This article explains how JD Supply Chain’s self‑built low‑code rule‑engine platform separates business logic from code, supports ultra‑high concurrency, enables non‑technical users to configure rules, and reduces a typical 10‑day delivery cycle to as fast as 0.5 day across dozens of core projects.
This article walks through the background, features, installation, core concepts, and practical code examples of the URule rule engine, showing how to configure variable, constant, parameter, and action libraries, create decision tables and rule sets, and integrate the engine with a Spring Boot application.
This article describes JD Supply Chain's self‑developed low‑code rule engine platform, outlining its motivation, high‑concurrency performance challenges, value‑centric design, visual rule‑authoring workflow, SDK integration, and the resulting efficiency gains for both development and business teams.
This article introduces the URule rule engine, explains how to install and configure its open‑source version with Spring Boot, describes its core concepts such as variable, constant, parameter, and action libraries, and demonstrates rule set and decision table creation for real‑world business scenarios.
This article explains how Tencent builds a trillion‑scale real‑time signal system for its content ecosystem, covering signal applications, data source and processing challenges, a layered architecture with Flink‑based streaming, dynamic topic detection, high‑throughput ID mapping, large‑window calculations, rule‑engine triggering, and future roadmap for scalability and cost reduction.
Vivo’s Points Task System evolved from a simple configuration‑driven task model into a scalable, multi‑source behavior incentive platform that uses an AviatorScript engine, unified SDK, and three isolated services—event collection, computation, and task handling—to deliver configurable tasks, real‑time rewards, and flexible user notifications while ensuring stability and extensibility.
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.
The article describes an intelligent merchant‑side diagnostic platform that unifies ad‑operation data in a centralized lake, uses a low‑code rule engine with arithmetic, code, and Java class modes to orchestrate reusable SOPs, and employs an acceleration layer for fast large‑scale queries, achieving over 90% coverage and outlining future expansion.
This article describes JD Supply Chain's self‑developed low‑code rule engine platform, outlining its motivation, high‑concurrency challenges, architectural solutions, visual design capabilities, performance achievements, and how it enables both developers and business users to rapidly deliver complex business rules.
This article explains how a real‑time rule engine and performance‑optimized architecture are used to detect supply‑demand imbalances, enforce mutually exclusive adjustment rules, and efficiently scale capacity management for a nationwide logistics platform.
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 Baidu Tieba rule engine is a low‑code platform that lets non‑developers define, compile, and execute dynamic business rules through modular components, variable handling, JSON‑based rule trees, and customizable disposition handlers, supporting synchronous, asynchronous and parallel execution to reduce hard‑coded logic, deployment frequency, and maintenance effort.
This article examines the ZCube rule engine, detailing its architecture, visual rule modeling tools, DSL and ANTLR integration, RETE algorithm implementation, knowledge package deployment, AB testing, and runtime execution flow, illustrating how it enables flexible, high‑performance business rule management.
This article explains the concepts, design principles, and practical implementation steps of an open‑source decision engine—covering rule vs. decision flow, DSL creation with YAML, operator handling, workflow orchestration, and deployment considerations for production environments.
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.
Shopee’s scalable fund‑routing decision engine combines a configurable Go‑based rule engine with Redis‑backed, Lua‑driven quota control to dynamically match loan orders to external capital providers, enforce multi‑dimensional volume limits, achieve tens of thousands TPS, and provide extensible risk‑management capabilities across markets.
This fifth installment explains vivo’s real‑time marketing automation engine, detailing its business need, layered architecture (access, processing, output, management, warehouse), scalable event‑queue design, dynamic configuration, unified dispatch, Flink‑based metric enrichment, and rule‑engine integration to achieve low‑latency, high‑throughput personalized targeting.
The Promotion Platform 2.0 upgrade replaces the fragmented 1.0 system with a unified promotion model, low‑code DSL‑driven workflow, and QLExpress rule engine, enabling rapid creation of diverse promotional tools, consistent pricing calculations, and high‑performance caching while preserving stability for the ACG e‑commerce ecosystem.
HiMock is a comprehensive mock platform designed to overcome traditional mock limitations by offering low‑code, code‑decoupled, high‑performance, and full‑scenario support across multiple protocols and front‑end/back‑end channels, enabling efficient testing, performance benchmarking, and parallel development for complex services such as bike, e‑bike, and ride‑hailing.
