Denormalization in Software

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they may exist because of information not available to the original designer Specifically, identify the key entities, foreign key relationships, and any entities and tables that exist only in the physical model that are purely for relationship support (such as many-tomany relationships) You can then review the key and non-key attributes of every entity, evaluating for each normal form Ask yourself whether or not each entity and its attributes follow the The key, the whole key, and nothing but the key ideal For each entity that seems to be in 3NF, evaluate for BCNF and 4NF This analysis will help you understand to what depth the original design was originally done If there are many-to-many relationships, ensure that 5NF is met unless there is a specific reason that 5NF is not necessary Identifying the normal form of each entity in a database should be fairly easy once you understand the normal forms Make sure to consider every attribute: does it depend entirely on the primary key Does it depend only on the primary key Is there only one candidate primary key in the entity Whenever you find that the answer to these questions is no, be sure to look at creating a separate entity from the existing entity This practice helps reduce redundancy and moves data to each element that is specific only to the entity that contains it If you follow these basic steps, you ll understand what forms the database meets, and you can identify areas of improvement This will help you complete a thorough review understanding where the existing design came from, where it s going, and how to get it there As always, document your work After you have finished, future designers and developers will thank you for leaving them a scalable, logical design
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Denormalization
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Generally, most online transactional processing (OLTP) systems will perform well if they ve been normalized to either 3NF or BCNF However, certain conditions may require that data be intentionally duplicated or that unrelated attributes be combined into single entities to expedite certain operations Additionally, online analytical processing (OLAP) systems, because of the way they are used, quite often require that data be denormalized to increase performance Denormalization, as the term implies,
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Normalizing a Data Model
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is the process of reversing the steps taken to achieve a normal form Often, it becomes necessary to violate certain normalization rules to satisfy the real-world requirements of specific queries Let s look at some examples In data models that have a completely normalized structure, there tend to be a great many entities and relationships To retrieve logical sets of data, you often need a great many joins to retrieve all the pertinent information about a given object Logically this is not a problem, but in the physical implementation of a database, joins tend to incur overhead in query processing time For every table that is joined, there is usually a cost to scan the indexes on that table and then retrieve the matching data from each object, combine the resulting data, and deliver it to the end user (for more on indexes and query optimization, see 10) When millions of rows are being scanned and tens or hundreds of rows are being returned, it is costly In these situations, creating a denormalized entity may offer a performance benefit, at the cost of violating one of the normal forms The trade-off is usually a matter of having redundant data, because you are storing an additional physical table that duplicates data being stored in other tables To mitigate the storage effects of this technique, you can often store subsets of data in the duplicate table, clearing it out and repopulating it based on the queries you know are running against it Additionally, this means that you have additional physical objects to maintain if there are schema changes in the original tables In this case, accurate documentation and a managed change control process are the only practices that can ensure that all the relevant denormalized objects stay in sync Denormalization also can help when you re working on reporting applications In larger environments, it is often necessary to generate reports based on application data Reporting queries often return large historical data sets, and when you join various types of data in a single report it incurs a lot of overhead on standard OLTP systems Running these queries on exactly the same databases that the applications are trying to use can result in an overloaded system, creating blocking situations and causing end users to wait an unacceptable amount of time for the data Additionally, it means storing large amounts of historical data in the OLTP system, something that may have other adverse effects, both internally to the database management system and to the physical server resources Denormalizing the data in the database to a set of tables (or even to a different physical database) specifically used for reporting can alleviate the
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