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Post-Debugging in Large Scale Big Data Analytic Systems

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dc.contributor.authorBergen, Eduard
dc.contributor.authorEdlich, Stefan
dc.contributor.editorMitschang, Bernhard
dc.contributor.editorNicklas, Daniela
dc.contributor.editorLeymann, Frank
dc.contributor.editorSchöning, Harald
dc.contributor.editorHerschel, Melanie
dc.contributor.editorTeubner, Jens
dc.contributor.editorHärder, Theo
dc.contributor.editorKopp, Oliver
dc.contributor.editorWieland, Matthias
dc.date.accessioned2017-06-21T11:24:45Z
dc.date.available2017-06-21T11:24:45Z
dc.date.issued2017
dc.description.abstractData scientists often need to fine tune and resubmit their jobs when processing a large quantity of data in big clusters because of a failed behavior of currently executed jobs. Consequently, data scientists also need to filter, combine, and correlate large data sets. Hence, debugging a job locally helps data scientists to figure out the root cause and increases efficiency while simplifying the working process. Discovering the root cause of failures in distributed systems involve a different kind of information such as the operating system type, executed system applications, the execution state, and environment variables. In general, log files contain this type of information in a cryptic and large structure. Data scientists need to analyze all related log files to get more insights about the failure and this is cumbersome and slow. Another possibility is to use our reference architecture. We extract remote data and replay the extraction on the developer’s local debugging environment.en
dc.identifier.isbn978-3-88579-660-2
dc.identifier.pissn1617-5468
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofDatenbanksysteme für Business, Technologie und Web (BTW 2017) - Workshopband
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-266
dc.subjectSoftware debugging
dc.subjectBug detection
dc.subjectlocalization and diagnosis
dc.subjectJava Virtual Machine
dc.subjectJVMTI
dc.subjectBytecode instrumentation
dc.subjectApache Flink
dc.subjectApplication-level failures
dc.titlePost-Debugging in Large Scale Big Data Analytic Systemsen
dc.typeText/Conference Paper
gi.citation.endPage74
gi.citation.publisherPlaceBonn
gi.citation.startPage65
gi.conference.date6.-10. März 2017
gi.conference.locationStuttgart
gi.conference.sessiontitleWorkshop Big Data Management Systems in Business and Industrial Applications (BigBIA17)

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P266 - BTW2017 - Datenbanksysteme für Business, Technologie und Web - Workshopband