Schema generation for document stores using workload-driven approach

Although there are numerous data modeling tools for relational databases, data modeling for NoSQL databases has seen another perspective. These databases (a) do not define any explicit schema, (b) store data in a denormalized manner, and (c) give many structure alternatives. The decision to structure the data always relies on rules of thumb, which do not guarantee an optimal structural solution. Based on this motivation, this paper offers a workload-driven model for the logical schema design of a NoSQL document database. It consists of Model input, Intermediate transformation, and Final schema generation. The proposed model takes the conceptual schema (EER model) and application workload (queries and anticipated data volume) as input and describes a procedure to convert it into a logical model for NoSQL document stores. The conversion process initially converts the application queries into query graphs. The query graphs, along with the anticipated data volume, are used to generate the query labels. The resulting query labels are assigned on the schema graph designed from the EER model. The schema graph and labels are used to transform the EER model into the appropriate logical schema model based on the actions defined for each label. We evaluate the model using a case study in the eCommerce application domain. The experimental evaluation shows the proposed model outperforms the existing conventional, optimized, and query path graphs models in multiple aspects, including query performance, storage space efficiency, aggregate pipeline efficiency, read–write latency, collection-wise performance, scalability, throughput and latency. By effectively addressing the challenges associated with managing the variety and volume of big data through a well-designed schema, our proposed model significantly reduces the time, cost, and effort required for schema development and repair.