Dr. Darius Sidlauskas' talk on DB seminar

Title: "Parallel Processing of Massive Update and Query Workloads in Main Memory"

Abstract: The efficient processing of workloads that interleave moving-object updates and queries is challenging. In addition to the conflicting needs for update-efficient versus query-efficient data structures, the increasing parallel capabilities of multi-core processors yield challenges. To prevent concurrency anomalies and to ensure correct system behavior, conflicting update and query operations must be serialized. In this setting, it is a key concern to avoid that operations are blocked, which leaves processing cores idle.

This talk investigates spatial indexing techniques that aim to support these workloads on modern processors. As hard drives, involving moving mechanical parts, are often too slow for such workloads, the talk focuses on main memory solutions. It first compares the performance of two broad existing classes of spatial indexes-data partitioning and space partitioning indexes. The results show that a static grid-based index excels under update-intensive loads. Then, we look at a number of our proposed grid-based techniques that enable highly parallel workload processing. Since some techniques trade query-freshness for higher parallelism, the talk also examines concurrency degrees from traditional transaction processing in the context of our target domain. This includes proposing new semantics that enable a high degree of parallelism and ensure up-to-date query results. Finally, the talk includes empirical results conducted on modern processors showing that our proposals scale near-linearly with the number of hardware threads and thus are able to benefit from increasing on-chip parallelism.

Bio: Darius Sidlauskas is a postdoctoral researcher at the MADALGO (MAssive Data structures and ALGOrithms) research center at Aarhus University, Denmark. In 2012, he received his Ph.D. degree in Computer Science from Aalborg University, Denmark. His research interests include data-intensive systems and their practical applications on modern hardware.