Jennifer Lam will present her General Exam "Thermopylae: Exploiting Skew in High Throughput Distributed Databases" on Wednesday, May 11, 2022 at 2:00 PM in Friend 202 and via Zoom.
Committee Members: Wyatt Lloyd (advisor), Michael Freedman, Amit Levy
Abstract:
Distributed databases scale out by spreading data across many machines, which allows them to support large-scale applications whose data is too large to fit on a single-machine database. This scalability introduces a dilemma, however, as the throughput of distributed databases is much lower than that of modern single-machine databases for skewed workloads. This paper eliminates this dilemma as much as possible. We present Thermopylae, the first distributed database that is able to both support large-scale applications and match its throughput to single-machine databases under skewed workloads. Central to Thermopylae is a novel hybrid architecture that embeds a high-performance single-machine database into a highly scalable distributed database. Thermopylae applies a specialized concurrency control protocol designed for its hybrid architecture. Our evaluation shows that Thermopyale achieves orders of magnitude better performance than a state-of-the-art distributed database and closely matches its throughput to a modern single-machine database under skewed workloads.
Reading List:
Everyone is invited to attend the talk, and those faculty wishing to remain for the oral exam following are welcome to do so.