Implement Dynamic Discount Rules in Spring Boot with Drools

Introduction

When an e‑commerce system needs to calculate discounts based on flexible business rules—such as extra 5 % for VIP customers or an additional 10 % for orders over 1000 CNY—hard‑coding the logic would require code changes, retesting and redeployment each time a rule changes. Using a rule engine like Drools allows the rules to be externalised and modified without touching the business code.

Adding Maven Dependencies

Create a Spring Boot project and add the following Drools dependencies to

pom.xml

:

<code>&lt;dependency&gt;
  &lt;groupId&gt;org.drools&lt;/groupId&gt;
  &lt;artifactId&gt;drools-core&lt;/artifactId&gt;
  &lt;version&gt;7.59.0.Final&lt;/version&gt;
&lt;/dependency&gt;
&lt;dependency&gt;
  &lt;groupId&gt;org.drools&lt;/groupId&gt;
  &lt;artifactId&gt;drools-compiler&lt;/artifactId&gt;
  &lt;version&gt;7.59.0.Final&lt;/version&gt;
&lt;/dependency&gt;
&lt;dependency&gt;
  &lt;groupId&gt;org.drools&lt;/groupId&gt;
  &lt;artifactId&gt;drools-decisiontables&lt;/artifactId&gt;
  &lt;version&gt;7.59.0.Final&lt;/version&gt;
&lt;/dependency&gt;</code>

Drools Configuration Class

Create a

DroolsConfig

class that registers a

KieContainer

bean and loads the DRL files from

src/main/resources/rules

:

<code>@Configuration
public class DroolsConfig {
    // Path to the rule file
    private static final String RULES_CUSTOMER_RULES_DRL = "rules/customer-discount.drl";
    private static final KieServices kieServices = KieServices.Factory.get();

    @Bean
    public KieContainer kieContainer() {
        KieFileSystem kieFileSystem = kieServices.newKieFileSystem();
        kieFileSystem.write(ResourceFactory.newClassPathResource(RULES_CUSTOMER_RULES_DRL));
        KieBuilder kb = kieServices.newKieBuilder(kieFileSystem);
        kb.buildAll();
        KieModule kieModule = kb.getKieModule();
        return kieServices.newKieContainer(kieModule.getReleaseId());
    }
}</code>

Defines a Spring bean of type

KieContainer

that loads the DRL file from the classpath.

Creates a

KieFileSystem

to write the rule file.

Builds the Drools module with

KieBuilder

.

Exposes the container as a Spring bean.

Business Model Classes

Define the request object, an enum for customer type, and a DTO for the discount result.

<code>@Getter @Setter
public class OrderRequest {
    private String customerNumber;
    private Integer age;
    private Integer amount;
    private CustomerType customerType;
}</code>

<code>public enum CustomerType {
    LOYAL, NEW, DISSATISFIED;
    public String getValue() { return this.toString(); }
}</code>

<code>@Getter @Setter
public class OrderDiscount {
    private Integer discount = 0;
}</code>

Drools Rules (customer-discount.drl)

The DRL file contains four rules that adjust a global

orderDiscount

object.

<code>import com.alvin.drools.model.OrderRequest;
import com.alvin.drools.model.CustomerType;
global com.alvin.drools.model.OrderDiscount orderDiscount;

dialect "mvel"

// Rule 1: Age based discount
rule "Age based discount"
    when
        OrderRequest(age < 20 || age > 50)
    then
        System.out.println("==========Adding 10% discount for Kids/ senior customer=============");
        orderDiscount.setDiscount(orderDiscount.getDiscount() + 10);
end

// Rule 2: Loyal customer discount
rule "Customer type based discount - Loyal customer"
    when
        OrderRequest(customerType.getValue == "LOYAL")
    then
        System.out.println("==========Adding 5% discount for LOYAL customer=============");
        orderDiscount.setDiscount(orderDiscount.getDiscount() + 5);
end

// Rule 3: Other customer types
rule "Customer type based discount - others"
    when
        OrderRequest(customerType.getValue != "LOYAL")
    then
        System.out.println("==========Adding 3% discount for NEW or DISSATISFIED customer=============");
        orderDiscount.setDiscount(orderDiscount.getDiscount() + 3);
end

// Rule 4: Amount based discount
rule "Amount based discount"
    when
        OrderRequest(amount > 1000L)
    then
        System.out.println("==========Adding 5% discount for amount more than 1000$=============");
        orderDiscount.setDiscount(orderDiscount.getDiscount() + 5);
end</code>

Service Layer

The

OrderDiscountService

obtains a

KieSession

, injects the global discount object, inserts the request, fires all rules and returns the calculated discount.

<code>@Service
public class OrderDiscountService {

    @Autowired
    private KieContainer kieContainer;

    public OrderDiscount getDiscount(OrderRequest orderRequest) {
        OrderDiscount orderDiscount = new OrderDiscount();
        KieSession kieSession = kieContainer.newKieSession();
        kieSession.setGlobal("orderDiscount", orderDiscount);
        kieSession.insert(orderRequest);
        kieSession.fireAllRules();
        kieSession.dispose();
        return orderDiscount;
    }
}</code>

Controller

Expose a REST endpoint that receives an

OrderRequest

JSON and returns the calculated

OrderDiscount

.

<code>@RestController
public class OrderDiscountController {

    @Autowired
    private OrderDiscountService orderDiscountService;

    @PostMapping("/get-discount")
    public ResponseEntity<OrderDiscount> getDiscount(@RequestBody OrderRequest orderRequest) {
        OrderDiscount discount = orderDiscountService.getDiscount(orderRequest);
        return new ResponseEntity<>(discount, HttpStatus.OK);
    }
}</code>

Testing

Run the Spring Boot application and POST a JSON payload such as:

<code>{
  "customerNumber": "C001",
  "age": 18,
  "amount": 1200,
  "customerType": "LOYAL"
}</code>

For a loyal customer younger than 20 with an order amount greater than 1000, the rules above produce a total discount of 20 % (10 % age discount + 5 % loyal‑customer discount + 5 % amount discount).

Conclusion

By integrating Drools with Spring Boot we can externalise discount logic into a DRL file, allowing business users or product managers to add or modify rules—such as a new rule for customers in a specific city—without changing Java code, thus speeding up iteration and reducing deployment risk.