Using shared DRAM as persistence for an in-memory DBMS

A shared-nothing architecture is state-of-the-art for deploying a distributed analytical in-memory database management system: it preserves the in-memory performance advantage by processing data locally on each node but is difficult to scale out. Modern switched fabric communication links such as InfiniBand narrow the performance gap between local and remote DRAM data access to a single order of magnitude. Based on these premises, this project introduces a distributed in-memory database architecture that separates the query execution engine and data access: this enables a) the usage of a large-scale DRAM-based storage system such as Stanford's RAMCloud and b) the push-down of bandwidth-intensive database operators into the storage system. We address the resulting challenges such as finding the optimal operator execution strategy and partitioning scheme. The project demonstrates that such an architecture delivers both: the elasticity of a shared-storage approach and the performance characteristics of operating on local DRAM. In our project, we created AnalyticsDB which is a prototypical analytical query processor with a pluggable storage layer.

Contact: Christian Tinnefeld

Research Area: In-Memory Data Management for Enterprise Systems

"A Course in In-Memory Data Management" by Prof. Dr. h.c. Hasso Plattner. This book is the culmination of six years work of in-memory research. As such, it provides the technical foundation for combined transactional and analytical workloads inside one single database as well as examples of new applications that are now possible given the availability of the new technology. The book is available at Springer.