Scalability is the ability of a system to handle increased load without compromising performance. Designing scalable systems is crucial for applications that expect growth in users, data, or traffic.

Key principles of scalable system design:

1. Horizontal Scaling
   Add more machines to handle increased load. This is more flexible than vertical scaling (upgrading hardware).
2. Statelessness
   Design services to avoid storing user state on the server. Use databases or caching layers instead (e.g., Redis).
3. Load Balancing
   Distribute incoming traffic across multiple servers using a load balancer to prevent any single point of failure.
4. Caching
   Store frequently accessed data in memory to reduce database load. Use tools like Redis or Memcached.
5. Asynchronous Processing
   Handle long-running tasks in the background using queues (e.g., RabbitMQ, Kafka).
6. Database Sharding and Replication
   Split large databases into smaller shards or replicate them across regions for faster access and higher availability.
7. Auto-Scaling and Monitoring
   Use cloud tools (AWS Auto Scaling, Azure Monitor) to scale services based on metrics like CPU or request rate.

Design tip: Always design with failure in mind. Use retries, circuit breakers, and graceful degradation to ensure resilience.

Scalable systems can grow efficiently with demand, making these principles essential knowledge for backend developers, cloud architects, and DevOps engineers.