();
InputStream dis = new FileInputStream( new File( "rangeTemp.drl" ) );
ex.expand( kBase, dis, cfl );

StatefulKnowledgeSession session = kBase.newStatefulKnowledgeSession();
session.insert( new Item( "A", 130, 42, ItemCode.LOCK ) );

65


session.insert( new Item( "B", 44, 100, ItemCode.STOCK ) );
session.insert( new Item( "C", 123, 180, ItemCode.BARREL ) );
session.insert( new Item( "D", 85, 9, ItemCode.LOCK ) );

session.fireAllRules();

Notice that the two resulting rules deal with different fields, one with an item's weight, the other
one with its price. - Below is the output.

Item E price in range: 25 code: BARREL
Item A weight in range: 42 code: LOCK

2.6. More on building and deploying

2.6.1. Knowledge Base by Configuration Using Changesets

So far, the programmatic API has been used to build a Knowledge Base. Quite often it's more
desirable to do this via configuration. To facilitate this, Drools supports the "Changeset" feature.
The file changeset.xml contains a list of resources, and it may also point recursively to another
changeset XML file. Currently there is no XML schema for the changeset XML, but we hope to
add one soon. A few examples will be shown to give you the gist of things. A resource approach is
employed that uses a prefix to indicate the protocol. All the protocols provided by java.net.URL,
such as "file" and "http", are supported, as well as an additional "classpath". Currently the type
attribute must always be specified for a resource, as it is not inferred from the file name extension.
Here is a simple example that points to a http location for some rules.

<change-set xmlns='http://drools.org/drools-5.0/change-set'
xmlns:xs='http://www.w3.org/2001/XMLSchema-instance'
xs:schemaLocation='http://drools.org/drools-5.0/change-
set http://anonsvn.jboss.org/repos/labs/labs/jbossrules/trunk/drools-api/src/
main/resources/change-set-1.0.0.xsd' >
<add>
<resource source='http:org/domain/myrules.drl' type='DRL' />
</add>
</change-set>

To use the above XML, the code is almost identical as before, except we change the resource
type to CHANGE_SET.

kbuilder.add( ResourceFactory.newClasspathResource( "myChangeSet.xml", getClass() ),

66

Knowledge Agent

ResourceType.CHANGE_SET );
System.err.println( builder.getErrors().toString() );
}

Changesets can include any number of resources, and they even support additional configuration
information, which currently is only needed for decision tables. The example below is expanded
to load the rules from a http URL location, and an Excel decision table from the classpath.

set.xsd http://anonsvn.jboss.org/repos/labs/labs/jbossrules/trunk/drools-api/
src/main/resources/change-set-1.0.0.xsd' >
<add>
<resource source='classpath:data/
IntegrationExampleTest.xls' type="DTABLE">
<decisiontable-conf input-type="XLS" worksheet-name="Tables_2" />
</resource>
</add>
</change-set>

It is also possible to specify a directory, to add the contents of that directory. It is expected that all
the files are of the specified type, since type is not yet inferred from the file name extensions.

<add>
<resource source='file://myfolder/' type='DRL' />
</add>
</change-set>

2.6.2. Knowledge Agent

The Knowledge Agent provides automatic loading, caching and re-loading of resources and is
configured from a properties files. The Knowledge Agent can update or rebuild this Knowledge
Base as the resources it uses are changed. The strategy for this is determined by the configuration
given to the factory, but it is typically pull-based using regular polling. We hope to add push-based
updates and rebuilds in future versions.

67


KnowledgeAgent kagent = KnowledgeAgentFactory.newKnowledgeAgent( "MyAgent" );
kagent.applyChangeSet( ResourceFactory.newUrlResource( url ) );
KnowledgeBase kbase = kagent.getKnowledgeBase();

A KnowledgeAgent object will continuously scan all the added resources, using a default polling
interval of 60 seconds and, when some last modification date is updated, it will applied the
changes into the cached Knowledge Base using the new resources. Note that the previous
KnowledgeBase reference will still exist and you'll have to call getKnowledgeBase() to access
the newly built KnowledgeBase. If a directory is specified as part of the change set, the entire
contents of that directory will be scanned for changes. The way modifications are applied depends
on drools.agent.newInstance property present in the KnowledgeAgentConfiguration object
passed to the agent.

For polling to occur, the polling and notifier services must be started:

ResourceFactory.getResourceChangeNotifierService().start();
ResourceFactory.getResourceChangeScannerService().start();

2.6.2.1. Knowledge Agent and Custom ClassLoaders
Because Knowledge Agent could scan and process remote resources, it could ends up failing
when compiling or executing rules, queries, functions, etc. that use classes outside the agent's
classloader. If this is your case, you could take 2 approach: use a custom classloader for agent's
kbuilder or force the agent to use the same classloader that its kbase has.

2.6.2.1.1. Custom ClassLoaders for KnowledgeBuilder

Knowledge Agent uses KnowledgeBuilder internally in order to compile managed resources.
If you need to pass custom configuration to these compilers you could pass a
KnowledgeBuilderConfiguration object to KnowledgeAgentFactory.newKnowledgeAgent(). This
object will be used in every builder the agent creates. Using a KnowledgeBuilderConfiguration
you can specify a custom classloader.

2.6.2.1.2. Reuse KnowledgeBase ClassLoader

Most of the times, the classloader you want to use in the compilation process of remote resources
is the same needed in the agent's kbase, so the rules could be executed. If you want to use
this approach, you will need to setup the desired ClassLoader to the agent kbase and use the
"drools.agent.useKBaseClassLoaderForCompiling" property of KnowledgeAgentConfiguration
object.

This approach lets you modify agent's kbuilder classloader in runtime by modifying the classloader
the agent's kbase uses. This will serve also when not using incremental change set processing
(see the section below). When the kbase is recreated its configuration is reused, so the classloader
is maintained.

68

Knowledge Agent

KnowledgeBaseConfiguration kbaseConfig =
KnowledgeBaseFactory.newKnowledgeBaseConfiguration(null, customClassLoader);
KnowledgeBase kbase =
KnowledgeBaseFactory.newKnowledgeBase(kbaseConfig); //kbase with custom
classloader
KnowledgeAgentConfiguration aconf =
KnowledgeAgentFactory.newKnowledgeAgentConfiguration();
aconf.setProperty("drools.agent.newInstance", "false"); //incremental change set
processing enabled
aconf.setProperty("drools.agent.useKBaseClassLoaderForCompiling", "true");
KnowledgeAgent kagent = KnowledgeAgentFactory.newKnowledgeAgent(
"test agent", kbase, aconf);

2.6.2.2. Incremental Change Set Processing

Knowledge Agent can process change sets in two different ways: recreating the knowledge base
every time a new change set is processed or applying the change set in the cached knowledge
base without destroying it. This behavior is controlled by the "newInstance" property of the
KnowledgeAgentConfiguration object passed to the Agent's constructor.

When "newInstance" is set to true (the default value), the agent will destroy the cached Knowledge
Base it contains and populate a new one containing the change set modifications. When
"newInstance" is set to "false" change sets are applied directly to the cached Knowledge Base.
The rule that were not modified in the change sets' resources are not replaced in the Knowledge
Base, the modified or deleted rules are modified or deleted from the cached Knowledge Base.
Functions, Queries and Definition Types are always replaced in the cached Knowledge Base
whether they are modified or not.

The following code snippet creates a new Knowledge Agent with its "newInstance" property set
to false

KnowledgeAgentConfiguration aconf = KnowledgeAgentFactory.newKnowledgeAgentConfiguration();
aconf.setProperty("drools.agent.newInstance", "false");
KnowledgeAgent kagent = KnowledgeAgentFactory.newKnowledgeAgent("test
agent", null, aconf);

2.6.2.3. Remote HTTP resource caching

A note on remote HTTP Url Resources: if your knowledge agent is "pulling" resources from a
http(s) URL, then you might rightly be concerned if that resource (remote web server) suddenly
disappears. To survive a restart when a resource is no longer available remotely (eg the remote
server is being restarted) then you can set a System Property: drools.resource.urlcache to a

69


directory that has write permissions for the application: the Knowledge Agent will cache copies of
the remote resources in that local directory.

For example, using the java command line: -Ddrools.resource.urlcache=/users/someone/
KnowledgeCache - will keep local copies of the resources (rules, packages etc) in that directory,
for the agent to use should it be restarted (when a remote resource becomes available, and is
updated, it will automatically update the local cache copy).

70

Chapter 3.

Chapter 3. Api Reference
3.1. Building

Figure 3.1. org.drools.builder

3.1.1. Building using Code

The KnowledgeBuilder is responsible for taking source files, such as a DRL file or an Excel file,
and turning them into a Knowledge Package of rule and process definitions which a Knowledge
Base can consume. An object of the class ResourceType indicates the type of resource it is being
asked to build.

71


The ResourceFactory provides capabilities to load resources from a number of sources, such as
Reader, ClassPath, URL, File, or ByteArray. Binaries, such as decision tables (Excel .xls files),
should not use a Reader based resource handler, which is only suitable for text based resources.

Figure 3.2. KnowledgeBuilder

The KnowledgeBuilder is created using the KnowledgeBuilderFactory.

72

Building using Code

Figure 3.3. KnowledgeBuilderFactory

A KnowledgeBuilder can be created using the default configuration.

Example 3.1. Creating a new KnowledgeBuilder


A configuration can be created using the KnowledgeBuilderFactory. This allows the behavior
of the Knowledge Builder to be modified. The most common usage is to provide a custom class
loader so that the KnowledgeBuilder object can resolve classes that are not in the default
classpath. The first parameter is for properties and is optional, i.e., it may be left null, in which
case the default options will be used. The options parameter can be used for things like changing
the dialect or registering new accumulator functions.

Example 3.2. Creating a new KnowledgeBuilder with a custom ClassLoader

KnowledgeBuilderConfiguration kbuilderConf = KnowledgeBuilderFactory.newKnowledgeBuilderConfigu
KnowledgeBuilder kbuilder = KnowledgeBuilderFactory.newKnowledgeBuilder(kbuilderConf);

Resources of any type can be added iteratively. Below, a DRL file is added. Unlike Drools 4.0
Package Builder, the Knowledge Builder can now handle multiple namespaces, so you can just
keep adding resources regardless of namespace.

73


Example 3.3. Adding DRL Resources

kbuilder.add( ResourceFactory.newFileResource( "/project/myrules.drl" ),
ResourceType.DRL);

It is a best practice to always check the compilation results after a change an addition. The
KnowledgeBuilder can report compilation results of 3 different severities: ERROR, WARNING and
INFO.

An ERROR indicates that the compilation of the resources failed and you should not add more
resources or retrieve the Knowledge Packages if there are errors. getKnowledgePackages()
returns an empty list if there are errors.

WARNING and INFO results can be ignored, but are available for inspection nonetheless.
hasErrors() method after an addition.

To check and retrieve the building results for a list of severities, the KnowledgeBuilder interface
offers a couple methods:

Example 3.4. KnowledgeBuilder result inspection methods

/**
* Return the knowledge builder results for the listed severities.
*
* @param severities
* @return
*/
KnowledgeBuilderResults getResults(ResultSeverity...severities );

/**
* Checks if the builder generated any results of the listed severities
* @param severities
* @return
*/
boolean hasResults(ResultSeverity...severities );

The KnowledgeBuilder interface also has two helper methods to inspect for errors only:
hasErrors() and getErrors():

Example 3.5. Validating

if( kbuilder.hasErrors() ) {
System.out.println( kbuilder.getErrors() );
return;

74

Building using Code

}

When all the resources have been added and there are no errors the collection of Knowledge
Packages can be retrieved. It is a Collection because there is one Knowledge Package per
package namespace. These Knowledge Packages are serializable and often used as a unit of
deployment.

Example 3.6. Getting the KnowledgePackages

Collection<KnowledgePackage> kpkgs = kbuilder.getKnowledgePackages();

The final example puts it all together.

Example 3.7. Putting it all together

return;
}

kbuilder.add( ResourceFactory.newFileResource( "/project/myrules1.drl" ),
ResourceType.DRL);
kbuilder.add( ResourceFactory.newFileResource( "/project/myrules2.drl" ),
ResourceType.DRL);

return;
}


The KnowledgeBuilder also has a batch mode, with a fluent interface, that allows to build multiple
DRLs at once as in the following example:

Example 3.8. Using KnowledgeBuilder in batch mode

kbuilder.batch()
.add( ResourceFactory.newFileResource( "/project/
myrules1.drl" ), ResourceType.DRL )

75


myrules2.drl" ), ResourceType.DRL )
mytypes1.drl" ), ResourceType.DRL )
.build();

In this way it is no longer necessary to build the DRLs files in the right order (e.g. first the DRLs
containing the type declarations and then the ones with the rules using them) and it will also be
possible to have circular references among them.

In the end the KnowledgeBuilder (regardless if you are using the batch mode or not) also allows
to discard what has been added with the last DRL(s) building. This can be useful to recover from
having added a wrong DRL to the KnowledgeBuilder as it follows:

Example 3.9. Discard the building of the last added DRL

kbuilder.add( ResourceFactory.newFileResource( "/project/
wrong.drl" ), ResourceType.DRL );
kbuilder.undo();
}

3.1.2. Building using Configuration and the ChangeSet XML
Instead of adding the resources to create definitions programmatically it is also possible to do it by
configuration, via the ChangeSet XML. The simple XML file supports three elements: add, remove,
and modify, each of which has a sequence of <resource> subelements defining a configuration
entity. The following XML schema is not normative and intended for illustration only.

Example 3.10. XML Schema for ChangeSet XML (not normative)

<xs:schema xmlns:xs="http://www.w3.org/2001/XMLSchema"
xmlns="http://drools.org/drools-5.0/change-set"
targetNamespace="http://drools.org/drools-5.0/change-set">

<xs:element name="change-set" type="ChangeSet"/>

<xs:complexType name="ChangeSet">
<xs:choice maxOccurs="unbounded">
<xs:element name="add" type="Operation"/>
<xs:element name="remove" type="Operation"/>
<xs:element name="modify" type="Operation"/>
</xs:choice>

76

Building using Configuration and the ChangeSet XML

</xs:complexType>

<xs:complexType name="Operation">
<xs:sequence>
<xs:element name="resource" type="Resource"
maxOccurs="unbounded"/>
</xs:sequence>
</xs:complexType>

<xs:complexType name="Resource">
<xs:sequence>

<xs:element name="decisiontable-conf" type="DecTabConf"
minOccurs="0"/>
</xs:sequence>

<xs:attribute name="source" type="xs:string"/>
<xs:attribute name="type" type="ResourceType"/>
</xs:complexType>

<xs:complexType name="DecTabConf">
<xs:attribute name="input-type" type="DecTabInpType"/>
<xs:attribute name="worksheet-name" type="xs:string"
use="optional"/>
</xs:complexType>

<xs:simpleType name="ResourceType">
<xs:restriction base="xs:string">
<xs:enumeration value="DRL"/>
<xs:enumeration value="XDRL"/>
<xs:enumeration value="DSL"/>
<xs:enumeration value="DSLR"/>
<xs:enumeration value="DRF"/>
<xs:enumeration value="DTABLE"/>
<xs:enumeration value="PKG"/>
<xs:enumeration value="BRL"/>
<xs:enumeration value="CHANGE_SET"/>
</xs:restriction>
</xs:simpleType>

<xs:simpleType name="DecTabInpType">
<xs:restriction base="xs:string">
<xs:enumeration value="XLS"/>
<xs:enumeration value="CSV"/>
</xs:restriction>
</xs:simpleType>

77


</xs:schema>

Currently only the add element is supported, but the others will be implemented to support iterative
changes. The following example loads a single DRL file.

Example 3.11. Simple ChangeSet XML

<add>
<resource source='file:/project/myrules.drl' type='DRL' />
</add>
</change-set>

Notice the file: prefix, which signifies the protocol for the resource. The Change Set supports
all the protocols provided by java.net.URL, such as "file" and "http", as well as an additional
"classpath". Currently the type attribute must always be specified for a resource, as it is not inferred
from the file name extension. Using the ClassPath resource loader in Java allows you to specify
the Class Loader to be used to locate the resource but this is not possible from XML. Instead, the
Class Loader will default to the one used by the Knowledge Builder unless the ChangeSet XML
is itself loaded by the ClassPath resource, in which case it will use the Class Loader specified
for that resource.

Currently you still need to use the API to load that ChangeSet, but we will add support for
containers such as Spring in the future, so that the process of creating a Knowledge Base can be
done completely by XML configuration. Loading resources using an XML file couldn't be simpler,
as it's just another resource type.

Example 3.12. Loading the ChangeSet XML

kbuilder.add( ResourceFactory.newUrlResource( url ), ResourceType.CHANGE_SET );

ChangeSets can include any number of resources, and they even support additional configuration
information, which currently is only needed for decision tables. Below, the example is expanded
to load rules from a http URL location, and an Excel decision table from the classpath.

Example 3.13. ChangeSet XML with resource configuration


78

Changing default building result severity

<add>
<resource source='classpath:data/
IntegrationExampleTest.xls' type="DTABLE">
<decisiontable-conf input-type="XLS" worksheet-name="Tables_2" />
</resource>
</add>
</change-set>

The ChangeSet is especially useful when working with a Knowledge Agent, as it allows for change
notification and automatic rebuilding of the Knowledge Base, which is covered in more detail in
the section on the Knowledge Agent, under Deploying.

Directories can also be specified, to add all resources in that folder. Currently it is expected that
all resources in that folder are of the same type. If you use the Knowledge Agent it will provide a
continous scanning for added, modified or removed resources and rebuild the cached Knowledge
Base. The KnowledgeAgent provides more information on this.

Example 3.14. ChangeSet XML which adds a directory's contents

<add>
<resource source='file:/projects/myproject/myrules' type='DRL' />
</add>
</change-set>

3.1.3. Changing default building result severity
In some cases, it is possible to change the default severity of a type of building result. For instance,
when a new rule with the same name of an existing rule is added to a package, the default behavior
is to replace the old rule by the new rule and report it as an INFO. This is probably ideal for most
use cases, but in some deployements, the user might want to prevent the rule update and report
it as an error.

Changing the default severity for a result type is the same as configuring any other option in Drools
and can be done either by API calls, system properties or configuration files. As of this version,
Drools supports configurable result severity for rule updates and function updates. To configure
it using system properties or configuration files, the user has to use the following properties:

79


Example 3.15. Setting the severity using properties

// sets the severity of rule updates
drools.kbuilder.severity.duplicateRule = <INFO|WARNING|ERROR>
// sets the severity of function updates
drools.kbuilder.severity.duplicateFunction = <INFO|WARNING|ERROR>

To configure it through the API:

Example 3.16. Setting the severity using the API

KnowledgeBuilderConfiguration kbconf = ...

// sets the severity of rule updates to error
kbconf.setOption(
KBuilderSeverityOption.get(
"drools.kbuilder.severity.duplicateRule",
ResultSeverity.ERROR ) );
// sets the severity of function updates to error
kbconf.setOption(
KBuilderSeverityOption.get(
"drools.kbuilder.severity.duplicateFunction",
ResultSeverity.ERROR ) );

3.2. Deploying

3.2.1. KnowledgePackage and Knowledge Definitions

A Knowledge Package is a collection of Knowledge Definitions, such as rules and processes. It is
created by the Knowledge Builder, as described in the chapter "Building". Knowledge Packages
are self-contained and serializable, and they currently form the basic deployment unit.

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KnowledgeBase

Figure 3.4. KnowledgePackage

Knowledge Packages are added to the Knowledge Base. However, a Knowledge Package
instance cannot be reused once it's added to the Knowledge Base. If you need to add it to another
Knowledge Base, try serializing it first and using the "cloned" result. We hope to fix this limitation
in future versions of Drools.

3.2.2. KnowledgeBase

The Knowledge Base is a repository of all the application's knowledge definitions. It may contain
rules, processes, functions, and type models. The Knowledge Base itself does not contain
instance data, known as facts; instead, sessions are created from the Knowledge Base into which
data can be inserted and where process instances may be started. Creating the Knowledge Base
can be heavy, whereas session creation is very light, so it is recommended that Knowledge Bases
be cached where possible to allow for repeated session creation.

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Figure 3.5. KnowledgeBase

A KnowledgeBase object is also serializable, and some people may prefer to build and then store
a KnowledgeBase, treating it also as a unit of deployment, instead of the Knowledge Packages.

The KnowledgeBase is created using the KnowledgeBaseFactory.

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In-Process Building and Deployment

Figure 3.6. KnowledgeBaseFactory

A KnowledgeBase can be created using the default configuration.

Example 3.17. Creating a new KnowledgeBase


If a custom class loader was used with the KnowledgeBuilder to resolve types not in the default
class loader, then that must also be set on the KnowledgeBase. The technique for this is the same
as with the KnowledgeBuilder.

Example 3.18. Creating a new KnowledgeBase with a custom ClassLoader

KnowledgeBaseConfiguration kbaseConf =
KnowledgeBaseFactory.createKnowledgeBaseConfiguration( null, cl );
KnowledgeBase kbase = KnowledgeBaseFactory.newKnowledgeBase( kbaseConf );

3.2.3. In-Process Building and Deployment

This is the simplest form of deployment. It compiles the knowledge definitions and adds them to the
Knowledge Base in the same JVM. This approach requires drools-core.jar and drools-compiler.jar
to be on the classpath.

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Example 3.19. Add KnowledgePackages to a KnowledgeBase


kbase.addKnowledgePackages( kpkgs );

Note that the addKnowledgePackages(kpkgs) method can be called iteratively to add additional
knowledge.

3.2.4. Building and Deployment in Separate Processes

Both the KnowledgeBase and the KnowledgePackage are units of deployment and serializable.
This means you can have one machine do any necessary building, requiring drools-
compiler.jar, and have another machine deploy and execute everything, needing only drools-
core.jar.

Although serialization is standard Java, we present an example of how one machine might write
out the deployment unit and how another machine might read in and use that deployment unit.

Example 3.20. Writing the KnowledgePackage to an OutputStream

ObjectOutputStream out = new ObjectOutputStream( new FileOutputStream( fileName ) );
out.writeObject( kpkgs );
out.close();

Example 3.21. Reading the KnowledgePackage from an InputStream

ObjectInputStream in = new ObjectInputStream( new FileInputStream( fileName ) );
// The input stream might contain an individual
// package or a collection.
@SuppressWarnings( "unchecked" )
Collection<KnowledgePackage> kpkgs =
()in.readObject( Collection<KnowledgePackage> );
in.close();

kbase.addKnowledgePackages( kpkgs );

The KnowledgeBase is also serializable and some people may prefer to build and then store the
KnowledgeBase itself, instead of the Knowledge Packages.

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StatefulknowledgeSessions and KnowledgeBase Modifications

Drools Guvnor, our server side management system, uses this deployment approach. After
Guvnor has compiled and published serialized Knowledge Packages on a URL, Drools can use
the URL resource type to load them.

3.2.5. StatefulknowledgeSessions and KnowledgeBase
Modifications

Stateful Knowledge Sessions will be discussed in more detail in section "Running". The
KnowledgeBase creates and returns StatefulKnowledgeSession objects, and it may optionally
keep references to those. When KnowledgeBase modifications occur those modifications are
applied against the data in the sessions. This reference is a weak reference and it is also optional,
which is controlled by a boolean flag.

3.2.6. KnowledgeAgent

The KnowlegeAgent provides automatic loading, caching and re-loading of resources and is
configured from a properties files. The Knowledge Agent can update or rebuild this Knowlege
Base as the resources it uses are changed. The strategy for this is determined by the configuration
given to the factory, but it is typically pull-based using regular polling. We hope to add push-based
updates and rebuilds in future versions. The Knowledge Agent will continuously scan all the added
resources, using a default polling interval of 60 seconds. If their date of the last modification is
updated it will rebuild the cached Knowledge Base using the new resources.

Figure 3.7. KnowledgeAgent

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The KnowlegeBuilder is created using a KnowledgeBuilderFactory object. The agent must
specify a name, which is used in the log files to associate a log entry with the corresponding agent.

Example 3.22. Creating the KnowledgeAgent


Figure 3.8. KnowledgeAgentFactory

The following example constructs an agent that will build a new KnowledgeBase from the specified
ChangeSet. (See section "Building" for more details on the ChangeSet format.) Note that the
method can be called iteratively to add new resources over time. The Knowledge Agent polls
the resources added from the ChangeSet every 60 seconds, the default interval, to see if they
are updated. Whenever changes are found it will construct a new Knowledge Base or apply the
modifications to the existing Knowledge Base according to its configuration. If the change set
specifies a resource that is a directory its contents will be scanned for changes, too.

Example 3.23. Writing the KnowledgePackage to an OutputStream

kagent.applyChangeSet( ResourceFactory.newUrlResource( url ) );

Resource scanning is not on by default, it's a service and must be started, and the same is true
for notification. Both can be done via the ResourceFactory.

Example 3.24. Starting the Scanning and Notification Services

ResourceFactory.getResourceChangeNotifierService().start();

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KnowledgeAgent

ResourceFactory.getResourceChangeScannerService().start();

The default resource scanning period may be changed via the ResourceChangeScannerService.
A suitably updated ResourceChangeScannerConfiguration object is passed to the service's
configure() method, which allows for the service to be reconfigured on demand.

Example 3.25. Changing the Scanning Intervals

ResourceChangeScannerConfiguration sconf =
ResourceFactory.getResourceChangeScannerService().newResourceChangeScannerConfiguration();
// Set the disk scanning interval to 30s, default is 60s.
sconf.setProperty( "drools.resource.scanner.interval", "30" );
ResourceFactory.getResourceChangeScannerService().configure( sconf );

Knowledge Agents can take an empty Knowledge Base or a populated one. If a populated
Knowledge Base is provided, the Knowledge Agent will run an iterator from Knowledge Base
and subscribe to the resources that it finds. While it is possible for the Knowledge Builder to
build all resources found in a directory, that information is lost by the Knowledge Builder so
that those directories will not be continuously scanned. Only directories specified as part of the
applyChangeSet(Resource) method are monitored.

One of the advantages of providing KnowledgeBase as the starting point is that you can
provide it with a KnowledgeBaseConfiguration. When resource changes are detected and a
new KnowledgeBase object is instantiated, it will use the KnowledgeBaseConfiguration of the
previous KnowledgeBase object.

Example 3.26. Using an existing KnowledgeBase

KnowledgeBaseConfiguration kbaseConf =
KnowledgeBaseFactory.createKnowledgeBaseConfiguration( null, cl );
KnowledgeBase kbase KnowledgeBaseFactory.newKnowledgeBase( kbaseConf );
// Populate kbase with resources here.

KnowledgeAgent kagent =
KnowledgeAgentFactory.newKnowledgeAgent( "MyAgent", kbase );

In the above example getKnowledgeBase() will return the same provided kbase instance until
resource changes are detected and a new Knowledge Base is built. When the new Knowledge
Base is built, it will be done with the KnowledgeBaseConfiguration that was provided to the
previous KnowledgeBase.

As mentioned previously, a ChangeSet XML can specify a directory and all of its contents will
be added. If this ChangeSet XML is used with the applyChangeSet() method it will also add

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any directories to the scanning process. When the directory scan detects an additional file, it will
be added to the Knowledge Base; any removed file is removed from the Knowledge Base, and
modified files will be removed from the Knowledge Base.

Example 3.27. ChangeSet XML which adds a directories contents

xs:schemaLocation='http://drools.org/drools-5.0/change-set.xsd' >
<add>
<resource source='file:/projects/myproject/myrules' type='PKG' />
</add>
</change-set>

Note that for the resource type PKG the drools-compiler dependency is not needed as the
Knowledge Agent is able to handle those with just drools-core.

The KnowledgeAgentConfiguration can be used to modify a Knowledge Agent's default
behavior. You could use this to load the resources from a directory, while inhibiting the continuous
scan for changes of that directory.

Example 3.28. Change the Scanning Behavior


KnowledgeAgentConfiguration kaconf =
KnowledgeAgentFactory.newKnowledgeAgentConfiguation();
// Do not scan directories, just files.
kaconf.setProperty( "drools.agent.scanDirectories", "false" );
KnowledgeAgent kagent =
KnowledgeAgentFactory.newKnowledgeAgent( "test agent", kaconf );

Previously we mentioned Drools Guvnor and how it can build and publish serialized Knowledge
Packages on a URL, and that the ChangeSet XML can handle URLs and Packages. Taken
together, this forms an importanty deployment scenario for the Knowledge Agent.

3.3. Running

3.3.1. KnowledgeBase
The KnowledgeBase is a repository of all the application's knowledge definitions. It will contain
rules, processes, functions, and type models. The Knowledge Base itself does not contain data;
instead, sessions are created from the KnowledgeBase into which data can be inserted and from

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StatefulKnowledgeSession

which process instances may be started. Creating the KnowledgeBase can be heavy, whereas
session creation is very light, so it is recommended that KnowledgeBases be cached where
possible to allow for repeated session creation.

Example 3.29. Creating a new KnowledgeBase


3.3.2. StatefulKnowledgeSession
The StatefulKnowledgeSession stores and executes on the runtime data. It is created from the
KnowledgeBase.

Figure 3.9. StatefulKnowledgeSession

Example 3.30. Create a StatefulKnowledgeSession from a KnowledgeBase


3.3.3. KnowledgeRuntime

3.3.3.1. WorkingMemoryEntryPoint
The WorkingMemoryEntryPoint provides the methods around inserting, updating and retrieving
facts. The term "entry point" is related to the fact that we have multiple partitions in a Working
Memory and you can choose which one you are inserting into, although this use case is aimed at
event processing and covered in more detail in the Fusion manual. Most rule based applications
will work with the default entry point alone.

The KnowledgeRuntime interface provides the main interaction with the engine. It is available
in rule consequences and process actions. In this manual the focus is on the methods and
interfaces related to rules, and the methods pertaining to processes will be ignored for now.

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But you'll notice that the KnowledgeRuntime inherits methods from both the WorkingMemory and
the ProcessRuntime, thereby providing a unified API to work with processes and rules. When
working with rules, three interfaces form the KnowledgeRuntime: WorkingMemoryEntryPoint,
WorkingMemory and the KnowledgeRuntime itself.

Figure 3.10. WorkingMemoryEntryPoint

3.3.3.1.1. Insertion

Insertion is the act of telling the WorkingMemory about a fact, which you do by
ksession.insert(yourObject), for example. When you insert a fact, it is examined for matches
against the rules. This means all of the work for deciding about firing or not firing a rule is done
during insertion; no rule, however, is executed until you call fireAllRules(), which you call after
you have finished inserting your facts. It is a common misunderstanding for people to think the
condition evaluation happens when you call fireAllRules(). Expert systems typically use the
term assert or assertion to refer to facts made available to the system. However, due to "assert"
being a keyword in most languages, we have decided to use the insert keyword; so expect to
hear the two used interchangeably.

When an Object is inserted it returns a FactHandle. This FactHandle is the token used to
represent your inserted object within the WorkingMemory. It is also used for interactions with the
WorkingMemory when you wish to retract or modify an object.

Cheese stilton = new Cheese("stilton");
FactHandle stiltonHandle = ksession.insert( stilton );

As mentioned in the Knowledge Base section, a Working Memory may operate in two assertion
modes, i.e., equality or identity, with identity being the default.

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KnowledgeRuntime

Identity means that the Working Memory uses an IdentityHashMap to store all asserted objects.
New instance assertions always result in the return of a new FactHandle, but if an instance is
asserted again then it returns the original fact handle, i.e., it ignores repeated insertions for the
same fact.

Equality means that the Working Memory uses a HashMap to store all asserted Objects. New
instance assertions will only return a new FactHandle if no equal objects have been asserted.

3.3.3.1.2. Retraction

Retraction is the removal of a fact from Working Memory, which means that it will no longer track
and match that fact, and any rules that are activated and dependent on that fact will be cancelled.
Note that it is possible to have rules that depend on the nonexistence of a fact, in which case
retracting a fact may cause a rule to activate. (See the not and exist keywords.) Retraction is
done using the FactHandle that was returned by the insert call.

FactHandle stiltonHandle = ksession.insert( stilton );
....
ksession.retract( stiltonHandle );

3.3.3.1.3. Update

The Rule Engine must be notified of modified facts, so that they can be reprocessed. You must use
the update() method to notify the WorkingMemory of changed objects for those objects that are
not able to notify the WorkingMemory themselves. Notice that update() always takes the modified
object as a second parameter, which allows you to specify new instances for immutable objects.
On the right hand side of a rule the modify statement is recommended, as it makes the changes
and notifies the engine in a single statement. After a fact's field values have changed you must
call update before changing another fact, or you will cause problems with the indexing within the
rule engine. The modify keyword avoids this problem.

FactHandle stiltonHandle = workingMemory.insert( stilton );
...
stilton.setPrice( 100 );
workingMemory.update( stiltonHandle, stilton );

3.3.3.2. WorkingMemory

The WorkingMemory provides access to the Agenda, permits query executions, and lets you
access named Entry Points.

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Figure 3.11. WorkingMemory

3.3.3.2.1. Query

Queries are used to retrieve fact sets based on patterns, as they are used in rules. Patterns may
make use of optional parameters. Queries can be defined in the Knowledge Base, from where they
are called up to return the matching results. While iterating over the result collection, any bound
identifier in the query can be accessed using the get(String identifier) method and any FactHandle
for that identifier can be retrieved using getFactHandle(String identifier).

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KnowledgeRuntime

Figure 3.12. QueryResults

Figure 3.13. QueryResultsRow

93


Example 3.31. Simple Query Example

QueryResults results =
ksession.getQueryResults( "my query", new Object[] { "string" } );
for ( QueryResultsRow row : results ) {
System.out.println( row.get( "varName" ) );
}

3.3.3.2.2. Live Querries

Drools has always had query support, but the result was returned as an iterable set; this makes
it hard to monitor changes over time.

We have now complimented this with Live Querries, which has a listener attached instead of
returning an iterable result set. These live querries stay open creating a view and publish change
events for the contents of this view. So now you can execute your query, with parameters and
listen to changes in the resulting view.

Example 3.32. Implementing ViewChangedEventListener

final List updated = new ArrayList();
final List removed = new ArrayList();
final List added = new ArrayList();

ViewChangedEventListener listener = new ViewChangedEventListener() {
public void rowUpdated(Row row) {
updated.add( row.get( "$price" ) );
}

public void rowRemoved(Row row) {
removed.add( row.get( "$price" ) );
}

public void rowAdded(Row row) {
added.add( row.get( "$price" ) );
}
};

// Open the LiveQuery
LiveQuery query = ksession.openLiveQuery( "cheeses",
new Object[] { "cheddar", "stilton" },
listener );
...
...
query.dispose() // make sure you call dispose when you want the query to close

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KnowledgeRuntime

A Drools blog article contains an example of Glazed Lists integration for live queries,

http://blog.athico.com/2010/07/glazed-lists-examples-for-drools-live.html

3.3.3.3. KnowledgeRuntime

The KnowledgeRuntime provides further methods that are applicable to both rules and processes,
such as setting globals and registering Channels (previously exit points, some references may
remain in docs for a while).

Figure 3.14. KnowledgeRuntime

3.3.3.3.1. Globals

Globals are named objects that can be passed to the rule engine, without needing to insert them.
Most often these are used for static information, or for services that are used in the RHS of a rule,
or perhaps as a means to return objects from the rule engine. If you use a global on the LHS of

95


a rule, make sure it is immutable. A global must first be declared in a rules file before it can be
set on the session.

global java.util.List list

With the Knowledge Base now aware of the global identifier and its type, it is now possible
to call ksession.setGlobal() for any session. Failure to declare the global type and
identifier first will result in an exception being thrown. To set the global on the session use
ksession.setGlobal(identifier, value):

List list = new ArrayList();
ksession.setGlobal("list", list);

If a rule evaluates on a global before you set it you will get a NullPointerException.

3.3.3.4. StatefulRuleSession

The StatefulRuleSession is inherited by the StatefulKnowledgeSession and provides the rule
related methods that are relevant from outside of the engine.

Figure 3.15. StatefulRuleSession

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Agenda

3.3.3.4.1. Agenda Filters

Figure 3.16. AgendaFilters

AgendaFilter objects are optional implementations of the filter interface which are used to allow
or deny the firing of an activation. What you filter on is entirely up to the implementation. Drools 4.0
used to supply some out of the box filters, which have not be exposed in drools 5.0 knowledge-
api, but they are simple to implement and the Drools 4.0 code base can be referred to.

To use a filter specify it while calling fireAllRules(). The following example permits only rules
ending in the string "Test". All others will be filtered out.

ksession.fireAllRules( new RuleNameEndsWithAgendaFilter( "Test" ) );

3.3.4. Agenda

The Agenda is a Rete feature. During actions on the WorkingMemory, rules may become fully
matched and eligible for execution; a single Working Memory Action can result in multiple eligible
rules. When a rule is fully matched an Activation is created, referencing the rule and the matched
facts, and placed onto the Agenda. The Agenda controls the execution order of these Activations
using a Conflict Resolution strategy.

The engine cycles repeatedly through two phases:

1. Working Memory Actions. This is where most of the work takes place, either in the
Consequence (the RHS itself) or the main Java application process. Once the Consequence
has finished or the main Java application process calls fireAllRules() the engine switches
to the Agenda Evaluation phase.

2. Agenda Evaluation. This attempts to select a rule to fire. If no rule is found it exits, otherwise it
fires the found rule, switching the phase back to Working Memory Actions.

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Figure 3.17. Two Phase Execution

The process repeats until the agenda is clear, in which case control returns to the calling
application. When Working Memory Actions are taking place, no rules are being fired.

Figure 3.18. Agenda

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Agenda

3.3.4.1. Conflict Resolution
Conflict resolution is required when there are multiple rules on the agenda. (The basics to this are
covered in chapter "Quick Start".) As firing a rule may have side effects on the working memory,
the rule engine needs to know in what order the rules should fire (for instance, firing ruleA may
cause ruleB to be removed from the agenda).

The default conflict resolution strategies employed by Drools are: Salience and LIFO (last in, first
out).

The most visible one is salience (or priority), in which case a user can specify that a certain rule
has a higher priority (by giving it a higher number) than other rules. In that case, the rule with
higher salience will be preferred. LIFO priorities are based on the assigned Working Memory
Action counter value, with all rules created during the same action receiving the same value. The
execution order of a set of firings with the same priority value is arbitrary.

As a general rule, it is a good idea not to count on rules firing in any particular order, and to
author the rules without worrying about a "flow". However when a flow is needed a number of
possibilities exist, including but not limited to: agenda groups, rule flow groups, activation groups,
control/semaphore facts. These are discussed in later sections.

Drools 4.0 supported custom conflict resolution strategies; while this capability still exists in Drools
it has not yet been exposed to the end user via knowledge-api in Drools 5.0.

3.3.4.2. AgendaGroup

Figure 3.19. AgendaGroup

Agenda groups are a way to partition rules (activations, actually) on the agenda. At any one time,
only one group has "focus" which means that activations for rules in that group only will take effect.
You can also have rules with "auto focus" which means that the focus is taken for its agenda group
when that rule's conditions are true.

Agenda groups are known as "modules" in CLIPS terminology. While it best to design rules that do
not need control flow, this is not always possible. Agenda groups provide a handy way to create

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a "flow" between grouped rules. You can switch the group which has focus either from within the
rule engine, or via the API. If your rules have a clear need for multiple "phases" or "sequences"
of processing, consider using agenda-groups for this purpose.

