Parameter Curation for Benchmark Queries

Andrey Gubichev, Peter Boncz

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Abstract

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).
Original languageEnglish
Title of host publicationPerformance Characterization and Benchmarking: Traditional to Big Data - 6th TPC Technology Conference, TPCTC 2014, Revised Selected Papers
PublisherSpringer/Verlag
Pages113-129
Number of pages17
Volume8904
ISBN (Electronic)9783319153490
DOIs
Publication statusPublished - 2014
Event6th TPC Technology Conference on Performance Evaluation and Benchmarking, TPCTC 2014 held in conjunction with 40th International Conference on Very Large Data Bases, VLDB 2014 - Hangzhou, China
Duration: 1 Sept 20145 Sept 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8904
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference6th TPC Technology Conference on Performance Evaluation and Benchmarking, TPCTC 2014 held in conjunction with 40th International Conference on Very Large Data Bases, VLDB 2014
Country/TerritoryChina
CityHangzhou
Period1/09/145/09/14

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