Getting Started with Drools Rule Language (DRL) in Aletyx Enterprise Build of Kogito and Drools 10.1.0-aletyx

Your First DRL Rule

Let’s create a simple loan approval rule to demonstrate the basics of DRL. This example will help you understand the fundamental structure and components of Drools rules.

Step 1: Set Up Your Project

First, create a new Java project with the required dependencies. If you’re using Maven, add the following to your pom.xml:

<dependencies>
    <!-- Aletyx Enterprise Build of Kogito and Drools -->
    <dependency>
        <groupId>ai.aletyx</groupId>
        <artifactId>kie-api</artifactId>
        <version>10.1.0-aletyx</version>
    </dependency>
    <dependency>
        <groupId>ai.aletyx</groupId>
        <artifactId>kie-internal</artifactId>
        <version>10.1.0-aletyx</version>
    </dependency>
    <dependency>
        <groupId>ai.aletyx</groupId>
        <artifactId>drools-core</artifactId>
        <version>10.1.0-aletyx</version>
    </dependency>
    <dependency>
        <groupId>ai.aletyx</groupId>
        <artifactId>drools-compiler</artifactId>
        <version>10.1.0-aletyx</version>
    </dependency>
</dependencies>

Step 2: Create Your Fact Model

Let’s define simple Java classes that our rules will work with:

package ai.aletyx.examples.loan;

/**
 * This class represents a loan application in our system.
 * It contains all the information needed for loan processing rules.
 *
 * In Drools, these domain objects are called "facts" - they're the data
 * that the rules engine reasons about.
 */
public class LoanApplication {
    // Core applicant information
    private String applicantName;
    private int amount;
    private int creditScore;

    // Decision information - these will be modified by our rules
    private boolean approved;
    private String explanation;

    /**
     * Creates a new loan application with initial values.
     * Note that approved is set to false by default -
     * it will be changed by rules if the application meets criteria.
     */
    public LoanApplication(String applicantName, int amount, int creditScore) {
        this.applicantName = applicantName;
        this.amount = amount;
        this.creditScore = creditScore;
        this.approved = false; // Default to not approved
    }

    // Getters and setters - these will be used by the rules engine
    // to both check conditions and modify the fact
    public String getApplicantName() { return applicantName; }
    public void setApplicantName(String applicantName) { this.applicantName = applicantName; }

    public int getAmount() { return amount; }
    public void setAmount(int amount) { this.amount = amount; }

    public int getCreditScore() { return creditScore; }
    public void setCreditScore(int creditScore) { this.creditScore = creditScore; }

    public boolean isApproved() { return approved; }
    public void setApproved(boolean approved) { this.approved = approved; }

    public String getExplanation() { return explanation; }
    public void setExplanation(String explanation) { this.explanation = explanation; }

    /**
     * String representation for debugging purposes.
     * This is helpful when printing rule execution results.
     */
    @Override
    public String toString() {
        return "LoanApplication{" +
                "applicantName='" + applicantName + '\'' +
                ", amount=" + amount +
                ", creditScore=" + creditScore +
                ", approved=" + approved +
                ", explanation='" + explanation + '\'' +
                '}';
    }
}

Step 3: Create Your First DRL File

Create a file named loan-rules.drl in the src/main/resources directory:

// Package declaration - this defines the namespace for our rules
package ai.aletyx.examples.loan

// Unit declaration - this defines a self-contained group of rules

unit LoanRules;

// Import statements - bringing in Java classes we'll need in our rules
import ai.aletyx.examples.loan.LoanApplication;
import org.drools.ruleunits.api.RuleUnitData;
import org.drools.ruleunits.api.DataStore;

/**
 * Rule: High Credit Score Approval
 * Purpose: Automatically approve applications with excellent credit
 * Criteria: Credit score of 700 or higher
 */
rule "High Credit Score Approval"
  when
    // Pattern matching: Find applications with high credit scores
    // The $application variable will reference each matching application
    $application: /applications[ creditScore >= 700 ]
  then
    // Actions to take when the condition is met
    $application.setApproved(true);  // Approve the application
    $application.setExplanation("Automatically approved due to high credit score");
    System.out.println("Approved application for " + $application.getApplicantName());
end