Each time setFocus() is called it pushes that Agenda Group onto a stack. When the focus group
is empty it is popped from the stack and the focus group that is now on top evaluates. An Agenda
Group can appear in multiple locations on the stack. The default Agenda Group is "MAIN", with all
rules which do not specify an Agenda Group being in this group. It is also always the first group
on the stack, given focus initially, by default.

ksession.getAgenda().getAgendaGroup( "Group A" ).setFocus();

3.3.4.3. ActivationGroup

Figure 3.20. ActivationGroup

An activation group is a set of rules bound together by the same "activation-group" rule attribute. In
this group only one rule can fire, and after that rule has fired all the other rules are cancelled from
the agenda. The clear() method can be called at any time, which cancels all of the activations
before one has had a chance to fire.

ksession.getAgenda().getActivationGroup( "Group B" ).clear();

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Event Model

3.3.4.4. RuleFlowGroup

Figure 3.21. RuleFlowGroup

A rule flow group is a group of rules associated by the "ruleflow-group" rule attribute. These
rules can only fire when the group is activate. The group itself can only become active when
the elaboration of the ruleflow diagram reaches the node representing the group. Here too, the
clear() method can be called at any time to cancels all activations still remaining on the Agenda.

ksession.getAgenda().getRuleFlowGroup( "Group C" ).clear();

3.3.5. Event Model

The event package provides means to be notified of rule engine events, including rules firing,
objects being asserted, etc. This allows you, for instance, to separate logging and auditing
activities from the main part of your application (and the rules).

The KnowlegeRuntimeEventManager interface is implemented by the KnowledgeRuntime which
provides two interfaces, WorkingMemoryEventManager and ProcessEventManager. We will only
cover the WorkingMemoryEventManager here.

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Figure 3.22. KnowledgeRuntimeEventManager

The WorkingMemoryEventManager allows for listeners to be added and removed, so that events
for the working memory and the agenda can be listened to.

Figure 3.23. WorkingMemoryEventManager

The following code snippet shows how a simple agenda listener is declared and attached to a
session. It will print activations after they have fired.

Example 3.33. Adding an AgendaEventListener

ksession.addEventListener( new DefaultAgendaEventListener() {
public void afterActivationFired(AfterActivationFiredEvent event) {
super.afterActivationFired( event );
System.out.println( event );
}
});

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Event Model

Drools also provides DebugWorkingMemoryEventListener and DebugAgendaEventListener
which implement each method with a debug print statement. To print all Working Memory events,
you add a listener like this:

Example 3.34. Creating a new KnowledgeBuilder

ksession.addEventListener( new DebugWorkingMemoryEventListener() );

All emitted events implement the KnowlegeRuntimeEvent interface which can be used to retrieve
the actual KnowlegeRuntime the event originated from.

Figure 3.24. KnowlegeRuntimeEvent

The events currently supported are:

• ActivationCreatedEvent

• ActivationCancelledEvent

• BeforeActivationFiredEvent

• AfterActivationFiredEvent

• AgendaGroupPushedEvent

• AgendaGroupPoppedEvent

• ObjectInsertEvent

• ObjectRetractedEvent

• ObjectUpdatedEvent

• ProcessCompletedEvent

• ProcessNodeLeftEvent

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• ProcessNodeTriggeredEvent

• ProcessStartEvent

3.3.6. KnowledgeRuntimeLogger

The KnowledgeRuntimeLogger uses the comprehensive event system in Drools to create an audit
log that can be used to log the execution of an application for later inspection, using tools such
as the Eclipse audit viewer.

Figure 3.25. KnowledgeRuntimeLoggerFactory

Example 3.35. FileLogger

KnowledgeRuntimeLogger logger =
KnowledgeRuntimeLoggerFactory.newFileLogger(ksession, "logdir/mylogfile");
...
logger.close();

3.3.7. StatelessKnowledgeSession

The StatelessKnowledgeSession wraps the StatefulKnowledgeSession, instead of extending
it. Its main focus is on decision service type scenarios. It avoids the need to call dispose().
Stateless sessions do not support iterative insertions and the method call fireAllRules()
from Java code; the act of calling execute() is a single-shot method that will internally
instantiate a StatefulKnowledgeSession, add all the user data and execute user commands, call
fireAllRules(), and then call dispose(). While the main way to work with this class is via the
BatchExecution (a subinterface of Command) as supported by the CommandExecutor interface,
two convenience methods are provided for when simple object insertion is all that's required. The
CommandExecutor and BatchExecution are talked about in detail in their own section.

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StatelessKnowledgeSession

Figure 3.26. StatelessKnowledgeSession

Our simple example shows a stateless session executing a given collection of Java objects using
the convenience API. It will iterate the collection, inserting each element in turn.

Example 3.36. Simple StatelessKnowledgeSession execution with a
Collection

kbuilder.add( ResourceFactory.newFileSystemResource( fileName ), ResourceType.DRL );
if (kbuilder.hasErrors() ) {
} else {
ksession.execute( collection );
}

If this was done as a single Command it would be as follows:

Example 3.37. Simple StatelessKnowledgeSession execution with
InsertElements Command

ksession.execute( CommandFactory.newInsertElements( collection ) );

If you wanted to insert the collection itself, and the collection's individual elements, then
CommandFactory.newInsert(collection) would do the job.

Methods of the CommandFactory create the supported commands, all of which can be marshalled
using XStream and the BatchExecutionHelper. BatchExecutionHelper provides details on the

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XML format as well as how to use Drools Pipeline to automate the marshalling of BatchExecution
and ExecutionResults.

StatelessKnowledgeSession supports globals, scoped in a number of ways. I'll cover the non-
command way first, as commands are scoped to a specific execution call. Globals can be resolved
in three ways.

• The Stateless Knowledge Session method getGlobals() returns a Globals instance which
provides access to the session's globals. These are shared for all execution calls. Exercise
caution regarding mutable globals because execution calls can be executing simultaneously in
different threads.

Example 3.38. Session scoped global

// Set a global hbnSession, that can be used for DB interactions in the rules.
ksession.setGlobal( "hbnSession", hibernateSession );
// Execute while being able to resolve the "hbnSession" identifier.
ksession.execute( collection );

• Using a delegate is another way of global resolution. Assigning a value to a global (with
setGlobal(String, Object)) results in the value being stored in an internal collection
mapping identifiers to values. Identifiers in this internal collection will have priority over any
supplied delegate. Only if an identifier cannot be found in this internal collection, the delegate
global (if any) will be used.

• The third way of resolving globals is to have execution scoped globals. Here, a Command to set
a global is passed to the CommandExecutor.

The CommandExecutor interface also offers the ability to export data via "out" parameters. Inserted
facts, globals and query results can all be returned.

Example 3.39. Out identifiers

// Set up a list of commands
List cmds = new ArrayList();
cmds.add( CommandFactory.newSetGlobal( "list1", new ArrayList(), true ) );
cmds.add( CommandFactory.newInsert( new Person( "jon", 102 ), "person" ) );
cmds.add( CommandFactory.newQuery( "Get People" "getPeople" );

// Execute the list
ExecutionResults results =
ksession.execute( CommandFactory.newBatchExecution( cmds ) );

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StatelessKnowledgeSession

// Retrieve the ArrayList
results.getValue( "list1" );
// Retrieve the inserted Person fact
results.getValue( "person" );
// Retrieve the query as a QueryResults instance.
results.getValue( "Get People" );

3.3.7.1. Sequential Mode

With Rete you have a stateful session where objects can be asserted and modified over time,
and where rules can also be added and removed. Now what happens if we assume a stateless
session, where after the initial data set no more data can be asserted or modified and rules cannot
be added or removed? Certainly it won't be necessary to re-evaluate rules, and the engine will
be able to operate in a simplified way.

1. Order the Rules by salience and position in the ruleset (by setting a sequence attribute on the
rule terminal node).

2. Create an array, one element for each possible rule activation; element position indicates firing
order.

3. Turn off all node memories, except the right-input Object memory.

4. Disconnect the Left Input Adapter Node propagation, and let the Object plus the Node be
referenced in a Command object, which is added to a list on the Working Memory for later
execution.

5. Assert all objects, and, when all assertions are finished and thus right-input node memories are
populated, check the Command list and execute each in turn.

6. All resulting Activations should be placed in the array, based upon the determined sequence
number of the Rule. Record the first and last populated elements, to reduce the iteration range.

7. Iterate the array of Activations, executing populated element in turn.

8. If we have a maximum number of allowed rule executions, we can exit our network evaluations
early to fire all the rules in the array.

The LeftInputAdapterNode no longer creates a Tuple, adding the Object, and then propagate
the Tuple – instead a Command object is created and added to a list in the Working Memory.
This Command object holds a reference to the LeftInputAdapterNode and the propagated
object. This stops any left-input propagations at insertion time, so that we know that a right-
input propagation will never need to attempt a join with the left-inputs (removing the need for left-
input memory). All nodes have their memory turned off, including the left-input Tuple memory
but excluding the right-input object memory, which means that the only node remembering an
insertion propagation is the right-input object memory. Once all the assertions are finished and all

107


right-input memories populated, we can then iterate the list of LeftInputAdatperNode Command
objects calling each in turn. They will propagate down the network attempting to join with the right-
input objects, but they won't be remembered in the left input as we know there will be no further
object assertions and thus propagations into the right-input memory.

There is no longer an Agenda, with a priority queue to schedule the Tuples; instead, there is simply
an array for the number of rules. The sequence number of the RuleTerminalNode indicates the
element within the array where to place the Activation. Once all Command objects have finished
we can iterate our array, checking each element in turn, and firing the Activations if they exist. To
improve performance, we remember the first and the last populated cell in the array. The network
is constructed, with each RuleTerminalNode being given a sequence number based on a salience
number and its order of being added to the network.

Typically the right-input node memories are Hash Maps, for fast object retraction; here, as we
know there will be no object retractions, we can use a list when the values of the object are not
indexed. For larger numbers of objects indexed Hash Maps provide a performance increase; if
we know an object type has only a few instances, indexing is probably not advantageous, and
a list can be used.

Sequential mode can only be used with a Stateless Session and is off by default. To turn it on,
either call RuleBaseConfiguration.setSequential(true), or set the rulebase configuration
property drools.sequential to true. Sequential mode can fall back to a dynamic agenda
by calling setSequentialAgenda with SequentialAgenda.DYNAMIC. You may also set the
"drools.sequential.agenda" property to "sequential" or "dynamic".

3.3.8. Commands and the CommandExecutor

Drools has the concept of stateful or stateless sessions. We've already covered stateful sessions,
which use the standard working memory that can be worked with iteratively over time. Stateless
is a one-off execution of a working memory with a provided data set. It may return some results,
with the session being disposed at the end, prohibiting further iterative interactions. You can think
of stateless as treating a rule engine like a function call with optional return results.

In Drools 4 we supported these two paradigms but the way the user interacted with them
was different. StatelessSession used an execute(...) method which would insert a collection of
objects as facts. StatefulSession didn't have this method, and insert used the more traditional
insert(...) method. The other issue was that the StatelessSession did not return any results,
so that users themselves had to map globals to get results, and it wasn't possible to do anything
besides inserting objects; users could not start processes or execute queries.

Drools 5.0 addresses all of these issues and more. The foundation for this is the CommandExecutor
interface, which both the stateful and stateless interfaces extend, creating consistency and
ExecutionResults:

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Commands and the CommandExecutor

Figure 3.27. CommandExecutor

Figure 3.28. ExecutionResults

The CommandFactory allows for commands to be executed on those sessions, the only difference
being that the Stateless Knowledge Session executes fireAllRules() at the end before
disposing the session. The currently supported commands are:

• FireAllRules

• GetGlobal

• SetGlobal

• InsertObject

• InsertElements

• Query

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• StartProcess

• BatchExecution

InsertObject will insert a single object, with an optional "out" identifier. InsertElements will
iterate an Iterable, inserting each of the elements. What this means is that a Stateless Knowledge
Session is no longer limited to just inserting objects, it can now start processes or execute queries,
and do this in any order.

Example 3.40. Insert Command

ExecutionResults bresults =
ksession.execute( CommandFactory.newInsert( new Cheese( "stilton" ), "stilton_id" ) );
Stilton stilton = bresults.getValue( "stilton_id" );

The execute method always returns an ExecutionResults instance, which allows access to any
command results if they specify an out identifier such as the "stilton_id" above.

Example 3.41. InsertElements Command

Command cmd = CommandFactory.newInsertElements( Arrays.asList( Object[] {
new Cheese( "stilton" ),
new Cheese( "brie" ),
new Cheese( "cheddar" ),
});
ExecutionResults bresults = ksession.execute( cmd );

The execute method only allows for a single command. That's where BatchExecution comes
in, which represents a composite command, created from a list of commands. Now, execute will
iterate over the list and execute each command in turn. This means you can insert some objects,
start a process, call fireAllRules and execute a query, all in a single execute(...) call, which
is quite powerful.

As mentioned previosly, the Stateless Knowledge Session will execute fireAllRules()
automatically at the end. However the keen-eyed reader probably has already noticed the
FireAllRules command and wondered how that works with a StatelessKnowledgeSession. The
FireAllRules command is allowed, and using it will disable the automatic execution at the end;
think of using it as a sort of manual override function.

Commands support out identifiers. Any command that has an out identifier set on it will add its
results to the returned ExecutionResults instance. Let's look at a simple example to see how this
works.

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Example 3.42. BatchExecution Command


List cmds = new ArrayList();
cmds.add( CommandFactory.newInsertObject( new Cheese( "stilton", 1), "stilton") );
cmds.add( CommandFactory.newStartProcess( "process cheeses" ) );
cmds.add( CommandFactory.newQuery( "cheeses" ) );
ExecutionResults bresults = ksession.execute( CommandFactory.newBatchExecution( cmds ) );
Cheese stilton = ( Cheese ) bresults.getValue( "stilton" );
QueryResults qresults = ( QueryResults ) bresults.getValue( "cheeses" );

In the above example multiple commands are executed, two of which populate the
ExecutionResults. The query command defaults to use the same identifier as the query name,
but it can also be mapped to a different identifier.

A custom XStream marshaller can be used with the Drools Pipeline to achieve XML scripting,
which is perfect for services. Here are two simple XML samples, one for the BatchExecution and
one for the ExecutionResults.

Example 3.43. Simple BatchExecution XML

<batch-execution>
<insert out-identifier='outStilton'>
<org.drools.Cheese>
<type>stilton</type>
<price>25</price>
<oldPrice>0</oldPrice>
</org.drools.Cheese>
</insert>
</batch-execution>

Example 3.44. Simple ExecutionResults XML

<execution-results>
<result identifier='outStilton'>
<org.drools.Cheese>
<price>30</price>
</result>
</execution-results>

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Spring and Camel, covered in the integrations book, facilitate declarative services.

Example 3.45. BatchExecution Marshalled to XML

<batch-execution>
<insert out-identifier="stilton">
<org.drools.Cheese>
<price>1</price>
</insert>
<query out-identifier='cheeses2' name='cheesesWithParams'>
<string>stilton</string>
<string>cheddar</string>
</query>
</batch-execution>

The CommandExecutor returns an ExecutionResults, and this is handled by the pipeline code
snippet as well. A similar output for the <batch-execution> XML sample above would be:

Example 3.46. ExecutionResults Marshalled to XML

<execution-results>
<result identifier="stilton">
<org.drools.Cheese>
<price>2</price>
</result>
<result identifier='cheeses2'>
<query-results>
<identifiers>
<identifier>cheese</identifier>
</identifiers>
<row>
<org.drools.Cheese>
<type>cheddar</type>
<price>2</price>
</row>
<row>
<org.drools.Cheese>
<type>cheddar</type>
<price>1</price>

112

</row>
</query-results>
</result>
</execution-results>

The BatchExecutionHelper provides a configured XStream instance to support the marshalling
of Batch Executions, where the resulting XML can be used as a message format, as shown above.
Configured converters only exist for the commands supported via the Command Factory. The
user may add other converters for their user objects. This is very useful for scripting stateless or
stateful knowledge sessions, especially when services are involved.

There is currently no XML schema to support schema validation. The basic format is outlined here,
and the drools-pipeline module has an illustrative unit test in the XStreamBatchExecutionTest
unit test. The root element is <batch-execution> and it can contain zero or more commands
elements.

Example 3.47. Root XML element

<batch-execution>
...
</batch-execution>

This contains a list of elements that represent commands, the supported commands is limited
to those Commands provided by the Command Factory. The most basic of these is the <insert>
element, which inserts objects. The contents of the insert element is the user object, as dictated
by XStream.

Example 3.48. Insert

<batch-execution>
<insert>
...
</insert>
</batch-execution>

The insert element features an "out-identifier" attribute, demanding that the inserted object will
also be returned as part of the result payload.

Example 3.49. Insert with Out Identifier Command

<batch-execution>

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<insert out-identifier='userVar'>
...
</insert>
</batch-execution>

It's also possible to insert a collection of objects using the <insert-elements> element. This
command does not support an out-identifier. The org.domain.UserClass is just an illustrative
user object that XStream would serialize.

Example 3.50. Insert Elements command

<batch-execution>
<insert-elements>
<org.domain.UserClass>
...
</org.domain.UserClass>
...
...
</insert-elements>
</batch-execution>

Next, there is the <set-global> element, which sets a global for the session.

Example 3.51. Insert Elements command

<batch-execution>
<set-global identifier='userVar'>
...
</set-global>
</batch-execution>

<set-global> also supports two other optional attributes, out and out-identifier. A true value
for the boolean out will add the global to the <batch-execution-results> payload, using the
name from the identifier attribute. out-identifier works like out but additionally allows you
to override the identifier used in the <batch-execution-results> payload.

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Example 3.52. Set Global Command

<batch-execution>
<set-global identifier='userVar1' out='true'>
...
</set-global>
<set-global identifier='userVar2' out-identifier='alternativeUserVar2'>
...
</set-global>
</batch-execution>

There is also a <get-global> element, without contents, with just an out-identifier attribute.
(There is no need for an out attribute because retrieving the value is the sole purpose of a <get-
global> element.

Example 3.53. Get Global Command

<batch-execution>
<get-global identifier='userVar1' />
<get-global identifier='userVar2' out-identifier='alternativeUserVar2'/>
</batch-execution>

While the out attribute is useful in returning specific instances as a result payload, we often wish to
run actual queries. Both parameter and parameterless queries are supported. The name attribute
is the name of the query to be called, and the out-identifier is the identifier to be used for the
query results in the <execution-results> payload.

Example 3.54. Query Command

<batch-execution>
<query out-identifier='cheeses' name='cheeses'/>
<query out-identifier='cheeses2' name='cheesesWithParams'>
<string>stilton</string>
<string>cheddar</string>
</query>
</batch-execution>

The <start-process> command accepts optional parameters. Other process related methods
will be added later, like interacting with work items.

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Example 3.55. Start Process Command

<batch-execution>
<startProcess processId='org.drools.actions'>
<parameter identifier='person'>
<org.drools.TestVariable>
<name>John Doe</name>
</org.drools.TestVariable>
</parameter>
</startProcess>
</batch-execution

Example 3.56. Signal Event Command

<signal-event process-instance-id='1' event-type='MyEvent'>
<string>MyValue</string>
</signal-event>

Example 3.57. Complete Work Item Command

<complete-work-item id='" + workItem.getId() + "' >
<result identifier='Result'>
<string>SomeOtherString</string>
</result>
</complete-work-item>

Example 3.58. Abort Work Item Command

<abort-work-item id='21' />

Support for more commands will be added over time.

3.3.9. Marshalling

The MarshallerFactory is used to marshal and unmarshal Stateful Knowledge Sessions.

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Marshalling

Figure 3.29. MarshallerFactory

At the simplest the MarshallerFactory can be used as follows:

Example 3.59. Simple Marshaller Example

// ksession is the StatefulKnowledgeSession
// kbase is the KnowledgeBase
ByteArrayOutputStream baos = new ByteArrayOutputStream();
Marshaller marshaller = MarshallerFactory.newMarshaller( kbase );
marshaller.marshall( baos, ksession );
baos.close();

However, with marshalling you need more flexibility when dealing with referenced
user data. To achieve this we have the ObjectMarshallingStrategy interface.
Two implementations are provided, but users can implement their own. The two
supplied strategies are IdentityMarshallingStrategy and SerializeMarshallingStrategy.
SerializeMarshallingStrategy is the default, as used in the example above, and it just calls the
Serializable or Externalizable methods on a user instance. IdentityMarshallingStrategy
instead creates an integer id for each user object and stores them in a Map, while the id is written to
the stream. When unmarshalling it accesses the IdentityMarshallingStrategy map to retrieve
the instance. This means that if you use the IdentityMarshallingStrategy, it is stateful for the
life of the Marshaller instance and will create ids and keep references to all objects that it attempts
to marshal. Below is he code to use an Identity Marshalling Strategy.

Example 3.60. IdentityMarshallingStrategy

ObjectMarshallingStrategy oms = MarshallerFactory.newIdentityMarshallingStrategy()
Marshaller marshaller =

117


MarshallerFactory.newMarshaller( kbase, new ObjectMarshallingStrategy[]{ oms } );
baos.close();

For added flexability we can't assume that a single strategy is suitable. Therefore we have added
the ObjectMarshallingStrategyAcceptor interface that each Object Marshalling Strategy
contains. The Marshaller has a chain of strategies, and when it attempts to read or write a user
object it iterates the strategies asking if they accept responsability for marshalling the user object.
One of the provided implementations is ClassFilterAcceptor. This allows strings and wild cards
to be used to match class names. The default is "*.*", so in the above example the Identity
Marshalling Strategy is used which has a default "*.*" acceptor.

Assuming that we want to serialize all classes except for one given package, where we will use
identity lookup, we could do the following:

Example 3.61. IdentityMarshallingStrategy with Acceptor

ObjectMarshallingStrategyAcceptor identityAcceptor =
MarshallerFactory.newClassFilterAcceptor( new String[] { "org.domain.pkg1.*" } );
ObjectMarshallingStrategy identityStrategy =
MarshallerFactory.newIdentityMarshallingStrategy( identityAcceptor );
ObjectMarshallingStrategy sms = MarshallerFactory.newSerializeMarshallingStrategy();
Marshaller marshaller =
MarshallerFactory.newMarshaller( kbase,
new ObjectMarshallingStrategy[]{ identityStrategy, sms } );
baos.close();

Note that the acceptance checking order is in the natural order of the supplied array.

3.3.10. Persistence and Transactions
Longterm out of the box persistence with Java Persistence API (JPA) is possible with Drools.
You will need to have some implementation of the Java Transaction API (JTA) installed. For
development purposes we recommend the Bitronix Transaction Manager, as it's simple to set up
and works embedded, but for production use JBoss Transactions is recommended.

Example 3.62. Simple example using transactions

Environment env = KnowledgeBaseFactory.newEnvironment();
env.set( EnvironmentName.ENTITY_MANAGER_FACTORY,
Persistence.createEntityManagerFactory( "emf-name" ) );
env.set( EnvironmentName.TRANSACTION_MANAGER,
TransactionManagerServices.getTransactionManager() );

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Persistence and Transactions

// KnowledgeSessionConfiguration may be null, and a default will be used
StatefulKnowledgeSession ksession =
JPAKnowledgeService.newStatefulKnowledgeSession( kbase, null, env );
int sessionId = ksession.getId();

UserTransaction ut =
(UserTransaction) new InitialContext().lookup( "java:comp/UserTransaction" );
ut.begin();
ksession.insert( data1 );
ksession.insert( data2 );
ksession.startProcess( "process1" );
ut.commit();

To use a JPA, the Environment must be set with both the EntityManagerFactory and the
TransactionManager. If rollback occurs the ksession state is also rolled back, so you can continue
to use it after a rollback. To load a previously persisted Stateful Knowledge Session you'll need
the id, as shown below:

Example 3.63. Loading a StatefulKnowledgeSession

StatefulKnowledgeSession ksession =
JPAKnowledgeService.loadStatefulKnowledgeSession( sessionId, kbase, null, env );

To enable persistence several classes must be added to your persistence.xml, as in the example
below:

Example 3.64. Configuring JPA

<persistence-unit name="org.drools.persistence.jpa" transaction-type="JTA">
<provider>org.hibernate.ejb.HibernatePersistence</provider>
<jta-data-source>jdbc/BitronixJTADataSource</jta-data-source>
<class>org.drools.persistence.session.SessionInfo</class>
<class>org.drools.persistence.processinstance.ProcessInstanceInfo</class>
<class>org.drools.persistence.processinstance.ProcessInstanceEventInfo</
class>
<class>org.drools.persistence.processinstance.WorkItemInfo</class>
<properties>

<property name="hibernate.dialect" value="org.hibernate.dialect.H2Dialect"/>

<property name="hibernate.max_fetch_depth" value="3"/>
<property name="hibernate.hbm2ddl.auto" value="update" />
<property name="hibernate.show_sql" value="true" />
<property name="hibernate.transaction.manager_lookup_class"

119


value="org.hibernate.transaction.BTMTransactionManagerLookup" />
</properties>
</persistence-unit>

The jdbc JTA data source would have to be configured first. Bitronix provides a number of ways
of doing this, and its documentation should be contsulted for details. For a quick start, here is the
programmatic approach:

Example 3.65. Configuring JTA DataSource

PoolingDataSource ds = new PoolingDataSource();
ds.setUniqueName( "jdbc/BitronixJTADataSource" );
ds.setClassName( "org.h2.jdbcx.JdbcDataSource" );
ds.setMaxPoolSize( 3 );
ds.setAllowLocalTransactions( true );
ds.getDriverProperties().put( "user", "sa" );
ds.getDriverProperties().put( "password", "sasa" );
ds.getDriverProperties().put( "URL", "jdbc:h2:mem:mydb" );
ds.init();

Bitronix also provides a simple embedded JNDI service, ideal for testing. To use it add a
jndi.properties file to your META-INF and add the following line to it:

Example 3.66. JNDI properties

java.naming.factory.initial=bitronix.tm.jndi.BitronixInitialContextFactory

3.3.11. Drools Clips
Drools Clips is an alpha level research project to provide a Clips like front end ot Drools. As of
Drools 5.2 this module as stopped working, we will try and fix for 5.3.

Deftemplates are working, the knowledge base handles multiple name spaces and you can attach
the knoweldge base to the session for interative building, to provide a more "shell" like environment
suitable for Clips.

• deftemplate

• defrule

• deffunction

• and/or/not/exists/test conditional elements

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Drools Clips

• Literal, Variable, Return Value and Predicate field constarints

This project is very early stages and in need of love. If you want to help, open up eclipse import api,
core, compiler and clips and you should be good to go. The unit tests should be self explanatory.

121

Chapter 4.

Chapter 4. Rule Language
Reference
4.1. Overview
Drools has a "native" rule language. This format is very light in terms of punctuation, and supports
natural and domain specific languages via "expanders" that allow the language to morph to your
problem domain. This chapter is mostly concerted with this native rule format. The diagrams used
to present the syntax are known as "railroad" diagrams, and they are basically flow charts for the
language terms. The technically very keen may also refer to DRL.g which is the Antlr3 grammar
for the rule language. If you use the Rule Workbench, a lot of the rule structure is done for you with
content assistance, for example, type "ru" and press ctrl+space, and it will build the rule structure
for you.

4.1.1. A rule file
A rule file is typically a file with a .drl extension. In a DRL file you can have multiple rules, queries
and functions, as well as some resource declarations like imports, globals and attributes that
are assigned and used by your rules and queries. However, you are also able to spread your
rules across multiple rule files (in that case, the extension .rule is suggested, but not required) -
spreading rules across files can help with managing large numbers of rules. A DRL file is simply
a text file.

The overall structure of a rule file is:

Example 4.1. Rules file

package package-name

imports

globals

functions

queries

rules

The order in which the elements are declared is not important, except for the package name that,
if declared, must be the first element in the rules file. All elements are optional, so you will use
only those you need. We will discuss each of them in the following sections.

123

Chapter 4. Rule Language Refe...

4.1.2. What makes a rule
For the inpatients, just as an early view, a rule has the following rough structure:

rule "name"
attributes
when
LHS
then
RHS
end

It's really that simple. Mostly punctuation is not needed, even the double quotes for "name" are
optional, as are newlines. Attributes are simple (always optional) hints to how the rule should
behave. LHS is the conditional parts of the rule, which follows a certain syntax which is covered
below. RHS is basically a block that allows dialect specific semantic code to be executed.

It is important to note that white space is not important, except in the case of domain specific
languages, where lines are processed one by one and spaces may be significant to the domain
language.

4.2. Keywords
Drools 5 introduces the concept of hard and soft keywords.

Hard keywords are reserved, you cannot use any hard keyword when naming your domain objects,
properties, methods, functions and other elements that are used in the rule text.

Here is the list of hard keywords that must be avoided as identifiers when writing rules:

• true

• false

• null

Soft keywords are just recognized in their context, enabling you to use these words in any other
place if you wish, although, it is still recommended to avoid them, to avoid confusions, if possible.
Here is a list of the soft keywords:

• lock-on-active

• date-effective

• date-expires

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Keywords

• no-loop

• auto-focus

• activation-group

• agenda-group

• ruleflow-group

• entry-point

• duration

• package

• import

• dialect

• salience

• enabled

• attributes

• rule

• extend

• when

• then

• template

• query

• declare

• function

• global

• eval

• not

• in

• or

125


• and

• exists

• forall

• accumulate

• collect

• from

• action

• reverse

• result

• end

• over

• init

Of course, you can have these (hard and soft) words as part of a method name in camel case,
like notSomething() or accumulateSomething() - there are no issues with that scenario.

Although the 3 hard keywords above are unlikely to be used in your existing domain models, if
you absolutely need to use them as identifiers instead of keywords, the DRL language provides
the ability to escape hard keywords on rule text. To escape a word, simply enclose it in grave
accents, like this:

Holiday( `true` == "yes" ) // please note that Drools will resolve that reference
to the method Holiday.isTrue()

4.3. Comments
Comments are sections of text that are ignored by the rule engine. They are stripped out when
they are encountered, except inside semantic code blocks, like the RHS of a rule.

4.3.1. Single line comment

To create single line comments, you can use '//'. The parser will ignore anything in the line after
the comment symbol. Example:

rule "Testing Comments"

126

Multi-line comment

when
// this is a single line comment
eval( true ) // this is a comment in the same line of a pattern
then
// this is a comment inside a semantic code block
end

Warning

Drools also supports for backward compatibility the use of '#' for single line
comments, but this is deprecated and will be removed in future releases.

4.3.2. Multi-line comment

Figure 4.1. Multi-line comment

Multi-line comments are used to comment blocks of text, both in and outside semantic code blocks.
Example:

rule "Test Multi-line Comments"
when
/* this is a multi-line comment
in the left hand side of a rule */
eval( true )
then
/* and this is a multi-line comment
in the right hand side of a rule */
end

4.4. Error Messages
Drools 5 introduces standardized error messages. This standardization aims to help users to find
and resolve problems in a easier and faster way. In this section you will learn how to identify and
interpret those error messages, and you will also receive some tips on how to solve the problems
associated with them.

4.4.1. Message format
The standardization includes the error message format and to better explain this format, let's use
the following example:

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Figure 4.2. Error Message Format

1st Block: This area identifies the error code.

2nd Block: Line and column information.

3rd Block: Some text describing the problem.

4th Block: This is the first context. Usually indicates the rule, function, template or query where
the error occurred. This block is not mandatory.

5th Block: Identifies the pattern where the error occurred. This block is not mandatory.

4.4.2. Error Messages Description

4.4.2.1. 101: No viable alternative
Indicates the most common errors, where the parser came to a decision point but couldn't identify
an alternative. Here are some examples:

Example 4.2.

1: rule one
2: when
3: exists Foo()
4: exits Bar()
5: then
6: end

The above example generates this message:

• [ERR 101] Line 4:4 no viable alternative at input 'exits' in rule one

At first glance this seems to be valid syntax, but it is not (exits != exists). Let's take a look at next
example:

Example 4.3.

1: package org.drools;
2: rule
3: when
4: Object()
5: then

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Error Messages Description

6: System.out.println("A RHS");
7: end

Now the above code generates this message:

• [ERR 101] Line 3:2 no viable alternative at input 'WHEN'

This message means that the parser encountered the token WHEN, actually a hard keyword, but
it's in the wrong place since the the rule name is missing.

The error "no viable alternative" also occurs when you make a simple lexical mistake. Here is a
sample of a lexical problem:

Example 4.4.

1: rule simple_rule
2: when
3: Student( name == "Andy )
4: then
5: end

The above code misses to close the quotes and because of this the parser generates this error
message:

• [ERR 101] Line 0:-1 no viable alternative at input '<eof>' in rule simple_rule in pattern Student

Note

Usually the Line and Column information are accurate, but in some cases (like
unclosed quotes), the parser generates a 0:-1 position. In this case you should
check whether you didn't forget to close quotes, apostrophes or parentheses.

4.4.2.2. 102: Mismatched input

This error indicates that the parser was looking for a particular symbol that it didn't #nd at the
current input position. Here are some samples:

Example 4.5.

1: rule simple_rule
2: when
3: foo3 : Bar(

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The above example generates this message:

• [ERR 102] Line 0:-1 mismatched input '<eof>' expecting ')' in rule simple_rule in pattern Bar

To fix this problem, it is necessary to complete the rule statement.

Note

Usually when you get a 0:-1 position, it means that parser reached the end of
source.

The following code generates more than one error message:

Example 4.6.

1: package org.drools;
2:
3: rule "Avoid NPE on wrong syntax"
4: when
5: not( Cheese( ( type == "stilton", price == 10 ) || ( type == "brie",
price == 15 ) ) from $cheeseList )
6: then
7: System.out.println("OK");
8: end

These are the errors associated with this source:

• [ERR 102] Line 5:36 mismatched input ',' expecting ')' in rule "Avoid NPE on wrong syntax" in
pattern Cheese

• [ERR 101] Line 5:57 no viable alternative at input 'type' in rule "Avoid NPE on wrong syntax"

• [ERR 102] Line 5:106 mismatched input ')' expecting 'then' in rule "Avoid NPE on wrong syntax"

Note that the second problem is related to the first. To fix it, just replace the commas (',') by AND
operator ('&&').

Note

In some situations you can get more than one error message. Try to fix one
by one, starting at the first one. Some error messages are generated merely as
consequences of other errors.

130

Error Messages Description

4.4.2.3. 103: Failed predicate

A validating semantic predicate evaluated to false. Usually these semantic predicates are used to
identify soft keywords. This sample shows exactly this situation:

Example 4.7.

1: package nesting;
2: dialect "mvel"
3:
4: import org.drools.Person
5: import org.drools.Address
6:
7: fdsfdsfds
8:
9: rule "test something"
10: when
11: p: Person( name=="Michael" )
12: then
13: p.name = "other";
14: System.out.println(p.name);
15: end

With this sample, we get this error message:

• [ERR 103] Line 7:0 rule 'rule_key' failed predicate:
{(validateIdentifierKey(DroolsSoftKeywords.RULE))}? in rule

The fdsfdsfds text is invalid and the parser couldn't identify it as the soft keyword rule.

Note

This error is very similar to 102: Mismatched input, but usually involves soft
keywords.

4.4.2.4. 104: Trailing semi-colon not allowed

This error is associated with the eval clause, where its expression may not be terminated with
a semicolon. Check this example:

Example 4.8.

1: rule simple_rule
2: when

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3: eval(abc();)
4: then
5: end

Due to the trailing semicolon within eval, we get this error message:

• [ERR 104] Line 3:4 trailing semi-colon not allowed in rule simple_rule

This problem is simple to fix: just remove the semi-colon.

4.4.2.5. 105: Early Exit

The recognizer came to a subrule in the grammar that must match an alternative at least once,
but the subrule did not match anything. Simply put: the parser has entered a branch from where
there is no way out. This example illustrates it:

Example 4.9.

1: template test_error
2: aa s 11;
3: end

This is the message associated to the above sample:

• [ERR 105] Line 2:2 required (...)+ loop did not match anything at input 'aa' in template test_error

To fix this problem it is necessary to remove the numeric value as it is neither a valid data type
which might begin a new template slot nor a possible start for any other rule file construct.

4.4.3. Other Messages
Any other message means that something bad has happened, so please contact the development
team.

4.5. Package
A package is a collection of rules and other related constructs, such as imports and globals. The
package members are typically related to each other - perhaps HR rules, for instance. A package
represents a namespace, which ideally is kept unique for a given grouping of rules. The package
name itself is the namespace, and is not related to files or folders in any way.

It is possible to assemble rules from multiple rule sources, and have one top level package
configuration that all the rules are kept under (when the rules are assembled). Although, it is not
possible to merge into the same package resources declared under different names. A single

132

import

Rulebase may, however, contain multiple packages built on it. A common structure is to have
all the rules for a package in the same file as the package declaration (so that is it entirely self-
contained).

The following railroad diagram shows all the components that may make up a package. Note that
a package must have a namespace and be declared using standard Java conventions for package
names; i.e., no spaces, unlike rule names which allow spaces. In terms of the order of elements,
they can appear in any order in the rule file, with the exception of the package statement, which
must be at the top of the file. In all cases, the semicolons are optional.

Figure 4.3. package

Notice that any rule attribute (as described the section Rule Attributes) may also be written at
package level, superseding the attribute's default value. The modified default may still be replaced
by an attribute setting within a rule.

4.5.1. import

Figure 4.4. import

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Import statements work like import statements in Java. You need to specify the fully qualified paths
and type names for any objects you want to use in the rules. Drools automatically imports classes
from the Java package of the same name, and also from the package java.lang.

4.5.2. global

Figure 4.5. global

With global you define global variables. They are used to make application objects available
to the rules. Typically, they are used to provide data or services that the rules use, especially
application services used in rule consequences, and to return data from the rules, like logs or
values added in rule consequences, or for the rules to interact with the application, doing callbacks.
Globals are not inserted into the Working Memory, and therefore a global should never be used to
establish conditions in rules except when it has a constant immutable value. The engine cannot be
notified about value changes of globals and does not track their changes. Incorrect use of globals
in constraints may yield surprising results - surprising in a bad way.

If multiple packages declare globals with the same identifier they must be of the same type and
all of them will reference the same global value.

In order to use globals you must:

1. Declare your global variable in your rules file and use it in rules. Example:

global java.util.List myGlobalList;

rule "Using a global"
when
eval( true )
then
myGlobalList.add( "Hello World" );
end

2. Set the global value on your working memory. It is a best practice to set all global values before
asserting any fact to the working memory. Example:

WorkingMemory wm = rulebase.newStatefulSession();
wm.setGlobal( "myGlobalList", list );

134

Function

Note that these are just named instances of objects that you pass in from your application to
the working memory. This means you can pass in any object you want: you could pass in a
service locator, or perhaps a service itself. With the new from element it is now common to pass
a Hibernate session as a global, to allow from to pull data from a named Hibernate query.

One example may be an instance of a Email service. In your integration code that is calling the
rule engine, you obtain your emailService object, and then set it in the working memory. In the
DRL, you declare that you have a global of type EmailService, and give it the name "email". Then
in your rule consequences, you can use things like email.sendSMS(number, message).

Globals are not designed to share data between rules and they should never be used for that
purpose. Rules always reason and react to the working memory state, so if you want to pass data
from rule to rule, assert the data as facts into the working memory.

It is strongly discouraged to set or change a global value from inside your rules. We recommend
to you always set the value from your application using the working memory interface.

4.6. Function

Figure 4.6. function

Functions are a way to put semantic code in your rule source file, as opposed to in normal Java
classes. They can't do anything more than what you can do with helper classes. (In fact, the
compiler generates the helper class for you behind the scenes.) The main advantage of using
functions in a rule is that you can keep the logic all in one place, and you can change the functions
as needed (which can be a good or a bad thing). Functions are most useful for invoking actions
on the consequence (then) part of a rule, especially if that particular action is used over and over
again, perhaps with only differing parameters for each rule.

A typical function declaration looks like:

function String hello(String name) {

135


return "Hello "+name+"!";
}

Note that the function keyword is used, even though its not really part of Java. Parameters to
the function are defined as for a method, and you don't have to have parameters if they are not
needed. The return type is defined just like in a regular method.

Alternatively, you could use a static method in a helper class, e.g., Foo.hello(). Drools supports
the use of function imports, so all you would need to do is:

import function my.package.Foo.hello

Irrespective of the way the function is defined or imported, you use a function by calling it by its
name, in the consequence or inside a semantic code block. Example:

rule "using a static function"
when
eval( true )
then
System.out.println( hello( "Bob" ) );
end

4.7. Type Declaration

Figure 4.7. meta_data

136

Declaring New Types

Figure 4.8. type_declaration

Type declarations have two main goals in the rules engine: to allow the declaration of new types,
and to allow the declaration of metadata for types.

