Skip to main content
SpringerLink
Log in

Big Data Benchmark Compendium

  • Conference paper
Book cover Performance Evaluation and Benchmarking: Traditional to Big Data to Internet of Things (TPCTC 2015)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 9508))

Included in the following conference series:

Abstract

The field of Big Data and related technologies is rapidly evolving. Consequently, many benchmarks are emerging, driven by academia and industry alike. As these benchmarks are emphasizing different aspects of Big Data and, in many cases, covering different technical platforms and uses cases, it is extremely difficult to keep up with the pace of benchmark creation. Also with the combinations of large volumes of data, heterogeneous data formats and the changing processing velocity, it becomes complex to specify an architecture which best suits all application requirements. This makes the investigation and standardization of such systems very difficult. Therefore, the traditional way of specifying a standardized benchmark with pre-defined workloads, which have been in use for years in the transaction and analytical processing systems, is not trivial to employ for Big Data systems. This document provides a summary of existing benchmarks and those that are in development, gives a side-by-side comparison of their characteristics and discusses their pros and cons. The goal is to understand the current state in Big Data benchmarking and guide practitioners in their approaches and use cases.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Alexandrov, A., Brücke, C., Markl, V.: Issues in big data testing and benchmarking. In: Proceedings of the Sixth International Workshop on Testing Database Systems, DBTest 2013, New York, NY, USA, June 24, pp. 1: 1–1: 5 (2013)

    Google Scholar 

  2. AMP Lab: AMP Lab Big Data Benchmark (2013). https://amplab.cs.berkeley.edu/benchmark/

  3. Pavlo, A.: Benchmark (2011). http://database.cs.brown.edu/projects/mapreduce-vs-dbms/

  4. Hadoop, A.: Package org.apache.hadoop.examples.pi (2015). http://hadoop.apache.org/docs/r0.23.11/api/org/apache/hadoop/examples/pi/package-summary.html

  5. Hadoop, A.: TPC Express Benchmark HS - Standard Specification (2015). http://hadoop.apache.org/docs/current/api/org/apache/hadoop/examples/terasort/package-summary.html

  6. Apache Software Foundation: Grep (2009). http://wiki.apache.org/hadoop/Grep

  7. Apache Software Foundation: DataGeneratorHadoop (2010). http://wiki.apache.org/pig/DataGeneratorHadoop

  8. Apache Software Foundation: Running TPC-H Benchmark on Pig (2012). https://issues.apache.org/jira/browse/PIG-2397

  9. Apache Software Foundation: GridMix (2013). https://hadoop.apache.org/docs/stable1/gridmix.html

  10. Apache Software Foundation: Hive performance benchmarks (2013). https://issues.apache.org/jira/browse/HIVE-396

  11. Apache Software Foundation: PigMix (2013).https://cwiki.apache.org/confluence/display/PIG/PigMix

  12. Apache Software Foundation: TPC-H and TPC-DS for Hive (2015). https://github.com/hortonworks/hive-testbench/tree/hive14

  13. Baru, C., Bhandarkar, M., Nambiar, R., Poess, M., Rabl, T.: Setting the direction for big data benchmark standards. In: Nambiar, R., Poess, M. (eds.) TPCTC 2012. LNCS, vol. 7755, pp. 197–208. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  14. Baru, C., et al.: Discussion of BigBench: a proposed industry standard performance benchmark for big data. In: Nambiar, R., Poess, M. (eds.) TPCTC 2014. LNCS, vol. 8904, pp. 44–63. Springer, Heidelberg (2015)

    Chapter  Google Scholar 

  15. BigBench: BigBench (2015). https://github.com/intel-hadoop/Big-Data-Benchmark-for-Big-Bench

  16. Team, B.: BigFrame (2013). https://github.com/bigframeteam/BigFrame/wiki

  17. BSC: Aloja home page (2014). http://aloja.bsc.es/

  18. Chang, J., Lim, K.T., Byrne, J., Ramirez, L., Ranganathan, P.: Workload diversity and dynamics in big data analytics: implications to system designers. In: Proceedings of the 2nd Workshop on Architectures and Systems for Big Data. ASBD 2012, pp. 21–26. ACM, NY (2012)

    Google Scholar 

  19. Chen, Y.: We dont know enough to make a big data benchmark suite-an academia-industry view. Technical report No. UCB/EECS-2012-71 (2012)

