Predictive Translation: High-Performance Buffer Management Without the Trade-Offs

To efficiently manage larger-than-memory datasets, storage-based database management systems (DBMSs) rely on buffer managers. These are traditionally implemented using hash tables to translate page identifiers (PIDs) to memory pointers. While this design offers many practical advantages, prior studies have shown its performance limitations and proposed alternative designs to close the gap with optimized in-memory DBMSs. However, these modern designs introduce systematic issues, such as intrusive implementations or reliance on kernel modules, which ultimately hinder their adoption. This paper challenges the notion that hash-table-based buffer pools cannot deliver high performance. We introduce predictive translation , a novel approach that combines the high performance of modern approaches with the qualitative benefits of traditional designs. Predictive translation achieves this by exploiting the capabilities of commodity CPUs – particularly their superscalar execution – through deterministic placement of pages to hide the excessive latency of software-level hash table lookups. Our evaluation demonstrates that our approach meets all practical requirements while delivering performance at least on par with state-of-the-art alternatives. We show that our design is a compelling solution for buffer management in modern DBMSs running on fast storage devices.