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Apache Arrow

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Apache Arrow

Basisdaten

Entwickler Wes McKinney, Antoine Pitrou, Sutou Kouhei, Matt Topol[1]
Erscheinungsjahr 17. Februar 2016[2]
Aktuelle Version 22.0.0[3]
(24. Oktober 2025)
Lizenz Apache-Lizenz, Version 2.0
arrow.apache.org

Apache Arrow is a language-agnostic software framework for developing applications that efficiently load and consume in-memory columnar data in a standardized manner. It also specifies a standard memory format that represents flat and hierarchical data in an optimised columnar manner for efficient analytic operations on modern CPU and GPU hardware.[4][5][6][7][8] This reduces or eliminates factors that limit the feasibility of working with large sets of data, such as the cost, volatility, or physical constraints of dynamic random-access memory.[9]

Interoperability

Arrow can be used with Apache Parquet, Apache Spark, NumPy, PySpark, pandas and other data processing libraries. The project provides an open source software library written in C++ with bindings for many other programming languages, e.g. Python and Java. Arrow allows for zero-copy reads and fast data access and interchange without serialisation overhead between these languages and systems.[4]

Applications

Arrow has been used in diverse domains, including analytics,[10] genomics,[11][9] and cloud computing.[12]

Comparison to Apache Parquet and ORC

Apache Parquet and Apache ORC are popular examples of on-disk columnar data formats. Arrow is designed as a complement to these formats for processing data in-memory.[13] The hardware resource engineering trade-offs for in-memory processing vary from those associated with on-disk storage.[14] The Arrow and Parquet projects includes libraries that allow for reading and writing data between the two formats.[15]

Reception

Daniel Abadi, Darnell-Kanal Professor of Computer Science at the University of Maryland[16] and a pioneer of column-oriented databases,[17] reviewed Apache Arrow in March 2018.[18] "The time is right for database systems architects to agree on and adhere to a main memory data representation standard," he concluded. "[If your] workloads are typically scanning through a few attributes of many entities, I do not see any reason not to embrace the Arrow standard."

Governance

Arrow was announced by Cloudera[19] and donated to the Apache Software Foundation[20] in 2016, where it has been maintained and extended since.[20][21][8][22][23] In October 2019, the Apache Arrow team announced that it plans to split the Arrow format and library versioning starting with the planned v1.0 release.[24]

References

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  1. ↑ github.com.
  2. ↑ Origin and History of Apache Arrow. (abgerufen am 16. November 2025).
  3. ↑ Release 22.0.0. 24. Oktober 2025 (abgerufen am 11. November 2025).
  4. ↑ a b Apache Arrow and Distributed Compute with Kubernetes. 13. Dezember 2018;.
  5. ↑ Tony Baer: Apache Arrow: Lining Up The Ducks In A Row... Or Column. In: Seeking Alpha. 17. Februar 2016;.
  6. ↑ Tony Baer: Apache Arrow: The little data accelerator that could. In: ZDNet. 25. Februar 2019;.
  7. ↑ Susan Hall: Apache Arrow's Columnar Layouts of Data Could Accelerate Hadoop, Spark. In: The New Stack. 23. Februar 2016;.
  8. ↑ a b Serdar Yegulalp: Apache Arrow aims to speed access to big data. In: InfoWorld. 27. Februar 2016;.
  9. ↑ a b Tanveer Ahmad: ArrowSAM: In-Memory Genomics Data Processing through Apache Arrow Framework. In: bioRxiv. 2019, doi:10.1101/741843v1 (biorxiv.org).
  10. ↑ Dinsmore T.W.: In-Memory Analytics. In: Disruptive Analytics. Apress, Berkeley, CA, 2016, ISBN 978-1-4842-1312-4, In-Memory Analytics, S. 97–116, doi:10.1007/978-1-4842-1311-7_5.
  11. ↑ Versaci F, Pireddu L, Zanetti G: Scalable genomics: from raw data to aligned reads on Apache YARN. In: IEEE International Conference on Big Data. 2016, S. 1232–1241 (biorxiv.org [PDF]).
  12. ↑ Maas M, Asanović K, Kubiatowicz J: Return of the runtimes: rethinking the language runtime system for the cloud 3.0 era. In: Proceedings of the 16th Workshop on Hot Topics in Operating Systems (ACM). 2017, S. 138–143, doi:10.1145/3110000/3103003/p138-Maas (acm.org [PDF]).
  13. ↑ Julien LeDem: Apache Arrow and Apache Parquet: Why We Needed Different Projects for Columnar Data, On Disk and In-Memory. In: KDnuggets.
  14. ↑ Apache Arrow vs. Parquet and ORC: Do we really need a third Apache project for columnar data representation?
  15. ↑ PyArrow:Reading and Writing the Apache Parquet Format.
  16. ↑ Daniel Abadi. In: Department of Computer Science, University of Maryland.
  17. ↑ Prof. Abadi Wins VLDB 10-Year Best Paper Award.
  18. ↑ An analysis of the strengths and weaknesses of Apache Arrow.
  19. ↑ Introducing Apache Arrow.
  20. ↑ a b Alexander J. Martin: Apache Foundation rushes out Apache Arrow as top-level project. In: The Register. 17. Februar 2016;.
  21. ↑ Big data gets a new open-source project, Apache Arrow: It offers performance improvements of more than 100x on analytical workloads, the foundation says.
  22. ↑ Julien LeDem: The first release of Apache Arrow. In: SD Times. 28. November 2016;.
  23. ↑ Julien Le Dem on the Future of Column-Oriented Data Processing with Apache Arrow.
  24. ↑ pmc: Apache Arrow 0.15.0 Release. In: Apache Arrow. 6. Oktober 2019, abgerufen am 18. Dezember 2019 (amerikanisches Englisch).
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