Writing a locustfile

A locustfile is a normal python file. The only requirement is that it declares at least one class— let’s call it the locust class—that inherits from the class Locust.

The Locust class

A locust class represents one user (or a swarming locust if you will). Locust will spawn (hatch) one instance of the locust class for each user that is being simulated. There are a few attributes that a locust class should typically define.

The task_set attribute

The task_set attribute should point to a TaskSet class which defines the behaviour of the user and is described in more detail below.

The wait_time attribute

In addition to the task_set attribute, one should also declare a wait_time method. It’s used to determine for how long a simulated user will wait between executing tasks. Locust comes with a few built in functions that return a few common wait_time methods.

The most common one is between. It’s used to make the simulated users wait a random time between a min and max value after each task execution. Other built in wait time functions are constant and constant_pacing.

With the following locustfile, each user would wait between 5 and 15 seconds between tasks:

from locust import Locust, TaskSet, task, between

class MyTaskSet(TaskSet):
    def my_task(self):
        print("executing my_task")

class User(Locust):
    task_set = MyTaskSet
    wait_time = between(5, 15)

The wait_time method should return a number of seconds (or fraction of a second) and can also be declared on a TaskSet class, in which case it will only be used for that TaskSet.

It’s also possible to declare your own wait_time method directly on a Locust or TaskSet class. The following locust class would start sleeping for one second and then one, two, three, etc.

class MyLocust(Locust):
    task_set = MyTaskSet
    last_wait_time = 0

    def wait_time(self):
        self.last_wait_time += 1
        return self.last_wait_time

The weight attribute

If more than one locust class exists in the file, and no locusts are specified on the command line, each new spawn will choose randomly from the existing locusts. Otherwise, you can specify which locusts to use from the same file like so:

$ locust -f locust_file.py WebUserLocust MobileUserLocust

If you wish to make one of these locusts execute more often you can set a weight attribute on those classes. Say for example, web users are three times more likely than mobile users:

class WebUserLocust(Locust):
    weight = 3

class MobileUserLocust(Locust):
    weight = 1

The host attribute

The host attribute is a URL prefix (i.e. “http://google.com”) to the host that is to be loaded. Usually, this is specified in Locust’s web UI or on the command line, using the --host option, when locust is started.

If one declares a host attribute in the locust class, it will be used in the case when no --host is specified on the command line or in the web request.

TaskSet class

If the Locust class represents a swarming locust, you could say that the TaskSet class represents the brain of the locust. Each Locust class must have a task_set attribute set, that points to a TaskSet.

A TaskSet is, like its name suggests, a collection of tasks. These tasks are normal python callables and—if we were load-testing an auction website—could do stuff like “loading the start page”, “searching for some product” and “making a bid”.

When a load test is started, each instance of the spawned Locust classes will start executing their TaskSet. What happens then is that each TaskSet will pick one of its tasks and call it. It will then wait a number of seconds, specified by the Locust class’ wait_time method (unless a wait_time method has been declared directly on the TaskSet, in which case it will use its own method instead). Then it will again pick a new task to be called, wait again, and so on.

Declaring tasks

The typical way of declaring tasks for a TaskSet it to use the task decorator.

Here is an example:

from locust import Locust, TaskSet, task

class MyTaskSet(TaskSet):
    def my_task(self):
        print("Locust instance (%r) executing my_task" % (self.locust))

class MyLocust(Locust):
    task_set = MyTaskSet

@task takes an optional weight argument that can be used to specify the task’s execution ratio. In the following example task2 will be executed twice as much as task1:

from locust import Locust, TaskSet, task
from locust.wait_time import between

class MyTaskSet(TaskSet):
    wait_time = between(5, 15)

    def task1(self):

    def task2(self):

class MyLocust(Locust):
    task_set = MyTaskSet

tasks attribute

Using the @task decorator to declare tasks is a convenience, and usually the best way to do it. However, it’s also possible to define the tasks of a TaskSet by setting the tasks attribute (using the @task decorator will actually just populate the tasks attribute).

The tasks attribute is either a list of python callables, or a <callable : int> dict. The tasks are python callables that receive one argument—the TaskSet class instance that is executing the task. Here is an extremely simple example of a locustfile (this locustfile won’t actually load test anything):

from locust import Locust, TaskSet

def my_task(l):

class MyTaskSet(TaskSet):
    tasks = [my_task]

class MyLocust(Locust):
    task_set = MyTaskSet

If the tasks attribute is specified as a list, each time a task is to be performed, it will be randomly chosen from the tasks attribute. If however, tasks is a dict—with callables as keys and ints as values—the task that is to be executed will be chosen at random but with the int as ratio. So with a tasks that looks like this:

{my_task: 3, another_task: 1}

my_task would be 3 times more likely to be executed than another_task.

