Encyclopedia of Big Data Technologies

Living Edition
| Editors: Sherif Sakr, Albert Zomaya

NoSQL Database Systems

Living reference work entry
DOI: https://doi.org/10.1007/978-3-319-63962-8_50-1

Synonyms

Definition

NoSQL ( Not Only SQL) is a new generation of high-performance database systems that have been designed to deal with the increasing scaling requirement of modern Web-scale applications. In particular, the new NoSQL systems had a number of design features in common:
  • The ability to horizontally scale out throughput over many servers.

  • A simple call level interface or protocol.

  • Supporting weaker consistency models in contrast to ACID guaranteed properties for transactions in most traditional RDBMS. These models are usually referred to as BASE models (Basically Available, Soft state, Eventually consistent) (Pritchett 2008).

  • Efficient use of distributed indexes and RAM for data storage.

  • The ability to dynamically define new attributes or data schema.

These design features are made in order to achieve the following system goals (Sakr 2014; Zhao et al. 2014):
  • Availability: They must always be accessible even on the...

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References

  1. Bermbach D, Zhao L, Sakr S (2013) Towards comprehensive measurement of consistency guarantees for cloud-hosted data storage services. In: TPCTC. Springer, pp 32–47Google Scholar
  2. Bernstein PA, Goodman N (1981) Concurrency control in distributed database systems. ACM Comput Surv (CSUR) 13(2):185–221Google Scholar
  3. Brewer EA (2000) Towards robust distributed systems (abstract). In: PODC, p 7Google Scholar
  4. Cattell R (2011) Scalable SQL and NoSQL data stores. SIGMOD Rec 39(4):12–27. https://doi.org/10.1145/1978915.1978919
  5. Chang F, Dean J, Ghemawat S, Hsieh WC, Wallach DA, Burrows M, Chandra T, Fikes A, Gruber RE (2008a) Bigtable: a distributed storage system for structured data. ACM Trans Comput Syst 26(2):4:1–4:26. https://doi.org/10.1145/1365815.1365816
  6. Chang F, Dean J, Ghemawat S, Hsieh WC, Wallach DA, Burrows M, Chandra T, Fikes A, Gruber RE (2008b) Bigtable: a distributed storage system for structured data. ACM Transactions on Computer Systems (TOCS) 26(2):4Google Scholar
  7. DeCandia G, Hastorun D, Jampani M, Kakulapati G, Lakshman A, Pilchin A, Sivasubramanian S, Vosshall P, Vogels W (2007) Dynamo: Amazon’s highly available key-value store. SIGOPS Oper Syst Rev 41(6): 205–220. https://doi.org/10.1145/1323293.1294281
  8. Fitzpatrick B (2004) Distributed caching with memcached. Linux J 2004(124):5Google Scholar
  9. George L (2011) HBase: the definitive guide. O’Reilly Media, Inc., BeijingGoogle Scholar
  10. Ghemawat S, Gobioff H, Leung ST (2003) The Google file system. SIGOPS Oper Syst Rev 37(5):29–43. https://doi.org/10.1145/1165389.945450
  11. Hunt P, Konar M, Junqueira FP, Reed B (2010) Zookeeper: wait-free coordination for internet-scale systems. In: USENIX annual technical conference, vol 8, p 9Google Scholar
  12. Karger D, Lehman E, Leighton T, Panigrahy R, Levine M, Lewin D (1997) Consistent hashing and random trees: distributed caching protocols for relieving hot spots on the World Wide Web. In: Proceedings of the 29th annual ACM symposium on theory of computing (STOC ’97). ACM, El Paso, pp 654–663. https://doi.org/10.1145/258533.258660
  13. Lakshman A, Malik P (2010) Cassandra: a decentralized structured storage system. ACM SIGOPS Operating Systems Review 44(2):35–40Google Scholar
  14. Pritchett D (2008) BASE: an ACID alternative. Queue 6(3):48–55. https://doi.org/10.1145/1394127.1394128
  15. Sakr S (2014) Cloud-hosted databases: technologies, challenges and opportunities. Cluster Computing 17(2):487–502Google Scholar
  16. Sakr S, Liu A (2013) Is your cloud-hosted database truly elastic? In: Proceedings of the ninth IEEE 2013 world congress on services (SERVICES). IEEE, pp 444–447Google Scholar
  17. Sakr S, Liu A, Batista DM, Alomari M (2011a) A survey of large scale data management approaches in cloud environments. IEEE Commun Surv Tutorials 13(3): 311–336.  https://doi.org/10.1109/SURV.2011.032211.00087
  18. Sakr S, Zhao L, Wada H, Liu A (2011b) Clouddb autoadmin: towards a truly elastic cloud-based data store. In: Proceedings of the 2011 IEEE international conference on web services (ICWS). IEEE, pp 732–733Google Scholar
  19. Sivasubramanian S (2012) Amazon dynamodb: a seamlessly scalable non-relational database service. In: Proceedings of the 2012 ACM SIGMOD international conference on management of data. ACM, pp 729–730Google Scholar
  20. Stonebraker M (2008) One size fits all: an idea whose time has come and gone. Commun ACM 51(12):76Google Scholar
  21. Vogels W (2008) Eventually Consistent. Queue 6:14–19. https://doi.org/10.1145/1466443.1466448
  22. Zawodny J (2009) Redis: lightweight key/value store that goes the extra mile. Linux Mag 79Google Scholar
  23. Zhao L, Sakr S, Liu A, Bouguettaya A (2014) Cloud data management. Springer, ChamGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.School of Computer Science and Engineering (CSE)University of New South WalesSydneyAustralia

Section editors and affiliations

  • Rodrigo N Calheiros
    • 1
  • Marcos Dias de Assuncao
    • 2
  1. 1.School of Computing, Engineering and MathematicsWestern Sydney UniversityPenrithAustralia
  2. 2.Inria, LIP, ENS LyonLyonFrance