Dragan Tomic

Array requirements for scientific applications and an implementation for microsoft SQL server

This paper outlines certain scenarios from the fields of astrophysics and fluid dynamics simulations which require high performance data warehouses that support array data type. A common feature of all these use cases is that subsetting and preprocessing the data on the server side (as far as possible inside the database server process) is necessary to avoid the client-server overhead and to minimize IO utilization. Analyzing and summarizing the requirements of the various fields help software engineers to come up with a comprehensive design of an array extension to relational database systems that covers a wide range of scientific applications. We also present a working implementation of an array data type for Microsoft SQL Server 2008 to support large-scale scientific applications. We introduce the design of the array type, results from a performance evaluation, and discuss the lessons learned from this implementation. The library can be downloaded from our website at http://voservices.net/sqlarray/.

Mapping XML to a Wide Sparse Table

XML is commonly supported by SQL database systems. However, existing mappings of XML to tables can only deliver satisfactory query performance for limited use cases. In this paper, we propose a novel mapping of XML data into one wide table whose columns are sparsely populated. This mapping provides good performance for document types and queries that are observed in enterprise applications but are not supported efficiently by existing work. XML queries are evaluated by translating them into SQL queries over the wide sparsely-populated table. We show how to translate full XPath 1.0 into SQL. Based on the characteristics of the new mapping, we present rewriting optimizations that dramatically reduce the number of joins. Experiments demonstrate that query evaluation over the new mapping delivers considerable improvements over existing techniques for the target use cases.

Managing collections of XML schemas in microsoft SQL server 2005

Schema evolution is of two kinds: (a) those requiring instance transformation because the application is simpler to develop when it works only with one version of the schema, and (b) those in which the old data must be preserved and instance transformation must be avoided. The latter is important in practice but has received scant attention in the literature. Data conforming to multiple versions of the XML schema must be maintained, indexed, and manipulated using the same query. Microsoft’s SQL Server 2005 introduces XML schema collections to address both types of schema evolution.