System design, AWS, microservices, AI/ML deployment — practical posts for engineers who want to build production-grade systems.
What the nines actually mean, how to eliminate single points of failure, active-active vs active-passive redundancy, multi-region failover, and setting RPO/RTO targets that match the business, not just the architecture diagram.
Delivery guarantees, Kafka partitions and consumer groups, RabbitMQ exchanges, dead letter queues, and how to decide when async processing is the right call and when it just adds latency.
Treating prompts as versioned, tested, observable production code — prompt structure, few-shot examples, chain-of-thought, template reuse, compression for cost, and catching prompt regressions before users do.
How distributed tracing actually works — span propagation, correlation IDs, sampling strategy, and how to go from 'the API feels slow' to the exact service and query causing it.
Instrumenting a Spring Boot service end to end — OpenTelemetry setup, Prometheus metrics and alerting rules, Grafana dashboards, structured logs with correlation IDs, and how to design alerts that don't cause fatigue.
Database decisions outlive most code. This post covers relational vs. document vs. key-value vs. time-series vs. graph databases, polyglot persistence, sharding vs. replication, schema evolution, and the decision framework senior engineers use to pick the right store for the job.
How production systems survive dependency failures — circuit breaker states, timeout strategy, bulkhead isolation, retries with jitter, and fallback design, with real Resilience4j code.
Scaling RAG past a demo: choosing a vector database, chunking strategy, hybrid search and reranking, measuring retrieval quality, and where the cost actually goes at high query volume.
Master distributed systems consistency guarantees. Explore ACID vs BASE, CAP theorem nuances, 2PC limitations, consensus algorithms, and when to accept inconsistency. Covers Raft, Paxos, CRDTs, and real-world trade-offs senior engineers face.
A practical guide to cache layers, invalidation strategies, cache stampede protection, and the consistency-vs-performance trade-offs that decide whether caching helps or quietly corrupts your data.