SRIndex: Bridging the Last Mile between Learned-Index Scalability and Workload Dynamics for Memory Pooling Systems

As data volumes continue to escalate, conventional index structures struggle to keep pace with high-performance demands. Learned indexing is a data structure that leverages machine learning models to predict the locations of data, which significantly improves search performance compared to traditional indexing methods. However, existing learned index schemes often focus solely on optimizing read and write operations, neglecting scalability and robustness in their index design. This oversight leads to significant performance variations across workloads with different characteristics. In this paper, we present SRIndex , a scalable and robust learned index, to address the abovementioned problems. SRIndex consists of the following design primitives: (i) It implements a model layer , which is optimized with a select number of keys to reduce asynchronous retraining time and accelerate query processes; (ii) It incorporates a transition layer to achieve the scalability of the index; and (iii) It introduces a leaf layer to provide efficient storage and high access efficiency. Extensive experimental results demonstrate that SRIndex outperforms state-of-the-art learned indexes by improving 1.27 × of write throughput under dynamic workloads.