This large long-term project introduces a revolutionary staged design for high-performance, evolvable DBMS that are easy to tune and maintain. We break the database system into modules and encapsulate them into self-contained stages connected to each other through queues.

With the advent of highly-parallel chip multiprocessors database system designers are called to revisit their designs. We study the performance of commercial database systems in evolving computer architectures and we believe that conventional database designs are inherently restricted in performing highly in such environments. On the other hand, the different approach taken by staged database designs makes them more suitable for high performance in the new computing landscape.

Our focus is in four directions:

• Study the performance of database systems when running OLTP and DSS workload on emerging hardware, such as the highly-parallel chip multiprocessors.
• Build a staged relational query engine that can optimally manage available disk bandwidth, RAM, and CPU cycles across multiple concurrent queries, and provide a significant performance boost over conventional query engines.
• Apply the Staged DB design coupled with smart scheduling to Online Transaction Processing (OLTP) engines in order to optimize both instruction and data cache (processor cache) performance, as well as, to improve the (intra-transaction) parallelism in those workloads.
• Optimize chip multiprocessors for commercial workloads, especially database applications, such as on-line transaction processing (OLTP) and decision support applications (DSS).

People

• Anastasia Ailamaki
• Babak Falsafi
• Nikos Hardavellas
• Ippokratis Pandis
• Ryan Johnson
• Stavros Harizopoulos
• Kun Gao
• Naju G. Mancheril
• Vladislav Shkapenyuk

Acknowledgements

This work is supported in part from the project named "III-COR: Staged Database Systems: Maximizing Locality through Service-based Data Management (National Science Foundation AWARD #0713409)".

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