{"ID":2880752,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.13935","arxiv_id":"2508.13935","title":"Scavenger+: Revisiting Space-Time Tradeoffs in Key-Value Separated LSM-trees","abstract":"Key-Value Stores (KVS) based on log-structured merge-trees (LSM-trees) are widely used in storage systems but face significant challenges, such as high write amplification caused by compaction. KV-separated LSM-trees address write amplification but introduce significant space amplification, a critical concern in cost-sensitive scenarios. Garbage collection (GC) can reduce space amplification, but existing strategies are often inefficient and fail to account for workload characteristics. Moreover, current key-value (KV) separated LSM-trees overlook the space amplification caused by the index LSM-tree. In this paper, we systematically analyze the sources of space amplification in KV-separated LSM-trees and propose Scavenger+, which achieves a better performance-space trade-off. Scavenger+ introduces (1) an I/O-efficient garbage collection scheme to reduce I/O overhead, (2) a space-aware compaction strategy based on compensated size to mitigate index-induced space amplification, and (3) a dynamic GC scheduler that adapts to system load to make better use of CPU and storage resources. Extensive experiments demonstrate that Scavenger+ significantly improves write performance and reduces space amplification compared to state-of-the-art KV-separated LSM-trees, including BlobDB, Titan, and TerarkDB.","short_abstract":"Key-Value Stores (KVS) based on log-structured merge-trees (LSM-trees) are widely used in storage systems but face significant challenges, such as high write amplification caused by compaction. KV-separated LSM-trees address write amplification but introduce significant space amplification, a critical concern in cost-s...","url_abs":"https://arxiv.org/abs/2508.13935","url_pdf":"https://arxiv.org/pdf/2508.13935v1","authors":"[\"Jianshun Zhang\",\"Fang Wang\",\"Jiaxin Ou\",\"Yi Wang\",\"Ming Zhao\",\"Sheng Qiu\",\"Junxun Huang\",\"Baoquan Li\",\"Peng Fang\",\"Dan Feng\"]","published":"2025-08-19T15:26:36Z","proceeding":"cs.DB","tasks":"[\"cs.DB\"]","methods":"[]","has_code":false}