Important Notice
Grool rule engine has been donated to Hyperjump.tech so now its part of Hyperjump's OSS Family. You can visit grule-rule-engine to continue using this software.
This project is archived.
"Gopher Holds The Rules"
Groolimport "github.com/newm4n/grool"
Rule Engine for Go
Grool is a Rule Engine library for Golang programming language. Inspired by the acclaimed JBOSS Drools, done in a much simple manner.
Like Drools, Grools have its own DSL comparable as follows.
Drools's DRL be like :
rule "SpeedUp"
salience 10
when
$TestCar : TestCarClass( speedUp == true && speed < maxSpeed )
$DistanceRecord : DistanceRecordClass()
then
$TestCar.setSpeed($TestCar.Speed + $TestCar.SpeedIncrement);
update($TestCar);
$DistanceRecord.setTotalDistance($DistanceRecord.getTotalDistance() + $TestCar.Speed)
update($DistanceRecord)
end
And Grools's GRL be like :
rule SpeedUp "When testcar is speeding up we keep increase the speed." salience 10 {
when
TestCar.SpeedUp == true && TestCar.Speed < TestCar.MaxSpeed
then
TestCar.Speed = TestCar.Speed + TestCar.SpeedIncrement;
DistanceRecord.TotalDistance = DistanceRecord.TotalDistance + TestCar.Speed;
}
There are no better explanation compared to the article authored by Martin Fowler. You can read the article here (RulesEngine by Martin Fowler).
Taken from TutorialsPoint website (with very slight modification),
Grools is Rule Engine or a Production Rule System that uses the rule-based approach to implement and Expert System. Expert Systems are knowledge-based systems that use knowledge representation to process acquired knowledge into a knowledge base that can be used for reasoning.
A Production Rule System is Turing complete with a focus on knowledge representation to express propositional and first-order logic in a concise, non-ambiguous and declarative manner.
The brain of a Production Rules System is an Inference Engine that can scale to a large number of rules and facts. The Inference Engine matches facts and data against Production Rules – also called Productions or just Rules – to infer conclusions which result in actions.
A Production Rule is a two-part structure that uses first-order logic for reasoning over knowledge representation. A business rule engine is a software system that executes one or more business rules in a runtime production environment.
A Rule Engine allows you to define “What to Do” and not “How to do it.”
What is a Rule?
(also taken from TutorialsPoint)
Rules are pieces of knowledge often expressed as, "When some conditions occur, then do some tasks."
When
<Condition is true>
Then
<Take desired Action>
The most important part of a Rule is its when part. If the when part is satisfied, the then part is triggered.
rule <rule_name> <rule_description>
<attribute> <value> {
when
<conditions>
then
<actions>
}
Advantages of a Rule Engine
Declarative Programming
Rules make it easy to express solutions to difficult problems and get the solutions verified as well. Unlike codes, Rules are written in less complex language; Business Analysts can easily read and verify a set of rules.
Logic and Data Separation
The data resides in the Domain Objects and the business logic resides in the Rules. Depending upon the kind of project, this kind of separation can be very advantageous.
Centralization of Knowledge
By using Rules, you create a repository of knowledge (a knowledge base) which is executable. It is a single point of truth for business policy. Ideally, Rules are so readable that they can also serve as documentation.
Agility To Change
Since business rules are actually treated as data. Adjusting the rule according to business dynamic nature become trivial. No need to re-build codes, deploy as normal software development do, you only need to roll out sets of rule and apply them to knowledge repository.
Hello Grool
Grool Rule Language (GRL)
The GRL is a DSL (Domain Specific Language) designed for Grool. Its a simplified language to be used for defining rule evaluation criteria and action to be executed if the criteria were met.
Generally, the language have the following structure :
rule <RuleName> <RuleDescription> [salience <priority>] {
when
<boolean expression>
then
<assignment or operation expression>
}
RuleName identify a speciffic rule. The name should be unique in the entire knowledge base, consist of one word thus it should not contains white-spece.
RuleDescription describes the rule. The description should be enclosed with a double-quote.
Salience defines the importance of the rule. Its an optional rule configuration, and by default, when you don't specify them, all rule have the salience of 0 (zero). The lower the value, the less important the rule. Whenever multiple rule are a candidate for execution, highest salience rule will be executed first. You may define negative value for the salience, to make the salience even lower. Like any implementation of Rule-Engine, there are no definitive algorithm to specify which rule to be execute in case of conflicting candidate, the engine may run which ever they like. Salience is one way of hinting the rule engine of which rule have more importance compared to the other.
Boolean Expression is an expression that will be used by rule engine to identify if that speciffic rule are a candidate for execution for the current facts.
Assignment or Operation Expression contains list of expressions (each expression should be ended with ";" symbol.) The expression are designed to modify the current fact values, making calculation, make some logging, etc.
