Version 4.0 of the RabbitMQ Java Client brings support for runtime metrics. This can be especially useful to know how a client application is behaving. Let's see how to enable metrics collection and how to monitor those metrics on JMX or even inside a Spring Boot application.
This blog post was written for RabbitMQ 3.5, released in 2015. While some parts still apply, there's a lot of outdated information. For example, RabbitMQ 4.0 doesn't support queue mirroring anymore and "paging messages to disk" is no longer something that RabbitMQ has to do, since messages are almost always persisted to disk right away.
In order to prevent fast publishers from overflowing the broker with more messages than it can handle at any particular moment, RabbitMQ implements an internal mechanism called credit flow that will be used by the various systems inside RabbitMQ to throttle down publishers, while allowing the message consumers to catch up. In this blog post we are going to see how credit flow works, and what we can do to tune its configuration for an optimal behaviour.
One of the goals for RabbitMQ 3.3 was that you should be able to find bottlenecks in running systems more easily. Older versions of RabbitMQ let you see that you were rate-limited but didn't easily let you see why. In this blog post we'll talk through some of the new performance indicators in version 3.3.
I warn you before we start: this is another wordy blog post about performance-ish changes in RabbitMQ 3.3. Still with us? Good.
So in the previous post I mentioned "a new feature which I'll talk about in a future blog post". That feature is consumer bias.
Well, we got the bad news out of the way yesterday, so today let's talk about (some of) the good news: some types of publishing and consuming are now a great deal faster, especially in clusters.
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.