In DataCell, we design streaming functionalities in a mod- ern relational database kernel which targets big data analyt- ics. This includes exploitation of both its storage/execution engine and its optimizer infrastructure. We investigate the opportunities and challenges that arise with such a direction and we show that it carries significant advantages for mod- ern applications in need for online analytics such as web logs, network monitoring and scientific data management. The major challenge then becomes the efficient support for specialized stream features, e.g., multi-query processing and incremental window-based processing as well as exploiting standard DBMS functionalities in a streaming environment such as indexing. In this demo, we present the DataCell system, an exten- sion of the MonetDB open-source column-store for online an- alytics. The demo gives the user the opportunity to experi- ence the features of DataCell such as processing both stream and persistent data and performing window based process- ing. The demo provides a visual interface to monitor the critical system components, e.g., how query plans transform from typical DBMS query plans to online query plans, how data flows through the query plans as the streams evolve, how DataCell maintains intermediate results in columnar form to avoid repeated evaluation of the same stream por- tions, etc. The demo also provides the ability to interac- tively set the test scenarios regarding input data and various DataCell knobs.

Additional Metadata
THEME Information (theme 2)
Publisher PVLDB
Conference International Conference on Very Large Databases
Liarou, E, Idreos, S, Manegold, S, & Kersten, M.L. (2012). MonetDB/DataCell: Online Analytics in a Streaming Column-Store. In Proceedings of International Conference on Very Large Data Bases 2012 (38). PVLDB.