system-design-patterns

Scalable system design combines horizontal scaling (adding servers), load balancing to distribute traffic, and database scaling techniques like sharding and read replicas. Choose based on your traffic patterns, data size, and consistency requirements to ensure your architecture grows with demand.

Caching strategies like Cache-Aside and Write-Through improve read performance by storing frequently accessed data in memory. Cache-Aside suits read-heavy workloads; Write-Through ensures consistency in write-intensive systems. Select based on your application's read/write ratio and consistency needs.

Reliability patterns like Circuit Breaker, Retry with Exponential Backoff, and Bulkhead isolation prevent one failing component from taking down your entire system. These patterns detect failures early, control retry behavior, and isolate faults to maintain overall system resilience.

Horizontal scaling (adding more machines) handles unlimited growth and provides fault tolerance; vertical scaling (upgrading hardware) is simpler but has physical limits. Use horizontal scaling for distributed systems requiring high availability; vertical scaling for single-node workloads with simpler operational needs.

CAP theorem requires choosing between Consistency, Availability, and Partition tolerance. ACID guarantees strong consistency; BASE trades consistency for availability. Your choice depends on whether your system prioritizes data accuracy (financial systems) or availability (social media).

Read replicas distribute query load across multiple database copies; sharding partitions data by key to enable parallel processing. Together they enable horizontal scaling of both reads and writes while maintaining fault tolerance through data redundancy.