Scalable Online First-Order Monitoring

Joshua Schneider, David Basin, Frederik Brix, Srđan Krstić, Dmitriy Traytel

Abstract

Online monitoring is the task of identifying complex temporal patterns while incrementally processing streams of events. Existing state-of-the-art monitors can process streams of modest velocity in real-time: a few thousands events per second. We scale up monitoring to higher velocities by slicing the stream, based on the events' data values, into substreams that can be independently monitored. Because monitoring is not data parallel in general, slicing can lead to data duplication. To reduce this overhead, we adapt hash-based partitioning techniques from databases to the monitoring setting. We implement the resulting automatic data slicer in Apache Flink and use the MonPoly tool to monitor the substreams. We empirically evaluate this setup, demonstrating a substantial scalability improvement.

Paper draft