Dynamic Field Processing: When database table structures frequently change

Frequent changes to database table structures are a common problem in development, and how to efficiently process dynamic fields becomes a key question.Share dynamic field management techniques, covering database table optimization methods and structural change strategies, to help developers respond flexibly to changing requirements, reduce maintenance costs, and increase the flexibility of table design, JSON field application, and version control.

Why do we need dynamic field management?

Many developers have encountered this problem: The database's structure has to be changed every few days to keep up with the changing demands of the application. Every time a new field is added, the SQL code must be rewritten and the program code altered, which not only reduces efficiency, but also makes errors more likely.At this point, the importance of dynamic field management becomes clear.It will allow your database to be more "robust" and reduce the cost of modifying code when demands change.

Technique 1: Use a design pattern.

If you find that you frequently need to add or subtract fields, you can try the design of an extended table.

The system can be easily adjusted to accommodate new fields of knowledge.

Create a single "user_metadata" table, and associate it with the main table using the "user_id" field. Design the fields to be in key-value form.For example, the following:

The following is a translation of his interview.

user_id | field_name | field_value

1 Hobbies: Mountain climbing.

1 | skill | Python.

Please use natural-sounding language, and translate only the text in quotation marks.

When a new field needs to be added, all that is necessary is to insert a new data row, and there is no need to alter the table structure.

Pay attention to the problem of search efficiency.

This scheme is flexible, but it adds expense when the tables are linked.It is suggested that fields with high frequency queries be kept in the main table, while low frequency dynamic data be stored in the extension table.

Practical tip # 2: Use JSON fields.

The mainstream databases MySQL 5.7 and PostgreSQL both support JSON types, which are perfect for dealing with dynamic fields.

Store data with uncertain structures.

For example, the parameters of different types of goods vary greatly.

Please use natural-sounding English.

ALTER TABLE products ADD COLUMN attributes JSON;

Please translate the following into clear, natural English, and output only the final translation.

When storing data, simply insert a JSON object:

Q: What is the most important thing in your life?

IP68 water resistance rating.

Please translate the following text into English.

Creating virtual columns to speed up search times.

You can use the generated column feature to extract commonly used JSON fields into virtual columns.

Q: What is the most important thing you have learned from your experience?

ALTER TABLE products

ADD COLUMN screen_size VARCHAR(10) AS (attributes->>' $.屏幕尺寸');

Please translate the following into natural-sounding English.

This way, the flexibility of the system is maintained and the speed of the search is increased.

Practical technique 3: Versioning the database structure.

It is unrealistic to completely avoid changing the structure of the clock, but the key is to make sure the changes are controllable.

Establish a mechanism for recording changes.

Create a schema_migrations table to record every change.

(Chang Chia-wei / photos by Huang Chih-wei / tr.

version | change_description | executed_at.

v1.2 | Added user level field | 2024-03-15.

The following is a list of the 10 most popular Chinese names in Taiwan.

Use automated migration tools.

Tools like Liquibase or Flyway allow SQL scripts to be included in version control.Each time a change is made, a rollback migration script is generated, and the CI / CD process is followed.

Choosing the right plan.

There is no absolute good or bad among these three methods, it all depends on the business situation.

- Frequently adding or deleting fields and needing complex queries → choose extended table.

- Variable data structures and simple queries → Use JSON fields.

– Needing strict data consistency → doing a good job of version migration.

Finally, it should be noted that it is not a good idea to pursue flexibility to the point of overdesign.I've seen teams that store every field as a key-value pair, and then have to join ten tables to do a query, which really kills performance.A good framework is always the result of careful weighing of the options.