August 30, 2022
Team RabbitMQ and community members have recently identified a curious scenario where a freshly started node could
consume a surprisingly high amount of memory, say, 1.5 GiB or so. We’d like to share our findings with the community
and explain what short term and longer term workarounds are available.
August 1, 2022
We intend to release RabbitMQ 3.11.0 on 5 September 2022. While we have been testing
it internally for some time, with production-like workloads, we need your help to
check that it is as stable and reliable as we believe it is.
July 22, 2022
Today when we use the rabbitmq-management with the rabbitmq_auth_backend_oauth2 plugin, the only supported Authorization server is UAA, making it difficult to connect to other OAuth 2.0 servers. Additionally, rabbitmq-management plugin uses the OAuth 2.0 implicit flow which is no longer recommended for security reasons.
RabbitMQ 3.11 will support practically any Authorization server compliant with OpenID Connect and OAuth 2.0 protocols. Furthermore, OAuth 2.0 authorization code grant becomes the default grant and implicit grant is no longer supported.
July 20, 2022
RabbitMQ 3.11.0 will make all feature flags introduced during the life of RabbitMQ 3.8.x required.
People who initially created clusters using RabbitMQ 3.8.9 or older should enable all feature flags before upgrading to RabbitMQ 3.11! If the feature flags are not enabled, RabbitMQ 3.11.0+ will refuse to start.
Feature flags are a mechanism to make breaking changes to RabbitMQ while still maintaining the compatibility between several versions of RabbitMQ. They usually come with code to migrate data structures in the internal schema database or on disk for instance.
July 13, 2022
RabbitMQ 3.11 will bring a feature with one of the coolest names in its history: super streams.
Super streams are a way to scale out by partitioning a large stream into smaller streams.
They integrate with single active consumer to preserve message order within a partition.
This blog post gives an overview of super streams and the use cases they unlock.
Read on to learn more, we value your feedback to make this feature the best it can be.
July 5, 2022
RabbitMQ 3.11 will bring a noteworthy feature to streams: single active consumer.
Single active consumer provides exclusive consumption and consumption continuity on a stream.
It is also critical to get the most out of super streams, our solution for partitioning, that provide scalability for streams.
Read on to find out more about single active consumer for streams and don’t hesitate to experiment with what is already available: try it, break it, tell us what you like and don’t like, what’s missing.
Your feedback is essential to make this feature the best it can be.
May 31, 2022
Recent Erlang/OTP versions ship with Linux perf support.
This blog post provides step by step instructions on how you can create CPU and memory flame graphs in RabbitMQ to quickly and accurately detect performance bottlenecks.
We also provide examples of how flame graphs have helped us to increase message throughput in RabbitMQ.
May 16, 2022
RabbitMQ 3.10 was released on the 3rd of May 2022, with many new features and improvements.
This blog post gives an overview of the performance improvements
in that release. Long story short, you can expect higher throughput, lower latency and faster node startups,
especially with large definitions files imported on startup.
May 5, 2022
Arnaud Cogoluègnes & Michael Klishin
RabbitMQ 3.10 has recently been released and has some major new features
which focus on optimizations, performance, and stability.
Release notes page
includes information about the specific changes in this version as well as various installation assets.
See our upgrade guide for more information about upgrading to 3.10.0.
Let’s have a tour!
April 26, 2022
RabbitMQ RPM packages for CentOS 7 will be discontinued from May 2022 because
that CentOS release series provides outdated versions of OpenSSL and Linux kernel.
CentOS 7 users are recommended to migrate to a new cluster which uses a more recent distribution
via one of the options: