NHtapDB: Native HTAP Databases
Guoxin Kang, Lei Wang, Simin Chen, and Jianfeng Zhan
TL;DR
NHtapDB introduces a native HTAP database that combines real-time analytics with hybrid workload performance, utilizing a machine learning framework for dynamic model updates and a mixed-format store for efficiency.
Contribution
It presents a novel native HTAP database integrating machine learning for real-time insights and a mixed-format storage system optimized for hybrid workloads.
Findings
Enhanced performance on HTAP workloads
Effective real-time model training and deployment
Validated with an advanced HTAP benchmark
Abstract
Native database (1) provides a near-data machine learning framework to facilitate generating real-time business insight, and predefined change thresholds will trigger online training and deployment of new models, and (2) offers a mixed-format store to guarantee the performance of HTAP workloads, especially the hybrid workloads that consist of OLAP queries in-between online transactions. We make rigorous test plans for native database with an enhanced state-of-the-art HTAP benchmark.
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Taxonomy
TopicsAdvanced Database Systems and Queries · Stock Market Forecasting Methods · Time Series Analysis and Forecasting