The "solar-stellar connection" began as a relatively small field of research focused on understanding the processes that generate magnetic fields in stars and sometimes lead to a cyclic pattern of long-term variability in activity, as demonstrated by our Sun. This area of study has recently become more broadly pertinent to questions of exoplanet habitability and exo-space weather, as well as stellar evolution. In contrast to other areas of stellar research, individual stars in the solar-stellar connection often have a distinct identity and character in the literature, due primarily to the rarity of the decades-long time-series that are necessary for studying stellar activity cycles. Furthermore, the underlying stellar dynamo is not well understood theoretically, and is thought to be sensitive to several stellar properties, e.g., luminosity, differential rotation, and the depth of the convection zone, which in turn are often parameterized by other more readily available properties. Relevant observations are scattered throughout the literature and existing stellar databases, and consolidating information for new studies is a tedious and laborious exercise. To accelerate research in this area I developed sunstardb, a relational database of stellar properties and magnetic activity proxy time-series keyed by individual named stars. The organization of the data eliminates the need for the problematic catalog cross-matching operations inherent when building an analysis data set from heterogeneous sources. In this article I describe the principles behind sunstardb, the data structures and programming interfaces, as well as use cases from solar-stellar connection research.
