Selection of Doris Data Models (Aggregate, Unique, etc.)

Data modeling is an important component of building data storage systems, and choosing the right data model is crucial for effectively managing and processing large volumes of data. Here are some common data model choices:

Aggregate (Aggregate)

Aggregate is a simplified data model that aggregates multiple data records together to perform a certain operation or calculation. This pattern is typically used for simple storage requirements, such as storing user information.

Advantages

Simple and easy to maintain
Supports storage of multiple data records

Disadvantages

Cannot support complex data relationships and calculations
Does not support transaction processing

Unique (Unique)

Unique is a special type of aggregate that allows each aggregate group to have one unique value. This pattern is typically used to ensure data consistency and reliability.

Advantages

Supports unique value storage
Can guarantee data consistency

Disadvantages

Cannot support complex data relationships and calculations
Does not support transaction processing

Composite (Composite)

Composite is an extended aggregate pattern that allows multiple Aggregates to be combined together to meet more complex storage requirements. This pattern is typically used for medium-level storage requirements, such as storing user information and order information.

Advantages

Supports storage of multiple data records
Can support complex data relationships and calculations

Disadvantages

Requires more complex storage structures and index management
Supports transaction processing

Hash (Hash)

Hash is a special type of composite that allows data to be compressed into a smaller space to achieve fast storage and retrieval. This pattern is typically used for large dataset storage requirements.

Advantages

Supports fast storage and retrieval
Can effectively handle large datasets

Disadvantages

Cannot support complex data relationships and calculations
Does not support transaction processing