PixelsDB: Serverless and NL-Aided Data Analytics with Flexible Service Levels and Prices
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| Udgivet i: | arXiv.org (Dec 23, 2024), p. n/a |
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| Andre forfattere: | , , , |
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Cornell University Library, arXiv.org
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| Online adgang: | Citation/Abstract Full text outside of ProQuest |
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| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3148948993 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2331-8422 | ||
| 035 | |a 3148948993 | ||
| 045 | 0 | |b d20241223 | |
| 100 | 1 | |a Bian, Haoqiong | |
| 245 | 1 | |a PixelsDB: Serverless and NL-Aided Data Analytics with Flexible Service Levels and Prices | |
| 260 | |b Cornell University Library, arXiv.org |c Dec 23, 2024 | ||
| 513 | |a Working Paper | ||
| 520 | 3 | |a Serverless query processing has become increasingly popular due to its advantages, including automated resource management, high elasticity, and pay-as-you-go pricing. For users who are not system experts, serverless query processing greatly reduces the cost of owning a data analytic system. However, it is still a significant challenge for non-expert users to transform their complex and evolving data analytic needs into proper SQL queries and select a serverless query service that delivers satisfactory performance and price for each type of query. This paper presents PixelsDB, an open-source data analytic system that allows users who lack system or SQL expertise to explore data efficiently. It allows users to generate and debug SQL queries using a natural language interface powered by fine-tuned language models. The queries are then executed by a serverless query engine that offers varying prices for different performance service levels (SLAs). The performance SLAs are natively supported by dedicated architecture design and heterogeneous resource scheduling that can apply cost-efficient resources to process non-urgent queries. We demonstrate that the combination of a serverless paradigm, a natural-language-aided interface, and flexible SLAs and prices will substantially improve the usability of cloud data analytic systems. | |
| 653 | |a Resource scheduling | ||
| 653 | |a Data analysis | ||
| 653 | |a Prices | ||
| 653 | |a Resource management | ||
| 653 | |a Queries | ||
| 653 | |a User satisfaction | ||
| 653 | |a Query processing | ||
| 653 | |a Query languages | ||
| 700 | 1 | |a Geng, Dongyang | |
| 700 | 1 | |a Li, Haoyang | |
| 700 | 1 | |a Chai, Yunpeng | |
| 700 | 1 | |a Ailamaki, Anastasia | |
| 773 | 0 | |t arXiv.org |g (Dec 23, 2024), p. n/a | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3148948993/abstract/embedded/BH75TPHOCCPB476R?source=fedsrch |
| 856 | 4 | 0 | |3 Full text outside of ProQuest |u http://arxiv.org/abs/2405.19784 |