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.
How a team serving mobile, microservices, and third-party integrations ended up running REST, gRPC, and GraphQL together.
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 with Go: cache-aside, write-through, write-behind, stampede prevention, and event-based invalidation.
Scale Redis: cluster sharding, hot key replication, connection pooling, pipelining, and eviction tuning for 1M+ ops/sec.