High-Performance Query Processing of a Real-World OLAP Database with ParGRES

Abstract

Abstract. Typical OLAP queries take a long time to be processed so speeding up the execution of each single query is imperative to decision making. ParGRES is an open-source database cluster middleware for high performance OLAP query processing. By exploiting intra-query parallelism on PC clusters, ParGRES has shown excellent performance using the TPC-H benchmark. In this paper, we evaluate ParGRES on a real-world OLAP database. Through adaptive virtual partitioning of the database, ParGRES yields linear and very often super-linear speedup for frequent queries. This shows that ParGRES is a very cost-effective solution for OLAP query processing in real settings

Parallel OLAP query processing in database clusters with data replication

“…Unlike TPC-H, this dataset has a lot of skew within its attribute values and joins. By issuing typical user queries, we confirmed the efficiency of this approach by obtaining very good superlinear results while keeping low costs [22]. We also intend to explore QueCh techniques in the context of data grids in GParGRES [23].…”

Event Stream Processing with Multiple Threads

“…In Event Stream Processing Much more work on parallelism and distribution was undertaken from the database point of view. The evaluation of SQL queries using multiple cores has spawned a large line of works [5,15,17,24,26,32]. However, relational database queries differ significantly from runtime verification properties, where the order of events is an essential matter.…”

A Cloud-Based Framework for Supporting Effective and Efficient OLAP in Big Data Environments

“…• ParGRES Paes et al[23] implements ParGRES middleware. The main features of ParGRES are: automatic parsing of SQL queries to allow for intra-query parallel execution; query processing with inter-and intra-query parallelism; virtual dynamic partition definition; result composition; update processing; and dynamic load balancing.…”