You can run JMeter and Gatling performance tests on HyperExecute either by uploading a project through the web portal or by configuring a CLI job for repeatable terminal and pipeline runs. The portal is the simpler route when you want to set up and launch a test interactively; CLI and YAML suit teams that need execution triggered and configured from automation.
Choose the right HyperExecute workflow
| Workflow | Best fit | What you do |
|---|---|---|
| Portal | One-off or manually configured JMeter and Gatling runs | Create a project, upload the test files, set load parameters, and launch the test in the dashboard. |
| CLI with YAML | Repeatable runs launched from a terminal or CI/CD pipeline | Prepare credentials, a compatible HyperExecute CLI, and a YAML configuration, then invoke the CLI and inspect the resulting job. |
The documented no-code portal flows cover JMeter and Gatling. HyperExecute documentation also lists k6 in its performance-testing category, but that does not establish an equivalent portal-upload workflow for every framework. Check the current documentation for the workflow your framework supports. HyperExecute performance testing guide
Run a JMeter test through the portal
Prepare the test plan
Create and save a JMeter test plan as a .jmx file. Include any data files the plan needs, such as CSV input, and ensure that file paths and test settings will work in the remote execution environment.
Upload and configure the project
- Open the HyperExecute Projects dashboard and create a project.
- Upload the
.jmxplan and select it for the test run. - Configure the user count, test duration, and ramp-up period to match the workload you want to model.
- Set the load distribution, machine count, and any CSV splitting options. Review the selected region; the guide lists East US as a default, but regional settings may change.
- Review the expected aggregate load, then select Run Test.
Do not assume the thread count in your plan is the total across the whole job. The vendor guide warns that, without load-distribution overrides, JMeter thread counts can be replicated on each machine in each region. In its example, a 250-user plan on three machines across two regions can produce 1,500 concurrent users (250 × 3 × 2). Treat that as a configuration example, not a universal result: set the distribution explicitly and verify the configured and reported load for your run. HyperExecute performance testing guide
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Run a Gatling test through the portal
Upload the simulation
- Create a project in the HyperExecute dashboard and select Gatling as the framework.
- Upload the simulation files required by the current HyperExecute Gatling guide.
- Select the simulation and choose the test type that matches the question you need to answer.
- Configure the load model and distribution, including duration and the relevant user or arrival-rate settings.
- Check the region and machine configuration before launching the test.
Choose Capacity, Stress, or Soak
| Test type | Workload model described in the guide | Question it helps answer |
|---|---|---|
| Capacity | Duration plus initial and final user-arrival rates | How does the system scale as arrival rates rise, and where are its limits? |
| Stress | Duration plus total injected users | How does the system behave during peaks, including failure and recovery? |
| Soak | Duration plus a constant arrival rate | Does prolonged load reveal memory leaks or performance degradation? |
These labels describe the modes in the vendor guide; use the current UI and framework configuration to confirm exactly how a selected test is parameterized. HyperExecute Gatling testing guide
Run tests from a terminal or pipeline with CLI and YAML
The CLI route is for repeatable, automation-driven execution. The exact CLI syntax, YAML schema, supported runner options, and version requirements can change, so use the current HyperExecute guide and the documentation matching your installed CLI rather than treating an older sample as a universal configuration. The Gatling guide describes a flow involving credentials, the CLI, a hyperexecute.yaml file, Maven dependency resolution, mvn gatling:test, and report artifact upload.
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Prepare credentials and project files
- Have a HyperExecute account and the project files needed by the test.
- Install or obtain the HyperExecute CLI version supported by the current guide, and confirm its version before running.
- Set account credentials as environment variables using the variable names specified in the current HyperExecute documentation. Do not put a real access key in a committed YAML file, script, or article example.
- Place the test project and its configuration in the working directory expected by your YAML runner.
Configure and invoke the job
Use the current Gatling guide’s example as the starting point for your project-specific YAML. Its described runner flow resolves Maven dependencies, runs mvn gatling:test, and uploads the generated report as an artifact. These are guide-specific examples, not a complete, version-independent YAML file.
- Validate that the installed CLI version and YAML schema match the current documentation.
- Configure the runner, dependency resolution, test command, and report artifact paths for your project.
- Run the CLI with the configuration file, using the invocation syntax in the current guide.
- In the HyperExecute job view, check job status and logs; confirm that the report files were uploaded and are accessible as artifacts.
For a JMeter project, use the applicable current CLI documentation and job configuration rather than assuming that the Gatling runner example applies unchanged. The vendor’s December 2025 release notes describe JMeter project workflows as a CI/CD orchestration feature; check current availability and instructions before relying on that capability. Gatling guide · Performance testing guide · HyperExecute release notes
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Plan aggregate load before launch
Distributed load generation makes the relationship between a test plan’s users and the job’s total load easy to misread. Before running, record the intended aggregate load and check how the chosen framework’s users or threads are assigned across generators and regions.
- Confirm whether the configured user or thread count is per machine, per region, or aggregate.
- Set distribution overrides when you need a controlled total rather than replication across machines and regions.
- Review machine count, region count, and region percentages together; a change to any of them can affect generated load.
- For JMeter CSV data, confirm whether splitting is enabled and that each generator receives the intended data.
- Validate ramp-up and duration against your target workload, not just the maximum user count.
The vendor guide cites 2,000 users as a ceiling under favorable conditions, not a guarantee or independent benchmark. It says outcomes depend on factors such as lightweight requests, suitable timeouts, and having enough machines and regions. Size the test for your workload and verify what the configured run actually delivers. HyperExecute performance testing guide
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Read results and diagnose failed runs
Start with the HyperExecute job status and logs, then inspect the report or artifacts produced by the selected framework. For Gatling, the guide specifically describes uploading a report artifact and accessing it through the HyperExecute logs UI. Interpret response times, throughput, errors, and saturation using the metrics emitted by your test and the behavior your test is designed to measure.
Common problems to check
- The actual load is much higher than expected: Check whether the plan’s users or threads are replicated per machine and region. Set distribution overrides and verify the aggregate configuration.
- CSV-driven JMeter tests behave unexpectedly: Check the CSV splitting setting and ensure data distribution matches the number of generators.
- The CLI rejects a configuration: Confirm that the CLI version and YAML schema correspond to the current guide; configuration formats and supported options can change.
- Gatling dependencies or the test command fail: Review the job logs for Maven resolution and
mvn gatling:testerrors, then check project dependencies and runner setup against the current Gatling instructions. - No report appears in artifacts: Check that the report path in the job configuration matches the path the test actually generates and that artifact upload is configured.
- The run does not use the intended region: Review regional settings instead of relying on a default, which the vendor guide identifies as East US for its documented configuration.
For current project-specific instructions, consult the performance testing guide and Gatling guide before changing CLI flags, YAML keys, region names, or UI steps.
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Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
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