FPCache: A Fingerprint-Rectified Learned Index Cache for Disaggregated Memory

The rapid growth of data-intensive applications has increased the demand for efficient storage in large-scale key-value (KV) stores. Disaggregated memory architectures provide a scalable solution by separating compute and memory resources via RDMA. However, existing indexing schemes in these environments suffer from poor read efficiency, significantly degrading overall system throughput and scalability. Specifically, learned indexes often encounter substantial read amplification during remote data retrieval due to prediction errors. In addition, caching full keys incurs a high cache footprint, limiting the effective cache capacity on compute nodes and leading to additional remote memory accesses. This paper presents FPCache, a fingerprint-rectified learned index cache for disaggregated memory. We propose a fingerprint-assisted two-stage read approach to mitigate read amplification. FPCache first retrieves a compact fingerprint array for local matching. It then converts range reads into precise point accesses and directly reads the corresponding data item, thereby avoiding reading the entire range and reducing extra data transfers. Next, we design a fingerprint-offset compression strategy to maximize cache density. Leveraging fixed-length fingerprints and position offsets enables compute nodes to retain significantly more hotspot data within limited memory resources. Experimental evaluations using various YCSB workloads demonstrate that FPCache consistently outperforms state-of-the-art methods. Compared to systems like CHIME and ROLEX, FPCache improves system throughput by up to 62% and effectively maintains stable access efficiency under diverse data distributions.