/**
 * Rule: Low Credit Score Rejection
 * Purpose: Automatically reject applications with poor credit
 * Criteria: Credit score below 600
 */
rule "Low Credit Score Rejection"
  when
    // Pattern matching: Find applications with low credit scores
    $application: /applications[ creditScore < 600 ]
  then
    // Actions to take when the condition is met
    $application.setApproved(false);  // Reject the application
    $application.setExplanation("Automatically rejected due to low credit score");
    System.out.println("Rejected application for " + $application.getApplicantName());
end

/**
 * Rule: Medium Credit Score Review
 * Purpose: Flag applications with moderate credit for manual review
 * Criteria: Credit score between 600 and 699
 */
rule "Medium Credit Score Review"
  when
    // Pattern matching: Find applications with medium credit scores
    // Note the comma between constraints acts as AND
    $application: /applications[ creditScore >= 600, creditScore < 700 ]
  then
    // We don't explicitly approve or reject - these need human review
    $application.setExplanation("Requires manual review - medium credit score");
    System.out.println("Flagged for review: application for " + $application.getApplicantName());
end

Step 4: Create Your Rule Unit Class

Now create the Java class that implements the rule unit:

package ai.aletyx.examples.loan;

import org.drools.ruleunits.api.RuleUnitData;
import org.drools.ruleunits.api.DataStore;
import org.drools.ruleunits.api.DataSource;

/**
 * This Java class is the implementation of our Rule Unit.
 * It must match the unit declaration in our DRL file and
 * implement the RuleUnitData interface.
 *
 * Rule Units encapsulate the data sources that rules operate on.
 * They provide clean separation between different sets of rules.
 */
public class LoanRules implements RuleUnitData {
    // This data store will hold all loan applications
    // The name must match what we declared in our DRL
    private final DataStore<LoanApplication> applications;

    /**
     * Constructor initializes the data store.
     * We use DataSource.createStore() to create a mutable collection
     * that allows adding, removing, and updating facts.
     */
    public LoanRules() {
        this.applications = DataSource.createStore();
    }

    /**
     * Getter method for the applications data store.
     * This method name must match the field name in the DRL unit declaration.
     * The rules engine uses this getter to access the data store.
     */
    public DataStore<LoanApplication> getApplications() {
        return applications;
    }
}

Step 5: Create a Test Application

Finally, create a simple Java application to test your rules:

package ai.aletyx.examples.loan;

import org.drools.ruleunits.api.RuleUnitInstance;
import org.drools.ruleunits.api.RuleUnitProvider;

/**
 * This class demonstrates how to use the Drools rule engine
 * to process loan applications using our rules.
 */
public class LoanRulesTest {

    public static void main(String[] args) {
        // Step 1: Create a new rule unit
        // This holds our data stores that rules will operate on
        LoanRules loanRules = new LoanRules();

        // Step 2: Create a rule unit instance
        // This is the entry point to the rules engine
        RuleUnitInstance<LoanRules> instance = RuleUnitProvider.get()
            .createRuleUnitInstance(loanRules);

        try {
            // Step 3: Create the facts (domain objects) that rules will evaluate
            // Create three applications with different credit scores:
            // - High credit score (750) - should be auto-approved
            // - Low credit score (550) - should be auto-rejected
            // - Medium credit score (650) - should require manual review
            LoanApplication application1 = new LoanApplication("John Doe", 10000, 750);
            LoanApplication application2 = new LoanApplication("Jane Smith", 15000, 550);
            LoanApplication application3 = new LoanApplication("Bob Johnson", 5000, 650);

            // Step 4: Insert the facts into the data store
            // This makes them available to the rules engine
            loanRules.getApplications().add(application1);
            loanRules.getApplications().add(application2);
            loanRules.getApplications().add(application3);

