Matthias Nicola

XML parsing: a threat to database performance

XML parsing is generally known to have poor performance characteristics relative to transactional database processing. Yet, its potentially fatal impact on overall database performance is being underestimated. We report real-word database applications where XML parsing performance is a key obstacle to a successful XML deployment. There is a considerable share of XML database applications which are prone to fail at an early and simple road block: XML parsing. We analyze XML parsing performance and quantify the extra overhead of DTD and schema validation. Comparison with relational database performance shows that the desired response times and transaction rates over XML data can not be achieved without major improvements in XML parsing technology. Thus, we identify research topics which are most promising for XML parser performance in database systems.

Generating SQL/XML query and update statements

The XML support in relational databases and the SQL/XML language are still relatively new as compared to purely relational databases and traditional SQL. Today, most database users have a strong relational and SQL background. SQL/XML enables users to perform queries and updates across XML and relational data, but many struggle with writing SQL/XML statements or XQuery update expressions. One reason is the novelty of SQL/XML and of the XQuery expressions that must be included. Another problem is that the tree structure of the XML data may be unknown or difficult to understand for the user. Evolving XML Schemas as well as hybrid XML/relational schemas make it even harder to write SQL/XML statements. Also, legacy applications use SQL but may require access to XML data without costly code changes. Motivated by these challenges, we developed a method to generate SQL/XML query and update statements automatically. The input is either a GUI or a regular SQL statement that uses logical data item names irrespective of their actual location in relational or XML columns in the database. The output is a SQL/XML statement that queries or updates relational and XML data as needed to carry out the original user statement. This relieves the user and simplifies schema evolution and integration. We have prototyped and tested the proposed method on top of DB2 9.5.

Lessons learned from DB2 pureXML applications: a practitioner's perspective

Beyond using XML as a message format, more and more companies are storing XML data permanently in a database. Database researchers and vendors alike have spent a lot of effort on designing, studying, and implementing XML database technology. In this paper we report our experiences from working with a broad variety of companies that have developed and deployed XML applications on top of DB2. We discuss three real-world XML database scenarios and their design considerations, and describe recurring patterns in XML applications. We highlight common concepts and observations, and document challenges that point to future work for the database community.

Comparing XML processing performance in middleware and database: a case study

XML processing is at the core of many middleware systems. In recent years XML databases have become widely available. This article identifies three common XML processing use cases, and compares their performance when XML manipulation is performed in an XML database with equivalent XML manipulation implemented through middleware application code. The article concludes with guidelines for XML processing placement and identifies areas for further study.

DB2 10.5 mit BLU Acceleration: Optimiert für komplexe analytische Anfragen

ZusammenfassungMit der Einführung von DB2 10.5 im Juni 2013 bietet DB2 eine neue Technologie, die einen deutlichen Leistungssprung für die Verarbeitung komplexer Datenbankanfragen erlaubt, zum Beispiel in Data Warehouse- und Data Mart-Anwendungen. Hinter dem Namen „BLU Acceleration“ verbirgt sich eine Kombination mehrerer fundamentaler Neuheiten in DB2, wie zum Beispiel spaltenorientierte Speicherung, ordnungserhaltende Komprimierung, Anfrageauswertung auf komprimierten Daten, Vektorverarbeitung der Daten auf Hardwareebene, und „Data Skipping“. Dieser Artikel bietet ein Überblick über DB2 10.5 mit BLU Acceleration (kurz: BLU).

Case studies in hardware XPath acceleration

The high increase in usage of XML in electronic data exchange introduces new challenges for efficient processing of XML data. Applications that heavily use XML need to be able to quickly extract the relevant parts of the XML data, often using the XPath language for addressing XML document parts. High speed execution of XPath requests and queries is therefore becoming a critical requirement in many application domains, including XML databases and event processing. This work explores the potential for accelerating XPath processing in these domains using specialized hardware. This in turn poses the challenges of integrating specialized hardware with general-purpose application code. We present the design decisions behind building an integration layer to bridge between applications and the hardware, and describe our implementation. We discuss the factors that affect the acceleration potential, and show that despite the transmission overheads associated with off-loading XPath processing to the specialized co-processor, significant speedups can be obtained, ranging from modest 11% improvements in the event-processing domain, to over 6x speedup factor in the healthcare domain.