Denormalization is a strategy used on a previously-
normalized database to increase performance. In
computing
Computing is any goal-oriented activity requiring, benefiting from, or creating computing machinery. It includes the study and experimentation of algorithmic processes, and development of both hardware and software. Computing has scientific, ...
, denormalization is the process of trying to improve the read performance of a
database
In computing, a database is an organized collection of data stored and accessed electronically. Small databases can be stored on a file system, while large databases are hosted on computer clusters or cloud storage. The design of databases s ...
, at the expense of losing some write performance, by adding
redundant copies of data or by grouping data.
[S. K. Shin and G. L. Sanders]
Denormalization strategies for data retrieval from data warehouses
Decision Support Systems, 42(1):267-282, October 2006. It is often motivated by
performance or
scalability
Scalability is the property of a system to handle a growing amount of work by adding resources to the system.
In an economic context, a scalable business model implies that a company can increase sales given increased resources. For example, a ...
in
relational database software
In computing, a database is an organized collection of data stored and accessed electronically. Small databases can be stored on a file system, while large databases are hosted on computer clusters or cloud storage. The design of databases spa ...
needing to carry out very large numbers of read operations. Denormalization differs from the
unnormalized form In database normalization, unnormalized form (UNF), also known as an unnormalized relation or non-first normal form (N1NF or NF2), is a database data model (organization of data in a database) which does not meet any of the conditions of database no ...
in that denormalization benefits can only be fully realized on a data model that is otherwise normalized.
Implementation
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.
DBMS support
One method is to keep the logical design normalized, but allow the
database management system (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 (
Microsoft SQL Server
Microsoft SQL Server is a relational database management system developed by Microsoft. As a database server, it is a software product with the primary function of storing and retrieving data as requested by other software applications—which ...
) or
materialized view
In computing, a materialized view is a database object that contains the results of a query. For example, it may be a local copy of data located remotely, or may be a subset of the rows and/or columns of a table or join result, or may be a summary ...
s (
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 optimized physically.
DBA implementation
Another 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 synchronized, 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
A trade-off (or tradeoff) is a situational decision that involves diminishing or losing one quality, quantity, or property of a set or design in return for gains in other aspects. In simple terms, a tradeoff is where one thing increases, and anot ...
, speeding up reads (
SELECT in SQL) while slowing down writes (
INSERT,
UPDATE, and
DELETE). This means a denormalized database under heavy write load may offer ''worse'' performance than its functionally equivalent normalized counterpart.
Denormalization versus not normalized data
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
Third normal form (3NF) is a database schema design approach for relational databases which uses normalizing principles to reduce the duplication of data, avoid data anomalies, ensure referential integrity, and simplify data management. It was ...
and any relations with join and multi-valued dependencies are handled appropriately.
Examples of denormalization techniques include:
* "Storing" the count of the "many" elements in a one-to-many relationship as an attribute of the "one" relation
* Adding attributes to a relation from another relation with which it will be joined
*
Star schema
In computing, the star schema is the simplest style of data mart schema and is the approach most widely used to develop data warehouses and dimensional data marts. The star schema consists of one or more fact tables referencing any number of dim ...
s, which are also known as fact-dimension models and have been extended to
snowflake schema
In computing, a snowflake schema is a logical arrangement of tables in a multidimensional database such that the entity relationship diagram resembles a snowflake shape. The snowflake schema is represented by centralized fact tables which ...
s
* Prebuilt summarization or
OLAP cube
An OLAP cube is a multi-dimensional array of data. Online analytical processing (OLAP) is a computer-based technique of analyzing data to look for insights. The term ''cube'' here refers to a multi-dimensional dataset, which is also sometimes c ...
s
With the continued dramatic increase in all three of storage, processing power and bandwidth, on all levels, denormalization in databases has moved from being an unusual or extension technique, to the commonplace, or even the norm. For example, one specific downside of denormalization was, simply, that it "uses more storage" (that is to say, literally more columns in a database). With the exception of truly enormous systems, this particular aspect has been made irrelevant and using more storage is a non-issue.
See also
*
Cache (computing)
*
Normalization
*
Scalability
Scalability is the property of a system to handle a growing amount of work by adding resources to the system.
In an economic context, a scalable business model implies that a company can increase sales given increased resources. For example, a ...
References
{{Database normalization