Design a highly available and fault-tolerant distributed caching system for a global application, detailing cache invalidation strategies, consistency models, and how you would handle cache misses and data synchronization across multiple regions.

MECE Framework: Design involves a multi-layered caching strategy. 1. Architecture: Global CDN (edge caching), regional in-memory caches (e.g., Redis Cluster), and a distributed persistent cache (e.g., Apache Cassandra for hot data). 2. Consistency Models: Eventual consistency for most reads (e.g., read-through, write-behind), strong consistency for critical writes (e.g., write-through, cache-aside with database locks). 3. Invalidation: Time-to-Live (TTL) for volatile data, publish/subscribe (Pub/Sub) for immediate invalidation upon data changes, and versioning for complex objects. 4. Cache Misses: Read-through pattern to fetch from the database, populate cache, and return data. Implement circuit breakers to prevent database overload. 5. Data Synchronization: Cross-region replication for persistent caches, active-passive or active-active for regional caches with conflict resolution (e.g., last-write-wins).

A highly available and fault-tolerant distributed caching system for a global application requires a multi-tiered approach using the MECE framework. Architecturally, I'd implement a global CDN for static assets and edge caching, regional Redis Clusters for in-memory caching, and a distributed persistent cache like Apache Cassandra for frequently accessed dynamic data. For consistency, eventual consistency (read-through, write-behind) would be used for most reads, while critical writes would employ strong consistency (write-through, cache-aside with database locks). Cache invalidation would leverage TTL for time-sensitive data, Pub/Sub for immediate invalidation on data changes, and versioning for complex objects. Cache misses would trigger a read-through pattern, fetching data from the primary database, populating the cache, and returning the result, with circuit breakers to prevent database overload. Data synchronization across regions would involve active-passive or active-active replication for regional caches with conflict resolution (e.g., last-write-wins) and cross-region replication for persistent caches, ensuring data consistency and availability.