Disco: Efficient Distributed Window Aggregation
Lawrence Benson (HPI, University of Potsdam), Philipp M. Grulich, Steffen Zeuch, Volker Markl, Tilmann Rabl (HPI, University of Potsdam)
Proceedings of the 23rd International Conference on Extending Database Technology (EDBT 2020) | March 2020


Many business applications benefit from fast analysis of online data streams. Modern stream processing engines (SPEs) provide complex window types and user-defined aggregation functions to analyze streams. While SPEs run in central data centers, wireless sensors networks (WSNs) perform distributed aggregations close to the data sources, which is beneficial especially in modern IoT setups. However, WSNs support only basic aggregations and windows. To bridge the gap between complex central aggregations and simple distributed analysis, we propose Disco, a distributed complex window aggregation approach. Disco processes complex window types on multiple independent nodes while efficiently aggregating incoming data streams. Our evaluation shows that Disco’s throughput scales linearly with the number of nodes and that Disco already outperforms a centralized solution in a two-node setup. Furthermore, Disco reduces the network cost significantly compared to the centralized approach. Disco’s treelike topology handles thousands of nodes per level and scales to support future data-intensive streaming applications.