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JMeter JMesPath Extractor

Summary

JMESPath offers a powerful and predictable way to extract and validate JSON data in JMeter, complementing JSONPath. It provides cleaner syntax, richer filtering options, and a fully specified grammar, making it reliable across platforms.

The article shows how JMESPath extractors and assertions work in JMeter, including variable handling, match modes, and real examples. Octoperf also integrates JMESPath with an expression builder that simplifies selector creation and testing.

Advanced features like projections, pipes, multiselect, and functions allow complex JSON transformations. Although it lacks recursive traversal and arithmetic operations, JMESPath covers most extraction needs for performance testing scripts.

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Install OctoPerf in your company

Article Summary

Installing OctoPerf on-premise has become straightforward and flexible, even in highly constrained enterprise environments.
The platform runs entirely on containers and supports Docker, Kubernetes, and Rancher deployments.
SSO integration, fine-grained administration, and workspace isolation make it compatible with strict security policies.
Load generators can be managed on-premise or provisioned dynamically via cloud providers.
The setup scales from small teams to large organizations while keeping full control over data and infrastructure.

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Troubleshoot your tests

Article Summary

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.

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OctoPerf MCP Server

Summary

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.

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JMeter ecosystem for your Performance test through docker-compose, monitor, and mock your services

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.