Prefetching is a crucial technique employed in traditional databases to enhance interactivity, particularly in the context of data exploitation. Data exploration is a query processing paradigm in which users search for insights buried in the data, often not knowing what exactly they are looking for. Data exploratory tools deal with multiple challenges such as the need for interactivity with no a priori knowledge being present to help with the system tuning. The state-of-the-art prefetchers are specifically designed for navigational workloads only, where the number of possible actions is limited. The prefetchers that work with SQL-based workloads, on the other hand, mainly rely on data logical addresses rather than the data semantics. They fail to predict complex access patterns in cases where the database size is substantial, resulting in an extensive address space, or when there is frequent co-accessing of data. In this paper, we propose SeLeP, a semantic prefetcher that makes prefetching decisions for both types of workloads, based on the encoding of the data values contained inside the accessed blocks. Following the popular path of using machine learning approaches to automatically learn the hidden patterns, we formulate the prefetching task as a time-series forecasting problem and use an encoder-decoder LSTM architecture to learn the data access pattern. Our extensive experiments, across real-life exploratory workloads, demonstrate that SeLeP improves the hit ratio up to 40% and reduces I/O time up to 45% compared to the state-of-the-art, attaining impressive 95% hit ratio and 80% I/O reduction on average.

翻译：预取是传统数据库中用于增强交互性的关键技术在数据开发利用场景中尤为重要。数据探索是一种查询处理范式，用户在此过程中搜索隐藏在数据中的洞察，往往并不明确自己究竟在寻找什么。数据探索工具面临多重挑战，例如需要实现交互性，但缺乏先验知识来辅助系统调优。现有最先进的预取器仅针对导航型工作负载设计，这类场景中可能的操作数量有限。而适用于SQL型工作负载的预取器主要依赖数据逻辑地址而非数据语义。当数据库规模较大导致地址空间广泛，或存在频繁的数据协同访问时，此类预取器无法预测复杂的访问模式。本文提出SeLeP，一种基于访问块内数据值编码的语义预取器，可为两种类型的工作负载做出预取决策。遵循使用机器学习方法自动学习隐藏模式的流行路径，我们将预取任务建模为时间序列预测问题，并采用编码器-解码器LSTM架构学习数据访问模式。基于真实探索性工作负载的大量实验表明，与现有最优方案相比，SeLeP将命中率提升高达40%，I/O时间减少高达45%，平均命中率可达95%，I/O减少率达80%。