Aggregates caching for enterprise applications

Modern enterprise applications generate a mixed workload comprised of short-running transactional queries and long-running analytical queries containing expensive aggregations. Based on the fact that columnar in-memory databases are capable of handling these mixed workloads, we evaluate how existing materialized view maintenance strategies can accelerate the execution of aggregate queries. We contribute by introducing a novel materialized view maintenance approach that leverages the main-delta architecture of columnar storage, outperforming existing strategies for a wide range of workloads. As an optimization, we further propose an approach that adapts the aggregate maintenance strategy based upon the currently monitored workload characteristics.