SQL for Revision Tracking: Choosing the Right Strategy for Your Needs

1. Revision Table:

   • Create a separate table specifically for revisions.
   • This table will have columns to store the original data's ID (like a product ID), the revision number (like version 1.0, 1.1), and possibly the actual changed data itself.
   • You can also include timestamps and user IDs to track who made the changes and when.
   • This approach is simple but can become bulky if you have a lot of revisions or large data chunks.

2. Separate Flag and History Table:

   • In your main data table, add a flag indicating if the record is currently active or not (a "soft delete").
   • Create a separate history table with columns for the original data's ID, the revision number, and all the data fields that can be changed.
   • This approach saves space in the main table but requires joining the tables to retrieve current or past data.

Here are some additional points to consider:

• Views: Create views to simplify fetching the latest data or specific revisions.
• Storage Optimization: Depending on your data type (text vs. numbers), you might choose different storage mechanisms for historical data.
• Versioning vs. Full History: Decide if you need to store the complete data for every revision or just track changes.

Example Codes (MySQL)

CREATE TABLE main_data (
  id INT PRIMARY KEY AUTO_INCREMENT,
  data VARCHAR(255) NOT NULL
);

CREATE TABLE revisions (
  id INT PRIMARY KEY AUTO_INCREMENT,
  data_id INT NOT NULL,
  revision_number INT NOT NULL DEFAULT 1,
  data_content TEXT,  -- Can be different data types based on your needs
  modified_by INT,
  modified_at DATETIME DEFAULT CURRENT_TIMESTAMP,
  FOREIGN KEY (data_id) REFERENCES main_data(id)
);

CREATE TABLE main_data (
  id INT PRIMARY KEY AUTO_INCREMENT,
  data VARCHAR(255) NOT NULL,
  is_active BOOLEAN DEFAULT TRUE
);

CREATE TABLE revisions (
  id INT PRIMARY KEY AUTO_INCREMENT,
  data_id INT NOT NULL,
  revision_number INT NOT NULL DEFAULT 1,
  field1 VARCHAR(255),
  field2 TEXT,  -- Adjust columns based on your actual data fields
  modified_by INT,
  modified_at DATETIME DEFAULT CURRENT_TIMESTAMP,
  FOREIGN KEY (data_id) REFERENCES main_data(id)
);

• This approach is flexible and offers powerful branching and merging capabilities, but requires additional setup and management overhead compared to pure SQL solutions.
• Each commit acts as a revision point, allowing you to track changes and revert to previous versions if needed.
• Store your data files (documents, configurations etc.) within the VCS repository.
• Originally used for code management, VCS like Git can be adapted for data revision control.

Document-Oriented Databases:

• This approach offers good scalability and flexibility for complex data structures, but querying historical data might require specialized tools compared to traditional SQL.
• Each document revision can be a separate object within the main document.
• These databases store data as JSON-like documents, which can inherently hold revision history.

Event Sourcing:

• This approach provides a complete audit trail and simplifies data consistency, but requires a different mindset for data retrieval compared to traditional databases.
• By replaying the event stream, you can reconstruct the state of your data at any point in time.
• Each event represents a specific action taken and its associated data.
• This architectural pattern captures all changes to data as a sequence of events.

Choosing the best alternative depends on your specific needs:

• For detailed audit trails and complex data changes: Event sourcing could be a powerful option.
• For complex data structures and scalability: Document-oriented databases could be a good fit.
• For simple revision history with structured data: VCS or a revision table in SQL might suffice.