Global Field CTO | Author | International Speaker | Follow me with Data in Motion
Apache Kafka is getting native support for queues—and it’s a game changer for the #datastreaming community. With the #opensource Kafka 4.0 release, #ApacheKafka went beyond logs and stream processing by introducing queue-style semantics through KIP-932. This new capability unlocks parallelism that isn’t limited by partition count—perfect for real-world #jobqueue and #taskdistribution scenarios. The key innovation is the introduction of share groups, allowing multiple consumers to process messages from the same partition. This enhances Kafka’s scalability for event-driven applications and simplifies patterns previously handled by external #messagequeues like RabbitMQ or ActiveMQ. Kafka’s new queue support adds: - Fine-grained control with message-level acknowledgements (ack, release, reject) - High-throughput flexibility beyond the limits of traditional consumer groups - A powerful hybrid model combining queue semantics with Kafka’s durable event log For use cases like inventory systems, sales event processing, or real-time analytics, this makes Kafka a unified solution—blending the benefits of #streamprocessing with the needs of flexible, scalable queuing. This isn’t production-ready yet—no Dead Letter Queue (DLQ) support, no delayed retries, and no strict ordering per key. But for teams already invested in Kafka, it avoids the operational overhead of maintaining separate queue infrastructure for simpler tasks. Kafka continues evolving into the central nervous system for modern #cloudnative data architectures. Deep dive slide deck and video recording: https://lnkd.in/eHihr_Yr What’s the first use case you’d move to Kafka queues?
Insightful
Kafka purists reading this be like: “It’s not a queue, it’s a distributed streaming platform!” Well… looks like Kafka just said, “Hold my partition key.”
eventually
Still not proper queuing IMHO. Some may call me biased so don't take it from me: https://www.linkedin.com/pulse/queues-kafka-my-opinion-david-ware-xcvme/
Thanks for sharing, Kai
BSc Digital Transformation, Chief Software Architect for Data Integration and Big Data
4moFrankly, I do not see the value. If one consumer cannot cope with the messages within a partition, there is something wrong in the implementation. Consumer reads polls the data, processes one record after the other (no multi-threading) and auto-commits? Partition count is just one? Queues are good for point-to-point connection. In that case Kafka is the wrong solution, not to mention that this is seldom the reality today. Kafka replaced message queuing systems because - consumer is in charge and not the producer or event broker - order guarantees - rewind support - read the same data multiple times - add new consumers for tests without impact on the broker - (infinite storage option) And now you implemented a queue feature to bring back all downsides of message queues again. I wouldn't have done that given my current sentiment, but I have not played with the queues enough to be certain. Can't think of a single reason using it.