# KIP-932 explores queue semantics and Share Groups for Apache Kafka

DevFeed: [KIP-932 explores queue semantics and Share Groups for Apache Kafka](<https://devfeed.tech/articles/let-s-take-a-look-at-kip-932-queues-for-kafka-18845.md>)

Original publisher: [Read original article](<https://www.morling.dev/blog/kip-932-queues-for-kafka/>)

Published: 2025-03-05T11:35:00Z

Content type: opinion

Language: en

Sources: [Gunnar Morling](<https://devfeed.tech/sources/gunnar-morling.md>)

Topics: [Kafka](<https://devfeed.tech/topics/kafka.md>), [Streaming](<https://devfeed.tech/topics/streaming.md>), [stream-processing](<https://devfeed.tech/topics/stream-processing.md>), [Architecture & Design](<https://devfeed.tech/topics/architecture-design.md>)

Tags: [apache-kafka](<https://devfeed.tech/tags/apache-kafka.md>), [fraud](<https://devfeed.tech/tags/fraud.md>), [fraud-detection](<https://devfeed.tech/tags/fraud-detection.md>), [job](<https://devfeed.tech/tags/job.md>), [kafka](<https://devfeed.tech/tags/kafka.md>), [microservices](<https://devfeed.tech/tags/microservices.md>), [stream-processing](<https://devfeed.tech/tags/stream-processing.md>), [streaming](<https://devfeed.tech/tags/streaming.md>)

## AI overview

The article examines KIP-932, which explores adding queue semantics to Apache Kafka through Share Groups. It explains how Kafka's partition-based consumer model limits parallelism and ordered processing, and why individual message acknowledgment and rejection are important for queueing workloads such as independent job processing.

## Source excerpt

Table of Contents Towards Queue Support in Kafka--Introducing Share Groups Share Groups in Action Retry Behavior and State Management Share Group State Persistence Summary and Outlook In the "Let's Take a Look at...!" blog series I am going to explore interesting projects, developments and technologies in the data and streaming space. This can be KIPs and FLIPs, open-source projects, services, and more. The idea is to get some hands-on experience, learn about potential use cases and applications, and understand the trade-offs involved. If you think there's a specific subject I should take a look at, let me know in the comments below! That guy above? Yep, that's me, whenever someone says "Kafka queue". Because, that's not what Apache Kafka is. At its core, Kafka is a distributed durable event log. Producers write events to a topic, organized in partitions which are distributed amongst the brokers of a Kafka cluster. Consumers, organized in groups, divide the partitions they process amongst themselves, so that each partition of a topic is read by exactly one consumer in the group.