Approximate Nearest Neighbor Search (ANNS) has become a fundamental component in many real-world applications. Among various ANNS algorithms, graph-based methods are state-of-the-art. However, ANNS often suffers from a significant drop in accuracy for certain queries, especially in Out-of-Distribution (OOD) scenarios. To address this issue, a recent approach named RoarGraph constructs a bipartite graph between the base data and historical queries to bridge the gap between two different distributions. However, it suffers from some limitations: (1) Building a bipartite graph between two distributions lacks theoretical support, resulting in the query distribution not being effectively utilized by the graph index. (2) Requires a sufficient number of historical queries before graph construction and suffers from high construction times. (3) When the query workload changes, it requires reconstruction to maintain high search accuracy. In this paper, we first propose Escape Hardness, a metric to evaluate the quality of the graph structure around the query. Then we divide the graph search into two stages and dynamically identify and fix defective graph regions in each stage based on Escape Hardness. (1) From the entry point to the vicinity of the query. We propose R eachability Fix ing (RFix), which enhances the navigability of some key nodes. (2) Searching within the vicinity of the query. We propose N eighboring G raph Defects Fix ing (NGFix) to improve graph connectivity in regions where queries are densely distributed. The results of extensive experiments show that our method outperforms other state-of-the-art methods on real-world datasets, achieving up to 2.25× faster search speed for OOD queries at 99% recall compared with RoarGraph and 6.88× faster speed compared with HNSW. It also accelerates index construction by 2.35-9.02× compared to RoarGraph.

The emerging persistent memory (PM) is increasingly being leveraged to construct high-performance and persistent indexes. By exploiting data distribution, recent learned indexes open up a new index design paradigm. Some prior studies try to refit the learned index according to the features of PM. However, they neglect to analyze the performance of existing learned index schemes on PM. In this paper, we provide a comprehensive analysis of learned indexes on PM and propose two optimization methods to improve the performance. In particular, we evaluate ALEX, PGM-index, and XIndex after converting them to persistent indexes. With appropriate modifications, some design choices of volatile learned index still show favorable performance on PM under workloads with simple data distribution. But they perform poorly when the data distribution becomes complex. According to the experiment results, we summarize some instructive insights and optimize persistent learned indexes for complex data distributions with two methods: 1) a cost-based insertion pattern selection to minimize PM writes and 2) recoverable internal nodes selective persistence to decrease the overhead of internal lookups. Our evaluations demonstrate the performance of optimized ALEX is 2.09x/1.53x of the original ALEX in insert/search. Meanwhile, it also outperforms the specific-designed persistent learned index.

In-memory Key-Value (KV) stores are widely deployed in modern data centers. These systems generally use DRAM as their storage medium, causing huge hardware costs. The emerging persistent memory (PMEM) is a potential substitute for DRAM, which has a lower price and larger capacity, but lower access speed and bandwidth. Many prior studies strive to build hybrid memory systems to retain both the advantages of DRAM and PMEM. However, they are either application agnostic or simply take DRAM as a cache, which are both not efficient for in-memory KV stores. In this paper, we propose SwapKV, a well-designed in-memory KV store for hybrid DRAM-PMEM system. SwapKV has several promising properties. First, SwapKV combines DRAM and PMEM to a uniform memory pool and only stores one copy of data, which maximizes capacity utilization. Second, SwapKV maps all writing operations to DRAM and migrates data to PMEM with large blocks asynchronously, which mitigates the intrinsic inefficiency of PMEM for writing operations. Third, SwapKV maintains the hot data in DRAM through an efficient hotness filtering and data swapping mechanism, which ensures high system throughput and responsiveness. We implement SwapKV and evaluate it under various workload patterns. The results demonstrate that SwapKV improves the throughput by 11\%$\sim$41\% compared to the state-of-the-art alternatives.