The article details a practical, reusable enterprise architecture for data classification and grading that combines scanning tools, a rule‑engine with hot‑updates, a high‑performance identification service, and a security enforcement platform, addressing massive real‑time data volumes, diverse storage types, cross‑department isolation, and compliance with China’s data security laws.
Facing a tangled SMS routing system with many third‑party providers, I replaced cumbersome if‑else and serial queries with LiteFlow’s lightweight rule engine, using parallel checks, EL expressions, sub‑variables, and context‑based data flow to create a flexible, maintainable, and hot‑reloadable workflow, even for multithreaded tasks.
This article introduces LiteFlow, a lightweight, fast, and stable component‑based rule engine, explains its design principles, shows step‑by‑step usage in both non‑Spring and Spring Boot environments, and dives into the core source code that drives parsing, chain execution, and node processing.
This article explains the background, design principles, high‑concurrency handling, storage optimization, rule‑engine implementation, and intelligent scheduling needed to create a universal, stable, extensible, and intelligent recommendation slot that enriches short‑video app ecosystems.
The e‑commerce platform’s loss‑prevention architecture combines domain‑modeled scenario identification, pre‑emptive checks, automated testing, and a real‑time data‑reconciliation pipeline using Dcheck and rule factories to detect anomalies, trigger alerts, and execute emergency response plans, thereby minimizing financial risk and ensuring transaction stability.
This article presents a lightweight solution for dynamic business rule management by abstracting rules into variable‑condition‑result models, employing the QLExpress script engine for execution, and demonstrating how to bind Java methods, enabling flexible, maintainable backend logic without extensive if‑else code.
This article explains how to design a lightweight rule engine in Go by first using JSON‑defined rules, then leveraging Go's token scanner and AST parser to evaluate boolean expressions such as header and cookie checks, and finally discusses extending the engine with custom primitives and GoYACC for more complex grammars.
This article explains what a rule engine is, why it is useful, common products, core concepts such as rule files, LHS/RHS, facts, and various modeling patterns, as well as management, execution modes, real‑world use cases and best‑practice recommendations for building scalable backend rule‑processing systems.
This article analyzes the shortcomings of a button‑heavy rule‑expression editor for B2B customer‑service whitelists and proposes keyboard‑centric input, card‑based visual grouping, and drag‑and‑drop modularity to dramatically improve editing speed, readability, and maintenance efficiency.
At iTech Salon, iQIYI’s Peng Tao outlined a three‑layer data‑quality monitoring framework—pingback, middle, and business report layers—detailing anomaly‑detection techniques such as thresholds, statistical, correlation and Prophet forecasting, and announced future plans for intelligent rule generation and automated attribution to pinpoint root causes.
This article explains how to extend a trial‑user application rule by designing a flexible Java rule engine, detailing the logical conditions, abstract and concrete rule classes, a builder service supporting AND/OR composition, and discussing its advantages and drawbacks.
This article describes the background, requirements, architecture, script language selection, performance testing, and future plans of a rule engine used to send notifications to couriers in Dada Logistics, highlighting the trade‑offs among Groovy, MVEL, SpEL and Aviator.
The article details Vivo Mall’s new promotion pricing engine, which replaces duplicated site‑level logic with a layered model (product, shop, platform) that supports configurable stacking, processes discounts through three calculation stages, and uses a meta‑driven rule engine to flexibly interpret JSON promotion templates, ensuring extensible, stable pricing functionality.
This article explains the origins and concepts of rule engines, demonstrates practical JavaScript implementations, dives into the Rete pattern‑matching algorithm, and explores design considerations for visual rule editors and lightweight Node.js components.
This article walks through designing and implementing a Java rule engine that supports AND/OR relationships and short‑circuit evaluation, presents the core abstractions, shows concrete rule examples, and evaluates the pros and cons of the approach.
This article walks through a real‑world Java rule‑engine implementation, covering the business requirements, abstract rule design, executor construction, sample code, and a balanced discussion of its advantages and drawbacks for backend developers.
This article describes the design, short‑circuit execution logic, and refactoring of a backend rule engine that evaluates user application criteria using AND/OR relationships, outlines its implementation steps, and discusses its advantages and drawbacks for maintainability.
This article walks through a real‑world scenario of extending trial‑user eligibility rules, explains the logical flow using AND/OR short‑circuiting, and presents a modular Java rule engine design with abstract templates, concrete rules, a service builder, and a test case, highlighting its advantages and drawbacks.
In 2020 I reflected on my role as a front‑end engineer working with a mixed‑tech team on Alibaba's Xianyu content creator business, detailing how I built the team, set goals, made data‑driven decisions, and delivered front‑end innovations such as a rule engine, Flutter expansion, backend tooling, and container strategy.
This article walks through the design and implementation of a Java rule engine that supports both AND and OR logical relationships, demonstrates short‑circuit evaluation, and shows how to structure rules, a service, and test code for maintainable, extensible business logic.
This article explains how to design and implement a rule engine in Java using abstract base rules, template methods, and logical connectors (AND, OR, NOT), providing code examples, execution flow, and a discussion of its advantages and drawbacks.
The Optimus platform centralizes Xianyu feed tag configuration into a three‑layer system of tags, scenes, and experiments, providing a console for AB testing, time‑ and version‑based targeting, fast HSF/Diamond client integration, parallel rule‑based data fetching, cutting latency from 120 ms to 15 ms and enabling rapid tag updates that boosted pCTR and pCVR, with plans to broaden coverage and add smarter personalization.
The paper presents a configurable real‑time rule engine for live‑streaming product audits that decouples data aggregation from rule execution, uses QLExpress for dynamic conditions, supports Dubbo and HTTP sources, and enables safe gray‑release updates, cutting the rule‑change cycle from weeks to near‑real‑time.
This article describes the background, metadata concepts, SPU and category‑attribute modeling, MySQL tree‑storage techniques, dynamic business configuration, rule‑engine and workflow‑engine solutions adopted in Tripyun‑Ctrip Cloud to build a reusable, scalable middle‑platform for the tourism industry.
Xianyu’s product‑information structuring has progressed from simple text mining to multimodal AI pipelines that now boost coverage by nearly 50 %, while facing precision and engineering hurdles, and it plans to adopt a standardized VID attribute system, plug‑in multimodal models, and rule‑based input assistance to enable seamless, photo‑driven publishing.
This article explains how to replace repetitive if‑else rule checks in PHP with a generic rule module using the Strategy design pattern and reflection, providing step‑by‑step code examples, architectural diagrams, and a clear path to extend rules without modifying client code.
This article examines Meituan's evolution from hard‑coded validation to a custom rule‑engine framework, detailing case studies of store information checks, audit workflows, and performance metric calculations, and explains the design of the Maze framework that separates business logic from system code while improving maintainability and scalability.
This article examines Meituan's custom rule engine Zeus, detailing the security challenges of a massive multi‑service platform, the architectural decisions made to decouple risk logic, the implementation of reusable factors and rule groups, and the ongoing push toward automated, intelligent risk mitigation.
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.
To accelerate growth on Xianyu’s 20 million‑DAU platform, the team built an event‑driven rule engine with a SQL‑like DSL that translates user‑behavior streams into real‑time Flink/Blink queries, cutting rule development from four days to half a day and achieving sub‑5‑second processing latency.
This article examines the problems of deeply nested if‑else statements in Java code and demonstrates how to replace them with cleaner alternatives such as the Factory pattern, enums, the Command pattern, and a simple rule engine, improving readability and maintainability.
The article outlines the design of a prepaid‑card marketing platform that uses configurable rule templates, condition‑behavior chains, and rule orders to manage membership recharge rewards and gift‑card usage limits, providing a generic core engine with adapters for merchants while supporting future migration to payment systems.
Xianyu’s Mach system tackles the e‑commerce challenge of instantly selecting high‑quality items from billions of products by leveraging Blink’s low‑latency stream computing, detailing its architecture—including state, windows, custom UDX functions, data merging, rule execution, and SQL‑to‑MVEL conversion—to achieve sub‑second processing at massive scale.
Mahé, Xianyu’s real‑time product selection platform, uses Alibaba’s Blink stream engine to merge, evaluate roughly 300 rule‑based filters per item and emit only changed results, processing 1.4 billion daily messages at up to 50 k TPS through a four‑layer, stateful architecture.
