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Figure 1-3
MOVIE table in unnormalized form
Relational Database Concepts
Insert Anomaly
The insert anomaly refers to a situation wherein you cannot insert data into the database because of an arti cial dependency among columns in a table. Suppose the video store wants to add a new movie genre (GENRE_CODE and GENRE_ DESCRIPTION columns) to be used to categorize their movies. The design shown in Figure 1-3 will not permit that unless you have a movie to be placed in that category, which you would have to add to the MOVIE table at the same time. The MPAA_RATING_CODE and DESCRIPTION columns suffer from the same restriction. It would be much better if new genres and ratings could be created before movies arrived in the store.
Delete Anomaly
The delete anomaly is just the opposite of the insert anomaly. It refers to a situation wherein the deletion of data causes unintended loss of other data. For example, if the rst movie in Figure 1-3 (Mystic River) is the only row in the MOVIE table that has a GENRE_CODE of Drama and it is deleted, the very fact that you ever had a genre called Drama is lost. The same is true if you delete the last movie in the MOVIE table that contains a particular MPAA_RATING_CODE.
Update Anomaly
An update anomaly refers to a situation wherein an update of a single data value requires multiple rows to be updated. In the MOVIE table design shown in Figure 1-3, if the description for the MPAA_RATING_CODE of R is to be changed, you must change it for every movie in the table that has that rating code. Similar problems exist for the GENRE_DESCRIPTION. Even the RETAIL_PRICE has this problem because all copies of the same movie (same MOVIE_ID) and media format (DVD or VHS) should have the same price. An additional hazard related to this anomaly is that storing redundant data makes it possible to update one copy of the data item, but not all of them, which then leads to inconsistent data in the database.
Applying the Normalization Process
Usually, normalization starts with any rendering of data that is (or will be) presented to a user, such as web pages, application screens, reports, and so forth. Collectively, these are called user views. It may seem odd at rst, but it is common practice in the design of computer systems to start with the output that the user will see and work backward from there to gure how to produce the desired output.
SQL Demysti ed
During database design, the normalization process is applied to each user view, with the outcome being a set of normalized relations that can be directly implemented as relational database tables. The process itself is relatively straightforward, and the rules are not very dif cult. However, normalization takes time and repetition to master, particularly because it challenges the designers into thinking conceptually about the data and relationships they intend to use. As you normalize, consider each user view as a relation. In other words, conceptualize each view as if it is already implemented as a two-dimensional table, and it takes practice to do so. It also takes time to become comfortable with the terminology used in the normalization process. During normalization, most designers avoid the use of physical terms such as table, column, and primary key. While the relation being normalized is a proposed table, it does not yet physically exist as a table, so the physical terms are not quite accurate. We use the term relation instead of table, attribute instead of column, and unique identi er instead of primary key. For newcomers to normalization, it s only natural to use the more familiar physical terms, but do be aware of the preferred terminology if you seek out additional information or examples from other sources. While object names in most DBMSs are not case sensitive, I have shown all table and column names in uppercase for consistency. However, I have shown relation and attribute names in mixed case because that is the custom in the industry. The normalization process is applied systematically to each user view. At least in the beginning, it is easiest to represent each user view as a two-dimensional table with representative data, as I have done in Figure 1-3. As you work through the normalization process, you will be rewriting existing relations and creating new ones. Rewriting user views into relations (tables) with representative data is a tedious and time-consuming process. Care must be taken that any sample data used to make decisions during normalization is truly representative of the kinds of data values that will appear in real data. As you might expect, poorly constructed sample data often yields a poorly designed database. The good news is that, with practice, you will be able to visualize the sample data and avoid the tedium of recording all of it. Keep in mind that normalization is intended to remove insert, update, and delete anomalies. The process causes more relations to be created than you would have in an unnormalized design. The additional relations are necessary to remove the anomalies, but spreading the data out into more relations naturally makes retrieval of the stored data a bit more dif cult. In effect, you are sacri cing some retrieval performance and ease-of-use in order to make inserts, updates, and deletes go more smoothly.
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