Encyclopedia of Database Systems

2018 Edition
| Editors: Ling Liu, M. Tamer Özsu

Distributed Join

  • Kai-Uwe SattlerEmail author
Reference work entry
DOI: https://doi.org/10.1007/978-1-4614-8265-9_705


Distributed query; Join processing


The distributed join is a query operator that combines two relations stored at different sites in the following way: each tuple from the first relation is concatenated with each tuple from the second relation that satisfies a given join condition, e.g., the match in two attributes. The main characteristics of a distributed join are that at least one of the operand relations has to be transferred to another site.

Historical Background

Techniques for evaluating joins on distributed relations have already been discussed in the context of the first prototypes of distributed database systems such as SDD-1, Distributed INGRES and R*. In Ref. [6] the basic strategies ship whole vs. fetch matches were discussed and results of experimental evaluations were reported. Another report on an experimental comparison of distributed join strategies was given in Ref. [5].

Special strategies for distributed join evaluation that aim at reducing the...

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

Recommended Reading

  1. 1.
    Babb E. Implementing a relational database by means of specialized hardware. ACM Trans Database Syst. 1979;4(1):1–29.CrossRefGoogle Scholar
  2. 2.
    Bernstein PA, Goodman N, Wong E, Reeve CL, Rothnie Jr JB. Query processing in a system for distributed databases (SDD-1). ACM Trans Database Syst. 1981;6(4):602–25.CrossRefzbMATHGoogle Scholar
  3. 3.
    Hevner AR, Yao SB. Query processing in distributed database systems. IEEE Trans Softw Eng. 1979;5(3):177–82.CrossRefzbMATHGoogle Scholar
  4. 4.
    Kossmann D. The state of the art in distributed query processing. ACM Comput Surv. 2000;32(4):422–69.CrossRefGoogle Scholar
  5. 5.
    Lu H, Carey M. Some experimental results on distributed join algorithms in a local network. In: Proceedings of the 11th International Conference on Very Large Data Bases; 1985. p. 229–304.Google Scholar
  6. 6.
    Mackert LF, Lohman G. R* Optimizer validation and performance evaluation for local queries. In: Proceedings of the ACM SIGMOD International Conference on Management of Data; 1986. p. 84–95.CrossRefGoogle Scholar
  7. 7.
    Özsu MT, Valduriez P. Principles of distributed database systems. 2nd ed. London: Prentice Hall; 1999.Google Scholar
  8. 8.
    Roth MT, Schwarz P. Don’t scrap it, wrap it! A wrapper architecture for legacy data sources. In: Proceedings of the 23rd International Conference on Very Large Data Bases; 1997. p. 266–75.Google Scholar
  9. 9.
    Stonebraker M. The design and implementation of distributed INGRES. In: Stonebraker M, editor. The INGRES papers. Reading: Addison-Wesley; 1986.zbMATHGoogle Scholar
  10. 10.
    Urhan T, Franklin MJ. XJoin: a reactively-scheduled pipelined join operator. Bull Tech Comm Data Eng. 2000;23(2):27–33.Google Scholar
  11. 11.
    Valduriez P. Semi-join algorithms for distributed database machines. In: Schneider H-J, editor. Distributed data bases. Amsterdam: North-Holland; 1982. p. 23–37.Google Scholar
  12. 12.
    Williams R, Daniels D, Hass L, Lapis G, Lindsay B, Ng P, Obermarck R, Selinger P, Walker A, Wilms P, Yost RR. An overview of the architecture. San Jose: IBM Research Lab; 1981.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Technische Universität IlmenauIlmenauGermany

Section editors and affiliations

  • Kian-Lee Tan
    • 1
  1. 1.Dept. of Computer ScienceNational Univ. of SingaporeSingaporeSingapore