The Go module proxy is designed to cache requests for consistent user experiences, but it may inadvertently contribute to upstream host instability by repeatedly downloading modules inefficiently. The author shares logs highlighting the proxy's behavior, which shows a tendency to clone repositories from scratch rather than utilizing more efficient update methods, leading to unnecessary load and delays. Ultimately, the article critiques the design of the system and its handling of updates.

Consistent hashing is an algorithm designed to maintain efficient data distribution across multiple nodes in a hash table, minimizing the need for data reassignment when nodes are added or removed. Unlike naive hashing methods that require entire data sets to be rehashed when node counts change, consistent hashing enables most items to retain their mappings, thus reducing cache misses during dynamic scaling of systems. Implementing consistent hashing can be enhanced with virtual nodes to improve load balancing across servers.

The article discusses the complexities and performance considerations of implementing a distributed database cache. It highlights the challenges of cache synchronization, data consistency, and the trade-offs between speed and accuracy in data retrieval. Additionally, it offers insights into strategies for optimizing caching methods to enhance overall system performance.

Sharing a single Redis cache cluster across multiple services can lead to significant issues, such as key eviction affecting all services, complicating monitoring and debugging processes. While it may seem simpler initially, this approach can create confusion and performance problems as the system scales. In some cases, a shared cache is acceptable, but it's often better to maintain separate clusters for improved reliability and clarity.