Instrumenting Python with Kieker

dc.contributor.author	Simonov, Serafim
dc.contributor.author	Düllmann, Thomas F.
dc.contributor.author	Jung, Reiner
dc.contributor.author	Gundlach, Sven
dc.contributor.editor	Herrmann, Andrea
dc.date.accessioned	2024-02-22T10:37:52Z
dc.date.available	2024-02-22T10:37:52Z
dc.date.issued	2023
dc.description.abstract	Python has become a widely used programming language in big data, machine learning, and scientific modeling. In all these domains, performance is a key factor to success and requires the ability to understand the runtime behavior of software. Therefore, we ported Kieker monitoring to Python and evaluated different approaches to introduce probes into code. In this paper, we evaluate these approaches, show their benefits and limitations and provide a perfor mance evaluation of the Kieker 4 Python framework.	en
dc.identifier.issn	0720-8928
dc.identifier.uri	https://dl.gi.de/handle/20.500.12116/43639
dc.language.iso	en
dc.pubPlace	Bonn
dc.publisher	Gesellschaft für Informatik e.V.
dc.relation.ispartof	Softwaretechnik-Trends Band 43, Heft 1
dc.relation.ispartofseries	Softwaretechnik-Trends
dc.subject	Python
dc.subject	performance
dc.subject	Kieker
dc.subject	monitoring
dc.title	Instrumenting Python with Kieker	en
dc.type	Text/Conference Paper
mci.conference.date	7.-9.11.2022
mci.conference.location	Stuttgart
mci.conference.sessiontitle	13th Symposium on Software Performance (SSP)
mci.reference.pages	26-28

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