I Don&#39t Want to be the Mitt Romney of Databases

What can I say? Yes, I helped build a database management system (DBMS) for the &#34one percent.&#34 This previous system (H-Store) is able to get up to 40x higher throughput over traditional, disk-oriented DBMSs for on-line transaction processing workloads. But getting this great performance requires a significant upfront deployment cost (e.g., application rewriting, pre-partitioning). It is also unable to perform non-trivial analysis operations without the use of a separate data warehouse, which further increases costs and overhead. This makes a DBMS like ours accessible to only those organizations with ample resources.

In this talk, I outline our vision for a new distributed DBMS (codenamed &#34The Brain&#34) that we are building at CMU that is truly for the 99%. It will enable any application to get the same kind of performance as a specialized system like H-Store without any expensive setup or maintenance. The crux of the system is to employ machine learning techniques to support the efficient execution of hybrid workloads (transactions + analytics) through intelligent pre-fetching and automatic partitioning/tuning. In essence, our new DBMS is able to learn about how an application uses the database without any human intervention and reconfigure itself accordingly.
Andy Pavlo is an Assistant Professor of Databaseology in the Computer Science Department at Carnegie Mellon University.