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Load Testing Blog

Defining and Maintaining Performance Test Coverage

In this post we are going to look at performance test coverage. What functionality to performance test can range from very little to most of the application under test and both are valid under the right circumstances. We have talked about what to performance test in other posts available in the OctoPerf Blog but as part of a wider post about performance testing rather than as the subject of the post. This is an important topic and deserves a post devoted to it.

We are going to discuss the performance coverage topic through a series of questions which we will explore in detail. These questions are:

  • in JMeter
  • 9 min read

Postman Collection to JMeter

In this blog post we are going to look at how we take a postman request or collection and translate these into JMeter tests. When web services are being build it is common for Postman to be used to test the endpoints. This is done by:

  • Development teams
  • Wider Quality Assurance community
  • Business users
  • Product owners

the list goes on.

What naturally happens during programmes where web services are part of the design is that postman requests and collections are built and grow to support all manner of requirements. Using these collections to quickly build your performance tests can save you a significant amount of time and effort when it comes to understanding the web service you are testing along with the payload that the web service expects.

You will find that in some cases you can inherit a fully functioning set of web service requests that support all your requirements from a performance testing perspective.

Build your own JMeter Docker Image and execute your Performance Test

If you'd like to run load tests in a simple way, and possibly share them, while benefiting from a simplified configuration, with a focus on writing your test plan, and its test typology, this article is for you!

Docker offers virtualization services that simplify the replication of working environments.

Furthermore, each virtualized service is isolated from unrelated services on other containers or the host machine, ensuring portability across host machines and the network.

Using JMeter within a Docker container offers several advantages :

Portability :

Docker enables the creation of lightweight and portable containers that can run on any Docker-compatible system, whether it's Linux, macOS, or Windows. This simplifies the deployment and management of JMeter, avoiding compatibility issues related to different system configurations.

Isolation :

Docker containers provide an isolated environment for running applications, meaning JMeter's dependencies and runtime environment are encapsulated within the container. This reduces potential conflicts with other applications or system components.

Ease of deployment :

With Docker, distributing and deploying JMeter across multiple machines or environments is straightforward.

You can create a Docker image containing JMeter and distribute it to your team or various test environments, streamlining the deployment process.

Version management :

By using Docker, you can version your Docker images containing JMeter, facilitating the management of different versions of JMeter used in your tests. You can also share these images via public or private Docker registries

  • in JMeter
  • 8 min read

JMeter Throughput Controller

In this blog post we are going to be looking at the Throughput Controller. Its name is a little misleading as it does not control the throughput in terms of managing load across the duration of a test, this is handled by elements such as the Constant Throughput Timer. The Throughput Controller can support you in building quite complex scenarios especially when coupled with other Controllers and we will explore this in this blog post as well as looking at how this Controller can provide benefit in real-world performance testing scenarios. OctoPerf support this behaviour natively on their SAAS platform through the use of the Flow Control Action that can support the same patterns of behaviour that we will discuss in this blog post.

Basic example

Let’s create a JMeter test to demonstrate how this Throughput Controller Works. We will use a set of Dummy Samplers to achieve this. If we create this test where we have 2 Samplers:

  • Sampler A
  • Sampler B

With Sampler B being a child of a Throughput Controller.

initial-basic-test.png

We can see that we have a single Thread with the Loop Count set to 10.

  • in JMeter
  • 11 min read

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.