TaskSets can be nested

A very important property of TaskSets is that they can be nested, because real websites are usually built up in an hierarchical way, with multiple sub-sections. Nesting TaskSets will therefore allow us to define a behaviour that simulates users in a more realistic way. For example we could define TaskSets with the following structure:

  • Main user behaviour
  • Index page
  • Forum page
  • Read thread
  • Reply
  • New thread
  • View next page
  • Browse categories
  • Watch movie
  • Filter movies
  • About page

The way you nest TaskSets is just like when you specify a task using the tasks attribute, but instead of referring to a python function, you refer to another TaskSet:

class ForumPage(TaskSet):
    def read_thread(self):

    def new_thread(self):

    def stop(self):

class UserBehaviour(TaskSet):
    tasks = {ForumPage:10}

    def index(self):

So in the above example, if the ForumPage would get selected for execution when the UserBehaviour TaskSet is executing, then the ForumPage TaskSet would start executing. The ForumPage TaskSet would then pick one of its own tasks, execute it, wait, and so on.

There is one important thing to note about the above example, and that is the call to self.interrupt() in the ForumPage’s stop method. What this does is essentially to stop executing the ForumPage task set and the execution will continue in the UserBehaviour instance. If we didn’t have a call to the interrupt() method somewhere in ForumPage, the Locust would never stop running the ForumPage task once it has started. But by having the interrupt function, we can—together with task weighting—define how likely it is that a simulated user leaves the forum.

It’s also possible to declare a nested TaskSet, inline in a class, using the @task decorator, just like when declaring normal tasks:

class MyTaskSet(TaskSet):
    class SubTaskSet(TaskSet):
        def my_task(self):

Referencing the Locust instance, or the parent TaskSet instance

A TaskSet instance will have the attribute locust point to its Locust instance, and the attribute parent point to its parent TaskSet (it will point to the Locust instance, in the base TaskSet).

TaskSequence class

TaskSequence class is a TaskSet but its tasks will be executed in order. To define this order you should do the following:

class MyTaskSequence(TaskSequence):
    def first_task(self):

    def second_task(self):

    def third_task(self):

In the above example, the order is defined to execute first_task, then second_task and lastly the third_task for 10 times. As you can see, you can compose @seq_task with @task decorator, and of course you can also nest TaskSets within TaskSequences and vice versa.

Setups, Teardowns, on_start, and on_stop

Locust optionally supports Locust level setup and teardown, TaskSet level setup and teardown, and TaskSet on_start and on_stop

Setups and Teardowns

setup and teardown, whether it’s run on Locust or TaskSet, are methods that are run only once. setup is run before tasks start running, while teardown is run after all tasks have finished and Locust is exiting. This enables you to perform some preparation before tasks start running (like creating a database) and to clean up before the Locust quits (like deleting the database).

To use, simply declare a setup and/or teardown on the Locust or TaskSet class. These methods will be run for you.

The on_start and on_stop methods

A TaskSet class can declare an on_start method or an on_stop method. The on_start method is called when a simulated user starts executing that TaskSet class, while the on_stop <locust.core.TaskSet.on_stop() method is called when the TaskSet is stopped.

Order of events

Since many setup and cleanup operations are dependent on each other, here is the order which they are run:

  1. Locust setup (once)
  2. TaskSet setup (once)
  3. TaskSet on_start (once per locust)
  4. TaskSet tasks…
  5. TaskSet on_stop (once per locust)
  6. TaskSet teardown (once)
  7. Locust teardown (once)

In general, the setup and teardown methods should be complementary.

Making HTTP requests

So far, we’ve only covered the task scheduling part of a Locust user. In order to actually load test a system we need to make HTTP requests. To help us do this, the HttpLocust class exists. When using this class, each instance gets a client attribute which will be an instance of HttpSession which can be used to make HTTP requests.

class HttpLocust

Represents an HTTP “user” which is to be hatched and attack the system that is to be load tested.

The behaviour of this user is defined by the task_set attribute, which should point to a TaskSet class.

