Testing Microservices and Distributed Systems with JMeter
This blog post is about testing microservices and distributed systems with JMeter. It will focus on the principles of performance testing applications that are architected this way. We will not look at which JMeter samplers to use in order to generate a load against microservices or how to configure these samplers. This post will consider best practise and consideration in designing your performance testing when faced with these applications. Let’s just remind ourselves what the definition of microservices is, be mindful that there are many definitions that vary, but in principle:
Microservices are smaller, loosely coupled services that you can deploy independently. Here, “services” refer to different functions of an application. In a microservices architecture, an application’s functions are divided into many smaller components serving specific purposes. These components or services are fine-grained and usually have separate technology stacks, data management methods, and databases. They can communicate with other services of the application via REST APIs, message brokers, and streaming. Microservices are a way of structuring an application as a collection of small, independently deployable services that communicate with each other over a network. This is different from the traditional monolithic architecture, where all components of the application are tightly coupled and run as a single unit.
The way microservices are called depends on their implementation, they are commonly scripted in JMeter using a HTTP Sampler or a GraphQL Sampler both of which have OctoPerf blog posts which can be found here and here. If the microservices you are testing are accessed in a different way then you will probably find a post on the protocol on our Blog Post pages, which can be found here. If you are unable to find one, please get in touch and we’ll look at writing one.