    Google Scholar 

  20. Chen, Y., Alspaugh, S., Katz, R.H.: Interactive analytical processing in big data systems: a cross-industry study of mapreduce workloads. PVLDB 5(12), 1802–1813 (2012)

    Google Scholar 

  21. Chen, Y., Ganapathi, A., Griffith, R., Katz, R.H.: The case for evaluating mapreduce performance using workload suites. In: 19th Annual IEEE/ACM International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems. MASCOTS 2011, Singapore, 25–27 July 2011, pp. 390–399 (2011)

    Google Scholar 

  22. Chen, Y., Raab, F., Katz, R.: From TPC-C to big data benchmarks: a functional workload model. In: Rabl, T., Poess, M., Baru, C., Jacobsen, H.-A. (eds.) WBDB 2012. LNCS, vol. 8163, pp. 28–43. Springer, Heidelberg (2014)

    Chapter  Google Scholar 

  23. Cooper, B.F., Silberstein, A., Tam, E., Ramakrishnan, R., Sears, R.: Benchmarking cloud serving systems with YCSB. In: Proceedings of the 1st ACM Symposium on Cloud Computing. SoCC 2010, Indianapolis, Indiana, USA, 10–11 June 2010, pp. 143–154 (2010)

    Google Scholar 

  24. Dimitrov, M., Kumar, K., Lu, P., Viswanathan, V., Willhalm, T.: Memory system characterization of big data workloads. In: Proceedings of the 2013 IEEE International Conference on Big Data, 6–9 October 2013, Santa Clara, CA, US, pp. 15–22 (2013)

    Google Scholar 

  25. Ferdman, M., Adileh, A., Koçberber, Y.O., Volos, S., Alisafaee, M., Jevdjic, D., Kaynak, C., Popescu, A.D., Ailamaki, A., Falsafi, B.: Clearing the clouds: a study of emerging scale-out workloads on modern hardware. In: Proceedings of the 17th International Conference on Architectural Support for Programming Languages and Operating Systems, ASPLOS 2012, London, UK, 3–7 March 2012, pp. 37–48 (2012)

    Google Scholar 

  26. Ferrarons, J., Adhana, M., Colmenares, C., Pietrowska, S., Bentayeb, F., Darmont,J.: PRIMEBALL: A parallel processing framework benchmark for big dataapplications in the cloud. In: Performance Characterization and Benchmarking -5th TPC Technology Conference. TPCTC 2013, Trento, Italy, 26 August 2013, pp. 109–124 (2013)

    Google Scholar 

  27. Ghazal, A., Rabl, T., Hu, M., Raab, F., Poess, M., Crolotte, A., Jacobsen., H.A.: BigBench: towards an industry standard benchmark for big data analytics. In: SIGMOD (2013)

    Google Scholar 

  28. Huang, S., Huang, J., Dai, J., Xie, T., Huang, B.: The hibench benchmark suite: characterization of the mapreduce-based data analysis. In: Workshops Proceedings of the 26th International Conference on Data Engineering, ICDE 2010, 1–6 March 2010, Long Beach, California, USA, pp. 41–51 (2010)

    Google Scholar 

  29. ICT, Chinese Academy of Sciences: CloudRank-D (2013). http://prof.ict.ac.cn/CloudRank/

  30. ICT, Chinese Academy of Sciences: DCBench (2013). http://prof.ict.ac.cn/DCBench/

  31. ICT, Chinese Academy of Sciences: BigDataBench 3.1 (2015). http://prof.ict.ac.cn/BigDataBench/

  32. Intel: HiBench Suite (2015). https://github.com/intel-hadoop/HiBench

  33. Kim, K., Jeon, K., Han, H., Kim, S.G., Jung, H., Yeom, H.Y.: Mrbench: a benchmark for mapreduce framework. In: 14th International Conference on Parallel and Distributed Systems, ICPADS 2008, Melbourne, Victoria, Australia, 8–10 December 2008, pp. 11–18 (2008)

    Google Scholar 

  34. Kunjir, M., Kalmegh, P., Babu, S.: Thoth: towards managing a multi-system cluster. PVLDB 7(13), 1689–1692 (2014)

    Google Scholar 

  35. Li, M., Tan, J., Wang, Y., Zhang, L., Salapura, V.: Sparkbench: a comprehensive benchmarking suite for in memory data analytic platform spark. In: Proceedings of the 12th ACM International Conference on Computing Frontiers. CF 2015, pp. 53:1–53:8. ACM, New York, NY, USA (2015)

