Serverless Streaming Graph Analytics

Streaming graph analytics is an emerging field of applications that aim to extract knowledge from evolving networks in a timely and efficient manner. Graph streams are (possibly unbounded) sequences of timestamped events that represent relationships between entities: user interactions in social networks, online financial transactions, product purchases, driver and user locations in ride-sharing services.

In this project, we will focus on graph streams that can be used to model distributed systems, where workers are represented as nodes connected with edges that denote communication or dependencies. In this model, monitoring and performance analysis can be expressed as graph streaming queries. For example, if the dynamic topology of an OpenShift cluster fleet is modeled as a graph, a streaming query can continuously detect disconnected regions. We will design a prototype open-source streaming graph analytics system on top of Apache Flink Stateful Functions and develop a temporal graph processing API for expressing continuous and ad-hoc queries on graph streams.

Principal Investigator: Vasia Kalavri

Watch: Temporal graph analytics on Apache Flink Stateful Functions from Research Interest Group Meeting, September 20, 2022

Accompanying slides