Read-Optimized Persistent Hash Index for Query Acceleration through Fingerprint Filtering and Lock-Free Prefetching

Hash indexes are widely used in key-value storage systems due to their ability to perform rapid single-point queries. The persistent memory (PM) technology has received significant attention in both academia and industry due to its high performance, non-volatility, and large capacity characteristics. Currently, hash indexes tailored for persistent memories have been extensively researched. However, through an in-depth experimental study, we have discovered that existing persistent hash indexes suffer from low query performance. This is primarily due to persistent memory's higher read latency than DRAM's, which reduces the performance of both positive and negative queries in persistent hash indexes. Additionally, the former's higher read lock overhead further diminishes query performance. To address the above problems, we propose in this paper a Read-Optimized Persistent Hash Index, referred to as ROPHI, based on fingerprint filtering and lock-free prefetching. By employing a fingerprint filtering method, ROPHI introduces a DRAM-based Cuckoo filter to store fingerprints of keys on top of the PM-based hash table, effectively mitigating the time-consuming access overhead of persistent memory hash tables by accessing only the DRAM-based filter. Additionally, ROPHI employs lock-free prefetching for positive query acceleration, utilizing lock-free optimistic concurrent read techniques to avoid read lock overhead and high-speed cache prefetching techniques to reduce access overhead to persistent memory. Experimental results on the Intel Optane DC Persistent Memory Module (DCPMM) platform demonstrate that ROPHI significantly improves query performance over existing persistent hash index schemes. Specifically, ROPHI achieves an improvement of 2.67×-13.59× in negative query performance and 1.72x-7.86x in positive query performance. ROPHI outperforms the state-of-the-art SmartHT in positive query throughput by 34.5%, and in insertion and deletion throughput by 9.20% and 19.87% respectively, while sacrificing only 1.93% of negative query throughput. Additionally, it achieves a 5.07x improvement in recovery efficiency.