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System Design: Build Your Own Caching System (Like Redis/Memcached)
Anyone can build an app now just like anyone can take a photo. But not everyone can capture light. Understanding data structures and architecture is how you learn where to point the camera
Database Scaling Rules 1 table → fine 10 tables → add indexes 100 tables → need query optimization + caching 1000 tables → need sharding, read replicas, schema governance
You have millions of cache entries (Redis) and want to reduce cache misses when keys are dynamically generated. How do you approach it?
Linked Lists walk - arrays teleport One node knows only the next - nothing more. Used in: - LRU caches (LinkedHashMap) - Undo/Redo stacks - Memory allocators - TCP/IP packet buffers - Concurrent queues - Blockchain history Not fast, but flexible.
Rate limiting isn’t about rejection , it’s about survival. Used in: - APIs → fair usage - Auth → brute-force prevention - Queues → backpressure control It protects good systems from good users who go wild
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