• Declaring new types: Drools works out of the box with plain Java objects as facts. Sometimes,
however, users may want to define the model directly to the rules engine, without worrying about
creating models in a lower level language like Java. At other times, there is a domain model
already built, but eventually the user wants or needs to complement this model with additional
entities that are used mainly during the reasoning process.

• Declaring metadata: facts may have meta information associated to them. Examples of meta
information include any kind of data that is not represented by the fact attributes and is consistent
among all instances of that fact type. This meta information may be queried at runtime by the
engine and used in the reasoning process.

4.7.1. Declaring New Types
To declare a new type, all you need to do is use the keyword declare, followed by the list of fields,
and the keyword end. A new fact must have a list of fields, otherwise the engine will look for an
existing fact class in the classpath and raise an error if not found.

137


Example 4.10. Declaring a new fact type: Address

declare Address
number : int
streetName : String
city : String
end

The previous example declares a new fact type called Address. This fact type will have three
attributes: number, streetName and city. Each attribute has a type that can be any valid Java
type, including any other class created by the user or even other fact types previously declared.

For instance, we may want to declare another fact type Person:

Example 4.11. declaring a new fact type: Person

declare Person
name : String
dateOfBirth : java.util.Date
address : Address
end

As we can see on the previous example, dateOfBirth is of type java.util.Date, from the Java
API, while address is of the previously defined fact type Address.

You may avoid having to write the fully qualified name of a class every time you write it by using
the import clause, as previously discussed.

Example 4.12. Avoiding the need to use fully qualified class names by using
import

import java.util.Date

declare Person
name : String
dateOfBirth : Date
address : Address
end

When you declare a new fact type, Drools will, at compile time, generate bytecode that implements
a Java class representing the fact type. The generated Java class will be a one-to-one Java Bean
mapping of the type definition. So, for the previous example, the generated Java class would be:

138

Declaring Metadata

Example 4.13. generated Java class for the previous Person fact type
declaration

public class Person implements Serializable {
private java.util.Date dateOfBirth;
private Address address;

// empty constructor
public Person() {...}

// constructor with all fields
public Person( String name, Date dateOfBirth, Address address ) {...}

// if keys are defined, constructor with keys
public Person( ...keys... ) {...}

// getters and setters
// equals/hashCode
// toString
}

Since the generated class is a simple Java class, it can be used transparently in the rules, like
any other fact.

Example 4.14. Using the declared types in rules

rule "Using a declared Type"
when
$p : Person( name == "Bob" )
then
// Insert Mark, who is Bob's mate.
Person mark = new Person();
mark.setName("Mark");
insert( mark );
end

4.7.2. Declaring Metadata
Metadata may be assigned to several different constructions in Drools: fact types, fact attributes
and rules. Drools uses the at sign ('@') to introduce metadata, and it always uses the form:

@metadata_key( metadata_value )

139


The parenthesized metadata_value is optional.

For instance, if you want to declare a metadata attribute like author, whose value is Bob, you
could simply write:

Example 4.15. Declaring a metadata attribute

@author( Bob )

Drools allows the declaration of any arbitrary metadata attribute, but some will have special
meaning to the engine, while others are simply available for querying at runtime. Drools allows the
declaration of metadata both for fact types and for fact attributes. Any metadata that is declared
before the attributes of a fact type are assigned to the fact type, while metadata declared after an
attribute are assigned to that particular attribute.

Example 4.16. Declaring metadata attributes for fact types and attributes


declare Person
@author( Bob )
@dateOfCreation( 01-Feb-2009 )

name : String @key @maxLength( 30 )
dateOfBirth : Date
address : Address
end

In the previous example, there are two metadata items declared for the fact type (@author and
@dateOfCreation) and two more defined for the name attribute (@key and @maxLength). Please
note that the @key metadata has no required value, and so the parentheses and the value were
omitted.:

@position can be used to declare the position of a field, overriding the default declared order. This
is used for positional constraints in patterns.

declare Cheese
name : String @position(1)
shop : String @position(2)
price : int @position(0)
end

140

Declaring Metadata

4.7.2.1. Predefined class level annotations
Some annotations have predefined semantics that are interpreted by the engine. The following is
a list of some of these predefined annotations and their meaning.

4.7.2.1.1. @role( <fact | event> )

See Drools Fusion documentation for details.

4.7.2.1.2. @typesafe( <boolean> )

By default all type declarations are compiled with type safety enabled; @typesafe( false ) provides
a means to override this behaviour by permitting a fall-back, to type unsafe evaluation where all
constraints are generated as MVEL constraints and executed dynamically. This can be important
when dealing with collections that do not have any generics or mixed type collections.

4.7.2.1.3. @timestamp( <attribute name> )


4.7.2.1.4. @duration( <attribute name> )


4.7.2.1.5. @expires( <time interval> )


4.7.2.1.6. @propertyChangeSupport

Facts that implement support for property changes as defined in the Javabean(tm) spec, now can
be annotated so that the engine register itself to listen for changes on fact properties. The boolean
parameter that was used in the insert() method in the Drools 4 API is deprecated and does not
exist in the drools-api module.

Example 4.17. @propertyChangeSupport

declare Person
@propertyChangeSupport
end

4.7.2.1.7. @propertyReactive

Make this type property reactive. See Fine grained property change listeners section for details.

4.7.2.2. Predefined attribute level annotations
As noted before, Drools also supports annotations in type attributes. Here is a list of predefined
attribute annotations.

141


4.7.2.2.1. @key

Declaring an attribute as a key attribute has 2 major effects on generated types:

1. The attribute will be used as a key identifier for the type, and as so, the generated class
will implement the equals() and hashCode() methods taking the attribute into account when
comparing instances of this type.

2. Drools will generate a constructor using all the key attributes as parameters.
For instance:

Example 4.18. example of @key declarations for a type

declare Person
firstName : String @key
lastName : String @key
age : int
end

For the previous example, Drools will generate equals() and hashCode() methods that will check
the firstName and lastName attributes to determine if two instances of Person are equal to each
other, but will not check the age attribute. It will also generate a constructor taking firstName and
lastName as parameters, allowing one to create instances with a code like this:

Example 4.19. creating an instance using the key constructor

Person person = new Person( "John", "Doe" );

4.7.2.2.2. @position

Patterns support positional arguments on type declarations.

Positional arguments are ones where you don't need to specify the field name, as the position
maps to a known named field. i.e. Person( name == "mark" ) can be rewritten as Person( "mark"; ).
The semicolon ';' is important so that the engine knows that everything before it is a positional
argument. Otherwise we might assume it was a boolean expression, which is how it could be
interpreted after the semicolon. You can mix positional and named arguments on a pattern by
using the semicolon ';' to separate them. Any variables used in a positional that have not yet been
bound will be bound to the field that maps to that position.

declare Cheese
name : String
shop : String

142

Declaring Metadata for Existing Types

price : int
end

The default order is the declared order, but this can be overridden using @position

declare Cheese
end

The @Position annotation, in the org.drools.definition.type package, can be used to annotate
original pojos on the classpath. Currently only fields on classes can be annotated. Inheritance of
classes is supported, but not interfaces of methods yet.

Example patterns, with two constraints and a binding. Remember semicolon ';' is used to
differentiate the positional section from the named argument section. Variables and literals and
expressions using just literals are supported in positional arguments, but not variables.

Cheese( "stilton", "Cheese Shop", p; )
Cheese( "stilton", "Cheese Shop"; p : price )
Cheese( "stilton"; shop == "Cheese Shop", p : price )
Cheese( name == "stilton"; shop == "Cheese Shop", p : price )

4.7.3. Declaring Metadata for Existing Types

Drools allows the declaration of metadata attributes for existing types in the same way as when
declaring metadata attributes for new fact types. The only difference is that there are no fields
in that declaration.

For instance, if there is a class org.drools.examples.Person, and one wants to declare metadata
for it, it's possible to write the following code:

Example 4.20. Declaring metadata for an existing type

import org.drools.examples.Person

declare Person
@author( Bob )
end

143


Instead of using the import, it is also possible to reference the class by its fully qualified name,
but since the class will also be referenced in the rules, it is usually shorter to add the import and
use the short class name everywhere.

Example 4.21. Declaring metadata using the fully qualified class name

declare org.drools.examples.Person
@author( Bob )
end

4.7.4. Parametrized constructors for declared types

Generate constructors with parameters for declared types.

Example: for a declared type like the following:

declare Person
firstName : String @key
lastName : String @key
age : int
end

The compiler will implicitly generate 3 constructors: one without parameters, one with the @key
fields, and one with all fields.

Person() // parameterless constructor
Person( String firstName, String lastName )
Person( String firstName, String lastName, int age )

4.7.5. Non Typesafe Classes

@typesafe( <boolean>) has been added to type declarations. By default all type declarations are
compiled with type safety enabled; @typesafe( false ) provides a means to override this behaviour
by permitting a fall-back, to type unsafe evaluation where all constraints are generated as MVEL
constraints and executed dynamically. This can be important when dealing with collections that
do not have any generics or mixed type collections.

4.7.6. Accessing Declared Types from the Application Code

Declared types are usually used inside rules files, while Java models are used when sharing the
model between rules and applications. Although, sometimes, the application may need to access

144

Accessing Declared Types from the Application Code

and handle facts from the declared types, especially when the application is wrapping the rules
engine and providing higher level, domain specific user interfaces for rules management.

In such cases, the generated classes can be handled as usual with the Java Reflection API, but,
as we know, that usually requires a lot of work for small results. Therefore, Drools provides a
simplified API for the most common fact handling the application may want to do.

The first important thing to realize is that a declared fact will belong to the package
where it was declared. So, for instance, in the example below, Person will belong to the
org.drools.examples package, and so the fully qualified name of the generated class will be
org.drools.examples.Person.

Example 4.22. Declaring a type in the org.drools.examples package

package org.drools.examples


declare Person
name : String
dateOfBirth : Date
address : Address
end

Declared types, as discussed previously, are generated at knowledge base compilation time, i.e.,
the application will only have access to them at application run time. Therefore, these classes are
not available for direct reference from the application.

Drools then provides an interface through which users can handle declared types from the
application code: org.drools.definition.type.FactType. Through this interface, the user can
instantiate, read and write fields in the declared fact types.

Example 4.23. Handling declared fact types through the API

// get a reference to a knowledge base with a declared type:
KnowledgeBase kbase = ...

// get the declared FactType
FactType personType = kbase.getFactType( "org.drools.examples",
"Person" );

// handle the type as necessary:
// create instances:
Object bob = personType.newInstance();

// set attributes values

145


personType.set( bob,
"name",
"Bob" );
personType.set( bob,
"age",
42 );

// insert fact into a session
StatefulKnowledgeSession ksession = ...
ksession.insert( bob );

// read attributes
String name = personType.get( bob, "name" );
int age = personType.get( bob, "age" );

The API also includes other helpful methods, like setting all the attributes at once, reading values
from a Map, or reading all attributes at once, into a Map.

Although the API is similar to Java reflection (yet much simpler to use), it does not use reflection
underneath, relying on much more performant accessors implemented with generated bytecode.

4.7.7. Type Declaration 'extends'

Type declarations now support 'extends' keyword for inheritance

In order to extend a type declared in Java by a DRL declared subtype, repeat the supertype in
a declare statement without any fields.

import org.people.Person

declare Person
end

declare Student extends Person
school : String
end

declare LongTermStudent extends Student
years : int
course : String
end

146

Traits

4.7.8. Traits

The same fact may have multiple dynamic types which do not fit naturally in a class hierarchy.
Traits allow to model this very common scenario. A trait is an interface that can be applied (and
eventually removed) to an individual object at runtime. To create a trait out of an interface, one
has to add a @format(trait) annotation to its declaration in DRL as in the following example:

declare GoldenCustomer
@format(trait)
// fields will map to getters/setters
code : String
balance : long
discount : int
maxExpense : long
end

In order to apply a trait to an object, we provide the new don keyword, which can be used as
simply as this:

when
$c : Customer()
then
GoldenCustomer gc = don( $c, Customer.class );
end

when a core object dons a trait, a proxy class is created on the fly (one such class will be generated
lazily for each core/trait class combination). The proxy instance, which wraps the core object and
implements the trait interface, is inserted automatically and will possibly activate other rules. An
immediate advantage of declaring and using interfaces, getting the implementation proxy for free
from the engine, is that multiple inheritance hierarchies can be exploited when writing rules. The
core classes, however, need not implement any of those interfaces statically, also facilitating the
use of legacy classes as cores. In fact, any object can don a trait. For efficiency reasons, however,
one can add the @Traitable annotation to a declared bean class to reduce the amount of glue
code that the compiler will have to generate. This is optional and will not change the behavior
of the engine:

declare Customer
@Traitable
code : String
balance : long
end

147


The only connection between core classes and trait interfaces is at the proxy level, a trait is not
specifically tied to a core class. This means that the same trait can be applied to totally different
objects. For this reason, the trait does not transparently expose the fields of its core object. So,
when writing a rule using a trait interface, only the fields of the interface will be available, as usual.
However, any field in the interface that corresponds to a core object field, will be mapped by the
proxy class:

when
$o: OrderItem( $p : price, $code : custCode )
$c: GoldenCustomer( code == $code, $a : balance, $d: discount )
then
$c.setBalance( $a - $p*$d );
end

In this case, the code and balance would be read from the underlying Customer object. Likewise,
the setAccount will modify the underlying object, preserving a strongly typed access to the data
structures. Now, one might wonder what happens when a core class does NOT provide the
implementation for a field defined in an interface. We call hard fields those trait fields which are also
core fields and thus readily available, while we define soft those fields which are NOT provided
by the core class. Hidden fields, instead, are fields in the core class not exposed by the interface.

So, while hard field management is intuitive, there remains the problem of soft and hidden fields.
The solution we have adopted is to use a two-part proxy. Internally, proxies are formed by a proper
proxy and a wrapper. The former implements the interface, while the latter manages the core
object fields, implementing a name/value map to supports soft fields. The proxy, then, uses both
the core object and the map wrapper to implement the interface, as needed. So, you can write:

when
$sc : GoldenCustomer( $c : code, // hard getter
$maxExpense : maxExpense > 1000 // soft getter
)
then
$sc.setDiscount( ... ); // soft setter
end

The wrapper, then, provides a looser form of typing when writing rules. However, it has also other
uses. The wrapper is specific to the object it wraps, regardless of how many traits have been
attached to an object: all the proxies on the same object will share the same wrapper. Secondly, the
wrapper also contains a back-reference to all proxies attached to the wrapped object, effectively
allowing traits to see each other. To this end, we have provided the new isA operator:

$sc : GoldenCustomer( $maxExpense : maxExpense > 1000,
this isA "SeniorCustomer"

148

Traits

)

Eventually, the business logic may require that a trait is removed from a wrapped object. To this
end, we provide two options. The first is a "logical don", which will result in a logical insertion of
the proxy resulting from the traiting operation:

then
don( $x, // core object
Customer.class, // trait class
true // optional flag for logical insertion
)

The second is the use of the shed keyword, which causes the retraction of the proxy corresponding
to the given argument type:

then
Thing t = shed( $x, GoldenCustomer.class )

This operation returns another proxy implementing the org.drools.factmodel.traits.Thing interface,
where the getFields() and getCore() methods are defined. Internally, in fact, all declared traits are
generated to extend this interface (in addition to any others specified). This allows to preserve the
wrapper with the soft fields which would otherwise be lost.

149


4.8. Rule

Figure 4.9. rule

A rule specifies that when a particular set of conditions occur, specified in the Left Hand Side
(LHS), then do what queryis specified as a list of actions in the Right Hand Side (RHS). A common
question from users is "Why use when instead of if?" "When" was chosen over "if" because "if"
is normally part of a procedural execution flow, where, at a specific point in time, a condition is
to be checked. In contrast, "when" indicates that the condition evaluation is not tied to a specific
evaluation sequence or point in time, but that it happens continually, at any time during the life
time of the engine; whenever the condition is met, the actions are executed.

A rule must have a name, unique within its rule package. If you define a rule twice in the same
DRL it produces an error while loading. If you add a DRL that includes a rule name already in the
package, it replaces the previous rule. If a rule name is to have spaces, then it will need to be
enclosed in double quotes (it is best to always use double quotes).

Attributes - described below - are optional. They are best written one per line.

The LHS of the rule follows the when keyword (ideally on a new line), similarly the RHS follows
the then keyword (again, ideally on a newline). The rule is terminated by the keyword end. Rules
cannot be nested.

150

Rule Attributes

Example 4.24. Rule Syntax Overview

rule "<name>"
<attribute>*
when
<conditional element>*
then
<action>*
end

Example 4.25. A simple rule

rule "Approve if not rejected"
salience -100
agenda-group "approval"
when
not Rejection()
p : Policy(approved == false, policyState:status)
exists Driver(age > 25)
Process(status == policyState)
then
log("APPROVED: due to no objections.");
p.setApproved(true);
end

4.8.1. Rule Attributes

Rule attributes provide a declarative way to influence the behavior of the rule. Some are quite
simple, while others are part of complex subsystems such as ruleflow. To get the most from Drools
you should make sure you have a proper understanding of each attribute.

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Figure 4.10. rule attributes

no-loop
default value: false

type: Boolean

When a rule's consequence modifies a fact it may cause the rule to activate again, causing
an infinite loop. Setting no-loop to true will skip the creation of another Activation for the rule
with the current set of facts.

ruleflow-group
default value: N/A

type: String

Ruleflow is a Drools feature that lets you exercise control over the firing of rules. Rules that
are assembled by the same ruleflow-group identifier fire only when their group is active.

152

Rule Attributes

lock-on-active

type: Boolean

Whenever a ruleflow-group becomes active or an agenda-group receives the focus, any rule
within that group that has lock-on-active set to true will not be activated any more; irrespective
of the origin of the update, the activation of a matching rule is discarded. This is a stronger
version of no-loop, because the change could now be caused not only by the rule itself. It's
ideal for calculation rules where you have a number of rules that modify a fact and you don't
want any rule re-matching and firing again. Only when the ruleflow-group is no longer active or
the agenda-group loses the focus those rules with lock-on-active set to true become eligible
again for their activations to be placed onto the agenda.

salience
default value: 0

type: integer

Each rule has an integer salience attribute which defaults to zero and can be negative or
positive. Salience is a form of priority where rules with higher salience values are given higher
priority when ordered in the Activation queue.

Drools also supports dynamic salience where you can use an expression involving bound
variables.

Example 4.26. Dynamic Salience

rule "Fire in rank order 1,2,.."
salience( -$rank )
when
Element( $rank : rank,... )
then
...
end

agenda-group
default value: MAIN

type: String

Agenda groups allow the user to partition the Agenda providing more execution control. Only
rules in the agenda group that has acquired the focus are allowed to fire.

auto-focus

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type: Boolean

When a rule is activated where the auto-focus value is true and the rule's agenda group
does not have focus yet, then it is given focus, allowing the rule to potentially fire.

activation-group
default value: N/A

type: String

Rules that belong to the same activation-group, identified by this attribute's string value, will
only fire exclusively. In other words, the first rule in an activation-group to fire will cancel the
other rules' activations, i.e., stop them from firing.

Note: This used to be called Xor group, but technically it's not quite an Xor. You may still hear
people mention Xor group; just swap that term in your mind with activation-group.

dialect
default value: as specified by the package

type: String

possible values: "java" or "mvel"

The dialect species the language to be used for any code expressions in the LHS or the RHS
code block. Currently two dialects are available, Java and MVEL. While the dialect can be
specified at the package level, this attribute allows the package definition to be overridden
for a rule.

date-effective
default value: N/A

type: String, containing a date and time definition

A rule can only activate if the current date and time is after date-effective attribute.

date-expires
default value: N/A

type: String, containing a date and time definition

A rule cannot activate if the current date and time is after the date-expires attribute.

duration
default value: no default value

type: long

The duration dictates that the rule will fire after a specified duration, if it is still true.

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Timers and Calendars

Example 4.27. Some attribute examples

rule "my rule"
salience 42
agenda-group "number 1"
when ...

4.8.2. Timers and Calendars
Rules now support both interval and cron based timers, which replace the now deprecated duration
attribute.

Example 4.28. Sample timer attribute uses

timer ( int: <initial delay> <repeat interval>? )
timer ( int: 30s )
timer ( int: 30s 5m )

timer ( cron: <cron expression> )
timer ( cron:* 0/15 * * * ? )

Interval (indicated by "int:") timers follow the semantics of java.util.Timer objects, with an initial
delay and an optional repeat interval. Cron (indicated by "cron:") timers follow standard Unix cron
expressions:

Example 4.29. A Cron Example

rule "Send SMS every 15 minutes"
timer (cron:* 0/15 * * * ?)
when
$a : Alarm( on == true )
then
channels[ "sms" ].insert( new Sms( $a.mobileNumber, "The alarm is still on" );
end

Calendars are used to control when rules can fire. The Calendar API is modelled on Quartz [http://
www.quartz-scheduler.org/]:

Example 4.30. Adapting a Quartz Calendar

Calendar weekDayCal = QuartzHelper.quartzCalendarAdapter(org.quartz.Calendar quartzCal)

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Calendars are registered with the StatefulKnowledgeSession:

Example 4.31. Registering a Calendar

ksession.getCalendars().set( "weekday", weekDayCal );

They can be used in conjunction with normal rules and rules including timers. The rule attribute
"calendars" may contain one or more comma-separated calendar names written as string literals.

Example 4.32. Using Calendars and Timers together

rule "weekdays are high priority"
calendars "weekday"
timer (int:0 1h)
when
Alarm()
then
send( "priority high - we have an alarm# );
end

rule "weekend are low priority"
calendars "weekend"
timer (int:0 4h)
when
Alarm()
then
send( "priority low - we have an alarm# );
end

4.8.3. Left Hand Side (when) syntax

4.8.3.1. What is the Left Hand Side?

The Left Hand Side (LHS) is a common name for the conditional part of the rule. It consists of zero
or more Conditional Elements. If the LHS is empty, it will be considered as a condition element
that is always true and it will be activated once, when a new WorkingMemory session is created.

Figure 4.11. Left Hand Side

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Left Hand Side (when) syntax

Example 4.33. Rule without a Conditional Element

rule "no CEs"
when
// empty
then
... // actions (executed once)
end

// The above rule is internally rewritten as:

rule "eval(true)"
when
eval( true )
then
... // actions (executed once)
end

Conditional elements work on one or more patterns (which are described below). The most
common conditional element is "and". Therefore it is implicit when you have multiple patterns in
the LHS of a rule that are not connected in any way:

Example 4.34. Implicit and

rule "2 unconnected patterns"
when
Pattern1()
Pattern2()
then
... // actions
end

// The above rule is internally rewritten as:

rule "2 and connected patterns"
when
Pattern1()
and Pattern2()
then
... // actions
end

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Note

An "and" cannot have a leading declaration binding (unlike for example or). This
is obvious, since a declaration can only reference a single fact at a time, and when
the "and" is satisfied it matches both facts - so which fact would the declaration
bind to?

// Compile error
$person : (Person( name == "Romeo" ) and Person( name == "Juliet"))

4.8.3.2. Pattern (conditional element)

4.8.3.2.1. What is a pattern?

A pattern element is the most important Conditional Element. It can potentially match on each fact
that is inserted in the working memory.

A pattern contains of zero or more constraints and has an optional pattern binding. The railroad
diagram below shows the syntax for this.

Figure 4.12. Pattern

In its simplest form, with no constraints, a pattern matches against a fact of the given type. In
the following case the type is Cheese, which means that the pattern will match against all Person
objects in the Working Memory:

Person()

The type need not be the actual class of some fact object. Patterns may refer to superclasses or
even interfaces, thereby potentially matching facts from many different classes.

Object() // matches all objects in the working memory

Inside of the pattern parenthesis is where all the action happens: it defines the constraints for that
pattern. For example, with a age related constraint:

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Person( age == 100 )

Note
For backwards compatibility reasons it's allowed to suffix patterns with the ;
character. But it is not recommended to do that.

4.8.3.2.2. Pattern binding

For referring to the matched object, use a pattern binding variable such as $p.

Example 4.35. Pattern with a binding variable

rule ...
when
$p : Person()
then
System.out.println( "Person " + $p );
end

The prefixed dollar symbol ($) is just a convention; it can be useful in complex rules where it helps
to easily differentiate between variables and fields, but it is not mandatory.

4.8.3.3. Constraint (part of a pattern)

4.8.3.3.1. What is a constraint?

A constraint is an expression that returns true or false. This example has a constraint that states
5 is smaller than 6:

Person( 5 < 6 ) // just an example, as constraints like this would be useless
in a real pattern

In essence, it's a Java expression with some enhancements (such as property access) and a few
differences (such as equals() semantics for ==). Let's take a deeper look.

4.8.3.3.2. Property access on Java Beans (POJO's)

Any bean property can be used directly. A bean property is exposed using a standard Java bean
getter: a method getMyProperty() (or isMyProperty() for a primitive boolean) which takes no
arguments and return something. For example: the age property is written as age in DRL instead
of the getter getAge():

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Person( age == 50 )

// this is the same as:
Person( getAge() == 50 )

Drools uses the standard JDK Introspector class to do this mapping, so it follows the standard
Java bean specification.

Note

We recommend using property access (age) over using getters explicitly
(getAge()) because of performance enhancements through field indexing.

Warning

Property accessors must not change the state of the object in a way that may
effect the rules. Remember that the rule engine effectively caches the results of its
matching in between invocations to make it faster.

public int getAge() {
age++; // Do NOT do this
return age;
}

public int getAge() {
Date now = DateUtil.now(); // Do NOT do this
return DateUtil.differenceInYears(now, birthday);
}

To solve this latter case, insert a fact that wraps the current date into working
memory and update that fact between fireAllRules as needed.

Note

The following fallback applies: if the getter of a property cannot be found, the
compiler will resort to using the property name as a method name and without
arguments:

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Person( age == 50 )

// If Person.getAge() does not exists, this falls back to:
Person( age() == 50 )

Nested property access is also supported:

Person( address.houseNumber == 50 )

// this is the same as:
Person( getAddress().getHouseNumber() == 50 )

Nested properties are also indexed.

Warning

In a stateful session, care should be taken when using nested accessors as the
Working Memory is not aware of any of the nested values, and does not know when
they change. Either consider them immutable while any of their parent references
are inserted into the Working Memory. Or, instead, if you wish to modify a nested
value you should mark all of the outer facts as updated. In the above example,
when the houseNumber changes, any Person with that Address must be marked
as updated.

4.8.3.3.3. Java expression

You can use any Java expression that returns a boolean as a constraint inside the parentheses of
a pattern. Java expressions can be mixed with other expression enhancements, such as property
access:

Person( age == 50 )

It is possible to change the evaluation priority by using parentheses, as in any logic or
mathematical expression:

Person( age > 100 && ( age % 10 == 0 ) )

It is possible to reuse Java methods:

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Person( Math.round( weight / ( height * height ) ) < 25.0 )

Warning

As for property accessors, methods must not change the state of the object in a
way that may affect the rules. Any method executed on a fact in the LHS should
be a read only method.

Person( incrementAndGetAge() == 10 ) // Do NOT do this

Warning

The state of a fact should not change between rule invocations (unless those facts
are marked as updated to the working memory on every change):

Person( System.currentTimeMillis() % 1000 == 0 ) // Do NOT do this

Normal Java operator precedence applies, see the operator precedence list below.

Important

All operators have normal Java semantics except for == and !=.

The == operator has null-safe equals() semantics:

// Similar to: java.util.Objects.equals(person.getFirstName(),
"John")
// so (because "John" is not null) similar to:
// "John".equals(person.getFirstName())
Person( firstName == "John" )

The != operator has null-safe !equals() semantics:

// Similar to: !java.util.Objects.equals(person.getFirstName(),
"John")

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Person( firstName != "John" )

Type coercion is always attempted if the field and the value are of different types; exceptions will
be thrown if a bad coercion is attempted. For instance, if "ten" is provided as a string in a numeric
evaluator, an exception is thrown, whereas "10" would coerce to a numeric 10. Coercion is always
in favor of the field type and not the value type:

Person( age == "10" ) // "10" is coerced to 10

4.8.3.3.4. Comma separated AND

The comma character (',') is used to separate constraint groups. It has implicit AND connective
semantics.

// Person is at least 50 and weighs at least 80 kg
Person( age > 50, weight > 80 )

// Person is at least 50, weighs at least 80 kg and is taller than 2 meter.
Person( age > 50, weight > 80, height > 2 )

Note

Although the && and , operators have the same semantics, they are resolved with
different priorities: The && operator precedes the || operator. Both the && and ||
operator precede the , operator. See the operator precedence list below.

The comma operator should be preferred at the top level constraint, as it makes
constraints easier to read and the engine will often be able to optimize them better.

The comma (,) operator cannot be embedded in a composite constraint expression, such as
parentheses:

Person( ( age > 50, weight > 80 ) || height > 2 ) // Do NOT do this: compile error

// Use this instead
Person( ( age > 50 && weight > 80 ) || height > 2 )

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4.8.3.3.5. Binding variables

A property can be bound to a variable:

// 2 persons of the same age
Person( $firstAge : age ) // binding
Person( age == $firstAge ) // constraint expression

The prefixed dollar symbol ($) is just a convention; it can be useful in complex rules where it helps
to easily differentiate between variables and fields.

Note
For backwards compatibility reasons, It's allowed (but not recommended) to mix a
constraint binding and constraint expressions as such:

// Not recommended
Person( $age : age * 2 < 100 )

// Recommended (separates bindings and constraint expressions)
Person( age * 2 < 100, $age : age )

Bound variable restrictions using the operator == provide for very fast execution as it use hash
indexing to improve performance.

4.8.3.3.6. Unification

Drools does not allow bindings to the same declaration. However this is an important aspect to
derivation query unification. While positional arguments are always processed with unification
a special unification symbol, ':=', was introduced for named arguments named arguments. The
following "unifies" the age argument across two people.

Person( $age := age )
Person( $age := age)

In essence unification will declare a binding for the first occurrence and constrain to the same
value of the bound field for sequence occurrences.

4.8.3.3.7. Special literal support

Besides normal Java literals (including Java 5 enums), this literal is also supported:

164


4.8.3.3.7.1. Date literal

The date format dd-mmm-yyyy is supported by default. You can customize this by providing an
alternative date format mask as the System property named drools.dateformat. If more control
is required, use a restriction.

Example 4.36. Date Literal Restriction

Cheese( bestBefore < "27-Oct-2009" )

4.8.3.3.8. List and Map access

It's possible to directly access a List value by index:

// Same as childList(0).getAge() == 18
Person( childList[0].age == 18 )

It's also possible to directly access a Map value by key:

// Same as credentialMap.get("jsmith").isValid()
Person( credentialMap["jsmith"].valid )

4.8.3.3.9. Abbreviated combined relation condition

This allows you to place more than one restriction on a field using the restriction connectives &&
or ||. Grouping via parentheses is permitted, resulting in a recursive syntax pattern.

Figure 4.13. Abbreviated combined relation condition

Figure 4.14. Abbreviated combined relation condition with parentheses

// Simple abbreviated combined relation condition using a single &&

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Person( age > 30 && < 40 )

// Complex abbreviated combined relation using groupings
Person( age ( (> 30 && < 40) ||
(> 20 && < 25) ) )

// Mixing abbreviated combined relation with constraint connectives
Person( age > 30 && < 40 || location == "london" )

4.8.3.3.10. Special DRL operators

Figure 4.15. Operators

Coercion to the correct value for the evaluator and the field will be attempted.

4.8.3.3.10.1. The operators < <= > >=

These operators can be used on properties with natural ordering. For example, for Date fields, <
means before, for String fields, it means alphabetically lower.

Person( firstName < $otherFirstName )

Person( birthDate < $otherBirthDate )

Only applies on Comparable properties.

4.8.3.3.10.2. The operator matches

Matches a field against any valid Java Regular Expression. Typically that regexp is a string literal,
but variables that resolve to a valid regexp are also allowed.

Example 4.37. Regular Expression Constraint

Cheese( type matches "(Buffalo)?S*Mozarella" )

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Note

Like in Java, regular expressions written as string literals need to escape ''.

Only applies on String properties. Using matches against a null value always evaluates to false.

4.8.3.3.10.3. The operator not matches

The operator returns true if the String does not match the regular expression. The same rules
apply as for the matches operator. Example:

Example 4.38. Regular Expression Constraint

Cheese( type not matches "(Buffulo)?S*Mozarella" )

Only applies on String properties. Using not matches against a null value always evaluates
to true.

4.8.3.3.10.4. The operator contains

The operator contains is used to check whether a field that is a Collection or array contains the
specified value.

Example 4.39. Contains with Collections

CheeseCounter( cheeses contains "stilton" ) // contains with a String literal
CheeseCounter( cheeses contains $var ) // contains with a variable

Only applies on Collection properties.

4.8.3.3.10.5. The operator not contains

The operator not contains is used to check whether a field that is a Collection or array does
not contain the specified value.

Example 4.40. Literal Constraint with Collections

CheeseCounter( cheeses not contains "cheddar" ) // not contains with a String
literal
CheeseCounter( cheeses not contains $var ) // not contains with a variable

Only applies on Collection properties.

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Note
For backward compatibility, the excludes operator is supported
as a synonym for not contains.

4.8.3.3.10.6. The operator memberOf

The operator memberOf is used to check whether a field is a member of a collection or array; that
collection must be a variable.


CheeseCounter( cheese memberOf $matureCheeses )

4.8.3.3.10.7. The operator not memberOf

The operator not memberOf is used to check whether a field is not a member of a collection or
array; that collection must be a variable.


CheeseCounter( cheese not memberOf $matureCheeses )

4.8.3.3.10.8. The operator soundslike

This operator is similar to matches, but it checks whether a word has almost the same sound
(using English pronunciation) as the given value. This is based on the Soundex algorithm (see
http://en.wikipedia.org/wiki/Soundex).

Example 4.43. Test with soundslike

// match cheese "fubar" or "foobar"
Cheese( name soundslike 'foobar' )

4.8.3.3.10.9. The operator str

This operator str is used to check whether a field that is a String starts with or ends with a
certain value. It can also be used to check the length of the String.

Message( routingValue str[startsWith] "R1" )

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Message( routingValue str[endsWith] "R2" )

Message( routingValue str[length] 17 )

4.8.3.3.10.10. The operators in and not in (compound value restriction)

The compound value restriction is used where there is more than one possible value to match.
Currently only the in and not in evaluators support this. The second operand of this operator
must be a comma-separated list of values, enclosed in parentheses. Values may be given as
variables, literals, return values or qualified identifiers. Both evaluators are actually syntactic sugar,
internally rewritten as a list of multiple restrictions using the operators != and ==.

Figure 4.16. compoundValueRestriction

Example 4.44. Compound Restriction using "in"

Person( $cheese : favouriteCheese )
Cheese( type in ( "stilton", "cheddar", $cheese ) )

4.8.3.3.11. Inline eval operator (deprecated)

Figure 4.17. Inline Eval Expression

An inline eval constraint can use any valid dialect expression as long as it results to a primitive
boolean. The expression must be constant over time. Any previously bound variable, from the
current or previous pattern, can be used; autovivification is also used to auto-create field binding
variables. When an identifier is found that is not a current variable, the builder looks to see if the

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identifier is a field on the current object type, if it is, the field binding is auto-created as a variable
of the same name. This is called autovivification of field variables inside of inline eval's.

This example will find all male-female pairs where the male is 2 years older than the female; the
variable age is auto-created in the second pattern by the autovivification process.

Example 4.45. Return Value operator

Person( girlAge : age, sex = "F" )
Person( eval( age == girlAge + 2 ), sex = 'M' ) // eval() is actually obsolete
in this example

Note
Inline eval's are effectively obsolete as their inner syntax is now directly supported.
It's recommended not to use them. Simply write the expression without wrapping
eval() around it.

4.8.3.3.12. Operator precedence

The operators are evaluated in this precedence:

Table 4.1. Operator precedence
Operator type Operators Notes
(nested) property access . Not normal Java semantics
List/Map access [ ] Not normal Java semantics
constraint binding : Not normal Java semantics
multiplicative */%

additive +-

shift << >> >>>

relational < > <= >= instanceof

equality == != Does not use normal
Java (not) same semantics:
uses (not) equals semantics
instead.
non-short circuiting AND &

non-short circuiting exclusive ^
OR
non-short circuiting inclusive |
OR

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Operator type Operators Notes
logical AND &&

logical OR ||

ternary ? :

Comma separated AND , Not normal Java semantics

4.8.3.4. Positional Arguments
Patterns now support positional arguments on type declarations.

Positional arguments are ones where you don't need to specify the field name, as the position
maps to a known named field. i.e. Person( name == "mark" ) can be rewritten as Person( "mark"; ).
The semicolon ';' is important so that the engine knows that everything before it is a positional
argument. Otherwise we might assume it was a boolean expression, which is how it could be
interpreted after the semicolon. You can mix positional and named arguments on a pattern by
using the semicolon ';' to separate them. Any variables used in a positional that have not yet been
bound will be bound to the field that maps to that position.

declare Cheese
name : String
shop : String
price : int
end

Example patterns, with two constraints and a binding. Remember semicolon ';' is used to
differentiate the positional section from the named argument section. Variables and literals and
expressions using just literals are supported in positional arguments, but not variables. Positional
arguments are always resolved using unification.

Cheese( "stilton", "Cheese Shop", p; )
Cheese( "stilton", "Cheese Shop"; p : price )
Cheese( "stilton"; shop == "Cheese Shop", p : price )
Cheese( name == "stilton"; shop == "Cheese Shop", p : price )

Positional arguments that are given a previously declared binding will constrain against that using
unification; these are referred to as input arguments. If the binding does not yet exist, it will create
the declaration binding it to the field represented by the position argument; these are referred to
as output arguments.

4.8.3.5. Fine grained property change listeners
When you call modify() (see the modify statement section) on a given object it will trigger a
revaluation of all patterns of the matching object type in the knowledge base. This can can lead

171


to unwanted and useless evaluations and in the worst cases to infinite recursions. The only
workaround to avoid it was to split up your objects into smaller ones having a 1 to 1 relationship
with the original object.

This feature allows the pattern matching to only react to modification of properties actually
constrained or bound inside of a given pattern. That will help with performance and recursion and
avoid artificial object splitting.

By default this feature is off in order to make the behavior of the rule engine backward compatible
with the former releases. When you want to activate it on a specific bean you have to annotate it
with @propertyReactive. This annotation works both on drl type declarations:

declare Person
@propertyReactive
firstName : String
lastName : String
end

and on Java classes:

@PropertyReactive
public static class Person {
private String firstName;
private String lastName;
}

In this way, for instance, if you have a rule like the following:

rule "Every person named Mario is a male" when
$person : Person( firstName == "Mario" )
then
modify ( $person ) { setMale( true ) }
end

you won't have to add the no-loop attribute to it in order to avoid an infinite recursion because the
engine recognizes that the pattern matching is done on the 'firstName' property while the RHS of
the rule modifies the 'male' one. Note that this feature does not work for update(), and this is one of
the reasons why we promote modify() since it encapsulates the field changes within the statement.
Moreover, on Java classes, you can also annotate any method to say that its invocation actually
modifies other properties. For instance in the former Person class you could have a method like:

@Modifies( { "firstName", "lastName" } )

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public void setName(String name) {
String[] names = name.split("s");
this.firstName = names[0];
this.lastName = names[1];
}

That means that if a rule has a RHS like the following:

modify($person) { setName("Mario Fusco") }

it will correctly recognize that the values of both properties 'firstName' and 'lastName' could
have potentially been modified and act accordingly, not missing of reevaluating the patterns
constrained on them. At the moment the usage of @Modifies is not allowed on fields but only on
methods. This is coherent with the most common scenario where the @Modifies will be used for
methods that are not related with a class field as in the Person.setName() in the former example.
Also note that @Modifies is not transitive, meaning that if another method internally invokes
the Person.setName() one it won't be enough to annotate it with @Modifies( { "name" } ), but it
is necessary to use @Modifies( { "firstName", "lastName" } ) even on it. Very likely @Modifies
transitivity will be implemented in the next release.

