DiaLSM：基于分片的LSM树实现无写停顿性能
DiaLSM: Towards Write-Stall-Free Performance via Shard-based LSM-tree
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中文总结 AI 辅助
针对LSM树写停顿问题，提出基于内部片段的DiaLSM架构，通过拆分流水线并动态重定向，在RocksDB上实现高达2.4倍吞吐量提升和94%停顿减少。
中文摘要 AI 辅助
日志结构合并树（LSM）旨在实现高写入吞吐量，但在持续写入压力下会出现写停顿问题。我们使用排队模型对LSM中写-刷盘-压缩流水线的写停顿发生概率和平均持续时间进行了量化，超越了现有的经验分析。所提出的模型表明，具有单一流水线的整体式LSM无法消除写停顿，揭示了LSM内部的分片为从根本上缓解写停顿提供了机会。为了打破这一结构性瓶颈，我们提出了DiaLSM，一种基于内部片段的LSM架构。DiaLSM不是强制所有写入通过一个流水线，而是将写-刷盘-压缩路径拆分为多个独立的片段，并采用动态回退重定向，使得即使某些片段停顿，写入也能继续进行。在RocksDB上实现后，DiaLSM相比最先进的方法ADOC和Sub-Compaction，在db_bench、YCSB和Sysbench OLTP评估中实现了高达2.4倍的吞吐量提升、94%的停顿减少以及显著更低的延迟。
英文摘要
Log-Structured Merge-tree (LSM) aims to achieve high write throughput, but is known to experience the write stall problems when subjected to sustained write pressure. We quantify the occurrence probability and average duration of write stalls in LSM using a queuing model in the write--flush--compaction pipeline, moving beyond existing empirical analysis. The proposed model demonstrates that a monolithic LSM with a single pipeline cannot eliminate write stalls, revealing that internal sharding within the LSM offers an opportunity for fundamental write stall mitigation. To break this structural bottleneck, we propose DiaLSM, an internally shard-based LSM architecture. Instead of forcing all writes through one pipeline, DiaLSM splits the write--flush--compaction path into multiple independent shards and employs dynamic fallback redirection, allowing writes to proceed even when some shards stall. Implemented on RocksDB, DiaLSM achieves up to 2.4x higher throughput, 94% lower stalls, and significantly lower latency than state-of-the-art methods ADOC and Sub-Compaction, as demonstrated by db_bench, YCSB, and Sysbench OLTP evaluations.
发表机构
- Korea Aerospace University（韩国航空大学）
- Sogang University（西江大学）
- MangoBoost
- ETRI（电子通信研究所）
- Temple University（天普大学）
- Arizona State University（亚利桑那州立大学）
机构由 AI 辅助整理，请以论文原文为准。
