1. Why is Database Cleanup Important?

Improves performance – Reduces the size of active records, improving query speed.
Saves storage costs – Deletes old, unused, or expired links to free up space.
Ensures compliance – Removes URLs violating policies (e.g., spam, malicious links).
Reduces clutter – Keeps the system clean by removing inactive data.

2. What Needs to Be Purged?

Expired URLs – URLs with a predefined expiration time.
Unused URLs – URLs that haven’t been accessed for a long time.
Malicious URLs – URLs reported for phishing, spam, or illegal activities.
Orphaned Data – Any temporary or test data created by the system.

3. Strategies for Purging & Cleanup

A. Scheduled Expiry Cleanup (Automatic Deletion)

• Store an expires_at timestamp in the database.
• Run a scheduled cron job (daily/weekly) to delete expired URLs.

Example SQL Query:

Automation: Use a cron job to run the query daily at midnight:

B. Archiving Old URLs Instead of Deleting

Instead of deleting URLs immediately, move them to an archive table before permanent deletion.

Benefit: This allows recovery of URLs if needed before final deletion.

C. Detecting and Deleting Unused URLs

• Track URL access frequency in a hits column.
• If a URL hasn’t been accessed in X months, mark it for deletion.

Example SQL Query:

This deletes URLs that haven’t been visited in 6 months.

D. Removing Malicious or Blacklisted URLs

• Maintain a blacklist table with blocked keywords/domains.
• Run a job to check and delete matching URLs.

Example SQL Query:

Automation: Run this query every few hours to remove harmful links.

E. Index Optimization & Table Maintenance

After deleting large amounts of data, optimize the database to free up space and improve performance.

Optimize Table After Deletion:

This reclaims disk space and defragments the table.

4. When Should Purging Be Done?

Night-time Maintenance Windows – Low-traffic hours reduce impact on performance.
Incremental Cleanup – Instead of bulk deletes, remove small batches of records.
On-Demand Cleanup – Triggered manually when storage usage crosses a threshold.

5. Monitoring & Logging Cleanup Operations

It’s crucial to log all purging activities for debugging and tracking.

Example Log Entry Format:

Use Elasticsearch/Kibana/Grafana for log analysis.

6. Scaling Cleanup for Large Systems

For massive data volumes, optimize with partitioning and batch processing:

Partitioning by Date – Store URLs in separate tables based on year/month.
Batch Deletion – Instead of deleting millions of rows at once, delete 10,000 rows per batch.

Use Background Workers – Run cleanup asynchronously using Kafka, Celery, or AWS Lambda.

7. Final Takeaways

• Automate cleanup with scheduled jobs.
• Use archival instead of immediate deletion.
• Optimize database performance after deletion.
• Monitor logs to track purging efficiency.
• Ensure scalability with batch deletions and indexing.