论文标题
开箱即用的学习成本预测的零拍摄成本模型
Zero-Shot Cost Models for Out-of-the-box Learned Cost Prediction
论文作者
论文摘要
在本文中，我们介绍了零拍的成本模型，该模型可以使学习的成本估算能够概括为看不见的数据库。与最先进的工作驱动的方法相反，这些方法需要在每个新数据库上执行大量的培训查询（零摄像成本模型），因此可以在不昂贵的培训数据收集的情况下实例化学习的成本模型。为了启用此类零击成本模型，我们建议基于预先培训的成本模型的新学习范式。作为支持将这种预训练的成本模型转移到看不见数据库的核心贡献，我们引入了一种新的模型体系结构和表示技术，以编码查询工作负载作为这些模型的输入。正如我们将在评估中显示的那样，零摄像成本估算可以比最新的（现实世界中）数据库提供更准确的成本估算，而无需在看不见的数据库上进行任何查询执行。此外，我们表明，零击成本模型可以在几种射击模式下使用，通过在看不见的数据库上使用少量的其他培训查询来进一步改善其质量。
In this paper, we introduce zero-shot cost models which enable learned cost estimation that generalizes to unseen databases. In contrast to state-of-the-art workload-driven approaches which require to execute a large set of training queries on every new database, zero-shot cost models thus allow to instantiate a learned cost model out-of-the-box without expensive training data collection. To enable such zero-shot cost models, we suggest a new learning paradigm based on pre-trained cost models. As core contributions to support the transfer of such a pre-trained cost model to unseen databases, we introduce a new model architecture and representation technique for encoding query workloads as input to those models. As we will show in our evaluation, zero-shot cost estimation can provide more accurate cost estimates than state-of-the-art models for a wide range of (real-world) databases without requiring any query executions on unseen databases. Furthermore, we show that zero-shot cost models can be used in a few-shot mode that further improves their quality by retraining them just with a small number of additional training queries on the unseen database.
