利用内存数据库预计算共享因子，让机器学习训练快70%以上
Fast Factorized Learning: Powered by In-Memory Database Systems
- 在内存数据库中预计算共享因子，避免重复计算
- 相比非因子化学习提升70%性能，比磁盘系统快100倍
- 适合想加速数据预处理的机器学习工程师
在因子化连接上进行学习可避免冗余计算，通过识别并预先计算共享因子实现。以往研究探讨了在传统基于磁盘的数据库系统上计算因子的效果，但因缺乏公开代码，无法在内存数据库系统上复现实验。本文描述了在内存数据库中使用因子进行数据库内学习的实现。我们对PostgreSQL（磁盘系统）和HyPer（内存引擎）上的线性回归学习进行了基准测试。结果表明，在内存数据库系统上，因子化学习相比非因子化学习性能提升70%，且比磁盘系统快100倍。因此，现代数据库引擎可通过在数据提取前预计算聚合值，显著加速机器学习训练流程。
原文摘要 · Abstract (English)
Learning models over factorized joins avoids redundant computations by identifying and pre-computing shared cofactors. Previous work has investigated the performance gain when computing cofactors on traditional disk-based database systems. Due to the absence of published code, the experiments could not be reproduced on in-memory database systems. This work describes the implementation when using cofactors for in-database factorized learning. We benchmark our open-source implementation for learning linear regression on factorized joins with PostgreSQL -- as a disk-based database system -- and HyPer -- as an in-memory engine. The evaluation shows a performance gain of factorized learning on in-memory database systems by 70\% to non-factorized learning and by a factor of 100 compared to disk-based database systems. Thus, modern database engines can contribute to the machine learning pipeline by pre-computing aggregates prior to data extraction to accelerate training.
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