Designing an efficient and scalable MySQL database structure requires starting from four aspects: data type, indexing strategy, standardization and anti-standardization and naming specifications. 1. Reasonably select the field type, such as using INT instead of BIGINT, CHAR is better than VARCHAR (fixed length), and the time field is preferred to DATETIME or TIMESTAMP to avoid abuse of TEXT/BLOB, and enumeration classes can be used for ENUM or small table foreign keys; 2. Use the index correctly, do not create indexes in low-cardinal sequences, avoid fuzzy matching failure, combine indexes follow the principle of leftmost prefix, and regularly analyze slow query logs; 3. Fight normalization and anti-standardization, appropriately redundant commonly used fields to reduce JOIN, wide tables or JSON fields can be used for scenarios that read more and write less, but data consistency risks need to be prevented; 4. Clear and unified naming, use lowercase underscores to separate, add comments and maintain documents, and improve team collaboration efficiency.
Designing an efficient and scalable MySQL database structure is the basis for building stable applications. Many performance problems and maintenance problems have actually laid the groundwork as early as when building tables. The key is to plan the relationship between field types, index policies and tables in advance.
1. Reasonably select the data type
MySQL provides a wealth of data types, but choosing the right one is the key. For example, INT is enough for integers, so there is no need to use BIGINT; if the length of a string is fixed, CHAR is more suitable than VARCHAR; the time field is preferred to DATETIME or TIMESTAMP, pay attention to the differences in their range and time zone processing.
- Try to use smaller data types: the smaller the space, the higher the read and write efficiency
- Avoid abuse of TEXT/BLOB types, which may cause additional disk I/O and memory consumption
- Enumerated class fields can be considered to use ENUM or use foreign keys to associate small tables to avoid using magic values.
2. Use index correctly
The more indexes, the better, and not all queries can hit the index. Common misconceptions include adding indexes (such as gender) to low cardinality sequences, or using wildcards at the beginning of fuzzy matches to cause index failure.
The focus is on indexing fields that are often used to query conditions, sort, and group. When querying multi-field, combined indexes are more effective than multiple single-column indexes, but pay attention to the principle of leftmost prefix.
For example, there is such a joint index (name, age, city):
- You can hit the name query
- You can hit the name age query
- Can't hit age or city query alone
Also, remember to analyze the slow query log regularly to see which fields are missing indexes and which indexes are not used.
3. The trade-off between standardization and anti-normative
In database design, the third normal can reduce data redundancy, but sometimes some anti-specifications are needed to query performance. For example, save user names in the order table instead of joining the user table every time.
Common practices include:
- Redundant some commonly used fields to reduce join operations
- For scenarios where more reads and less writes, wide tables or materialized views can be used (although MySQL native support is limited)
- Use JSON fields to store unstructured data, but control how often you use it and how you access it
However, it should also be noted that excessive anti-standardization will bring about data consistency risks and need to be judged in combination with business logic.
4. Clearly named and complete documents
Try to use underscore-separated lowercase English, such as user_profile instead of UserProfile. Unified naming style helps with post-maintenance.
Also, don't forget to add comments. You can use the COMMENT keyword to add descriptions to tables and fields. When collaborating with a team, it is best to have a document that is updated simultaneously, even if it is just a simple ER diagram.
Basically that's it. A good schema design is not necessarily complicated, but it must be thought-provoked.
The above is the detailed content of Best practices for MySQL database schema design. For more information, please follow other related articles on the PHP Chinese website!
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