To validate backup and restore operations in CodeIgniter, you must move beyond checking if a backup file exists. Effective validation requires a three-tier approach: file integrity verification, functional restoration in an isolated environment, and data parity auditing.
1. File Integrity and Checksum Verification
Before attempting a restore, ensure the backup file was not corrupted during generation or transfer. If using a custom script or a tool like `mysqldump`, generate a checksum at the time of creation.
- Generate a SHA-256 hash of the source backup file:
sha256sum backup.sql > hash.sha256.
- Verify the hash of the file after it moves to your storage storage.
- If the hashes do not match, the backup is compromised and should not be used.
2. The Staging Environment Restore
Never validate a backup directly on production. Use a staging environment that mirrors your production configuration (PHP version, MySQL version, and extensions).
- Isolate the Database: Create a fresh database schema on your staging server.
- Execute the Restore: Use the CLI to avoid PHP memory limits:
mysql -u [user] -p [database_name] < backup.sql
- Update Configuration: Ensure you update
app/Config/database.php or your `.env` file to point to the staging database.
3. Data Parity Auditing
Once the restore is complete, you must verify that the data matches the source.
Row Count Comparison
The simplest high-level validation is comparing row counts for critical tables. You can run a query in CodeIgniter to export these counts:
| Table Name |
Production Count |
Staging Restore Count |
| users |
12,450 |
12,450 | n
| orders |
5,321 |
5,321 | n
Checksum Validations
For mission-critical data, use a checksum on specific table ranges. In MySQL, you can use:
CHECKSUM TABLE users, orders;
Compare the resulting hex strings between the source and the restored database. If the strings match, the data in those tables is bit-for-bit identical.
Automation and Error Handling
While CodeIgniter does not have a built-in "backup-validator," you can automate this using custom Spark commands.
- Spark Commands: Create a custom CLI command that triggers the restore and then immediately runs the row-count validation logic.
- Logging: Wrap your restore logic in a
try-catch block. Use log_message('error', $e->getMessage()) to capture failures during the import process.
- Rollbacks: Since large database imports are difficult to roll back within a single transaction, ensure your validation script drops and recreates the staging database before every test run to start from a clean state.
Note: Are you using a specific third-party library for backup generation, or custom scripts using native CLI commands?