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Robust Runtime Optimization and Skew-Resistant Execution of Analytical SPARQL Queries on Pig

  • Spyros Kotoulas
  • Jacopo Urbani
  • Peter Boncz
  • Peter Mika
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7649)

Abstract

We describe a system that incrementally translates SPARQL queries to Pig Latin and executes them on a Hadoop cluster. This system is designed to work efficiently on complex queries with many self-joins over huge datasets, avoiding job failures even in the case of joins with unexpected high-value skew. To be robust against cost estimation errors, our system interleaves query optimization with query execution, determining the next steps to take based on data samples and statistics gathered during the previous step. Furthermore, we have developed a novel skew-resistant join algorithm that replicates tuples corresponding to popular keys. We evaluate the effectiveness of our approach both on a synthetic benchmark known to generate complex queries (BSBM-BI) as well as on a Yahoo! case of data analysis using RDF data crawled from the web. Our results indicate that our system is indeed capable of processing huge datasets without pre-computed statistics while exhibiting good load-balancing properties.

Keywords

Query Processing Query Optimization Query Execution SPARQL Query Link Open Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Spyros Kotoulas
    • 1
    • 2
  • Jacopo Urbani
    • 1
  • Peter Boncz
    • 3
    • 2
  • Peter Mika
    • 4
  1. 1.IBM ResearchIreland
  2. 2.Vrije Universiteit AmsterdamThe Netherlands
  3. 3.CWI AmsterdamThe Netherlands
  4. 4.Yahoo! Research BarcelonaSpain

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