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Python

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The Python Load Test Engine is based on Locust. It models user behavior as Python classes and tasks. Use Python when your team works in Python, when you want to express user journeys as code with conditional logic, or when you need a straightforward ramp-up to steady-state load. Python runs on both Linux VM and Kubernetes infrastructure, so it fits everything from a single on-premises host to scalable distributed runs.

What you can do with Python​

  • Reuse Python skills and scripts. Write scenarios as Python classes, or upload an existing .py Locust script.
  • Test from a single host. Run on a Linux VM for on-premises targets or direct network access to internal services.
  • Scale out on Kubernetes. Run a master pod with worker pods for higher concurrency.
  • Model user behavior. Weight tasks so frequent actions run more often than rare ones.

Prerequisites​

  • Module access: Access to the Harness Resilience Testing module.
  • Infrastructure: A chaos infrastructure with load testing enabled, either a Linux Chaos Infrastructure or a Kubernetes Chaos Infrastructure (v1.85.3 or later).
  • Environment: An environment created in your project for the infrastructure.
  • An onboarded service: At least one service onboarded against that infrastructure.
  • Reachable target: Target application endpoints accessible from the test infrastructure.

Create a load test​

  1. Navigate to Resilience Testing > Load Testing.
  2. Click + New Load Test.
Try a sample test

Click the arrow beside + New Load Test and select Try Locust Sample Test to explore the flow with a pre-configured test before you build your own.

Configure the load test overview​

On the Overview tab, enter the test metadata, then select the infrastructure where the test runs. Set Name, an optional Description, and optional Tags, then choose a Target Type.

Select Linux VM as the target type, then select a Linux Chaos Infrastructure (with load testing enabled) from the Load Test Infrastructure dropdown. Set Load Test Engine to Python (Based on Locust).

The Harness chaos agent on the VM runs the Locust process locally and streams results back to Harness.

  • Best for simple setups, on-premises hosts, or direct network access to internal services.
  • Go to Linux Infrastructure for setup instructions.

Use lowercase letters, numbers, and dashes in Name. Harness derives the Id from it.

Select the services under test​

A Services section appears once you choose an infrastructure, and it is required. Select at least one onboarded service under Resilience Testing Services. The picker lists only the services onboarded against the infrastructure you selected.

If that infrastructure has no onboarded services, the section reads No resilience testing services yet and offers Onboard a Service. You cannot continue until at least one exists, because Harness reports load results against the service rather than against the test alone.

Click Next to proceed to Test Configuration.

Define the test​

On the Test Configuration tab, choose how you want to define the test workload. Locust supports two modes: upload a Python script, or reference a custom container image.

Upload a custom Locust .py script for advanced scenarios that require custom logic, authentication flows, or complex user behavior.

FieldDescription
Host URLBase URL of the application under test. Locust prepends this to all relative paths in your script.
Script fileDrag and drop or browse to upload. .py files only.
from locust import HttpUser, task, between

class WebsiteUser(HttpUser):
    wait_time = between(1, 3)

    @task
    def get_homepage(self):
        self.client.get("/")

    @task(2)
    def get_products(self):
        self.client.get("/api/products")

@task(weight) controls relative execution frequency across tasks.

The Test Configuration tab with Upload Python script selected, showing the Host URL and Locust Script File upload fields alongside the Load Configuration and Load Profile graph

The Upload Python script mode. Provide an optional Host URL that Locust prepends to relative paths, upload your .py script, then set Users, Duration, Ramp Up Duration, and Worker Count in the Load Configuration.

Configure the load profile​

Configure how virtual users are ramped up and sustained during the test:

ParameterDescriptionConstraint
UsersPeak concurrent virtual users.Must be a positive integer.
Duration (seconds)Total test runtime.Must be greater than Ramp Up Duration.
Ramp Up Duration (seconds)Time to reach peak users from zero.Must be less than Duration.
Worker CountNumber of worker processes that generate load.Must be a positive integer.

Steady-state duration = Duration - Ramp Up Duration.

Distributed load requires Kubernetes

Only a Kubernetes target spreads load across workers, where each worker runs as its own pod. On a Linux VM the test runs as a single process on the host, so raising Worker Count does not distribute the load. Use a Kubernetes infrastructure when one host cannot generate enough traffic.

The Load Profile graph updates in real time as you adjust values. The Load Profile Summary shows a plain-English breakdown, for example: Ramp up to 100 users in 120s, maintain steady state for 480s, total duration 600s (10m 0s).

Set a value at run time​

Every tool input carries a pin control at the end of the field. Select it to switch the field between Fixed value and Runtime input.

A fixed value is stored with the test and used on every run. A runtime input leaves the field unset, so the value is supplied when the test runs, which lets one load test serve several environments or load levels. Go to Run a load test in a pipeline to supply these values from a pipeline.

Define variables​

Variables is a drawer on the right edge of the Load Test Studio, available for both target types. A variable holds a value once and supplies it to the tool inputs, so a value such as a host name or a user count lives in one place instead of being repeated across fields.

Select + Add Variable and complete the New Variable dialog:

FieldDescription
TypeString, Number, or Secret. Use Secret for credentials so the value is not stored in the test definition.
NameThe variable name.
ValueThe value to use. This field carries its own pin control, so a variable can itself be a runtime input.
Description(Optional) What the variable is for.

Select Save to add the row, then Apply Changes to keep the drawer's edits. The drawer lists each variable with its Variable, Description, and Value.

Tune pods with Advanced Options​

Advanced Options is a drawer on the right edge of the Load Test Studio that controls how the load pods themselves behave. It applies to Kubernetes targets only, since both settings act on pods, and does not appear for a Linux VM target.

SettingDefaultWhat it does
Clean-up Load ResourcesOnDeletes the pods, configmaps, and secrets a run created once the run finishes. Turn it off to keep those resources for debugging, and remove them yourself afterwards.
Resource RequirementsOffSets CPU and memory requests and limits on the load pods. Turn it on when a run is throttled or evicted, or when your cluster enforces quotas.

Select Apply Changes to keep your edits, or Discard to close the drawer without saving.

Save and run the test​

  1. Click Save to create the load test.
  2. Find your test in the Load Tests list, which shows Type, Users, Duration, and recent executions at a glance.
  3. Click the Run (▶) button on any test to start an execution.
  4. Monitor real-time results during execution.

Next steps​

  • Go to Analyze load test results to interpret throughput, error rate, and response times.
  • Go to JavaScript to run a JavaScript-based test with thresholds on Kubernetes.
  • Go to Java to run an existing .jmx test plan on Kubernetes.
  • Go to Composite load tests to run this test alongside a probe that measures health while the load is applied.
  • Go to Key concepts to review virtual users, ramp-up, and load profiles.