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Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minuteTo run Azure Load Testing from GitHub Actions, check your test plan and YAML configuration into the repository, give the workflow permission to use your Azure Load Testing resource, log in to Azure, and call the azure/load-testing@v1 action with the configuration file, resource name, and resource group. Upload the generated loadTest folder so the CSV results and HTML report stay attached to the run. Pass/fail gating in this workflow works through client-side failure criteria defined in the test YAML; server-side metric criteria cannot be enforced from GitHub Actions (details below).
What you need before the workflow can run
Most failed first runs trace back to one of the prerequisites below. Confirm them before editing the workflow file.
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- An Azure Load Testing resource in the Azure subscription you intend to test from, plus at least one existing load test created in that resource.
- A test plan (a JMeter
.jmxfile or a Locust.pyfile) and a test configuration YAML file committed to the same repository. Any supporting CSV or properties files the plan reads should be committed too. - A workflow file under
.github/workflows/in that repository. - An identity that GitHub Actions can use to sign in to Azure. Section three compares the options.
- Access for that identity to the Azure Load Testing resource, using the Azure RBAC role
Load Test Contributor, scoped to the resource.
How the workflow runs, step by step
Microsoft Learn’s CI/CD guide for Azure Load Testing describes this sequence. The steps below follow it in order.
- Check out the repository with
actions/checkoutso the test plan and YAML are present on the runner. - Authenticate to Azure with
azure/login. - Invoke
azure/load-testing@v1and passloadTestConfigFile,loadTestResource, andresourceGroup. Paths inloadTestConfigFileare relative to the repository root. - Optionally publish the generated
loadTestdirectory withactions/upload-artifact.
The guide also documents env and secrets inputs for passing values into the test, and waitForCompletion: false for letting the workflow continue without waiting for the test to finish. The guide was reviewed in October 2026, but action versions and inputs change. Check the current action listing in the GitHub Marketplace before copying a sample. The guide’s older example uses azure/login@v1; the Azure Login documentation now shows azure/login@v2, which is what the example below uses.
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Example workflow
name: Load test
on:
workflow_dispatch:
permissions:
id-token: write
contents: read
jobs:
load-test:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- uses: azure/login@v2
with:
client-id: ${{ secrets.AZURE_CLIENT_ID }}
tenant-id: ${{ secrets.AZURE_TENANT_ID }}
subscription-id: ${{ secrets.AZURE_SUBSCRIPTION_ID }}
- uses: azure/load-testing@v1
with:
loadTestConfigFile: 'tests/config.yaml'
loadTestResource: 'perf-alt-resource'
resourceGroup: 'rg-perf-testing'
secrets: |
{
"APP_PASSWORD": "${{ secrets.APP_PASSWORD }}"
}
- uses: actions/upload-artifact@v4
if: always()
with:
name: loadTest
path: loadTest
The id-token: write permission is required only for the OIDC sign-in method described below. The if: always() condition on the upload step keeps results available when the load test fails the job, which is usually when you most need the report.
Choosing how the workflow signs in to Azure
Microsoft’s documentation describes three patterns that matter for this workflow. They differ in what you must store in GitHub and in where the trust comes from.
| Approach | What is stored in GitHub | Where it fits | Notes from the documentation |
|---|---|---|---|
| Microsoft Entra service principal with a client secret | Service principal credentials as a GitHub Actions secret | Pipelines that cannot use federated identity | The Azure Load Testing CI/CD guide uses this pattern and scopes Load Test Contributor to the resource. The guide’s example uses azure/login@v1; prefer the current login action. |
| OpenID Connect (OIDC) federated credential | Client ID, tenant ID, and subscription ID, which are identifiers rather than passwords | GitHub-hosted or self-hosted runners that can request a GitHub OIDC token | Azure Login’s OIDC flow is the pattern the CI/CD guide points to. It requires id-token: write and a federated credential on the Entra app for your repository. |
| Managed identity on a self-hosted runner | Identity values as secrets; no client secret for the runner identity | Self-hosted runners running on Azure compute | Azure Login’s guidance includes managed identity examples for self-hosted runners. Assigning the identity to the runner’s host is outside the scope of the workflow file. |
Whichever method you choose, keep the identity values in GitHub secrets rather than in the workflow file. Scope the role to the Azure Load Testing resource, not to the whole subscription, so a leaked credential can only operate on that resource.