    Google Scholar 

  36. Luo, C., Zhan, J., Jia, Z., Wang, L., Lu, G., Zhang, L., Xu, C., Sun, N.: Cloudrank-d: benchmarking and ranking cloud computing systems for data processing applications. Front. Comput. Sci. 6(4), 347–362 (2012)

    MathSciNet  Google Scholar 

  37. Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., Byers, A.H.: Big data: The Next Frontier for Innovation, Competition, and Productivity. Technical report, McKinsey Global Institute (2011). http://www.mckinsey.com/insights/mgi/research/technology_and_innovation/big_data_the_next_frontier_for_innovation

  38. Li, M.: SparkBench (2015). https://bitbucket.org/lm0926/sparkbench

  39. Ming, Z., Luo, C., Gao, W., Han, R., Yang, Q., Wang, L., Zhan, J.: BDGS: a scalable big data generator suite in big data benchmarking. In: Rabl, T., Raghunath, N., Poess, M., Bhandarkar, M., Jacobsen, H.-A., Baru, C. (eds.) Advancing Big Data Benchmarks. LNCS, vol. 8585, pp. 138–154. Springer, Heidelberg (2013)

    Google Scholar 

  40. MRBS: MRBS (2013). http://sardes.inrialpes.fr/research/mrbs/index.html

  41. Nambiar, R., Poess, M., Dey, A., Cao, P., Magdon-Ismail, T., Qi Ren, D., Bond, A.: Introducing TPCx-HS: the first industry standard for benchmarking big data systems. In: Nambiar, R., Poess, M. (eds.) TPCTC 2014. LNCS, vol. 8904, pp. 1–12. Springer, Heidelberg (2015)

    Chapter  Google Scholar 

  42. Olston, C., Reed, B., Srivastava, U., Kumar, R., Tomkins, A.: Pig latin: a not-so-foreign language for data processing. In: Proceedings of the ACM SIGMOD International Conference on Management of Data. SIGMOD 2008, Vancouver, BC, Canada, 10–12 June 2008, pp. 1099–1110 (2008)

    Google Scholar 

  43. Patil, S., Polte, M., Ren, K., Tantisiriroj, W., Xiao, L., López, J., Gibson, G., Fuchs, A., Rinaldi, B.: YCSB++: benchmarking and performance debugging advanced features in scalable table stores. In: ACM Symposium on Cloud Computing in conjunction with SOSP 2011, SOCC 2011, Cascais, Portugal, 26–28 October 2011, p. 9 (2011)

    Google Scholar 

  44. Pavlo, A., Paulson, E., Rasin, A., Abadi, D.J., DeWitt, D.J., Madden, S., Stonebraker, M.: A comparison of approaches to large-scale data analysis. In: SIGMOD, pp. 165–178 (2009)

    Google Scholar 

  45. Poggi, N., Carrera, D., Call, A., Mendoza, S., Becerra, Y., Torres, J., Ayguadé, E., Gagliardi, F., Labarta, J., Reinauer, R., Vujic, N., Green, D., Blakeley, J.: ALOJA: a systematic study of hadoop deployment variables to enable automated characterization of cost-effectiveness. In: 2014 IEEE International Conference on Big Data, Big Data 2014, Washington, DC, USA, 27–30 October 2014, pp. 905–913 (2014)

    Google Scholar 

  46. Rabl, T., Frank, M., Sergieh, H.M., Kosch, H.: A data generator for cloud-scale benchmarking. In: Nambiar, R., Poess, M. (eds.) TPCTC 2010. LNCS, vol. 6417, pp. 41–56. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  47. Rabl, T., Poess, M.: Parallel data generation for performance analysis of large, complex RDBMS. In: DBTest 2011, p. 5 (2011)

    Google Scholar 

  48. Sakr, S., Casati, F.: Liquid benchmarks: towards an online platform for collaborative assessment of computer science research results. In: Nambiar, R., Poess, M. (eds.) TPCTC 2010. LNCS, vol. 6417, pp. 10–24. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  49. Sakr, S., Shafaat, A., Bajaber, F., Barnawi, A., Batarfi, O., Altalhi, A.H.: Liquid benchmarking: a platform for democratizing the performance evaluation process. In: Proceedings of the 18th International Conference on Extending Database Technology, EDBT 2015, Brussels, Belgium, 23–27 March 2015, pp. 537–540 (2015)