Boolean Expression
Boolean expression comes natural for java or golang developer in GRL.
when
contains(User.Name, "robert") &&
User.Age > 35
then
...
Constants and Literalss
13442344-553234.4553-234.3trueTRUEFalse+-/*&&||<<=>>===!=Comments
You can always put a comment inside your GRL script. Such as :
// This is a comment // And this /* And also this */ /* As well as this */
Examples
rule SpeedUp "When testcar is speeding up we keep increase the speed." {
when
TestCar.SpeedUp == true && TestCar.Speed < TestCar.MaxSpeed
then
TestCar.Speed = TestCar.Speed + TestCar.SpeedIncrement;
DistanceRecord.TotalDistance = DistanceRecord.TotalDistance + TestCar.Speed;
}
rule StartSpeedDown "When testcar is speeding up and over max speed we change to speed down." {
when
TestCar.SpeedUp == true && TestCar.Speed >= TestCar.MaxSpeed
then
TestCar.SpeedUp = false;
log("Now we slow down");
}
rule SlowDown "When testcar is slowing down we keep decreasing the speed." {
when
TestCar.SpeedUp == false && TestCar.Speed > 0
then
TestCar.Speed = TestCar.Speed - TestCar.SpeedIncrement;
DistanceRecord.TotalDistance = DistanceRecord.TotalDistance + TestCar.Speed;
}
rule SetTime "When Distance Recorder time not set, set it." {
when
isNil(DistanceRecord.TestTime)
then
log("Set the test time");
DistanceRecord.TestTime = now();
}
Loading GRL on to Knowledge
One knowledge base may consist of many rules. Tens to hundreds of rules. They may be loaded from multiple sources. Those rules will go to the same knowledge as long as you use the same knowledge when loading each of the resource.
Before you load any rule, you need to have your own knowledge
knowledgeBase := model.NewKnowledgeBase() ruleBuilder := builder.NewRuleBuilder(knowledgeBase)
From File
fileRes := NewFileResource("/path/to/rules.grl")
err := ruleBuilder.BuildRuleFromResource(fileRes)
if err != nil {
panic(err)
}
From String or ByteArray
byteArr := NewBytesResource([]byte(rules))
err := ruleBuilder.BuildRuleFromResource(byteArr)
if err != nil {
panic(err)
}
From URL
urlRes := NewUrlResource("http://host.com/path/to/rule.grl")
err := ruleBuilder.BuildRuleFromResource(urlRes)
if err != nil {
panic(err)
}
Preparing Facts
structstructtype TestCar struct {
SpeedUp bool
Speed int
MaxSpeed int
SpeedIncrement int
}
type DistanceRecorder struct {
TotalDistance int
TestTime time.Time
}
And then you instantiate those struct.
tc := &TestCar{
SpeedUp: true,
Speed: 0,
MaxSpeed: 100,
SpeedIncrement: 2,
}
dr := &DistanceRecorder{
TotalDistance: 0,
}
Add those struct instances (fact) into data context.
dataContext := context.NewDataContext()
dataContext.Add("TestCar", tc)
dataContext.Add("DistanceRecord", dr)
Now your fact is ready to be executed in the rule engine that already prepared with some knowledge.
Executing A Knowledge On Facts and get result
You already know how to load rules into knowledge base, and you also know how to prepare your fact. Now we can execute the knowledge base agains facts.
engine := NewGroolEngine()
err = engine.Execute(dataContext, knowledgeBase)
if err != nil {
t.Errorf("Got error : %v", err)
t.FailNow()
} else {
t.Log(dr.TotalDistance)
}
The rule engine will use loaded knowledgebase to work upon sets of fact data in data context.
Calling Function in Grool
All invocable functions which are invocable from the DataContext is Invocable from within the rule, both in the "When" scope and "Then" scope.
Assuming you have a struct with some functions.
type MyPoGo struct {
}
func (p *MyPoGo) GetStringLength(sarg string) int {
return len(sarg)
}
func (p *MyPoGo) AppendString(aString, subString string) string {
return sprintf("%s%s", aString, subString)
}
And add the struct into knowledge base
dctx := context.NewDataContext()
dctx.Add("Pogo", &MyPoGo{})
You can call the fuction within the rule
when
Pogo.GetStringLength(some.variable) < 100
then
some.variable = Pogo.AppendString(some.variable, "Groooling");
Default Functions
context/GroolFunction.gocontext/GroolFunction.goImportant Thing you must know about Custom Function in Grool
When you make your own function to be called from rule engine, you need to know the following rules.
int64float64int64float64- Need to do more and more and more test.
- Better code coverage test.
- Better commenting for go doc best practice.
- Improve function argument handling to be more fluid and intuitive.