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This guide shows how to integrate the Drools rule engine into a Spring Boot application to calculate e‑commerce discounts dynamically, covering Maven dependencies, configuration, model definitions, DRL rule creation, service and controller layers, and a simple test scenario demonstrating rule‑driven discount computation.
This tutorial explains how to integrate the Drools business rule engine into a Spring Boot project to calculate flexible e‑commerce discounts based on customer type, age, and order amount, showing dependency setup, configuration, model definitions, DRL rules, service and controller layers, and a test run.
This article provides an in‑depth technical comparison between the mature Drools rule engine and the newer LiteFlow framework, covering definitions, expression syntax, Java integration, API usage, coupling, learning curve, tooling, storage, hot‑reloading, UI support, performance benchmarks, and overall suitability for complex business logic.
This article describes the progressive evolution of a consumer‑finance risk decision engine—from an initial rule‑set implementation, through a Drools‑based configuration, to a fully self‑developed micro‑service engine—detailing architectural changes, component designs, execution flow, operational challenges, and solutions such as empty‑run testing.
The article explains how Drools, a business rule management system using the Rete algorithm, separates decision logic from code by generating DRL files from decision‑table data, enabling non‑technical users to manage collection‑system rules that assign overdue cases to queues, while noting current limitations and future upgrades.
This article explains the concepts of DMN and Drools, why Drools is chosen, and provides step‑by‑step instructions—including online editing with Kogito and Maven‑free deployment techniques—to run DMN rules in Java projects without relying on a Kie Server.
This article describes a real-time log processing platform that integrates Kafka, Flink, Drools rule engine, Redis, and Elasticsearch to unify heterogeneous log formats, extract business metrics, and provide configurable, dynamic data processing for large‑scale logging scenarios.
The article analyzes the difficulties of testing a large‑scale transaction middle platform—such as data diversity, volume, consistency, and lack of standards—and presents a rule‑driven, layered automation framework using Drools, jOOQ, and data‑objectification to dramatically improve testing efficiency and reliability.
This article presents a modular architecture for real‑time ETL log analysis that combines Flume for log collection, Kafka as a buffering layer, Storm for stream processing, Drools for rule‑based data transformation, and Redis for fast storage, detailing installation, configuration, and code integration steps.
This article details the design, installation, and modular integration of Flume, Kafka, Storm, Drools, and Redis to build a real‑time log analysis pipeline for ETL systems, discussing architecture, configuration, code examples, and practical considerations for scalability and fault tolerance.
This article presents a modular architecture for real‑time ETL log collection and analysis, detailing the installation and configuration of Flume, Kafka, Storm, Drools, and Redis, and explains how their integration improves fault tolerance, scalability, and processing speed.
This article describes the design, implementation, and practical application of a Drools-based rule engine for automating sales performance calculations, detailing the background challenges, rule definitions, data processing steps, SpringBoot integration, and code examples to achieve flexible, maintainable, and secure business logic separation.
Xiaomi senior R&D engineer Deng Wenjun shares the evolution of Xiaomi's internet‑finance risk‑control system, describing early rule‑based limits, the adoption of Drools for fast rule deployment, data‑driven modeling with random‑forest classifiers, and ongoing challenges in scalability, latency, and privacy.
The article details Xiaomi's evolution of internet‑finance risk control—from early limit and frequency rules that cut bad‑debt by a third, through adopting the Drools rules engine for rapid deployment and gray‑release, to leveraging random‑forest machine‑learning models and extensive user profiling that reduced fraud by roughly 40%, while addressing privacy and operational challenges.