Designing scalable, reliable architectures for real-world backend systems.
Adding one cache server shouldn't invalidate every key. Consistent hashing with virtual nodes and bounded loads — full Go and Java implementations.
When to decompose a monolith, how to define boundaries, and the patterns that work: API gateways, sagas, and event-driven comms.
Kafka vs RabbitMQ vs NATS vs SQS: delivery semantics, ordering, throughput, ops complexity, and a decision framework with Go code.
REST, gRPC, and GraphQL solve different client problems. A decision guide for when each one wins, and what running all three side by side costs.
What head-of-line blocking costs, how QUIC solves it, and how to choose the right transport for real-time and API workloads.
Four caching patterns (cache-aside, write-through, write-behind, read-through), plus Go code for stampede prevention, multi-tier caching, and event-based invalidation.
Scale Redis: cluster sharding, hot key replication, connection pooling, pipelining, and eviction tuning for high-throughput workloads.