Why Monoliths crash hard and Distributed Systems stay alive. We explore the concepts of High Availability, Single Points of Failure (SPOF), and how Replication saves the day.
Your cache is full. What do you delete to make space? We explore LRU, LFU, FIFO, and why Netflix had to rethink their eviction strategy.
Why fetch from the database every time? Learn how Caching (In-Memory, Distributed, CDN) dramatically improves performance and reduces latency.
When multiple users access your app, do they see the same data? We break down Strong vs Eventual Consistency and the challenges of keeping distributed data in sync.
How to distribute traffic efficiently across your servers. We cover Algorithms (Round Robin, IP Hash), Health Checks, and Active-Passive setups.
Is having a backup server 'Redundancy' or 'Replication'? We clarify the difference—Redundancy is about survival, Replication is about data consistency.
Should you buy a bigger server or buy more servers? We explore the trade-offs between Vertical Scaling (Scale Up) and Horizontal Scaling (Scale Out).
Consistency, Availability, Partition Tolerance. Pick two. We explore the ultimate trade-off in distributed systems and why 'P' is not optional.
Why can't we just store everything in text files? We explore the major challenges: Inconsistency, Security, and Redundancy.
Why distributed systems achieve higher Throughput than Monoliths. We explore the relationship between Latency and Throughput, and how Load Balancers and CDNs optimize data flow.