  Prescriptive Performance Analysis (PPA) has shown to be more useful than
traditional descriptive and diagnostic analyses for making sense of Big Data
(BD) frameworks' performance. In practice, when processing large (RDF) graphs
on top of relational BD systems, several design decisions emerge and cannot be
decided automatically, e.g., the choice of the schema, the partitioning
technique, and the storage formats. PPA, and in particular ranking functions,
helps enable actionable insights on performance data, leading practitioners to
an easier choice of the best way to deploy BD frameworks, especially for graph
processing. However, the amount of experimental work required to implement PPA
is still huge. In this paper, we present PAPyA 1, a library for implementing
PPA that allows (1) preparing RDF graphs data for a processing pipeline over
relational BD systems, (2) enables automatic ranking of the performance in a
user-defined solution space of experimental dimensions; (3) allows user-defined
flexible extensions in terms of systems to test and ranking methods. We
showcase PAPyA on a set of experiments based on the SparkSQL framework. PAPyA
simplifies the performance analytics of BD systems for processing large (RDF)
graphs.We provide PAPyA as a public open-source library under an MIT license
that will be a catalyst for designing new research prescriptive analytical
techniques for BD applications.
