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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Custom Metrics in Spring Boot 3.5 with Micrometer and Prometheus: Names, Exposure, Cardinality, and the Test That Returns 404
A Spring Boot 3.5.16 counter, gauge, and timer scraped by a real Prometheus 3.14, with the naming rules, the exposure property, a measured user-id cardinality blow-up and its MeterFilter cap, and the @AutoConfigureObservability trap in tests.
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Building a Production-Ready Java Rate Limiter with Token Bucket Algorithm
Step-by-step guide to implementing a robust Java Token Bucket rate limiter for production systems.
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Designing Automated Bias Detection and Mitigation Workflows for Production AI Models
Explore automated workflows to detect and mitigate bias in production AI models, ensuring fairness, transparency, and ethical compliance at scale.
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Implementing Rate Limiting with Token Bucket Algorithm in High-Traffic APIs: A Dev Guide
A practical guide to applying the token bucket algorithm for effective rate limiting in high-traffic API environments.
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Building and Scaling Kafka Stream Processing with State Stores and RocksDB in Production
Explore building stateful Kafka Streams apps with RocksDB for persistence, scalability, and fault tolerance in real-time processing.
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Securing Spring Boot Applications with JWT and Refresh Token Rotation
Learn to secure Spring Boot REST APIs with JWT combined with rotating refresh tokens, enhancing authentication security by preventing token replay and enabling smooth token lifecycle management.
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Distributed Tracing in Spring Boot 3 with Micrometer Tracing and Zipkin: Migrating from Spring Cloud Sleuth
Spring Cloud Sleuth is end-of-life. Two Spring Boot 3.5.16 services, Micrometer Tracing with Brave, Zipkin 3.6.1: the same traceId in both logs, the trace fetched from Zipkin’s API, W3C headers on the wire, and sampling measured at exactly 10 of 100.
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Implementing Real-Time AI Model Personalization with User Feedback Loops in Production
This comprehensive guide helps ML engineers implement real-time AI personalization using user feedback loops with incremental model updates, thread-safe serving, and production safeguards.