For what regards nested accessors, the engine will be notified only for top level fields. In other
words a pattern matching like:

Person ( address.city.name == "London )

will be revaluated only for modification of the 'address' property of a Person object. In the same
way the constraints analysis is currently strictly limited to what there is inside a pattern. Another
example could help to clarify this. An LHS like the following:

$p : Person( )
Car( owner = $p.name )

will not listen on modifications of the person's name, while this one will do:

Person( $name : name )
Car( owner = $name )

To overcome this problem it is possible to annotate a pattern with @watch as it follows:

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$p : Person( ) @watch ( name )
Car( owner = $p.name )

Indeed, annotating a pattern with @watch allows you to modify the inferred set of properties for
which that pattern will react. Note that the properties named in the @watch annotation are actually
added to the ones automatically inferred, but it is also possible to explicitly exclude one or more
of them prepending their name with a ! and to make the pattern to listen for all or none of the
properties of the type used in the pattern respectively with the wildcrds * and !*. So, for example,
you can annotate a pattern in the LHS of a rule like:

// listens for changes on both firstName (inferred) and lastName
Person( firstName == $expectedFirstName ) @watch( lastName )

// listens for all the properties of the Person bean
Person( firstName == $expectedFirstName ) @watch( * )

// listens for changes on lastName and explicitly exclude firstName
Person( firstName == $expectedFirstName ) @watch( lastName, !firstName )

// listens for changes on all the properties except the age one
Person( firstName == $expectedFirstName ) @watch( *, !age )

Since doesn't make sense to use this annotation on a pattern using a type not annotated with
@PropertyReactive the rule compiler will raise a compilation error if you try to do so. Also the
duplicated usage of the same property in @watch (for example like in: @watch( firstName, !
firstName ) ) will end up in a compilation error. In a next release we will make the automatic
detection of the properties to be listened smarter by doing analysis even outside of the pattern.

It also possible to enable this feature by default on all the types of your model or to completely
disallow it by using on option of the KnowledgeBuilderConfiguration. In particular this new
PropertySpecificOption can have one of the following 3 values:

- DISABLED => the feature is turned off and all the other related annotations
are just ignored
- ALLOWED => this is the default behavior: types are not property
reactive unless they are not annotated with @PropertySpecific
- ALWAYS => all types are property reactive by default

So, for example, to have a KnowledgeBuilder generating property reactive types by default you
could do:

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KnowledgeBuilderConfiguration config =
KnowledgeBuilderFactory.newKnowledgeBuilderConfiguration();
config.setOption(PropertySpecificOption.ALWAYS);
KnowledgeBuilder kbuilder =
KnowledgeBuilderFactory.newKnowledgeBuilder(config);

In this last case it will be possible to disable the property reactivity feature on a specific type by
annotating it with @ClassReactive.

4.8.3.6. Basic conditional elements

4.8.3.6.1. Conditional Element and

The Conditional Element "and" is used to group other Conditional Elements into a logical
conjunction. Drools supports both prefix and and infix and.

Figure 4.18. infixAnd

Traditional infix and is supported:

//infixAnd
Cheese( cheeseType : type ) and Person( favouriteCheese == cheeseType )

Explicit grouping with parentheses is also supported:

//infixAnd with grouping
( Cheese( cheeseType : type ) and
( Person( favouriteCheese == cheeseType ) or
Person( favouriteCheese == cheeseType ) )

Note

The symbol && (as an alternative to and) is deprecated. But it is still supported in
the syntax for backwards compatibility.

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Figure 4.19. prefixAnd

Prefix and is also supported:

(and Cheese( cheeseType : type )

The root element of the LHS is an implicit prefix and and doesn't need to be specified:

Example 4.46. implicit root prefixAnd

when
Cheese( cheeseType : type )
Person( favouriteCheese == cheeseType )
then
...

4.8.3.6.2. Conditional Element or

The Conditional Element or is used to group other Conditional Elements into a logical disjunction.
Drools supports both prefix or and infix or.

Figure 4.20. infixOr

Traditional infix or is supported:

//infixOr
Cheese( cheeseType : type ) or Person( favouriteCheese == cheeseType )

Explicit grouping with parentheses is also supported:

//infixOr with grouping
( Cheese( cheeseType : type ) or
( Person( favouriteCheese == cheeseType ) and

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Note

The symbol || (as an alternative to or) is deprecated. But it is still supported in
the syntax for backwards compatibility.

Figure 4.21. prefixOr

Prefix or is also supported:

(or Person( sex == "f", age > 60 )
Person( sex == "m", age > 65 )

Note

The behavior of the Conditional Element or is different from the connective ||
for constraints and restrictions in field constraints. The engine actually has no
understanding of the Conditional Element or. Instead, via a number of different
logic transformations, a rule with or is rewritten as a number of subrules. This
process ultimately results in a rule that has a single or as the root node and one
subrule for each of its CEs. Each subrule can activate and fire like any normal rule;
there is no special behavior or interaction between these subrules. - This can be
most confusing to new rule authors.

The Conditional Element or also allows for optional pattern binding. This means that each resulting
subrule will bind its pattern to the pattern binding. Each pattern must be bound separately, using
eponymous variables:

pensioner : ( Person( sex == "f", age > 60 ) or Person( sex == "m", age > 65 ) )

(or pensioner : Person( sex == "f", age > 60 )
pensioner : Person( sex == "m", age > 65 ) )

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Since the conditional element or results in multiple subrule generation, one for each possible
logically outcome, the example above would result in the internal generation of two rules. These
two rules work independently within the Working Memory, which means both can match, activate
and fire - there is no shortcutting.

The best way to think of the conditional element or is as a shortcut for generating two or more
similar rules. When you think of it that way, it's clear that for a single rule there could be multiple
activations if two or more terms of the disjunction are true.

4.8.3.6.3. Conditional Element not

Figure 4.22. not

The CE not is first order logic's non-existential quantifier and checks for the non-existence of
something in the Working Memory. Think of "not" as meaning "there must be none of...".

The keyword not may be followed by parentheses around the CEs that it applies to. In the simplest
case of a single pattern (like below) you may optionally omit the parentheses.

Example 4.47. No Busses

not Bus()

Example 4.48. No red Busses

// Brackets are optional:
not Bus(color == "red")
// Brackets are optional:
not ( Bus(color == "red", number == 42) )
// "not" with nested infix and - two patterns,
// brackets are requires:
not ( Bus(color == "red") and
Bus(color == "blue") )

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4.8.3.6.4. Conditional Element exists

Figure 4.23. exists

The CE exists is first order logic's existential quantifier and checks for the existence of something
in the Working Memory. Think of "exists" as meaning "there is at least one..". It is different from
just having the pattern on its own, which is more like saying "for each one of...". If you use exists
with a pattern, the rule will only activate at most once, regardless of how much data there is in
working memory that matches the condition inside of the exists pattern. Since only the existence
matters, no bindings will be established.

The keyword exists must be followed by parentheses around the CEs that it applies to. In the
simplest case of a single pattern (like below) you may omit the parentheses.

Example 4.49. At least one Bus

exists Bus()

Example 4.50. At least one red Bus

exists Bus(color == "red")
// brackets are optional:
exists ( Bus(color == "red", number == 42) )
// "exists" with nested infix and,
// brackets are required:
exists ( Bus(color == "red") and
Bus(color == "blue") )

4.8.3.7. Advanced conditional elements

4.8.3.7.1. Conditional Element forall

Figure 4.24. forall

The Conditional Element forall completes the First Order Logic support in Drools. The
Conditional Element forall evaluates to true when all facts that match the first pattern match all
the remaining patterns. Example:

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rule "All English buses are red"
when
forall( $bus : Bus( type == 'english')
Bus( this == $bus, color = 'red' ) )
then
// all English buses are red
end

In the above rule, we "select" all Bus objects whose type is "english". Then, for each fact that
matches this pattern we evaluate the following patterns and if they match, the forall CE will
evaluate to true.

To state that all facts of a given type in the working memory must match a set of constraints,
forall can be written with a single pattern for simplicity. Example:

Example 4.51. Single Pattern Forall

rule "All Buses are Red"
when
forall( Bus( color == 'red' ) )
then
// all Bus facts are red
end

Another example shows multiple patterns inside the forall:

Example 4.52. Multi-Pattern Forall

rule "all employees have health and dental care programs"
when
forall( $emp : Employee()
HealthCare( employee == $emp )
DentalCare( employee == $emp )
)
then
// all employees have health and dental care
end

Forall can be nested inside other CEs. For instance, forall can be used inside a not CE. Note
that only single patterns have optional parentheses, so that with a nested forall parentheses
must be used:

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Example 4.53. Combining Forall with Not CE

rule "not all employees have health and dental care"
when
not ( forall( $emp : Employee()
HealthCare( employee == $emp )
DentalCare( employee == $emp ) )
)
then
// not all employees have health and dental care
end

As a side note, forall( p1 p2 p3...) is equivalent to writing:

not(p1 and not(and p2 p3...))

Also, it is important to note that forall is a scope delimiter. Therefore, it can use any previously
bound variable, but no variable bound inside it will be available for use outside of it.

4.8.3.7.2. Conditional Element from

Figure 4.25. from

The Conditional Element from enables users to specify an arbitrary source for data to be matched
by LHS patterns. This allows the engine to reason over data not in the Working Memory. The
data source could be a sub-field on a bound variable or the results of a method call. It is a
powerful construction that allows out of the box integration with other application components
and frameworks. One common example is the integration with data retrieved on-demand from
databases using hibernate named queries.

The expression used to define the object source is any expression that follows regular MVEL
syntax. Therefore, it allows you to easily use object property navigation, execute method calls and
access maps and collections elements.

Here is a simple example of reasoning and binding on another pattern sub-field:

rule "validate zipcode"
when
Person( $personAddress : address )
Address( zipcode == "23920W") from $personAddress
then

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// zip code is ok
end

With all the flexibility from the new expressiveness in the Drools engine you can slice and dice this
problem many ways. This is the same but shows how you can use a graph notation with the 'from':

rule "validate zipcode"
when
$p : Person( )
$a : Address( zipcode == "23920W") from $p.address
then
// zip code is ok
end

Previous examples were evaluations using a single pattern. The CE from also support object
sources that return a collection of objects. In that case, from will iterate over all objects in the
collection and try to match each of them individually. For instance, if we want a rule that applies
10% discount to each item in an order, we could do:

rule "apply 10% discount to all items over US$ 100,00 in an order"
when
$order : Order()
$item : OrderItem( value > 100 ) from $order.items
then
// apply discount to $item
end

The above example will cause the rule to fire once for each item whose value is greater than 100
for each given order.

You must take caution, however, when using from, especially in conjunction with the lock-on-
active rule attribute as it may produce unexpected results. Consider the example provided earlier,
but now slightly modified as follows:

rule "Assign people in North Carolina (NC) to sales region 1"
ruleflow-group "test"
lock-on-active true
when
$p : Person( )
$a : Address( state == "NC") from $p.address
then
modify ($p) {} // Assign person to sales region 1 in a modify block
end

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rule "Apply a discount to people in the city of Raleigh"
lock-on-active true
when
$p : Person( )
$a : Address( city == "Raleigh") from $p.address
then
modify ($p) {} // Apply discount to person in a modify block
end

In the above example, persons in Raleigh, NC should be assigned to sales region 1 and receive
a discount; i.e., you would expect both rules to activate and fire. Instead you will find that only
the second rule fires.

If you were to turn on the audit log, you would also see that when the second rule fires, it
deactivates the first rule. Since the rule attribute lock-on-active prevents a rule from creating
new activations when a set of facts change, the first rule fails to reactivate. Though the set of facts
have not changed, the use of from returns a new fact for all intents and purposes each time it
is evaluated.

First, it's important to review why you would use the above pattern. You may have many rules
across different rule-flow groups. When rules modify working memory and other rules downstream
of your RuleFlow (in different rule-flow groups) need to be reevaluated, the use of modify is
critical. You don't, however, want other rules in the same rule-flow group to place activations on
one another recursively. In this case, the no-loop attribute is ineffective, as it would only prevent
a rule from activating itself recursively. Hence, you resort to lock-on-active.

There are several ways to address this issue:

• Avoid the use of from when you can assert all facts into working memory or use nested object
references in your constraint expressions (shown below).

• Place the variable assigned used in the modify block as the last sentence in your condition
(LHS).

• Avoid the use of lock-on-active when you can explicitly manage how rules within the same
rule-flow group place activations on one another (explained below).

The preferred solution is to minimize use of from when you can assert all your facts into working
memory directly. In the example above, both the Person and Address instance can be asserted
into working memory. In this case, because the graph is fairly simple, an even easier solution is
to modify your rules as follows:


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lock-on-active true
when
$p : Person(address.state == "NC" )
then
end

lock-on-active true
when
$p : Person(address.city == "Raleigh" )
then
modify ($p) {} //Apply discount to person in a modify block
end

Now, you will find that both rules fire as expected. However, it is not always possible to access
nested facts as above. Consider an example where a Person holds one or more Addresses and
you wish to use an existential quantifier to match people with at least one address that meets
certain conditions. In this case, you would have to resort to the use of from to reason over the
collection.

There are several ways to use from to achieve this and not all of them exhibit an issue with the use
of lock-on-active. For example, the following use of from causes both rules to fire as expected:

lock-on-active true
when
$p : Person($addresses : addresses)
exists (Address(state == "NC") from $addresses)
then
end

lock-on-active true
when
$p : Person($addresses : addresses)
exists (Address(city == "Raleigh") from $addresses)
then
modify ($p) {} // Apply discount to person in a modify block
end

However, the following slightly different approach does exhibit the problem:

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lock-on-active true
when
$assessment : Assessment()
$p : Person()
$addresses : List() from $p.addresses
exists (Address( state == "NC") from $addresses)
then
modify ($assessment) {} // Modify assessment in a modify block
end

lock-on-active true
when
$assessment : Assessment()
$p : Person()
$addresses : List() from $p.addresses
exists (Address( city == "Raleigh") from $addresses)
then
modify ($assessment) {} // Modify assessment in a modify block
end

In the above example, the $addresses variable is returned from the use of from. The example
also introduces a new object, assessment, to highlight one possible solution in this case. If the
$assessment variable assigned in the condition (LHS) is moved to the last condition in each rule,
both rules fire as expected.

Though the above examples demonstrate how to combine the use of from with lock-on-active
where no loss of rule activations occurs, they carry the drawback of placing a dependency on the
order of conditions on the LHS. In addition, the solutions present greater complexity for the rule
author in terms of keeping track of which conditions may create issues.

A better alternative is to assert more facts into working memory. In this case, a person's addresses
may be asserted into working memory and the use of from would not be necessary.

There are cases, however, where asserting all data into working memory is not practical and we
need to find other solutions. Another option is to reevaluate the need for lock-on-active. An
alternative to lock-on-active is to directly manage how rules within the same rule-flow group
activate one another by including conditions in each rule that prevent rules from activating each
other recursively when working memory is modified. For example, in the case above where a
discount is applied to citizens of Raleigh, a condition may be added to the rule that checks whether
the discount has already been applied. If so, the rule does not activate.

185


4.8.3.7.3. Conditional Element collect

Figure 4.26. collect

The Conditional Element collect allows rules to reason over a collection of objects obtained
from the given source or from the working memory. In First Oder Logic terms this is the cardinality
quantifier. A simple example:

import java.util.ArrayList

rule "Raise priority if system has more than 3 pending alarms"
when
$system : System()
$alarms : ArrayList( size >= 3 )
from collect( Alarm( system == $system, status == 'pending' ) )
then
// Raise priority, because system $system has
// 3 or more alarms pending. The pending alarms
// are $alarms.
end

In the above example, the rule will look for all pending alarms in the working memory for each
given system and group them in ArrayLists. If 3 or more alarms are found for a given system,
the rule will fire.

The result pattern of collect can be any concrete class that implements the
java.util.Collection interface and provides a default no-arg public constructor. This means
that you can use Java collections like ArrayList, LinkedList, HashSet, etc., or your own class, as
long as it implements the java.util.Collection interface and provide a default no-arg public
constructor.

Both source and result patterns can be constrained as any other pattern.

Variables bound before the collect CE are in the scope of both source and result patterns
and therefore you can use them to constrain both your source and result patterns. But note that
collect is a scope delimiter for bindings, so that any binding made inside of it is not available
for use outside of it.

186


Collect accepts nested from CEs. The following example is a valid use of "collect":

import java.util.LinkedList;

rule "Send a message to all mothers"
when
$town : Town( name == 'Paris' )
$mothers : LinkedList()
from collect( Person( gender == 'F', children > 0 )
from $town.getPeople()
)
then
// send a message to all mothers
end

4.8.3.7.4. Conditional Element accumulate

Figure 4.27. accumulate

The Conditional Element accumulate is a more flexible and powerful form of collect, in the
sense that it can be used to do what collect does and also achieve results that the CE collect
is not capable of doing. Basically, what it does is that it allows a rule to iterate over a collection of
objects, executing custom actions for each of the elements, and at the end it returns a result object.

Accumulate supports both the use of pre-defined accumulate functions, or the use of inline custom
code. Inline custom code should be avoided though, as it is harder for rule authors to maintain,
and frequently leads to code duplication. Accumulate functions are easier to test and reuse.

187


The Accumulate CE also supports multiple different syntaxes. The preferred syntax is the top level
accumulate, as noted bellow, but all other syntaxes are supported for backward compatibility.

4.8.3.7.4.1. Accumulate CE (preferred syntax)

The top level accumulate syntax is the most compact and flexible syntax. The simplified syntax
is as follows:

accumulate( <source pattern>; <functions> [;<constraints>] )

For instance, a rule to calculate the minimum, maximum and average temperature reading for a
given sensor and that raises an alarm if the minimum temperature is under 20C degrees and the
average is over 70C degrees could be written in the following way, using Accumulate:

rule "Raise alarm"
when
$s : Sensor()
accumulate( Reading( sensor == $s, $temp : temperature );
$min : min( $temp ),
$max : max( $temp ),
$avg : average( $temp );
$min < 20, $avg > 70 )
then
// raise the alarm
end

In the above example, min, max and average are Accumulate Functions and will calculate the
minimum, maximum and average temperature values over all the readings for each sensor.

Drools ships with several built-in accumulate functions, including:

• average

• min

• max

• count

• sum

• collectList

• collectSet

These common functions accept any expression as input. For instance, if someone wants to
calculate the average profit on all items of an order, a rule could be written using the average
function:

188


rule "Average profit"
when
$order : Order()
accumulate( OrderItem( order == $order, $cost : cost, $price : price );
$avgProfit : average( 1 - $cost / $price ) )
then
// average profit for $order is $avgProfit
end

Accumulate Functions are all pluggable. That means that if needed, custom, domain specific
functions can easily be added to the engine and rules can start to use them without any restrictions.
To implement a new Accumulate Function all one needs to do is to create a Java class that
implements the org.drools.runtime.rule.TypedAccumulateFunction interface and add a line
to the configuration file or set a system property to let the engine know about the new function.
As an example of an Accumulate Function implementation, the following is the implementation
of the average function:

/**
* An implementation of an accumulator capable of calculating average values
*/
public class AverageAccumulateFunction implements org.drools.runtime.rule.TypedAccumulateFuncti

public void readExternal(ObjectInput in) throws IOException, ClassNotFoundException {

}

public void writeExternal(ObjectOutput out) throws IOException {

}

public static class AverageData implements Externalizable {
public int count = 0;
public double total = 0;

public AverageData() {}

public void readExternal(ObjectInput in) throws IOException, ClassNotFoundException {
count = in.readInt();
total = in.readDouble();
}

public void writeExternal(ObjectOutput out) throws IOException {
out.writeInt(count);
out.writeDouble(total);
}

189


}

/* (non-Javadoc)
* @see org.drools.base.accumulators.AccumulateFunction#createContext()
*/
public Serializable createContext() {
return new AverageData();
}

/* (non-Javadoc)
* @see
org.drools.base.accumulators.AccumulateFunction#init(java.lang.Object)
*/
public void init(Serializable context) throws Exception {
AverageData data = (AverageData) context;
data.count = 0;
data.total = 0;
}

/* (non-Javadoc)
* @see

java.lang.Object)
*/
public void accumulate(Serializable context,
Object value) {
data.count++;
data.total += ((Number) value).doubleValue();
}

/* (non-Javadoc)
* @see

java.lang.Object)
*/
public void reverse(Serializable context,
Object value) throws Exception {
data.count--;
data.total -= ((Number) value).doubleValue();
}

/* (non-Javadoc)
* @see
org.drools.base.accumulators.AccumulateFunction#getResult(java.lang.Object)
*/
public Object getResult(Serializable context) throws Exception {

190


return new Double( data.count == 0 ? 0 : data.total / data.count );
}

/* (non-Javadoc)
* @see org.drools.base.accumulators.AccumulateFunction#supportsReverse()
*/
public boolean supportsReverse() {
return true;
}

/**
* {@inheritDoc}
*/
public Class< ? > getResultType() {
return Number.class;
}

}

The code for the function is very simple, as we could expect, as all the "dirty" integration work is
done by the engine. Finally, to plug the function into the engine, we added it to the configuration file:

drools.accumulate.function.average =
org.drools.base.accumulators.AverageAccumulateFunction

Here, "drools.accumulate.function." is a prefix that must always be used,
"average" is how the function will be used in the rule file, and
"org.drools.base.accumulators.AverageAccumulateFunction" is the fully qualified name of the
class that implements the function behavior.

4.8.3.7.4.2. Alternate Syntax: single function with return type

The accumulate syntax evolved over time with the goal of becoming more compact and
expressive. Nevertheless, Drools still supports previous syntaxes for backward compatibility
purposes.

In case the rule is using a single accumulate function on a given accumulate, the author may
add a pattern for the result object and use the "from" keyword to link it to the accumulate result.
Example: a rule to apply a 10% discount on orders over $100 could be written in the following way:

rule "Apply 10% discount to orders over US$ 100,00"
when
$order : Order()
$total : Number( doubleValue > 100 )
from accumulate( OrderItem( order == $order, $value : value ),
sum( $value ) )

191


then
# apply discount to $order
end

In the above example, the accumulate element is using only one function (sum), and so, the rules
author opted to explicitly write a pattern for the result type of the accumulate function (Number)
and write the constraints inside it. There are no problems in using this syntax over the compact
syntax presented before, except that is is a bit more verbose. Also note that it is not allowed to
use both the return type and the functions binding in the same accumulate statement.

4.8.3.7.4.3. Accumulate with inline custom code

Warning
The use of accumulate with inline custom code is not a good practice for several
reasons, including difficulties on maintaining and testing rules that use them, as
well as the inability of reusing that code. Implementing your own accumulate
functions is very simple and straightforward, they are easy to unit test and to use.
This form of accumulate is supported for backward compatibility only.

Another possible syntax for the accumulate is to define inline custom code, instead of using
accumulate functions. As noted on the previous warned, this is discouraged though for the stated
reasons.

The general syntax of the accumulate CE with inline custom code is:

<result pattern> from accumulate( <source pattern>,
init( <init code> ),
action( <action code> ),
reverse( <reverse code> ),
result( <result expression> ) )

The meaning of each of the elements is the following:

• <source pattern>: the source pattern is a regular pattern that the engine will try to match against
each of the source objects.

• <init code>: this is a semantic block of code in the selected dialect that will be executed once
for each tuple, before iterating over the source objects.

• <action code>: this is a semantic block of code in the selected dialect that will be executed for
each of the source objects.

• <reverse code>: this is an optional semantic block of code in the selected dialect that if present
will be executed for each source object that no longer matches the source pattern. The objective

192


of this code block is to undo any calculation done in the <action code> block, so that the engine
can do decremental calculation when a source object is modified or retracted, hugely improving
performance of these operations.

• <result expression>: this is a semantic expression in the selected dialect that is executed after
all source objects are iterated.

• <result pattern>: this is a regular pattern that the engine tries to match against the object
returned from the <result expression>. If it matches, the accumulate conditional element
evaluates to true and the engine proceeds with the evaluation of the next CE in the rule. If it
does not matches, the accumulate CE evaluates to false and the engine stops evaluating CEs
for that rule.

It is easier to understand if we look at an example:

rule "Apply 10% discount to orders over US$ 100,00"
when
$order : Order()
$total : Number( doubleValue > 100 )
from accumulate( OrderItem( order == $order, $value : value ),
init( double total = 0; ),
action( total += $value; ),
reverse( total -= $value; ),
result( total ) )
then
# apply discount to $order
end

In the above example, for each Order in the Working Memory, the engine will execute the init
code initializing the total variable to zero. Then it will iterate over all OrderItem objects for that
order, executing the action for each one (in the example, it will sum the value of all items into
the total variable). After iterating over all OrderItem objects, it will return the value corresponding
to the result expression (in the above example, the value of variable total). Finally, the engine
will try to match the result with the Number pattern, and if the double value is greater than 100,
the rule will fire.

The example used Java as the semantic dialect, and as such, note that the usage of the semicolon
as statement delimiter is mandatory in the init, action and reverse code blocks. The result is an
expression and, as such, it does not admit ';'. If the user uses any other dialect, he must comply
to that dialect's specific syntax.

As mentioned before, the reverse code is optional, but it is strongly recommended that the user
writes it in order to benefit from the improved performance on update and retract.

The accumulate CE can be used to execute any action on source objects. The following example
instantiates and populates a custom object:

193


rule "Accumulate using custom objects"
when
$person : Person( $likes : likes )
$cheesery : Cheesery( totalAmount > 100 )
from accumulate( $cheese : Cheese( type == $likes ),
init( Cheesery cheesery = new Cheesery(); ),
action( cheesery.addCheese( $cheese ); ),
reverse( cheesery.removeCheese( $cheese ); ),
result( cheesery ) );
then
// do something
end

4.8.3.8. Conditional Element eval

Figure 4.28. eval

The conditional element eval is essentially a catch-all which allows any semantic code (that
returns a primitive boolean) to be executed. This code can refer to variables that were bound in the
LHS of the rule, and functions in the rule package. Overuse of eval reduces the declarativeness
of your rules and can result in a poorly performing engine. While eval can be used anywhere in
the patterns, the best practice is to add it as the last conditional element in the LHS of a rule.

Evals cannot be indexed and thus are not as efficient as Field Constraints. However this makes
them ideal for being used when functions return values that change over time, which is not allowed
within Field Constraints.

For folks who are familiar with Drools 2.x lineage, the old Drools parameter and condition tags are
equivalent to binding a variable to an appropriate type, and then using it in an eval node.

p1 : Parameter()
p2 : Parameter()
eval( p1.getList().containsKey( p2.getItem() ) )

p1 : Parameter()
p2 : Parameter()
// call function isValid in the LHS
eval( isValid( p1, p2 ) )

194


4.8.3.9. Railroad diagrams

195


196


197


198


199


200


201


202


203


204


205


206

The Right Hand Side (then)

4.8.4. The Right Hand Side (then)

4.8.4.1. Usage

The Right Hand Side (RHS) is a common name for the consequence or action part of the rule;
this part should contain a list of actions to be executed. It is bad practice to use imperative or
conditional code in the RHS of a rule; as a rule should be atomic in nature - "when this, then
do this", not "when this, maybe do this". The RHS part of a rule should also be kept small, thus
keeping it declarative and readable. If you find you need imperative and/or conditional code in the
RHS, then maybe you should be breaking that rule down into multiple rules. The main purpose
of the RHS is to insert, retractor modify working memory data. To assist with that there are a few
convenience methods you can use to modify working memory; without having to first reference
a working memory instance.

update(object, handle); will tell the engine that an object has changed (one that has been bound
to something on the LHS) and rules may need to be reconsidered.

update(object); can also be used; here the Knowledge Helper will look up the facthandle for you,
via an identity check, for the passed object. (Note that if you provide Property Change Listeners
to your Java beans that you are inserting into the engine, you can avoid the need to call update()
when the object changes.). After a fact's field values have changed you must call update before

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changing another fact, or you will cause problems with the indexing within the rule engine. The
modify keyword avoids this problem.

insert(new Something()); will place a new object of your creation into the Working Memory.

insertLogical(new Something()); is similar to insert, but the object will be automatically
retracted when there are no more facts to support the truth of the currently firing rule.

retract(handle); removes an object from Working Memory.

These convenience methods are basically macros that provide short cuts to the KnowledgeHelper
instance that lets you access your Working Memory from rules files. The predefined variable
drools of type KnowledgeHelper lets you call several other useful methods. (Refer to the
KnowledgeHelper interface documentation for more advanced operations).

• The call drools.halt() terminates rule execution immediately. This is required for returning
control to the point whence the current session was put to work with fireUntilHalt().

• Methods insert(Object o), update(Object o) and retract(Object o) can be called on
drools as well, but due to their frequent use they can be called without the object reference.

• drools.getWorkingMemory() returns the WorkingMemory object.

• drools.setFocus( String s) sets the focus to the specified agenda group.

• drools.getRule().getName(), called from a rule's RHS, returns the name of the rule.

• drools.getTuple() returns the Tuple that matches the currently executing rule, and
drools.getActivation() delivers the corresponding Activation. (These calls are useful for
logging and debugging purposes.)

The full Knowledge Runtime API is exposed through another predefined variable, kcontext,
of type KnowledgeContext. Its method getKnowledgeRuntime() delivers an object of type
KnowledgeRuntime, which, in turn, provides access to a wealth of methods, many of which are
quite useful for coding RHS logic.

• The call kcontext.getKnowledgeRuntime().halt() terminates rule execution immediately.

• The accessor getAgenda() returns a reference to this session's Agenda, which in turn provides
access to the various rule groups: activation groups, agenda groups, and rule flow groups. A
fairly common paradigm is the activation of some agenda group, which could be done with the
lengthy call:

// give focus to the agenda group CleanUp
kcontext.getKnowledgeRuntime().getAgenda().getAgendaGroup( "CleanUp" ).setFocus();

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The Right Hand Side (then)

(You can achieve the same using drools.setFocus( "CleanUp" ).)

• To run a query, you call getQueryResults(String query), whereupon you may process the
results, as explained in section “Query”.

• A set of methods dealing with event management lets you, among other things, add and remove
event listeners for the Working Memory and the Agenda.

• MethodgetKnowledgeBase() returns the KnowledgeBase object, the backbone of all the
Knowledge in your system, and the originator of the current session.

• You can manage globals with setGlobal(...), getGlobal(...) and getGlobals().

• Method getEnvironment() returns the runtime's Environment which works much like what
you know as your operating system's environment.

4.8.4.2. The modify Statement
This language extension provides a structured approach to fact updates. It combines the update
operation with a number of setter calls to change the object's fields. This is the syntax schema
for the modify statement:

modify ( <fact-expression> ) {
<expression> [ , <expression> ]*
}

The parenthesized <fact-expression> must yield a fact object reference. The expression list in
the block should consist of setter calls for the given object, to be written without the usual object
reference, which is automatically prepended by the compiler.

The example illustrates a simple fact modification.

Example 4.54. A modify statement

rule "modify stilton"
when
$stilton : Cheese(type == "stilton")
then
modify( $stilton ){
setPrice( 20 ),
setAge( "overripe" )
}
end

The advantages in using the modify statment are particularly clear when used in conjuction with
fine grained property change listeners. See the corresponding section for more details.

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4.8.5. A Note on Auto-boxing and Primitive Types

Drools attempts to preserve numbers in their primitive or object wrapper form, so a variable bound
to an int primitive when used in a code block or expression will no longer need manual unboxing;
unlike Drools 3.0 where all primitives were autoboxed, requiring manual unboxing. A variable
bound to an object wrapper will remain as an object; the existing JDK 1.5 and JDK 5 rules to
handle auto-boxing and unboxing apply in this case. When evaluating field constraints, the system
attempts to coerce one of the values into a comparable format; so a primitive is comparable to
an object wrapper.

4.9. Query

Figure 4.29. query

A query is a simple way to search the working memory for facts that match the stated conditions.
Therefore, it contains only the structure of the LHS of a rule, so that you specify neither "when"
nor "then". A query has an optional set of parameters, each of which can be optionally typed. If
the type is not given, the type Object is assumed. The engine will attempt to coerce the values
as needed. Query names are global to the KnowledgeBase; so do not add queries of the same
name to different packages for the same RuleBase.

To return the results use ksession.getQueryResults("name"), where "name" is the query's
name. This returns a list of query results, which allow you to retrieve the objects that matched
the query.

The first example presents a simple query for all the people over the age of 30. The second one,
using parameters, combines the age limit with a location.

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Query

Example 4.55. Query People over the age of 30

query "people over the age of 30"
person : Person( age > 30 )
end

Example 4.56. Query People over the age of x, and who live in y

query "people over the age of x" (int x, String y)
person : Person( age > x, location == y )
end

We iterate over the returned QueryResults using a standard "for" loop. Each element is a
QueryResultsRow which we can use to access each of the columns in the tuple. These columns
can be accessed by bound declaration name or index position.

Example 4.57. Query People over the age of 30

QueryResults results = ksession.getQueryResults( "people over the age of 30" );
System.out.println( "we have " + results.size() + " people over the age of 30" );

System.out.println( "These people are are over 30:" );

for ( QueryResultsRow row : results ) {
Person person = ( Person ) row.get( "person" );
System.out.println( person.getName() + "n" );
}

Support for positional syntax has been added for more compact code. By default the declared
type order in the type declaration matches the argument position. But it possible to override these
using the @position annotation. This allows patterns to be used with positional arguments, instead
of the more verbose named arguments.

declare Cheese
end

The @Position annotation, in the org.drools.definition.type package, can be used to annotate
original pojos on the classpath. Currently only fields on classes can be annotated. Inheritance of

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classes is supported, but not interfaces or methods. The isContainedIn query below demonstrates
the use of positional arguments in a pattern; Location(x, y;) instead of Location( thing ==
x, location == y).

Queries can now call other queries, this combined with optional query arguments provides
derivation query style backward chaining. Positional and named syntax is supported for
arguments. It is also possible to mix both positional and named, but positional must come first,
separated by a semi colon. Literal expressions can be passed as query arguments, but at this
stage you cannot mix expressions with variables. Here is an example of a query that calls another
query. Note that 'z' here will always be an 'out' variable. The '?' symbol means the query is pull only,
once the results are returned you will not receive further results as the underlying data changes.

declare Location
thing : String
location : String
end

query isContainedIn( String x, String y )
Location(x, y;)
or
( Location(z, y;) and ?isContainedIn(x, z;) )
end

As previously mentioned you can use live "open" queries to reactively receive changes over time
from the query results, as the underlying data it queries against changes. Notice the "look" rule
calls the query without using '?'.

query isContainedIn( String x, String y )
Location(x, y;)
or
( Location(z, y;) and isContainedIn(x, z;) )
end

rule look when
Person( $l : likes )
isContainedIn( $l, 'office'; )
then
insertLogical( $l 'is in the office' );
end

Drools supports unification for derivation queries, in short this means that arguments are optional.
It is possible to call queries from java leaving arguments unspecified using the static field
org.drools.runtime.rule.Variable.v - note you must use 'v' and not an alternative instance of
Variable. These are referred to as 'out' arguments. Note that the query itself does not declare at

212

Domain Specific Languages

compile time whether an argument is in or an out, this can be defined purely at runtime on each
use. The following example will return all objects contained in the office.

results = ksession.getQueryResults( "isContainedIn", new Object[] { Variable.v,
"office" } );
l = new ArrayList<List<String>>();
for ( QueryResultsRow r : results ) {
l.add( Arrays.asList( new String[] { (String) r.get( "x" ), (String)
r.get( "y" ) } ) );
}

The algorithm uses stacks to handle recursion, so the method stack will not blow up.

The following is not yet supported:

• List and Map unification

• Variables for the fields of facts

• Expression unification - pred( X, X + 1, X * Y / 7 )

4.10. Domain Specific Languages
Domain Specific Languages (or DSLs) are a way of creating a rule language that is dedicated to
your problem domain. A set of DSL definitions consists of transformations from DSL "sentences"
to DRL constructs, which lets you use of all the underlying rule language and engine features.
Given a DSL, you write rules in DSL rule (or DSLR) files, which will be translated into DRL files.

DSL and DSLR files are plain text files, and you can use any text editor to create and modify them.
But there are also DSL and DSLR editors, both in the IDE as well as in the web based BRMS,
and you can use those as well, although they may not provide you with the full DSL functionality.

4.10.1. When to Use a DSL
DSLs can serve as a layer of separation between rule authoring (and rule authors) and the
technical intricacies resulting from the modelling of domain object and the rule engine's native
language and methods. If your rules need to be read and validated by domain experts (such as
business analysts, for instance) who are not programmers, you should consider using a DSL; it
hides implementation details and focuses on the rule logic proper. DSL sentences can also act as
"templates" for conditional elements and consequence actions that are used repeatedly in your
rules, possibly with minor variations. You may define DSL sentences as being mapped to these
repeated phrases, with parameters providing a means for accommodating those variations.

DSLs have no impact on the rule engine at runtime, they are just a compile time feature, requiring
a special parser and transformer.

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4.10.2. DSL Basics
The Drools DSL mechanism allows you to customise conditional expressions and consequence
actions. A global substitution mechanism ("keyword") is also available.

Example 4.58. Example DSL mapping

[when]Something is {colour}=Something(colour=="{colour}")

In the preceding example, [when] indicates the scope of the expression, i.e., whether it is valid
for the LHS or the RHS of a rule. The part after the bracketed keyword is the expression that you
use in the rule; typically a natural language expression, but it doesn't have to be. The part to the
right of the equal sign ("=") is the mapping of the expression into the rule language. The form of
this string depends on its destination, RHS or LHS. If it is for the LHS, then it ought to be a term
according to the regular LHS syntax; if it is for the RHS then it might be a Java statement.

Whenever the DSL parser matches a line from the rule file written in the DSL with an expression in
the DSL definition, it performs three steps of string manipulation. First, it extracts the string values
appearing where the expression contains variable names in braces (here: {colour}). Then, the
values obtained from these captures are then interpolated wherever that name, again enclosed
in braces, occurs on the right hand side of the mapping. Finally, the interpolated string replaces
whatever was matched by the entire expression in the line of the DSL rule file.

Note that the expressions (i.e., the strings on the left hand side of the equal sign) are used as
regular expressions in a pattern matching operation against a line of the DSL rule file, matching all
or part of a line. This means you can use (for instance) a '?' to indicate that the preceding character
is optional. One good reason to use this is to overcome variations in natural language phrases of
your DSL. But, given that these expressions are regular expression patterns, this also means that
all "magic" characters of Java's pattern syntax have to be escaped with a preceding backslash ('').

It is important to note that the compiler transforms DSL rule files line by line. In the above example,
all the text after "Something is " to the end of the line is captured as the replacement value for
"{colour}", and this is used for interpolating the target string. This may not be exactly what you
want. For instance, when you intend to merge different DSL expressions to generate a composite
DRL pattern, you need to transform a DSLR line in several independent operations. The best way
to achieve this is to ensure that the captures are surrounded by characteristic text - words or even
single characters. As a result, the matching operation done by the parser plucks out a substring
from somewhere within the line. In the example below, quotes are used as distinctive characters.
Note that the characters that surround the capture are not included during interpolation, just the
contents between them.

As a rule of thumb, use quotes for textual data that a rule editor may want to enter. You can also
enclose the capture with words to ensure that the text is correctly matched. Both is illustrated by
the following example. Note that a single line such as Something is "green" and another
solid thing is now correctly expanded.

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DSL Basics

Example 4.59. Example with quotes

[when]something is "{colour}"=Something(colour=="{colour}")
[when]another {state} thing=OtherThing(state=="{state}"

It is a good idea to avoid punctuation (other than quotes or apostrophes) in your DSL expressions
as much as possible. The main reason is that punctuation is easy to forget for rule authors
using your DSL. Another reason is that parentheses, the period and the question mark are magic
characters, requiring escaping in the DSL definition.

