In-depth technical writeups on distributed backends, concurrency patterns, platform reliability, and cloud architecture at scale.
Featured GuideMeasure Node.js event loop lag with perf_hooks, read it with event loop utilization, and use it to tell CPU-bound code from Kubernetes CPU throttling and GC.
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Kubernetes OperationsWhy rolling deploys drop requests, how Kubernetes pod termination really works, and a step-by-step graceful shutdown for Go and Node.js services.
Overload ProtectionWhy unbounded queues turn overload into outages, and how to add backpressure, concurrency limits, and load shedding to Go and Node.js services.
Resilience PatternsHow to set timeouts from real latency data, retry with exponential backoff and jitter, cap retries with budgets, and avoid retry storms that cause outages.
Event-Driven DesignWhy writing to your database and publishing to Kafka together breaks, how the transactional outbox pattern fixes it, and how to build idempotent consumers.
PostgreSQL PerformanceHow to read PostgreSQL EXPLAIN ANALYZE plans with BUFFERS, spot bad row estimates, choose the right index, and fix the slow queries backend services hit most.
Kafka at ScaleRunning Kafka at scale: sizing partitions, handling hot keys, measuring consumer lag, avoiding rebalance storms, tuning producers, and balancing brokers.
Kafka InternalsKafka internals explained: partitions as append-only logs, segments and indexes, leaders and the ISR, the high watermark, idempotent producers, and KRaft.
Go PerformanceA hands-on Go performance debugging workflow: CPU and heap profiles with pprof, block and mutex profiles, the execution tracer, and GC tuning with GOMEMLIMIT.
Node.js PerformanceMeasure Node.js event loop lag with perf_hooks, read it with event loop utilization, and use it to tell CPU-bound code from Kubernetes CPU throttling and GC.
Node.js InternalsA practical tour of Node.js event loop internals: libuv phases, microtasks vs process.nextTick, the libuv thread pool, and mistakes that block every request.
Architecture & ScaleDistributed locking patterns for scaled-out services: safe Redis locks with tokens and atomic release, fencing tokens, and when you don't need a lock.
Go & KafkaHow to build a Go Kafka pipeline that stays fast under load: key-sharded workers that keep ordering, bounded channels for backpressure, and client batching.