Jul 2026· ACM Symposium on Parallelism in Algorithms and Architectures· pp. 116-128· 0 citations· 47 references
Computer Science
TL;DR
KDB is introduced, a novel persistent key-value data store (a concurrent index) with rich linearizable semantics that offers highly scalable performance across varied workloads thanks to its unique multiversioned architecture.
Abstract
In this paper, we introduce KDB, a novel persistent key-value data store (a concurrent index) with rich linearizable semantics. In contrast to state-of-the-art systems which offer only lookup and put/remove operations, KDB supports both snapshots (which are used by range scans) and atomic batch updates—put and remove operations that are executed atomically. Despite its rich semantics, our system offers highly scalable performance across varied workloads thanks to its unique multiversioned architecture. It features a hybrid lock-CAS synchronization mechanism that allows lookup operations and scans to proceed in a wait-free fashion. Under the hood, KDB maintains all key-value entries in persistent memory (PM) for failure atomicity, but it heavily relies on an efficient DRAM-backed multiversion index based on skip lists to hide the costs of accessing PM. For better PM utilization, entries are arranged in PM in preallocated arrays that occasionally undergo compaction.
Large-scale online services—including web search, recommendation, and LLM inference workloads such as Retrieval-Augmented Generation (RAG) and KV-cache offloading—demand storage that handles petabyte-scale data under millisecond tail-latency SLAs. In-memory stores are cost-prohibitive at scale; disk-based systems sacri...
Ying-Xin Li, Kai Liu, Hanglun Xie· Proceedings of the VLDB Endo...· 0 citations
Adopting replicated persistent key-value stores (RPKVSs) as metadata backends is becoming increasingly popular in modern large-scale storage systems. Conventional RPC-based KV replication is infamously subject to its replication overheads due to the inherent architectural mismatch between the application-level network...
Liang Bao, Rui-Song Zhou, Hua Wang et al.· ACM Transactions on Architec...· 0 citations
Log-structured merge-tree (LSM-tree) key-value stores rely on caching to mitigate multi-component lookups and long tails, yet block and KP caches are prone to compactioninduced expiry, and all three cache types suffer from scan pollution under LRU eviction. We present AutoThermKV, a two-level in-memory architecture tha...
Yunfan Chi, E. Sha, Longshan Xu et al.· IEEE International Conferenc...· 0 citations
Disaggregated memory architecture has gained wide adoption in cloud and high-performance systems [18, 31, 43] due to its decoupled resource model, elasticity, and low-latency access. In such architectures, transaction mechanisms must ensure atomic and consistent access to remote memory. Prior designs use array-based ve...
Ao-Xin Wei, Jin-Tian Wu, Jian Zhou et al.· Proceedings of the Internati...· 0 citations
A novel architecture called S !"#$, designed to enhance the performance of hash indexes in disaggregated memory, is introduced and the results show that S !"#$ outperforms state-of-the-art DM-optimized hash indexes by at most 6.7 → (RACE), 3.6 → (SepHash), and 1.8 → (Outback) in YCSB workloads, respectively.
Han-Tian Zha, Teng Ma, Bao-Tong Lu et al.· 0 citations
On EnterpriseRAG-Bench, MEMONDEMAND outperforms the strongest published LB#1 result at every evaluated scale from 10M tokens through the complete 618M- token collection, and results on FinanceBench, HotpotQA, and FRAMES further show strong performance across financial, multi-hop, and fact-retrieval settings.
Xin-Yuan Song, Bo-Wen Zhu, H. Haque et al.· 0 citations
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