An Empirical Evaluation of XQuery Processors

This paper presents an extensive and detailed experimental evaluation of XQuery processors. The study consists of running five publicly available XQuery benchmarks --- the Michigan benchmark (MBench), XBench, XMach-1, XMark and X007 --- on six XQuery processors, three stand-alone (file-based) XQuery processors (Galax, Qizx/Open, Saxon-B) and three XML/XQuery database systems (BerkeleyDB/XML, MonetDB/XQuery, X-Hive/DB). Next to assessing and comparing the functionality, performance and scalability for the various systems, the major focus of this work is to report in detail about the experiences made while performing such an exhaustive study, to discuss all the problems that we encountered and how we solved them, and hence to hopefully provide some guidelines (or even a recipe) for performing reproducible large-scale experime

An Empirical Evaluation of XQuery Processors

This paper presents an extensive and detailed experimental evaluation of XQuery processors. The study consists of running five publicly available XQuery benchmarks --- the Michigan benchmark (MBench), XBench, XMach-1, XMark and X007 --- on six XQuery processors, three stand-alone (file-based) XQuery processors (Galax, Qizx/Open, Saxon-B) and three XML/XQuery database systems (BerkeleyDB/XML, MonetDB/XQuery, X-Hive/DB). Next to assessing and comparing the functionality, performance and scalability for the various systems, the major focus of this work is to report in detail about the experiences made while performing such an exhaustive study, to discuss all the problems that we encountered and how we solved them, and hence to hopefully provide some guidelines (or even a recipe) for performing reproducible large-scale experimental research and system evaluation

Efficient resource utilization in shared-everything environments

Efficient resource usage is a key to achieve better performance in parallel database systems. Up to now, most research has focussed on balancing the load on several resources of the same type, i.e. balancing either CPU load or I/O load. In this paper, we present emph{floating probe, a strategy for parallel evaluation of pipelining segments in a shared-everything environment that provides dynamic load balancing between CPU- and I/O-resources. The key idea of floating probe is to overlap---as much as possible with respect to data dependencies---I/O-bound build phase and CPU-bound probe phase of pipelining segments to improve resource utilization. Simulation results show, that floating probe achieves shorter execution times while consuming less memory than conventional pipelining strategies

Cracking the database store

Query performance strongly depends on finding an execution plan that touches as few superfluous tuples as possible. The access structures d

Pathfinder: XQuery - The Relational Way

Relational query processors are probably the best understood (as well as the best engineered) query engines available today. Although carefully tuned to process instances of the relational model (tables of tuples), these processors can also provide a foundation for the evaluation of "alien" (non-relational) query languages: if a relational encoding of the alien data model and its associated query language is given, the RDBMS may act like a special-purpose processor for the new language

Adaptive indexing in modern database kernels

Physical design represents one of the hardest problems for database management systems. Without proper tuning, systems cannot achieve good performance. Offline indexing creates indexes a priori assuming good workload knowledge and idle time. More recently, online indexing monitors the workload trends and creates or drops indexes online. Adaptive indexing takes another step towards completely automating the tuning process of a database system, by enabling incremental and partial online indexing. The main idea is that physical design changes continuously, adaptively, partially, incrementally and on demand while processing queries as part of the execution operators. As such it brings a plethora of opportunities for rethinking and improving every single corner of database system design. We will analyze the indexing space between offline, online and adaptive indexing through several state of the art indexing techniques, e. g., what-if analysis and soft indexes. We will discuss in detail adaptive indexing techniques such as database cracking, adaptive merging, sideways cracking and various hybrids that try to balance the online tuning overhead with the convergence speed to optimal performance. In addition, we will discuss how various aspects of modern techniques for database architectures, such as vectorization, bulk processing, column-store execution and storage affect adaptive indexing. Finally, we will discuss several open research topics towards fully automomous database kernels

Storing XML Documents in Databases

The authors introduce concepts for loading large amounts of XML documents into databases where the documents are stored and maintained. The goal is to make XML databases as unobtrusive in multi-tier systems as possible and at the same time provide as many services defined by the XML standards as possible. The ubiquity of XML has sparked great interest in deploying concepts known from Relational Database Management Systems such as declarative query languages, transactions, indexes and integrity constraints. This chapter presents now bulkloading is done in Monet XML, a main memory XML database system, and evaluates the cost of bulkloading and bulk deletion with respect to strategies which base on insertion and deletion of individual nodes. Additionally, we survey the applicability of the techniques to a wider class of XML storage schemas

MonetDB/XQuery - Consistent & Efficient Updates on the Pre/Post Plane

Relational XQuery processors aim at leveraging mature relational DBMS query processing technology to provide scalability and efficiency. To achieve this goal, various storage schemes have been proposed to encode the tree structure of XML documents in flat relational tables. Basically, two classes can be identified: (1) encodings using fixed-length surrogates, like the preorder ranks in the pre/post encoding [5] or the equivalent pre/size/level encoding [8], and (2) encodings using variable-length surrogates, like, e.g., ORDPATH [9] or P-PBiTree [12]. Recent research [1] showed a clear advantage of the former for efficient evaluation of XPath location steps, exploiting techniques like cheap node order tests, positional lookup, and node skipping in staircase join [7]. However, once updates are involved, variable-length surrogates are often considered the better choice, mainly as a straightforward implementation of structural XML updates using fixed-length surrogates faces two performance bottlenecks: (i) high physical cost (the preorder ranks of all nodes following the update position must be modified—on average 50% of the document), and (ii) low transaction concurrency (updating the size of all ancestor nodes causes lock contention on the document root)

Data Vaults: A Symbiosis between Database Technology and Scientific File Repositories

In this short paper we outline the Data Vault, a database-attached external file repository. It provides a true symbiosis between a DBMS and existing file-based repositories. Data is kept in its original format while scalable processing functionality is provided through the DBMS facilities. In particular, it provides transparent access to all data kept in the repository through an (array-based) query language using the file-type specific scientific libraries. The design space for data vaults is characterized by requirements coming from various fields. We present a reference architecture for their realization in (commercial) DBMSs and a concrete implementation in MonetDB for remote sensing data geared at content-based image retrieval