Elastic in-memory OLTP Systems

On-line transaction processing (OLTP) database management systems (DBMSs) often serve time-varying workloads due to daily, weekly or seasonal fluctuations in demand, or because of rapid growth in demand due to a company’s business success. In addition, many OLTP workloads are heavily skewed to “hot” tuples or ranges of tuples. For example, the majority of NYSE volume involves only 40 stocks. To deal with such fluctuations, an OLTP DBMS needs to be elastic; that is, it must be able to expand and contract resources in response to load fluctuations and dynamically balance load as hot tuples vary over time.

This project investigated elasticity in in-memory OLTP systems, where we developed two systems E-Store and Accordion. E-Store is designed to maintain system performance over a highly variable and diverse load. It accomplishes this goal by balancing tuple accesses across an elastic set of partitions. In Accordion, we explicitly considered the affinity between partitions, which indicates the frequency in which they are accessed together by the same transactions. Accordion estimates the capacity of a server by explicitly considering the impact of distributed transactions and affinity on the maximum throughput of the server.


  • Marco Serafini, Essam Mansour, Ashraf Aboulnaga. “A Method and System for Processing Data,” US Patent application 14/910970, filed February 2016 (pending)