Denormalization is the process of attempting to optimize the performance of a database by adding redundant data or by grouping data. In some cases, denormalization helps cover up the inefficiencies inherent in relational database software. A relational normalized database imposes a heavy access load over physical storage of data even if it is well tuned for high performance.
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 DBMS to store additional redundant information on disk to optimize 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 (MS SQL) or materialized views (Oracle). A view represents information in a format convenient for querying, and the index ensures that queries against the view are optimized.
The more usual approach is to demoralize 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 demoralized database does not become inconsistent. This is done by creating rules in the database called constraints, which specify how the redundant copies of information must be kept synchronized. It is the increase in logical complexity of the database design and the added complexity of the additional constraints that make this approach hazardous. Sometimes a denormalized database under heavy write load may actually offer worse performance than its functionally equivalent normalized counterpart.
A denormalized data model is not the same as a data model that has not been normalized, and denormalization should only take place after a satisfactory level of normalization has taken place and that any required constraints and/or rules have been created to deal with the inherent anomalies in the design. For example, all the relations are in third normal form and any relations with join and multi-valued dependencies are handled appropriately.
Uses of Denormalization
Databases intended for Online Transaction Processing (OLTP) are typically more normalized than databases intended for Online Analytical Processing (OLAP). OLTP Applications are characterized by a high volume of small transactions such as updating a sales record at a super market checkout counter. The expectation is that each transaction will leave the database in a consistent state. By contrast, databases intended for OLAP operations are primarily “read mostly” databases. OLAP applications tend to extract historical data that has accumulated over a long period of time. For such databases, redundant or “denormalized” data may facilitate Business Intelligence applications. Specifically, dimensional tables in a star schema often contain denormalized data.
Denormalization is also used to improve performance on smaller computers as in computerized cash-registers and mobile devices, since these may use the data for look-up only (e.g. price lookups).
De-normalization may also be used when no RDBMS exists for a platform (such as Palm), or no changes are to be made to the data and a swift response is crucial.
Dinesh Thakur holds an B.C.A, MCDBA, MCSD certifications. Dinesh authors the hugely popular