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research-article

An Empirical Evaluation of Columnar Storage Formats

Authors:

Huanchen ZhangAuthors Info & Claims

Proceedings of the VLDB Endowment, Volume 17, Issue 2

Pages 148 - 161

https://doi.org/10.14778/3626292.3626298

Published: 01 October 2023 Publication History

Abstract

Columnar storage is a core component of a modern data analytics system. Although many database management systems (DBMSs) have proprietary storage formats, most provide extensive support to open-source storage formats such as Parquet and ORC to facilitate cross-platform data sharing. But these formats were developed over a decade ago, in the early 2010s, for the Hadoop ecosystem. Since then, both the hardware and workload landscapes have changed.

In this paper, we revisit the most widely adopted open-source columnar storage formats (Parquet and ORC) with a deep dive into their internals. We designed a benchmark to stress-test the formats' performance and space efficiency under different workload configurations. From our comprehensive evaluation of Parquet and ORC, we identify design decisions advantageous with modern hardware and real-world data distributions. These include using dictionary encoding by default, favoring decoding speed over compression ratio for integer encoding algorithms, making block compression optional, and embedding finer-grained auxiliary data structures. We also point out the inefficiencies in the format designs when handling common machine learning workloads and using GPUs for decoding. Our analysis identified important considerations that may guide future formats to better fit modern technology trends.

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An Empirical Evaluation of Columnar Storage Formats

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cover image Proceedings of the VLDB Endowment

Proceedings of the VLDB Endowment Volume 17, Issue 2

October 2023

185 pages

ISSN:2150-8097

Editors:
Meihui Zhang
Beijing Institute of Technology
,
Cyrus Shahabi
University of Southern California

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VLDB Endowment

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Published: 01 October 2023

Published in PVLDB Volume 17, Issue 2

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Cited By

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https://doi.org/10.14778/3685800.3685827
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https://dl.acm.org/doi/10.14778/3681954.3682001
Zeng XMeng RPavlo AMcKinney WZhang H(2024)NULLS!: Revisiting Null Representation in Modern Columnar FormatsProceedings of the 20th International Workshop on Data Management on New Hardware10.1145/3662010.3663452(1-10)Online publication date: 10-Jun-2024
https://dl.acm.org/doi/10.1145/3662010.3663452
Liu YZeng XZhang H(2024)LeCo: Lightweight Compression via Learning Serial CorrelationsProceedings of the ACM on Management of Data10.1145/36393202:1(1-28)Online publication date: 26-Mar-2024
https://dl.acm.org/doi/10.1145/3639320
Mišev DRodionychev MBaumann P(2023)Performance of Null Handling in Array Databases2023 IEEE International Conference on Big Data (BigData)10.1109/BigData59044.2023.10386100(247-254)Online publication date: 15-Dec-2023
https://doi.org/10.1109/BigData59044.2023.10386100

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