prnsh's notes

converting-logical-to-physical-model

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**Low Level Steps for conversion**

  1. Identify and Define Storage Structures:

    • Choose physical storage structures (e.g., tablespaces, files) based on the system’s storage and performance requirements.

  2. Determine Indexing Strategy:

    • Identify frequently queried columns and create indexes on them to speed up retrieval.
    • Consider types of indexes: B-tree, hash indexes, full-text indexes.

  3. Define Primary and Foreign Keys:

    • Enforce referential integrity by specifying primary and foreign keys, and determine the indexing of these keys for performance optimization.

  4. Consider Partitioning:

    • For large tables, consider partitioning based on relevant criteria (e.g., by range, list, or hash).
    • Partitioning helps with large-scale data management and enhances query performance.

  5. Storage Optimization:

    • Optimize the physical layout by considering techniques like clustering tables and compressing data to save space.
    • Choose appropriate file formats (e.g., row-based or columnar storage) based on query types.

  6. Data Redundancy and Replication:

    • Decide on replication methods if required for high availability or fault tolerance (e.g., master-slave replication).
    • Ensure that data redundancy is controlled while optimizing for availability.

  7. Data Types and Constraints:

    • Map logical data types to physical data types appropriate for the DBMS (e.g., VARCHAR to TEXT).
    • Define physical constraints like NOT NULL, CHECK, or UNIQUE for performance and data integrity.

  8. Consider Access Paths:

    • Define optimal access paths (e.g., full table scans, range scans, index seeks) based on query patterns.

  9. Create Views and Materialized Views:

    • Define views for simplifying complex queries and materialized views for optimized querying in read-heavy environments.

  10. Define Backup and Recovery Strategies:

    • Plan physical data backup and recovery processes considering the database’s growth and criticality.

Converting a Logical Data Model to Physical Data Model

  1. Identify the Appropriate Constraints:

    • Apply NOT NULL, CHECK, DEFAULT, UNIQUE, and FOREIGN KEY constraints as per the requirements of the data model.
    • Ensure referential integrity by defining primary keys and foreign keys for relationships between tables.

  2. Determine Data Types:

    • Map logical data types to corresponding physical data types that are compatible with the DBMS (e.g., INTEGER, VARCHAR, DATE).
    • Adjust data types based on storage requirements and performance considerations.

  3. Optimize for Storage:

    • Plan the physical storage of tables, considering table spaces and partitioning options to handle large data volumes efficiently.
    • Consider compression techniques to reduce disk usage.

  4. Design Indexes for Performance:

    • Identify frequently accessed columns and create indexes to optimize query performance.
    • Create primary indexes on primary keys and secondary indexes on foreign keys or other search fields.

  5. Determine the Access Paths:

    • Define the most efficient access paths for retrieving data (e.g., full table scans, index scans, hash joins) based on query patterns.

  6. Plan for Data Redundancy and Replication:

    • Decide on replication strategies (e.g., master-slave, multi-master) for high availability and fault tolerance.
    • Ensure that data redundancy is controlled but optimized for performance.

  7. Partition Large Tables:

    • For large tables, use partitioning strategies (e.g., range, list, hash) to split the table into smaller, more manageable chunks for better query performance.

  8. Define Data Integrity Rules:

    • Enforce data integrity rules such as referential integrity and domain integrity to ensure the consistency of the data.
    • Implement triggers or stored procedures where applicable for automatic actions (e.g., updates, deletions).

  9. Define Backup and Recovery Plans:

    • Plan for data backup and recovery strategies that cater to the size and criticality of the data.
    • Ensure transaction log management for recovery purposes.

  10. Design Views and Materialized Views:

    • Create views to simplify complex queries and improve performance for end-users.
    • If necessary, define materialized views for pre-aggregated data and faster query results in read-heavy environments.

Example of Conversion:

  1. Logical Model:

    • Table “Customer” with columns: CustomerID, Name, Address.
    • Table “Order” with columns: OrderID, CustomerID, OrderDate.
    • Relationship: “CustomerID” in “Order” is a foreign key referencing “Customer”.

  2. Physical Model Conversion:

    • Partitioning: “Order” table could be partitioned by “OrderDate” for better performance.
    • Indexing: Index on “CustomerID” in both “Customer” and “Order” for faster join performance.
    • Table Spaces: Use separate table spaces for “Customer” and “Order” to optimize storage and I/O performance.
    • Data Types: “CustomerID” may be stored as an INT, and “OrderDate” as a DATE in the physical model.
    • Backup/Recovery: Schedule regular backups for both “Customer” and “Order” tables.

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