FunctionGuard: A query engine for expensive scientific functions in relational databases

Expensive user-defined functions impose unique challenges to database management systems at query time. This is mostly due to the black-box nature of these functions, the in-ability to optimize their internals, and the potential inefficiency of the common optimization heuristics, e.g., "selection-push-down'. Moreover, the increasing diversity of modern scientific applications that depend on DBMSs and, at the same time, extensively use expensive UDFs is mandating the design and development of efficient techniques to support these expensive functions. In this paper, we propose the "FunctionGuard" system that leverages disk-based persistent caching in novel ways to achieve across-queries optimizations for expensive UDFs. The unique features of FunctionGuard include: (1) Dynamic extraction of dependencies between the UDFs and the data sources and identifying the potential cacheable functions, (2) Cache-aware query optimization through newly introduced query operators, (3) Proactive cache refreshing that partially migrates the cost of the expensive calls from the query time to the idle and under-utilized times, and (4) Integration with the state-of-art techniques that generate efficient query plans under the presence of expensive functions. The system is implemented within PostgreSQL DBMS, and the results show the effectiveness of the proposed algorithms and optimizations.