In a DSL mapping, the braces "{" and "}" should only be used to enclose a variable definition or
reference, resulting in a capture. If they should occur literally, either in the expression or within the
replacement text on the right hand side, they must be escaped with a preceding backslash (""):

[then]do something= if (foo) { doSomething(); }

Note

If braces "{" and "}" should appear in the replacement string of a DSL definition,
escape them with a backslash ('').

Example 4.60. Examples of DSL mapping entries

# This is a comment to be ignored.
[when]There is a person with name of "{name}"=Person(name=="{name}")
[when]Person is at least {age} years old and lives in "{location}"=
Person(age >= {age}, location=="{location}")
[then]Log "{message}"=System.out.println("{message}");
[when]And = and

Given the above DSL examples, the following examples show the expansion of various DSLR
snippets:

Example 4.61. Examples of DSL expansions

There is a person with name of "Kitty"
==> Person(name="Kitty")
Person is at least 42 years old and lives in "Atlanta"
==> Person(age >= 42, location="Atlanta")

215


Log "boo"
==> System.out.println("boo");
There is a person with name of "Bob" and Person is at least 30 years old and
lives in "Utah"
==> Person(name="Bob") and Person(age >= 30, location="Utah")

Note
Don't forget that if you are capturing plain text from a DSL rule line and want to
use it as a string literal in the expansion, you must provide the quotes on the right
hand side of the mapping.

You can chain DSL expressions together on one line, as long as it is clear to the parser where
one ends and the next one begins and where the text representing a parameter ends. (Otherwise
you risk getting all the text until the end of the line as a parameter value.) The DSL expressions
are tried, one after the other, according to their order in the DSL definition file. After any match,
all remaining DSL expressions are investigated, too.

The resulting DRL text may consist of more than one line. Line ends are in the replacement text
are written as n.

4.10.3. Adding Constraints to Facts
A common requirement when writing rule conditions is to be able to add an arbitrary combination
of constraints to a pattern. Given that a fact type may have many fields, having to provide an
individual DSL statement for each combination would be plain folly.

The DSL facility allows you to add constraints to a pattern by a simple convention: if your DSL
expression starts with a hyphen (minus character, "-") it is assumed to be a field constraint and,
consequently, is is added to the last pattern line preceding it.

For an example, lets take look at class Cheese, with the following fields: type, price, age and
country. We can express some LHS condition in normal DRL like the following

Cheese(age < 5, price == 20, type=="stilton", country=="ch")

The DSL definitions given below result in three DSL phrases which may be used to create any
combination of constraint involving these fields.

[when]There is a Cheese with=Cheese()
[when]- age is less than {age}=age<{age}
[when]- type is '{type}'=type=='{type}'
[when]- country equal to '{country}'=country=='{country}'

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Adding Constraints to Facts

You can then write rules with conditions like the following:

There is a Cheese with
- age is less than 42
- type is 'stilton'

The parser will pick up a line beginning with "-" and add it as a constraint to the preceding pattern,
inserting a comma when it is required. For the preceding example, the resulting DRL is:

Cheese(age<42, type=='stilton')

Combining all all numeric fields with all relational operators (according to the DSL expression "age
is less than..." in the preceding example) produces an unwieldy amount of DSL entries. But you
can define DSL phrases for the various operators and even a generic expression that handles
any field constraint, as shown below. (Notice that the expression definition contains a regular
expression in addition to the variable name.)

[when][]is less than or equal to=<=
[when][]is less than=<
[when][]is greater than or equal to=>=
[when][]is greater than=>
[when][]is equal to===
[when][]equals===
[when][]There is a Cheese with=Cheese()

[when][]- {field:w*} {operator} {value:d*}={field} {operator} {value}

Given these DSL definitions, you can write rules with conditions such as:

There is a Cheese with
- age is less than 42
- rating is greater than 50
- type equals 'stilton'

In this specific case, a phrase such as "is less than" is replaced by <, and then the line matches
the last DSL entry. This removes the hyphen, but the final result is still added as a constraint to
the preceding pattern. After processing all of the lines, the resulting DRL text is:

Cheese(age<42, rating > 50, type=='stilton')

217


Note

The order of the entries in the DSL is important if separate DSL expressions are
intended to match the same line, one after the other.

4.10.4. Developing a DSL

A good way to get started is to write representative samples of the rules your application requires,
and to test them as you develop. This will provide you with a stable framework of conditional
elements and their constraints. Rules, both in DRL and in DSLR, refer to entities according to
the data model representing the application data that should be subject to the reasoning process
defined in rules. Notice that writing rules is generally easier if most of the data model's types are
facts.

Given an initial set of rules, it should be possible to identify recurring or similar code snippets and
to mark variable parts as parameters. This provides reliable leads as to what might be a handy
DSL entry. Also, make sure you have a full grasp of the jargon the domain experts are using, and
base your DSL phrases on this vocabulary.

You may postpone implementation decisions concerning conditions and actions during this first
design phase by leaving certain conditional elements and actions in their DRL form by prefixing a
line with a greater sign (">"). (This is also handy for inserting debugging statements.)

During the next development phase, you should find that the DSL configuration stabilizes pretty
quickly. New rules can be written by reusing the existing DSL definitions, or by adding a parameter
to an existing condition or consequence entry.

Try to keep the number of DSL entries small. Using parameters lets you apply the same DSL
sentence for similar rule patterns or constraints. But do not exaggerate: authors using the DSL
should still be able to identify DSL phrases by some fixed text.

4.10.5. DSL and DSLR Reference

A DSL file is a text file in a line-oriented format. Its entries are used for transforming a DSLR file
into a file according to DRL syntax.

• A line starting with "#" or "//" (with or without preceding white space) is treated as a comment.
A comment line starting with "#/" is scanned for words requesting a debug option, see below.

• Any line starting with an opening bracket ("[") is assumed to be the first line of a DSL entry
definition.

• Any other line is appended to the preceding DSL entry definition, with the line end replaced
by a space.

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DSL and DSLR Reference

A DSL entry consists of the following four parts:

• A scope definition, written as one of the keywords "when" or "condition", "then" or
"consequence", "*" and "keyword", enclosed in brackets ("[" and "]"). This indicates whether the
DSL entry is valid for the condition or the consequence of a rule, or both. A scope indication
of "keyword" means that the entry has global significance, i.e., it is recognized anywhere in a
DSLR file.

• A type definition, written as a Java class name, enclosed in brackets. This part is optional unless
the the next part begins with an opening bracket. An empty pair of brackets is valid, too.

• A DSL expression consists of a (Java) regular expression, with any number of embedded
variable definitions, terminated by an equal sign ("="). A variable definition is enclosed in braces
("{" and "}"). It consists of a variable name and two optional attachments, separated by colons
(":"). If there is one attachment, it is a regular expression for matching text that is to be assigned
to the variable; if there are two attachments, the first one is a hint for the GUI editor and the
second one the regular expression.

Note that all characters that are "magic" in regular expressions must be escaped with a
preceding backslash ("") if they should occur literally within the expression.

• The remaining part of the line after the delimiting equal sign is the replacement text for any
DSLR text matching the regular expression. It may contain variable references, i.e., a variable
name enclosed in braces. Optionally, the variable name may be followed by an exclamation
mark ("!") and a transformation function, see below.

Note that braces ("{" and "}") must be escaped with a preceding backslash ("") if they should
occur literally within the replacement string.

Debugging of DSL expansion can be turned on, selectively, by using a comment line starting with
"#/" which may contain one or more words from the table presented below. The resulting output
is written to standard output.

Table 4.2. Debug options for DSL expansion

Word Description
result Prints the resulting DRL text, with line numbers.
steps Prints each expansion step of condition and
consequence lines.
keyword Dumps the internal representation of all DSL
entries with scope "keyword".
when Dumps the internal representation of all DSL
entries with scope "when" or "*".
then Dumps the internal representation of all DSL
entries with scope "then" or "*".

219


Word Description
usage Displays a usage statistic of all DSL entries.

Below are some sample DSL definitions, with comments describing the language features they
illustrate.

# Comment: DSL examples

#/ debug: display result and usage

# keyword definition: replaces "regula" by "rule"
[keyword][]regula=rule

# conditional element: "T" or "t", "a" or "an", convert matched word
[when][][Tt]here is an? {entity:w+}=
${entity!lc}: {entity!ucfirst} ()

# consequence statement: convert matched word, literal braces
[then][]update {entity:w+}=modify( ${entity!lc} ){ }

The transformation of a DSLR file proceeds as follows:

1. The text is read into memory.

2. Each of the "keyword" entries is applied to the entire text. First, the regular expression from the
keyword definition is modified by replacing white space sequences with a pattern matching any
number of white space characters, and by replacing variable definitions with a capture made
from the regular expression provided with the definition, or with the default (".*?"). Then, the
DSLR text is searched exhaustively for occurrences of strings matching the modified regular
expression. Substrings of a matching string corresponding to variable captures are extracted
and replace variable references in the corresponding replacement text, and this text replaces
the matching string in the DSLR text.

3. Sections of the DSLR text between "when" and "then", and "then" and "end", respectively, are
located and processed in a uniform manner, line by line, as described below.

For a line, each DSL entry pertaining to the line's section is taken in turn, in the order it appears
in the DSL file. Its regular expression part is modified: white space is replaced by a pattern
matching any number of white space characters; variable definitions with a regular expression
are replaced by a capture with this regular expression, its default being ".*?". If the resulting
regular expression matches all or part of the line, the matched part is replaced by the suitably
modified replacement text.

Modification of the replacement text is done by replacing variable references with the text
corresponding to the regular expression capture. This text may be modified according to the
string transformation function given in the variable reference; see below for details.

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DSL and DSLR Reference

If there is a variable reference naming a variable that is not defined in the same entry, the
expander substitutes a value bound to a variable of that name, provided it was defined in one
of the preceding lines of the current rule.

4. If a DSLR line in a condition is written with a leading hyphen, the expanded result is inserted
into the last line, which should contain a pattern CE, i.e., a type name followed by a pair of
parentheses. if this pair is empty, the expanded line (which should contain a valid constraint)
is simply inserted, otherwise a comma (",") is inserted beforehand.

If a DSLR line in a consequence is written with a leading hyphen, the expanded result is inserted
into the last line, which should contain a "modify" statement, ending in a pair of braces ("{" and
"}"). If this pair is empty, the expanded line (which should contain a valid method call) is simply
inserted, otherwise a comma (",") is inserted beforehand.

Note

It is currently not possible to use a line with a leading hyphen to insert text into
other conditional element forms (e.g., "accumulate") or it may only work for the first
insertion (e.g., "eval").

All string transformation functions are described in the following table.

Table 4.3. String transformation functions

Name Description
uc Converts all letters to upper case.
lc Converts all letters to lower case.
ucfirst Converts the first letter to upper case, and all
other letters to lower case.
num Extracts all digits and "-" from the string. If the
last two digits in the original string are preceded
by "." or ",", a decimal period is inserted in the
corresponding position.
a?b/c Compares the string with string a, and if they
are equal, replaces it with b, otherwise with c.
But c can be another triplet a, b, c, so that the
entire structure is, in fact, a translation table.

The following DSL examples show how to use string transformation functions.

# definitions for conditions
[when][]There is an? {entity}=${entity!lc}: {entity!ucfirst}()

221


[when][]- with an? {attr} greater than {amount}={attr} <= {amount!num}
[when][]- with a {what} {attr}={attr} {what!positive?>0/negative?%lt;0/zero?==0/
ERROR}

A file containing a DSL definition is customarily given the extension .dsl. It is passed to the
Knowledge Builder with ResourceType.DSL. For a file using DSL definition, the extension .dslr
should be used. The Knowledge Builder expects ResourceType.DSLR. The IDE, however, relies
on file extensions to correctly recognize and work with your rules file.

The DSL must be passed to the Knowledge Builder ahead of any rules file using the DSL.

KnowledgeBuilder kBuilder = new KnowledgeBuilder();
Resource dsl = ResourceFactory.newClassPathResource( dslPath, getClass() );
kBuilder.add( dsl, ResourceType.DSL );
Resource dslr = ResourceFactory.newClassPathResource( dslrPath, getClass() );
kBuilder.add( dslr, ResourceType.DSLR );

For parsing and expanding a DSLR file the DSL configuration is read and supplied to the parser.
Thus, the parser can "recognize" the DSL expressions and transform them into native rule
language expressions.

4.11. XML Rule Language

Warning

The XML rule language has not been updated to support functionality introduced
in Drools 5.x and is consider a deprecated feature.

As an option, Drools also supports a "native" rule language as an alternative to DRL. This allows
you to capture and manage your rules as XML data. Just like the non-XML DRL format, the XML
format is parsed into the internal "AST" representation - as fast as possible (using a SAX parser).
There is no external transformation step required.

4.11.1. Legacy Drools 2.x XML rule format

The Drools 2.x legacy XML format is no longer supported by Drools XML parser

4.11.2. When to use XML

There are several scenarios that XML is desirable. However, we recommend that it is not a default
choice, as XML is not readily human readable (unless you like headaches) and can create visually
bloated rules.

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The XML format

If you do want to edit XML by hand, use a good schema aware editor that provides nice hierarchical
views of the XML, ideally visually (commercial tools like XMLSpy, Oxygen etc are good, but cost
money, but then so do headache tablets).

Other scenarios where you may want to use the XML format are if you have a tool that generates
rules from some input (programmatically generated rules), or perhaps interchange from another
rule language, or from another tool that emits XML (using XSLT you can easily transform between
XML formats). Note you can always generate normal DRL as well.

Alternatively you may be embedding Drools in a product that already uses XML for configuration,
so you would like the rules to be in an XML format. You may be creating your own rule language
on XML - note that you can always use the AST objects directly to create your own rule language
as well (the options are many, due to the open architecture).

4.11.3. The XML format
A full W3C standards (XMLSchema) compliant XSD is provided that describes the XML language,
which will not be repeated here verbatim. A summary of the language follows.

Example 4.62. A rule in XML

<?xml version="1.0" encoding="UTF-8"?>

<package name="com.sample"
xmlns="http://drools.org/drools-5.0"
xmlns:xs="http://www.w3.org/2001/XMLSchema-instance"
xs:schemaLocation="http://drools.org/drools-5.0 drools-5.0.xsd">

<import name="java.util.HashMap" />
<import name="org.drools.*" />

<global identifier="x" type="com.sample.X" />
<global identifier="yada" type="com.sample.Yada" />

<function return-type="void" name="myFunc">
<parameter identifier="foo" type="Bar" />
<parameter identifier="bada" type="Bing" />

<body>
System.out.println("hello world");
</body>
</function>

<rule name="simple_rule">
<rule-attribute name="salience" value="10" />
<rule-attribute name="no-loop" value="true" />
<rule-attribute name="agenda-group" value="agenda-group" />
<rule-attribute name="activation-group" value="activation-group" />

223


<lhs>
<pattern identifier="foo2" object-type="Bar" >
<or-constraint-connective>
<and-constraint-connective>
<field-constraint field-name="a">
<or-restriction-connective>
<and-restriction-connective>
<literal-restriction evaluator=">" value="60" />
<literal-restriction evaluator="<" value="70" />
</and-restriction-connective>
<and-restriction-connective>
<literal-restriction evaluator="<" value="50" />
<literal-restriction evaluator=">" value="55" />
</and-restriction-connective>
</or-restriction-connective>
</field-constraint>

<field-constraint field-name="a3">
<literal-restriction evaluator="==" value="black" />
</field-constraint>
</and-constraint-connective>

<literal-restriction evaluator="==" value="40" />
</field-constraint>

<literal-restriction evaluator="==" value="pink" />
</field-constraint>

<literal-restriction evaluator="==" value="12"/>
</field-constraint>

<literal-restriction evaluator="==" value="yellow"/>
<literal-restriction evaluator="==" value="blue" />
</field-constraint>
</or-constraint-connective>
</pattern>

<not>

224

The XML format

<pattern object-type="Person">
<field-constraint field-name="likes">
<variable-restriction evaluator="==" identifier="type"/>
</field-constraint>
</pattern>

<exists>
<pattern object-type="Person">
<field-constraint field-name="likes">
<variable-restriction evaluator="==" identifier="type"/>
</field-constraint>
</pattern>
</exists>
</not>

<or-conditional-element>
</field-constraint>
<literal-restriction evaluator="==" value="hello" />
</field-constraint>
<literal-restriction evaluator="==" value="null" />
</field-constraint>
</pattern>

<field-binding field-name="a" identifier="a4" />
<literal-restriction evaluator="!=" value="4" />
<literal-restriction evaluator="!=" value="5" />
</field-constraint>
</pattern>
</or-conditional-element>

<field-constraint field-name="b">
<return-value-restriction evaluator="==" >a4 + 1</return-
value-restriction>
<variable-restriction evaluator=">" identifier="a4" />
<qualified-identifier-restriction evaluator="==">
org.drools.Bar.BAR_ENUM_VALUE
</qualified-identifier-restriction>

225


</field-constraint>
</pattern>

<field-binding field-name="a" identifier="a4" />
<field-constraint field-name="b">
</field-constraint>
</pattern>
</lhs>
<rhs>
if ( a == b ) {
assert( foo3 );
} else {
retract( foo4 );
}
System.out.println( a4 );
</rhs>
</rule>

</package>

In the preceding XML text you will see the typical XML element, the package declaration, imports,
globals, functions, and the rule itself. Most of the elements are self explanatory if you have some
understanding of the Drools features.

The import elements import the types you wish to use in the rule.

The global elements define global objects that can be referred to in the rules.

The function contains a function declaration, for a function to be used in the rules. You have to
specify a return type, a unique name and parameters, in the body goes a snippet of code.

The rule is discussed below.

Example 4.63. Detail of rule element

<rule name="simple_rule">
<rule-attribute name="salience" value="10" />
<rule-attribute name="no-loop" value="true" />
<rule-attribute name="agenda-group" value="agenda-group" />
<rule-attribute name="activation-group" value="activation-group" />

<lhs>
<pattern identifier="cheese" object-type="Cheese">
<from>
<accumulate>

226

The XML format

<pattern object-type="Person"></pattern>
<init>
int total = 0;
</init>
<action>
total += $cheese.getPrice();
</action>
<result>
new Integer( total ) );
</result>
</accumulate>
</from>
</pattern>

<pattern identifier="max" object-type="Number">
<from>
<accumulate>
<pattern identifier="cheese" object-type="Cheese"></pattern>
<external-function evaluator="max" expression="$price"/>
</accumulate>
</from>
</pattern>
</lhs>
<rhs>
list1.add( $cheese );
</rhs>
</rule>

In the above detail of the rule we see that the rule has LHS and RHS (conditions and consequence)
sections. The RHS is simple, it is just a block of semantic code that will be executed when the rule
is activated. The LHS is slightly more complicated as it contains nested elements for conditional
elements, constraints and restrictions.

A key element of the LHS is the Pattern element. This allows you to specify a type (class) and
perhaps bind a variable to an instance of that class. Nested under the pattern object are constraints
and restrictions that have to be met. The Predicate and Return Value constraints allow Java
expressions to be embedded.

That leaves the conditional elements, not, exists, and, or etc. They work like their DRL
counterparts. Elements that are nested under and an "and" element are logically "anded" together.
Likewise with "or" (and you can nest things further). "Exists" and "Not" work around patterns, to
check for the existence or nonexistence of a fact meeting the pattern's constraints.

The Eval element allows the execution of a valid snippet of Java code - as long as it evaluates to a
boolean (do not end it with a semi-colon, as it is just a fragment) - this can include calling a function.
The Eval is less efficient than the columns, as the rule engine has to evaluate it each time, but it
is a "catch all" feature for when you can express what you need to do with Column constraints.

227


4.11.4. Legacy Drools 2.x XML rule format
The Drools 2.x legacy XML format is no longer supported by Drools XML parser

4.11.5. Automatic transforming between formats (XML and DRL)
Drools comes with some utility classes to transform between formats. This works by parsing the
rules from the source format into the AST, and then "dumping" out to the appropriate target format.
This allows you, for example, to write rules in DRL, and when needed, export to XML if necessary
at some point in the future.

The classes to look at if you need to do this are:

XmlDumper - for exporting XML.
DrlDumper - for exporting DRL.
DrlParser - reading DRL.
XmlPackageReader - reading XML.

Using combinations of the above, you can convert between any format (including round trip). Note
that DSLs will not be preserved (from DRLs that are using a DSL) - but they will be able to be
converted.

Feel free to make use of XSLT to provide all sorts of possibilities for XML, XSLT and its ilk are
what make XML powerful.

228

Chapter 5.

Chapter 5. The Java Rule Engine
API (JSR94)
5.1. Introduction
Drools provides an implementation of the Java Rule Engine API (known as JSR94), which allows
for support of multiple rule engines from a single API. JSR94 does not deal in any way with the
rule language itself. W3C is working on the Rule Interchange Format (RIF) [http://www.w3.org/
TR/2006/WD-rif-ucr-20060323/] and the OMG has started to work on a standard based on RuleML
[http://ruleml.org/]. Recently Haley Systems has also proposed a rule language standard called
RML.

It should be remembered that the JSR94 standard represents the "least common denominator" in
features across rule engines. This means that there is less functionality in the JSR94 API than in
the standard Knowledge (Drools and jBPM) API. So, by using JSR94, you forfeit the advantage of
using the full capabilities of the Drools Rule Engine. It is necessary to expose further functionality,
like globals and support for DRL, DSL and XML, via property maps due to the very basic feature
set of JSR94; this introduces non-portable functionality. Furthermore, as JSR94 does not provide
a rule language, you are only solving a small fraction of the complexity of switching rule engines
with very little gain. So, while we support JSR94, for those that insist on using it, we strongly
recommend you program against the Knowledge (Drools and jBPM) API.

5.2. How To Use
There are two parts to working with JSR94. The first part is the administrative API that deals with
building and registering RuleExecutionSet objects, the second part is runtime session execution
of these RuleExecutionSets.

5.2.1. Building and Registering RuleExecutionSets
The RuleServiceProviderManager manages the registration and retrieval of
RuleServiceProviders. The Drools RuleServiceProvider implementation is automatically
registered via a static block when the class is loaded using Class.forName, in much the same
way as JDBC drivers.

Example 5.1. Automatic RuleServiceProvider Registration

// RuleServiceProviderImpl is registered to "http://drools.org/"
// via a static initialization block
Class.forName("org.drools.jsr94.rules.RuleServiceProviderImpl");

// Get the rule service provider from the provider manager.

229

Chapter 5. The Java Rule Engi...

RuleServiceProvider ruleServiceProvider =
RuleServiceProviderManager.getRuleServiceProvider("http://drools.org/");

The RuleServiceProvider provides access to the RuleRuntime and RuleAdministrator APIs. The
RuleAdministrator provides an administration API for the management of RuleExecutionSet
objects, making it possible to register a RuleExecutionSet that can then be retrieved via the
RuleRuntime.

First, you need to create a RuleExecutionSet before it can be registered;
RuleAdministrator provides factory methods to return an empty LocalRuleExecutionSetProvider
or RuleExecutionSetProvider. The LocalRuleExecutionSetProvider should be used
to load a RuleExecutionSets from local sources that are not serializable,
like Streams. The RuleExecutionSetProvider can be used to load
RuleExecutionSets from serializable sources, like DOM Elements or Packages.
Both the "ruleAdministrator.getLocalRuleExecutionSetProvider( null );" and the
"ruleAdministrator.getRuleExecutionSetProvider( null );" take null as a parameter, as the
properties map for these methods is not currently used.

Example 5.2. Registering a LocalRuleExecutionSet with the
RuleAdministrator API

// Get the RuleAdministration
RuleAdministrator ruleAdministrator = ruleServiceProvider.getRuleAdministrator();
LocalRuleExecutionSetProvider ruleExecutionSetProvider =
ruleAdministrator.getLocalRuleExecutionSetProvider( null );

// Create a Reader for the drl
URL drlUrl = new URL("http://mydomain.org/sources/myrules.drl");
Reader drlReader = new InputStreamReader( drlUrl.openStream() );

// Create the RuleExecutionSet for the drl
RuleExecutionSet ruleExecutionSet =
ruleExecutionSetProvider.createRuleExecutionSet( drlReader, null );

"ruleExecutionSetProvider.createRuleExecutionSet( reader, null )" in the above example takes a
null parameter for the properties map; however it can actually be used to provide configuration for
the incoming source. When null is passed the default is used to load the input as a drl. Allowed
keys for a map are "source" and "dsl". The key "source" takes "drl" or "xml" as its value; you set
"source" to "drl" to load a DRL, or to "xml" to load an XML source; "xml" will ignore any "dsl" key/
value settings. The "dsl" key can take a Reader or a String (the contents of the dsl) as a value.

Example 5.3. Specifying a DSL when registering a LocalRuleExecutionSet

// Get the RuleAdministration

230

Using Stateful and Stateless RuleSessions

RuleAdministration ruleAdministrator = ruleServiceProvider.getRuleAdministrator();
LocalRuleExecutionSetProvider ruleExecutionSetProvider =
ruleAdministrator.getLocalRuleExecutionSetProvider( null );

// Create a Reader for the drl
URL drlUrl = new URL("http://mydomain.org/sources/myrules.drl");
Reader drlReader = new InputStreamReader( drlUrl.openStream() );

// Create a Reader for the dsl and a put in the properties map
URL dslUrl = new URL("http://mydomain.org/sources/myrules.dsl");
Reader dslReader = new InputStreamReader( dslUrl.openStream() );
Map properties = new HashMap();
properties.put( "source", "drl" );
properties.put( "dsl", dslReader );

// Create the RuleExecutionSet for the drl and dsl
RuleExecutionSet ruleExecutionSet =
ruleExecutionSetProvider.createRuleExecutionSet( reader, properties );

When registering a RuleExecutionSet you must specify the name to be used for its retrieval. There
is also a field to pass properties, which is currently unused - so just pass null.

Example 5.4. Register the RuleExecutionSet

// Register the RuleExecutionSet with the RuleAdministrator
String uri = ruleExecutionSet.getName();
ruleAdministrator.registerRuleExecutionSet(uri, ruleExecutionSet, null);

5.2.2. Using Stateful and Stateless RuleSessions

The Runtime, obtained from the RuleServiceProvider, is used to create stateful and stateless rule
engine sessions.

Example 5.5. Getting the RuleRuntime

RuleRuntime ruleRuntime = ruleServiceProvider.getRuleRuntime();

To create a rule session you must use one of the two RuleRuntime
public constants. These are "RuleRuntime.STATEFUL_SESSION_TYPE" and
"RuleRuntime.STATELESS_SESSION_TYPE", accompanying the URI to the RuleExecutionSet
you wish to instantiate a RuleSession for. The properties map can be null, or it can be used
to specify globals, as shown in the next section. The createRuleSession(...) method returns a
RuleSession instance which must then be cast to StatefulRuleSession or StatelessRuleSession.

231

Chapter 5. The Java Rule Engi...

Example 5.6. Stateful Rule

(StatefulRuleSession) session =
ruleRuntime.createRuleSession( uri,
null,
RuleRuntime.STATEFUL_SESSION_TYPE );
session.addObject( new PurchaseOrder( "lots of cheese" ) );
session.executeRules();

The StatelessRuleSession has a very simple API; you can only call executeRules(List list) passing
a list of objects, and an optional filter, the resulting objects are then returned.

Example 5.7. Stateless

(StatelessRuleSession) session =
ruleRuntime.createRuleSession( uri,
null,
RuleRuntime.STATELESS_SESSION_TYPE );
list.add( new PurchaseOrder( "even more cheese" ) );

List results = new ArrayList();
results = session.executeRules( list );

5.2.2.1. Globals

It is possible to support globals with JSR94, in a manner that is not portable, by using the properties
map passed to the RuleSession factory method. Globals must be defined in the DRL or XML
file first, otherwise an exception will be thrown. The key represents the identifier declared in the
DRL or XML, and the value is the instance you wish to be used in the execution. In the following
example the results are collected in a java.util.List which is used as global:

java.util.List globalList = new java.util.ArrayList( );
java.util.Map map = new java.util.HashMap( );
map.put( "list", globalList );
//Open a stateless Session
StatelessRuleSession srs =
(StatelessRuleSession) runtime.createRuleSession( "SistersRules",
map,
RuleRuntime.STATELESS_SESSION_TYPE );
...
// Persons added to List
// call executeRules( ) giving a List of Objects as parameter
// There are rules which will put Objects in the List

232

References

// fetch the list from the map
List list = (java.util.List) map.get("list");

Do not forget to declare the global "list" in your DRL:

package SistersRules;
import org.drools.jsr94.rules.Person;
global java.util.List list
rule FindSisters
when
$person1 : Person ( $name1:name )
$person2 : Person ( $name2:name )
eval( $person1.hasSister($person2) )
then
list.add($person1.getName() + " and " + $person2.getName() +" are sisters");
assert( $person1.getName() + " and " + $person2.getName() +" are sisters");
end

5.3. References
If you need more information on JSR 94, please refer to the following references

1. Official JCP Specification for Java Rule Engine API (JSR 94)

• http://www.jcp.org/en/jsr/detail?id=94

2. The Java Rule Engine API documentation

• http://www.javarules.org/api_doc/api/index.html

3. The Logic From The Bottom Line: An Introduction to The Drools Project. By N. Alex Rupp,
published on TheServiceSide.com in 2004

• http://www.theserverside.com/articles/article.tss?l=Drools

4. Getting Started With the Java Rule Engine API (JSR 94): Toward Rule-Based Applications. By
Dr. Qusay H. Mahmoud, published on Sun Developer Network in 2005

• http://java.sun.com/developer/technicalArticles/J2SE/JavaRule.html

5. Jess and the javax.rules API. By Ernest Friedman-Hill, published on TheServerSide.com in
2003

• http://www.theserverside.com/articles/article.tss?l=Jess

233

Chapter 6.

Chapter 6. The Rule IDE (Eclipse)
The Eclipse based IDE provides developers (and very technical users) with an environment to edit
and test rules in various formats, and integrate it deeply with their applications. In cases where
you prefer business rules and web tooling, you will want to look at the BRMS (but using the BRMS
and the IDE together is not uncommon).

The Drools IDE is delivered as an Eclipse plug-in, which allows you to author and manage rules
from within Eclipse, as well as integrate rules with your application. This is an optional tool, and
not all components are required to be used, you can use what components are relevant to you.
The Drools IDE is also a part of the Red Hat Developer Studio (formerly known as JBoss IDE).

This guide will cover some of the features of JBoss Drools, in as far as the IDE touches on them
(it is assumed that the reader has some familiarity with rule engines, and Drools in particular. It is
important to note that none of the underlying features of the rule engine are dependent on Eclipse,
and integrators are free to use their tools of choice, as always ! Plenty of people use IntelliJ with
rules, for instance.

Note

You can get the plug-in either as a zip to download, or from an update site. Refer
to the chapter on installation.

235


Figure 6.1. Overview

6.1. Features Outline
The rules IDE has the following features

1. Textual/graphical rule editor

a. An editor that is aware of DRL syntax, and provides content assistance (including an outline
view)

b. An editor that is aware of DSL (domain specific language) extensions, and provides content
assistance.

2. RuleFlow graphical editor

You can edit visual graphs which represent a process (a rule flow). The RuleFlow can then be
applied to your rule package to have imperative control.

3. Wizards for fast creation of

a. a "rules" project

b. a rule resource, either as a DRL file or a "guided rule editor" file (.brl)

c. a Domain Specific language

236

Drools Runtimes

d. a decision table

e. a ruleflow

4. A domain specific language editor

a. Create and manage mappings from your user's language to the rule language

5. Rule validation

a. As rules are entered, the rule is "built" in the background and errors reported via the problem
view where possible

The above features make use of Eclipse infrastructure and features, with all of the power of Eclipse
being available.

6.2. Drools Runtimes
A Drools runtime is a collection of jar files that represent one specific release of the Drools project
jars. To create a runtime, you must point the IDE to the release of your choice. If you want to create
a new runtime based on the latest Drools project jars included in the plugin itself, you can also
easily do that. You are required to specify a default Drools runtime for your Eclipse workspace,
but each individual project can override the default and select the appropriate runtime for that
project specifically.

6.2.1. Defining a Drools Runtime

To define one or more Drools runtimes using the Eclipse preferences view you open up your
Preferences, by selecting the "Preferences" menu item in the menu "Window". A "Preferences"
dialog should show all your settings. On the left side of this dialog, under the Drools category,
select "Installed Drools runtimes". The panel on the right should then show the currently defined
Drools runtimes. If you have not yet defined any runtimes, it should look like the figure below.

237


To define a new Drools runtime, click on the add button. A dialog such as the one shown below
should pop up, asking for the name of your runtime and the location on your file system where
it can be found.

In general, you have two options:

238

Defining a Drools Runtime

1. If you simply want to use the default jar files as included in the Drools Eclipse plugin, you can
create a new Drools runtime automatically by clicking the "Create a new Drools 5 runtime ..."
button. A file browser will show up, asking you to select the folder on your file system where
you want this runtime to be created. The plugin will then automatically copy all required
dependencies to the specified folder. After selecting this folder, the dialog should look like the
figure shown below.

2. If you want to use one specific release of the Drools project, you should create a folder on
your file system that contains all the necessary Drools libraries and dependencies. Instead of
creating a new Drools runtime as explained above, give your runtime a name and select the
location of this folder containing all the required jars.

After clicking the OK button, the runtime should show up in your table of installed Drools runtimes,
as shown below. Click on checkbox in front of the newly created runtime to make it the default
Drools runtime. The default Drools runtime will be used as the runtime of all your Drools project
that have not selected a project-specific runtime.

239


You can add as many Drools runtimes as you need. For example, the screenshot below shows a
configuration where two runtimes have been defined: a Drools 5.1.1 runtime and a Drools 5.2.0.M2
runtime. The Drools 5.1.1 runtime is selected as the default one.

240

Selecting a runtime for your Drools project

Note that you will need to restart Eclipse if you changed the default runtime and you want to make
sure that all the projects that are using the default runtime update their classpath accordingly.

6.2.2. Selecting a runtime for your Drools project

Whenever you create a Drools project (using the New Drools Project wizard or by converting an
existing Java project to a Drools project using the action "Convert to Drools Project" that is shown
when you are in the Drools perspective and you right-click an existing Java project), the plugin
will automatically add all the required jars to the classpath of your project.

When creating a new Drools project, the plugin will automatically use the default Drools runtime for
that project, unless you specify a project-specific one. You can do this in the final step of the New
Drools Project wizard, as shown below, by deselecting the "Use default Drools runtime" checkbox
and selecting the appropriate runtime in the drop-down box. If you click the "Configure workspace
settings ..." link, the workspace preferences showing the currently installed Drools runtimes will
be opened, so you can add new runtimes there.

241


You can change the runtime of a Drools project at any time by opening the project properties
and selecting the Drools category, as shown below. Mark the "Enable project specific settings"
checkbox and select the appropriate runtime from the drop-down box. If you click the "Configure
workspace settings ..." link, the workspace preferences showing the currently installed Drools
runtimes will be opened, so you can add new runtimes there. If you deselect the "Enable project
specific settings" checkbox, it will use the default runtime as defined in your global preferences.

242

Creating a Rule Project

6.3. Creating a Rule Project
The aim of the new project wizard is to set up an executable scaffold project to start using rules
immediately. This will set up a basic structure, the classpath, sample rules and a test case to get
you started.

243


Figure 6.2. New rule project scaffolding

When you choose to create a new "rule project" you will get a choice to add some default artifacts
to it, like rules, decision tables, ruleflows, etc. These can serve as a starting point, and will give
you something executable almost immediately, which you can then modify and mould to your
needs. The simplest case (a hello world rule) is shown below. Feel free to experiment with the
plug-in at this point.

244

Creating a Rule Project

Figure 6.3. New rule project result

The newly created project contains an example rule file (Sample.drl) in the src/rules directory
and an example Java file (DroolsTest.java) that can be used to execute the rules in a Drools
engine. You'll find this in the folder src/java, in the com.sample package. All the other jars that are
necessary during execution are also added to the classpath in a custom classpath container called
Drools Library. Rules do not have to be kept in "Java" projects at all, this is just a convenience for
people who are already using Eclipse as their Java IDE.

Important note: The Drools plug-in adds a "Drools Builder" capability to your Eclipse instance.
This means you can enable a builder on any project that will build and validate your rules when
resources change. This happens automatically with the Rule Project Wizard, but you can also
enable it manually on any project. One downside of this is that if you have rule files with a large
number of rules (more than 500 rules per file), it means that the background builder may be doing
a lot of work to build the rules on each change. An option here is to turn off the builder, or put
the large rules into .rule files, where you can still use the rule editor, but it won't build them in the
background. To fully validate the rules you will need to run them in a unit test of course.

245


6.4. Creating a New Rule and Wizards
You can create a rule simple as an empty text ".drl" file, or use the wizard to do so. The wizard
menu can be invoked with Control+N, or by choosing it from the toolbar, where there is a menu
with the JBoss Drools icon.

Figure 6.4. The wizard menu

The wizard will ask for some basic options for generating a rule resource. These are just hints -
you can change your mind later. For storing rule files you would typically create a directory src/
rules and create suitably named subdirectories. The package name is mandatory, and is similar
to a package name in Java; i.e., it establishes a namespace for grouping related rules.

246

Textual Rule Editor

Figure 6.5. New rule wizard

The result of this wizard is a rule skeleton, for you to expand. As with all wizards, they are an
optional help - you don't have to use them if you don't want to.

6.5. Textual Rule Editor
The rule editor is where rule managers and developers will be spending most of their time. The
rule editor follows the pattern of a normal text editor in Eclipse, with all the customary features of
a text editor. On top of this, the rule editor provides pop-up content assistance. You invoke pop-
up content assistance the "normal" way by pressing Control+Space.

247


Figure 6.6. The rule editor in action

The rule editor works on files that have a .drl (or .rule) extension. Usually these contain related
rules, but it would also be possible to have rules in individual files, grouped by being in the same
package "namespace", if you so prefer. These DRL files are plain text files.

You can see from the example above that the rule group is using a domain specific language.
Note the expander keyword, which tells the rule compiler to look for a dsl file of that name, to
resolve the rule language. Even with the domain specific language (DSL) the rules are still stored
as plain text as you see it onscreen, which allows simpler management of rules and versions,
e.g., comparing versions of rules.

The editor has an outline view that is kept in sync with the structure of the rules; it is updated on
save. This provides a quick way of navigating around rules by name, even in a file which may
have hundreds of rules. The items are sorted alphabetically by default.

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Drools Views

Figure 6.7. The rule outline view

6.6. Drools Views
When debugging an application using a Drools engine, these views can be used to check the
state of the Drools engine itself: the Working Memory View, the Agenda View, and the Global
Data View. To be able to use these views, create breakpoints in your code invoking the working
memory. For example, the line where you call workingMemory.fireAllRules() is a good candidate.
If the debugger halts at that joinpoint, you should select the working memory variable in the debug
variables view. The available views can then be used to show the details of the selected working
memory:

1. The Working Memory View shows all elements of the Drools working memory.

2. The Agenda View shows all elements on the agenda. For each rule on the agenda, the rule
name and bound variables are shown.

3. The Global Data View shows all global data currently defined in the Drools working memory.

The Audit view can be used to display audit logs containing events that were logged during the
execution of a rules engine, in tree form.

6.6.1. The Working Memory View

The Working Memory View shows all elements in the working memory of the Drools engine.

An action is added to the right of the view, to customize what is shown:

249


1. The Show Logical Structure toggles showing the logical structure of the elements in the working
memory, or all their details. Logical structures allow for visualizing sets of elements in a more
obvious way.

6.6.2. The Agenda View

The Agenda View shows all elements on the agenda. For each rule on the agenda, the rule name
and bound variables are shown.


1. The Show Logical Structure toggles showing the logical structure of the agenda item, or all their
details. Logical structures allow for example visualizing sets of elements in a more obvious way.
The logical structure of AgendaItems shows the rule that is represented by the AgendaItem,
and the values of all the parameters used in the rule.

6.6.3. The Global Data View

The Global Data View shows all global data currently defined in the Drools engine.


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The Audit View

1. The Show Logical Structure toggles showing the logical structure of the elements in the working
memory, or all their details. Logical structures allow for example visualizing sets of elements
in a more obvious way.

