Database decay and how to avoid it

Michael Stonebraker, Dong Deng, Michael L. Brodie

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Scopus citations

Abstract

The traditional wisdom for designing database schemas is to use a design tool (typically based on a UML or E-R model) to construct an initial data model for one's data. When one is satisfied with the result, the tool will automatically construct a collection of 3rd normal form relations for the model. Then applications are coded against this relational schema. When business circumstances change (as they do frequently) one should run the tool again to produce a new data model and a new resulting collection of tables. The new schema is populated from the old schema, and the applications are altered to work on the new schema, using relational views whenever possible to ease the migration. In this way, the database remains in 3rd normal form, which represents a 'good' schema, as defined by DBMS researchers. 'In the wild', schemas often change once a quarter or more often, and the traditional wisdom is to repeat the above exercise for each alteration. In this paper we report that the traditional wisdom appears to be rarely-to-never followed for large, multi-department applications. Instead DBAs appear to attempt to minimize application maintenance (and hence schema changes) instead of maximizing schema quality. This leads to schemas which quickly diverge from E-R or UML models and actual database semantics tend to drift farther and farther from 3rd normal form. We term this divergence of reality from 3rd normal form principles database decay. Obviously, this is a very undesirable state of affairs, and should be avoided if possible. The paper continues with tactics to slow down database decay. We argue that the traditional development methodology, that of coding applications in ODBC or JDBC, is at least partly to blame for decay. Hence, we propose an alternate methodology that should be more resilient to decay.

Original languageEnglish (US)
Title of host publicationProceedings - 2016 IEEE International Conference on Big Data, Big Data 2016
EditorsRonay Ak, George Karypis, Yinglong Xia, Xiaohua Tony Hu, Philip S. Yu, James Joshi, Lyle Ungar, Ling Liu, Aki-Hiro Sato, Toyotaro Suzumura, Sudarsan Rachuri, Rama Govindaraju, Weijia Xu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages7-16
Number of pages10
ISBN (Electronic)9781467390040
DOIs
StatePublished - 2016
Externally publishedYes
Event4th IEEE International Conference on Big Data, Big Data 2016 - Washington, United States
Duration: Dec 5 2016Dec 8 2016

Publication series

NameProceedings - 2016 IEEE International Conference on Big Data, Big Data 2016

Other

Other4th IEEE International Conference on Big Data, Big Data 2016
CountryUnited States
CityWashington
Period12/5/1612/8/16

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Information Systems
  • Hardware and Architecture

Fingerprint Dive into the research topics of 'Database decay and how to avoid it'. Together they form a unique fingerprint.

Cite this