Bigtable

Bigtable: A Distributed Storage System for Structured Data. Bigtable is a distributed storage system for managing structured data that is designed to scale to a very large size: petabytes of data across thousands of commodity servers. Many projects at Google store data in Bigtable, including web indexing, Google Earth, and Google Finance. These applications place very different demands on Bigtable, both in terms of data size (from URLs to web pages to satellite imagery) and latency requirements (from backend bulk processing to real-time data serving). Despite these varied demands, Bigtable has successfully provided a flexible, high-performance solution for all of these Google products. In this article, we describe the simple data model provided by Bigtable, which gives clients dynamic control over data layout and format, and we describe the design and implementation of Bigtable.


References in zbMATH (referenced in 22 articles )

Showing results 1 to 20 of 22.
Sorted by year (citations)

1 2 next

  1. Li, Xiaoyan; Fan, Jianxi; Lin, Cheng-Kuan; Cheng, Baolei; Jia, Xiaohua: The extra connectivity, extra conditional diagnosability and (t/k)-diagnosability of the data center network DCell (2019)
  2. Marinković, Bojan; Glavan, Paola; Ognjanović, Zoran: Proving properties of the Chord protocol using the ASM formalism (2019)
  3. Kepner, Jeremy; Jananthan, Hayden: Mathematics of big data. Spreadsheets, databases, matrices, and graphs. With a foreword by Charles E. Leiserson (2018)
  4. Scott, Steven L.: Comparing consensus Monte Carlo strategies for distributed Bayesian computation (2017)
  5. Cuzzocrea, Alfredo; Cosulschi, Mirel; de Virgilio, Roberto: An effective and efficient MapReduce algorithm for computing BFS-based traversals of large-scale RDF graphs (2016)
  6. Derbeko, Philip; Dolev, Shlomi; Gudes, Ehud; Sharma, Shantanu: Security and privacy aspects in MapReduce on clouds: a survey (2016)
  7. Rocha, Vladimir; Kon, Fabio; Cobe, Raphael; Wassermann, Renata: A hybrid cloud-P2P architecture for multimedia information retrieval on VoD services (2016) ioport
  8. Yang, Chao-Tung; Shih, Wen-Chung; Huang, Chih-Lin; Jiang, Fuu-Cheng; Chu, William Cheng-Chung: On construction of a distributed data storage system in cloud (2016) ioport
  9. Jiang, Wenbin; Zhang, Lei; Liao, Xiaofei; Jin, Hai; Peng, Yaqiong: A novel clustered MongoDB-based storage system for unstructured data with high availability (2014) ioport
  10. Kuznetsov, S.; Poskonin, A.: NoSQL data management systems (2014) ioport
  11. Philip Chen, C. L.; Zhang, Chun-Yang: Data-intensive applications, challenges, techniques and technologies: a survey on big data (2014) ioport
  12. Qiang, Yan; Pei, Bo; Wu, Weili; Zhao, Juanjuan; Zhang, Xiaolong; Li, Yue; Wu, Lidong: Improvement of path analysis algorithm in social networks based on HBase (2014)
  13. Walraven, Stefan; Truyen, Eddy; Joosen, Wouter: Comparing PaaS offerings in light of SaaS development. A comparison of PaaS platforms based on a practical case study (2014) ioport
  14. Liu, Lei; Liu, Dongqing; Lü, Shuai; Zhang, Peng: An abstract description method of map-reduce-merge using Haskell (2013) ioport
  15. Zhu, Ming-Dong; Shen, De-Rong; Yue, Kou; Nie, Tie-Zheng; Yu, Ge: A framework for supporting tree-like indexes on the chord overlay (2013) ioport
  16. Droz-Bartholet, L.; Lapayre, J.-C.; Bouquet, F.; Garcia, E.; Heinisch, A.: \textscRamos: concurrent writing and reconfiguration for collaborative systems (2012)
  17. Wang, Qing: Abstract state machines for data-parallel computing (2012)
  18. Ferragina, Paolo; Nitto, Igor; Venturini, Rossano: On optimally partitioning a text to improve its compression (2011)
  19. Lü, XueFeng; Cheng, ChengQi; Gong, JianYa; Guan, Li: Review of data storage and management technologies for massive remote sensing data (2011) ioport
  20. Christensen, Ken; Roginsky, Allen; Jimeno, Miguel: A new analysis of the false positive rate of a Bloom filter (2010)

1 2 next