By adopting to more domains, database management systems (DBMSs) increase their functionality continously. This leads to DBMSs that often include unnecessary functionality, which decreases performance. A result of this trend is that new specialized systems arise that focus only on a certain application scenario but often reimplement already existing functionality. To avoid bloated DBMSs, we propose to introduce variability in DBMS implementations that allows users to select only needed functionality for a specific application scenario. In this paper, we focus on the query optimizer as it is a key component of DBMSs. We describe the potentials of tailoring query optimizers. Furthermore, we analyze common and differing functionality of three query optimizers of industrial DBMSs (SQLite, Oracle, and PostgreSQL) to create a variability model for query optimizers that can be used as a basis for future variability-aware implementations.
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