用主动学习优化日志结构合并树，显著提升键值存储性能。
CAMAL: Optimizing LSM-trees via Active Learning
- 通过解耦参数的主动学习，高效调优日志结构合并树。
- 在RocksDB中平均提升28%，最高达8倍性能改善。
- 支持数据增长与动态负载下的在线自适应调优，适合数据库系统研发者。
本文提出一种名为CAMAL的新方法，利用机器学习优化基于日志结构合并树（LSM-tree）的键值存储结构，以降低各类读写操作的成本。CAMAL是首个将主动学习应用于调优LSM-tree的尝试，其学习过程与传统代价模型结合，提升训练效率；采用解耦的主动学习范式，分别优化各参数，进一步加速学习过程；具备有效机制，可随数据规模增长增量更新模型；支持在动态工作负载下在线调优；集成至完整系统RocksDB后，平均性能提升28%，最高可达8倍，优于当前最先进的RocksDB设计。
原文摘要 · Abstract (English)
We use machine learning to optimize LSM-tree structure, aiming to reduce the cost of processing various read/write operations. We introduce a new approach Camal, which boasts the following features: (1) ML-Aided: Camal is the first attempt to apply active learning to tune LSM-tree based key-value stores. The learning process is coupled with traditional cost models to improve the training process; (2) Decoupled Active Learning: backed by rigorous analysis, Camal adopts active learning paradigm based on a decoupled tuning of each parameter, which further accelerates the learning process; (3) Easy Extrapolation: Camal adopts an effective mechanism to incrementally update the model with the growth of the data size; (4) Dynamic Mode: Camal is able to tune LSM-tree online under dynamically changing workloads; (5) Significant System Improvement: By integrating Camal into a full system RocksDB, the system performance improves by 28% on average and up to 8x compared to a state-of-the-art RocksDB design.
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