Designing a robust and scalable system involves understanding both functional and non-functional requirements, choosing the right architecture (monolithic vs microservices), and implementing strategies for scalability, database design, fault tolerance, security, and monitoring. Techniques like caching, load balancing, redundancy, and message queues can enhance performance, while considerations like distributed locking, data replication, and API gateways ensure reliability and efficiency in operations.
Table of contents
1. Low Latency Requirement: Make use of Cache and CDN.2. Read-Heavy System: Use Cache for faster reads.3. Write-Heavy System: Employ Message Queues for async writing.4. Unstructured Data: Use NoSQL Database.5. ACID Compliant DB: Choose RDBMS/SQL Database.5. Complex Data (Videos, Images, Files): Prefer Blob/Object storage.7. High Availability: Use Load Balancer.8. Scaling SQL Database: Implement Database Sharding.11. Global Data Delivery: Consider CDN.12. Graph Data: Utilize Graph Database.13. High-Performing Database Queries: Utilize Database Indexes.14. Single Point of Failure: Introduce Redundancy.15. Bulk Job Processing: Use Batch Processing and Message Queues.16. Server Load Management: Apply Rate Limiter.17. Microservices Architecture: Employ API Gateway.18. Data Integrity: Ensure Checksum Algorithm.19. Analytics and Audit Trails: Consider data lakes or append-only databases.20. Fault-Tolerance: Implement Data Replication.21. User-to-User Fast Communication: Use Websockets.22. Failure Detection: Implement Heartbeat.23. Efficient Server Scaling: Apply Consistent Hashing.24. Decentralized Data Transfer: Consider Gossip Protocol.25. High Availability Trade-Off: Embrace Eventual Consistency.26. Handling Large Data: Implement Pagination.27. Handling Traffic Spikes: Use Autoscaling.1 Comment
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