Towards Automatic and Efficient Prediction Query Processing in Analytical Database

Data analysts nowadays are keen to have analytical capabilities involving deep learning (DL). Prediction queries, which combine relational operations with DL models to analyze multi-modal data, provide a powerful facility for smart in-database analysis. However, loose integration systems, which support such queries via User-Defined Functions (UDFs) and external runtimes, often impose high economic costs, particularly in cloud-based environments; while tight integration systems, which implement model inference through a sequence of explic-itly written SQL queries, incur heavy user burdens and huge optimization space. In this paper, we introduce PEPS, an end-to-end analytical database for automatic and efficient prediction query processing. PEPS automates the process of prediction query synthesis and ensures usability through declarative schemes. Additionally, it improves query performance with offline optimization using the DB-oriented Computation Graph Optimization (DBCGO) algorithm and online optimization via heuristic query rewriting. Empirical evaluations show that PEPS offers better usability than baseline methods, provides lower economic costs, and achieves performance speedup compared to advanced Python UDFs.