Key-Value Stores (KVS) implemented with log-structured merge-tree (LSM-tree) have gained widespread acceptance in storage systems. Nonetheless, a significant challenge arises in the form of high write amplification due to the compaction process. While KV-separated LSM-trees successfully tackle this issue, they also bring about substantial space amplification problems, a concern that cannot be overlooked in cost-sensitive scenarios. Garbage collection (GC) holds significant promise for space amplification reduction, yet existing GC strategies often fall short in optimization performance, lacking thorough consideration of workload characteristics. Additionally, current KV-separated LSM-trees also ignore the adverse effect of the space amplification in the index LSM-tree. In this paper, we systematically analyze the sources of space amplification of KV-separated LSM-trees and introduce Scavenger, which achieves a better trade-off between performance and space amplification. Scavenger initially proposes an I/O-efficient garbage collection scheme to reduce I/O overhead and incorporates a space-aware compaction strategy based on compensated size to minimize the space amplification of index LSM-trees. Extensive experiments show that Scavenger significantly improves write performance and achieves lower space amplification than other KV-separated LSM-trees (including BlobDB, Titan, and TerarkDB).

翻译：采用日志结构合并树（LSM-tree）实现的键值存储系统已在存储领域获得广泛应用。然而，合并过程导致的高写入放大问题构成了重大挑战。虽然键值分离的LSM树成功解决了这一问题，却同时引发了不容忽视的空间放大难题，这在成本敏感的应用场景中尤为突出。垃圾回收机制在降低空间放大方面展现出巨大潜力，但现有策略往往优化效果有限，且缺乏对工作负载特性的全面考量。此外，当前键值分离LSM树的设计也忽视了索引LSM树中空间放大带来的负面影响。本文系统分析了键值分离LSM树的空间放大来源，并提出清道夫系统，在性能与空间放大之间实现了更优权衡。清道夫首先提出了一种I/O高效的垃圾回收方案以降低I/O开销，同时引入基于补偿大小的空间感知合并策略，以最小化索引LSM树的空间放大。大量实验表明，相较于其他键值分离LSM树系统（包括BlobDB、Titan和TerarkDB），清道夫能显著提升写入性能并实现更低的空间放大。