What is Denormalization in Databases?
It will not surprise anyone that there is more than one database design. Creating different structures from databases is necessary because one size does not always fit all. Certainly, in many cases, we can use standard methods like database normalization, and everything will run smoothly. However, there are also situations where this common procedure will not work and will require a different approach. If you want the highest flexibility, performance, and convenience of database management, you need to think carefully about your database design. It may turn out that you primarily care about efficient data retrieval in a short time, for example, to generate reports. Then, it is worth considering the implementation of something that streamlines such processes. We are talking about denormalizing the database. Have you heard of such a procedure before? Dig in to find out more about what it looks like and what benefits it has.
What is denormalization
Before moving on to the denormalization definition, we should first explain what database normalization is. It is the process of structuring a database, usually a relational database, following a series of so-called normal forms to reduce data redundancy and improve data integrity. Database denormalization, on the other hand, is the introduction of controlled redundancy into the database to accelerate the performance of operations on it. Due to the denormalization of the database, costly table joining operations are avoided. Denormalizing a database means generating redundant data in it, that is, mutual consideration of keys or columns between tables that are expected to be linked frequently. Therefore, each table contains not only the information that is relevant to it. As a result, the tables are larger, but queries can be faster. It is also important to use keys in denormalized tables, which results in much faster data processing.
What is the purpose of denormalization?
Properly performed denormalization can facilitate the execution of specific queries. These benefits come at the expense of redundant data. Denormalized data takes up more space because the same data is in several places. However, this is not a bad phenomenon in this case. Some related data is together on the same lines, and fewer connections are needed, and it makes all operations on them faster. Queries are easier to write, and also, they will work better.
In addition to the significant acceleration of data download, denormalization can also be used for other purposes. This strategy can make database management easier. The normalized database does not have the computed values that your application needs. Calculating them every moment they are needed would take more time. This would slow down query execution. Denormalizing the data provides the already calculated values. After generating and adding them to tables, you can create queries and reports without in-depth knowledge of application code or API. Moreover, when it comes to reporting, with denormalization, it will be much simpler and faster. With a normalized database, generating reports from up-to-date data is time-consuming and can negatively affect the overall performance of the system. If the application is to provide a lot of information for analysis, denormalization can be a very effective solution.
Bear in mind that denormalization is not always necessary. Even if there are no performance issues in the application, there is no need to implement it. But once you notice that this is happening and your system is slowing down, or you are sure it will happen soon, consider using this strategy. However, it is recommended that you optimize other elements, such as queries, and make sure that indexing is correct before implementing this technique.
Normalization and denormalization
Normalization, as mentioned above, is the structuring of data and dividing it into multiple tables. It is performed to reduce data redundancy and inconsistency and to achieve data integrity. However, it requires multiple, often complicated joining tables. On the other hand, by denormalizing the database, we put all the data into one table and speed up their searches. With everything in one place, your query does not have to jump from table to table to find what you need.
The dark side of denormalization, however, will be taking up a lot of space. The same data will be stored in many places. After all, this strategy is based on redundancy. With normalization, you optimize the disk space. What other differences can we see in the two strategies? For example, normalization increases the number of tables and joins, as opposed to denormalization which reduces them. In the normalization process, data integrity is also maintained, which is more difficult to maintain after database denormalization. When normalization is performed, redundant data is eliminated, while denormalization increases this repetitive data.
What is denormalization in SQL?
SQL is a structured query language used to create and modify databases and to place and retrieve data from databases. It is a declarative language, and it is left to the database management system (DBMS) to decide how to store and retrieve the data. It is one of the most famous languages of this type, so it’s no wonder that denormalization in SQL can be interesting. In the case of SQL, denormalization is standardized and is also based on redundant data. Because this procedure also has many opponents, the efficient use of denormalization cases, according to some, is very limited. Data warehouses are an example of such use of this strategy. They are relational databases that are designed for querying and analysis, not transaction processing. Data warehouses typically contain historical data from transactional data but may include data from other sources. They separate the analysis workload from the transactional workload and enable the organization to consolidate data from several sources. Of course, denormalization in SQL can also be used in other cases, not only in data warehouses. We emphasize once again that it all depends on the appropriate study of your particular case.
Undoubtedly, denormalization is a concept with many benefits that makes it easier to manage databases. However, it all depends on the proper implementation of this technique and choosing it appropriately for the project. This is especially important, as it also carries many risks if it is not used skilfully. While this is not the first method you should use to improve performance, in some situations, it may be the best or even the only solution. Always do a proper analysis first, track system performance, and start optimizing smaller items, like queries.
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