Workload Partitioning for Window Aggregates

This lecture discusses workload partitioning strategies for efficient sharing of window aggregates in data-intensive applications and systems. It covers topics such as ad-hoc analytics on streams, sharing for window aggregation, and the dynamic partitioner. The instructor explains the optimization considerations, group-filters cost, and data characteristics affecting partitioning. The lecture also explores the system architecture, aggregate throughput evaluation, and conclusions on work-sharing and workload partitioning approaches.