Description

Document stores have become one of the most popular NoSQL systems, mainly due to their semi-structured data storage structure and well-developed query capabilities. The semi-structured nature allows them to have database designs beyond traditional normalization theories. This makes the database design decisions more complicated with a myriad of possibilities. Thus, the database design process for them has resorted to ad-hoc trial and error methods. However, having a good database design is essential for any data storage system’s performance, and bad design decisions cannot always be compensated by adding more powerful hardware. Thus, in this work, we propose DocDesign, a decision aid tool for document store database design. DocDesign allows its users to evaluate different database designs for data storage requirements under a particular workload. Through DocDesign, users can make informed decisions for a design by evaluating the estimated storage statistics and query runtimes without testing it on an actual document store. DocDesign also generates design specific queries for the input workload. This not only cuts down the time and the effort taken in design decision making and development but also save money spent on fixing poor designs in the long run. On-site, we will showcase how DocDesign facilitates the design decision-making process for MongoDB with both synthetic and real-world examples.

DocDesign-Demo from Moditha Hewasinghage on Vimeo.

DocDesign 2.0, a novel system that supports data-base design for document stores. DocDesign 2.0 automatically generates a document store design driven by a query workloadand a set of optimization objectives. In the presence of a massive search space, DocDesign 2.0 adopts multi-objective optimization techniques that, with high probability, guarantee to yield the optimal design based on the preferences (i.e., weights) providedby the end-user. In this work, we demonstrate how DocDesign2.0 improves the productivity on the task of designing a document store, as well as how the quality of the results is improved with respect to those obtained by manually generating the design

DocDesign V2 from Moditha Hewasinghage on Vimeo.

Related publications

2021
Moditha Hewasinghage, Sergi Nadal, Alberto Abelló: DocDesign 2.0: Automated Database Design for Document Stores with Multi-criteria Optimization. EDBT 2021

2020
Moditha Hewasinghage, Alberto Abelló, Jovan Varga, Esteban Zimányi: DocDesign: Cost-Based Database Design for Document Stores. SSDBM 2020