NoDB in Action: Adaptive Query Processing on Raw Data


I. Alagiannis, R. Borovica, M. Branco, S. Idreos, and A. Ailamaki, “NoDB in Action: Adaptive Query Processing on Raw Data,” Proceedings of the Very Large Databases Endowment (PVLDB), vol. 5, no. 12, pp. 1942-1945, 2012.
NoDBvldb2012.pdf2.18 MB


As data collections become larger and larger, users are faced with increasing bottlenecks in their data analysis. More data means more time to prepare the data, to load the data into the database and to execute the desired queries. Many applications already avoid using traditional database systems, e.g., scientific data analysis and social networks, due to their complexity and the increased data-to-query time, i.e. the time between getting the data and retrieving its first useful results. For many applications data collections keep growing fast, even on a daily basis, and this data deluge will only increase in the future, where it is expected to have much more data than what we can move or store, let alone analyze. In this demonstration, we will showcase a new philosophy for designing database systems called NoDB. NoDB aims at minimizing the data-to-query time, most prominently by removing the need to load data before launching queries. We will present our prototype implementation, PostgresRaw, built on top of PostgreSQL, which allows for efficient query execution over raw data files with zero initialization overhead. We will visually demonstrate how PostgresRaw incrementally and adaptively touches, parses, caches and indexes raw data files autonomously and exclusively as a side-effect of user queries.

Last updated on 12/21/2013