Practical knowledge from real-world experience.
A graph, a directed graph, and rules on top. What a state machine is, why finite matters, and how modeling a flow this way avoids the bugs that live between loose booleans.
How an HTTP request and a Kafka message get traced with the native OpenTelemetry integration in Spring Boot 4, what the quickstart doesn't document, and why the backend decision (ELK, Grafana, whatever) no longer touches the application code.
Before instrumenting anything, it's worth understanding what problem observability solves and why OpenTelemetry has become the standard.
You disabled open-in-view. Now what? @EntityGraph, @Transactional, batch_fetch_size, Split Queries, and DTO Projection — measured on real PostgreSQL.
How a corruption bug looked like a gRPC problem, but was actually caused by unordered async writes during client-side file persistence.
How Spring Boot's default open-in-view setting caused a silent deadlock with virtual threads and TransactionTemplate.
You already know you need a broker. Now the question is which one — and the answer changes how you design your entire event system.
Why REST APIs are not always the best choice for heavy scheduled workloads, and when batch processing is the better architectural fit.
Your server shouldn't do everything at the same time. Understanding the real problem a message broker solves.