6.6.4. The Audit View

The audit view visualizes an audit log, that is optionally created when executing the rules engine.
To create an audit log, use the following code:

// Create a new Knowledge Runtime Logger, that logs to file.
// An event.log file is created in the subdirectory log dir (which must exist)
of the working directory
KnowledgeRuntimeLogger KnowledgeRuntimeLoggerFactory.newFileLogger(
logger
= ksession,
"log/
event");

ksession.insert(...);

// stop logging
logger.close();

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Open the log by clicking the Open Log action, the first icon in the Audit View, and select the file.
The Audit View now shows all events that where logged during the executing of the rules. There
are different types of events, each with a different icon:

1. Object inserted, a green square:

2. Object updated, a yellow square:

3. Object removed, a red square:

4. Activation created, a right arrow:

5. Activation cancelled, a left arrow:

6. Activation executed, a blue diamond:

7. Ruleflow start or end, the "process" icon:

8. Ruleflow-group activation or deactivation, the "activity" icon:

9. Rule package addition or removal, the Drools icon:

10.Rule addition or removal, the Drools icon:

All these events show extra information concerning the event, like the id and toString
representation of the object in case of working memory events (insert, modify and retract), the
name of the rule and all the variables bound in the activation in case of an activation event (created,
cancelled or executed). If an event occurs when executing an activation, it is shown as a child of
the activation's execution event. For some events, you can retrieve the "cause":

1. The cause of an object modified or retracted event is the last object event for that object. This
is either the object asserted event, or the last object modified event for that object.

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Domain Specific Languages

2. The cause of an activation cancelled or executed event is the corresponding activation created
event.

When selecting an event, the cause of that event is shown in green in the audit view (if visible
of course). You can also right click the action and select the "Show Cause" menu item. This will
scroll you to the cause of the selected event.

6.7. Domain Specific Languages
Domain Specific Languages (DSL) enable you to create a language that allows your rules to look
like - rules! Most often the domain specific language reads like natural language. Typically you
would look at how a business analyst would describe the rule, in their own words, and then map
this to your object model, via rule constructs. A side benefit of this is that it can provide an insulation
layer between your domain objects and the rules themselves (as we know you like to refactor).
A domain specific language will grow as the rules grow, and works best when there are common
terms used over an over, with different parameters.

To aid with this, the rule workbench provides an editor for Domain Specific Languages. They are
stored in a plain text format, so you can use any editor of your choice; this format is simply a
slightly enhanced version of the "Properties" file format. The editor will be invoked on any files
with a .dsl extension. There is also a wizard to create a sample DSL.

6.7.1. Editing languages

Figure 6.8. The Domain Specific Language editor

The DSL editor provides a tabular view of the mapping of Language to Rule Expressions. The
Language Expression is what is used in the rules. This also feeds the content assistance for the

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rule editor, so that it can suggest Language Expressions from the DSL configuration. (The rule
editor loads the DSL configuration when the rule resource is loaded for editing.) The Rule language
mapping defines the "code" for the rules into which the language expression will be compiled by
the rule engine compiler. The form of this Rule language expression depends on it being intended
for the condition or the action part of a rule. (For the RHS it may be a snippet of Java, for instance).
The "scope" item indicates where the expression belongs, "when" indicating the LHS, "then" the
RHS, and "*" meaning anywhere. It's also possible to create aliases for keywords.

By selecting a mapping item (a row in the table) you can see the expression and mapping in the
text fields below the table. Double clicking or pressing the edit button will open the edit dialog.
Other buttons let you remove and add mappings. Don't remove mappings while they are still in use.

Figure 6.9. Language Mapping editor dialog

Here is a short description of the DSL translation process. The parser reads the rule text in a DSL,
line by line, and tries to match some "Language Expression", depending on the scope. After a
match, the values that correspond to a placeholder between curly braces (e.g., {age}) are extracted
from the rule source. The placeholders in the corresponding "Rule Expression" are replaced by
their corresponding value. In the example above, the natural language expression maps to two
constraints on a fact of type Person, based on the fields age and location, and the {age} and
{location} values are extracted from the original rule text.

If you do not wish to use a language mapping for a particular rule in a drl, prefix the expression
with > and the compiler will not try to translate it according to the language definition. Also note
that Domain Specific Languages are optional. When the rule is compiled, the .dsl file will also
need to be available.

6.8. The Rete View
The Rete Tree View shows you the current Rete Network for your DRL file. You display it by clicking
on the tab "Rete Tree" at the bottom of the DRL Editor window. With the Rete Network visualization
being open, you can use drag-and-drop on individual nodes to arrange optimal network overview.
You may also select multiple nodes by dragging a rectangle over them; then the entire group can

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Large DRL Files

be moved around. The Eclips toolbar icons for zooming in and out can be used in the customary
manner.

In the current release there is no export function to creates a gif or jpg file. Meanwhile, please use
ctrl + alt + print to create a copy of your current Eclipse window, and cut it off.

The Rete View is an advanced feature which takes full advantage of the Eclipse Graphical Editing
Framework (GEF).

The Rete view works only in Drools Rule Projects, where the Drools Builder is set in the project
´s properties.

If you are using Drools in another type of project where you are not having a Drools Rule Project
with the appropriate Drools Builder, you can use a workaround: Set up a little Drools Rule Project
next to it, putting needed libraries into it, and the DRLs you want to inspect with the Rete View.
Just click on the right tab below in the DRL Editor, followed by a click on "Generate Rete View".

6.9. Large DRL Files
Depending on the JDK you use, it may be necessary to increase the permanent generation max
size. Both the SUN and the IBM JDK have a permanent generation, whereas BEA JRockit does
not.

To increase the permanent generation, start Eclipse with -XX:MaxPermSize=###m

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Example: c:EclipseEclipse.exe -XX:MaxPermSize=128m

Rulesets of 4,000 rules or greater should set the permanent generation to at least 128Mb.

Note

This may also apply to compiling large numbers of rules in general, as there is
generally one or more classes per rule.

As an alternative to the above, you may put rules in a file with the ".rule" extension, and the
background builder will not try to compile them with each change, which may provide performance
improvements if your IDE becomes sluggish with very large numbers of rules.

6.10. Debugging Rules

Figure 6.10. Debugging

You can debug rules during the execution of your Drools application. You can add breakpoints
in the consequences of your rules, and whenever such a breakpoint is encountered during the

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Creating Breakpoints

execution of the rules, execution is halted. You can then inspect the variables known at that point
and use any of the default debugging actions to decide what should happen next: step over,
continue, etc. You can also use the debug views to inspect the content of the working memory
and the Agenda.

6.10.1. Creating Breakpoints

You can add and remove rule breakpoints in DRL files in two ways, similar to adding breakpoints
to Java files:

1. Double-click the ruler of the DRL editor at the line where you want to add a breakpoint. Note
that rule breakpoints can only be created in the consequence of a rule. Double-clicking on a
line where no breakpoint is allowed will do nothing. A breakpoint can be removed by double-
clicking the ruler once more.

2. If you right-click the ruler, a popup menu will show up, containing the "Toggle breakpoint" action.
Note that rule breakpoints can only be created in the consequence of a rule. The action is
automatically disabled if no rule breakpoint is allowed at that line. Clicking the action will add a
breakpoint at the selected line, or remove it if there was one already.

The Debug Perspective contains a Breakpoints view which can be used to see all defined
breakpoints, get their properties, enable/disable or remove them, etc.

6.10.2. Debugging Rules

Drools breakpoints are only enabled if you debug your application as a Drools Application. You
can do this like this:

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Figure 6.11. Debug as Drools Application

1. Select the main class of your application. Right click it and select the "Debug As >" sub-menu
and select Drools Application. Alternatively, you can also select the "Debug ..." menu item to
open a new dialog for creating, managing and running debug configurations (see the screenshot
below).

2. Select the "Drools Application" item in the left tree and click the "New launch configuration"
button (leftmost icon in the toolbar above the tree). This will create a new configuration with
some of the properties (like project and main class)already filled in, based on the main class
you selected in the beginning. All properties shown here are the same as for any standard
Java program.

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Debugging Rules

3. Change the name of your debug configuration to something meaningful. You can just accept
the defaults for all other properties. For more information about these properties, please check
the Eclipse JDT documentation.

4. Click the "Debug" button on the bottom to start debugging your application. You only have to
define your debug configuration once. The next time you run your Drools application, you don't
have to create a new one but select the previously defined one in the tree on the left, as a sub-
element of the "Drools Application" tree node, and then click the Debug button. The Eclipse
toolbar also contains shortcut buttons to quickly re-execute one of your previous configurations
(at least when one of the Java, Java Debug, or Drools perspectives has been selected).

Figure 6.12. Debug as Drools Application Configuration

After clicking the "Debug" button, the application starts executing and will halt if any breakpoint
is encountered. This can be a Drools rule breakpoint, or any other standard Java breakpoint.
Whenever a Drools rule breakpoint is encountered, the corresponding DRL file is opened and the
active line is highlighted. The Variables view also contains all rule parameters and their value. You
can then use the default Java debug actions to decide what to do next: resume, terminate, step

259


over, etc. The debug view can also be used to inspect the contents of the Working Memory and
the Agenda at that time as well. You don't have to select a Working Memory now, as the current
executing working memory is automatically shown.

Figure 6.13. Debugging

6.11. Preferences
The rules IDE also comes with a set of customizable preferences.

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Preferences

In particular they allow to configure the following options:

1. Automatically reparse all rules if a Java resource is changed: triggers a rebuilding of all the
rules when a Java class is modified

2. Allow cross reference in DRL files: makes it possible to have a resource in a DRL file referencing
another resource defined in a different file. For example you could have a rule in a file using
a type declared in another file. Note that by enabling this option it will no longer possible to
declare the same resource (i.e. two rule with the same name in the same package) in two
different DRL files.

3. Internal Drools classes use: allows, disallows or discourages (generating warning) the use of
Drools classes not exposed in the public API

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Chapter 7.

Chapter 7. Examples
7.1. Getting the Examples
Make sure the Drools Eclipse plugin is installed, which needs the Graphical Editing Framework
(GEF) dependency installed first. Then download and extract the drools-examples zip file, which
includes an already created Eclipse project. Import that project into a new Eclipse workspace. The
rules all have example classes that execute the rules. If you want to try the examples in another
project (or another IDE) then you will need to set up the dependencies by hand, of course. Many,
but not all of the examples are documented below, enjoy!

Some examples require Java 1.6 to run.

7.2. Hello World

Name: Hello World
Main class: org.drools.examples.helloworld.HelloWorldExample
Module: drools-examples
Type: Java application
Rules file: HelloWorld.drl
Objective: demonstrate basic rules in use

The "Hello World" example shows a simple example of rules usage, and both the MVEL and Java
dialects.

This example demonstrates how to build Knowledge Bases and Sessions. Also, audit logging
and debug outputs are shown, which is omitted from other examples as it's all very similar. A
KnowledgeBuilder is used to turn a DRL source file into Package objects which the Knowledge
Base can consume. The add method takes a Resource interface and a Resource Type as
parameters. The Resource can be used to retrieve a DRL source file from various locations; in
this case the DRL file is being retrieved from the classpath using a ResourceFactory, but it could
come from a disk file or a URL. Here, we only add a single DRL source file, but multiple DRL files
can be added. Also, DRL files with different namespaces can be added, where the Knowledge
Builder creates a package for each namespace. Multiple packages of different namespaces can
be added to the same Knowledge Base. When all the DRL files have been added, we should
check the builder for errors. While the Knowledge Base will validate the package, it will only have
access to the error information as a String, so if you wish to debug the error information you should
do it on the KnowledgeBuilder instance. Once you know the builder is error free, get the Package
collection, instantiate a KnowledgeBase from the KnowledgeBaseFactory and add the package
collection.

Example 7.1. HelloWorld: Creating the KnowledgeBase and Session

final KnowledgeBuilder kbuilder = KnowledgeBuilderFactory.newKnowledgeBuilder();

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Chapter 7. Examples

// this will parse and compile in one step
kbuilder.add(ResourceFactory.newClassPathResource("HelloWorld.drl",
HelloWorldExample.class), ResourceType.DRL);

// Check the builder for errors
if (kbuilder.hasErrors()) {
System.out.println(kbuilder.getErrors().toString());
throw new RuntimeException("Unable to compile "HelloWorld.drl".");
}

// get the compiled packages (which are serializable)
final Collection<KnowledgePackage> pkgs = kbuilder.getKnowledgePackages();

// add the packages to a KnowledgeBase (deploy the knowledge packages).
final KnowledgeBase kbase = KnowledgeBaseFactory.newKnowledgeBase();
kbase.addKnowledgePackages(pkgs);

final StatefulKnowledgeSession ksession = kbase.newStatefulKnowledgeSession();

Drools has an event model that exposes much of what's happening internally. Two default debug
listeners are supplied, DebugAgendaEventListener and DebugWorkingMemoryEventListener
which print out debug event information to the System.err stream displayed in the Console
window. Adding listeners to a Session is trivial, as shown below. The KnowledgeRuntimeLogger
provides execution auditing, the result of which can be viewed in a graphical viewer. The logger is
actually a specialised implementation built on the Agenda and Working Memory listeners. When
the engine has finished executing, logger.close() must be called.

Most of the examples use the Audit logging features of Drools to record execution flow for later
inspection.

Example 7.2. HelloWorld: Event logging and Auditing

// setup the debug listeners
ksession.addEventListener( new DebugAgendaEventListener() );
ksession.addEventListener( new DebugWorkingMemoryEventListener() );

// setup the audit logging
KnowledgeRuntimeLoggerFactory.newFileLogger(ksession, "log/helloworld");

The single class used in this example is very simple. It has two fields: the message, which is a
String and the status which can be one of the two integers HELLO or GOODBYE.

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Hello World

Example 7.3. HelloWorld example: Message Class

public static class Message {
public static final int HELLO = 0;
public static final int GOODBYE = 1;

private String message;
private int status;
...
}

A single Message object is created with the message text "Hello World" and the status HELLO and
then inserted into the engine, at which point fireAllRules() is executed. Remember that all
the network evaluation is done during the insert time, so that by the time the program execution
reaches the fireAllRules() method call the engine already knows which rules are fully matches
and able to fire.

Example 7.4. HelloWorld: Execution

final Message message = new Message();
message.setMessage("Hello World");
message.setStatus(Message.HELLO);
ksession.insert(message);


logger.close();

ksession.dispose();

To execute the example as a Java application:

1. Open the class org.drools.examples.helloworld.HelloWorldExample in your Eclipse IDE

2. Right-click the class and select "Run as..." and then "Java application"

If we put a breakpoint on the fireAllRules() method and select the ksession variable, we can
see that the "Hello World" rule is already activated and on the Agenda, confirming that all the
pattern matching work was already done during the insert.

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Chapter 7. Examples

Figure 7.1. Hello World: fireAllRules Agenda View

The may application print outs go to to System.out while the debug listener print outs go to
System.err.

Example 7.5. HelloWorld: System.out in the Console window

Hello World
Goodbye cruel world

Example 7.6. HelloWorld: System.err in the Console window

==>[ActivationCreated(0): rule=Hello World;
tuple=[fid:1:1:org.drools.examples.helloworld.HelloWorldExample
$Message@17cec96]]
[ObjectInserted:

$Message@17cec96];
object=org.drools.examples.helloworld.HelloWorldExample
$Message@17cec96]
[BeforeActivationFired: rule=Hello World;

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Hello World

$Message@17cec96]]
==>[ActivationCreated(4): rule=Good Bye;
$Message@17cec96]]
[ObjectUpdated:

$Message@17cec96];
old_object=org.drools.examples.helloworld.HelloWorldExample
$Message@17cec96;
new_object=org.drools.examples.helloworld.HelloWorldExample
$Message@17cec96]
[AfterActivationFired(0): rule=Hello World]
[BeforeActivationFired: rule=Good Bye;
$Message@17cec96]]
[AfterActivationFired(4): rule=Good Bye]

The LHS (after when) section of the rule states that it will be activated for each Message object
inserted into the Working Memory whose status is Message.HELLO. Besides that, two variable
bindings are created: the variable message is bound to the message attribute and the variable m
is bound to the matched Message object itself.

The RHS (after then) or consequence part of the rule is written using the MVEL expression
language, as declared by the rule's attribute dialect. After printing the content of the bound
variable message to System.out, the rule changes the values of the message and status
attributes of the Message object bound to m. This is done MVEL's modify statement, which allows
you to apply a block of assignments in one statement, with the engine being automatically notified
of the changes at the end of the block.

Example 7.7. HelloWorld: rule "Hello World"

rule "Hello World"
dialect "mvel"
when
m : Message( status == Message.HELLO, message : message )
then
System.out.println( message );
modify ( m ) { message = "Goodbye cruel world",
status = Message.GOODBYE };
end

We can set a breakpoint into the DRL, on the modify call, and inspect the Agenda view again
during the execution of the rule's consequence. This time we start the execution via "Debug As"
and "Drools application" and not by running a "Java application":

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Chapter 7. Examples

1. Open the class org.drools.examples.HelloWorld in your Eclipse IDE.

2. Right-click the class and select "Debug as..." and then "Drools application".

Now we can see that the other rule "Good Bye", which uses the Java dialect, is activated and
placed on the Agenda.

Figure 7.2. Hello World: rule "Hello World" Agenda View

The "Good Bye" rule, which specifies the "java" dialect, is similar to the "Hello World" rule except
that it matches Message objects whose status is Message.GOODBYE.

Example 7.8. HelloWorld: rule "Good Bye"

rule "Good Bye"
dialect "java"
when

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State Example

Message( status == Message.GOODBYE, message : message )
then
System.out.println( message );
end

Remember the Java code where we used the KnowledgeRuntimeLoggerFactory method
newFileLogger to create a KnowledgeRuntimeLogger and called logger.close() at the end.
This created an audit log file that can be shown in the Audit view. We use the Audit view in many
of the examples to demonstrate the example execution flow. In the view screen shot below we
can see that the object is inserted, which creates an activation for the "Hello World" rule; the
activation is then executed which updates the Message object causing the "Good Bye" rule to
activate; finally the "Good Bye" rule also executes. Selecting an event in the Audit view highlights
the origin event in green; therefore the "Activation created" event is highlighted in green as the
origin of the "Activation executed" event.

Figure 7.3. Hello World: Audit View

7.3. State Example
This example is implemented in three different versions to demonstrate different ways of
implementing the same basic behavior: forward chaining, i.e., the ability the engine has to
evaluate, activate and fire rules in sequence, based on changes on the facts in the Working
Memory.

7.3.1. Understanding the State Example

Name: State Example
Main class: org.drools.examples.state.StateExampleUsingSalience
Rules file: StateExampleUsingSalience.drl
Objective: Demonstrates basic rule use
and Conflict Resolution for rule firing priority.

Each State class has fields for its name and its current state (see the class
org.drools.examples.state.State). The two possible states for each objects are:

• NOTRUN

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Chapter 7. Examples

• FINISHED

Example 7.9. State Class

public class State {
public static final int NOTRUN = 0;
public static final int FINISHED = 1;

private final PropertyChangeSupport changes =
new PropertyChangeSupport( this );

private int state;

... setters and getters go here...
}

Ignoring the PropertyChangeSupport, which will be explained later, we see the creation of four
State objects named A, B, C and D. Initially their states are set to NOTRUN, which is default for the
used constructor. Each instance is asserted in turn into the Session and then fireAllRules()
is called.

Example 7.10. Salience State: Execution

State a = new State( "A" );
State b = new State( "B" );
State c = new State( "C" );
final State d = new State( "D" );

// By setting dynamic to TRUE, Drools will use JavaBean
// PropertyChangeListeners so you don't have to call modify or update().
boolean dynamic = true;

session.insert( a, dynamic );
session.insert( b, dynamic );
session.insert( c, dynamic );
session.insert( d, dynamic );

session.fireAllRules();
session.dispose(); // Stateful rule session must always be disposed when finished

To execute the application:

1. Open the class org.drools.examples.state.StateExampleUsingSalience in your Eclipse
IDE.

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Understanding the State Example


You will see the following output in the Eclipse console window:

Example 7.11. Salience State: Console Output

A finished
B finished
C finished
D finished

There are four rules in total. First, the Bootstrap rule fires, setting A to state FINISHED, which
then causes B to change its state to FINISHED. C and D are both dependent on B, causing a
conflict which is resolved by the salience values. Let's look at the way this was executed.

The best way to understand what is happening is to use the Audit Logging feature to graphically
see the results of each operation. To view the Audit log generated by a run of this example:

1. If the Audit View is not visible, click on "Window" and then select "Show View", then "Other..."
and "Drools" and finally "Audit View".

2. In the "Audit View" click the "Open Log" button and select the file "<drools-examples-dir>/log/
state.log".

After that, the "Audit view" will look like the following screenshot:

Figure 7.4. Salience State Example Audit View

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Chapter 7. Examples

Reading the log in the "Audit View", top to bottom, we see every action and the corresponding
changes in the Working Memory. This way we observe that the assertion of the State object A
in the state NOTRUN activates the Bootstrap rule, while the assertions of the other State objects
have no immediate effect.

Example 7.12. Salience State: Rule "Bootstrap"

rule Bootstrap
when
a : State(name == "A", state == State.NOTRUN )
then
System.out.println(a.getName() + " finished" );
a.setState( State.FINISHED );
end

The execution of rule Bootstrap changes the state of A to FINISHED, which, in turn, activates rule
"A to B".

Example 7.13. Salience State: Rule "A to B"

rule "A to B"
when
State(name == "A", state == State.FINISHED )
b : State(name == "B", state == State.NOTRUN )
then
System.out.println(b.getName() + " finished" );
b.setState( State.FINISHED );
end

The execution of rule "A to B" changes the state of B to FINISHED, which activates both, rules "B
to C" and "B to D", placing their Activations onto the Agenda. From this moment on, both rules
may fire and, therefore, they are said to be "in conflict". The conflict resolution strategy allows the
engine's Agenda to decide which rule to fire. As rule "B to C" has the higher salience value (10
versus the default salience value of 0), it fires first, modifying object C to state FINISHED. The
Audit view shown above reflects the modification of the State object in the rule "A to B", which
results in two activations being in conflict. The Agenda view can also be used to investigate the
state of the Agenda, with debug points being placed in the rules themselves and the Agenda view
opened. The screen shot below shows the breakpoint in the rule "A to B" and the state of the
Agenda with the two conflicting rules.

272


Figure 7.5. State Example Agenda View

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Chapter 7. Examples

Example 7.14. Salience State: Rule "B to C"

rule "B to C"
salience 10
when
State(name == "B", state == State.FINISHED )
c : State(name == "C", state == State.NOTRUN )
then
System.out.println(c.getName() + " finished" );
c.setState( State.FINISHED );
end

Rule "B to D" fires last, modifying object D to state FINISHED.

Example 7.15. Salience State: Rule "B to D"

rule "B to D"
when
d : State(name == "D", state == State.NOTRUN )
then
System.out.println(d.getName() + " finished" );
d.setState( State.FINISHED );
end

There are no more rules to execute and so the engine stops.

Another notable concept in this example is the use of dynamic facts, based on
PropertyChangeListener objects. As described in the documentation, in order for the engine
to see and react to changes of fact properties, the application must tell the engine that changes
occurred. This can be done explicitly in the rules by using the modify statement, or implicitly
by letting the engine know that the facts implement PropertyChangeSupport as defined by
the JavaBeans specification. This example demonstrates how to use PropertyChangeSupport
to avoid the need for explicit modify statements in the rules. To make use of this
feature, ensure that your facts implement PropertyChangeSupport, the same way the class
org.drools.example.State does, and use the following code to insert the facts into the Working
Memory:

Example 7.16. Inserting a Dynamic Fact

// By setting dynamic to TRUE, Drools will use JavaBean
// PropertyChangeListeners so you don't have to call modify or update().
final boolean dynamic = true;

274


session.insert( fact, dynamic );

When using PropertyChangeListener objects, each setter must implement a little extra code for
the notification. Here is the setter for state in the class org.drools.examples:
:

Example 7.17. Setter Example with PropertyChangeSupport

public void setState(final int newState) {
int oldState = this.state;
this.state = newState;
this.changes.firePropertyChange( "state",
oldState,
newState );
}

There are two other classes in this example: StateExampleUsingAgendaGroup and
StateExampleWithDynamicRules. Both execute from A to B to C to D, as just shown. The
StateExampleUsingAgendaGroup uses agenda-groups to control the rule conflict and which one
fires first. StateExampleWithDynamicRules shows how an additional rule can be added to an
already running Working Memory with all the existing data applying to it at runtime.

Agenda groups are a way to partition the Agenda into groups and to control which groups can
execute. By default, all rules are in the agenda group "MAIN". The "agenda-group" attribute lets
you specify a different agenda group for the rule. Initially, a Working Memory has its focus on the
Agenda group "MAIN". A group's rules will only fire when the group receives the focus. This can be
achieved either ny using the method by setFocus() or the rule attribute auto-focus. "auto-focus"
means that the rule automatically sets the focus to its agenda group when the rule is matched and
activated. It is this "auto-focus" that enables rule "B to C" to fire before "B to D".

Example 7.18. Agenda Group State Example: Rule "B to C"

rule "B to C"
agenda-group "B to C"
auto-focus true
when
c : State(name == "C", state == State.NOTRUN )
then
System.out.println(c.getName() + " finished" );
c.setState( State.FINISHED );
kcontext.getKnowledgeRuntime().getAgenda().getAgendaGroup( "B to
D" ).setFocus();
end

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The rule "B to C" calls setFocus() on the agenda group "B to D", allowing its active rules to fire,
which allows the rule "B to D" to fire.

Example 7.19. Agenda Group State Example: Rule "B to D"

rule "B to D"
agenda-group "B to D"
when
d : State(name == "D", state == State.NOTRUN )
then
System.out.println(d.getName() + " finished" );
d.setState( State.FINISHED );
end

The example StateExampleWithDynamicRules adds another rule to the Rule Base after
fireAllRules(). The added rule is just another state transition.

Example 7.20. Dynamic State Example: Rule "D to E"

rule "D to E"
when
State(name == "D", state == State.FINISHED )
e : State(name == "E", state == State.NOTRUN )
then
System.out.println(e.getName() + " finished" );
e.setState( State.FINISHED );
end

This produces the following expected output:

Example 7.21. Dynamic Sate Example Output

A finished
B finished
C finished
D finished
E finished

7.4. Fibonacci Example
Name: Fibonacci
Main class: org.drools.examples.fibonacci.FibonacciExample

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Fibonacci Example

Rules file: Fibonacci.drl
Objective: Demonstrates Recursion,
the CE not and cross product matching

The Fibonacci Numbers (see http://en.wikipedia.org/wiki/Fibonacci_number) discovered by
Leonardo of Pisa (see http://en.wikipedia.org/wiki/Fibonacci) is a sequence that starts with 0 and
1. The next Fibonacci number is obtained by adding the two preceding Fibonacci numbers. The
Fibonacci sequence begins with 0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 377, 610, 987,
1597, 2584, 4181, 6765, 10946,... The Fibonacci Example demonstrates recursion and conflict
resolution with salience values.

The single fact class Fibonacci is used in this example. It has two fields, sequence and value.
The sequence field is used to indicate the position of the object in the Fibonacci number sequence.
The value field shows the value of that Fibonacci object for that sequence position, using -1 to
indicate a value that still needs to be computed.

Example 7.22. Fibonacci Class

public static class Fibonacci {
private int sequence;
private long value;

public Fibonacci( final int sequence ) {
this.sequence = sequence;
this.value = -1;
}

... setters and getters go here...
}

Execute the example:

1. Open the class org.drools.examples.fibonacci.FibonacciExample in your Eclipse IDE.


Eclipse shows the following output in its console window (with "...snip..." indicating lines that were
removed to save space):

Example 7.23. Fibonacci Example: Console Output

recurse for 50

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Chapter 7. Examples

recurse for 49
recurse for 48
recurse for 47
...snip...
recurse for 5
recurse for 4
recurse for 3
recurse for 2
1 == 1
2 == 1
3 == 2
4 == 3
5 == 5
6 == 8
...snip...
47 == 2971215073
48 == 4807526976
49 == 7778742049
50 == 12586269025

To kick this off from Java we only insert a single Fibonacci object, with a sequence field of 50.
A recursive rule is then used to insert the other 49 Fibonacci objects. This example doesn't
use PropertyChangeSupport. It uses the MVEL dialect, which means we can use the modify
keyword, which allows a block setter action which also notifies the engine of changes.

Example 7.24. Fibonacci Example: Execution

ksession.insert( new Fibonacci( 50 ) );

The rule Recurse is very simple. It matches each asserted Fibonacci object with a value of -1,
creating and asserting a new Fibonacci object with a sequence of one less than the currently
matched object. Each time a Fibonacci object is added while the one with a sequence field equal
to 1 does not exist, the rule re-matches and fires again. The not conditional element is used to
stop the rule's matching once we have all 50 Fibonacci objects in memory. The rule also has a
salience value, because we need to have all 50 Fibonacci objects asserted before we execute
the Bootstrap rule.

Example 7.25. Fibonacci Example: Rule "Recurse"

rule Recurse
salience 10
when
f : Fibonacci ( value == -1 )
not ( Fibonacci ( sequence == 1 ) )

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Fibonacci Example

then
insert( new Fibonacci( f.sequence - 1 ) );
System.out.println( "recurse for " + f.sequence );
end

The Audit view shows the original assertion of the Fibonacci object with a sequence field of 50,
done from Java code. From there on, the Audit view shows the continual recursion of the rule,
where each asserted Fibonacci object causes the Recurse rule to become activated and to fire
again.

Figure 7.6. Fibonacci Example: "Recurse" Audit View 1

When a Fibonacci object with a sequence field of 2 is asserted the "Bootstrap" rule is matched
and activated along with the "Recurse" rule. Note the multi-restriction on field sequence, testing
for equality with 1 or 2.

Example 7.26. Fibonacci Example: Rule "Bootstrap"

rule Bootstrap
when
f : Fibonacci( sequence == 1 || == 2, value == -1 ) // multi-restriction
then
modify ( f ){ value = 1 };

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Chapter 7. Examples

System.out.println( f.sequence + " == " + f.value );
end

At this point the Agenda looks as shown below. However, the "Bootstrap" rule does not fire
because the "Recurse" rule has a higher salience.

Figure 7.7. Fibonacci Example: "Recurse" Agenda View 1

When a Fibonacci object with a sequence of 1 is asserted the Bootstrap rule is matched again,
causing two activations for this rule. Note that the "Recurse" rule does not match and activate
because the not conditional element stops the rule's matching as soon as a Fibonacci object
with a sequence of 1 exists.

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Fibonacci Example

Figure 7.8. Fibonacci Example: "Recurse" Agenda View 2

Once we have two Fibonacci objects with values not equal to -1 the "Calculate" rule is able
to match. It was the "Bootstrap" rule that set the objects with sequence 1 and 2 to values of
1. At this point we have 50 Fibonacci objects in the Working Memory. Now we need to select
a suitable triple to calculate each of their values in turn. Using three Fibonacci patterns in a
rule without field constraints to confine the possible cross products would result in 50x49x48
possible combinations, leading to about 125,000 possible rule firings, most of them incorrect. The
"Calculate" rule uses field constraints to correctly constraint the thee Fibonacci patterns in the
correct order; this technique is called cross product matching. The first pattern finds any Fibonacci
with a value != -1 and binds both the pattern and the field. The second Fibonacci does this, too,
but it adds an additional field constraint to ensure that its sequence is greater by one than the
Fibonacci bound to f1. When this rule fires for the first time, we know that only sequences 1
and 2 have values of 1, and the two constraints ensure that f1 references sequence 1 and f2
references sequence 2. The final pattern finds the Fibonacci with a value equal to -1 and with a
sequence one greater than f2. At this point, we have three Fibonacci objects correctly selected
from the available cross products, and we can calculate the value for the third Fibonacci object
that's bound to f3.

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Chapter 7. Examples

Example 7.27. Fibonacci Example: Rule "Calculate"

rule Calculate
when
// Bind f1 and s1
f1 : Fibonacci( s1 : sequence, value != -1 )
// Bind f2 and v2; refer to bound variable s1
f2 : Fibonacci( sequence == (s1 + 1), v2 : value != -1 )
// Bind f3 and s3; alternative reference of f2.sequence
f3 : Fibonacci( s3 : sequence == (f2.sequence + 1 ), value == -1 )
then
// Note the various referencing techniques.
modify ( f3 ) { value = f1.value + v2 };
System.out.println( s3 + " == " + f3.value );
end

The modify statement updated the value of the Fibonacci object bound to f3. This means we
now have another new Fibonacci object with a value not equal to -1, which allows the "Calculate"
rule to rematch and calculate the next Fibonacci number. The Audit view below shows how the
firing of the last "Bootstrap" modifies the Fibonacci object, enabling the "Calculate" rule to match,
which then modifies another Fibonacci object allowing the "Calculate" rule to match again. This
continues till the value is set for all Fibonacci objects.

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Banking Tutorial

Figure 7.9. Fibonacci Example: "Bootstrap" Audit View

7.5. Banking Tutorial

Name: BankingTutorial
Main class: org.drools.tutorials.banking.BankingExamplesApp.java
Rules file: org.drools.tutorials.banking.*.drl
Objective: Demonstrate pattern matching, basic sorting and calculation
rules.

This tutorial demonstrates the process of developing a complete personal banking application to
handle credits and debits on multiple accounts. It uses a set of design patterns that have been
created for the process.

The class RuleRunner is a simple harness to execute one or more DRL files against a set of data.
It compiles the Packages and creates the Knowledge Base for each execution, allowing us to
easily execute each scenario and inspect the outputs. In reality this is not a good solution for a

283

Chapter 7. Examples

production system, where the Knowledge Base should be built just once and cached, but for the
purposes of this tutorial it shall suffice.

Example 7.28. Banking Tutorial: RuleRunner

public class RuleRunner {

public RuleRunner() {
}

public void runRules(String[] rules,
Object[] facts) throws Exception {


for ( int i = 0; i < rules.length; i++ ) {
String ruleFile = rules[i];
System.out.println( "Loading file: " + ruleFile );
kbuilder.add( ResourceFactory.newClassPathResource( ruleFile,
RuleRunner.class ),
ResourceType.DRL );
}

Collection<KnowledgePackage> pkgs = kbuilder.getKnowledgePackages();
kbase.addKnowledgePackages( pkgs );

for ( int i = 0; i < facts.length; i++ ) {
Object fact = facts[i];
System.out.println( "Inserting fact: " + fact );
ksession.insert( fact );
}

}
}

The first of our sample Java classes loads and executes a single DRL file, Example.drl, but
without inserting any data.

Example 7.29. Banking Tutorial : Java Example1

public class Example1 {
public static void main(String[] args) throws Exception {
new RuleRunner().runRules( new String[] { "Example1.drl" },

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Banking Tutorial

new Object[0] );
}
}

The first simple rule to execute has a single eval condition that will always be true, so that this
rule will match and fire, once, after the start.

Example 7.30. Banking Tutorial: Rule in Example1.drl

rule "Rule 01"
when
eval( 1==1 )
then
System.out.println( "Rule 01 Works" );
end

The output for the rule is below, showing that the rule matches and executes the single print
statement.

Example 7.31. Banking Tutorial: Output of Example1.java

Loading file: Example1.drl
Rule 01 Works

The next step is to assert some simple facts and print them out.

Example 7.32. Banking Tutorial: Java Example2

Number[] numbers = new Number[] {wrap(3), wrap(1), wrap(4), wrap(1), wrap(5)};
numbers );
}

private static Integer wrap( int i ) {
return new Integer(i);
}
}

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Chapter 7. Examples

This doesn't use any specific facts but instead asserts a set of java.lang.Integer objects. This
is not considered "best practice" as a number is not a useful fact, but we use it here to demonstrate
basic techniques before more complexity is added.

Now we will create a simple rule to print out these numbers.


rule "Rule 02"
when
Number( $intValue : intValue )
then
System.out.println( "Number found with value: " + $intValue );
end

Once again, this rule does nothing special. It identifies any facts that are Number objects and prints
out the values. Notice the use of the abstract class Number: we inserted Integer objects but we
now look for any kind of number. The pattern matching engine is able to match interfaces and
superclasses of asserted objects.

The output shows the DRL being loaded, the facts inserted and then the matched and fired rules.
We can see that each inserted number is matched and fired and thus printed.


Inserting fact: 3
Inserting fact: 1
Inserting fact: 4
Inserting fact: 1
Inserting fact: 5
Number found with value: 5

There are certainly many better ways to sort numbers than using rules, but since we will need to
apply some cashflows in date order when we start looking at banking rules we'll develop simple
rule based sorting technique.

Example 7.35. Banking Tutorial: Example3.java


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Banking Tutorial

Number[] numbers = new Number[] {wrap(3), wrap(1), wrap(4), wrap(1), wrap(5)};
numbers );
}

private static Integer wrap(int i) {
return new Integer(i);
}
}

Again we insert our Integer objects, but this time the rule is slightly different:


rule "Rule 03"
when
$number : Number( )
not Number( intValue < $number.intValue )
then
System.out.println("Number found with value: " + $number.intValue() );
retract( $number );
end

The first line of the rule identifies a Number and extracts the value. The second line ensures that
there does not exist a smaller number than the one found by the first pattern. We might expect
to match only one number - the smallest in the set. However, the retraction of the number after it
has been printed means that the smallest number has been removed, revealing the next smallest
number, and so on.

The resulting output shows that the numbers are now sorted numerically.


Inserting fact: 3
Inserting fact: 1
Inserting fact: 4
Inserting fact: 1
Inserting fact: 5

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Chapter 7. Examples


We are ready to start moving towards our personal accounting rules. The first step is to create
a Cashflow object.

Example 7.38. Banking Tutorial: Class Cashflow

public class Cashflow {
private Date date;
private double amount;

public Cashflow() {
}

public Cashflow(Date date, double amount) {
this.date = date;
this.amount = amount;
}

public Date getDate() {
return date;
}

public void setDate(Date date) {
this.date = date;
}

public double getAmount() {
return amount;
}

public void setAmount(double amount) {
this.amount = amount;
}

public String toString() {
return "Cashflow[date=" + date + ",amount=" + amount + "]";
}
}

Class Cashflow has two simple attributes, a date and an amount. (Note that using the type double
for monetary units is generally not a good idea because floating point numbers cannot represent
most numbers accurately.) There is also an overloaded constructor to set the values, and a method
toString to print a cashflow. The Java code of Example4.java inserts five Cashflow objects,
with varying dates and amounts.

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Banking Tutorial


Object[] cashflows = {
new Cashflow(new SimpleDate("01/01/2007"), 300.00),
};

cashflows );
}
}

The convenience class SimpleDate extends java.util.Date, providing a constructor taking a
String as input and defining a date format. The code is listed below

Example 7.40. Banking Tutorial: Class SimpleDate

public class SimpleDate extends Date {
private static final SimpleDateFormat format = new SimpleDateFormat("dd/
MM/yyyy");

public SimpleDate(String datestr) throws Exception {
setTime(format.parse(datestr).getTime());
}
}

Now, let’s look at Example4.drl to see how we print the sorted Cashflow objects:


rule "Rule 04"
when
$cashflow : Cashflow( $date : date, $amount : amount )
not Cashflow( date < $date)
then
System.out.println("Cashflow: "+$date+" :: "+$amount);
retract($cashflow);
end

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Chapter 7. Examples

Here, we identify a Cashflow and extract the date and the amount. In the second line of the rule
we ensure that there is no Cashflow with an earlier date than the one found. In the consequence,
we print the Cashflow that satisfies the rule and then retract it, making way for the next earliest
Cashflow. So, the output we generate is:


Inserting fact: Cashflow[date=Mon Jan 01 00:00:00 GMT 2007,amount=300.0]
Inserting fact: Cashflow[date=Fri Jan 05 00:00:00 GMT 2007,amount=100.0]
Inserting fact: Cashflow[date=Thu Jan 11 00:00:00 GMT 2007,amount=500.0]
Inserting fact: Cashflow[date=Sun Jan 07 00:00:00 GMT 2007,amount=800.0]
Inserting fact: Cashflow[date=Tue Jan 02 00:00:00 GMT 2007,amount=400.0]
Cashflow: Mon Jan 01 00:00:00 GMT 2007 :: 300.0
Cashflow: Tue Jan 02 00:00:00 GMT 2007 :: 400.0
Cashflow: Fri Jan 05 00:00:00 GMT 2007 :: 100.0
Cashflow: Sun Jan 07 00:00:00 GMT 2007 :: 800.0
Cashflow: Thu Jan 11 00:00:00 GMT 2007 :: 500.0

Next, we extend our Cashflow, resulting in a TypedCashflow which can be a credit or a debit
operation. (Normally, we would just add this to the Cashflow type, but we use extension to keep
the previous version of the class intact.)

