The tasks of database logical design involve transforming the conceptual data model (often created during the conceptual design phase) into a logical structure that can be implemented in a specific database management system (DBMS). This phase focuses on organizing data logically, defining relationships, and ensuring efficient data access while adhering to the constraints of the chosen DBMS.
Key tasks include:
Translating the Conceptual Model: Convert entities, attributes, and relationships from the Entity-Relationship (ER) model or other conceptual models into relational tables, columns, and keys. For example, an entity like "Customer" with attributes (CustomerID, Name, Email) becomes a table with corresponding columns.
Defining Primary and Foreign Keys: Establish primary keys to uniquely identify each record in a table (e.g., CustomerID as the primary key for the Customer table) and foreign keys to enforce relationships between tables (e.g., OrderID in the Orders table referencing CustomerID in the Customer table).
Normalization: Apply normalization rules (1NF, 2NF, 3NF, etc.) to eliminate data redundancy and ensure data integrity. For instance, splitting a table with repeating groups (like multiple phone numbers in one row) into separate tables.
Designing Relationships: Determine the type of relationships (one-to-one, one-to-many, many-to-many) and implement them using foreign keys or junction tables. For example, a many-to-many relationship between Students and Courses might require a junction table (Enrollments).
Optimizing Data Access: Structure the database to support efficient querying, such as indexing frequently searched columns (e.g., indexing the Email column in the Customer table for faster lookups).
Handling Constraints: Define constraints like NOT NULL, UNIQUE, or CHECK to enforce business rules. For example, ensuring the "Age" column in a table does not accept negative values.
Mapping to DBMS Features: Adapt the logical design to the specific capabilities and limitations of the target DBMS, such as supported data types or storage engines.
In cloud environments, services like Tencent Cloud's Relational Database Service (TencentDB for MySQL/PostgreSQL) can help implement logical designs efficiently, offering scalable, managed database solutions with built-in optimization tools. For NoSQL needs, Tencent Cloud's TencentDB for MongoDB or Redis can handle non-relational logical designs.