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Pass/fail criteria: what CI can and cannot enforce
Client-side failure criteria in the test YAML
For CI, define failure criteria in the failureCriteria section of the test configuration YAML. Microsoft’s examples use client-side metrics such as average response time and error percentage. Typical entries look like this:
failureCriteria:
- avg(response_time_ms) > 300
- percentage(error) > 5
You can also tie a criterion to a single named request. The name must match the JMeter sampler or the Locust request exactly, or the criterion will not apply to the intended traffic. The workflow log reports the outcome and reflects the load-test status, so a breach shows up in the run.
What GitHub Actions cannot gate on
Microsoft states explicitly that Azure Load Testing does not support configuring failure criteria on server-side metrics from Azure Pipelines or GitHub Actions. Server-side criteria, such as resource utilization on the application under test, are configured through the Azure portal. If you need a build to fail when a server metric crosses a threshold, the documented YAML workflow will not do that for you. You would need to check those metrics in a separate step, through a tool outside this action, or in the portal.
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Setting waitForCompletion: false has a related consequence. The step returns before the test finishes, so the job cannot use the result as a gate. Leave waiting enabled whenever the pass/fail outcome must block a deployment.
Secrets, Key Vault, and secured endpoints
Passing secrets to the test script
Microsoft’s GitHub Actions example passes a secrets parameter to azure/load-testing. Each entry maps a named test secret to a GitHub Actions secret, as shown in the example above. The name on the left is the secret name your test script reads. Keep the GitHub secret itself out of the YAML and out of the script file.
Secrets and certificates stored in Azure Key Vault
If the secret or client certificate lives in Azure Key Vault, the Azure Load Testing resource needs its own identity. Configure a system-assigned or user-assigned managed identity on the resource, then grant that identity access to the vault. The workflow’s Azure login does not give the test engine vault access on its own.
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Authenticating to the target endpoint
To call an endpoint protected by Microsoft Entra authentication, assign a managed identity to the Azure Load Testing resource and select that identity in the test configuration. Your test script must then acquire an access token for the target endpoint and send it with requests. The identity also needs permission on the target resource itself; the load-testing role does not grant that.
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The configuration YAML sets the test specification version to v0.1 and requires a testId of 2 to 50 characters, using lowercase letters, digits, underscores, or hyphens. The YAML reference also covers engine instance count, environment variables, client certificates, app components, private-network settings, regional configuration, and managed identities.
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| Item | JMeter | Locust |
|---|---|---|
| Test plan file | .jmx file |
.py file |
| Supporting data | CSV or properties files referenced by the plan | CSV or other files the script loads |
| Request-specific failure criteria | Name must match the JMeter sampler | Name must match the Locust request |
| Secrets | Passed through the secrets input and read by the plan |
Passed through the secrets input and read by the script |
Keep the file paths in the YAML relative to the repository, and commit every file the plan opens. A plan that works on your laptop but reads a file outside the repository will fail on the runner.
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Retaining results for review
Azure Load Testing writes results and an HTML report into the loadTest folder in the GitHub Actions workspace. The folder has two parts:
- A results folder with a separate CSV file for each test engine, containing per-request details.
- A report folder with an HTML summary and performance graphs.
The upload step in the example saves that folder as a workflow artifact named loadTest. Download it from the workflow run’s summary page in GitHub. Artifacts are kept according to your repository or organization retention settings, so check those if you need results for a longer audit period.
Troubleshooting common failures
- The workflow cannot find the test. Confirm that
loadTestConfigFileis relative to the repository root and that the YAML and plan are committed, not just present locally. - Azure login succeeds but the load test is rejected. Check that the role assignment is
Load Test Contributorand that its scope is the Azure Load Testing resource named inloadTestResource. - OIDC sign-in fails. Check that the workflow has
id-token: writeand that the federated credential on the Entra app matches your repository and branch or environment. - A named-request criterion never fires. The request name in the YAML does not match the JMeter sampler or Locust request exactly.
- The test cannot read a Key Vault secret. The Azure Load Testing resource’s managed identity has no access to the vault.
- The results folder is missing from the artifact. The upload step ran before the test wrote its output, or the step does not reference
loadTestas its path. Check the step order.
After changing failure criteria, run the workflow once with a threshold you know will be breached. Confirm the job fails before relying on the criterion as a gate.
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