Example 7.43. Banking Tutorial: Class TypedCashflow

public class TypedCashflow extends Cashflow {
public static final int CREDIT = 0;
public static final int DEBIT = 1;

private int type;

public TypedCashflow() {
}

public TypedCashflow(Date date, int type, double amount) {
super( date, amount );
this.type = type;
}

public int getType() {
return type;
}

public void setType(int type) {
this.type = type;
}

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Banking Tutorial

return "TypedCashflow[date=" + getDate() +
",type=" + (type == CREDIT ? "Credit" : "Debit") +
",amount=" + getAmount() + "]";
}
}

There are lots of ways to improve this code, but for the sake of the example this will do.

Now let's create Example5, a class for running our code.


new TypedCashflow(new SimpleDate("01/01/2007"),
TypedCashflow.CREDIT, 300.00),
TypedCashflow.DEBIT, 800.00),
};

cashflows );
}
}

Here, we simply create a set of Cashflow objects which are either credit or debit operations. We
supply them and Example5.drl to the RuleEngine.

Now, let’s look at a rule printing the sorted Cashflow objects.


rule "Rule 05"
when
$cashflow : TypedCashflow( $date : date,
$amount : amount,
type == TypedCashflow.CREDIT )

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Chapter 7. Examples

not TypedCashflow( date < $date,
then
System.out.println("Credit: "+$date+" :: "+$amount);
retract($cashflow);
end

Here, we identify a Cashflow fact with a type of CREDIT and extract the date and the amount. In
the second line of the rule we ensure that there is no Cashflow of the same type with an earlier
date than the one found. In the consequence, we print the cashflow satisfying the patterns and
then retract it, making way for the next earliest cashflow of type CREDIT.

So, the output we generate is


Inserting fact: TypedCashflow[date=Mon Jan 01 00:00:00 GMT
2007,type=Credit,amount=300.0]
Inserting fact: TypedCashflow[date=Fri Jan 05 00:00:00 GMT
Inserting fact: TypedCashflow[date=Thu Jan 11 00:00:00 GMT
Inserting fact: TypedCashflow[date=Sun Jan 07 00:00:00 GMT
2007,type=Debit,amount=800.0]
Inserting fact: TypedCashflow[date=Tue Jan 02 00:00:00 GMT
2007,type=Debit,amount=400.0]
Credit: Mon Jan 01 00:00:00 GMT 2007 :: 300.0
Credit: Fri Jan 05 00:00:00 GMT 2007 :: 100.0
Credit: Thu Jan 11 00:00:00 GMT 2007 :: 500.0

Continuing our banking exercise, we are now going to process both credits and debits on two bank
accounts, calculating the account balance. In order to do this, we create two separate Account
objects and inject them into the Cashflows objects before passing them to the Rule Engine. The
reason for this is to provide easy access to the correct account without having to resort to helper
classes. Let’s take a look at the Account class first. This is a simple Java object with an account
number and balance:

Example 7.47. Banking Tutorial: Class Account

public class Account {
private long accountNo;
private double balance = 0;

public Account() {
}

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public Account(long accountNo) {
this.accountNo = accountNo;
}

public long getAccountNo() {
return accountNo;
}

public void setAccountNo(long accountNo) {
this.accountNo = accountNo;
}

public double getBalance() {
return balance;
}

public void setBalance(double balance) {
this.balance = balance;
}

return "Account[" + "accountNo=" + accountNo + ",balance=" + balance + "]";
}
}

Now let’s extend our TypedCashflow, resulting in AllocatedCashflow, to include an Account
reference.

Example 7.48. Banking Tutorial: Class AllocatedCashflow

public class AllocatedCashflow extends TypedCashflow {
private Account account;

public AllocatedCashflow() {
}

public AllocatedCashflow(Account account, Date date, int type, double amount) {
super( date, type, amount );
this.account = account;
}

public Account getAccount() {
return account;
}

public void setAccount(Account account) {

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Chapter 7. Examples

this.account = account;
}

return "AllocatedCashflow[" +
"account=" + account +
",date=" + getDate() +
",type=" + (getType() == CREDIT ? "Credit" : "Debit") +
",amount=" + getAmount() + "]";
}
}

The Java code of Example5.java creates two Account objects and passes one of them into each
cashflow, in the constructor call.


Account acc1 = new Account(1);
Account acc2 = new Account(2);

new AllocatedCashflow(acc1,new SimpleDate("01/01/2007"),
TypedCashflow.DEBIT, 100.00)
};

cashflows );

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}
}

Now, let’s look at the rule in Example6.drl to see how we apply each cashflow in date order and
calculate and print the balance.


rule "Rule 06 - Credit"
when
$cashflow : AllocatedCashflow( $account : account,
$date : date,
$amount : amount,
not AllocatedCashflow( account == $account, date < $date)
then
System.out.println("Credit: " + $date + " :: " + $amount);
$account.setBalance($account.getBalance()+$amount);
System.out.println("Account: " + $account.getAccountNo() +
" - new balance: " + $account.getBalance());
retract($cashflow);
end

rule "Rule 06 - Debit"
when
$cashflow : AllocatedCashflow( $account : account,
$date : date,
$amount : amount,
type == TypedCashflow.DEBIT )
not AllocatedCashflow( account == $account, date < $date)
then
System.out.println("Debit: " + $date + " :: " + $amount);
$account.setBalance($account.getBalance() - $amount);
System.out.println("Account: " + $account.getAccountNo() +
" - new balance: " + $account.getBalance());
retract($cashflow);
end

Although we have separate rules for credits and debits, but we do not specify a type when checking
for earlier cashflows. This is so that all cashflows are applied in date order, regardless of the
cashflow type. In the conditions we identify the account to work with, and in the consequences
we update it with the cashflow amount.

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Chapter 7. Examples


Inserting fact:
AllocatedCashflow[account=Account[accountNo=1,balance=0.0],date=Mon Jan 01
00:00:00 GMT 2007,type=Credit,amount=300.0]
Inserting fact:
AllocatedCashflow[account=Account[accountNo=1,balance=0.0],date=Mon Feb 05
Inserting fact:
AllocatedCashflow[account=Account[accountNo=2,balance=0.0],date=Sun Mar 11
Inserting fact:
AllocatedCashflow[account=Account[accountNo=1,balance=0.0],date=Wed Feb 07
00:00:00 GMT 2007,type=Debit,amount=800.0]
Inserting fact:
AllocatedCashflow[account=Account[accountNo=2,balance=0.0],date=Fri Mar 02
00:00:00 GMT 2007,type=Debit,amount=400.0]
Inserting fact:
AllocatedCashflow[account=Account[accountNo=1,balance=0.0],date=Sun Apr 01
00:00:00 BST 2007,type=Credit,amount=200.0]
Inserting fact:
AllocatedCashflow[account=Account[accountNo=1,balance=0.0],date=Thu Apr 05
Inserting fact:
AllocatedCashflow[account=Account[accountNo=2,balance=0.0],date=Fri May 11
Inserting fact:
AllocatedCashflow[account=Account[accountNo=1,balance=0.0],date=Mon May 07
00:00:00 BST 2007,type=Debit,amount=900.0]
Inserting fact:
AllocatedCashflow[account=Account[accountNo=2,balance=0.0],date=Wed May 02
00:00:00 BST 2007,type=Debit,amount=100.0]
Debit: Fri Mar 02 00:00:00 GMT 2007 :: 400.0
Account: 2 - new balance: -400.0
Credit: Sun Mar 11 00:00:00 GMT 2007 :: 500.0
Account: 2 - new balance: 100.0
Debit: Wed May 02 00:00:00 BST 2007 :: 100.0
Credit: Fri May 11 00:00:00 BST 2007 :: 700.0
Credit: Mon Jan 01 00:00:00 GMT 2007 :: 300.0
Credit: Mon Feb 05 00:00:00 GMT 2007 :: 100.0
Debit: Wed Feb 07 00:00:00 GMT 2007 :: 800.0
Credit: Sun Apr 01 00:00:00 BST 2007 :: 200.0

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Pricing Rule Decision Table Example

Credit: Thu Apr 05 00:00:00 BST 2007 :: 300.0
Debit: Mon May 07 00:00:00 BST 2007 :: 900.0

7.6. Pricing Rule Decision Table Example
The Pricing Rule decision table demonstrates the use of a decision table in a spreadsheet, in
Excel's XLS format, in calculating the retail cost of an insurance policy. The purpose of the provide
set of rules is to calculate a base price and a discount for a car driver applying for a specific policy.
The driver's age, history and the policy type all contribute to what the basic premium is, and an
additional chunk of rules deals with refining this with a discount percentage.

Name: Example Policy Pricing
Main class: org.drools.examples.decisiontable.PricingRuleDTExample
Rules file: ExamplePolicyPricing.xls
Objective: demonstrate spreadsheet-based decision tables.

7.6.1. Executing the example
Open the file PricingRuleDTExample.java and execute it as a Java application. It should
produce the following output in the Console window:

Cheapest possible
BASE PRICE IS: 120
DISCOUNT IS: 20

The code to execute the example follows the usual pattern. The rules are loaded, the facts inserted
and a Stateless Session is created. What is different is how the rules are added.

DecisionTableConfiguration dtableconfiguration =
KnowledgeBuilderFactory.newDecisionTableConfiguration();
dtableconfiguration.setInputType( DecisionTableInputType.XLS );


Resource xlsRes = ResourceFactory.newClassPathResource( "ExamplePolicyPricing.xls",
getClass() );
kbuilder.add( xlsRes,
ResourceType.DTABLE,

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dtableconfiguration );

Note the use of the DecisionTableConfiguration object. Its input type is set to
DecisionTableInputType.XLS. If you use the BRMS, all this is of course taken care of for you.

There are two fact types used in this example, Driver and Policy. Both are used with their default
values. The Driver is 30 years old, has had no prior claims and currently has a risk profile of LOW.
The Policy being applied for is COMPREHENSIVE, and it has not yet been approved.

7.6.2. The decision table

In this decision table, each row is a rule, and each column is a condition or an action.

Figure 7.10. Decision table configuration

Referring to the spreadsheet show above, we have the RuleSet declaration, which provides the
package name. There are also other optional items you can have here, such as Variables for
global variables, and Imports for importing classes. In this case, the namespace of the rules is
the same as the fact classes we are using, so we can omit it.

Moving further down, we can see the RuleTable declaration. The name after this (Pricing bracket)
is used as the prefix for all the generated rules. Below that, we have "CONDITION or ACTION",
indicating the purpose of the column, i.e., whether it forms part of the condition or the consequence
of the rule that will be generated.

You can see that there is a driver, his data spanned across three cells, which means that the
template expressions below it apply to that fact. We observe the driver's age range (which
uses $1 and $2 with comma-separated values), locationRiskProfile, and priorClaims in the
respective columns. In the action columns, we are set the policy base price and log a message.

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Figure 7.11. Base price calculation

In the preceding spreadsheet section, there are broad category brackets, indicated by the
comment in the leftmost column. As we know the details of our drivers and their policies, we can
tell (with a bit of thought) that they should match row number 18, as they have no prior accidents,
and are 30 years old. This gives us a base price of 120.

Figure 7.12. Discount calculation

The above section contains the conditions for the discount we might grant our driver. The discount
results from the Age bracket, the number of prior claims, and the policy type. In our case, the driver
is 30, with no prior claims, and is applying for a COMPREHENSIVE policy, which means we can give
a discount of 20%. Note that this is actually a separate table, but in the same worksheet, so that
different templates apply.

It is important to note that decision tables generate rules. This means they aren't simply top-down
logic, but more a means to capture data resulting in rules. This is a subtle difference that confuses

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some people. The evaluation of the rules is not necessarily in the given order, since all the normal
mechanics of the rule engine still apply.

7.7. Pet Store Example

Name: Pet Store
Main class: org.drools.examples.petstore.PetStoreExample
Rules file: PetStore.drl
Objective: Demonstrate use of Agenda Groups, Global Variables and integration
with a GUI,
including callbacks from within the rules

The Pet Store example shows how to integrate Rules with a GUI, in this case a Swing based
desktop application. Within the rules file, it demonstrates how to use Agenda groups and auto-
focus to control which of a set of rules is allowed to fire at any given time. It also illustrates the
mixing of the Java and MVEL dialects within the rules, the use of accumulate functions and the
way of calling Java functions from within the ruleset.

All of the Java code is contained in one file, PetStore.java, defining the following principal
classes (in addition to several classes to handle Swing Events):

• Petstore contains the main() method that we will look at shortly.

• PetStoreUI is responsible for creating and displaying the Swing based GUI. It contains several
smaller classes, mainly for responding to various GUI events such as mouse button clicks.

• TableModel holds the table data. Think of it as a JavaBean that extends the Swing class
AbstractTableModel.

• CheckoutCallback allows the GUI to interact with the Rules.

• Ordershow keeps the items that we wish to buy.

• Purchase stores details of the order and the products we are buying.

• Product is a JavaBean holding details of the product available for purchase, and its price.

Much of the Java code is either plain JavaBeans or Swing-based. Only a few Swing-related points
will be discussed in this section, but a good tutorial about Swing components can be found at
Sun's Swing website, in http://java.sun.com/docs/books/tutorial/uiswing/.

The pieces of Java code in Petstore.java that relate to rules and facts are shown below.

Example 7.52. Creating the PetStore RuleBase in PetStore.main


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kbuilder.add( ResourceFactory.newClassPathResource( "PetStore.drl",
PetStore.class ),
ResourceType.DRL );

// Create the stock.
Vector<Product> stock = new Vector<Product>();
stock.add( new Product( "Gold Fish", 5 ) );
stock.add( new Product( "Fish Tank", 25 ) );
stock.add( new Product( "Fish Food", 2 ) );

// A callback is responsible for populating the
// Working Memory and for firing all rules.
PetStoreUI ui = new PetStoreUI( stock,
new CheckoutCallback( kbase ) );
ui.createAndShowGUI();

The code shown above loads the rules from a DRL file on the classpath. Unlike other examples
where the facts are asserted and fired straight away, this example defers this step to later.
The way it does this is via the second last line where a PetStoreUI object is created using a
constructor accepting the Vector object stock collecting our products, and an instance of the
CheckoutCallback class containing the Rule Base that we have just loaded.

The Java code that fires the rules is within the CheckoutCallBack.checkout() method. This is
triggered (eventually) when the Checkout button is pressed by the user.

Example 7.53. Firing the Rules - extract from CheckoutCallBack.checkout()

public String checkout(JFrame frame, List<Product> items) {
Order order = new Order();

// Iterate through list and add to cart
for ( Product p: items ) {
order.addItem( new Purchase( order, p ) );
}

// Add the JFrame to the ApplicationData to allow for user interaction

ksession.setGlobal( "frame", frame );
ksession.setGlobal( "textArea", this.output );

ksession.insert( new Product( "Gold Fish", 5 ) );
ksession.insert( new Product( "Fish Tank", 25 ) );
ksession.insert( new Product( "Fish Food", 2 ) );

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ksession.insert( new Product( "Fish Food Sample", 0 ) );

ksession.insert( order );

// Return the state of the cart
return order.toString();
}

Two items get passed into this method. One is the handle to the JFrame Swing component
surrounding the output text frame, at the bottom of the GUI. The second is a list of order items;
this comes from the TableModel storing the information from the "Table" area at the top right
section of the GUI.

The for loop transforms the list of order items coming from the GUI into the Order JavaBean, also
contained in the file PetStore.java. Note that it would be possible to refer to the Swing dataset
directly within the rules, but it is better coding practice to do it this way, using simple Java objects.
It means that we are not tied to Swing if we wanted to transform the sample into a Web application.

It is important to note that all state in this example is stored in the Swing components, and that
the rules are effectively stateless. Each time the "Checkout" button is pressed, this code copies
the contents of the Swing TableModel into the Session's Working Memory.

Within this code, there are nine calls to the Working Memory. The first of these creates a new
Working Memory, as a Stateful Knowledge Session from the Knowledge Base. Remember that
we passed in this Knowledge Base when we created the CheckoutCallBack class in the main()
method. The next two calls pass in two objects that we will hold as global variables in the rules:
the Swing text area and the Swing frame used for writing messages.

More inserts put information on products into the Working Memory, as well as the order list. The
final call is the standard fireAllRules(). Next, we look at what this method causes to happen
within the rules file.

Example 7.54. Package, Imports, Globals and Dialect: extract from
PetStore.drl

package org.drools.examples

import org.drools.WorkingMemory
import org.drools.examples.petstore.PetStoreExample.Order
import org.drools.examples.petstore.PetStoreExample.Purchase
import org.drools.examples.petstore.PetStoreExample.Product
import java.util.ArrayList
import javax.swing.JOptionPane;

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import javax.swing.JFrame

global JFrame frame
global javax.swing.JTextArea textArea

The first part of file PetStore.drl contains the standard package and import statements to make
various Java classes available to the rules. New to us are the two globals frame and textArea.
They hold references to the Swing components JFrame and JTextArea components that were
previously passed on by the Java code calling the setGlobal() method. Unlike variables in rules,
which expire as soon as the rule has fired, global variables retain their value for the lifetime of
the Session.

The next extract from the file PetStore.drl contains two functions that are referenced by the
rules that we will look at shortly.

Example 7.55. Java Functions in the Rules: extract from PetStore.drl

function void doCheckout(JFrame frame, WorkingMemory workingMemory) {
Object[] options = {"Yes",
"No"};

int n = JOptionPane.showOptionDialog(frame,
"Would you like to checkout?",
"",
JOptionPane.YES_NO_OPTION,
JOptionPane.QUESTION_MESSAGE,
null,
options,
options[0]);

if (n == 0) {
workingMemory.setFocus( "checkout" );
}
}

function boolean requireTank(JFrame frame, WorkingMemory workingMemory, Order order, Product fi
Object[] options = {"Yes",
"No"};

int n = JOptionPane.showOptionDialog(frame,
"Would you like to buy a tank for your " + total + " fish?",
"Purchase Suggestion",
JOptionPane.YES_NO_OPTION,
JOptionPane.QUESTION_MESSAGE,
null,
options,
options[0]);

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System.out.print( "SUGGESTION: Would you like to buy a tank for your "
+ total + " fish? - " );

if (n == 0) {
Purchase purchase = new Purchase( order, fishTank );
workingMemory.insert( purchase );
order.addItem( purchase );
System.out.println( "Yes" );
} else {
System.out.println( "No" );
}
return true;
}

Having these functions in the rules file just makes the Pet Store example more compact. In
real life you probably have the functions in a file of their own, within the same rules package,
or as a static method on a standard Java class, and import them, using import function
my.package.Foo.hello.

The purpose of these two functions is:

• doCheckout() displays a dialog asking users whether they wish to checkout. If they do, focus
is set to the checkOut agenda-group, allowing rules in that group to (potentially) fire.

• requireTank() displays a dialog asking users whether they wish to buy a tank. If so, a new
fish tank Product is added to the order list in Working Memory.

We'll see the rules that call these functions later on. The next set of examples are from the Pet
Store rules themselves. The first extract is the one that happens to fire first, partly because it has
the auto-focus attribute set to true.

Example 7.56. Putting items into working memory: extract from PetStore.drl

// Insert each item in the shopping cart into the Working Memory
// Insert each item in the shopping cart into the Working Memory
rule "Explode Cart"
agenda-group "init"
auto-focus true
salience 10
dialect "java"
when
$order : Order( grossTotal == -1 )
$item : Purchase() from $order.items
then
insert( $item );

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kcontext.getKnowledgeRuntime().getAgenda().getAgendaGroup( "show
items" ).setFocus();

kcontext.getKnowledgeRuntime().getAgenda().getAgendaGroup( "evaluate" ).setFocus();
end

This rule matches against all orders that do not yet have their grossTotal calculated . It loops
for each purchase item in that order. Some parts of the "Explode Cart" rule should be familiar:
the rule name, the salience (suggesting the order for the rules being fired) and the dialect set to
"java". There are three new features:

• agenda-group "init" defines the name of the agenda group. In this case, there is only one
rule in the group. However, neither the Java code nor a rule consequence sets the focus to this
group, and therefore it relies on the next attribute for its chance to fire.

• auto-focus true ensures that this rule, while being the only rule in the agenda group, gets a
chance to fire when fireAllRules() is called from the Java code.

• kcontext....setFocus() sets the focus to the "show items" and "evaluate" agenda groups
in turn, permitting their rules to fire. In practice, we loop through all items on the order, inserting
them into memory, then firing the other rules after each insert.

The next two listings show the rules within the "show items" and evaluate agenda groups. We
look at them in the order that they are called.

Example 7.57. Show Items in the GUI - extract from PetStore.drl

rule "Show Items"
agenda-group "show items"
dialect "mvel"
when
$order : Order( )
$p : Purchase( order == $order )
then
textArea.append( $p.product + "n");
end

The "show items" agenda-group has only one rule, called "Show Items" (note the difference
in case). For each purchase on the order currently in the Working Memory (or Session), it logs
details to the text area at the bottom of the GUI. The textArea variable used to do this is one of
the global variables we looked at earlier.

The evaluate Agenda group also gains focus from the "Explode Cart" rule listed previously.
This Agenda group has two rules, "Free Fish Food Sample" and "Suggest Tank", shown below.

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Example 7.58. Evaluate Agenda Group: extract from PetStore.drl

// Free Fish Food sample when we buy a Gold Fish if we haven't already bought
// Fish Food and don't already have a Fish Food Sample
rule "Free Fish Food Sample"
agenda-group "evaluate"
dialect "mvel"
when
$order : Order()
not ( $p : Product( name == "Fish Food") && Purchase( product == $p ) )
not ( $p : Product( name == "Fish Food Sample") && Purchase( product
== $p ) )
exists ( $p : Product( name == "Gold Fish") && Purchase( product
== $p ) )
$fishFoodSample : Product( name == "Fish Food Sample" );
then
System.out.println( "Adding free Fish Food Sample to cart" );
purchase = new Purchase($order, $fishFoodSample);
insert( purchase );
$order.addItem( purchase );
end

// Suggest a tank if we have bought more than 5 gold fish and don't already
have one
rule "Suggest Tank"
agenda-group "evaluate"
dialect "java"
when
$order : Order()
not ( $p : Product( name == "Fish Tank") && Purchase( product == $p ) )
ArrayList( $total : size > 5 ) from collect( Purchase( product.name ==
"Gold Fish" ) )
$fishTank : Product( name == "Fish Tank" )
then
requireTank(frame, drools.getWorkingMemory(), $order, $fishTank, $total);
end

The rule "Free Fish Food Sample" will only fire if

• we don't already have any fish food, and

• we don't already have a free fish food sample, and

• we do have a Gold Fish in our order.

If the rule does fire, it creates a new product (Fish Food Sample), and adds it to the order in
Working Memory.

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The rule "Suggest Tank" will only fire if

• we don't already have a Fish Tank in our order, and

• we do have more than 5 Gold Fish Products in our order.

If the rule does fire, it calls the requireTank() function that we looked at earlier (showing a Dialog
to the user, and adding a Tank to the order / working memory if confirmed). When calling the
requireTank() function the rule passes the global frame variable so that the function has a handle
to the Swing GUI.

The next rule we look at is "do checkout".

Example 7.59. Doing the Checkout - extract (6) from PetStore.drl

rule "do checkout"
dialect "java"
when
then
doCheckout(frame, drools.getWorkingMemory());
end

The rule "do checkout" has no agenda group set and no auto-focus attribute. As such, is is
deemed part of the default (MAIN) agenda group. This group gets focus by default when all the
rules in agenda-groups that explicitly had focus set to them have run their course.

There is no LHS to the rule, so the RHS will always call the doCheckout() function. When calling
the doCheckout() function, the rule passes the global frame variable to give the function a handle
to the Swing GUI. As we saw earlier, the doCheckout() function shows a confirmation dialog to
the user. If confirmed, the function sets the focus to the checkout agenda-group, allowing the next
lot of rules to fire.

Example 7.60. Checkout Rules: extract from PetStore.drl

rule "Gross Total"
agenda-group "checkout"
dialect "mvel"
when
$order : Order( grossTotal == -1)
Number( total : doubleValue )
from accumulate( Purchase( $price : product.price ), sum( $price ) )
then
modify( $order ) { grossTotal = total };
textArea.append( "ngross total=" + total + "n" );

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Chapter 7. Examples

end

rule "Apply 5% Discount"
dialect "mvel"
when
$order : Order( grossTotal >= 10 && < 20 )
then
$order.discountedTotal = $order.grossTotal * 0.95;
textArea.append( "discountedTotal total=" + $order.discountedTotal + "n" );
end

rule "Apply 10% Discount"
dialect "mvel"
when
$order : Order( grossTotal >= 20 )
then
$order.discountedTotal = $order.grossTotal * 0.90;
textArea.append( "discountedTotal total=" + $order.discountedTotal + "n" );
end

There are three rules in the checkout agenda-group:

• If we haven't already calculated the gross total, Gross Total accumulates the product prices
into a total, puts this total into Working Memory, and displays it via the Swing JTextArea, using
the textArea global variable yet again.

• If our gross total is between 10 and 20, "Apply 5% Discount" calculates the discounted total
and adds it to the Working Memory and displays it in the text area.

• If our gross total is not less than 20, "Apply 10% Discount" calculates the discounted total
and adds it to the Working Memory and displays it in the text area.

Now that we've run through what happens in the code, let's have a look at what happens when
we actually run the code. The file PetStore.java contains a main() method, so that it can be run
as a standard Java application, either from the command line or via the IDE. This assumes you
have your classpath set correctly. (See the start of the examples section for more information.)

The first screen that we see is the Pet Store Demo. It has a list of available products (top left),
an empty list of selected products (top right), checkout and reset buttons (middle) and an empty
system messages area (bottom).

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Pet Store Example

Figure 7.13. PetStore Demo just after Launch

To get to this point, the following things have happened:

1. The main() method has run and loaded the Rule Base but not yet fired the rules. So far, this
is the only code in connection with rules that has been run.

2. A new PetStoreUI object has been created and given a handle to the Rule Base, for later use.

3. Various Swing components do their stuff, and the above screen is shown and waits for user
input.

Clicking on various products from the list might give you a screen similar to the one below.

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Figure 7.14. PetStore Demo with Products Selected

Note that no rules code has been fired here. This is only Swing code, listening for mouse click
events, and adding some selected product to the TableModel object for display in the top right
hand section. (As an aside, note that this is a classic use of the Model View Controller design
pattern).

It is only when we press the "Checkout" button that we fire our business rules, in roughly the same
order that we walked through the code earlier.

1. Method CheckOutCallBack.checkout() is called (eventually) by the Swing class waiting for
the click on the "Checkout" button. This inserts the data from the TableModel object (top right
hand side of the GUI), and inserts it into the Session's Working Memory. It then fires the rules.

2. The "Explode Cart" rule is the first to fire, given that it has auto-focus set to true. It loops
through all the products in the cart, ensures that the products are in the Working Memory, and
then gives the "Show Items" and Evaluation agenda groups a chance to fire. The rules in
these groups add the contents of the cart to the text area (at the bottom of the window), decide
whether or not to give us free fish food, and to ask us whether we want to buy a fish tank. This
is shown in the figure below.

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Pet Store Example

Figure 7.15. Do we want to buy a fish tank?

1. The Do Checkout rule is the next to fire as it (a) No other agenda group currently has focus
and (b) it is part of the default (MAIN) agenda group. It always calls the doCheckout() function
which displays a 'Would you like to Checkout?' Dialog Box.

2. The doCheckout() function sets the focus to the checkout agenda-group, giving the rules in
that group the option to fire.

3. The rules in the the checkout agenda-group display the contents of the cart and apply the
appropriate discount.

4. Swing then waits for user input to either checkout more products (and to cause the rules to fire
again), or to close the GUI - see the figure below.

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Figure 7.16. Petstore Demo after all rules have fired.

We could add more System.out calls to demonstrate this flow of events. The output, as it currently
appears in the Console window, is given in the listing below.

Example 7.61. Console (System.out) from running the PetStore GUI

Adding free Fish Food Sample to cart
SUGGESTION: Would you like to buy a tank for your 6 fish? - Yes

7.8. Honest Politician Example

Name: Honest Politician
Main class: org.drools.examples.honestpolitician.HonestPoliticianExample
Rules file: HonestPoliticianExample.drl

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Honest Politician Example

Objective: Illustrate the concept of "truth maintenance" based on the logical
insertion of facts

The Honest Politician example demonstrates truth maintenance with logical assertions. The basic
premise is that an object can only exist while a statement is true. A rule's consequence can logically
insert an object with the insertLogical() method. This means the object will only remain in the
Working Memory as long as the rule that logically inserted it remains true. When the rule is no
longer true the object is automatically retracted.

In this example there is the class Politician, with a name and a boolean value for being honest.
Four politicians with honest state set to true are inserted.

Example 7.62. Class Politician

public class Politician {
private boolean honest;
...
}

Example 7.63. Honest Politician: Execution

Politician blair = new Politician("blair", true);
Politician bush = new Politician("bush", true);
Politician chirac = new Politician("chirac", true);
Politician schroder = new Politician("schroder", true);

ksession.insert( blair );
ksession.insert( bush );
ksession.insert( chirac );
ksession.insert( schroder );


The Console window output shows that, while there is at least one honest politician, democracy
lives. However, as each politician is in turn corrupted by an evil corporation, so that all politicians
become dishonest, democracy is dead.

Example 7.64. Honest Politician: Console Output

Hurrah!!! Democracy Lives
I'm an evil corporation and I have corrupted schroder
I'm an evil corporation and I have corrupted chirac

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Chapter 7. Examples

I'm an evil corporation and I have corrupted bush
I'm an evil corporation and I have corrupted blair
We are all Doomed!!! Democracy is Dead

As soon as there is at least one honest politician in the Working Memory a new Hope object is
logically asserted. This object will only exist while there is at least one honest politician. As soon
as all politicians are dishonest, the Hope object will be automatically retracted. This rule is given
a salience of 10 to ensure that it fires before any other rule, as at this stage the "Hope is Dead"
rule is actually true.

Example 7.65. Honest Politician: Rule "We have an honest politician"

rule "We have an honest Politician"
salience 10
when
exists( Politician( honest == true ) )
then
insertLogical( new Hope() );
end

As soon as a Hope object exists the "Hope Lives" rule matches and fires. It has a salience of 10
so that it takes priority over "Corrupt the Honest".

Example 7.66. Honest Politician: Rule "Hope Lives"

rule "Hope Lives"
salience 10
when
exists( Hope() )
then
System.out.println("Hurrah!!! Democracy Lives");
end

Now that there is hope and we have, at the start, four honest politicians, we have four activations
for this rule, all in conflict. They will fire in turn, corrupting each politician so that they are no longer
honest. When all four politicians have been corrupted we have no politicians with the property
honest == true. Thus, the rule "We have an honest Politician" is no longer true and the object
it logical inserted (due to the last execution of new Hope()) is automatically retracted.

Example 7.67. Honest Politician: Rule "Corrupt the Honest"

rule "Corrupt the Honest"
when

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Honest Politician Example

politician : Politician( honest == true )
exists( Hope() )
then
System.out.println( "I'm an evil corporation and I have corrupted "
+ politician.getName() );
modify ( politician ) { honest = false };
end

With the Hope object being automatically retracted, via the truth maintenance system, the
conditional element not applied to Hope is no longer true so that the following rule will match and
fire.

Example 7.68. Honest Politician: Rule "Hope is Dead"

rule "Hope is Dead"
when
not( Hope() )
then
System.out.println( "We are all Doomed!!! Democracy is Dead" );
end

Let's take a look at the Audit trail for this application:

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Chapter 7. Examples

Figure 7.17. Honest Politician Example Audit View

The moment we insert the first politician we have two activations. The rule "We have an honest
Politician" is activated only once for the first inserted politician because it uses an exists
conditional element, which matches once for any number. The rule "Hope is Dead" is also activated
at this stage, because we have not yet inserted the Hope object. Rule "We have an honest
Politician" fires first, as it has a higher salience than "Hope is Dead", which inserts the Hope object.
(That action is highlighted green.) The insertion of the Hope object activates "Hope Lives" and de-
activates "Hope is Dead"; it also activates "Corrupt the Honest" for each inserted honest politician.
Rule "Hope Lives" executes, printing "Hurrah!!! Democracy Lives". Then, for each politician, rule
"Corrupt the Honest" fires, printing "I'm an evil corporation and I have corrupted X", where X is the
name of the politician, and modifies the politician's honest value to false. When the last honest
politician is corrupted, Hope is automatically retracted, by the truth maintenance system, as shown
by the blue highlighted area. The green highlighted area shows the origin of the currently selected
blue highlighted area. Once the Hope fact is retracted, "Hope is dead" activates and fires printing
"We are all Doomed!!! Democracy is Dead".

7.9. Sudoku Example

Name: Sudoku
Main class: org.drools.examples.sudoku.SudokuExample
Rules file: sudoku.drl, validate.drl

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Sudoku Overview

Objective: Demonstrates the solving of logic problems, and complex pattern
matching.

This example demonstrates how Drools can be used to find a solution in a large potential solution
space based on a number of constraints. We use the popular puzzle of Sudoku. This example
also shows how Drools can be integrated into a graphical interface and how callbacks can be used
to interact with a running Drools rules engine in order to update the graphical interface based on
changes in the Working Memory at runtime.

7.9.1. Sudoku Overview

Sudoku is a logic-based number placement puzzle. The objective is to fill a 9x9 grid so that each
column, each row, and each of the nine 3x3 zones contains the digits from 1 to 9, once, and only
once.

The puzzle setter provides a partially completed grid and the puzzle solver's task is to complete
the grid with these constraints.

The general strategy to solve the problem is to ensure that when you insert a new number it should
be unique in its particular 3x3 zone, row and column.

See Wikipedia [http://en.wikipedia.org/wiki/Sudoku] for a more detailed description.

7.9.2. Running the Example

Download and install drools-examples as described above and then execute java
org.drools.examples.DroolsExamplesApp and click on "SudokuExample".

The window contains an empty grid, but the program comes with a number of grids stored internally
which can be loaded and solved. Click on "File", then "Samples" and select "Simple" to load one
of the examples. Note that all buttons are disabled until a grid is loaded.

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Loading the "Simple" example fills the grid according to the puzzle's initial state.

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Running the Example

Click on the "Solve" button and the Drools-based engine will fill out the remaining values, and the
buttons are inactive once more.

Alternatively, you may click on the "Step" button to see the next digit found by the rule set. The
Console window will display detailed information about the rules which are executing to solve the
step in a human readable form. Some examples of these messages are presented below.

single 8 at [0,1]
column elimination due to [1,2]: remove 9 from [4,2]
hidden single 9 at [1,2]
row elimination due to [2,8]: remove 7 from [2,4]
remove 6 from [3,8] due to naked pair at [3,2] and [3,7]
hidden pair in row at [4,6] and [4,4]

Click on the "Dump" button to see the state of the grid, with cells showing either the established
value or the remaining possibilitiescandidates.

Col: 0 Col: 1 Col: 2 Col: 3 Col: 4 Col: 5
Col: 6 Col: 7 Col: 8
Row 0: 2 4 7 9 2 456 4567 9 23 56 9 --- 5 --- --- 1 ---
3 67 9 --- 8 --- 4 67
Row 1: 12 7 9 --- 8 --- 1 67 9 23 6 9 --- 4 --- 23 67 1
3 67 9 3 67 9 --- 5 ---

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Row 2: 1 4 7 9 1 456 --- 3 --- 56 89 5 78 5678
--- 2 --- 4 67 9 1 4 67
Row 3: 1234 12345 1 45 12 5 8 --- 6 --- 2 5 78
5 78 45 7 --- 9 ---
Row 4: --- 6 --- --- 7 --- 5 --- 4 --- 2 5 8 --- 9 ---
5 8 --- 1 --- --- 3 ---
Row 5: --- 8 --- 12 45 1 45 9 12 5 --- 3 --- 2 5 7
567 4567 2 4 67
Row 6: 1 3 7 1 3 6 --- 2 --- 3 56 8 5 8 3 56 8
--- 4 --- 3 567 9 1 678
Row 7: --- 5 --- 1 34 6 1 4 678 3 6 8 --- 9 --- 34 6 8 1
3 678 --- 2 --- 1 678
Row 8: 34 --- 9 --- 4 6 8 --- 7 --- --- 1 --- 23456 8
3 56 8 3 56 6 8

Now, let us load a Sudoku grid that is deliberately invalid. Click on "File", "Samples" and "!
DELIBERATELY BROKEN!". Note that this grid starts with some issues, for example the value
5 appears twice in the first row.

A few simple rules perform a sanity check, right after loading a grid. In this case, the following
messages are printed on standard output:

cell [0,8]: 5 has a duplicate in row 0
cell [0,0]: 5 has a duplicate in row 0
cell [6,0]: 8 has a duplicate in col 0

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cell [4,0]: 8 has a duplicate in col 0
Validation complete.

Nevertheless, click on the "Solve" button to apply the solving rules to this invalid grid. This will not
complete; some cells remain empty.

The solving functionality has been achieved by the use of rules that implement standard solving
techniques. They are based on the sets of values that are still candidates for a cell. If, for instance,
such a set contains a single value, then this is the value for the cell. A little less obvious is the single
occurrence of a value in one of the groups of nine cells. The rules detecting these situations insert
a fact of type Setting with the solution value for some specific cell. This fact causes the elimination
of this value from all other cells in any of the groups the cell belongs to. Finally, it is retracted.

Other rules merely reduce the permissible values for some cells. Rules "naked pair", "hidden pair
in row", "hidden pair in column" and "hidden pair in square" merely eliminate possibilities but do
not establish solutions. More sophisticated eliminations are done by "X-wings in rows", "X-wings
in columns", "intersection removal row" and "intersection removal column".

7.9.3. Java Source and Rules Overview
The Java source code can be found in the /src/main/java/org/drools/examples/sudoku directory,
with the two DRL files defining the rules located in the /src/main/rules/org/drools/examples/sudoku
directory.

The package org.drools.examples.sudoku.swing contains a set of classes which implement
a framework for Sudoku puzzles. Note that this package does not have any dependencies on

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the Drools libraries. SudokuGridModel defines an interface which can be implemented to store a
Sudoku puzzle as a 9x9 grid of Cell objects. SudokuGridView is a Swing component which can
visualize any implementation of SudokuGridModel. SudokuGridEvent and SudokuGridListener
are used to communicate state changes between the model and the view: events are fired when
a cell's value is resolved or changed. If you are familiar with the model-view-controller patterns in
other Swing components such as JTable then this pattern should be familiar. SudokuGridSamples
provides a number of partially filled Sudoku puzzles for demonstration purposes.

Package org.drools.examples.sudoku.rules contains a utility class with a method for
compiling DRL files.

The package org.drools.examples.sudoku contains a set of classes implementing the
elementary Cell object and its various aggregations: the CellFile subtypes CellRow and
CellCol as well as CellSqr, all of which are subtypes of CellGroup. It's interesting to note that
Cell and CellGroup are subclasses of SetOfNine, which provides a property free with the type
Set<Integer>. For a Cell it represents the individual candidate set; for a CellGroup the set is
the union of all candidate sets of its cells, or, simply, the set of digits that still need to be allocated.