            // Step 5: Execute all matching rules
            // The fire() method activates the rule engine to evaluate
            // all rules against the facts we've provided
            instance.fire();

            // Step 6: Check the results
            // The facts have been modified by the rules that fired
            System.out.println("\nFinal application statuses after rules execution:");
            System.out.println(application1); // Should be approved
            System.out.println(application2); // Should be rejected
            System.out.println(application3); // Should need review
        } finally {
            // Step 7: Clean up resources
            // Always dispose of the rule unit instance when done
            // to free resources properly
            instance.close();
        }
    }
}

Step 6: Run Your Application

Execute the LoanRulesTest class. You should see output showing which rules fired and the final state of each loan application.

Basic Rule Structure

Let’s break down the structure of a DRL rule:

rule "Rule Name"
    // Optional attributes
    when
        // Conditions (Left Hand Side, LHS)
    then
        // Actions (Right Hand Side, RHS)
end

Components Explained:

rule "Rule Name": Every rule starts with the rule keyword followed by a unique name in double quotes.
Attributes: Optional specifications that modify rule behavior, like priority (salience), activation timing (date-effective), etc.
when: Marks the beginning of the conditions section.
Conditions: Patterns that match facts in working memory. In our loan example:
$application: /applications[ creditScore >= 700 ]
This pattern:
- Binds the matching LoanApplication object to variable $application
- Searches the applications data source
- Matches only applications with a credit score of 700 or higher
then: Marks the beginning of the actions section.
Actions: Java code that executes when the conditions are met. In our example:
$application.setApproved(true); $application.setExplanation("Automatically approved due to high credit score");
These actions modify the matched fact by setting properties.
end: Marks the end of the rule.

Common Rule Patterns

Let’s look at some common rule patterns:

1. Simple Constraint Matching

rule "Simple Constraint"
  when
    $application: /applications[ amount < 5000 ]
  then
    $application.setCategory("Small Loan");
end

2. Multiple Constraints

rule "Multiple Constraints"
  when
    $application: /applications[ amount < 5000, creditScore > 700 ]
  then
    $application.setCategory("Low-Risk Small Loan");
end

3. Multiple Patterns

rule "Co-signer Rule"
  when
    $application: /applications[ creditScore < 600 ]
    $cosigner: /cosigners[ applicationId == $application.id, creditScore > 700 ]
  then
    $application.setApproved(true);
    $application.setExplanation("Approved with qualified co-signer");
end

4. Nested Property Access

rule "Address Verification"
  when
    $application: /applications[ address.state == "CA", address.yearsAtAddress > 2 ]
  then
    $application.setAddressVerified(true);
end

Common Use Cases for DRL

DRL excels in a variety of business scenarios. Here are four important use cases where Drools really shines:

1. Validation Rules

Rules can validate data against business constraints and requirements.

/**
 * Validation Rule: Check for valid application data
 * Purpose: Ensure applications contain reasonable values before processing
 *
 * In validation rules, we typically look for invalid conditions
 * and flag the object accordingly.
 */
rule "Validate Loan Application"
  when
    // Find applications with invalid data
    // The || operator works as a logical OR
    $app: /applications[ amount <= 0 || creditScore <= 0 ]
  then
    // Mark the application as invalid with an explanation
    $app.setValid(false);
    $app.setExplanation("Invalid amount or credit score");

    // We could also log the validation failure
    System.out.println("Validation failed for: " + $app.getApplicantName());
end

2. Classification Rules

Rules can categorize data based on multiple criteria.

/**
 * Classification Rule: High Risk Application
 * Purpose: Label applications that carry elevated risk
 *
 * Classification rules help categorize objects to enable
 * different downstream processing.
 */
rule "Classify as High Risk"
  when
    // Find applications meeting high-risk criteria
    $app: /applications[ creditScore < 600 || bankruptcies > 0 ]
  then
    // Set the risk category
    $app.setRiskCategory("HIGH");

    // We could also assign a numerical risk score
    $app.setRiskScore(calculateRiskScore($app));
end

3. Decision Making

Rules can determine values and outcomes based on sophisticated criteria.

