In a first post in this sizing series we covered the workload, cluster and storage volume configurations on AWS ec2. In this post we’ll run a sizing analysis with mirrored queues.
The first phase of our sizing analysis will be assessing what intensities each of our clusters and storage volumes can handle easily and which are too much.
All tests use the following policy:
This is the start of a short series where we look at sizing your RabbitMQ clusters. The actual sizing wholly depends on your hardware and workload, so rather than tell you how many CPUs and how much RAM you should provision, we’ll create some general guidelines and use a case study to show what things you should consider.
There can be many reasons to do benchmarking:
In this post we’ll take a look at the various options for running RabbitMQ benchmarks. But before we do, you’ll need a way to see the results and look at system metrics.
In the last post we ran some simple benchmarks on a single queue to see what effect pipelining publisher confirms and consumer acknowledgements had on flow control.
Specifically we looked at:
Unsurprisingly, we saw when we restricted publishers and the brokers to a small number of in-flight messages at a time, that throughput was low. When we increased that limit, throughput increased, but only to a point, after which we saw no more throughput gains but instead just latency increases. We also saw that allowing consumers to use the multiple flag was beneficial to throughput.
In this post we’re going to look at those same three settings, but with many clients, many queues and different amounts of load, including stress tests. We’ll see that publisher confirms and consumer acknowledgements play a role in flow control to help prevent overload of a broker.
In the last post we covered what flow control is, both as a general concept and the various flow control mechanisms available in RabbitMQ. We saw that publisher confirms and consumer acknowledgements are not just data safety measures, but also play a role in flow control.
In this post we’re going to look at how application developers can use publisher confirms and consumer acknowledgements to get a balance of safety and high performance, in the context of a single queue.
Flow control becomes especially important when a broker is being overloaded. A single queue is unlikely to overload your broker. If you send large messages then sure, you can saturate your network, or if you only have a single CPU core, then one queue could max it out. But most of us are on 8, 16 or 30+ core machines. But it’s interesting to break down the effects of confirms and acks on a single queue. From there we can take our learnings and see if they apply to larger deployments (the next post).
As part of our quorum queue series we’re taking a look at flow control, how it protects RabbitMQ from being overloaded and how that relates to quorum queues.
Flow control is a concept that has been in computer networking and networked software for decades. Essentially it is a mechanism for applying back pressure to senders to avoid overloading receivers. Receivers typically buffer incoming packets/messages as a way of dealing with a send rate that exceeds its processing rate. But receiver buffers cannot grow forever so either the send rate should only transiently exceed receiver processing capacity (bursty traffic) or the sender must be slowed down (back pressure).
Flow control is a way of applying this back pressure on the sender, slowing them down so that the receiver’s buffers do not overflow and latencies do not grow too large. In a chain of sender/receivers, this back pressure can propagate up the chain to the origin of the traffic. In more complex graphs of connected components, flow control can balance incoming traffic between fast and slow senders, avoiding overload but allowing the system to reach full utilisation despite different numbers of senders, different rates and different load patterns (steady or bursty).
Quorum queues are still relatively new to RabbitMQ and many people have still not made the jump from classic mirrored queues. Before you migrate to this new queue type you need to make sure that your hardware can support your workload and a big factor in that is what storage drives you use.
In this blog post we’re going to take a closer look at quorum queues and their performance characteristics on different storage configurations.
The TL;DR is that we highly recommend SSDs when using quorum queues. The reason for this is that quorum queues are sensitive to IO latency and SSDs deliver lower latency IO than HDDs. With higher IO latency, you'll see lower throughput, higher end-to-end latency and some other undesirable effects.
Further down in this post we’ll demonstrate why we recommend this, using various benchmarks with different SSD and HDD configurations.
In this post we'll cover how the RabbitMQ Java client library gathers runtime metrics and sends them to monitoring systems like JMX and Datadog.
The RabbitMQ team is happy to announce the release of version 2.0 of HOP, RabbitMQ HTTP API client for Java and other JVM languages. This new release introduce a new reactive client based on Spring Framework 5.0 WebFlux.
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.