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Load Testing Blog

Performance Response Times

When performance testing you need a set of requirements to measure your response times against. When defining these you should do so with your end users or business teams.

It is relatively easy to predict volumes, load and users that will use your application as you will no doubt have some data based on your current systems. It is a lot harder to agree on what the response times of your application should be. Without this critical measure we really cannot say for certain that an application or service performs. The reason being that we are not measuring the response time under load against a value that has been agreed upfront.

We need to be able to answer these questions:

  • At what point does a response time become unacceptable?

  • How can response times be defined in the requirements gathering stage?

  • How can we ensure there is a measure to test against?

We are going to look in this post at how we might categorise response times based on the interaction model with the end-user. And use these metrics to build response time requirements as part of your wider non-functional requirements.

Open source Load Testing tools comparative study

Summary

Different open-source load-testing tools report response times in very different ways, and default settings can drastically affect results. A single test scenario was executed with JMeter, Locust, Gatling, and K6 using identical load, connection reuse disabled and a shared target URL. The study shows major discrepancies across tools due to differences in TCP/TLS handling, connection pooling, DNS resolution, and timing definitions.

JMeter and Gatling report values close to browser timings, while Locust reports significantly higher numbers and K6 under-reports unless metrics are combined. Understanding how each tool measures response time is essential before comparing results or drawing performance conclusions. The key takeaway: trust results only when you know exactly what the tool is measuring and how its configuration impacts the load.

Table of Contents

Performance Testing in Production

In this Blog Post we are going to discuss performance testing in production.

Now before you think we have gone mad and lost our minds completely this is not as crazy as it sounds.

Production is an environment that:

  • Is sized accurately,
  • Has a suitable diversity of data,
  • Has the correct data volumes,
  • Is on the correct infrastructure.

All the things you spend a significant amount of time getting right in your performance testing environment and that can be difficult to achieve.

  • in JMeter
  • 10 min read

How to load test LDAP with JMeter

We are going to discuss how we can test the Lightweight Directory Access Protocol (LDAP) using JMeter, the principles of LDAP can be quite complicated as their origins come from the X500 specification which documents a suite of protocols developed by the International Telecommunication Union in the 1980’s.

It is likely that you have heard of LDAP as the directory protocol used to underpin Active Directory (AD) which is a directory service for Microsoft and normally is used by many organisations to support user authentication and role profiles for company networks.

It is important to understand that LDAP is not exclusive to Microsoft but does allow users to query AD and authenticate access to it.

In order to demonstrate how we can test LDAP using JMeter we are going to use an Online Test Server provided by Forumsystems which means that should you wish to follow this tutorial but don’t have access to a LDAP server you can. If you need to look at the solution, the JMeter project can be found here.

A limitation of using this online LDAP test server is that we only have read-only access meaning we can only test the following functions:

  • Bind
  • Unbind
  • Search
  • Compare

But JMeter also offers the ability to:

  • Rename
  • Add
  • Delete
  • Modify

The LDAP entries which we will be unable to demonstrate using this service but nonetheless we will provide some examples of these test types.

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.