Detecting connections as they form: An introduction to streaming graph pattern matching

Cyril Scetbon introduces the concept of streaming graph pattern matching using SurrealDB, which integrates document, graph, relational, and vector models into a single engine, to incrementally detect meaningful subgraphs as they form in real-time without re-scanning the entire graph. The article delves into the use of labeled property graphs and SurrealDB's capabilities to model data, specifically focusing on creating a tool controlled by a YAML configuration file to transform raw data into graph elements via VRL (Vector Remap Language). The configuration allows for the specification of sources and patterns where sources define how raw data becomes graph elements and patterns describe the subgraphs of interest. Through a movie database example, Scetbon illustrates how to detect patterns such as a person acting in and directing the same movie, using a declarative DSL to define these patterns and automatically create derived edges when patterns are matched. The article emphasizes the benefits of a streaming approach where matches are detected and results are output immediately as data is processed, highlighting a design intent where users define desired graph shapes rather than writing queries, making it ideal for applications like fraud detection, recommendation engines, and event correlation.

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