Spring Data 2026.0.0 RC1: Upserts, Redis Pub/Sub, and Batch Cache Clearing
Spring Data 2026.0.0 has entered its release candidate phase, aligning with the Spring Boot 4.1 release train targeted for May 2026. The headline addition for relational database developers is native upsert support via MERGE or INSERT … ON CONFLICT … DO UPDATE syntax, exposed directly on the Template API. This replaces common workarounds — calling findById followed by a conditional save — with a single atomic operation, which matters for both correctness and performance under concurrent writes.
Redis developers gain a RedisMessageSendingTemplate that complements the existing annotation-driven @RedisListener infrastructure introduced in recent milestones. The template provides a programmatic, consistent API for publishing to Redis Pub/Sub channels, enabling the same message-sending patterns developers already know from Spring's JmsTemplate and RabbitTemplate without context-switching to lower-level RedisTemplate operations.
Cache management receives a focused improvement with an optimized RedisCache.resetCaches() implementation that clears multiple caches in a single batch command instead of one-by-one. For applications with many named caches — common in read-heavy services — this can significantly reduce the latency of cache invalidation operations during deployments or bulk data updates.
The RC release is feature-complete, with the final GA targeted for the Spring Boot 4.1 GA window. Comprehensive Javadoc and changelog entries for each Spring Data module (JPA, MongoDB, Redis, R2DBC, Elasticsearch, and others) are available in the official release notes.
Read more — Spring Blog
Spring AI 2.0.0-M5: Structured Output Improvements and New Builder Methods
Spring AI reached its fifth milestone ahead of its 2.0 general availability, with M5 focused on making structured output production-ready. The improved structured output converters allow developers to declare a target class once and have Spring AI handle the extraction, validation, and mapping of LLM responses into typed Java objects — a significant step up from earlier milestones that required manual JSON parsing or brittle string matching.
New builder methods added in M5 streamline message construction for common patterns: system messages with variable substitution, multi-turn conversation builders, and typed function call descriptors. These additions reduce boilerplate in the 80% of use cases involving RAG pipelines and chat-style agent interfaces, while leaving the low-level API intact for advanced patterns.
Spring AI 2.0 is part of the Spring Boot 4.1 release train and will GA alongside it in May 2026. The M5 release gives teams a stable enough base to begin integration testing in non-production environments, with the expectation that RC1 will follow shortly.
Read more — InfoQ
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