MonetDB/XQuery: a fast XQuery processor powered by a relational engine

Relational XQuery systems try to re-use mature relational data management infrastructures to create fast and scalable XML database technology. This paper describes the main features, key contributions, and lessons learned while implementing such a system. Its architecture consists of (i) a range-based encoding of XML documents into relational tables, (ii) a compilation technique that translates XQuery into a basic relational algebra, (iii) a restricted (order) property-aware peephole relational query optimization strategy, and (iv) a mapping from XML update statements into relational updates. Thus, this system implements all essential XML database functionalities (rather than a single feature) such that we can learn from the full consequences of our architectural decisions. While implementing this system, we had to extend the state-of-the-art with a number of new technical contributions, such as loop-lifted staircase join and efficient relational query evaluation strategies for XQuery theta-joins with existential semantics. These contributions as well as the architectural lessons learned are also deemed valuable for other relational back-end engines. The performance and scalability of the resulting system is evaluated on the XMark benchmark up to data sizes of 11GB. The performance section also provides an extensive benchmark comparison of all major XMark results published previously, which confirm that the goal of purely relational XQuery processing, namely speed and scalability, was met

AMaχoS—Abstract Machine for Xcerpt

Web query languages promise convenient and efficient access to Web data such as XML, RDF, or Topic Maps. Xcerpt is one such Web query language with strong emphasis on novel high-level constructs for effective and convenient query authoring, particularly tailored to versatile access to data in different Web formats such as XML or RDF. However, so far it lacks an efficient implementation to supplement the convenient language features. AMaχoS is an abstract machine implementation for Xcerpt that aims at efficiency and ease of deployment. It strictly separates compilation and execution of queries: Queries are compiled once to abstract machine code that consists in (1) a code segment with instructions for evaluating each rule and (2) a hint segment that provides the abstract machine with optimization hints derived by the query compilation. This article summarizes the motivation and principles behind AMaχoS and discusses how its current architecture realizes these principles

Algebraic incremental maintenance of XML views

International audienceMaterialized views can bring important performance benefits when querying XML documents. In the presence of XML document changes, materialized views need to be updated to faithfully reflect the changed document. In this work, we present an algebraic approach for propagating source updates to XML materialized views expressed in a powerful XML tree pattern formalism. Our approach differs from the state of the art in the area in two important ways. First, it relies on set-oriented, algebraic operations, to be contrasted with node-based previous approaches. Second, it exploits state-of-the-art features of XML stores and XML query evaluation engines, notably XML structural identifiers and associated structural join algorithms. We present algorithms for determining how updates should be propagated to views, and highlight the benefits of our approach over existing algorithms through a series of experiments

Database architecture evolution: Mammals flourished long before dinosaurs became extinct

The holy grail for database architecture research is to find a solution that is Scalable & Speedy, to run on anything from small ARM processors up to globally distributed compute clusters, Stable & Secure, to service a broad user community, Small & Simple, to be comprehensible to a small team of programmers, Self-managing, to let it run out-of-the-box without hassle. In this paper, we provide a trip report on this quest, covering both past experiences, ongoing research on hardware-conscious algorithms, and novel ways towards self-management specifically focused on column store solutions

Automaton Meets Query Algebra: Towards a Unified Model for XQuery Evaluation over XML Data Streams

Abstract. In this work, we address the efficient evaluation of XQuery expressions over continuous XML data streams, which is essential for a broad range of applications including monitoring systems and infor-mation dissemination systems. While previous work has shown that au-tomata theory is suited for on-the-fly pattern retrieval over XML data streams, we find that automata-based approaches suffer from being not as flexibly optimizable as algebraic query systems. In fact, they enforce a rigid data-driven paradigm of execution. We thus now propose a unified query model to augment automata-style processing with algebra-based query optimization techniques. The proposed model has been success-fully applied in the Raindrop stream processing system. Our experimen-tal study confirms considerable performance gains with both established optimization techniques and our novel query rewrite rules.