Vectorized UDFs in Column-Stores

Data Scientists rely on vector-based scripting languages such as R, Python and MATLAB to perform ad-hoc data analysis on potentially large data sets. When facing large data sets, they are only efficient when data is processed using vectorized or bulk operations. At the same time, overwhelming volume and variety of data as well as parsing overhead suggests that the use of specialized analytical data management systems would be beneficial. Data might also already be stored in a database. Efficient execution of data analysis programs such as data mining directly inside a database greatly improves analysis efficiency. We investigate how these vector-based languages can be efficiently integrated in the processing model of operator--at--a--time databases. We present MonetDB/Python, a new system that combines the open-source database MonetDB with the vector-based language Python. In our evaluation, we demonstrate efficiency gains of orders of magnitude.