David Hung-Chang Du | Searcharxiv

arXiv2025

LearnedKV: Integrating LSM and Learned Index for Superior Performance on Storage

We present LearnedKV, a novel tiered key-value store that seamlessly integrates a Log-Structured Merge (LSM) tree with a Learned Index to achieve superior read and write performance on storage systems. While existing approaches use learned indexes primarily as auxiliary components within LSM trees, LearnedKV employs a two-tier design where the LSM tree handles recent write operations while a separate Learned Index accelerates read performance. Our design includes a non-blocking conversion mechanism that efficiently transforms LSM data into a Learned Index during garbage collection, maintaining high performance without interrupting operations. LearnedKV dramatically reduces LSM size through this tiered approach, leading to significant performance gains in both reads and writes. Extensive evaluations across diverse workloads show that LearnedKV outperforms state-of-the-art LSM-based solutions by up to 4.32x for read operations and 1.43x for writes. The system demonstrates robust performance across different data distributions, access patterns, and storage media including both SSDs and HDDs.

cs.DB↗

arXiv2020

TurboKV: Scaling Up The Performance of Distributed Key-Value Stores With In-Switch Coordination

The power and flexibility of software-defined networks lead to a programmable network infrastructure in which in-network computation can help accelerating the performance of applications. This can be achieved by offloading some computational tasks to the network. However, what kind of computational tasks should be delegated to the network to accelerate applications performance? In this paper, we propose a way to exploit the usage of programmable switches to scale up the performance of distributed key-value stores. Moreover, as a proof-of-concept, we propose TurboKV, an efficient distributed key-value store architecture that utilizes programmable switches as: 1) partition management nodes to store the key-value store partitions and replicas information; and 2) monitoring stations to measure the load of storage nodes, this monitoring information is used to balance the load among storage nodes. We also propose a key-based routing protocol to route the search queries of clients based on the requested keys to targeted storage nodes. Our experimental results of an initial prototype show that our proposed architecture improves the throughput and reduces the latency of distributed key-value stores when compared to the existing architectures.

cs.DC↗