February 2005
Trends in High Performance Computing
Jim Gray, Microsoft
David T. Liu, University of California at Berkeley
Maria Nieto-Santisteban, Johns Hopkins University
Alex Szalay, Johns Hopkins University
David DeWitt, University of Wisconsin
Gerd Heber, Cornell University

Other useful database features

Database systems are also approaching the peta-scale data management problem driven largely by the need to manage huge information stores for the commercial and governmental sectors. They hide the file concept and deal with data collections. They can federate many different sources letting the program view them all as a single data collection. They also let the program pivot on any data attributes.

Database systems provide very powerful data definition tools to specify the abstract data formats and also specify how the data is organized. They routinely allow the data to be replicated so that it can be organized in several ways (e.g., by time, by space, by other attributes). These techniques have evolved from mere indices to materialized views that can combine data from many sources.

Database systems provide powerful associative search (search by value rather than by location) and provide automatic parallel access and execution essential to peta-scale data analysis. They provide non-procedural and parallel data search to quickly find data subsets, as well as many tools to automate data design and management.

In addition, data analysis using data cubes has made huge advances, and now efforts are focused on integrating machine learning algorithms that infer trends, do data clustering, and detect anomalies. All these tools are aimed at making it easy to analyze commercial data, but they are equally applicable to scientific data analysis.

Ending the impedance mismatch

Conventional tabular database systems are adequate for analyzing objects (e.g., galaxies, spectra, proteins, events, etc.). But even there, the support for time-sequence, spatial, text and other data types is often awkward. Database systems have not traditionally supported science’s core data type: the N-dimensional array. Arrays have had to masquerade as blobs (binary large objects) in most systems. This collection of problems is generally called the impedance mismatch, meaning the mismatch between the programming model and the database capabilities. The impedance mismatch has made it difficult to map many science applications into conventional tabular database systems.

But, database systems are changing. They are being integrated with programming languages so that they can support object-oriented databases. This new generation of object relational database systems treats any data type (be it a native float, an array, a string, or a compound object like an XML or HTML document) as an encapsulated type that can be stored as a value in a field of a record. Actually, these systems allow the values to be either stored directly in the record (embedded) or to be pointed to by the record (linked). This linking-embedding object model nicely accommodates the integration of database systems and file systems — files are treated as linked-objects. Queries can read and write these extended types using the same techniques they use on native types. Indeed we expect HDF and other file formats to be added as types to most database systems.

Once you can put your types and your programs inside the database you get the parallelism, non-procedural query, and data independence advantages of traditional database systems. We believe this database, file system, and programming language integration will be the key to managing and accessing peta-scale data management systems in the future.

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Reference this article
Gray, J., Liu, D., Nieto-Santisteban, M., Szalay, A., DeWitt, D., Heber, G. "Scientific Data Management in the Coming Decade," CTWatch Quarterly, Volume 1, Number 1, February 2005. http://www.ctwatch.org/quarterly/articles/2005/02/scientific-data-management/

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