abstract
- Automating physical design optimizations of database systems is challenging. Recent work on index selection or data compression has shown significant advantages of automated approaches. However, the impact on running systems is often hard to predict. Moreover, automated systems often lack the capabilities to help users understand the decisions taken. In this demonstration, we study the impact of optimal encoding configurations for in-memory database systems. We allow the user to set varying main memory budgets for which optimal encoding configurations are applied, as well as allow the user to manually configure the system. Effects on runtime performance and memory consumption can be directly observed. The user can further analyze the impact compression has on overall memory consumption and how compression ratios affect performance when the memory bandwidth is saturated.