39,719 research outputs found
A survey of parallel execution strategies for transitive closure and logic programs
An important feature of database technology of the nineties is the use of parallelism for speeding up the execution of complex queries. This technology is being tested in several experimental database architectures and a few commercial systems for conventional select-project-join queries. In particular, hash-based fragmentation is used to distribute data to disks under the control of different processors in order to perform selections and joins in parallel. With the development of new query languages, and in particular with the definition of transitive closure queries and of more general logic programming queries, the new dimension of recursion has been added to query processing. Recursive queries are complex; at the same time, their regular structure is particularly suited for parallel execution, and parallelism may give a high efficiency gain. We survey the approaches to parallel execution of recursive queries that have been presented in the recent literature. We observe that research on parallel execution of recursive queries is separated into two distinct subareas, one focused on the transitive closure of Relational Algebra expressions, the other one focused on optimization of more general Datalog queries. Though the subareas seem radically different because of the approach and formalism used, they have many common features. This is not surprising, because most typical Datalog queries can be solved by means of the transitive closure of simple algebraic expressions. We first analyze the relationship between the transitive closure of expressions in Relational Algebra and Datalog programs. We then review sequential methods for evaluating transitive closure, distinguishing iterative and direct methods. We address the parallelization of these methods, by discussing various forms of parallelization. Data fragmentation plays an important role in obtaining parallel execution; we describe hash-based and semantic fragmentation. Finally, we consider Datalog queries, and present general methods for parallel rule execution; we recognize the similarities between these methods and the methods reviewed previously, when the former are applied to linear Datalog queries. We also provide a quantitative analysis that shows the impact of the initial data distribution on the performance of methods
Partout: A Distributed Engine for Efficient RDF Processing
The increasing interest in Semantic Web technologies has led not only to a
rapid growth of semantic data on the Web but also to an increasing number of
backend applications with already more than a trillion triples in some cases.
Confronted with such huge amounts of data and the future growth, existing
state-of-the-art systems for storing RDF and processing SPARQL queries are no
longer sufficient. In this paper, we introduce Partout, a distributed engine
for efficient RDF processing in a cluster of machines. We propose an effective
approach for fragmenting RDF data sets based on a query log, allocating the
fragments to nodes in a cluster, and finding the optimal configuration. Partout
can efficiently handle updates and its query optimizer produces efficient query
execution plans for ad-hoc SPARQL queries. Our experiments show the superiority
of our approach to state-of-the-art approaches for partitioning and distributed
SPARQL query processing
Extending a multi-set relational algebra to a parallel environment
Parallel database systems will very probably be the future for high-performance data-intensive applications. In the past decade, many parallel database systems have been developed, together with many languages and approaches to specify operations in these systems. A common background is still missing, however. This paper proposes an extended relational algebra for this purpose, based on the well-known standard relational algebra. The extended algebra provides both complete database manipulation language features, and data distribution and process allocation primitives to describe parallelism. It is defined in terms of multi-sets of tuples to allow handling of duplicates and to obtain a close connection to the world of high-performance data processing. Due to its algebraic nature, the language is well suited for optimization and parallelization through expression rewriting. The proposed language can be used as a database manipulation language on its own, as has been done in the PRISMA parallel database project, or as a formal basis for other languages, like SQL
Performance Guarantees for Distributed Reachability Queries
In the real world a graph is often fragmented and distributed across
different sites. This highlights the need for evaluating queries on distributed
graphs. This paper proposes distributed evaluation algorithms for three classes
of queries: reachability for determining whether one node can reach another,
bounded reachability for deciding whether there exists a path of a bounded
length between a pair of nodes, and regular reachability for checking whether
there exists a path connecting two nodes such that the node labels on the path
form a string in a given regular expression. We develop these algorithms based
on partial evaluation, to explore parallel computation. When evaluating a query
Q on a distributed graph G, we show that these algorithms possess the following
performance guarantees, no matter how G is fragmented and distributed: (1) each
site is visited only once; (2) the total network traffic is determined by the
size of Q and the fragmentation of G, independent of the size of G; and (3) the
response time is decided by the largest fragment of G rather than the entire G.
In addition, we show that these algorithms can be readily implemented in the
MapReduce framework. Using synthetic and real-life data, we experimentally
verify that these algorithms are scalable on large graphs, regardless of how
the graphs are distributed.Comment: VLDB201
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