A System for Fast, Accurate, and Intuitive Querying of Large-Scale BGP Datasets

Abstract

BGP data collected from operational routers by platforms such as RIPE RIS or RouteViews is essential to address connectivity issues and analyze the global Internet's structure. However, the vast and ever-growing volume of this data makes deriving useful insights challenging: Existing APIs and dashboards offer limited perspectives, while analyzing large archives of MRT files is slow and cumbersome. We present ChatBGP, a domain-specific chatbot that turns plain-English questions about BGP data into Python code that runs swiftly and returns accurate answers. ChatBGP is enabled by four contributions: (i) a measurement study showing that BGP data is highly redundant and therefore highly compressible; (ii) a database scheme that exploits this redundancy to compress BGP data while enabling fast retrieval of specific data elements; (iii) an expressive API that exposes this database through a simple interface; and (iv) a prompt-engineering scheme that guides ChatGPT to synthesize optimized Python code that uses this API to accurately answer user queries, while retrieving only the data needed to answer each query. With ChatBGP, gaining insights into BGP data becomes effortless and significantly faster—e.g., delivering results in seconds instead of hours with existing tools. Network operators can rapidly diagnose connectivity issues, including security threats, researchers can analyze larger datasets while improving reproducibility, and students can engage with BGP monitoring in a more intuitive and interactive way.