1. Read-Heavy System: Consider using a cache.
2. Low Latency Requirement: Utilize cache and CDN.3. Write-Heavy System: Use message queues for async processing.
4. ACID Compliance: Opt for RDBMS or SQL Database.
5. Unstructured Data: Choose NoSQL Database.
6. Complex Data (Videos, Images, Files): Go for Blob/Object storage.
7. Complex Pre-computation: Use Message Queue & Cache.
8. High-Volume Data Search: Consider search index or search engine.
9. Scaling SQL Database: Implement Database Sharding.
10. High Availability, Performance, & Throughput: Use a Load Balancer.
11. Global Data Delivery: Employ a CDN.
12. Graph Data: Utilize Graph Database.
13. Scaling Various Components: Implement Horizontal Scaling.
14. High-Performing Database Queries: Use Database Indexes.
15. Bulk Job Processing: Consider Batch Processing & Message Queues.
16. Server Load Management: Use a Rate Limiter.
17. Microservices Architecture: Utilize an API Gateway.
18. Redundancy for Single Point of Failure: Implement Redundancy.
19. Fault-Tolerance and Durability: Employ Data Replication.
20. User-to-User Communication: Use Websockets.
21. Failure Detection in Distributed Systems: Implement a Heartbeat.
22. Data Integrity: Use Checksum Algorithm.
23. Efficient Server Scaling: Implement Consistent Hashing.
24. Decentralized Data Transfer: Consider Gossip Protocol.
25. Location-Based Functionality: Use Quadtree, Geohash, etc.
26. Avoid Specific Technology Names: Use generic terms.
27. High Availability and Consistency Trade-Off: Eventual Consistency.
28. Domain Name Query: Mention DNS (Domain Name System).
29. Handling Large Data in Network Requests: Implement Pagination.
30. Cache Eviction Policy: Preferred is LRU (Least Recently Used) Cache.
Credits: Dinesh Varyani. Your leetcode blog was an amazing list down!
