List of community contributors
New community contributors this week: Luo Tianxiang from Shanghai Telecom
https://github.com/AutoMQ/automq-for-kafka/pull/517
This PR simplifies the network configuration options for AutoMQ Kafka.
AutoMQ for RocketMQ Development Updates
Release Updates
We have launched the first Beta version, v0.1.0-beta, officially marking the entry of AutoMQ for RocketMQ into the Beta phase: [Release v0.1.0-beta]
In this release, we primarily focused on introducing new features and performance enhancements, along with augmenting our code quality through continuous unit testing
Support for Transactional Messages
In this version, we have implemented support for Apache RocketMQ's transactional message type, ensuring full compatibility with all three advanced message types of Apache RocketMQ (sequential messages, timed messages, transactional messages)
Moving forward, we will continue to address the pain points associated with message duplication in Apache RocketMQ, with message idempotence now added to our Roadmap. We invite developers to keep an eye on our ongoing updates
https://github.com/AutoMQ/automq-for-rocketmq/pull/790
https://github.com/AutoMQ/automq-for-rocketmq/pull/796
Added Operational Tools
To facilitate developer testing and use, we have added several MQAdmin tool commands, including:
Resetting consumer offsets
Querying consumer progress
Querying Topic positions and other metadata
Querying client connections
...
https://github.com/AutoMQ/automq-for-rocketmq/pull/800
https://github.com/AutoMQ/automq-for-rocketmq/pull/802
https://github.com/AutoMQ/automq-for-rocketmq/pull/807
Supporting queue reassignment
Leveraging Stream based on object storage for shared storage capabilities, we have implemented partition reassignment in a few seconds to another node, also providing a CLI tool for easy operation and maintenance. Developers can easily test this feature using [Docker compose to deploy clusters]
https://github.com/AutoMQ/automq-for-rocketmq/pull/818
Additionally, through message forwarding, we have achieved an almost imperceptible migration experience for clients: using two senders to send 4k messages at a fixed 500tps to one queue, after stabilizing the traffic, the queue is reassigned to another node, with the reassignment completed in seconds and traffic quickly restored. Detailed test data can be viewed in PR #821
https://github.com/AutoMQ/automq-for-rocketmq/pull/821
Enhanced cold read performance
This version has enhanced cold read performance by optimizing metadata interfaces and eliminating unnecessary long polling in cold read scenarios. Additionally, since cold read data is retrieved from object storage, it benefits from ultra-high throughput, independent of disk IOPS and bandwidth constraints.
https://github.com/AutoMQ/automq-for-rocketmq/pull/827
https://github.com/AutoMQ/automq-for-rocketmq/pull/828
AutoMQ for Kafka trunk update
Release of version 1.0.0-rc0
https://github.com/AutoMQ/automq-for-kafka/releases/tag/1.0.0-rc0
Enhanced observability: Metrics integrated with OpenTelemetry
https://github.com/AutoMQ/automq-for-kafka/pull/548
Kraft ObjectDelta GC optimization
https://github.com/AutoMQ/automq-for-kafka/pull/547
Added DeltaMap data structure to reduce the amount of data copied from ObjectDelta to ObjectImage.
Cold read GC optimization
https://github.com/AutoMQ/automq-for-kafka/issues/543
Broker consumption throughout the entire chain utilizes PooledByteBuf to reduce heap memory allocation, eliminating Allocation Stall under zgc.