Database denormalization

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 should not be confused with Unnormalized form. Databases/tables must first be normalized to efficiently denormalize them.

A normalized design will often "store" different but related pieces of information in separate logical tables (called relations). If these relations are stored physically as separate disk files, completing a database query that draws information from several relations (a join operation) can be slow. If many relations are joined, it may be prohibitively slow. There are two strategies for dealing with this. The preferred method is to keep the logical design normalized, but allow the database management system (DBMS) to store additional redundant information on disk to optimise query response. In this case it is the DBMS software's responsibility to ensure that any redundant copies are kept consistent. This method is often implemented in SQL as indexed views (Microsoft SQL Server) or materialised views (Oracle, PostgreSQL). A view may, among other factors, represent information in a format convenient for querying, and the index ensures that queries against the view are optimised physically.

The more common approach is to denormalize the logical data design. With care this can achieve a similar improvement in query response, but at a cost—it is now the database designer's responsibility to ensure that the denormalized database does not become inconsistent. This is done by creating rules in the database called constraints, that specify how the redundant copies of information must be kept synchronised, which may easily make the de-normalization procedure pointless. It is the increase in logical complexity of the database design and the added complexity of the additional constraints that make this approach hazardous. Moreover, constraints introduce a trade-off, speeding up reads (SELECT in SQL) while slowing down writes (INSERT, UPDATE, and DELETE). This means a denormalized database under heavy write load may actually offer worse performance than its functionally equivalent normalized counterpart.

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