Concurrent In-Memory OLAP Query Optimization Techniques

Recent researches not only focused on query-at-a-time query optimizations but also focused on group-at-a-time query optimizations due to the multicore hardware architecture support and highly concurrent workload requirements. By grouping concurrent queries into shared workload, some high latency operations, e.g., disk I/O, cache line access, can be shared for multiple queries. The existing approaches commonly lie in sharing query operators such as scan, join or predicate processing, and try to generate an optimized global executing plan for all the queries. For complex analytical workloads, how to generate an optimized shared execution plan is a challenging issue. In this paper, we present a template OLAP execution plan for widely adopted star schema to simplify execution plan for maximizing operator utilization. Firstly, we present a surrogate key oriented join index to transform traditional key probing based join operation to array index referencing (AIR) lookup to make join CPU efficient and support a lazy aggregation. Secondly, the predicate processing of concurrent queries is simplified as cache line conscious predicate vector to maximize concurrent predicate processing within single cache line access. Finally, we evaluate the concurrent template OLAP (on-line analytical processing) processing with multicore parallel implementation under the star schema benchmark(SSB), and the results prove that the shared scan and predicate processing can double the concurrent OLAP query performance.