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Auflistung nach Schlagwort "Data Stream Processing"

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  • Textdokument
    On-the-fly Reconfiguration of Query Plans for Stateful Stream Processing Engines
    (BTW 2019, 2019) Bartnik, Adrian; Del Monte, Bonaventura; Rabl, Tilmann; Markl, Volker
    Stream Processing Engines (SPEs) must tolerate the dynamic nature of unbounded data streams and provide means to quickly adapt to fluctuations in the data rate. Many major SPEs however provide very little functionality to adjust the execution of a potentially infinite streaming query at runtime. Each modification requires a complete query restart, which involves an expensive redistribution of the state of a query and may require external systems in order to guarantee correct processing semantics. This results in significant downtime, which increase the operational cost of those SPEs. We present a modification protocol that enables modifying specific operators as well as the data flow of a running query while ensuring exactly-once processing semantics. We provide an implementation for Apache Flink, which enables stateful operator migration across machines, the introduction of new operators into a running query, and changes to a specific operator based on external triggers. Our results on two benchmarks show that migrating operators for queries with small state is as fast as using the savepoint mechanism of Flink. Migrating operators in the presence of large state even outperforms the savepoint mechanism by a factor of more than 2.3. Introducing and replacing operators at runtime is performed in less than 10 s. Our modification protocol demonstrates the general feasibility of runtime modifications and opens the door for many other modification use cases, such as online algorithm tweaking and up-or downscaling operator instances.
  • Zeitschriftenartikel
    Query Optimization in Heterogenous Event Processing Federations
    (Datenbank-Spektrum: Vol. 15, No. 3, 2015) Pinnecke, Marcus; Hoßbach, Bastian
    Continuous processing of event streams evolved to an important class of data management over the last years and will become even more important due to novel applications such as the Internet of Things. Because systems for data stream and event processing have been developed independent of each other, often in competition and without the existence of any standards, the Stream Processing System (SPS) landscape is extremely heterogeneous today. To overcome the problems caused by this heterogeneity, a novel event processing middleware, the Java Event Processing Connectivity (JEPC), has been presented recently. However, despite the fact that SPSs can be accessed uniformly using JEPC, their different performance profiles caused by different algorithms and implementations remain. This gives the opportunity to query optimization, because individual system strengths can be exploited. In this paper, we present a novel query optimizer that exploits the technical heterogeneity in a federation of different unified SPSs. Taking into account different performance profiles of SPSs, we address query plan partitioning, candidate selection, and reducing inter-system communication in order to improve the overall query performance. We suggest a heuristic that finds a good initial mapping of sub-plans to a set of heterogenous SPSs. An experimental evaluation clearly shows that heterogeneous federations outperform homogeneous federations, in general, and that our heuristic performs well in practice.