Which password is the correct password? Chapter 4 Denormalized Design Creates Insert Problems A new member cannot be created unless they have already made a visit, otherwise there would be no primary key value. This field may not be left blank. Chapter 4 Denormalized Design Creates Delete Problems If a member makes only one visit, deleting that record will cause the loss of the member data.
Deleting visit would cause the loss of Sean Mc. The example above would be a violation of the 1 NF rule; therefore this table would have to be redesigned. Chapter 4 Determinants Determinant: a field or group of fields that controls or determines the values in another field.
The value of email will determine the values in all the other fields. Chapter 4 Second Normal Form 2 NF Definition: A table in which each non-key field is determined by the whole primary key and not part of the primary key by itself.
Therefore, the fname, lname, dorm, and phone non-key fields are determined by just part of the primary key: id. Chapter 4 Update Problem Caused by 2 NF Violation Data not determined by the whole primary key will be duplicated any updates may not be made to all instances of duplicate data. This will adversely impact the effectiveness and reliability of the database. Sometimes it is not possible to immediately update derived data elements when the columns upon which they rely change.
This can occur when the tables containing the derived elements are off-line or being operated upon by a utility. In this situation, time the update of the derived data such that it occurs immediately when the table is made available for update. Under no circumstances should outdated derived data be made available for reporting and inquiry purposes. A hierarchy is a structure that is easy to support using a relational database such as SQL Server, but is difficult to retrieve information from efficiently.
For this reason, applications which rely upon hierarchies very often contain denormalized tables to speed data retrieval. Two examples of these types of systems are the classic Bill of Materials application and a Departmental Reporting system. A Bill of Materials application typically records information about parts assemblies in which one part is composed of other parts.
A Department Reporting system typically records the departmental structure of an organization indicating which departments report to which other departments. A very effective way to denormalize a hierarchy is to create what are called "speed" tables. Figure 2 depicts a department hierarchy for a given organization. The hierarchic tree is built such that the top most node is the entire corporation and each of the other nodes represents a department at various levels within the corporation. In our example department is the entire corporation.
Departments and 56 report directly to Departments 12, 3, and 4 report directly to and indirectly to department And so on. The table shown under the tree in Figure 2 is the classic relational implementation of a hierarchy. There are two department columns, one for the parent and one for the child. This is an accurately normalized version of this hierarchy containing everything that is represented in the diagram. The complete hierarchy can be rebuilt with the proper data retrieval instructions.
This tree structure represents a classic department hierarchy. Department represents the entire organization.
Each of the other departments report to a higher department. For example, department 12 reports to department which in turn reports to the corporate office, department This can be represented in a SQL Server table as follows:. Even though the implementation effectively records the entire hierarchy, building a query to report all of the departments under any other given department can be time consuming to code and inefficient to process. Figure 4 shows a sample query that will return all of the departments that report to the corporate node However, this query can only be built if you know in advance the total number of possible levels the hierarchy can achieve.
A speed table is a denormalized version of a hierarchy. Data is replicated within a speed table to increase the speed of data retrieval. Every parent department has a row for every department that reports to it at any level, either directly or indirectly. A speed table optionally carries information such as level within the hierarchy and whether or not the given child department is at a detail most level within the hierarchy i.
This table depicts the parent department and all of the departments under it regardless of the level. Contrast this to the previous table which only recorded immediate children for each parent. A "speed" table also commonly contains other pertinent information that is needed by the given application. Typical information includes the level within the hierarchy for the given node, whether or not the given node of the hierarchy is a detail node at the bottom of the tree , and, if ordering within level is important, the sequence of the nodes at the given level.
After the "speed" table has been built, speedy queries can be written against this implementation of a hierarchy. A "speed" table is commonly built using a program written in Visual Basic or another high level language.
SQL alone is usually either too inefficient to handle the creation of a "speed" table or impractical because the number of levels in the hierarchy is either unknown or constantly changing. The following SQL shows various informative queries that would have been very inefficient to execute against the classical relational hierarchy.
But they work quite effectively against the "speed" tables. These queries work for any number of levels between the top and bottom of the hierarchy. We have discussed nine different types of denormalization.
The list below summarizes the types of denormalization that are available with a short description of when this type of denormalization is useful. The decision to denormalize should never be made lightly because it involves a lot of administrative dedication.
This dedication takes the form of documenting the denormalization decisions, ensuring valid data, scheduling of data migration, and keeping end users informed about the state of the tables. In addition, there is one more category of administrative overhead: periodic analysis.