/**
 * Decision Rule: Interest Rate Determination
 * Purpose: Calculate the appropriate interest rate for approved loans
 *
 * Decision rules evaluate conditions to generate a specific
 * value or outcome.
 */
rule "Determine Interest Rate"
  when
    // Only apply to approved applications with excellent credit
    $app: /applications[ approved == true, creditScore >= 750 ]
  then
    // Set a preferred interest rate for excellent credit
    $app.setInterestRate(3.75);

    // We could also calculate a rate based on multiple factors
    // $app.setInterestRate(calculateBaseRate() -
    //    (($app.getCreditScore() - 700) * 0.001));
end

4. Workflow Routing

Rules can determine the next steps in a business process.

/**
 * Workflow Rule: Senior Underwriter Assignment
 * Purpose: Route high-value or higher-risk loans to senior staff
 *
 * Workflow rules direct items through appropriate business processes
 * based on their characteristics.
 */
rule "Route to Senior Underwriter"
  when
    // Complex condition for high-value or moderate-value/higher-risk loans
    $app: /applications[ amount > 500000 ||
                         (creditScore < 650 && amount > 250000) ]
  then
    // Assign to the appropriate team and set priority
    $app.setAssignedTeam("SENIOR_UNDERWRITING");
    $app.setPriority("HIGH");

    // We could also notify the team lead or update a workflow system
    notifySeniorUnderwritingTeam($app);
end

Next Steps

Now that you’ve created your first DRL rules and understand the basic patterns, you’re ready to explore:

DRL Building Blocks: Diving deeper into packages, imports, and rule units
Working with Data in DRL: Understanding fact types and data binding
Advanced Condition Elements: Using exists, not, forall, and accumulate
Rule Attributes: Controlling rule execution with salience, no-loop, and more
Decision Tables: Spreadsheet-based rule authoring for business users
Implementing Decision Services: Best practices for production systems

For hands-on practice with more complex scenarios, explore our Rule Unit Guide and Decision Tables Tutorial.

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Getting Started with Drools Rule Language (DRL) in Aletyx Enterprise Build of Kogito and Drools 10.1.0-aletyx

Your First DRL Rule

Step 1: Set Up Your Project

Step 2: Create Your Fact Model

Step 3: Create Your First DRL File

Step 4: Create Your Rule Unit Class

Step 5: Create a Test Application

Step 6: Run Your Application

Basic Rule Structure

Components Explained:

Common Rule Patterns

1. Simple Constraint Matching

2. Multiple Constraints

3. Multiple Patterns

4. Nested Property Access

Common Use Cases for DRL

1. Validation Rules

2. Classification Rules

3. Decision Making

4. Workflow Routing

Next Steps

​Your First DRL Rule

​Step 1: Set Up Your Project

​Step 2: Create Your Fact Model

​Step 3: Create Your First DRL File

​Step 4: Create Your Rule Unit Class

​Step 5: Create a Test Application

​Step 6: Run Your Application

​Basic Rule Structure

​Components Explained:

​Common Rule Patterns

​1. Simple Constraint Matching

​2. Multiple Constraints

​3. Multiple Patterns

​4. Nested Property Access

​Common Use Cases for DRL

​1. Validation Rules

​2. Classification Rules

​3. Decision Making

​4. Workflow Routing

​Next Steps

Your First DRL Rule

Step 1: Set Up Your Project

Step 2: Create Your Fact Model

Step 3: Create Your First DRL File

Step 4: Create Your Rule Unit Class

Step 5: Create a Test Application

Step 6: Run Your Application

Basic Rule Structure

Components Explained:

Common Rule Patterns

1. Simple Constraint Matching

2. Multiple Constraints

3. Multiple Patterns

4. Nested Property Access

Common Use Cases for DRL

1. Validation Rules

2. Classification Rules

3. Decision Making

4. Workflow Routing

Next Steps