How do you document and communicate the reasons and effects of denormalization in SQL?

In my experience, denormalization often happens when there's a need for fast developing and/or there are some business analysis requirements. For fast developing, there are many times we cannot easily decide what to keep and what to leave in a database table, or we do not know how the table will be changed in the future. So we often begin with creating a table that trying to keep everything, and then change it depend on the need of future flexibility and extensibility. As for the business analysis, there are always requirements of collecting user behaviour and store them into the database for analysis. We want to know as much information as possible in the analysis process, so we may use a big table to store all the information we need.

Documenting and communicating the reasons and effects of denormalization in SQL involves several key steps to ensure clarity and understanding among stakeholders. Firstly, it's essential to clearly articulate the reasons behind the decision to denormalize, such as to optimize query performance, reduce data redundancy, or simplify complex joins. This rationale should be documented in a concise and comprehensible manner, outlining the specific business or technical requirements that led to the decision.Next, the effects of denormalization should be thoroughly documented, including both benefits and potential drawbacksThis documentation should highlight how denormalization impacts data integrity, storage requirements, and query performance.

Documenting denormalization in SQL involves outlining specific performance gains, such as reduced query complexity or improved retrieval speed. Communicate the rationale behind denormalization by detailing instances where it enhances read performance or simplifies complex joins. Document the trade-offs, including increased storage requirements or potential update anomalies. Use diagrams or performance metrics to illustrate improvements and emphasize how denormalization aligns with specific business needs, ensuring clear and comprehensive communication of its reasons and effects within the SQL environment.

When selecting denormalization techniques for our database, we will carefully evaluate each option based on the specific needs of our schema and queries. For instance, adding redundant columns can speed up read operations at the cost of additional storage and potential update anomalies, while creating materialized views can optimize complex queries by pre-computing aggregates.

Modifying the database is a serious job, unlike modifying the business logic, the code system modifies the process, and problems can be fixed by restoring the version or restarting the system (although they are also costly), but the database is where the data is kept, and changes to the database may lead to data loss, and even if we have backups, there will always be a lot of data that cannot be retrieved. So it is very important to choose the right method to do the denormalization and to record each change and the reason for the change. We may have a document to record every sql statement we have wrote to execute on the database, especially DDL statements.

The second step in database optimization is selecting the appropriate denormalization techniques for your schema and queries. Options include adding redundant columns, creating derived attributes, merging or splitting tables, pre-computing aggregates, creating materialized views, or using denormalized data structures. Document the rationale and implementation details of each technique, illustrating changes with diagrams, pseudocode, or SQL statements. Additionally, explain the impact on normal forms, functional dependencies, and referential integrity. This ensures clarity and understanding of the denormalization process

We should indeed perform a lot of tests to verify that the work we have done meets expectation. For example, if we want to improve the responsiveness of the database through denormalization, we should test that we are actually improving the speed by doing so. If we want to perform analytics (OLAP) through denormalization, we should verify that we can perform analytics better after modifying the database. Modifying the database is a serious task and we should be careful about doing it. In reality, however, there are many more conservative ways to achieve our goal, such as creating a View consisting of multiple database tables, rather than directly modifying the database tables, but it all depends on the specific requirements to achieve.

The third step is to test and evaluate the results of denormalization on your database's performance, functionality, and usability. Use realistic data sets, workloads, and scenarios to measure changes in query speed, response time, throughput, and resource consumption. Ensure the denormalized database continues to meet both functional and non-functional requirements of the application and users. Document test cases, methods, and outcomes, using charts, graphs, or tables to compare the before and after states. This thorough evaluation helps validate the effectiveness of denormalization efforts.

The fourth and final step is to communicate and collaborate with database stakeholders, including developers, analysts, managers, and clients. Present your documentation and evaluation of denormalization clearly and understandably, emphasizing the benefits, costs, and risks. Solicit and address feedback, suggestions, and concerns from stakeholders. Document the communication and collaboration process, utilizing tools, platforms, or formats that facilitate effective information and idea exchange. This ensures alignment and transparency throughout the denormalization process.

There is a very common term used in Chinese Internet companies - closed loop. This means that we should remain responsible for every work we carry out and close it in the right way. If we are working on development, we need to get new features live and collect feedback on them. If we're making changes, we need to be able to document our changes, test and validate the results of our changes, document and communicate those results to others, and make sure everyone involved is aware of them. It's a responsible approach to our work.