Horizontal vs Vertical Scaling in System Design.
➤ Vertical Scaling (Scaling Up)
→ Definition: Increasing the capacity of a single server by upgrading its hardware
→ How it works: Add more CPU cores, Increase RAM, Use faster storage
→ Pros: Simple to implement, Works well for smaller systems or monolithic apps
→ Cons: Hardware limits, Single point of failure, Expensive at scale
➤ Horizontal Scaling (Scaling Out)
→ Definition: Adding more servers to handle load by distributing traffic
→ How it works: Deploy multiple servers, Use load balancers, Replicate or shard databases
→ Pros: Better fault tolerance, Practically unlimited scalability, Cost-effective with commodity hardware
→ Cons: Higher complexity, Data consistency challenges, Requires sophisticated infrastructure
➤ Key Differences
→ Approach: Vertical = upgrade single machine | Horizontal = add more machines
→ Complexity: Vertical = low | Horizontal = high
→ Scalability: Vertical = limited | Horizontal = virtually unlimited
→ Fault Tolerance: Vertical = single point of failure | Horizontal = high availability
→ Cost: Vertical = expensive at high-end | Horizontal = cost-effective
→ Use Case: Vertical = small apps or monoliths | Horizontal = large-scale, cloud-native systems
➤ Real-World Example
→ Instagram initially relied on vertical scaling: they upgraded single servers with more memory and faster CPUs to handle growing traffic.
→ As user growth exploded, vertical scaling reached its limits.
→ They shifted to horizontal scaling: distributing traffic across many servers, using load balancers, caching layers (like Redis), and sharding their database.
→ This allowed them to support hundreds of millions of users worldwide without downtime.
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