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The author migrates a Spring AI Agent with 30+ business tools to Spring AI 2.0's Tool Search, which uses Lucene indexing to let the model search for relevant tools before calling them, reducing context overload and improving selection accuracy, while sharing best practices for tool descriptions and safe write-operation handling.
After scaling a Spring Boot app to three Kubernetes replicas, @Scheduled tasks ran thrice, generating duplicate reports. The author evaluates distributed locks but adopts JobRunr for persistent, retryable, and monitorable background jobs, demonstrating integration with Spring Boot 4, code patterns for fire-and-forget, recurring, and delayed tasks, plus idempotency and deduplication strategies.
This article details the practical challenges and solutions when migrating from Spring Boot 3.x to 4.x, focusing on the Jackson 2 to 3 transition including package changes, exception handling updates, annotation exceptions, immutable ObjectMapper configuration, and coexistence strategies for legacy dependencies.
The author tests Kafka 4.2 Share Groups (Kafka Queues) by migrating a report-generation task queue from RabbitMQ, showing how Share Groups enable elastic consumer scaling beyond partition limits, manual acknowledgment modes (ACCEPT/RELEASE/REJECT/RENEW), and long-task handling with renew(), while noting RabbitMQ's routing strengths remain relevant.
This article walks through a production-ready Spring Boot multi-tenancy implementation using database-per-tenant isolation, JWT-based tenant identification via Spring Security, AbstractRoutingDataSource for dynamic routing, and ThreadLocal context management, while exposing critical pitfalls in transaction timing, async task propagation, and dynamic tenant registration.
The author refactors an old Spring Cloud project by removing Feign, replacing @FeignClient with Spring Boot 4.x’s native @HttpExchange and @ImportHttpServices, and demonstrates that Spring’s built‑in HTTP Service Client provides the same declarative convenience with less boilerplate, while still supporting custom configuration, error handling, and reactive use cases.
The article examines a common duplicate‑order bug caused by client retries after network timeouts, explains why @Transactional alone cannot guarantee idempotency, critiques simple Redis SETNX solutions, and presents a robust Spring Boot design that stores idempotency records together with order data in a single MySQL transaction, uses request fingerprints, unique keys, and owner tokens to safely replay or reject retries while handling concurrency and cleanup.
The article explains how the old pattern of GET‑ing an entire JSON string from Redis, deserializing it in Java, modifying a single field and writing it back is inefficient and error‑prone, and shows how Spring Data Redis 4.2’s RedisJsonTemplate lets you update or read individual JSON paths directly, reducing code, network traffic and race‑condition risks.
The article explains how the traditional find‑by‑id‑then‑save pattern causes two database round‑trips and concurrency bugs, and shows that Spring Data 4.1’s single‑statement Upsert (JdbcAggregateTemplate.upsert) lets the database decide INSERT or UPDATE in one SQL, reducing latency and race conditions while outlining its limitations and ideal use cases.
The article explains why a successful database transaction can leave an order incomplete when the accompanying Kafka message fails, examines the limitations of @Transactional, and demonstrates how Spring Modulith's Event Publication Registry and the Outbox pattern provide reliable, idempotent messaging solutions.
