Structured Query Language (SQL) has remained the standard query language for databases. SQL is highly optimized for processing structured data laid out in relations. Meanwhile, in the present application development landscape, it is highly desirable to utilize the power of learned models to perform complex tasks. Large language models (LLMs) have been shown to understand and extract information from unstructured textual data. However, SQL as a query language and accompanying relational database systems are either incompatible or inefficient for workloads that require leveraging learned models. This results in complex engineering and multiple data migration operations that move data between the data sources and the model inference platform. In this paper, we present iPDB, a relational system that supports in-database machine learning (ML) and large language model (LLM) inferencing using extended SQL syntax. In iPDB, LLMs and ML calls can function as semantic projects, as predicates to perform semantic selects and semantic joins, or for semantic aggregations in group-by clauses. iPDB has a new relational predict operator along with semantic query optimizations that enable users to write and efficiently execute semantic SQL queries, outperforming other state-of-the-art systems by 2.5x mean speedup, with speedups of up to 30x.

翻译：结构化查询语言（SQL）一直是数据库的标准查询语言。SQL针对关系型结构化数据的处理进行了高度优化。然而，在当前的应用程序开发环境中，利用学习模型执行复杂任务具有极大需求。大型语言模型（LLMs）已被证明能理解和提取非结构化文本数据中的信息。但作为查询语言的SQL及其配套的关系数据库系统，在需要借助学习模型的工作负载中要么不兼容，要么效率低下。这导致了复杂的工程问题以及数据源与模型推理平台之间多次数据迁移操作。本文提出iPDB——一个支持使用扩展SQL语法进行数据库内机器学习（ML）和大型语言模型（LLM）推理的关系系统。在iPDB中，LLM和ML调用既可作为语义投影，也可作为执行语义选择和语义连接的谓词，还可用于分组子句中的语义聚合。iPDB引入了新的关系预测算子及语义查询优化技术，使用户能够编写并高效执行语义SQL查询，平均速度较其他最先进系统提升2.5倍，最高加速比可达30倍。