SQL/MapReduce: a practical approach to self-describing, polymorphic, and parallelizable user-defined functions

A user-defined function (UDF) is a powerful database feature that allows users to customize database functionality. Though useful, present UDFs have numerous limitations, including install-time specification of input and output schema and poor ability to parallelize execution. We present a new approach to implementing a UDF, which we call SQL/MapReduce (SQL/MR), that overcomes many of these limitations. We leverage ideas from the MapReduce programming paradigm to provide users with a straightforward API through which they can implement a UDF in the language of their choice. Moreover, our approach allows maximum flexibility as the output schema of the UDF is specified by the function itself at query plan-time. This means that a SQL/MR function is polymorphic. It can process arbitrary input because its behavior as well as output schema are dynamically determined by information available at query plan-time, such as the function's input schema and arbitrary user-provided parameters. This also increases reusability as the same SQL/MR function can be used on inputs with many different schemas or with different user-specified parameters.

In this paper we describe the motivation for this new approach to UDFs as well as the implementation within Aster Data Systems' nCluster database. We demonstrate that in the context of massively parallel, shared-nothing database systems, this model of computation facilitates highly scalable computation within the database. We also include examples of new applications that take advantage of this novel UDF framework.