用合成SQL查询训练成本模型，效率提升45%。
Bootstrapping Learned Cost Models with Synthetic SQL Queries
- 基于生成式AI技术自动生成高质量合成SQL数据
- 仅需减少45%的查询量即可达到同等预测精度
- 适合数据库优化与性能测试的研究者使用
拥有针对特定数据库实例的真实工作负载对压力测试、漏洞检测以及成本和性能优化至关重要。近年来，学习型成本模型表明，只要有足够多样化的SQL查询，就能有效且高效地预测特定数据库引擎上查询的执行成本。本文介绍了我们利用受生成式AI和大语言模型启发的现代合成数据生成技术，构建高质量数据集以有效训练此类学习型成本模型的经验。初步结果表明，与现有生成方法相比，使用合成查询训练可使学习型成本模型的预测准确率在减少45%查询量的情况下仍保持一致。
原文摘要 · Abstract (English)
Having access to realistic workloads for a given database instance is extremely important to enable stress and vulnerability testing, as well as to optimize for cost and performance. Recent advances in learned cost models have shown that when enough diverse SQL queries are available, one can effectively and efficiently predict the cost of running a given query against a specific database engine. In this paper, we describe our experience in exploiting modern synthetic data generation techniques, inspired by the generative AI and LLM community, to create high-quality datasets enabling the effective training of such learned cost models. Initial results show that we can improve a learned cost model's predictive accuracy by training it with 45% fewer queries than when using competitive generation approaches.
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