Reactive and Proactive Sharing Across Concurrent Analytical Queries

Today an ever increasing amount of data is collected and analyzed by researchers, businesses, and scientists in data warehouses (DW). In addition to the data size, the number of users and applications querying data grows exponentially. The increasing concurrency is itself a challenge in query execution, but also introduces an oppor-tunity favoring synergy between concurrent queries. Traditional execution engines of DW follows a query-centric approach, where each query is optimized and executed independently. On the other hand, workloads with increased concurrency have several queries with common parts of data and work, creating the opportunity for sharing among concurrent queries. Sharing can be reactive to the inherently existing sharing opportunities, or proactive by redesign-ing query operators to maximize the sharing opportunities. This demonstration showcases the impact of proactive and re