This class creates a client attribute on instantiation which is an HTTP client with support for keeping a user session between requests.

client = None

Instance of HttpSession that is created upon instantiation of Locust. The client support cookies, and therefore keeps the session between HTTP requests.

When inheriting from the HttpLocust class, we can use its client attribute to make HTTP requests against the server. Here is an example of a locust file that can be used to load test a site with two URLs; / and /about/:

from locust import HttpLocust, TaskSet, task, between

class MyTaskSet(TaskSet):
    def index(self):

    def about(self):

class MyLocust(HttpLocust):
    task_set = MyTaskSet
    wait_time = between(5, 15)

Using the above Locust class, each simulated user will wait between 5 and 15 seconds between the requests, and / will be requested twice as much as /about/.

The attentive reader will find it odd that we can reference the HttpSession instance using self.client inside the TaskSet, and not self.locust.client. We can do this because the TaskSet class has a convenience property called client that simply returns self.locust.client.

Using the HTTP client

Each instance of HttpLocust has an instance of HttpSession in the client attribute. The HttpSession class is actually a subclass of requests.Session and can be used to make HTTP requests, that will be reported to Locust’s statistics, using the get, post, put, delete, head, patch and options methods. The HttpSession instance will preserve cookies between requests so that it can be used to log in to websites and keep a session between requests. The client attribute can also be referenced from the Locust instance’s TaskSet instances so that it’s easy to retrieve the client and make HTTP requests from within your tasks.

Here’s a simple example that makes a GET request to the /about path (in this case we assume self is an instance of a TaskSet or HttpLocust class:

response = self.client.get("/about")
print("Response status code:", response.status_code)
print("Response content:", response.text)

And here’s an example making a POST request:

response = self.client.post("/login", {"username":"testuser", "password":"secret"})

Safe mode

The HTTP client is configured to run in safe_mode. What this does is that any request that fails due to a connection error, timeout, or similar will not raise an exception, but rather return an empty dummy Response object. The request will be reported as a failure in Locust’s statistics. The returned dummy Response’s content attribute will be set to None, and its status_code will be 0.

Manually controlling if a request should be considered successful or a failure

By default, requests are marked as failed requests unless the HTTP response code is OK (<400). Most of the time, this default is what you want. Sometimes however—for example when testing a URL endpoint that you expect to return 404, or testing a badly designed system that might return 200 OK even though an error occurred—there’s a need for manually controlling if locust should consider a request as a success or a failure.

One can mark requests as failed, even when the response code is OK, by using the catch_response argument and a with statement:

with self.client.get("/", catch_response=True) as response:
    if response.content != b"Success":
        response.failure("Got wrong response")

Just as one can mark requests with OK response codes as failures, one can also use catch_response argument together with a with statement to make requests that resulted in an HTTP error code still be reported as a success in the statistics:

with self.client.get("/does_not_exist/", catch_response=True) as response:
    if response.status_code == 404:

Grouping requests to URLs with dynamic parameters

It’s very common for websites to have pages whose URLs contain some kind of dynamic parameter(s). Often it makes sense to group these URLs together in Locust’s statistics. This can be done by passing a name argument to the HttpSession's different request methods.


# Statistics for these requests will be grouped under: /blog/?id=[id]
for i in range(10):
    self.client.get("/blog?id=%i" % i, name="/blog?id=[id]")

Common libraries

Often, people wish to group multiple locustfiles that share common libraries. In that case, it is important to define the project root to be the directory where you invoke locust, and it is suggested that all locustfiles live somewhere beneath the project root.

A flat file structure works out of the box:

  • project root
    • commonlib_config.py
    • commonlib_auth.py
    • locustfile_web_app.py
    • locustfile_api.py
    • locustfile_ecommerce.py

The locustfiles may import common libraries using, e.g. import commonlib_auth. This approach does not cleanly separate common libraries from locust files, however.

Subdirectories can be a cleaner approach (see example below), but locust will only import modules relative to the directory in which the running locustfile is placed. If you wish to import from your project root (i.e. the location where you are running the locust command), make sure to write sys.path.append(os.getcwd()) in your locust file(s) before importing any common libraries—this will make the project root (i.e. the current working directory) importable.

  • project root
    • __init__.py
    • common/
      • __init__.py
      • config.py
      • auth.py
    • locustfiles/
      • __init__.py
      • web_app.py
      • api.py
      • ecommerce.py

With the above project structure, your locust files can import common libraries using:

import common.auth