{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T07:13:00Z","timestamp":1784099580100,"version":"3.55.0"},"reference-count":60,"publisher":"Association for Computing Machinery (ACM)","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2022,9]]},"abstract":"<jats:p>Many modern key-value stores, such as RocksDB, rely on log-structured merge trees (LSMs). Originally designed for spinning disks, LSMs optimize for write performance by only making sequential writes. But this optimization comes at the cost of reads: LSMs must rely on expensive compaction jobs and Bloom filters---all to maintain reasonable read performance. For NVMe SSDs, we argue that trading off read performance for write performance is no longer always needed. With enough parallelism, NVMe SSDs have comparable random and sequential access performance. This change makes update-in-place designs, which traditionally provide excellent read performance, a viable alternative to LSMs.<\/jats:p>\n          <jats:p>\n            In this paper, we close the gap between log-structured and update-in-place designs on modern SSDs with the help of new components that take advantage of data and workload patterns. Specifically, we explore three key ideas: (A)\n            <jats:italic>record caching<\/jats:italic>\n            for efficient point operations, (B)\n            <jats:italic>page grouping<\/jats:italic>\n            for high-performance range scans, and (C)\n            <jats:italic>insert forecasting<\/jats:italic>\n            to reduce the reorganization costs of accommodating new records. We evaluate these ideas by implementing them in a prototype update-in-place key-value store called\n            <jats:italic>TreeLine.<\/jats:italic>\n            On YCSB, we find that TreeLine outperforms RocksDB and LeanStore by 2.20\u00d7 and 2.07\u00d7 respectively on average across the point workloads, and by up to 10.95\u00d7 and 7.52\u00d7 overall.\n          <\/jats:p>","DOI":"10.14778\/3561261.3561270","type":"journal-article","created":{"date-parts":[[2022,11,16]],"date-time":"2022-11-16T15:32:50Z","timestamp":1668612770000},"page":"99-112","source":"Crossref","is-referenced-by-count":37,"title":["TreeLine"],"prefix":"10.14778","volume":"16","author":[{"given":"Geoffrey X.","family":"Yu","sequence":"first","affiliation":[{"name":"Massachusetts Institute of Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Markos","family":"Markakis","sequence":"additional","affiliation":[{"name":"Massachusetts Institute of Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andreas","family":"Kipf","sequence":"additional","affiliation":[{"name":"Massachusetts Institute of Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Per-\u00c5ke","family":"Larson","sequence":"additional","affiliation":[{"name":"University of Waterloo"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Umar Farooq","family":"Minhas","sequence":"additional","affiliation":[{"name":"Apple"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tim","family":"Kraska","sequence":"additional","affiliation":[{"name":"Massachusetts Institute of Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2022,11,16]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Lyric Doshi, Tim Kraska, Xiaozhou, Li, Andy Ly, and Christopher Olston.","author":"Abu-Libdeh Hussam","year":"2020","unstructured":"Hussam Abu-Libdeh , Deniz Alt\u0131nb\u00fcken , Alex Beutel , Ed H. 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