Shay Horovitz, Alon Ben-Lavi, Refael Auerbach, Bar Brownshtein, Chen Hamdani and Ortal Yona, College of Management Academic Studies, Israel

As modern applications and systems are growing fast and continuously changing, back-end services in general and database services in particular are being challenged with dynamic loads and differential query behaviour. The traditional best practice of designing database – creating fixed relational schemas prior to deployment - becomes irrelevant. While newer database technologies such as document based and columnar are more flexible, they perform better only under certain conditions that are hard to predict. Frequent manual modifications of database structures and technologies under production require expert skills, increase management costs and often ends up with sub-optimal performance. In this paper we propose AdaptaBase - a solution for performance optimization of database technologies in accordance with application query demands by using machine learning to model application query behavioural patterns and learning the optimal database technology per each behavioural pattern. Experiments present a reduction in query execution time of over 25% for the relational-columnar model selection, and over 30% for the relation-document based model selection.

Database, Cross-Technology, Machine Learning, Adaptive