Discussion of bigbench: A proposed industry standard performance benchmark for big data

Chaitanya Baru; Milind Bhandarkar; Carlo Curino; Manuel Danisch; Michael Frank; Bhaskar Gowda; Hans Arno Jacobsen; Huang Jie; Dileep Kumar; Raghunath Nambiar; Meikel Poess; Francois Raab; Tilmann Rabl; Nishkam Ravi; Kai Sachs; Saptak Sen; Lan Yi; Choonhan Youn

doi:10.1007/978-3-319-15350-6_4

Discussion of bigbench: A proposed industry standard performance benchmark for big data

Chaitanya Baru
, Milind Bhandarkar
, Carlo Curino
, Manuel Danisch
, Michael Frank
, Bhaskar Gowda
, Hans Arno Jacobsen
, Huang Jie
, Dileep Kumar
, Raghunath Nambiar
, Meikel Poess
, Francois Raab
, Tilmann Rabl
, Nishkam Ravi
, Kai Sachs
, Saptak Sen
, Lan Yi
, Choonhan Youn

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

21 Scopus citations

Abstract

Enterprises perceive a huge opportunity in mining information that can be found in big data. New storage systems and processing paradigms are allowing for ever larger data sets to be collected and analyzed. The high demand for data analytics and rapid development in technologies has led to a sizable ecosystem of big data processing systems. However, the lack of established, standardized benchmarks makes it difficult for users to choose the appropriate systems that suit their requirements. To address this problem, we have developed the BigBench benchmark specification. BigBench is the first end-to-end big data analytics benchmark suite. In this paper, we present the BigBench benchmark and analyze the workload from technical as well as business point of view. We characterize the queries in the workload along different dimensions, according to their functional characteristics, and also analyze their.

Original language	English
Title of host publication	Performance Characterization and Benchmarking
Subtitle of host publication	Traditional to Big Data - 6th TPC Technology Conference, TPCTC 2014, Revised Selected Papers
Editors	Meikel Poess, Raghunath Nambiar
Publisher	Springer Verlag
Pages	44-63
Number of pages	20
ISBN (Electronic)	9783319153490
DOIs	https://doi.org/10.1007/978-3-319-15350-6_4
State	Published - 2015
Externally published	Yes
Event	6th 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 Sep 2014 → 5 Sep 2014

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	8904
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	6th 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/Territory	China
City	Hangzhou
Period	1/09/14 → 5/09/14

Access to Document

10.1007/978-3-319-15350-6_4

Cite this

Baru, C., Bhandarkar, M., Curino, C., Danisch, M., Frank, M., Gowda, B., Jacobsen, H. A., Jie, H., Kumar, D., Nambiar, R., Poess, M., Raab, F., Rabl, T., Ravi, N., Sachs, K., Sen, S., Yi, L., & Youn, C. (2015). Discussion of bigbench: A proposed industry standard performance benchmark for big data. In M. Poess, & R. Nambiar (Eds.), Performance Characterization and Benchmarking: Traditional to Big Data - 6th TPC Technology Conference, TPCTC 2014, Revised Selected Papers (pp. 44-63). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8904). Springer Verlag. https://doi.org/10.1007/978-3-319-15350-6_4

Baru, Chaitanya ; Bhandarkar, Milind ; Curino, Carlo et al. / Discussion of bigbench : A proposed industry standard performance benchmark for big data. Performance Characterization and Benchmarking: Traditional to Big Data - 6th TPC Technology Conference, TPCTC 2014, Revised Selected Papers. editor / Meikel Poess ; Raghunath Nambiar. Springer Verlag, 2015. pp. 44-63 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{dc4355c539934f4a9b29e9ecd76ec81d,

title = "Discussion of bigbench: A proposed industry standard performance benchmark for big data",

abstract = "Enterprises perceive a huge opportunity in mining information that can be found in big data. New storage systems and processing paradigms are allowing for ever larger data sets to be collected and analyzed. The high demand for data analytics and rapid development in technologies has led to a sizable ecosystem of big data processing systems. However, the lack of established, standardized benchmarks makes it difficult for users to choose the appropriate systems that suit their requirements. To address this problem, we have developed the BigBench benchmark specification. BigBench is the first end-to-end big data analytics benchmark suite. In this paper, we present the BigBench benchmark and analyze the workload from technical as well as business point of view. We characterize the queries in the workload along different dimensions, according to their functional characteristics, and also analyze their.",

author = "Chaitanya Baru and Milind Bhandarkar and Carlo Curino and Manuel Danisch and Michael Frank and Bhaskar Gowda and Jacobsen, \{Hans Arno\} and Huang Jie and Dileep Kumar and Raghunath Nambiar and Meikel Poess and Francois Raab and Tilmann Rabl and Nishkam Ravi and Kai Sachs and Saptak Sen and Lan Yi and Choonhan Youn",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 6th TPC Technology Conference on Performance Evaluation and Benchmarking, TPCTC 2014 held in conjunction with 40th International Conference on Very Large Data Bases, VLDB 2014 ; Conference date: 01-09-2014 Through 05-09-2014",

year = "2015",

doi = "10.1007/978-3-319-15350-6\_4",

language = "English",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "44--63",

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booktitle = "Performance Characterization and Benchmarking",

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Baru, C, Bhandarkar, M, Curino, C, Danisch, M, Frank, M, Gowda, B, Jacobsen, HA, Jie, H, Kumar, D, Nambiar, R, Poess, M, Raab, F, Rabl, T, Ravi, N, Sachs, K, Sen, S, Yi, L & Youn, C 2015, Discussion of bigbench: A proposed industry standard performance benchmark for big data. in M Poess & R Nambiar (eds), Performance Characterization and Benchmarking: Traditional to Big Data - 6th TPC Technology Conference, TPCTC 2014, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8904, Springer Verlag, pp. 44-63, 6th 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, 1/09/14. https://doi.org/10.1007/978-3-319-15350-6_4

Discussion of bigbench: A proposed industry standard performance benchmark for big data. / Baru, Chaitanya; Bhandarkar, Milind; Curino, Carlo et al.
Performance Characterization and Benchmarking: Traditional to Big Data - 6th TPC Technology Conference, TPCTC 2014, Revised Selected Papers. ed. / Meikel Poess; Raghunath Nambiar. Springer Verlag, 2015. p. 44-63 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8904).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Discussion of bigbench

T2 - 6th TPC Technology Conference on Performance Evaluation and Benchmarking, TPCTC 2014 held in conjunction with 40th International Conference on Very Large Data Bases, VLDB 2014

AU - Baru, Chaitanya

AU - Bhandarkar, Milind

AU - Curino, Carlo

AU - Danisch, Manuel

AU - Frank, Michael

AU - Gowda, Bhaskar

AU - Jacobsen, Hans Arno

AU - Jie, Huang

AU - Kumar, Dileep

AU - Nambiar, Raghunath

AU - Poess, Meikel

AU - Raab, Francois

AU - Rabl, Tilmann

AU - Ravi, Nishkam

AU - Sachs, Kai

AU - Sen, Saptak

AU - Yi, Lan

AU - Youn, Choonhan

PY - 2015

Y1 - 2015

N2 - Enterprises perceive a huge opportunity in mining information that can be found in big data. New storage systems and processing paradigms are allowing for ever larger data sets to be collected and analyzed. The high demand for data analytics and rapid development in technologies has led to a sizable ecosystem of big data processing systems. However, the lack of established, standardized benchmarks makes it difficult for users to choose the appropriate systems that suit their requirements. To address this problem, we have developed the BigBench benchmark specification. BigBench is the first end-to-end big data analytics benchmark suite. In this paper, we present the BigBench benchmark and analyze the workload from technical as well as business point of view. We characterize the queries in the workload along different dimensions, according to their functional characteristics, and also analyze their.

AB - Enterprises perceive a huge opportunity in mining information that can be found in big data. New storage systems and processing paradigms are allowing for ever larger data sets to be collected and analyzed. The high demand for data analytics and rapid development in technologies has led to a sizable ecosystem of big data processing systems. However, the lack of established, standardized benchmarks makes it difficult for users to choose the appropriate systems that suit their requirements. To address this problem, we have developed the BigBench benchmark specification. BigBench is the first end-to-end big data analytics benchmark suite. In this paper, we present the BigBench benchmark and analyze the workload from technical as well as business point of view. We characterize the queries in the workload along different dimensions, according to their functional characteristics, and also analyze their.

UR - https://www.scopus.com/pages/publications/84922354917

U2 - 10.1007/978-3-319-15350-6_4

DO - 10.1007/978-3-319-15350-6_4

M3 - Conference contribution

AN - SCOPUS:84922354917

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 44

EP - 63

BT - Performance Characterization and Benchmarking

A2 - Poess, Meikel

A2 - Nambiar, Raghunath

PB - Springer Verlag

Y2 - 1 September 2014 through 5 September 2014

ER -

Baru C, Bhandarkar M, Curino C, Danisch M, Frank M, Gowda B et al. Discussion of bigbench: A proposed industry standard performance benchmark for big data. In Poess M, Nambiar R, editors, Performance Characterization and Benchmarking: Traditional to Big Data - 6th TPC Technology Conference, TPCTC 2014, Revised Selected Papers. Springer Verlag. 2015. p. 44-63. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-15350-6_4

Discussion of bigbench: A proposed industry standard performance benchmark for big data

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