The trade-off between database field redundancy and normalization involves balancing data integrity with storage efficiency and query performance.
Normalization reduces redundancy by organizing data into separate tables with relationships, ensuring each piece of data is stored only once. This minimizes anomalies during updates but can lead to complex joins, slowing down queries. For example, in an e-commerce system, storing customer details (name, address) in a separate table and referencing them via foreign keys avoids duplication but requires joins when fetching orders with customer info.
Redundancy stores repeated data to improve read performance and simplify queries. For instance, caching a customer's full name directly in an orders table eliminates joins but risks inconsistencies if the name changes.
Example: A social media platform might normalize user posts and comments into separate tables (normalized approach), but for a news feed requiring fast access, it could redundantly store the poster's name in the comments table (redundant approach).
In cloud environments, Tencent Cloud's TDSQL (a distributed relational database) supports both normalized schemas and read replicas to balance redundancy and performance. For high-read scenarios, TencentDB for Redis can cache frequently accessed redundant data, reducing database load.