Apache Kafka® 3.3 is released! With over two years of development, KIP-833 marks KRaft as production ready for new AK 3.3 clusters only. On behalf of the Kafka community, Danica Fine (Senior Developer Advocate, Confluent) shares highlights of this release, with KIPs from Kafka Core, Kafka Streams, and Kafka Connect.
To reduce request overhead and simplify client-side code, KIP-709 extends the OffsetFetch API requests to accept multiple consumer group IDs. This update has three changes, including extending the wire protocol, response handling changes, and enhancing the AdminClient to use the new protocol.
Log recovery is an important process that is triggered whenever a broker starts up after an unclean shutdown. And since there is no way to know the log recovery progress other than checking if the broker log is busy, KIP-831 adds metrics for the log recovery progress with `RemainingLogsToRecover` and `RemainingSegmentsToRecover`for each recovery thread. These metrics allow the admin to monitor the progress of the log recovery.
Additionally, updates on Kafka Core also include KIP-841: Fenced replicas should not be allowed to join the ISR in KRaft. KIP-835: Monitor KRaft Controller Quorum Health. KIP-859: Add metadata log processing error-related metrics.
KIP-834 for Kafka Streams added the ability to pause and resume topologies. This feature lets you reduce rescue usage when processing is not required or modifying the logic of Kafka Streams applications, or when responding to operational issues. While KIP-820 extends the KStream process with a new processor API.
Previously, KIP-98 added support for exactly-once delivery guarantees with Kafka and its Java clients. In the AK 3.3 release, KIP-618 offers the Exactly-Once Semantics support to Confluent’s source connectors. To accomplish this, a number of new connectors and worker-based configurations have been introduced, including `exactly.once.source.support`, `transaction.boundary`, and more.
Image attribution: Apache ZooKeeper™: https://zookeeper.apache.org/ and Raft logo: https://raft.github.io/
EPISODE LINKS
Confluent Platform 7.0: New Features + Updates
Real-Time Stream Processing with Kafka Streams ft. Bill Bejeck
Automating Infrastructure as Code with Apache Kafka and Confluent ft. Rosemary Wang
Getting Started with Spring for Apache Kafka ft. Viktor Gamov
Powering Event-Driven Architectures on Microsoft Azure with Confluent
Automating DevOps for Apache Kafka and Confluent ft. Pere Urbón-Bayes
Intro to Kafka Connect: Core Components and Architecture ft. Robin Moffatt
Designing a Cluster Rollout Management System for Apache Kafka ft. Twesha Modi
Apache Kafka 3.0 - Improving KRaft and an Overview of New Features
How to Build a Strong Developer Community with Global Engagement ft. Robin Moffatt and Ale Murray
What Is Data Mesh, and How Does it Work? ft. Zhamak Dehghani
Multi-Cluster Apache Kafka with Cluster Linking ft. Nikhil Bhatia
Using Apache Kafka and ksqlDB for Data Replication at Bolt
Placing Apache Kafka at the Heart of a Data Revolution at Saxo Bank
Advanced Stream Processing with ksqlDB ft. Michael Drogalis
Minimizing Software Speciation with ksqlDB and Kafka Streams ft. Mitch Seymour
Collecting Data with a Custom SIEM System Built on Apache Kafka and Kafka Connect ft. Vitalii Rudenskyi
Consistent, Complete Distributed Stream Processing ft. Guozhang Wang
Powering Real-Time Analytics with Apache Kafka and Rockset
Automated Event-Driven Architectures and Microservices with Apache Kafka and SmartBear
Create your
podcast in
minutes
It is Free
Insight Story: Tech Trends Unpacked
Zero-Shot
Fast Forward by Tomorrow Unlocked: Tech past, tech future
The Unbelivable Truth - Series 1 - 26 including specials and pilot
Acquired