Reliable load tests require solid preparation, controlled smoke testing, and structured error analysis.
Validation and smoke tests help detect script issues early and establish trustworthy baseline response times.
The guide explains how to identify common problems such as missing agents, premature test stops, and overloaded load generators.
It details how to distinguish script errors from server-side failures using response codes, error rates, and logs.
Clear troubleshooting patterns make it easier to isolate root causes and avoid misleading performance conclusions.
The rise of MCP is reshaping how AI interacts with technical tools, and this project shows how an MCP server can let AI assistants operate OctoPerf directly.
The goal is to make performance testing more accessible: running tests, analyzing results, and generating insights through natural language.
Built as an MVP, the server demonstrates how AI can automate workflows that normally require manual navigation and correlation.
The project highlights MCP’s key concepts, the supporting Go framework, and how developers can extend the system with new tools and analyses.
A demo illustrates end-to-end usage, from listing workspaces to running tests and comparing results.
The experience confirms how MCP can bridge AI and APIs to create smarter, more productive performance-testing workflows.
If you'd like to carry out load tests in a simple way, benefit from a simplified configuration with a focus on writing your test plan and its test typology, have the opportunity to monitor through detailed dashboards, store your metrics, and also mock one or several services: you are at the right place!
With this kind of configuration you will be able to make shift-left performance testing as well!
docker-compose is a small library that allows you to run docker-compose This is useful to bootstrap test environments.
Docker Compose offers a multitude of benefits which I'll detail below:
Simplified configuration: Docker Compose lets you define and manage all the services of a multi-container application in a single YAML file. This makes it easy to configure, start and stop all the containers in an application.
Automated deployment: With a single configuration file, you can automate the deployment of all the services required for your application, reducing manual errors and improving consistency between development, test and production environments.
Managing dependencies : Compose makes it easy to manage dependencies between services. You can define the startup order of containers and the links between them, ensuring that all services start up in the right order and are properly connected.
Portability: Once you've defined your Compose file, you can easily share it and run it on different machines. This ensures that developers and operational teams work in identical environments, reducing compatibility problems.
Service isolation: Docker Compose creates isolated networks for containers, ensuring that each service operates in a partitioned environment. This improves security and enables services to be tested without mutual interference.
Scalability: Compose makes it easy to scale services. You can quickly adjust the number of containers for a particular service by simply modifying the configuration file and redeploying.
Local development and easy testing: Developers can use Docker Compose to create local development environments that faithfully reproduce production environments. This enables problems to be detected and resolved early in the development cycle.
CI/CD integration: Docker Compose integrates well with continuous integration and deployment (CI/CD) pipelines. You can use Compose files to orchestrate automatic tests and deployments in your CI/CD workflows.
Simplified maintenance: With Docker Compose, updating configurations and services becomes simpler. You can update container images or modify configurations by modifying the Compose file and redeploying services.
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
The idea behind this blog post originated when we updated our documentation after the release of our new UI. We had to identify all links used in the OctoPerf website and update them from https://doc.octoperf.com to https://api.octoperf.com/doc. With more than 250 blog posts at the time I'm writing this one, you can see how this could prove challenging. And of course the twist is that we also took this opportunity to reorganize the documentation so it's not as simple as a search and replace of the domain.
This got us thinking on ways to automate it, because a lot of third party links could also be broken and of course one of those ways is to use OctoPerf itself to execute JMeter tests that will report on all the broken links.