Denormalizing a Relational Database for Performance
Introduction This post will explain how to denormalize your A one to many relationship is defined as each row in the related to table can be. Denormalization is the process of putting one fact in numerous places. . Sometimes, even one-to-many relationships can be combined into a. The second one is okay; the first is often the result of bad database performance: Some of the queries may use multiple tables to access data.
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. Instead, we can go a different way and add denormalized columns to the Users table: Repeating a single detail with its master 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.
Advantages No need to create joins for queries that need a single record Data inconsistencies are possible as a record value must be repeated Example 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.
Naturally, if a message contains more than one attachment, only the first attachment will be taken from the Messages table while other attachments will be stored in a separate Attachments table and, therefore, will require table joins.
In most cases, however, this denormalization technique will be really helpful. Adding short-circuit keys If a database has over three levels of master detail and you need to query only records from the lowest and highest levels, you can denormalize your database by creating short-circuit keys that connect the lowest-level grandchild records to higher-level grandparent records.
When and How You Should Denormalize a Relational Database
This technique helps you reduce the number of table joins when queries are executed. In a normalized database, such queries would need to join the Users and Categories tables. To improve database performance and avoid such joins, we can add a primary or unique key from the Users table directly to the Messages table.Converting SQL structures to Firebase structures - The Firebase Database For SQL Developers #2
This way we can provide information about users and messages without querying the Categories table, which means we can do without a redundant table join.
Though denormalization seems like the best way to increase performance of a database and, consequently, an application in general, you should resort to it only when other methods prove inefficient. For instance, often insufficient database performance can be caused by incorrectly written queries, faulty application code, inconsistent index design, or even improper hardware configuration.
Denormalization sounds tempting and extremely efficient in theory, but it comes with a number of drawbacks that you must be aware of before going with this strategy: What Are the Disadvantages of Denormalization?
Obviously, the biggest advantage of the denormalization process is increased performance. But we have to pay a price for it, and that price can consist of: We have to be very aware of the fact that data now can be changed in more than one place.
We must adjust every piece of duplicate data accordingly. That also applies to computed values and reports.
database design - Effect of denormalizing - Software Engineering Stack Exchange
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?
- Text description of the requirement
- Graphic description of the requirement
- Database denormalization techniques
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.
Think about it from the perspective of how your application interfaces with the reader; does it ever require you to list the details of a single course on which a single student is enrolled?
If so then that information may have already been selected from the database? The point is this. Look at the schema shown in Figure 3. Note how the SupportingAct entity does two things.
Firstly, it does resolve a many-to-many join between the Act and Show entities. Secondly and more importantly the SupportingAct entity has meaning in itself since it defines a second, third or even more supporting acts for a main event.
Technically speaking there can be many shows for every act since a pop music band could do many shows at many venues. Additionally every pop act could have one or more supporting pop music bands that perform with it as an introductory event.
Since the supporting acts are also Act entities by definition and they perform at the same show then it is thus meaningful to define those supporting acts as extensions of both the Act and Show entities. A finer point to note is that a pop music band traveling all over the world may not necessarily always play with the same supporting act or acts. Thus the same act could use different supporting acts when the main event performs at different venues. That is a little complicated and open to debate.
However, the point should now be clear about the practical application of 3rd Normal Form.
Many-to-many joins resolved into new entities should produce a meaningful entity, not meaningless unique identifiers, simply defined in order to define a theoretical but not necessarily practical required uniqueness.