Performance Characterization and Benchmarking. Traditional to Big Data

Volume 8904 of the series Lecture Notes in Computer Science pp 113-129


Parameter Curation for Benchmark Queries

  • Andrey GubichevAffiliated withTU Munich Email author 
  • , Peter BonczAffiliated withCWI

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In this paper we consider the problem of generating parameters for benchmark queries so these have stable behavior despite being executed on datasets (real-world or synthetic) with skewed data distributions and value correlations. We show that uniform random sampling of the substitution parameters is not well suited for such benchmarks, since it results in unpredictable runtime behavior of queries. We present our approach of Parameter Curation with the goal of selecting parameter bindings that have consistently low-variance intermediate query result sizes throughout the query plan. Our solution is illustrated with IMDB data and the recently proposed LDBC Social Network Benchmark (SNB).