Database standardization is suitable for systems that require high data consistency, write more and read less, and have stable structure, such as financial or order systems. It reduces redundancy and improves consistency by splitting data into multiple tables, but may bring problems such as degraded query performance and increased complexity; anti-standardization is suitable for scenarios with more reading more and less writing and high response speed requirements, such as reporting systems or distributed databases, which improve query efficiency through redundant data, but may sacrifice consistency. Normalization is divided into multiple paradigm stages, such as 1NF, 2NF to BCNF, and data storage and operation are optimized by reasonably splitting the table structure.

Database standardization is a problem that many developers and data engineers encounter when designing systems. Simply put, it is a way to reduce redundancy and improve consistency by organizing data. But standardization is not omnipotent, it is also accompanied by some trade-offs. If you are struggling with whether to standardize when designing a database, here are a few key points that can help you make a judgment.

What is database normalization?

The core goal of database standardization is to split the data into tables with clear logic and reasonable structure, thereby reducing duplicate storage and data exceptions. It is usually divided into multiple "paradigm" stages, such as the first normal form (1NF), the second normal form (2NF) and all the way to BCNF, etc.

To give a simple example: if you have an order table that contains both customer information and order details, this may cause duplicate data. After standardization, you may split the customer information into a single table, and the order table only retains the customer ID, so that the customer information only needs to be saved once.

Advantages of standardization

1. Reduce data redundancy
The data is stored only once, saving storage space and avoiding the risk of inconsistency between multiple replicas.

2. Improve data consistency
When you update the data, you only need to change one place, and there will be no problem of "this table is changed, that table is not synchronized".

3. Clearer data structure
The standardized database structure is clearer and the logic is clearer, which is convenient for later maintenance and expansion.

4. Support more flexible queries
The data can be flexibly combined through foreign key correlation between tables to adapt to different query needs.

Problems caused by standardization

1. Query performance may decline
Because the data is split into multiple tables, JOIN operations are often required during querying, which will increase the burden on the database, especially when the data volume is large and the concurrency is high.

2. Added complexity
There are more tables and more relationships, and more time is needed to understand structures and relationships when developing and maintaining.

3. Insert and update may be slower
Although update consistency is improved, multiple tables may be required to be manipulated when inserting new data, resulting in performance degradation.

4. Not suitable for all scenarios
For example, in some data analysis and log systems, in order to query efficiency, denormalization will be deliberately carried out.

When should I use standardization?

• You attach importance to data consistency : for example, financial systems and order systems, and data inconsistencies cannot be tolerated.
• Write operations are more than read operations : If the system updates data frequently, normalization can reduce errors.
• The data model is relatively stable : the structure does not need to be adjusted frequently, and is suitable for long-term maintenance.

When can anti-normalization be considered?

• Reading operations are far more common than writing operations : for example, report systems and data warehouses, query performance is more important.
• High requirements for response speed : For example, if there are too many JOINs in real time, it will affect the response time.
• Distributed databases or NoSQL are used : some systems themselves are not suitable for complex JOIN operations.

Basically that's it. Standardization is a good tool, but it is not the only answer. The key is to decide how to do it based on your business needs, data volume, and query mode. Sometimes finding a balance between the two is the most practical approach.

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