Skip to main content
SpringerLink
Log in

Evaluating Index Systems of High Energy Physics

  • Conference paper
  • First Online:
Big Scientific Data Benchmarks, Architecture, and Systems (SDBA 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 911))

Included in the following conference series:

Abstract

Nowadays, more and more scientific data has been produced through high-energy physics (HEP) facilities. Even in one particle physics experiment, the generated data reaches to petabytes scale. Retrieving data from massive data occupies a large proportion of data processing in HEP. Hence, the data query latency and throughput are the most important metrics for HEP data management. Inspired by the indexing technology of databases, the technology that improves the performance of data retrieval through the HEP data indexing, becomes the mainstream in the HEP data management. In this paper, focusing on two typical index systems–MySQL and HBase–for HEP data management, which are the typical SQL and NoSQL system respectively, we evaluate them from the perspectives of overall performance, system and micro-architecture behaviors. We find that HBase achieves higher performance than MySQL with the data scale increasing.

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. Hadoop. http://hadoop.apache.org/

  2. Hbase. https://hbase.apache.org/

  3. Mysql. https://www.mysql.com/

  4. Root. https://root.cern.ch/

  5. Brun, R., Rademakers, F.: Root-an object oriented data analysis framework. Nucl. Instrum. Methods Phys. Res. Sect. A 389(1–2), 81–86 (1997)

    Article  Google Scholar 

  6. 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, pp. 143–154. ACM (2010)

    Google Scholar 

  7. Gao, W., et al.: Data motif-based proxy benchmarks for big data and AI workloads. In: IISWC 2018 (2018)

    Google Scholar 

  8. Gao, W., et al.: Data motifs: a lens towards fully understanding big data and AI workloads. In: 2018 27th International Conference on Parallel Architectures and Compilation Techniques (PACT) (2018)

    Google Scholar 

  9. Jia, Z., et al.: Characterizing and subsetting big data workloads. In: 2014 IEEE International Symposium on Workload Characterization (IISWC), pp. 191–201. IEEE (2014)

    Google Scholar 

  10. Jia, Z., et al.: Understanding big data analytics workloads on modern processors. IEEE Trans. Parallel Distrib. Syst. 28(6), 1797–1810 (2017)

    Article  Google Scholar 

  11. Karkhanis, T.S., Smith, J.E.: A first-order superscalar processor model. In: 31st Annual International Symposium on Computer Architecture, Proceedings, pp. 338–349. IEEE (2004)

    Google Scholar 

  12. Liu, B., et al.: High performance computing activities in hadron spectroscopy at BESIII. J. Phys. Conf. Ser. 523, 012008 (2014)

    Article  Google Scholar 

  13. Vora, M.N.: Hadoop-HBase for large-scale data. In: 2011 International Conference on Computer Science and Network Technology (ICCSNT), vol. 1, pp. 601–605. IEEE (2011)

    Google Scholar 

  14. Wang, L., et al.: Bigdatabench: a big data benchmark suite from internet services. In: 2014 IEEE 20th International Symposium on High Performance Computer Architecture (HPCA), pp. 488–499. IEEE (2014)

    Google Scholar 

  15. Yaodong, C., et al.: Data management challenges and event index technologies in high energy physics. J. Comput. Res. Dev. 54(2), 258–266 (2017)

    Google Scholar 

  16. Zheng, C., Zhan, J., Jia, Z., Zhang, L.: Characterizing OS behaviors of datacenter and big data workloads. In: 2016 IEEE 18th International Conference on High Performance Computing and Communications; IEEE 14th International Conference on Smart City; IEEE 2nd International Conference on Data Science and Systems (HPCC/SmartCity/DSS), pp. 1079–1086. IEEE (2016)

    Google Scholar 

Download references

Acknowledgment

Our work in this paper is supported by NKRDPC, the National Key Research and Development Plan of China. (Grant No. 2016YFB1000600 and 2016YFB1000601).

Author information

Authors and Affiliations

  1. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

    Shaopeng Dai, Wanling Gao, Biwei Xie, Minghe Yu, Defei Kong & Rui Han

  2. University of Chinese Academy of Sciences, Beijing, China

    Shaopeng Dai, Wanling Gao, Biwei Xie, Minghe Yu, Jia’nan Chen & Defei Kong

  3. Sichuan University, Chengdu, China

    Jinheng Li

Authors
  1. Shaopeng Dai
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wanling Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Biwei Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Minghe Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jia’nan Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Defei Kong
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Rui Han
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Jinheng Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shaopeng Dai .

Editor information

Editors and Affiliations

  1. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

    Rui Ren

  2. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

    Chen Zheng

  3. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

    Jianfeng Zhan

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Dai, S. et al. (2019). Evaluating Index Systems of High Energy Physics. In: Ren, R., Zheng, C., Zhan, J. (eds) Big Scientific Data Benchmarks, Architecture, and Systems. SDBA 2018. Communications in Computer and Information Science, vol 911. Springer, Singapore. https://doi.org/10.1007/978-981-13-5910-1_2

Download citation

  • DOI: https://doi.org/10.1007/978-981-13-5910-1_2

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-5909-5

  • Online ISBN: 978-981-13-5910-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation