Using Adobe’s CEP platform, this tutorial shows how to set up a Photoshop extension, employ the Action Manager API to extract text layer properties such as color, font size, and shadow, and then recreate the design in HTML/CSS, streamlining marketing asset creation.
This article explains how Alibaba leverages Flink to handle over 40 billion events per second across all business units, detailing risk‑control concepts, rule types, architectural stages, resource tuning, dynamic CEP, shared computing, and the FY23 roadmap for large‑scale streaming risk management.
This article provides an extensive technical guide to Apache Flink, covering its exactly‑once consumption guarantees, checkpoint and two‑phase commit mechanisms, differences from Spark, state backends, watermark handling, time semantics, window joins, CEP, backpressure, architecture layers, deployment, resource management, and common operational issues.
This article explains the origins, concepts, and use‑cases of Event Stream Processing (ESP) and Complex Event Processing (CEP), contrasting their handling of ordered event streams versus unordered event clouds, and discusses how both technologies have evolved and are applied in modern real‑time analytics.
The Omega real‑time reach system uses a custom DSL that unifies input data, standardizes CEP APIs, and leverages an Antlr V4‑based translation framework to generate Python, C++, JavaScript, and Java code for cloud, edge, and front‑end containers, cutting rule development from days to minutes.
This article explains how Flink's Complex Event Processing (CEP) library implements pattern matching using a nondeterministic finite automaton with matching caches (NFAb), covering its theoretical foundation, construction, state transition semantics, event selection strategies, shared versioned match buffers, and computation state details.
This article introduces Complex Event Processing (CEP), explains its core concepts and features, details Flink's Pattern API with individual, combined, and group patterns, and provides a complete Scala example that detects three consecutive login failures within three seconds using Flink CEP.
The article presents a pure front‑end Complex Event Processing framework that captures user actions, uses an NFA‑based engine to evaluate dynamic strategies, synchronizes data across pages via local storage, and delivers millisecond‑level real‑time user reach on Mini‑program, Web, and Weex platforms.
The article describes how Xianyu’s recommendation system shifts complex event processing from server‑side Blink to client‑side Python using Flink CEP concepts, detailing the NFA‑based state and transition model, pattern‑building API, aggregation support, achieving sub‑second execution with modest memory, and outlines future optimizations such as NFA persistence, windowing, DSL script generation, and C++/TensorFlow Lite acceleration.
The Xianyu team built a data‑driven system that monitors seller online status and reply speed, uses Siddhi CEP to match behavior patterns, and orchestrates activities, tasks, and synchronization modules, boosting conversion by three percentage points and allowing new scenarios to launch without developer effort.
Xianyu’s Complex Event Processing system, built on Alibaba’s Blink (Flink) and a custom SQL‑like DSL, standardizes event I/O, lets users define sequence, window and aggregation rules, and combines an interactive rule service, SLS source, parser, job manager and MetaQ sink to achieve ~100 k QPS, sub‑second latency, fault‑tolerant, and rule‑to‑production turnaround in about thirty minutes.
This article explains how Flink CEP, a complex event processing library for Apache Flink, is employed at Haolo Mobility to detect intricate patterns in endless data streams by modeling patterns as states and using pattern conditions for state transitions, illustrating its practical application in real‑world big‑data scenarios.
This article provides a detailed introduction to FlinkCEP, covering how to add the library, define simple and composite patterns, use quantifiers and conditions, handle skip strategies, time constraints, and select results, with complete Java and Scala code examples for complex event processing.
This article outlines Didi's journey from fragmented, self‑built real‑time clusters to a unified, YARN‑managed platform that leverages Spark Streaming, Flink, and StreamSQL, detailing architectural choices, resource isolation, CEP enhancements, and the resulting impact on latency‑critical services.
From early self‑built Storm and Spark Streaming clusters to a unified YARN‑based Spark platform and finally a low‑latency Flink system with extended CEP and StreamSQL capabilities, Didi’s real‑time computing platform evolved through three stages, delivering multi‑tenant isolation, rich SQL processing, and dramatically reduced development costs.
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.
Transwarp Slipstream’s Complex Event Processing (CEP) engine enables developers to define and detect sophisticated event patterns using SQL, offering detailed syntax, configuration steps, and practical use cases such as bank fraud detection and traffic monitoring, thereby enhancing real‑time analytics in big‑data environments.
This article explains how GF Securities built a customized Lambda/Kappa‑style big‑data platform that integrates CEP, Spark, Flink and Kafka to deliver low‑latency stock price alerts, real‑time news, and capital‑flow trading strategies for the finance industry.
This article outlines the design of a payment risk control system, detailing functional and non‑functional requirements, core components such as real‑time, near‑real‑time, and batch engines, rule and penalty centers, and explains the role of CEP and Drools in achieving flexible, high‑performance fraud detection.
The article explains the functional and non‑functional requirements, common pitfalls, and detailed architecture—including real‑time, near‑real‑time, and batch engines, rule and penalty centers, and CEP technology—of a payment risk control system aimed at detecting and mitigating fraud while maintaining performance and flexibility.
