Resiliency-aware Data Compression for In-memory Database Systems.

Nowadays, database systems pursuit a main memory-centric architecture, where the entire business-related data is stored and processed in a compressed form in main memory. In this case, the performance gain is massive because database operations can benefit from its higher bandwidth and lower latency. However, current main memory-centric database systems utilize general-purpose error detection and correction solutions to address the emerging problem of increasing dynamic error rate of main memory. The costs of these generalpurpose methods dramatically increases with increasing error rates. To reduce these costs, we have to exploit context knowledge of database systems for resiliency. Therefore, we introduce our vision of resiliency-aware data compression in this paper, where we want to exploit the benefits of both fields in an integrated approach with low performance and memory overhead. In detail, we present and evaluate a first approach using AN encoding and two different compression schemes to show the potentials and challenges of our vision.