Locust and Gatling are both excellent load testing tools, but they approach the problem from fundamentally different angles, leading to distinct advantages and disadvantages depending on your needs.
Here’s a real-world example of a Locust test. Imagine you need to simulate 1000 users hitting an API endpoint that requires authentication.
from locust import HttpUser, task, between
class AuthenticatedUser(HttpUser):
wait_time = between(1, 5) # Users wait 1-5 seconds between tasks
host = "http://localhost:8080" # Target host
def on_start(self):
# Simulate login and store token
response = self.client.post("/login", json={"username": "testuser", "password": "password"})
self.auth_token = response.json()["token"]
@task
def get_protected_data(self):
self.client.get("/data", headers={"Authorization": f"Bearer {self.auth_token}"})
# To run this:
# 1. Save as locustfile.py
# 2. Run `locust -f locustfile.py`
# 3. Open http://localhost:8089 in your browser and specify 1000 users.
Now, let’s look at Gatling. Gatling uses a Scala DSL (Domain Specific Language) to define scenarios. Here’s the equivalent of the Locust example, simulating the same 1000 users hitting an authenticated API.
import io.gatling.core.Predef._
import io.gatling.http.Predef._
import scala.concurrent.duration._
class AuthApiSimulation extends Simulation {
val httpProtocol = http
.baseUrl("http://localhost:8080")
.acceptHeader("application/json")
.contentTypeHeader("application/json")
val loginScn = scenario("Login")
.exec(
http("Login Request")
.post("/login")
.body(StringBody("""{"username": "testuser", "password": "password"}"""))
.check(status.is(200), jsonPath("$.token").saveAs("authToken"))
)
val protectedDataScn = scenario("Protected Data Access")
.feed(loginScn) // This is a conceptual representation; in reality, you'd manage the token differently
.exec(
http("Get Protected Data")
.get("/data")
.header("Authorization", "Bearer #{authToken}")
)
setUp(
loginScn.inject(atOnceUsers(1000)).protocols(httpProtocol), // Inject 1000 users at once for login
protectedDataScn.inject(rampUsers(1000).during(10.seconds)).protocols(httpProtocol) // Ramp up 1000 users over 10 seconds for protected data
).maxDuration(30.seconds) // Overall simulation duration
// To run this:
// 1. Set up Gatling project (usually via Maven or Gradle).
// 2. Place this code in src/test/scala/simulations.
// 3. Run `mvn gatling:test` or `gradle gatling:test`.
}
Locust shines when you need to write load tests quickly, especially if your team is already proficient in Python. Its Python-based DSL makes it incredibly accessible. You can spin up a locustfile.py and start simulating users within minutes. The HttpUser class is straightforward, and defining tasks with @task decorators feels natural for Python developers. The on_start method is perfect for handling initial setup like authentication, storing the token directly in the user instance (self.auth_token) for subsequent requests. The between(1, 5) ensures a realistic pause between user actions, preventing a flood of identical requests.
Gatling, on the other hand, offers a more structured and performance-oriented approach, leveraging Scala and Akka. Its DSL is powerful, allowing for complex scenario definitions, advanced templating, and sophisticated control over user behavior. The separation of scenario and setUp is a key design principle. scenario defines what users do (the sequence of requests, checks, etc.), while setUp defines how these scenarios are executed (number of users, ramp-up, duration). The check method in Gatling is particularly robust, enabling complex assertions and data extraction from responses using JSONPath, Regex, etc. The saveAs functionality is crucial for passing data (like authentication tokens) between steps in a scenario.
The most surprising thing about Gatling’s performance is how it achieves high concurrency with relatively low resource consumption, even when simulating tens of thousands of users. This is largely due to its event-driven, non-blocking architecture powered by Akka. Instead of threads per user (which quickly exhausts memory and CPU), Gatling uses a small number of threads to manage a vast number of virtual users through an actor model. Each actor represents a user’s state and progresses through the scenario independently without blocking the underlying threads. This makes it incredibly efficient for large-scale tests.
Locust’s primary strength is its ease of use and Python integration. This makes it ideal for developers who want to incorporate load testing into their development workflow without a steep learning curve. The ability to distribute Locust workers across multiple machines (locust -f locustfile.py --master and locust -f locustfile.py --worker --master-host <master_ip>) is also a significant advantage for scaling up test execution itself.
Gatling’s strength lies in its performance, detailed reporting, and advanced scenario capabilities. If you need to simulate very high loads, require fine-grained control over user behavior and request pacing, or want sophisticated reporting out-of-the-box, Gatling is often the better choice. Its DSL, while requiring a Scala understanding, is designed specifically for load testing and offers features like feeders for external data sources and comprehensive statistics.
When you’re comfortable with Gatling’s DSL, the next step is often exploring its advanced templating and data feeding mechanisms, such as using CSV files or even databases to drive user input and scenario variations.