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Benchmarking Stream Processing Frameworks for Large Scale Data Shuffling

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dc.contributor.authorHenning, Sören
dc.contributor.authorVogel, Adriano
dc.contributor.authorLeichtfried, Michael
dc.contributor.authorErtl, Otmar
dc.contributor.authorRabiser, Rick
dc.contributor.editorHerrmann, Andrea
dc.date.accessioned2023-11-30T08:32:48Z
dc.date.available2023-11-30T08:32:48Z
dc.date.issued2023
dc.description.abstractDistributed stream processing frameworks help building scalable and reliable applications that perform transformations and aggregations on continuous data streams. We outline our ongoing research on designing a new benchmark for distributed stream processing frameworks. In contrast to other benchmarks, it focuses on use cases where stream processing frameworks are mainly used for redistributing data records to perform state-local aggregations, while the actual aggregation logic is considered as black-box software components. We describe our benchmark architecture based on a real-world use case, show how we imple mented it with four state-of-the-art frameworks, and give an overview of initial experimental results.en
dc.identifier.issn0720-8928
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/43247
dc.language.isoen
dc.pubPlaceBonn
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofSoftwaretechnik-Trends Band 43, Heft 4
dc.relation.ispartofseriesSoftwaretechnik-Trends
dc.subjectSPP
dc.subjectdata stream
dc.subjectbenchmark
dc.subjectperformance
dc.subjectexperiment
dc.titleBenchmarking Stream Processing Frameworks for Large Scale Data Shufflingen
dc.typeText/Conference Paper
mci.conference.date6-8 November 2023
mci.conference.locationKarlsruhe, Germany
mci.conference.sessiontitle14th Symposium on Software Performance 2023
mci.reference.pages14-16

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Softwaretechnik-Trends 43(4) - 2023