Efficient View Maintenance for Enterprise Applications in Columnar In-Memory Databases

Enterprise applications such as available-to-promise (ATP), financial accounting, and dunning typically employ a mixed database workload with short-running transactional as well as analytical queries with resource-intensive aggregations. The latter type of queries can be significantly accelerated by using materialized views with pre-calculated aggregates. However, this speed-up comes with the cost of view maintenance which is necessary to guarantee consistency when the underlying data changes. In this paper, we evaluate existing view maintenance strategies in the context of a columnar in-memory database that is designed for mixed workloads. We propose a novel view maintenance strategy that takes the main-delta architecture and resulting merge process of columnar storage into account. A further contribution is a cost model which determines the best maintenance strategy given a specific workload. Our experiments using an ATP application show that our novel strategy outperforms other strategies in mixed workloads with an insert-ratio of more than 40 percent.