Please note: This seminar will take place in DC 1302.
Zhuoyue Zhao, Assistant Professor
Department of Computer Science and Engineering, University at Buffalo
Approximate Query Processing enables users to trade slight loss of accuracy for very low query latencies. For today’s Hybrid Transactional/Analytical Processing workloads, this could be very useful to replace some of the expensive analytical queries if approximation is acceptable. However, traditional AQP systems rely on scan-based random sampling and thus still incur high latencies. Meanwhile, many AQP algorithms rely on specialized sampling indexes to perform random sampling without excessively scanning, but they are often not concurrency safe or updatable.
In this talk, I will present our recent work on a fast and concurrency-safe updatable sampling indexes for independent range sampling. It can sustain high rate of ingestion and sampling under snapshot isolation. It is fully integrated in PostgreSQL and we also built a new AQP extension around it. I will also discuss several challenges and promising directions for AQP in modern in-memory HTAP systems.
Bio: Zhuoyue Zhao is currently an assistant professor at University at Buffalo. He holds a PhD degree from University of Utah, where he was advised by Prof. Feifei Li and Prof. Jeff Phillips. His research interest is in database systems, specifically query processing and optimization, transaction processing, and storage and indexing.
He received an NSF CAREER award in 2024, and two SIGMOD best paper awards in 2016 and 2025.