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Performance Testing for large scale programmes

In this post we are going to look at performance testing on large scale programmes.

A few the posts we write define techniques and approaches based on a single application under test but sometimes you are faced with the prospect of performance testing:

  • A new solution that replaces several legacy applications,
  • A service migration from once cloud provider to another or one data center to another,
  • An infrastructure update that covers multiple applications or services,
  • A new solution that compliments and integrates with existing software.

Now, especially in the case of migration of services, performance is key, and you cannot afford to see a degradation in performance as the business users will have already become accustomed to the software and how it performs.

You can look to make it perform better but it is unlikely they will tolerate poorer performance just because you have migrated from one platform to another.

Equally, new solutions that replace legacy application will (rightly or not) be expected to perform better than their predecessor which is a challenge as your new solution will undoubtedly have a different workflow and approach to delivering what the end-users want.

These types of large-scale programmes can on the surface seem complex from a Quality Assurance perspective and we have put together this guide to help you understand some of the techniques you can use to ensure that the performance testing aspect of the testing is manageable and not overwhelming. We have set out in the sections below things to consider to assist in the performance testing of large-scale programmes of work.

Open source Load Testing tools comparative study

When using a testing tool, it is only logical to trust its results. And the more well-known the tool is, the more trust we put in it. Furthermore, how could we know it is wrong ? After all, who is in a position to judge the judge ? This phenomenon is particularly true in the load testing community, since the field is still something of a niche among the testing world. Finding deep-dive studies about the actual technical aspect of load testing is difficult.

Those observations led to the creation of this study. In this article, I will compare the results obtained for the exact same load test using 4 different open-source load testing tools: JMeter, Locust, Gatling and K6. These softwares were chosen because they are among the most used and/or discussed in the community, but in the future the goal will be to add others, including the ones that are not open-source.

The goal of this comparison is not to point any fingers and decide which tool is right or wrong. The objective is to try to understand what we measure within each tool, and what it means for our performance tests.

Producing JMeter Tests from OpenAPI

Swagger in conjunction with OpenAPI is a way for REST API’s to be build, documented and consumed. It is defined in either YAML or JSON. OpenAPI and Swagger are both open source, and their use is commonplace amongst development teams and can make the life of a performance tester a lot easier as it allows us to build JMeter tests directly from the definition of the endpoints.

The object of this post is to look at how we can produce JMeter tests directly from a Swagger definition. If you are unfamiliar with Swagger it is worth spending some time reading the online overview as it will provide a good understanding of how the specification works.

Now clearly in order for you to leverage the benefits of building performance tests from Swagger definitions you are going to need to have your development teams use it to describe the API contract for service they are producing, but for the purposes of this post we will use a test definition called Swagger Petstore.

Performance Testing in application Design

There are many articles on the huge benefits of performance testing integrated into the development process and the concept of shift-left performance testing.

We have also discussed the concepts of Load Test Driven Development which involves the creation of performance testing in parallel with the code development and sits alongside Test Driven Development.

We are going to consider in this post how involving performance testing resources in the application design process can be a benefit.