Prof. Dr. Tilmann Rabl


We try to keep an up to date list of all our publications. If you are interested in a PDF that we have not uploaded yet, feel free to send us an email to get a copy. All recent publications you will find below. For older, please click appropriate year.

Publications of the years 2020, 2019, 2018, 20172016, 2015, 2014, 2013, 2012, 2011, 2010, 2009, 2008, 2007

Analyzing Efficient Stream Processing on Modern Hardware

Zeuch, Steffen; Breß, Sebastian; Rabl, Tilmann; Monte, Bonaventura Del; Karimov, Jeyhun; Lutz, Clemens; Renz, Manuel; Traub, Jonas; Markl, Volker in PVLDB 2019 .

ModernStream Processing Engines(SPEs) process largedata volumes under tight latency constraints. Many SPEsexecute processing pipelines using message passing on shared-nothing architectures and apply a partition-basedscale-outstrategy to handle high-velocity input streams. Further-more, many state-of-the-art SPEs rely on a Java Virtual Ma-chine to achieve platform independence and speed up systemdevelopment by abstracting from the underlying hardware.In this paper, we show that taking the underlying hard-ware into account is essential to exploit modern hardwareefficiently. To this end, we conduct an extensive experimen-tal analysis of current SPEs and SPE design alternativesoptimized for modern hardware. Our analysis highlights po-tential bottlenecks and reveals that state-of-the-art SPEs arenot capable of fully exploiting current and emerging hard-ware trends, such as multi-core processors and high-speednetworks. Based on our analysis, we describe a set of designchanges to the common architecture of SPEs toscale-uponmodern hardware. We show that the single-node throughputcan be increased by up to two orders of magnitude comparedto state-of-the-art SPEs by applying specialized code genera-tion, fusing operators, batch-style parallelization strategies,and optimized windowing. This speedup allows for deploy-ing typical streaming applications on a single or a few nodesinstead of large clusters.
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Tagsmodernhardware  myown  streamprocessing  vldb