Finding the pitfalls in query performance

Despite their popularity, database benchmarks only highlight a small part of the capabilities of any given system. They do not necessarily highlight problematic components encountered in real life or provide hints for further research and engineering.In this paper we introduce discriminative performance benchmarking, which aids in exploring a larger search space to find performance outliers and their underlying cause. The approach is based on deriving a domain specific language from a sample query to identify a query workload. SQLscalpel subsequently explores the space using query morphing, and simulated annealing to find performance outliers, and the query components responsible. To speedup the exploration for often time-consuming experiments SQLscalpel has been designed to run asynchronously on a large cluster of machines.</p

SQALPEL: A database performance platform

Despite their popularity, database benchmarks only highlight a small fraction of the capabilities of any given DBMS. They often do not highlight problematic components encountered in real life database applications or provide hints for further research and engineering. To alleviate this problem we coined discriminative performance benchmarking as the way to go. It aids in exploring a larger query search space to find performance outliers and their underlying cause. The approach is based on deriving a domain specific language from a sample complex query to identify and execute a query workload. The demo illustrates sqalpel, a complete platform to collect, manage and selectively disseminate performance facts, that enables repeatability studies, and economy of scale by sharing performance experiences

A spatial column-store to triangulate the Netherlands on the fly

3D digital city models, important for urban planning, are currently constructed from massive point clouds obtained through airborne LiDAR (Light Detection and Ranging). They are semantically enriched with information obtained from auxiliary GIS data like Cadastral data which contains information about the boundaries of properties, road networks, rivers, lakes etc. Technical advances in the LiDAR data acquisition systems made possible the rapid acquisition of high resolution topographical information for an entire country. Such data sets are now reaching the trillion points barrier. To cope with this data deluge and provide up-to-date 3D digital city models on demand current geospatial management strategies should be re-thought. This work presents a column-oriented Spatial Database Management System which provides in-situ data access, effective data skipping, efficient spatial operations, and interactive data visualization. Its efficiency and scalability is demonstrated using a dense LiDAR scan of The Netherlands consisting of 640 billion points and the latest Cadastral information, and compared with PostGIS

MonetDB/MonetDB Jul2017_release

This is the official mirror of the MonetDB Mercurial repository. Please note that we do not accept pull requests on github. The regresession test results can be found on the MonetDB Testweb http://monetdb.cwi.nl/testweb/web/status.php .For contributions please see: https://www.monetdb.org/Developer