Whenever denormalized data exists for an application the data and environment should be periodically reviewed on an on-going basis. Hardware, software, and application requirements will evolve and change. This may alter the need for denormalization.
To verify whether or not denormalization is still a valid decision ask the following questions:. In general, periodically test whether the extra cost related to processing with normalized tables justifies the benefit of denormalization.
You should measure the following criteria:. It is important to remember that denormalization was initially implemented for performance reasons. If the environment changes it is only reasonable to re-evaluate the denormalization decision. Also, it is possible that, given a changing hardware and software environment, denormalized tables may be causing performance degradation instead of performance gains. Mullins, All rights reserved. Craig S. Return to Home Page.
Denormalization Guidelines by Craig S. Mullins Normalization is the process of putting one fact in one appropriate place. Although an in-depth discussion of normalization is beyond the scope of this article, brief definitions of the first three normal forms follow: In first normal form 1NF , all entities must have a unique identifier, or key, that can be composed of one or more attributes.
In addition, all attributes must be atomic and non-repeating. Atomic means that the attribute must not be composed of multiple attributes. For example, EMPNO should not be composed of social security number and last name because these are separate attributes.
In second normal form 2NF , all attributes that are not part of the key must depend on the entire key for that entity. In third normal form 3NF , all attributes that are not part of the key must not depend on any other non-key attributes. You should always consider these issues before denormalizing: can the system achieve acceptable performance without denormalizing? The Reason for Denormalization Only one valid reason exists for denormalizing a relational design - to enhance performance.
These are: Many critical queries and reports exist which rely upon data from more than one table. Often times these requests need to be processed in an on-line environment. Repeating groups exist which need to be processed in a group instead of individually. However, retrieving data from a normalized database can be slower, as queries need to address many different tables where different pieces of data are stored.
Updating, to the contrary, gets faster as all pieces of data are stored in a single place. The majority of modern applications need to be able to retrieve data in the shortest time possible.
As the name suggests, denormalization is the opposite of normalization. When you normalize a database, you organize data to ensure integrity and eliminate redundancies. Database denormalization means you deliberately put the same data in several places, thus increasing redundancy. The main purpose of denormalization is to significantly speed up data retrieval.
Developers should use this tool only for particular purposes:. Typically, a normalized database requires joining a lot of tables to fetch queries; but the more joins, the slower the query. As a countermeasure, you can add redundancy to a database by copying values between parent and child tables and, therefore, reducing the number of joins required for a query. Calculating these values on-the-fly would require time, slowing down query execution.
You can denormalize a database to provide calculated values. Often, applications need to provide a lot of analytical and statistical information. Generating reports from live data is time-consuming and can negatively impact overall system performance. Denormalizing your database can help you meet this challenge. Suppose you need to provide a total sales summary for one or many users; a normalized database would aggregate and calculate all invoice details multiple times.
Needless to say, this would be quite time-consuming, so to speed up this process, you could maintain the year-to-date sales summary in a table storing user details. There are several denormalization techniques, each appropriate for a particular situation. If the calculation contains detail records, you should store the derived calculation in the master table. Whenever you decide to store derivable values, make sure that denormalized values are always recalculated by the system.
But what if a user deletes a message from their account? To pre-join tables, you need to add a non-key column to a table that bears no business value. This way, you can dodge joining tables and therefore speed up queries. Yet you must ensure that the denormalized column gets updated every time the master column value is altered.
This denormalization technique can be used when you have to make lots of queries against many different tables — and as long as stale data is acceptable. Imagine that users of our email messaging service want to access messages by category. However, when using hardcoded values, you should create a check constraint to validate values against reference values.
This constraint must be rewritten each time a new value in the A table is required. This data denormalization technique should be used if values are static throughout the lifecycle of your system and as long as the number of these values is quite small.
Suppose we need to find out background information about users of an email messaging service, for example the kind, or type, of user. We can add a check constraint to the column or build the check constraint into the field validation for the application where users sign in to our email messaging service. There can be cases when the number of detail records per master is fixed or when detail records are queried with the master.
In these cases, you can denormalize a database by adding detail columns to the master table. This technique proves most useful when there are few records in the detail table. Imagine that we need to limit the maximum amount of storage space a user can get.
Since the amount of allowed storage space for each of these restraints is different, we need to track each restraint individually. Instead, we can go a different way and add denormalized columns to the Users table:. When you deal with historical data, many queries need a specific single record and rarely require other details. With this database denormalization technique, you can introduce a new foreign key column for storing this record with its master. Often, users send not only messages but attachments too.
The majority of messages are sent either without an attachment or with a single attachment, but in some cases users attach several files to a message.
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