DobLIX优化日志结构合并树的查询与数据访问，提升读性能。
DobLIX: A Dual-Objective Learned Index for Log-Structured Merge Trees
- 双目标学习索引同时优化查询效率和存储访问成本。
- 在RocksDB上读吞吐提升1.19至2.21倍，索引开销更低。
- 采用强化学习动态调参，适应不同工作负载变化。
本文提出DobLIX，一种专为基于日志结构合并（LSM）树的键值存储设计的双目标学习索引。传统学习索引仅优化查询，忽略存储访问影响，导致性能瓶颈。DobLIX在训练中引入数据访问优化作为第二目标，使索引查找效率与数据访问成本均最小化，在保持写入效率的同时显著提升读性能。系统还集成强化学习代理，实时动态调整参数以适应变化的工作负载。使用真实数据集的实验表明，DobLIX在RocksDB中相比现有最优方法，索引开销降低，吞吐量提升1.19至2.21倍。
原文摘要 · Abstract (English)
In this paper, we introduce DobLIX, a dual-objective learned index specifically designed for Log-Structured Merge(LSM) tree-based key-value stores. Although traditional learned indexes focus exclusively on optimizing index lookups, they often overlook the impact of data access from storage, resulting in performance bottlenecks. DobLIX addresses this by incorporating a second objective, data access optimization, into the learned index training process. This dual-objective approach ensures that both index lookup efficiency and data access costs are minimized, leading to significant improvements in read performance while maintaining write efficiency in real-world LSM-tree systems. Additionally, DobLIX features a reinforcement learning agent that dynamically tunes the system parameters, allowing it to adapt to varying workloads in real-time. Experimental results using real-world datasets demonstrate that DobLIX reduces indexing overhead and improves throughput by 1.19 to 2.21 times compared to state-of-the-art methods within RocksDB, a widely used LSM-tree-based storage engine.
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