RabbitMQ 3.12 will be released soon with many new features and improvements. This blog post focuses on the the performance-related differences. The most important change is that the lazy mode for classic queues is now the standard behavior (more on this below). The new implementation should be even more memory efficient while proving higher throughput and lower latency than both lazy or non-lazy implementations did in earlier versions.

For even better performance, we highly recommend switching to classic queues version 2 (CQv2).

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.

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.

Welcome back! Last time we talked about flow control and latency; today let’s talk about how different features affect the performance we see. Here are some simple scenarios. As before, they’re all variations on the theme of one publisher and one consumer publishing as fast as they can.

So today I would like to talk about some aspects of RabbitMQ’s performance. There are a huge number of variables that feed into the overall level of performance you can get from a RabbitMQ server, and today we’re going to try tweaking some of them and seeing what we can see.

Since the new persister arrived in RabbitMQ 2.0.0 (yes, it’s not so new anymore), Rabbit has had a relatively good story to tell about coping with queues that grow and grow and grow and reach sizes that preclude them from being able to be held in RAM. Rabbit starts writing out messages to disk fairly early on, and continues to do so at a gentle rate so that by the time RAM gets really tight, we’ve done most of the hard work already and thus avoid sudden bursts of writes. Provided your message rates aren’t too high or too bursty, this should all happen without any real impact on any connected clients.

Some recent discussion with a client made us return to what we’d thought was a fairly solved problem and has prompted us to make some changes.

In our previous blog post we talked about a few approaches to topic routing optimization and described the two more important of these in brief. In this post, we will talk about a few things we tried when implementing the DFA, as well as some performance benchmarking we have done on the trie and the DFA.

Among other things, lately we have been preoccupied with improving RabbitMQ’s routing performance. In particular we have looked into speeding up topic exchanges by using a few well-known algorithms as well as some other tricks. We were able to reach solutions many times faster than our current implementation.