    Google Scholar 

  50. Sangroya, A., Serrano, D., Bouchenak, S.: MRBS: A Comprehensive MapReduce Benchmark Suite. Technical report, LIG Grenoble Fr (2012)

    Google Scholar 

  51. Sangroya, A., Serrano, D., Bouchenak, S.: MRBS: towards dependability benchmarking for hadoop MapReduce. In: Caragiannis, I., Alexander, M., Badia, R.M., Cannataro, M., Costan, A., Danelutto, M., Desprez, F., Krammer, B., Sahuquillo, J., Scott, S.L., Weidendorfer, J. (eds.) Euro-Par Workshops 2012. LNCS, vol. 7640, pp. 3–12. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  52. Sakr, S.: Liquid benchmarking (2015). http://wiki.liquidbenchmark.net/doku.php/home

  53. Stonebraker, M., Abadi, D.J., DeWitt, D.J., Madden, S., Paulson, E., Pavlo, A., Rasin, A.: Mapreduce and parallel dbmss: friends or foes? Commun. ACM 53(1), 64–71 (2010)

    Article  Google Scholar 

  54. Transaction Processing Performance Council: TPC Benchmark H - Standard Specification, version 2.17.1 (2014)

    Google Scholar 

  55. Transaction Processing Performance Council: TPC Benchmark DS - Standard Specification, version 1.3.1 (2015)

    Google Scholar 

  56. Transaction Processing Performance Council: TPC Express Benchmark HS - Standard Specification, version 1.3.0 (2015)

    Google Scholar 

  57. Wang, L., Zhan, J., Luo, C., Zhu, Y., Yang, Q., He, Y., Gao, W., Jia, Z., Shi, Y., Zhang, S., Zhen, C., Lu, G., Zhan, K., Li, X., Qiu, B.: BigDataBench: a Big Data Benchmark Suite from Internet Services. In: HPCA (2014)

    Google Scholar 

  58. Xiong, W., Yu, Z., Bei, Z., Zhao, J., Zhang, F., Zou, Y., Bai, X., Li, Y., Xu, C.: A characterization of big data benchmarks. In: Proceedings of the 2013 IEEE International Conference on Big Data, 6–9 October 2013, Santa Clara, CA, USA, pp. 118–125 (2013)

    Google Scholar 

  59. Yahoo: YCSB (2015). https://github.com/brianfrankcooper/YCSB

  60. Chen, Y.: Statistical Workload Injector for MapReduce (SWIM) (2013). https://github.com/SWIMProjectUCB/SWIM/wiki

Download references

Acknowledgment

This research has been supported by the Research Group of the Standard Performance Evaluation Corporation (SPEC).

Author information

Authors and Affiliations

  1. Goethe University Frankfurt, Frankfurt, Germany

    Todor Ivanov

  2. bankmark, Passau, Germany

    Tilmann Rabl

  3. Oracle Corporation, Redwood Shores, USA

    Meikel Poess

  4. Barcelona Supercomputing Center (BSC), Barcelona, Spain

    Anna Queralt & Nicolas Poggi

  5. IBM, San Jose, USA

    John  Poelman

  6. VMware, Palo Alto, USA

    Jeffrey Buell

Authors
  1. Todor Ivanov
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tilmann Rabl
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Meikel Poess
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Anna Queralt
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. John  Poelman
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Nicolas Poggi
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Jeffrey Buell
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Todor Ivanov .

Editor information

Editors and Affiliations

  1. Cisco Systems, Inc., San Jose, CA, USA

    Raghunath Nambiar

  2. Oracle Corporation, Redwood City, CA, USA

    Meikel Poess

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Ivanov, T. et al. (2016). Big Data Benchmark Compendium. In: Nambiar, R., Poess, M. (eds) Performance Evaluation and Benchmarking: Traditional to Big Data to Internet of Things. TPCTC 2015. Lecture Notes in Computer Science(), vol 9508. Springer, Cham. https://doi.org/10.1007/978-3-319-31409-9_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-31409-9_9

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-31408-2

  • Online ISBN: 978-3-319-31409-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation