Author
Kim
Backend software engineer working with Java, Spring Boot, and Kafka. I write about engineering problems, performance work, and architecture decisions from development and operations.
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Automating Database Migrations in Spring Boot Using Flyway for Production Environments
Discover how to automate database migrations in Spring Boot with Flyway to ensure safe, consistent, and reliable schema management in production environments.
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Implementing Java Reactive Streams with Project Reactor for Backpressure Management
Learn to implement backpressure management in Java reactive streams using Project Reactor with practical examples, troubleshooting, and production safeguards to build resilient, scalable async systems.
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Implementing Feature Stores for Consistent AI Model Inputs in Production
This engineering guide details implementing feature stores for consistent, efficient AI model inputs, covering end-to-end implementation, testing, failure modes, trade-offs, and production best practices.
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Implementing Distributed Tracing in Production Systems for Improved Observability
A comprehensive guide to implementing distributed tracing for improved observability and performance in production microservices environments.
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Optimizing Kafka Producer Performance for High-Throughput Microservices
This guide provides backend engineers and DevOps a step-by-step Java example and comprehensive best practices to optimize Kafka producers for high throughput, balanced latency, and durability.
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Effective Exception Handling and Centralized Error Responses in Spring Boot REST APIs
Explore best practices to centralize and customize error handling in Spring Boot REST APIs for improved reliability and client experience.
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Building Resilient Java Applications Using the Bulkhead Pattern with Resilience4j
Explore applying the Bulkhead Pattern in Java with Resilience4j to isolate resources, prevent cascading failures, and enhance application resilience.
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Implementing Efficient Vector Search for Semantic Retrieval in Production Systems
Explore practical strategies and code examples to build scalable, high-performance vector search systems for semantic retrieval in AI-driven production environments.
