Year: 2025
Type: conference-paper
Abstract: In real-world tasks like data management and Web search, index operations often exhibit strong skewness, unlike standard benchmarks with uniform data distribution. While learned indexes improve query and update efficiency, they typically fail to address the skewed workload access, often prioritizing a single performance metric at the cost of overall index effectiveness. Additionally, the full reliance on learned models can increase vulnerability to attacks, compromising system stability. To address these challenges, we propose ShapeShifter, an adaptive evolutionary structure based on traditional indexes, capable of dynamically adjusting node structures according to the workload. ShapeShifter introduces a node evolution strategy with workload-skew-aware policies to adaptively adjust and optimize the partial index structure, leveraging a hybrid mechanism that combines traditional and learned structures for robust performance and optimal time-space tradeoff under skewed workloads and extreme data conditions. The evaluation results show that ShapeShifter achieves the optimal tradeoff while maintaining robustness.
Authors: Hui Wang, Xin Wang, Jiake Ge, Lei Liang, Peng Yi
Institutions: Tianjin University, Ant Group (China)
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Award: G7482657928
OpenAlex ID: W4409657298