Serverless query processing has become increasingly popular due to its advantages, including automated hardware and software 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 engine 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 service levels on query urgency. The service levels are natively supported by dedicated architecture design and heterogeneous resource scheduling that can apply cost-efficient resources to process non-urgent queries. We envision that the combination of a serverless paradigm, a natural-language-aided interface, and flexible service levels and prices will substantially improve the user experience in data analysis.

翻译：无服务器查询处理因其自动化软硬件管理、高弹性及按使用付费等优势日益普及。对于非系统专家的用户而言，无服务器查询处理极大降低了数据分析系统的拥有成本。然而，如何将复杂且动态变化的数据分析需求转化为恰当的SQL查询，并为每类查询选择能提供满意性能与价格的无服务器查询引擎，对非专业用户仍是重大挑战。本文提出PixelsDB——一个开源数据分析系统，使缺乏系统或SQL专业知识的用户能够高效探索数据。该系统允许用户通过基于微调语言模型构建的自然语言界面生成并调试SQL查询。查询随后由无服务器查询引擎执行，该引擎针对查询紧急程度提供不同服务等级与差异化定价。这些服务等级通过专用架构设计与异构资源调度实现原生支持，能够运用成本效益更高的资源处理非紧急查询。我们预见，无服务器范式、自然语言辅助界面与灵活服务等级及定价的结合，将显著提升数据分析的用户体验。