The Mimicking Octopus: Towards a one-size-fits-all Database Architecture

Modern enterprises need to pick the right DBMSs e.g. OLTP, OLAP, streaming systems, scan-oriented systems among others, each tailored to a specific use-case application, for their data managing problems. This makes using specialized solutions for each application costly due to licensing fees, integration overhead and DBA costs. Additionally, it is tedious to integrate these specialized solutions together. Alternatively, enterprises use a single specialized DBMS for all applications and thereby compromise heavily on performance. Further, a particular DBMS (e.g. row store) cannot adapt and change into a different DBMS (e.g. streaming system), as the workload changes, even though much of the code and technology is replicated anyways.

In this paper we discuss building a new type of database system which fits several use-cases while reducing costs, boosting performance, and improving the ease-of-use at the same time. We present the research challenges in building such a system. We believe that by dropping the assumption of a fixed store, as in traditional systems like row store and column store, and instead having a flexible storage scheme we can realize much better performance without compromising the cost. We outline OctopusDB as our plan for such a system and discuss how it can mimic several existing as well as newer systems. To do so, we present the concept of storage view as an abstraction of all storage layouts in OctopusDB. We discuss how the heterogenous optimization problems in OctopusDB can be reduced to a single problem: storage view selection; and describe how a Holistic Storage View Optimizer can deal with it. We present simulation results to justify our core idea and experimental evidence on our initial prototype to demonstrate our approach. Finally, we detail the next steps in our work.