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How a Spring Boot API handles over 7 million requests a day without scaling vertically or horizontally: asynchronous event processing and batching with RabbitMQ to collapse database connections, validated with a K6 load test.
Read →Performance and scaling in Spring Batch: parallel flows, step partitioning, and the memory and database considerations —heap, connection pools, ORM vs native SQL— that keep big data jobs fast and stable.
Read →An introduction to Spring Batch: jobs, chunk-oriented and tasklet steps, readers, processors and writers, fault tolerance, and how listeners and metadata tables make batch processing observable.
Read →How Gradle works under the hood —directories, core concepts, the build lifecycle, key files and properties— and how to use it to enforce a hexagonal architecture across modules.
Read →Understanding how Java works under the hood —JDK, JRE, JVM, JIT, memory and the Garbage Collector— to make better design decisions, optimize performance and reduce infrastructure costs.
Read →How to integrate Apache Kafka into a Spring project to communicate microservices asynchronously and solve the cascading deletion of a user and their notes across two databases.
Read →How the CQRS pattern separates read and write operations to scale an application horizontally, starting from a real database connection-exhaustion problem.
Read →Normalize or denormalize: how data modeling changes when you move from a SQL mindset to a NoSQL one in MongoDB, with the real example of folders and notes from the notes app.
Read →Thoughts on how to structure a React frontend project: decoupling HTTP calls, wrapping react-query in your own hooks, and applying the repository pattern on the client.
Read →How to combine Spring's conveniences with Domain-Driven Design and hexagonal architecture: value objects, an exception hierarchy, and mapping between entities and domain models.
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