A Workload-driven Document Database Schema Recommender (DBSR)

Database schema design requires careful consideration of the application's data model, workload, and target database technology to optimize for performance and data size. Traditional normalization schemes used in relational databases minimize data redundancy, whereas NoSQL document-oriented databases favor redundancy and optimize for horizontal scalability and performance. Systematic NoSQL schema design involves multiple dimensions, and a database designer is in practice required to carefully consider (i) which data elements to copy and co-locate, (ii) which data elements to normalize, and (iii) how to encode data, while taking into account factors such as the workload and data model. In this paper, we present a workload-driven document database schema recommender (DBSR), which takes a systematic, search-based approach in exploring the complex schema design space. The recommender takes as main inputs the application's data model and its read workload, and outputs (i) the suggested document schema (featuring secondary indexing), (ii) query plan recommendations, and (iii) a document utility matrix that encodes insights on their respective costs and relative utility. We evaluate recommended schema in MongoDB using YCSB, and show significant benefits to read query performance