Can you over normalize a database

Hope this helps. It's always a question of the application domain. It's usually a question of correctness, but occasionally a question of performance. Since that introduces temporal coupling, you've incorrectly normalized because the overnormalization affects already shipped orders, unless prices absolutely never change.

You can certainly argue that this is simply a modeling error as in the comments , but I see under-normalization as a modeling error in most cases, too.

The other category is performance related. In principle, I think there are generally better solutions to performance than denormalizing data, such as materialized views, but if your application suffers from the performance consequences of many joins, it may be worth assessing whether denormalizing can help you. I think these cases are often over-emphasized, because people sometimes reach for denormalization before they properly profile their application.

People also often forget about alternatives, like keeping a canonical form of the database and using warehousing or other strategies for frequently-read, but infrequently changed data. Normalization is absolute. A database follows Normal Forms or it does not. There are a half-dozen normal forms. Mostly, they have names like First through Fifth. Plus there's a Boyce-Codd Normal Form. Normalization isn't subjective. It isn't a judgement. Each table and relationship among tables either does or does not follow a normal form.

Having said that, normalization has a performance cost. Some people elect to denormalize in various ways to improve performance. The most common sensible denormalization is to break 3NF and include derived data. A common mistake is to break 2NF and have duplicate copies of a functional dependency between a key and non-key value. This requires extra updates or -- worse -- triggers to keep the copies in parallel. A data warehouse, also, rarely follows any of the transactional normalization rules because it's essentially never updated.

This may also mean that the database has outgrown the hardware. Or that the applications haven't been designed to scale.

The most common issue here is that folks try to use a transactional database for reporting while transactions are going on. The locking for transactions interferes with reporting. A lot of people are talking about performance.

I think a key issue is flexibility. In general, the more normalized your database, the more flexible it is. We currently use an "over-normalized" database because, in our operating environment, client requirements change on a monthly basis. By "over-normalizing" we can adopt our software accordingly, without changing the database structure. Each has a mission that dictates its schema. Like normalized transaction databases, data warehouses exist for a reason.

A complete system needs both. Always normalize as much as you are able to do. I usually go crazy on normalization, and try to design something that could handle every thinkable future extensions. What I end up with is a database design that is extremely flexible Then the real job starts: De-normalization. Edit: Documentations! I forgot to mention that documenting the de-normalization is very important. It is extremely helpful when you take over a project to know the reason behind the choices.

Third Normal Form 3NF is considered the optimal level of normalization for many a rational database application. This is a state in which, as Bill Kent once summarized , every "non-key field [in every table within a particular a relational database management system, or RDBMS] must provide a fact about the key, the whole key, and nothing but the key.

Codd , inventor of the relational model for database management. In contrast, overnormalization implies increasing the number of joins required in a query between related tables. This comes as a result of breaking down the database schema into a much more granular level than 3NF. However, though normalization past the 3rd degree can often be considered overnormalization, the negative connotation of the term "overnormalization" can sometimes be unwarranted.

Overnormalization may be desirable in some applications which by design require 4NF and beyond due to the complexity and versatility of the application software. An example of that is a highly customizable and extensible commercial database program for some industry in which it is sold to end users requiring an open API. In this case the data must by necessity reside in a highly denormalized format i. Database normalization is one of the sacred cows of application development.

Database normalization protects your data's integrity. It's a great idea in many cases, and you should begin any database design endeavor with normalization in mind. If you can normalize your database, go for it! The bottom line is that you should normalize your database unless you have a really good reason not to do so. Normalizing is usually sound design practice. That said, there are some good reasons not to normalize your database.

Database normalization is generally a good idea. You should try to follow the principles of normalization when it seems reasonable to do so. But if all indicators point to normalization being too complex to implement, consider an approach that will get the job done while still protecting your data. Finally — if you do choose to stray from the rules of normalization, be extra vigilant about how you enforce database integrity.

If you store redundant information, put triggers and other controls in place to make sure that information stays consistent. Actively scan device characteristics for identification. Use precise geolocation data. Select personalised content. Create a personalised content profile. An SQL KEY is a single column or combination of multiple columns used to uniquely identify rows or tuples in the table.

SQL Key is used to identify duplicate information, and it also helps establish a relationship between multiple tables in the database.

Note: Columns in a table that are NOT used to identify a record uniquely are called non-key columns. In our database, we have two people with the same name Robert Phil, but they live in different places. Hence, we require both Full Name and Address to identify a record uniquely. That is a composite key. We have divided our 1NF table into two tables viz. Table 1 and Table2. Table 1 contains member information. Table 2 contains information on movies rented.

Records can be uniquely identified in Table 1 using membership id. You will only be able to insert values into your foreign key that exist in the unique key in the parent table. This helps in referential integrity. The above problem can be overcome by declaring membership id from Table2 as foreign key of membership id from Table1.

Now, if somebody tries to insert a value in the membership id field that does not exist in the parent table, an error will be shown! A transitive functional dependency is when changing a non-key column, might cause any of the other non-key columns to change. Now our little example is at a level that cannot further be decomposed to attain higher normal form types of normalization in DBMS.