Using Object-Awareness to Optimize Join Processing in the SAP HANA Aggregate Cache.

The introduction of columnar in-memory databases, along with hardware evolution, has made the execution of transactional and analytical workloads on a single system both feasible and viable. Yet, doing analytics directly on the transactional data introduces an increasing amount of resourceintensive aggregate queries which can slow down the overall system performance in a multi-user environment. To increase the scalability of a system in the presence of multiple such queries, we propose an aggregate cache in the general delta-main architecture that provides an ecient means to handle costly aggregate queries by applying incremental materialized view maintenance and query compensation techniques. Handling aggregate queries based on joins of multiple tables however is still a challenge as query compensation can be very expensive in the delta-main architecture of columnar in-memory databases. Our analysis of enterprise applications has revealed several data schema and workload patterns that can be leveraged for addressing performance of query processing using the aggregate cache. We contribute by presenting an approach to transport the application object semantics into the database system, becoming object-aware, and optimize the query processing using the aggregate cache by applying partition pruning and predicate pushdown in such general delta-main architecture. Our experimental validation using customer data and workloads confirms that this type of optimizations enables ecient usage of the aggregate cache for an even higher share of aggregate queries as one mean to scale the system.