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Everything about Denormalization

Strategy used on previously-normalized databases

Denormalization is a strategy used on a previously-normalized database to increase performance. In computing, denormalization is the process of trying to improve the read performance of a database, at the expense of losing some write performance, by adding redundant copies of data or by grouping data. It is often motivated by performance or scalability in relational database software needing to carry out very large numbers of read operations. Denormalization differs from the unnormalized form in that denormalization benefits can only be fully realized on a data model that is otherwise normalized. Wikipedia →

🧭 Start here: a 6-step reading path

  1. Read the overview: DenormalizationA 5-minute orientation
  2. Skim a recent paper: AndroEvolve: automated Android API update with data flow analysis and variable denormalizationInfluential work since 2022
  3. Pick up a classic book: Introduction to numerical analysis and scientific computingThe most-reprinted book on the subject
  4. Read the landmark paper: Smoking, stigma and tobacco ‘denormalization’: Further reflections on the use of stigma as a public health tool. A commentary on Social Science & Medicine's Stigma, Prejudice, Discrimination and Health Special Issue (67: 3)Cited 242 times
  5. Go sideways: Database designA closely related topic
  6. Go sideways: DatabaseA closely related topic

📖 Definitions

Noun The act or process of denormalizing.

🗂️ The subject at a glance

  1. Implementation
  2. Denormalization versus not normalized data

🏆 Landmark papers (most cited)

🆕 Influential since 2022

📚 Books worth reading

Ranked by number of editions on Open Library, a rough measure of how lasting a book is.

🔗 Related topics