Data modeling in NoSQL databases is notoriously complex and driven by multiple and possibly conflicting requirements. Researchers have proposed methodologies to optimize schema design of a given domain for a given workload; however, due to the agile environment in which NoSQL databases are usually employed, both domain and workload are frequently subject to changes and evolution - possibly neutralizing the benefits of optimization. When this happens, the benefits of a new optimal schema design must be weighed against the costs of migrating the data. In this work, we empirically show the benefits of schema redesign in a real publicly available database. In particular, we identify multiple snapshots (in terms of domain extension and querying workload) in the 20+ years evolution of SkyServer, demonstrate how NoSQL schema optimization at a given time can later backfire, and evaluate the conditions under which data migration becomes beneficial. This takes us to define the foundations and challenges of a framework for continuous NoSQL database refactoring, with the goal of helping DBAs and data engineers decide if, when, and how a NoSQL database should be reconsidered to restore schema design optimality; special attention is paid to the optimization of data migration tasks scheduling.
Gallinucci et al. (Wed,) studied this question.
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