Bringing Data Transformations Near-Memory for Low-Latency Analytics in HTAP Environments
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| Soggetti: | |
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| _version_ | 1866918304715636736 |
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| author | Bernhardt, Arthur Volz, David Tamimi, Sajjad Koch, Andreas Petrov, Ilia |
| author_facet | Bernhardt, Arthur Volz, David Tamimi, Sajjad Koch, Andreas Petrov, Ilia |
| contents | In this paper we propose an approach for executing data transformations near- or in-storage on intelligent storage systems. The currently prevailing approach of extracting the data and then transforming it to a target format suffers degraded performance during transformation and causes heavy data movement. Our results show robust performance of foreground workloads and lower resource contention. Our vision draws architectural opportunities in multi-engine and multi-system settings, as well as for reuse. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_12456 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Bringing Data Transformations Near-Memory for Low-Latency Analytics in HTAP Environments Bernhardt, Arthur Volz, David Tamimi, Sajjad Koch, Andreas Petrov, Ilia Databases In this paper we propose an approach for executing data transformations near- or in-storage on intelligent storage systems. The currently prevailing approach of extracting the data and then transforming it to a target format suffers degraded performance during transformation and causes heavy data movement. Our results show robust performance of foreground workloads and lower resource contention. Our vision draws architectural opportunities in multi-engine and multi-system settings, as well as for reuse. |
| title | Bringing Data Transformations Near-Memory for Low-Latency Analytics in HTAP Environments |
| topic | Databases |
| url | https://arxiv.org/abs/2601.12456 |