Design Continuums and the Path Toward Self-Designing Key-Value Stores that Know and Learn


S. Idreos, et al., “Design Continuums and the Path Toward Self-Designing Key-Value Stores that Know and Learn,” in Biennial Conference on Innovative Data Systems Research (CIDR), 2019.
selfdesign.pdf4.55 MB


We introduce the concept of design continuums for the data layout of key-value stores. A design continuum unifies major distinct data structure designs under the same model. The critical insight and potential long-term impact is that such unifying models 1)~render what we consider up to now as fundamentally different data structures to be seen as ``views'' of the very same overall design space, and 2)~allow ``seeing'' new data structure designs with performance properties that are not feasible by existing designs. The core intuition behind the construction of design continuums is that all data structures arise from the very same set of fundamental design principles, i.e., a small set of data layout design concepts out of which we can synthesize any design that exists in the literature as well as new ones. We show how to construct, evaluate, and expand, design continuums and we also present the first continuum that unifies major data structure designs, i.e., B+Tree, BeTree, LSM-tree, and LSH-Table.

The practical benefit of a design continuum is that it creates a fast inference engine for the design of data structures. For example, we can near instantly predict how a specific design change in the underlying storage of a data system would affect performance, or reversely what would be the optimal data structure (from a given set of designs) given workload characteristics and a memory budget. In turn, these properties allow us to envision a new class of self-designing key-value stores with a substantially improved ability to adapt to workload and hardware changes by transitioning between drastically different data structure designs to assume a diverse set of performance properties at will. 

Last updated on 12/21/2018