Abstract
The diversity and complexity of modern data management applications led to the extension of the relational paradigm with syntactic and semantic support for User-Defined Functions (UDFs). Although well-established in traditional DBMS settings, UDFs have become even more central in many applications spanning data science, data analytics, etc. Still, a critical limitation of UDFs, which to some extent has turned data scientists towards NoSQL systems, is the impedance mismatch between their evaluation and relational processing. We present YeSQL, an SQL extension with rich UDF support along with a pluggable architecture to easily integrate it with either server-based or embedded database engines. We currently support UDFs written in Python, which are fully integrated with relational queries as scalar functions, aggregators, or table returning functions. Key novel characteristics of YeSQL include easy implementation of complex algorithms, tracing JIT compilation of Python UDFs, and seamless integration with a database engine. Our demonstration will showcase (a) the usability and expressiveness of our approach, and (b) that our techniques of minimizing context switching between the relational engine and the Python VM are very effective and achieve significant speedups in common, practical use cases.
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