Workload-aware aggregate maintenance in columnar in-memory databases

Database workloads generated by enterprise applications are comprised of short-running transactional as well as long-running analytical queries with resource-intensive aggregations. The expensive aggregate queries can be significantly accelerated by using materialized views. This speed-up, however, comes with the cost of materialized view maintenance which is necessary to guarantee consistency when the underlying data changes. While several view maintenance strategies are applicable in the context of an in-memory column store, their performance depends on various factors, most importantly the ratio between queries accessing the materialized view and queries altering the base data, called insert ratio. As a contribution in this paper, we propose algorithms that determine the best-performing view maintenance strategy based on the currently monitored factors. Using our novel materialized aggregate engine, we are able to switch between view maintenance strategies on demand. We have created cost models for the identified view maintenance strategies that determine at which insert ratio it is advisable to switch to another strategy. Our benchmarks in SanssouciDB reveal that for all identified workloads, switching between maintenance strategies is more beneficial than staying with a single strategy.