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 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.

The tasks attribute

A Locust class can have tasks declared as methods under it using the @task decorator, but one can also specify tasks using the tasks attribute which is descibed in more details below.


When a load test is started, an instance of a Locust class will be created for each simulated user and they will start running within their own green thread. When these users run they pick tasks that they execute, sleeps for awhile, and then picks a new task and so on.

The tasks are normal python callables and — if we were load-testing an auction website — they could do stuff like “loading the start page”, “searching for some product”, “making a bid”, etc.

Declaring tasks

The typical way of declaring tasks for a Locust class (or a TaskSet) it to use the task decorator.

Here is an example:

from locust import Locust, task
from locust.wait_time import constant

class MyLocust(Locust):
    wait_time = constant(1)

    def my_task(self):
        print("Locust instance (%r) executing my_task" % self)

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

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

class MyLocust(Locust):
    wait_time = between(5, 15)

    def task1(self):

    def task2(self):

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 Locust or 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 Task, or a <Task : int> dict, where Task is either a python callable or a TaskSet class (more on that below). If the task is a normal python function they receive a single argument which is the Locust instance that is executing the task.

Here is an example of a locust task declared as a normal python function:

from locust import Locust, constant

def my_task(l):

class MyLocust(Locust):
    tasks = [my_task]
    wait_time = constant(1)

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.

Internally the above dict will actually be expanded into a list (and the tasks attribute is updated) that looks like this:

[my_task, my_task, my_task, another_task]

and then Python’s random.choice() is used pick tasks from the list.

TaskSet class

Since real websites are usually built up in an hierarchical way, with multiple sub-sections, locust has the TaskSet class. A locust task can not only be a Python callable, but also a TaskSet class. A TaskSet is a collection of locust tasks that will be executed much like the tasks declared directly on a Locust class, with the user sleeping in between task executions. Here’s a short example of a locustfile that has a TaskSet:

from locust import Locust, TaskSet, between

class ForumSection(TaskSet):
    def view_thread(self):

    def create_thread(self):

    def stop(self):

class LoggedInUser(Locust):
    wait_time = between(5, 120)
    tasks = {ForumSection:2}

    def index_page(self):

A TaskSet can also be inlined directly under a Locust/TaskSet class using the @task decorator:

class MyUser(Locust):
    class MyTaskSet(TaskSet):

The tasks of a TaskSet class can be other TaskSet classes, allowing them to be nested any number of levels. This allows 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

When a running Locust thread picks a TaskSet class for execution an instance of this class will be created and execution will then go into this TaskSet. What happens then is that one of the TaskSet’s tasks will be picked and executed, and then the thread will sleep for a duration specified by the Locust’s wait_time function (unless a wait_time function has been declared directly on the TaskSet class, in which case it’ll use that function instead), then pick a new task from the TaskSet’s tasks, wait again, and so on.

Interrupting a TaskSet

One important thing to know about TaskSets is that they will never stop executing their tasks, and hand over execution back to their parent Locust/TaskSet, by themselves. This has to be done by the developer by calling the TaskSet.interrupt() method.

interrupt(self, reschedule=True)

Interrupt the TaskSet and hand over execution control back to the parent TaskSet.

If reschedule is True (default), the parent Locust will immediately re-schedule, and execute, a new task

This method should not be called by the root TaskSet (the one that is immediately, attached to the Locust class’ task_set attribute), but rather in nested TaskSet classes further down the hierarchy.

In the following example, if we didn’t have the stop task that calls self.interrupt(), the simulated user would never stop running tasks from the Forum taskset once it has went into it:

class RegisteredUser(Locust):
    class Forum(TaskSet):
        def view_thread(self):

        def stop(self):

    def frontpage(self):

Using the interrupt function, we can — together with task weighting — define how likely it is that a simulated user leaves the forum.

Differences between tasks in TaskSet and Locust classes

One difference for tasks residing under a TaskSet, compared to tasks residing directly under a Locust, is that the argument that they are passed when executed (self for tasks declared as methods with the @task decorator) is a reference to the TaskSet instance, instead of the Locust user instance. The Locust instance can be accessed from within a TaskSet instance through the TaskSet.locust. TaskSets also contains a convenience client attribute that refers to the client attribute on the Locust instance.

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 instance.

SequentialTaskSet class

SequentialTaskSet is a TaskSet but its tasks will be executed in the order that they are declared. Weights are ignored for tasks on a SequentialTaskSet class. Ofcourse you can also nest SequentialTaskSet within TaskSet and vice versa.

def function_task(taskset):

class SequenceOfTasks(SequentialTaskSet):
    def first_task(self):

    tasks = [functon_task]

    def second_task(self):

    def third_task(self):

In the above example, the tasks are executed in the order of declaration:

  1. first_task
  2. function_task
  3. second_task
  4. third_task

and then it will start over at first_task again.

on_start and on_stop methods

Locust and TaskSet classes can declare an on_start method and/or on_stop method. A Locust user will call it’s on_start method when it starts running, and it’s on_stop method when it stops running. For a TaskSet, the on_start method is called when a simulated user starts executing that TaskSet, and on_stop is called when the simulated user stops executing that TaskSet (when interrupt() is called, or the locust user is killed).

test_start and test_stop events

If you need to run some code at the start or stop of a load test, you should use the test_start and test_stop events. You can set up listeners for these events at the module level of your locustfile:

from locust import events

def on_test_start(**kwargs):
    print("A new test is starting")

def on_test_stop(**kwargs):
    print("A new test is ending")

When running Locust distributed the on_start and on_stop events will only be fired in the master node.

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(*args, **kwargs)

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 it’s tasks. Tasks can be declared either directly on the class by using the @task decorator on the methods, or by setting the tasks attribute.

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, task, between

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

    def index(self):

    def about(self):

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/.

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]")

HTTP Proxy settings

To improve performance, we configure requests to not look for HTTP proxy settings in the environment by setting requests.Session’s trust_env attribute to False. If you don’t want this you can manually set locust_instance.client.trust_env to True. For further details, refer to the documentation of requests.

How to structure your test code

It’s important to remember that the locustfile.py is just an ordinary Python module that is imported by Locust. From this module you’re free to import other python code just as you normally would in any Python program. The current working directory is automatically added to python’s sys.path, so any python file/module/packages that resides in the working directory can be imported using the python import statement.

For small tests, keeping all of the test code in a single locustfile.py should work fine, but for larger test suites, you’ll probably want to split the code into multiple files and directories.

How you structure the test source code is ofcourse entirely up to you, but we recommend that you follow Python best practices. Here’s an example file structure of an imaginary Locust project:

  • Project root
    • common/
      • __init__.py
      • auth.py
      • config.py
    • locustfile.py
    • requirements.txt (External Python dependencies is often kept in a requirements.txt)

A project with multiple different locustfiles could also keep them in a separate subdirectory:

  • Project root
    • common/
      • __init__.py
      • auth.py
      • config.py
    • locustfiles/
      • api.py
      • website.py
    • requirements.txt

With any of the above project structure, your locustfile can import common libraries using:

import common.auth