Algorithms for Windowed Aggregations and Joins on Distributed Stream Processing Systems
Juliane Verwiebe, Philipp M. Grulich, Jonas Traub, Volker Markl
Abstract
Abstract Window aggregations and windowed joins are central operators of modern real-time analytic workloads and significantly impact the performance of stream processing systems. This paper gives an overview of state-of-the-art research in this area conducted by the Berlin Institute for the Foundations of Learning and Data (BIFOLD) and the Technische Universität Berlin. To this end, we present different algorithms for efficiently processing windowed operators and discuss techniques for distributed stream processing. Recently, several approaches have leveraged modern hardware for windowed stream processing, which we will also include in this overview. Additionally, we describe the integration of windowed operators into various stream processing systems and diverse applications that use specialized window operations.