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Grizzly: Efficient Stream Processing Through Adaptive Query Compilation
Citation key GrulichBZTBCRM20
Author Philipp M. Grulich, Sebastian BreƟ, Steffen Zeuch, Jonas Traub, Janis von Bleichert, Zongxiong Chen, Tilmann Rabl, Volker Markl
Year 2020
Journal SIGMOD
Note A recording of the presentation is available here: https://www.youtube.com/watch?v=5ENRFANTHUA

Presentation slide are available here: https://www.redaktion.tu-berlin.de/fileadmin/fg131/Conferences/Presentations/Grulich_Sigmod-2020_Grizzly.pdf
Abstract Stream Processing Engines (SPEs) execute long-running queries on unbounded data streams. They rely on managed runtimes, an interpretation-based processing model, and do not perform runtime optimizations. Recent research states that this limits the utilization of modern hardware and neglects changing data characteristics at runtime. In this paper, we present Grizzly, a novel adaptive query compilation-based SPE to enable highly efficient query execution on modern hardware. We extend query-compilation and task-based parallelization for the unique requirements of stream processing and apply adaptive compilation to enable runtime re-optimizations. The combination of light-weight statistic gathering with just-in-time compilation enables Grizzly to dynamically adjust to changing data-characteristics at runtime. Our experiments show that Grizzly achieves up to an order of magnitude higher throughput and lower latency compared to state-of-the-art interpretation-based SPEs.
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