This article presents Alibaba Cloud's open‑source big‑data team's enhancements to Flink CEP, covering its core concepts, typical use cases, dynamic multi‑rule loading via FLIP‑200, extended CEP SQL syntax, performance optimizations, and real‑world risk‑control scenarios.
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.
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.
Slipstream 5.1 expands its real‑time stream processing capabilities with richer Complex Event Processing syntax, introduces Session Window support for session‑based analytics, and enhances the Morphling event‑driven engine, all accessible via SQL, making advanced streaming applications easier for both developers and business users.
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.
