Date: April 23, 2020
Time: 2:30-3:30pm ET


Network analytics spans a wide range of important tasks and network researchers have developed a broad range of tools to conduct network analytics. To date, however, existing work has focused on point-solutions targeted at particular tasks (e.g. network configuration verification) or protocols (e.g., BGP). This lack of generality forces researchers to repeatedly reinvent the wheel as new use cases arise which existing tools do not quite cover. We address this problem by observing that fundamental network state data drawn from network devices can be naturally stored in database tables. These tables, however, do not map directly into traditional database data models since their entries can contain “wildcard expressions”; can contain actions (e.g., logic), and have a priority ordering. We therefore extend the traditional relational model to the network by introducing (i) the flow relation, a novel representation of a traditional relation which can efficiently capture network data, and (ii) the flow algebra, a systematic extension of the relational algebra over flow relations. In doing so, we bring both the generality of the relational model and its bevy of optimizations from the literature to network analytics. We realize this model by building the FlowDB. FlowDB can execute a wide variety of real network analytics tasks practiced by Facebook operators on two Facebook data centers with 10K+ switches and over 100 million rules in 50-70 seconds. FlowDB outperforms state-of-the-art analytics tools by more than 50x.


Christopher Leet graduated from Yale in 2018 with distinction in Computer Science and distinction in Astrophysics, winning Yale’s Beckwith prize for Astronomy. His research focuses on highly-scalable network analytics and large-scale multi-robot coordination.