Safeguard Microservices With Unit Tests

Build an automated guardrail that pings a microservice endpoint, alerts Slack when it fails, and runs only when its unit tests pass.

Safeguard Microservices With Unit Tests

Modern microservices and API backends often expose health endpoints. With Kestra you can monitor those endpoints, write unit tests to validate the monitoring flow, and gate downstream automations on the test results. This guide walks through:

• Creating a flow that checks an HTTP endpoint and notifies Slack when it is down
• Writing Enterprise Edition unit tests that cover both success and failure paths
• Triggering a downstream flow only when the test suite passes

Prerequisites

• Kestra Enterprise Edition (required for Unit Tests and the RunTest task)
• A Slack Incoming Webhook URL (or another channel supported by the Notifications plugin)
• A Kestra API token stored as KESTRA_API_TOKEN (used by the test runner flow)

Step 1: Monitor the API endpoint

Create the following flow in the your namespace to send an alert when the target server is unreachable:

id: microservices-and-apis
namespace: tutorial
description: Microservices and APIs

inputs:
  - id: server_uri
    type: URI
    defaults: https://kestra.io

  - id: slack_webhook_uri
    type: URI
    defaults: https://kestra.io/api/mock

tasks:
  - id: http_request
    type: io.kestra.plugin.core.http.Request
    uri: "{{ inputs.server_uri }}"
    options:
      allowFailed: true

  - id: check_status
    type: io.kestra.plugin.core.flow.If
    condition: "{{ outputs.http_request.code != 200 }}"
    then:
      - id: server_unreachable_alert
        type: io.kestra.plugin.slack.SlackIncomingWebhook
        url: "{{ inputs.slack_webhook_uri }}"
        payload: |
          {
            "channel": "#alerts",
            "text": "The server {{ inputs.server_uri }} is down!"
          }
    else:
      - id: healthy
        type: io.kestra.plugin.core.log.Log
        message: Everything is fine!

This flow issues an HTTP request, lets it fail gracefully (allowFailed: true), then either sends a Slack alert or logs a healthy status.

Breakdown of the components:

• Inputs
  • server_uri: parameterizes the target so you can reuse the flow for staging, production, or any other health endpoint.
  • slack_webhook_uri: stores the Slack webhook that receives alerts without hardcoding secrets in the flow body. Instead of an input, you can also use the KV Store or a secret in the url property.
• http_request task: performs the status check and captures the HTTP code; allowFailed ensures the flow continues even if the request fails.
• check_status conditional: branches on the HTTP response, triggering the Slack alert when the service is down or logging “Everything is fine!” when the endpoint returns 200.

Step 2: Add unit tests

Next, define unit tests to cover both outcomes. Save the snippet below as a test resource in the same namespace.

id: test_microservices_and_apis
flowId: microservices-and-apis
namespace: tutorial
testCases:
  - id: server_should_be_reachable
    type: io.kestra.core.tests.flow.UnitTest
    fixtures:
      inputs:
        server_uri: https://kestra.io
    assertions:
      - value: "{{ outputs.http_request.code }}"
        equalTo: 200

  - id: server_should_be_unreachable
    type: io.kestra.core.tests.flow.UnitTest
    fixtures:
      inputs:
        server_uri: https://kestra.io/bad-url
      tasks:
        - id: server_unreachable_alert
          description: no Slack message from tests
    assertions:
      - value: "{{ outputs.http_request.code }}"
        notEqualTo: 200

Each test case supplies fixtures (inputs and optional task overrides) and assertions. The second test disables the Slack call while still confirming that the alert path runs when the endpoint fails.

Breakdown:

• Test definition: id, flowId, and namespace tie this test suite to the flow created in Step 1.
• server_should_be_reachable case: feeds a valid server_uri and asserts the HTTP response code is 200.
• server_should_be_unreachable case: points to a bad URL, stubs the Slack task so no message is sent during testing (reduce channel noise or spamming test messages), and asserts the HTTP code differs from 200.

Step 3: Run downstream logic only when tests pass

Finally, create a control flow that executes the test suite and gates additional work on the result. The RunTest task returns a boolean in outputs.run_test.result.state.

id: run_if_tests_pass
namespace: tutorial

tasks:
  - id: run_test
    type: io.kestra.plugin.kestra.ee.tests.RunTest
    auth:
      apiToken: "{{ secret('KESTRA_API_TOKEN') }}"
    namespace: tutorial
    testId: test_microservices_and_apis

  - id: run_if_tests_pass
    type: io.kestra.plugin.core.flow.If
    condition: "{{ outputs.run_test.result.state }}"
    then:
      - id: log
        type: io.kestra.plugin.core.log.Log
        message: hello

Replace the final log task with deployments, escalations, or other automations that should run only after the tests succeed.

Breakdown:

• run_test task: invokes the Enterprise Edition RunTest plugin with an API token, namespace, and test ID; the result includes a state boolean.
• run_if_tests_pass conditional: checks that boolean before proceeding, ensuring downstream work executes only when all test cases pass.

Step 4: Execute the tests

Run the unit tests from the Kestra UI or CLI to verify both assertions pass. A successful run confirms the monitor behaves correctly without sending Slack noise during testing.

Next steps

• Expand the monitoring flow to cover multiple endpoints by looping over inputs or using a namespace file.
• Send alerts to PagerDuty, Teams, or email by swapping the Slack task for a different Notifications plugin.
• Wire the gated flow into your CI/CD pipeline so every deployment validates critical monitors before rollout.