The article explains how G7's Financial Strategy Center unifies risk‑control rules into a strategy platform, detailing its benefits, underlying architecture, rule‑engine processing steps, and the supporting indicator service that together streamline development, deployment, and management of complex financial risk policies.
The article translates and explains a typical IoT platform architecture, outlining its core Gather‑Analyze‑Act functions, common building blocks such as device interfaces, messaging brokers, storage and analytics layers, and highlights key differentiators like multi‑tenancy, protocol support, security, and extensible rule engines.
This article explains how a lightweight, Groovy‑based rule engine can decouple complex ticketing business logic from core systems, outlines its architecture, implementation details, performance optimizations, and provides code examples and benchmark results.
This article explains how to design and implement a lightweight rule engine using condition‑based rules and a simple database schema, illustrated with a medical prescription recommendation example, and includes Java code that evaluates logical expressions to determine when to display results.
The article describes Meituan’s hotel real‑time reach platform, which replaces numerous hard‑coded Storm topologies with a unified Storm‑Aviator rule engine supporting time‑window and delayed triggers, offering configurable scenes, custom functions, monitoring, and alerting, and now processes nearly a billion daily events with improved conversion and scalability.
The article presents Hermes, Meituan’s O2O performance management platform for travel, detailing its six‑module, four‑engine architecture—including data, incentive, rule, calculation, and scheduling engines—while highlighting technical innovations such as two‑level caching, work‑stealing producer‑consumer processing, Map‑Reduce‑style calculations, and future AI‑driven enhancements.
This article examines the challenges of rule‑driven development at Meituan, compares hard‑coded, Drools‑based, and custom rule‑engine solutions, and presents the design of the Maze framework that separates decision logic, supports hot‑deployment, and improves maintainability and performance.
This article explains the architecture and key technologies of 360’s SSP advertising engine—including flexible micro‑service layers, DAG‑based topology, rule and template management, and high‑performance Go‑based HTTP frameworks—that enable billion‑scale ad delivery with low latency and high concurrency.
ZEUS, Suning’s decision analysis platform, integrates monitoring data from tools like Baymax and HIRO, applies CEP aggregation and Drools rule evaluation, and leverages big‑data storage and machine‑learning models to automatically identify root causes, provide real‑time alerts, and enable self‑healing in large‑scale distributed systems.
This article examines the rise of organized “wool‑pulling” fraud groups during China’s Double‑11 shopping festival, outlines their tools and tactics, and details NetEase Cloud Security’s multi‑layered anti‑fraud system—including captcha, SMS verification, IP rules, device fingerprinting, rule engines, user profiling, network graph analysis, and blacklist strategies—to protect e‑commerce platforms.
This guide explains the core Drools KIE components—including KieServices, KieContainer, KieBase, KieSession, KieRepository, KieProject, and ClasspathKieProject—detailing their roles, relationships, configuration attributes in kmodule.xml, and how they enable rule construction, management, and execution in Java applications.
This tutorial walks through setting up Drools 7, configuring Maven dependencies, kmodule.xml, and writing a simple Hello World rule test that demonstrates product discount calculation using the new KIE API.
The article describes how Ctrip’s IM+ platform integrates voice calls and instant messaging into a unified, multi‑channel customer service solution, detailing its background, current limitations, hybrid architecture, distributed intelligent routing, rule‑engine based allocation, and unified session management to improve efficiency and user experience.
The article describes how Meituan‑Dianping built the Maze rule‑engine framework—comprising MazeGO for policy parsing and MazeQL for data queries—to replace hard‑coded and Drools‑based solutions, delivering hot‑deployed, versioned, pre‑compiled rules that lower maintenance cost, boost developer productivity, and enable non‑technical analysts to manage complex business logic efficiently.
This article details the challenges of large‑scale database operations, the selection and design of a custom audit platform, its architecture, data collection, rule parsing, implementation, results, and lessons learned, while sharing the open‑source repository for the system.
This article presents a comprehensive overview of Ctrip's Aegis risk control system, detailing its modular architecture, rule engine, data service layer, Chloro analytics platform, and future directions, while highlighting the use of streaming, big‑data processing, and machine‑learning models for real‑time fraud detection.
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.
