Sphinx: A High-Performance Hybrid Index for Disaggregated Memory with Succinct Filter Cache

Abstract: Disaggregated memory (DM) architecture physically separates computing and memory resources into distinct pools interconnected via high-speed networks within data centers, with the aim of improving resource utilization compared to traditional architectures. Most existing range indexes for DM that support variable-length keys are based on adaptive radix trees. However, these indexes exhibit suboptimal performance on DM due to excessive network round trips during tree traversal and inefficient node-based caching mechanisms. To address these issues, we propose Sphinx, a novel hybrid index for DM. Sphinx introduces an Inner Node Hash Table to minimize the network round trips during index operations by replacing the sequential tree traversal with parallel hash reads. Sphinx incorporates a Succinct Filter Cache to further minimize network overhead while keeping the computing-side cache small and coherent. Experimental results show that Sphinx outperforms state-of-the-art counterparts by up to 7.3× in the YCSB benchmark.