📝 System Design Insights for a Multi-User Blogging Platform

(Databases, Text Handling, DateTime Storage, and Legal Realities)

🏛️ Database Design Principles

1. Soft Delete vs. Hard Delete

When users delete posts/comments, you must decide:

• Hard Delete (DELETE FROM blogs WHERE id=...)
  • ✅ Frees space immediately
  • ❌ Irrecoverable data
  • ❌ Analytics & ML signals lost
  • ❌ Legal/audit risks (evidence wiped)
  • ❌ Performance hit (B+ Tree rebalancing per delete)
• Soft Delete (UPDATE blogs SET deleted_at = NOW() WHERE id=...)
  • ✅ Recoverability (like a trash bin)
  • ✅ Archival for ML, analytics, audits
  • ✅ Legal compliance (retain abusive/illegal content)
  • ✅ Better performance (batch deletes later)

🔑 Recommendation: Always prefer soft deletes for user-generated content.

2. Handling Long Text Columns (e.g., Blog body)

• Problem: Large text fields (5k+ chars) bloat rows → slow scans.
• Database Optimization:
  • Small text (e.g., bio) stored inline.
  • Large text (e.g., body) stored separately; row holds a pointer.
  • Queries for metadata (title, author) stay fast.

💡 Best Practices:

• Split into blog_metadata vs. blog_content tables.
• Store summaries inline; full body separately.
• Pick correct data type (VARCHAR vs TEXT).

3. Storing Datetime Values (published_at)

Different approaches trade off readability vs performance:

📌 Takeaways:

• Native = convenience.
• Epoch = best performance, use UTC convention.
• Custom = practical for special cases (e.g., RedBus using YYYYMMDD to optimize range queries).

🔍 Hidden Real-World Insights

1. Legal Compliance & Police Station Reality

• Platforms must preserve deleted content for legal cases.
• Screenshots ≠ proof; DB rows with IDs + timestamps are authoritative.
• Engineers may face audits/legal requests.

👉 Always design audit trails from day one.

2. Why Soft Deletes Really Matter

Beyond recovery:

• ⚖️ Legal: Preserve evidence
• 📊 Analytics/ML: Keep hidden signals
• ⚡ Performance: Avoid costly rebalancing; batch deletes later

3. The Text Storage Trap

• Inline large text fields slow down queries.
• Separate storage → extra read, but worth it.
• Optimize by splitting frequently vs infrequently accessed data.

4. RedBus Migration Story (2016)

• Problem: Datetime columns slowed queries on huge tables.
• Solution: Store YYYYMMDD as integers.
• Result: Big performance boost.
• Lesson: Sometimes non-standard but practical formats outperform defaults.

🎯 System Design Process Framework

When making database decisions, evaluate in this order:

1. Legal & Compliance – retention, evidence, audits
2. Performance – query patterns, scans, indexing
3. Scalability – growth, cache alignment, deletes
4. Operational Readiness – recovery, monitoring, debugging

💡 Actionable Takeaways

For your next design:

• ✅ Use soft deletes for UGC
• ✅ Separate hot (frequent) vs cold (rare) data
• ✅ Benchmark datetime storage in your DB
• ✅ Plan cache with debouncing & load leaking
• ✅ Bake in legal compliance from day one

❓ Questions to Ask in Reviews

• “How will we handle legal data requests?”
• “What’s our cache failure recovery plan?”
• “Are queries optimized for actual access patterns?”
• “Are we prepared for viral traffic spikes?”

📌 Assignment: Identify every possible caching layer in your current system (e.g., client cache, CDN, DB query cache, Redis).

✨ Remember: These lessons are not theory – they come from real-world systems at scale, where design decisions can affect not just performance, but legal and business survival.

Would you like me to also visualize this as a structured diagram/mindmap (mermaid/graph) so you can use it in system design presentations?