CAP theorem, consistency models, and building reliable large-scale systems.
Kafka producer configuration: acks, idempotence, batching, compression, and the tradeoffs that matter for throughput and durability.
Adding one cache server shouldn't invalidate every key. Consistent hashing with virtual nodes and bounded loads — full Go and Java implementations.
Probabilistic drop rate limiting: uncoordinated enforcement bypassing Redis for 1M+ RPS with zero coordination overhead.
Why exactly-once is a myth, and how idempotency keys, database constraints, and the outbox pattern make retries safe in Go and Java.
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.
Reliable event-driven Go beyond connecting to Kafka: handling partial failures, duplicates, and distributed transactions safely.
Five rate limiting algorithms, their trade-offs, how to distribute them across a fleet, and client-side backoff that works.
Circuit breakers, bulkhead isolation, timeout propagation, and idempotent retries in Go — the patterns for surviving component failures.
How Raft works in production: leader election, log replication, safety guarantees, and failure modes in etcd and CockroachDB.