06472 Abstracts Collection - XQuery Implementation Paradigms

From 19.11.2006 to 22.11.2006, the Dagstuhl Seminar 06472 ``XQuery Implementation Paradigms'' was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available

The Family of MapReduce and Large Scale Data Processing Systems

In the last two decades, the continuous increase of computational power has produced an overwhelming flow of data which has called for a paradigm shift in the computing architecture and large scale data processing mechanisms. MapReduce is a simple and powerful programming model that enables easy development of scalable parallel applications to process vast amounts of data on large clusters of commodity machines. It isolates the application from the details of running a distributed program such as issues on data distribution, scheduling and fault tolerance. However, the original implementation of the MapReduce framework had some limitations that have been tackled by many research efforts in several followup works after its introduction. This article provides a comprehensive survey for a family of approaches and mechanisms of large scale data processing mechanisms that have been implemented based on the original idea of the MapReduce framework and are currently gaining a lot of momentum in both research and industrial communities. We also cover a set of introduced systems that have been implemented to provide declarative programming interfaces on top of the MapReduce framework. In addition, we review several large scale data processing systems that resemble some of the ideas of the MapReduce framework for different purposes and application scenarios. Finally, we discuss some of the future research directions for implementing the next generation of MapReduce-like solutions.Comment: arXiv admin note: text overlap with arXiv:1105.4252 by other author

HIGH PERFORMANCE XPATH EVALUATION IN XML STREAMS

This thesis presents methods for efficiently evaluating structural queries over tree-structured data streams. A data stream usually consists of a sequence of items that arrive in an order determined by the source. An application that uses such data cannot revisit an earlier item in the stream unless it buffers the item itself. Naive buffering methods are not practical due to the high throughput and indefinite length of data streams. Compared with the flat, relational-like data model for data streams that has received recent attention, processing a tree-structured XML data stream poses additional challenges, since a data item cannot, in general, be interpreted without taking structural information into account. In this thesis, we focus on the evaluation of XPath queries on streaming XML. As a W3C standard, XPath has become a core XML technology not only as a standalone query language but also as the foundation of XQuery and XSLT. Features such as subqueries and reverse axes make XPath a powerful query language but they also complicate XPath query processing. We present our work on XSQ, a streaming XPath query engine. Our methods are based on a novel segment-based evaluation scheme. XSQ uses very little memory and is able to process unbounded and unsegmented streaming data because it does not build a DOM tree in memory. It also provides high throughput by only processing the relevant portions of the data and low response time by returning results as early as possible. XSQ is the first streaming system to support complex XPath features such as multiple predicates, closure axes, aggregations, reverse axes, and subqueries. We also describe our work on XPaSS, an XPath-based publish-subscribe system that simultaneously evaluates a large number of XPath queries over XML streams. Unlike other similar systems that filter pre-segmented documents as results, XPaSS returns only the precisely delineated data specified by a user query. It uses a segment-sharing scheme instead of prefix- and suffix-sharing that are commonly used. In our experiments, XPaSS supports up to one million XPath subscriptions using a modest PC-class server, with a throughput comparable to that of the simpler filtering systems

Four Lessons in Versatility or How Query Languages Adapt to the Web

Exposing not only human-centered information, but machine-processable data on the Web is one of the commonalities of recent Web trends. It has enabled a new kind of applications and businesses where the data is used in ways not foreseen by the data providers. Yet this exposition has fractured the Web into islands of data, each in different Web formats: Some providers choose XML, others RDF, again others JSON or OWL, for their data, even in similar domains. This fracturing stifles innovation as application builders have to cope not only with one Web stack (e.g., XML technology) but with several ones, each of considerable complexity. With Xcerpt we have developed a rule- and pattern based query language that aims to give shield application builders from much of this complexity: In a single query language XML and RDF data can be accessed, processed, combined, and re-published. Though the need for combined access to XML and RDF data has been recognized in previous work (including the W3C’s GRDDL), our approach differs in four main aspects: (1) We provide a single language (rather than two separate or embedded languages), thus minimizing the conceptual overhead of dealing with disparate data formats. (2) Both the declarative (logic-based) and the operational semantics are unified in that they apply for querying XML and RDF in the same way. (3) We show that the resulting query language can be implemented reusing traditional database technology, if desirable. Nevertheless, we also give a unified evaluation approach based on interval labelings of graphs that is at least as fast as existing approaches for tree-shaped XML data, yet provides linear time and space querying also for many RDF graphs. We believe that Web query languages are the right tool for declarative data access in Web applications and that Xcerpt is a significant step towards a more convenient, yet highly efficient data access in a “Web of Data”

