Columnar database systems, designed for an optimal OLAP workload performance, strive for maximum multi-core utilization under concurrent query executions. However, multi-core parallel plan generated for isolated execution leads to suboptimal performance during concurrent query execution.

With the rise of multi-core CPU platforms, their optimal utilization for in-memory OLAP workloads using column store databases has become one of the biggest challenges. Some of the inherent limi- tations in the achievable query parallelism are due to the degree of parallelism dependency on the data skew, the overheads incurred by thread coordination, and the hardware resource limits. Finding the right balance between the degree of parallelism and the multi-core utilization is even more trickier. It makes parallel plan generation using traditional query optimizers a complex task. In this paper we introduce adaptive parallelization, which ex- ploits execution feedback to gradually increase the level of paral- lelism until we reach a sweet-spot. After each query has been exe- cuted, we replace an expensive operator (or a sequence) by a faster parallel version, i.e. the query plan is morphed into a faster one. A convergence algorithm is designed to reach the optimum as quick as possible. The approach is evaluated against a full-fledged column-store using micro-benchmarks and a subset of the TPC-H and TPC-DS queries. It confirms the feasibility of the design and proofs to be competitive against a statically optimized heuristic plan generator. Adaptively parallelized plans show optimal multi-core utilization and up to five times improvement compared to heuristically paral- lelized plans on the workload under evaluation.

With the rise of multi-socket multi-core CPUs a lot of effort is being put into how to best exploit their abundant CPU power. In a shared memory setting the multi-socket CPUs are equipped with their own memory module, and access memory modules across sockets in a non-uniform access pattern (NUMA). Memory access across socket is relatively expensive compared to memory access within a socket. One of the common solutions to minimize across socket memory access is to partition the data, such that the data affinity is maintained per socket.

Searching for the performance bottleneck in an execution trace is an error prone and time consuming activity. Existing tools offer some comfort by providing a visual representation of trace for analysis. In this paper we present the Stethoscope, an interactive visual tool to inspect and analyze columnar database query performance, both online and offline. It's unique interactive animated interface capitalizes the large data-flow graph representation of a query execution plan, augmented with query execution trace information. We demonstrate features of Stethoscope for both online and offline analysis of long running queries. It helps in understanding where time goes, how optimizers perform, and how parallel processing on multi-core systems is exploited.