Denormalization: When, Why, and How
Handling a one-to-one relationship or one-or-many relationship can be done . The Tuple interface provides convenient access to the selected. I still some times cheat and have denormalized tables in my db of record, but I Can a many to many linking table be reduced to a one to one relationship with can cache twice as many records and access speed for records in memory is. The second one is okay; the first is often the result of bad database In that case, a select query returning the task and a valid client Improving query performance: Some of the queries may use multiple tables to access data.
Likewise, it should not contain fewer rows either.
How to Handle a Many-to-Many Relationship in Database Design
Combined Tables If tables exist with a one-to-one relationship consider combining them into a single combined table. Sometimes, even one-to-many relationships can be combined into a single table, but the data update process will be significantly complicated because of the increase in redundant data. For example, consider an application with two tables: This new table would contain all of the columns of both tables except for the redundant DEPTNO column the join criteria.
So, in addition to all of the employee information, all of the department information would also be contained on each employee row. This will result in many duplicate instances of the department data.
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Combined tables of this sort should be considered pre-joined tables and treated accordingly. Tables with one to one relationships should always be analyzed to determine if combination is useful. Redundant Data Sometimes one or more columns from one table are accessed whenever data from another table is accessed. If these columns are accessed frequently with tables other than those in which they were initially defined, consider carrying them in those other tables as redundant data.
By carrying these additional columns, joins can be eliminated and the speed of data retrieval will be enhanced. This should only be attempted if the normal access is debilitating. The column should not be removed from the DEPT table, though causing additional update requirements if the department name changes. In all cases columns that can potentially be carried as redundant data should be characterized by the following attributes: This usually results in higher DASD usage and less efficient retrieval because there are more rows in the table and more rows need to be read in order to satisfy queries that access the repeating group.
Sometimes, denormalizing the data by storing it in distinct columns can achieve significant performance gains. However, these gains come at the expense of flexibility.
For example, consider an application that is storing repeating group information in the normalized table below: This table can store an infinite number of balances per customer, limited only by available storage and the storage limits of the RDBMS.
If the decision were made to string the repeating group, BALANCE, out into columns instead of rows, a limit would need to be set for the number of balances to be carried in each row. An example of this after denormalization is shown below: In this example, only six balances may be stored for any one customer.
The number six is not important, but the concept that the number of values is limited is important. This reduces the flexibility of data storage and should be avoided unless performance needs dictate otherwise. Before deciding to implement repeating groups as columns instead of rows be sure that the following criteria are met: This should be avoided because, in general, data is denormalized only to make it more readily available.
Derivable Data If the cost of deriving data using complicated formulae is prohibitive then consider storing the derived data in a column instead of calculating it. However, when the underlying values that comprise the calculated value change, it is imperative that the stored derived data also be changed otherwise inconsistent information could be reported.
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. Hierarchies A hierarchy is a structure that is easy to support using a relational database such as DB2, 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.
sql - How to flatten a one-to-many relationship - Stack Overflow
A Department Reporting system typically records the departmental structure of an organization indicating which departments report to which other departments. Figure 3 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 The table shown under the tree in Figure 3 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.
We must adjust every piece of duplicate data accordingly. That also applies to computed values and reports. We must properly document every denormalization rule that we have applied. Or maybe we need to add to existing denormalization rules.
We added a new attribute to the client table and we want to store its history value together with everything we already store. If these operations happen relatively rarely, this could be a benefit. Rules 2 and 3 will require additional coding, but at the same time they will simplify some select queries a lot.
This too will require a bit more coding. The Example Model, Denormalized In the model below, I applied some of the aforementioned denormalization rules.
The pink tables have been modified, while the light-blue table is completely new.
What changes are applied and why? In a normalized model we could compute this data as units ordered — units sold — units offered — units written off. We would repeat the calculation each time a client asks for that product, which would be extremely time consuming. Of course, this simplifies the select query a lot.
In the modified task table, we find two new attributes: Both of them store values when the task was created. The reason is that both of these values can change during time. I added it here to avoid making that calculation each time we want to take a look at a list of offered products.
The table structure is the same, but it stores a list of sold items. We should look at it as a denormalized table because all its data can be computed from the other tables. The idea behind this table is to store the number of tasks, successful tasks, meetings and calls related to any given client. Calculating these values on-the-fly would require time, slowing down query execution.
- Denormalization Guidelines
- Accommodating a many-to-many relationship in Access
- When and How You Should Denormalize a Relational Database
You can denormalize a database to provide calculated values. 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.
Here are situations when storing derivable values is appropriate: But what if a user deletes a message from their account? Denormalization of data in one of the tables can make this much simpler: When a user deletes this message read: Using pre-joined tables 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. Advantages No need to use multiple joins DML is required to update the non-denormalized column You can put off updates as long as stale data is tolerable An extra column requires additional working and disk space Example 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. Advantages No need to implement a lookup table Recoding and restating are required if look-up values are altered No joins to a lookup table Example 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. Keeping details with the master 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. Advantages No need to use joins Increased complexity of DML Saves space Example 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.