Efficient main memory-based XML stream processing

Applications that process XML documents as files or streams are naturally main-memory based. This makes main memory the bottleneck for scalability. This doctoral thesis addresses this problem and presents a toolkit for effective buffer management in main memory-based XML stream processors. XML document projection is an established technique for reducing the buffer requirements of main memory-based XML processors, where only data relevant to query evaluation is loaded into main memory buffers. We present a novel implementation of this task, where we use string matching algorithms designed for efficient keyword search in flat strings to navigate in tree-structured data. We then introduce an extension of the XQuery language, called FluX, that supports event-based query processing. Purely event-based queries of this language can be executed on streaming XML data in a very direct way. We develop an algorithm to efficiently rewrite XQueries into FluX. This algorithm is capable of exploiting order constraints derived from schemata to reduce the amount of buffering in query evaluation. During streaming query evaluation, we continuously purge buffers from data that is no longer relevant. By combining static query analysis with a dynamic analysis of the buffer contents, we effectively reduce the size of memory buffers. We have confirmed the efficacy of these techniques by extensive experiments and by publication at international venues. To compare our contributions to related work in a systematic manner, we contribute an abstract framework for XML stream processing. This framework allows us to gain a greater-picture view over the factors influencing the main memory consumption.Anwendungen, die XML-Dokumente als Dateien oder Ströme verarbeiten, sind natürlicherweise hauptspeicherbasiert. Für die Skalierbarkeit wird der Hauptspeicher damit zu einem Engpass. Diese Doktorarbeit widmet sich diesem Problem, zu dessen Lösung sie Werkzeuge für eine effektive Pufferverwaltung in hauptspeicherbasierten Prozessoren für XML-Datenströme vorstellt. Die Projektion von XML-Dokumenten ist eine etablierte Methode, um den Pufferverbrauch von hauptspeicherbasierten XML-Prozessoren zu reduzieren. Dabei werden nur jene Daten in den Hauptspeicherpuffer geladen, die für die Anfrageauswertung auch relevant sind. Wir präsentieren eine neue Implementierung dieser Aufgabe, wobei wir Algorithmen zur effizienten Suche in flachen Zeichenketten einsetzen, um in baumartig strukturierten Daten zu navigieren. Danach stellen wir eine Erweiterung der XQuery-Sprache vor, genannt FluX, welche eine ereignisbasierte Anfragebearbeitung erlaubt. Anfragen, die nur ereignisbasierte Konstrukte benutzen, können direkt über XML-Datenströmen ausgewertet werden. Dazu entwickeln wir einen Algorithmus, mit dessen Hilfe sich XQuery-Anfragen effizient in FluX übersetzen lassen. Dieser benutzt Ordnungsinformationen aus Datenschemata, womit das Puffern in der Anfragebearbeitung reduziert werden kann. Während der Verarbeitung des Datenstroms bereinigen wir laufend den Hauptspeicherpuffer von solchen Daten, die nicht länger relevant sind. Eine nachhaltige Reduzierung der Größe von Hauptspeicherpuffern gelingt durch die Kombination der statischen Anfrageanalyse mit einer dynamischen Analyse der Pufferinhalte. Die Effektivität dieser Puffermanagement-Techniken erfährt ihre Bestätigung in umfangreichen Experimenten und internationalen Publikationen. Für einen systematischen Vergleich unserer Beiträge mit der aktuellen Literatur entwickeln wir ein abstraktes System zur Modellierung von Prozessoren zur XML-Stromverarbeitung. So können wir die spezifischen Faktoren herausgreifen, die den Hauptspeicherverbrauch beeinflussen