With 81 Cell and 27 CellGroup objects and the linkage provided by the Cell properties cellRow,
cellCol and cellSqr and the CellGroup property cells, a list of Cell objects, it is possible to
write rules that detect the specific situations that permit the allocation of a value to a cell or the
elimination of a value from some candidate set.

An object of class Setting is used for triggering the operations that accompany the allocation of
a value: its removal from the candidate sets of sibling cells and associated cell groups. Moreover,
the presence of a Setting fact is used in all rules that should detect a new situation; this is to
avoid reactions to inconsistent intermediary states.

An object of class Stepping is used in a low priority rule to execute an emergency halt when
a "Step" does not terminate regularly. This indicates that the puzzle cannot be solved by the
program.

The class org.drools.examples.sudoku.SudokuExample implements a Java application
combining the components described.

7.9.4. Sudoku Validator Rules (validate.drl)

Validation rules detect duplicate numbers in cell groups. They are combined in an agenda group
which enables us to activate them, explicitly, after loading a puzzle.

The three rules "duplicate in cell..." are very similar. The first pattern locates a cell with an allocated
value. The second pattern pulls in any of the three cell groups the cell belongs to. The final pattern
would find a cell (other than the first one) with the same value as the first cell and in the same
row, column or square, respectively.

Rule "terminate group" fires last. It prints a message and calls halt.

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7.9.5. Sudoku Solving Rules (sudoku.drl)
There are three types of rules in this file: one group handles the allocation of a number to a cell,
another group detects feasible allocations, and the third group eliminates values from candidate
sets.

Rules "set a value", "eliminate a value from Cell" and "retract setting" depend on the presence of a
Setting object. The first rule handles the assignment to the cell and the operations for removing
the value from the "free" sets of the cell's three groups. Also, it decrements a counter that, when
zero, returns control to the Java application that has called fireUntilHalt(). The purpose of
rule "eliminate a value from Cell" is to reduce the candidate lists of all cells that are related to the
newly assigned cell. Finally, when all eliminations have been made, rule "retract setting" retracts
the triggering Setting fact.

There are just two rules that detect a situation where an allocation of a number to a cell is possible.
Rule "single" fires for a Cell with a candidate set containing a single number. Rule "hidden single"
fires when there is no cell with a single candidate but when there is a cell containing a candidate
but this candidate is absent from all other cells in one of the three groups the cell belongs to. Both
rules create and insert a Setting fact.

Rules from the largest group of rules implement, singly or in groups of two or three, various solving
techniques, as they are employed when solving Sudoku puzzles manually.

Rule "naked pair" detects identical candidate sets of size 2 in two cells of a group; these two
values may be removed from all other candidate sets of that group.

A similar idea motivates the three rules "hidden pair in..."; here, the rules look for a subset of two
numbers in exactly two cells of a group, with neither value occurring in any of the other cells of this
group. This, then, means that all other candidates can be eliminated from the two cells harbouring
the hidden pair.

A pair of rules deals with "X-wings" in rows and columns. When there are only two possible
cells for a value in each of two different rows (or columns) and these candidates lie also in the
same columns (or rows), then all other candidates for this value in the columns (or rows) can be
eliminated. If you follow the pattern sequence in one of these rules, you will see how the conditions
that are conveniently expressed by words such as "same" or "only" result in patterns with suitable
constraints or prefixed with "not".

The rule pair "intersection removal..." is based on the restricted occurrence of some number within
one square, either in a single row or in a single column. This means that this number must be in
one of those two or three cells of the row or column; hence it can be removed from the candidate
sets of all other cells of the group. The pattern establishes the restricted occurrence and then fires
for each cell outside the square and within the same cell file.

These rules are sufficient for many but certainly not for all Sudoku puzzles. To solve very difficult
grids, the rule set would need to be extended with more complex rules. (Ultimately, there are
puzzles that cannot be solved except by trial and error.)

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7.10. Number Guess

Name: Number Guess
Main class: org.drools.examples.numberguess.NumberGuessExample
Module: droolsjbpm-integration-examples (Note: this is in a different download,
the droolsjbpm-integration download.)
Rules file: NumberGuess.drl
Objective: Demonstrate use of Rule Flow to organise Rules

The "Number Guess" example shows the use of Rule Flow, a way of controlling the order in which
rules are fired. It uses widely understood workflow diagrams for defining the order in which groups
of rules will be executed.

Example 7.69. Creating the Number Guess RuleBase:
NumberGuessExample.main() - part 1

final KnowledgeBuilder kbuilder = KnowledgeBuilderFactory.newKnowledgeBuilder();
kbuilder.add( ResourceFactory.newClassPathResource( "NumberGuess.drl",
ShoppingExample.class ),
ResourceType.DRL );
kbuilder.add( ResourceFactory.newClassPathResource( "NumberGuess.rf",
ShoppingExample.class ),
ResourceType.DRF );

final KnowledgeBase kbase = KnowledgeBaseFactory.newKnowledgeBase();

The creation of the package and the loading of the rules (using the add() method) is the same as
the previous examples. There is an additional line to add the Rule Flow (NumberGuess.rf), which
provides the option of specifying different rule flows for the same Knowledge Base. Otherwise,
the Knowledge Base is created in the same manner as before.

Example 7.70. Starting the RuleFlow: NumberGuessExample.main() - part 2

final StatefulKnowledgeSession ksession = kbase.newStatefulKnowledgeSession();

KnowledgeRuntimeLoggerFactory.newFileLogger(ksession, "log/numberguess");

ksession.insert( new GameRules( 100, 5 ) );
ksession.insert( new RandomNumber() );
ksession.insert( new Game() );

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ksession.startProcess( "Number Guess" );

logger.close();

ksession.dispose();

Once we have a Knowledge Base, we can use it to obtain a Stateful Session. Into our session
we insert our facts, i.e., standard Java objects. (For simplicity, in this sample, these classes are
all contained within our NumberGuessExample.java file. Class GameRules provides the maximum
range and the number of guesses allowed. Class RandomNumber automatically generates a
number between 0 and 100 and makes it available to our rules, by insertion via the getValue()
method. Class Game keeps track of the guesses we have made before, and their number.

Note that before we call the standard fireAllRules() method, we also start the process that
we loaded earlier, via the startProcess() method. We'll learn where to obtain the parameter we
pass ("Number Guess", i.e., the identifier of the rule flow) when we talk about the rule flow file and
the graphical Rule Flow Editor below.

Before we finish the discussion of our Java code, we note that in some real-life application we
would examine the final state of the objects. (Here, we could retrieve the number of guesses, to
add it to a high score table.) For this example we are content to ensure that the Working Memory
session is cleared by calling the dispose() method.

Figure 7.18. RuleFlow for the NumberGuess Example

If you open the NumberGuess.rf file in the Drools IDE (provided you have the JBoss Rules
extensions installed correctly in Eclipse) you should see the above diagram, similar to a standard
flowchart. Its icons are similar (but not exactly the same) as in the JBoss jBPM workflow product.

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Should you wish to edit the diagram, a menu of available components should be available to the
left of the diagram in the IDE, which is called the palette. This diagram is saved in XML, an (almost)
human readable format, using XStream.

If it is not already open, ensure that the Properties View is visible in the IDE. It can be opened by
clicking "Window", then "Show View" and "Other", where you can select the "Properties" view. If
you do this before you select any item on the rule flow (or click on the blank space in the rule flow)
you should be presented with the following set of properties.

Figure 7.19. Properties for the Number Guess Rule Flow

Keep an eye on the Properties View as we progress through the example's rule flow, as it presents
valuable information. In this case, it provides us with the identification of the Rule Flow Process
that we used in our earlier code snippet, when we called session.startProcess().

In the "Number Guess" Rule Flow we encounter several node types, many of them identified by
an icon.

• The Start node (white arrow in a green circle) and the End node (red box) mark beginning and
end of the rule flow.

• A Rule Flow Group box (yellow, without an icon) represents a Rule Flow Groups defined in our
rules (DRL) file that we will look at later. For example, when the flow reaches the Rule Flow
Group "Too High", only those rules marked with an attribute of ruleflow-group "Too High"
can potentially fire.

• Action nodes (yellow, cog-shaped icon) perform standard Java method calls. Most action nodes
in this example call System.out.println(), indicating the program's progress to the user.

• Split and Join Nodes (blue ovals, no icon) such as "Guess Correct?" and "More guesses Join"
mark places where the flow of control can split, according to various conditions, and rejoin,
respectively

• Arrows indicate the flow between the various nodes.

The various nodes in combination with the rules make the Number Guess game work. For
example, the "Guess" Rule Flow Group allows only the rule "Get user Guess" to fire, because only
that rule has a matching attribute of ruleflow-group "Guess".

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Number Guess

Example 7.71. A Rule firing only at a specific point in the Rule Flow:
NumberGuess.drl

rule "Get user Guess"
ruleflow-group "Guess"
no-loop
when
$r : RandomNumber()
rules : GameRules( allowed : allowedGuesses )
game : Game( guessCount < allowed )
not ( Guess() )
then
System.out.println( "You have " + ( rules.allowedGuesses - game.guessCount )
+ " out of " + rules.allowedGuesses
+ " guesses left.nPlease enter your guess from 0 to "
+ rules.maxRange );
br = new BufferedReader( new InputStreamReader( System.in ) );
i = br.readLine();
modify ( game ) { guessCount = game.guessCount + 1 }
insert( new Guess( i ) );
end

The rest of this rule is fairly standard. The LHS section (after when) of the rule states that it will
be activated for each RandomNumber object inserted into the Working Memory where guessCount
is less than allowedGuesses from the GameRules object and where the user has not guessed
the correct number.

The RHS section (or consequence, after then) prints a message to the user and then awaits
user input from System.in. After obtaining this input (the readLine() method call blocks until
the return key is pressed) it modifies the guess count and inserts the new guess, making both
available to the Working Memory.

The rest of the rules file is fairly standard: the package declares the dialect as MVEL, and various
Java classes are imported. In total, there are five rules in this file:

1. Get User Guess, the Rule we examined above.

2. A Rule to record the highest guess.

3. A Rule to record the lowest guess.

4. A Rule to inspect the guess and retract it from memory if incorrect.

5. A Rule that notifies the user that all guesses have been used up.

One point of integration between the standard Rules and the RuleFlow is via the ruleflow-group
attribute on the rules, as discussed above. A second point of integration between the rules (.drl) file

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and the Rules Flow .rf files is that the Split Nodes (the blue ovals) can use values in the Working
Memory (as updated by the rules) to decide which flow of action to take. To see how this works,
click on the "Guess Correct Node"; then within the Properties View, open the Constraints Editor
by clicking the button at the right that appears once you click on the "Constraints" property line.
You should see something similar to the diagram below.

Figure 7.20. Edit Constraints for the "Guess Correct" Node

Click on the "Edit" button beside "To node Too High" and you'll see a dialog like the one below.
The values in the "Textual Editor" window follow the standard rule format for the LHS and can
refer to objects in Working Memory. The consequence (RHS) is that the flow of control follows
this node (i.e., "To node Too High") if the LHS expression evaluates to true.

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Number Guess

Figure 7.21. Constraint Editor for the "Guess Correct" Node: value too high

Since the file NumberGuess.java contains a main() method, it can be run as a standard Java
application, either from the command line or via the IDE. A typical game might result in the
interaction below. The numbers in bold are typed in by the user.

Example 7.72. Example Console output where the Number Guess Example
beat the human!

You have 5 out of 5 guesses left.
Please enter your guess from 0 to 100
50
Your guess was too high
25
Your guess was too low

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Chapter 7. Examples

37
44
47
You have no more guesses
The correct guess was 48

A summary of what is happening in this sample is:

1. The main() method of NumberGuessExample.java loads a Rule Base, creates a Stateful
Session and inserts Game, GameRules and RandomNumber (containing the target number)
objects into it. The method also sets the process flow we are going to use, and fires all rules.
Control passes to the Rule Flow.

2. File NumberGuess.rf, the Rule Flow, begins at the "Start" node.

3. Control passes (via the "More guesses" join node) to the Guess node.

4. At the Guess node, the appropriate Rule Flow Group ("Get user Guess") is enabled. In this
case the Rule "Guess" (in the NumberGuess.drl file) is triggered. This rule displays a message
to the user, takes the response, and puts it into Working Memory. Flow passes to the next Rule
Flow Node.

5. At the next node, "Guess Correct", constraints inspect the current session and decide which
path to take.

If the guess in step 4 was too high or too low, flow proceeds along a path which has an action
node with normal Java code printing a suitable message and a Rule Flow Group causing a
highest guess or lowest guess rule to be triggered. Flow passes from these nodes to step 6.

If the guess in step 4 was right, we proceed along the path towards the end of the Rule Flow.
Before we get there, an action node with normal Java code prints a statement "you guessed
correctly". There is a join node here (just before the Rule Flow end) so that our no-more-guesses
path (step 7) can also terminate the Rule Flow.

6. Control passes as per the Rule Flow via a join node, a guess incorrect Rule Flow Group
(triggering a rule to retract a guess from Working Memory) onto the "More guesses" decision
node.

7. The "More guesses" decision node (on the right hand side of the rule flow) uses constraints,
again looking at values that the rules have put into the working memory, to decide if we have

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more guesses and if so, goto step 3. If not, we proceed to the end of the rule flow, via a Rule
Flow Group that triggers a rule stating "you have no more guesses".

8. The loop over steps 3 to 7 continues until the number is guessed correctly, or we run out of
guesses.

7.11. Conway's Game Of Life

Name: Conway's Game Of Life
Main class: org.drools.examples.conway.ConwayAgendaGroupRun
org.drools.examples.conway.ConwayRuleFlowGroupRun
Module: droolsjbpm-integration-examples (Note: this is in a different download,
the droolsjbpm-integration download.)
Rules file: conway-ruleflow.drl conway-agendagroup.drl
Objective: Demonstrates 'accumulate', 'collect' and 'from'

Conway's Game Of Life, described in http://en.wikipedia.org/wiki/Conway's_Game_of_Life and in
http://www.math.com/students/wonders/life/life.html, is a famous cellular automaton conceived in
the early 1970's by the mathematician John Conway. While the system is well known as "Conway's
Game Of Life", it really isn't a game at all. Conway's system is more like a simulation of a form of
life. Don't be intimidated. The system is terribly simple and terribly interesting. Math and Computer
Science students alike have marvelled over Conway's system for more than 30 years now. The
application presented here is a Swing-based implementation of Conway's Game of Life. The
rules that govern the system are implemented as business rules using Drools. This document will
explain the rules that drive the simulation and discuss the Drools parts of the implementation.

We'll first introduce the grid view, shown below, designed for the visualisation of the game, showing
the "arena" where the life simulation takes place. Initially the grid is empty, meaning that there are
no live cells in the system. Each cell is either alive or dead, with live cells showing a green ball.
Preselected patterns of live cells can be chosen from the "Pattern" drop-down list. Alternatively,
individual cells can be doubled-clicked to toggle them between live and dead. It's important to
understand that each cell is related to its neighboring cells, which is fundamental for the game's
rules. Neighbors include not only cells to the left, right, top and bottom but also cells that are
connected diagonally, so that each cell has a total of 8 neighbors. Exceptions are the four corner
cells which have only three neighbors, and the cells along the four border, with five neighbors each.

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Chapter 7. Examples

Figure 7.22. Conway's Game of Life: Starting a new game

So what are the basic rules that govern this game? Its goal is to show the development of a
population, generation by generation. Each generation results from the preceding one, based on
the simultaneous evaluation of all cells. This is the simple set of rules that govern what the next
generation will look like:

• If a live cell has fewer than 2 live neighbors, it dies of loneliness.

• If a live cell has more than 3 live neighbors, it dies from overcrowding.

• If a dead cell has exactly 3 live neighbors, it comes to life.

That is all there is to it. Any cell that doesn't meet any of those criteria is left as is for the next
generation. With those simple rules in mind, go back and play with the system a little bit more and
step through some generations, one at a time, and notice these rules taking their effect.

The screenshot below shows an example generation, with a number of live cells. Don't worry about
matching the exact patterns represented in the screen shot. Just get some groups of cells added
to the grid. Once you have groups of live cells in the grid, or select a pre-designed pattern, click
the "Next Generation" button and notice what happens. Some of the live cells are killed (the green
ball disappears) and some dead cells come to life (a green ball appears). Step through several
generations and see if you notice any patterns. If you click on the "Start" button, the system will
evolve itself so you don't need to click the "Next Generation" button over and over. Play with the
system a little and then come back here for more details of how the application works.

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Figure 7.23. Conway's Game of Life: A running game

Now lets delve into the code. As this is an advanced example we'll assume that by now you
know your way around the Drools framework and are able to connect the presented highlight,
so that we'll just focus at a high level overview. The example has two ways to execute, one way
uses Agenda Groups to manage execution flow, and the other one uses Rule Flow Groups to
manage execution flow. These two versions are implemented in ConwayAgendaGroupRun and
ConwayRuleFlowGroupRun, respectively. Here, we'll discuss the Rule Flow version, as it's what
most people will use.

All the Cell objects are inserted into the Session and the rules in the ruleflow-group "register
neighbor" are allowed to execute by the Rule Flow process. This group of four rules creates
Neighbor relations between some cell and its northeastern, northern, northwestern and western
neighbors. This relation is bidirectional, which takes care of the other four directions. Border cells
don't need any special treatment - they simply won't be paired with neighboring cells where there
isn't any. By the time all activations have fired for these rules, all cells are related to all their
neighboring cells.

Example 7.73. Conway's Game of Life: Register Cell Neighbour relations

rule "register north east"
ruleflow-group "register neighbor"
when
$cell: Cell( $row : row, $col : col )
$northEast : Cell( row == ($row - 1), col == ( $col + 1 ) )
then
insert( new Neighbor( $cell, $northEast ) );
insert( new Neighbor( $northEast, $cell ) );
end

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Chapter 7. Examples

rule "register north"
when
$north : Cell( row == ($row - 1), col == $col )
then
insert( new Neighbor( $cell, $north ) );
insert( new Neighbor( $north, $cell ) );
end

rule "register north west"
when
$northWest : Cell( row == ($row - 1), col == ( $col - 1 ) )
then
insert( new Neighbor( $cell, $northWest ) );
insert( new Neighbor( $northWest, $cell ) );
end

rule "register west"
when
$west : Cell( row == $row, col == ( $col - 1 ) )
then
insert( new Neighbor( $cell, $west ) );
insert( new Neighbor( $west, $cell ) );
end

Once all the cells are inserted, some Java code applies the pattern to the grid, setting certain
cells to Live. Then, when the user clicks "Start" or "Next Generation", it executes the "Generation"
ruleflow. This ruleflow is responsible for the management of all changes of cells in each generation
cycle.

334


Figure 7.24. Conway's Game of Life: rule flow "Generation"

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Chapter 7. Examples

The rule flow process first enters the "evaluate" group, which means that any active rule in the
group can fire. The rules in this group apply the Game-of-Life rules discussed in the beginning of
the example, determining the cells to be killed and the ones to be given life. We use the "phase"
attribute to drive the reasoning of the Cell by specific groups of rules; typically the phase is tied
to a Rule Flow Group in the Rule Flow process definition. Notice that it doesn't actually change
the state of any Cell objectss at this point; this is because it's evaluating the grid in turn and it
must complete the full evaluation until those changes can be applied. To achieve this, it sets the
cell to a "phase" which is either Phase.KILL or Phase.BIRTH, used later to control actions applied
to the Cell object.

Example 7.74. Conway's Game of Life: Evaluate Cells with state changes

rule "Kill The Lonely"
ruleflow-group "evaluate"
no-loop
when
// A live cell has fewer than 2 live neighbors
theCell: Cell( liveNeighbors < 2, cellState == CellState.LIVE,
phase == Phase.EVALUATE )
then
modify( theCell ){
setPhase( Phase.KILL );
}
end

rule "Kill The Overcrowded"
no-loop
when
// A live cell has more than 3 live neighbors
theCell: Cell( liveNeighbors > 3, cellState == CellState.LIVE,
then
modify( theCell ){
setPhase( Phase.KILL );
}
end

rule "Give Birth"
no-loop
when
// A dead cell has 3 live neighbors
theCell: Cell( liveNeighbors == 3, cellState == CellState.DEAD,
then
modify( theCell ){

336


theCell.setPhase( Phase.BIRTH );
}
end

Once all Cell objects in the grid have been evaluated, we first clear any calculation activations that
occurred from any previous data changes. This is done via the "reset calculate" rule, which clears
any activations in the "calculate" group. We then enter a split in the rule flow which allows any
activations in both the "kill" and the "birth" group to fire. These rules are responsible for applying
the state change.

Example 7.75. Conway's Game of Life: Apply the state changes

rule "reset calculate"
ruleflow-group "reset calculate"
when
then
WorkingMemory wm = drools.getWorkingMemory();
wm.clearRuleFlowGroup( "calculate" );
end

rule "kill"
ruleflow-group "kill"
no-loop
when
theCell: Cell( phase == Phase.KILL )
then
modify( theCell ){
setCellState( CellState.DEAD ),
setPhase( Phase.DONE );
}
end

rule "birth"
ruleflow-group "birth"
no-loop
when
theCell: Cell( phase == Phase.BIRTH )
then
modify( theCell ){
setCellState( CellState.LIVE ),
setPhase( Phase.DONE );
}
end

At this stage, a number of Cell objects have been modified with the state changed to either LIVE
or DEAD. Now we get to see the power of the Neighbor facts defining the cell relations. When

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Chapter 7. Examples

a cell becomes live or dead, we use the Neighbor relation to iterate over all surrounding cells,
increasing or decreasing the liveNeighbor count. Any cell that has its count changed is also
set to to the EVALUATE phase, to make sure it is included in the reasoning during the evaluation
stage of the Rule Flow Process. Notice that we don't have to do any iteration ourselves; simply
by applying the relations in the rules we make the rule engine do all the hard work for us, with a
minimal amount of code. Once the live count has been determined and set for all cells, the Rule
Flow Process comes to and end. If the user has initially clicked the "Start" button, the engine will
restart the rule flow; otherwise the user may request another generation.

Example 7.76. Conway's Game of Life: Evaluate cells with state changes

rule "Calculate Live"
ruleflow-group "calculate"
lock-on-active
when
theCell: Cell( cellState == CellState.LIVE )
Neighbor( cell == theCell, $neighbor : neighbor )
then
modify( $neighbor ){
setLiveNeighbors( $neighbor.getLiveNeighbors() + 1 ),
setPhase( Phase.EVALUATE );
}
end

rule "Calculate Dead"
ruleflow-group "calculate"
lock-on-active
when
theCell: Cell( cellState == CellState.DEAD )
Neighbor( cell == theCell, $neighbor : neighbor )
then
modify( $neighbor ){
setLiveNeighbors( $neighbor.getLiveNeighbors() - 1 ),
setPhase( Phase.EVALUATE );
}
end

7.12. Pong
A Conversion for the classic game Pong. Use the keys A, Z and K, M. The ball should get faster
after each bounce.

Name: Example Pong
Main class: org.drools.games.pong.PongMain

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Adventures with Drools

Figure 7.25. Pong Screenshot

7.13. Adventures with Drools
Based on the Adventure in Prolog, over at the Amzi website, http://www.amzi.com/
AdventureInProlog/, we started to work on a text adventure game for Drools. They are ideal as
they can start off simple and build in complexity and size over time, they also demonstrate key
aspects of declarative relational programming.

Name: Example Text Adventure
Main class: org.drools.games.adventure.TextAdventure

You can view the 8 minute demonstration and introduction for the example at http://
downloads.jboss.org/drools/videos/text-adventures.swf

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Chapter 7. Examples

Figure 7.26. Pong Screenshot

7.14. Wumpus World

Name: Example Wumpus World
Main class: org.drools.games.wumpus.WumpusWorldMain

Wumpus World is an AI example covered in the book "Artificial Intelligence : A Modern Approach".
When the game first starts all the cells are greyed out. As you walk around they become visible.
The cave has pits, a wumpus and gold. When you are next to a pit you will feel a breeze, when
you are next to the wumpus you will smell a stench and see glitter when next to gold. The sensor
icons are shown above the move buttons. If you walk into a pit or the wumpus, you die. A more
detailed overview of Wumpus World can be found at http://www.cis.temple.edu/~giorgio/cis587/
readings/wumpus.shtml. A 20 minute video showing how the game is created and works is at http://
www.youtube.com/watch?v=4CvjKqUOEzM. [http://www.youtube.com/watch?v=4CvjKqUOEzM]

340

Wumpus World

Figure 7.27. Wumpus World

341

Chapter 7. Examples

Figure 7.28. Cave Screenshot

Figure 7.29. Signals Screenshot

342

Miss Manners and Benchmarking

Figure 7.30. Smell Stench

Figure 7.31. Move Up, Wumpus Collision

7.15. Miss Manners and Benchmarking

Name: Miss Manners
Main class: org.drools.benchmark.manners.MannersBenchmark

343

Chapter 7. Examples

Rules file: manners.drl
Objective: Advanced walkthrough on the Manners benchmark, covers Depth conflict
resolution in depth.

7.15.1. Introduction
Miss Manners is throwing a party and, being a good host, she wants to arrange good seating. Her
initial design arranges everyone in male-female pairs, but then she worries about people have
things to talk about. What is a good host to do? She decides to note the hobby of each guest so
she can then arrange guests not only pairing them according to alternating sex but also ensuring
that a guest has someone with a common hobby, at least on one side.

Figure 7.32. Miss Manners' Guests

7.15.1.1. BenchMarking
Five benchmarks were established in the 1991 paper "Effects of Database Size on Rule System
Performance: Five Case Studies" by David Brant, Timothy Grose, Bernie Lofaso and Daniel P.
Miranker:

344

Introduction

• Manners uses a depth-first search approach to determine the seating arrangements alternating
women and men and ensuring one common hobby for neighbors.

• Waltz establishes a three-dimensional interpretation of a line drawing by line labeling by
constraint propagation.

• WaltzDB is a more general version of Waltz, supporting junctions of more than three lines and
using a database.

• ARP is a route planner for a robotic air vehicle using the A* search algorithm to achieve minimal
cost.

• Weaver VLSI router for channels and boxes using a black-board technique.

Manners has become the de facto rule engine benchmark. Its behavior, however, is now well
known and many engines optimize for this, thus negating its usefulness as a benchmark which
is why Waltz is becoming more favorable. These five benchmarks are also published at the
University of Texas http://www.cs.utexas.edu/ftp/pub/ops5-benchmark-suite/.

7.15.1.2. Miss Manners Execution Flow

After the first seating arrangement has been assigned, a depth-first recursion occurs which
repeatedly assigns correct seating arrangements until the last seat is assigned. Manners uses a
Context instance to control execution flow. The activity diagram is partitioned to show the relation
of the rule execution to the current Context state.

345

Chapter 7. Examples

Figure 7.33. Manners Activity Diagram

7.15.1.3. The Data and Results

Before going deeper into the rules, let's first take a look at the asserted data and the resulting
seating arrangement. The data is a simple set of five guests who should be arranged so that sexes
alternate and neighbors have a common hobby.

The Data

The data is given in OPS5 syntax, with a parenthesized list of name and value pairs for each
attribute. Each person has only one hobby.

(guest (name n1) (sex m) (hobby h1) )
(guest (name n2) (sex f) (hobby h1) )

346

In depth Discussion

(last_seat (seat 5) )

The Results

Each line of the results list is printed per execution of the "Assign Seat" rule. They key bit to
notice is that each line has a "pid" value one greater than the last. (The significance of this will be
explained in the discussion of the rule "Assign Seating".) The "ls", "rs", "ln" and "rn" refer to the left
and right seat and neighbor's name, respectively. The actual implementation uses longer attribute
names (e.g., leftGuestName, but here we'll stick to the notation from the original implementation.

[Seating id=1, pid=0, done=true, ls=1, ln=n5, rs=1, rn=n5]
[Seating id=2, pid=1, done=false, ls=1, ln=n5, rs=2, rn=n4]
[Seating id=4, pid=3, done=false, ls=3, rn=n3, rs=4, rn=n2]

7.15.2. In depth Discussion

7.15.2.1. Cheating
Manners has been designed to exercise cross product joins and Agenda activities. Many people
not understanding this tweak the example to achieve better performance, making their port of the
Manners benchmark pointless. Known cheats or porting errors for Miss Manners are:

• Using arrays for a guests hobbies, instead of asserting each one as a single fact massively
reduces the cross products.

• Altering the sequence of data can also reduce the amount of matching, increasing execution
speed.

• It's possible to change the not Conditional Element so that the test algorithm only uses the
"first-best-match", which is, basically, transforming the test algorithm to backward chaining. The
results are only comparable to other backward chaining rule engines or ports of Manners.

• Removing the context so the rule engine matches the guests and seats prematurely. A proper
port will prevent facts from matching using the context start.

• It's possible to prevent the rule engine from performing combinatorial pattern matching.

• If no facts are retracted in the reasoning cycle, as a result of the not CE, the port is incorrect.

7.15.2.2. Conflict Resolution
The Manners benchmark was written for OPS5 which has two conflict resolution strategies, LEX
and MEA. LEX is a chain of several strategies including salience, recency and complexity. The

347

Chapter 7. Examples

recency part of the strategy drives the depth first (LIFO) firing order. The CLIPS manual documents
the Recency strategy as follows:

Every fact and instance is marked internally with a "time tag" to indicate its relative
recency with respect to every other fact and instance in the system. The pattern
entities associated with each rule activation are sorted in descending order for
determining placement. An activation with a more recent pattern entity is placed
before activations with less recent pattern entities. To determine the placement
order of two activations, compare the sorted time tags of the two activations one
by one starting with the largest time tags. The comparison should continue until
one activation’s time tag is greater than the other activation’s corresponding time
tag. The activation with the greater time tag is placed before the other activation
on the agenda. If one activation has more pattern entities than the other activation
and the compared time tags are all identical, then the activation with more time
tags is placed before the other activation on the agenda.
—CLIPS Reference Manual

However Jess and CLIPS both use the Depth strategy, which is simpler and lighter, which Drools
also adopted. The CLIPS manual documents the Depth strategy as:

Newly activated rules are placed above all rules of the same salience. For
example, given that fact-a activates rule-1 and rule-2 and fact-b activates rule-3
and rule-4, then if fact-a is asserted before fact-b, rule-3 and rule-4 will be above
rule-1 and rule-2 on the agenda. However, the position of rule-1 relative to rule-2
and rule-3 relative to rule-4 will be arbitrary.
—CLIPS Reference Manual

The initial Drools implementation for the Depth strategy would not work for Manners without the
use of salience on the "make_path" rule. The CLIPS support team had this to say:

The default conflict resolution strategy for CLIPS, Depth, is different than the
default conflict resolution strategy used by OPS5. Therefore if you directly
translate an OPS5 program to CLIPS, but use the default depth conflict resolution
strategy, you're only likely to get the correct behavior by coincidence. The LEX
and MEA conflict resolution strategies are provided in CLIPS to allow you to
quickly convert and correctly run an OPS5 program in CLIPS.
—Clips Support Forum

Investigation into the CLIPS code reveals there is undocumented functionality in the Depth
strategy. There is an accumulated time tag used in this strategy; it's not an extensively fact by
fact comparison as in the recency strategy, it simply adds the total of all the time tags for each
activation and compares.

7.15.2.3. Rule "assignFirstSeat"

Once the context is changed to START_UP, activations are created for all asserted guest. Because
all activations are created as the result of a single Working Memory action, they all have the same

348

In depth Discussion

Activation time tag. The last asserted Guest object would have a higher fact time tag, and its
Activation would fire because it has the highest accumulated fact time tag. The execution order in
this rule has little importance, but has a big impact in the rule "Assign Seat". The activation fires
and asserts the first Seating arrangement and a Path, and then sets the Context attribute state
to create an activation for rule findSeating.

rule assignFirstSeat
when
context : Context( state == Context.START_UP )
guest : Guest()
count : Count()
then
String guestName = guest.getName();

Seating seating =
new Seating( count.getValue(), 1, true, 1, guestName, 1, guestName);
insert( seating );

Path path = new Path( count.getValue(), 1, guestName );
insert( path );

modify( count ) { setValue ( count.getValue() + 1 ) }

System.out.println( "assign first seat : " + seating + " : " + path );

modify( context ) {
setState( Context.ASSIGN_SEATS )
}
end

7.15.2.4. Rule "findSeating"

This rule determines each of the Seating arrangements. The rule creates cross product solutions
for all asserted Seating arrangements against all the asserted guests except against itself or any
already assigned chosen solutions.

rule findSeating
when
context : Context( state == Context.ASSIGN_SEATS )
$s : Seating( pathDone == true )
$g1 : Guest( name == $s.rightGuestName )
$g2 : Guest( sex != $g1.sex, hobby == $g1.hobby )

count : Count()

not ( Path( id == $s.id, guestName == $g2.name) )

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Chapter 7. Examples

not ( Chosen( id == $s.id, guestName == $g2.name, hobby == $g1.hobby) )
then
int rightSeat = $s.getRightSeat();
int seatId = $s.getId();
int countValue = count.getValue();

Seating seating =
new Seating( countValue, seatId, false, rightSeat,
$s.getRightGuestName(), rightSeat + 1, $g2.getName() );
insert( seating );

Path path = new Path( countValue, rightSeat + 1, $g2.getName() );
insert( path );

Chosen chosen = new Chosen( seatId, $g2.getName(), $g1.getHobby() );
insert( chosen );

System.err.println( "find seating : " + seating + " : " + path +
" : " + chosen);

modify( count ) {setValue( countValue + 1 )}
modify( context ) {setState( Context.MAKE_PATH )}
end

However, as can be seen from the printed results shown earlier, it is essential that only the Seating
with the highest pid cross product be chosen. How can this be possible if we have activations, of
the same time tag, for nearly all existing Seating and Guest objects? For example, on the third
iteration of findSeating the produced activations will be as shown below. Remember, this is from
a very small data set, and with larger data sets there would be many more possible activated
Seating solutions, with multiple solutions per pid:

=>[ActivationCreated(35): rule=findSeating
[fid:19:33]:[Seating id=3, pid=2, done=true, ls=2, ln=n4, rs=3, rn=n3]
[fid:4:4]:[Guest name=n3, sex=m, hobbies=h3]
[fid:3:3]:[Guest name=n2, sex=f, hobbies=h3]



350

In depth Discussion

The creation of all these redundant activations might seem pointless, but it must be remembered
that Manners is not about good rule design; it's purposefully designed as a bad ruleset to fully
stress-test the cross product matching process and the Agenda, which this clearly does. Notice
that each activation has the same time tag of 35, as they were all activated by the change in the
Context object to ASSIGN_SEATS. With OPS5 and LEX it would correctly fire the activation with
the Seating asserted last. With Depth, the accumulated fact time tag ensures that the activation
with the last asserted Seating fires.

7.15.2.5. Rules "makePath" and "pathDone"

Rule makePath must always fire before pathDone. A Path object is asserted for each Seating
arrangement, up to the last asserted Seating. Notice that the conditions in pathDone are a subset
of those in makePath - so how do we ensure that makePath fires first?

rule makePath
when
Context( state == Context.MAKE_PATH )
Seating( seatingId:id, seatingPid:pid, pathDone == false )
Path( id == seatingPid, pathGuestName:guestName, pathSeat:seat )
not Path( id == seatingId, guestName == pathGuestName )
then
insert( new Path( seatingId, pathSeat, pathGuestName ) );
end

rule pathDone
when
context : Context( state == Context.MAKE_PATH )
seating : Seating( pathDone == false )
then
modify( seating ) {setPathDone( true )}

modify( context ) {setState( Context.CHECK_DONE)}
end

351

Chapter 7. Examples

Figure 7.34. Rete Diagram

352

Output Summary

Both rules end up on the Agenda in conflict and with identical activation time tags. However, the
accumulate fact time tag is greater for "Make Path" so it gets priority.

7.15.2.6. Rules "continue" and "areWeDone"

Rule areWeDone only activates when the last seat is assigned, at which point both rules will be
activated. For the same reason that makePath always wins over path Done, areWeDone will take
priority over rule continue.

rule areWeDone
when
context : Context( state == Context.CHECK_DONE )
LastSeat( lastSeat: seat )
Seating( rightSeat == lastSeat )
then
modify( context ) {setState(Context.PRINT_RESULTS )}
end

rule continue
when
context : Context( state == Context.CHECK_DONE )
then
modify( context ) {setState( Context.ASSIGN_SEATS )}
end

7.15.3. Output Summary

Assign First seat
=>[fid:13:13]:[Seating id=1, pid=0, done=true, ls=1, ln=n5, rs=1, rn=n5]
=>[fid:14:14]:[Path id=1, seat=1, guest=n5]

==>[ActivationCreated(16): rule=findSeating

==>[ActivationCreated(16): rule=findSeating
[fid:13:13]:[Seating id=1 , pid=0, done=true, ls=1, ln=n5, rs=1, rn=n5]
[fid:5:5]:[Guest name=n4, sex=m, hobbies=h1]*

Assign Seating
=>[fid:15:17] :[Seating id=2 , pid=1 , done=false, ls=1, lg=n5, rs=2, rn=n4]

353

Chapter 7. Examples

=>[fid:17:19]:[Chosen id=1, name=n4, hobbies=h1]

=>[ActivationCreated(21): rule=makePath
[fid:15:17] : [Seating id=2, pid=1, done=false, ls=1, ln=n5, rs=2, rn=n4]
[fid:14:14] : [Path id=1, seat=1, guest=n5]*

==>[ActivationCreated(21): rule=pathDone
[Seating id=2, pid=1, done=false, ls=1, ln=n5, rs=2, rn=n4]*

Make Path
=>[fid:18:22:[Path id=2, seat=1, guest=n5]]

Path Done

Continue Process
[fid:4:4] : [Guest name=n3, sex=m, hobbies=h3]*

[fid:2:2]:[Guest name=n2, sex=f, hobbies=h1], [fid:12:20] : [Count value=3]


Assign Seating
=>[fid:19:26]:[Seating id=3, pid=2, done=false, ls=2, lnn4, rs=3, rn=n3]]
=>[fid:20:27]:[Path id=3, seat=3, guest=n3]]
=>[fid:21:28]:[Chosen id=2, name=n3, hobbies=h3}]

[fid:19:26]:[Seating id=3, pid=2, done=false, ls=2, ln=n4, rs=3, rn=n3]
[fid:18:22]:[Path id=2, seat=1, guest=n5]*


354

Output Summary

=>[ActivationCreated(30): rule=done
[fid:19:26]:[Seating id=3, pid=2, done=false, ls=2, ln=n4, rs=3, rn=n3]*

Make Path

Make Path
=>[fid:23:32] [Path id=3, seat=2, guest=n4]

Path Done

Continue Processing
[fid:3:3]:[Guest name=n2, sex=f, hobbies=h3], [fid:12:29]*


[fid:9:9]:[Guest name=n5, sex=f, hobbies=h1], [fid:1:1] : [Guest name=n1, sex=m,
hobbies=h1]

Assign Seating
=>[fid:24:36]:[Seating id=4, pid=3, done=false, ls=3, ln=n3, rs=4, rn=n2]]
=>[fid:25:37]:[Path id=4, seat=4, guest=n2]]

==>[ActivationCreated(40): rule=makePath

==>[ActivationCreated(40): rule=makePath


355

Chapter 7. Examples

=>[ActivationCreated(40): rule=done
[fid:24:36]:[Seating id=4, pid=3, done=false, ls=3, ln=n3, rs=4, rn=n2]*

Make Path
=>fid:27:41:[Path id=4, seat=2, guest=n4]

Make Path
=>fid:28:42]:[Path id=4, seat=1, guest=n5]]

Make Path
=>fid:29:43]:[Path id=4, seat=3, guest=n3]]

Path Done

Continue Processing
[fid:5:5]:[Guest name=n4, sex=m, hobbies=h1], [fid:2:2]
[Guest name=n2, sex=f, hobbies=h1]

[fid:1:1]:[Guest name=n1, sex=m, hobbies=h1]*


Assign Seating
=>[fid:30:47]:[Seating id=5, pid=4, done=false, ls=4, ln=n2, rs=5, rn=n1]

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