MorphStore: Analytical Query Engine with a Holistic Compression-Enabled Processing Model
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Abstract (arXiv)
In this paper, we present MorphStore, an open-source in-memory columnar analytical query engine with a novel holistic compression-enabled processing model. Basically, compression using lightweight integer compression algorithms already plays an important role in existing in-memory column-store database systems, but mainly for base data. In particular, during query processing, these systems only keep the data compressed until an operator cannot process the compressed data directly, whereupon the data is decompressed, but not recompressed. Thus, the full potential of compression during query processing is not exploited. To overcome that, we developed a novel compression-enabled processing model as presented in this paper. As we are going to show, the continuous usage of compression for all base data and all intermediates is very beneficial to reduce the overall memory footprint as well as to improve the query performance.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). First posted on arXiv 20 Apr 2020.
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Citing papers per year: 2023: 1; 2024: 0; 2025: 1.
2 papers.
- 2025Morphing-based Compression for Data-centric ML Pipelines15 Apr 2025
- 2023LeCo: Lightweight Compression via Learning Serial Correlations27 Jun 2023