Striped Data Analysis Framework Article Swipe
O. Gutsche
,
I. V. Mandrichenko
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1051/epjconf/202024506042
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1051/epjconf/202024506042
A columnar data representation is known to be an efficient way for data storage, specifically in cases when the analysis is often done based only on a small fragment of the available data structures. A data representation like Apache Parquet is a step forward from a columnar representation, which splits data horizontally to allow for easy parallelization of data analysis. Based on the general idea of columnar data storage, working on the [LDRD Project], we have developed a striped data representation, which, we believe, is better suited to the needs of High Energy Physics data analysis. A traditional columnar approach allows for efficient data analysis of complex structures. While keeping all the benefits of columnar data representations, the striped mechanism goes further by enabling easy parallelization of computations without requiring special hardware. We will present an implementation and some performance characteristics of such a data representation mechanism using a distributed no-SQL database or a local file system, unified under the same API and data representation model. The representation is efficient and at the same time simple so that it allows for a common data model and APIs for wide range of underlying storage mechanisms such as distributed no-SQL databases and local file systems. Striped storage adopts Numpy arrays as its basic data representation format, which makes it easy and efficient to use in Python applications. The Striped Data Server is a web service, which allows to hide the server implementation details from the end user, easily exposes data to WAN users, and allows to utilize well known and developed data caching solutions to further increase data access efficiency. We are considering the Striped Data Server as the core of an enterprise scale data analysis platform for High Energy Physics and similar areas of data processing. We have been testing this architecture with a 2TB dataset from a CMS dark matter search and plan to expand it to multiple 100 TB or even PB scale. We will present the striped format, Striped Data Server architecture and performance test results.
Related Topics
Concepts
Computer science
Python (programming language)
Data structure
Representation (politics)
External Data Representation
Database
SQL
Computation
Data analysis
User-defined function
Data mining
Programming language
Information retrieval
Operating system
Search engine
Politics
Web search query
Query by Example
Law
Political science
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1051/epjconf/202024506042
- https://www.epj-conferences.org/articles/epjconf/pdf/2020/21/epjconf_chep2020_06042.pdf
- OA Status
- diamond
- Cited By
- 3
- References
- 2
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3101664573
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3101664573Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1051/epjconf/202024506042Digital Object Identifier
- Title
-
Striped Data Analysis FrameworkWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-01-01Full publication date if available
- Authors
-
O. Gutsche, I. V. MandrichenkoList of authors in order
- Landing page
-
https://doi.org/10.1051/epjconf/202024506042Publisher landing page
- PDF URL
-
https://www.epj-conferences.org/articles/epjconf/pdf/2020/21/epjconf_chep2020_06042.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
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https://www.epj-conferences.org/articles/epjconf/pdf/2020/21/epjconf_chep2020_06042.pdfDirect OA link when available
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Computer science, Python (programming language), Data structure, Representation (politics), External Data Representation, Database, SQL, Computation, Data analysis, User-defined function, Data mining, Programming language, Information retrieval, Operating system, Search engine, Politics, Web search query, Query by Example, Law, Political scienceTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
3Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2021: 2Per-year citation counts (last 5 years)
- References (count)
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2Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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