Implementation of Web Query Languages Reconsidered

Visions of the next generation Web such as the "Semantic Web" or the "Web 2.0" have triggered the emergence of a multitude of data formats. These formats have different characteristics as far as the shape of data is concerned (for example tree- vs. graph-shaped). They are accompanied by a puzzlingly large number of query languages each limited to one data format. Thus, a key feature of the Web, namely to make it possible to access anything published by anyone, is compromised. This thesis is devoted to versatile query languages capable of accessing data in a variety of Web formats. The issue is addressed from three angles: language design, common, yet uniform semantics, and common, yet uniform evaluation. % Thus it is divided in three parts: First, we consider the query language Xcerpt as an example of the advocated class of versatile Web query languages. Using this concrete exemplar allows us to clarify and discuss the vision of versatility in detail. Second, a number of query languages, XPath, XQuery, SPARQL, and Xcerpt, are translated into a common intermediary language, CIQLog. This language has a purely logical semantics, which makes it easily amenable to optimizations. As a side effect, this provides the, to the best of our knowledge, first logical semantics for XQuery and SPARQL. It is a very useful tool for understanding the commonalities and differences of the considered languages. Third, the intermediate logical language is translated into a query algebra, CIQCAG. The core feature of CIQCAG is that it scales from tree- to graph-shaped data and queries without efficiency losses when tree-data and -queries are considered: it is shown that, in these cases, optimal complexities are achieved. CIQCAG is also shown to evaluate each of the aforementioned query languages with a complexity at least as good as the best known evaluation methods so far. For example, navigational XPath is evaluated with space complexity O(q d) and time complexity O(q n) where q is the query size, n the data size, and d the depth of the (tree-shaped) data. CIQCAG is further shown to provide linear time and space evaluation of tree-shaped queries for a larger class of graph-shaped data than any method previously proposed. This larger class of graph-shaped data, called continuous-image graphs, short CIGs, is introduced for the first time in this thesis. A (directed) graph is a CIG if its nodes can be totally ordered in such a manner that, for this order, the children of any node form a continuous interval. CIQCAG achieves these properties by employing a novel data structure, called sequence map, that allows an efficient evaluation of tree-shaped queries, or of tree-shaped cores of graph-shaped queries on any graph-shaped data. While being ideally suited to trees and CIGs, the data structure gracefully degrades to unrestricted graphs. It yields a remarkably efficient evaluation on graph-shaped data that only a few edges prevent from being trees or CIGs

An Algebraic Approach to XQuery Optimization

As more data is stored in XML and more applications need to process this data, XML query optimization becomes performance critical. While optimization techniques for relational databases have been developed over the last thirty years, the optimization of XML queries poses new challenges. Query optimizers for XQuery, the standard query language for XML data, need to consider both document order and sequence order. Nevertheless, algebraic optimization proved powerful in query optimizers in relational and object oriented databases. Thus, this dissertation presents an algebraic approach to XQuery optimization. In this thesis, an algebra over sequences is presented that allows for a simple translation of XQuery into this algebra. The formal definitions of the operators in this algebra allow us to reason formally about algebraic optimizations. This thesis leverages the power of this formalism when unnesting nested XQuery expressions. In almost all cases unnesting nested queries in XQuery reduces query execution times from hours to seconds or milliseconds. Moreover, this dissertation presents three basic algebraic patterns of nested queries. For every basic pattern a decision tree is developed to select the most effective unnesting equivalence for a given query. Query unnesting extends the search space that can be considered during cost-based optimization of XQuery. As a result, substantially more efficient query execution plans may be detected. This thesis presents two more important cases where the number of plan alternatives leads to substantially shorter query execution times: join ordering and reordering location steps in path expressions. Our algebraic framework detects cases where document order or sequence order is destroyed. However, state-of-the-art techniques for order optimization in cost-based query optimizers have efficient mechanisms to repair order in these cases. The results obtained for query unnesting and cost-based optimization of XQuery underline the need for an algebraic approach to XQuery optimization for efficient XML query processing. Moreover, they are applicable to optimization in relational databases where order semantics are considered

Rewriting Declarative Query Languages

Queries against databases are formulated in declarative languages. Examples are the relational query language SQL and XPath or XQuery for querying data stored in XML. Using a declarative query language, the querist does not need to know about or decide on anything about the actual strategy a system uses to answer the query. Instead, the system can freely choose among the algorithms it employs to answer a query. Predominantly, query processing in the relational context is accomplished using a relational algebra. To this end, the query is translated into a logical algebra. The algebra consists of logical operators which facilitate the application of various optimization techniques. For example, logical algebra expressions can be rewritten in order to yield more efficient expressions. In order to query XML data, XPath and XQuery have been developed. Both are declarative query languages and, hence, can benefit from powerful optimizations. For instance, they could be evaluated using an algebraic framework. However, in general, the existing approaches are not directly utilizable for XML query processing. This thesis has two goals. The first goal is to overcome the above-mentioned misfits of XML query processing, making it ready for industrial-strength settings. Specifically, we develop an algebraic framework that is designed for the efficient evaluation of XPath and XQuery. To this end, we define an order-aware logical algebra and a translation of XPath into this algebra. Furthermore, based on the resulting algebraic expressions, we present rewrites in order to speed up the execution of such queries. The second goal is to investigate rewriting techniques in the relational context. To this end, we present rewrites based on algebraic equivalences that unnest nested SQL queries with disjunctions. Specifically, we present equivalences for unnesting algebraic expressions with bypass operators to handle disjunctive linking and correlation. Our approach can be applied to quantified table subqueries as well as scalar subqueries. For all our results, we present experiments that demonstrate the effectiveness of the developed approaches