Load testing in Rest API - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is load testing in the context of REST APIs?

Load testing is a way to check how a REST API behaves when many users or requests happen at the same time. It helps find out if the API can handle heavy use without slowing down or breaking.

beginner

Name one common tool used for load testing REST APIs.

One common tool is Apache JMeter. It lets you create many fake users to send requests to your API and see how it performs.

intermediate

Why is it important to simulate concurrent users during load testing?

Simulating concurrent users means sending many requests at the same time. This shows how the API handles real-world traffic when many people use it together, helping find bottlenecks or crashes.

beginner

What is a response time in load testing?

Response time is how long the API takes to answer a request. In load testing, we measure if response time stays fast even when many users send requests.

intermediate

What should you do if load testing shows your API slows down under heavy use?

You can improve your API by optimizing code, adding more servers, using caching, or fixing database queries. Load testing helps find these problems early.

What does load testing primarily check for in a REST API?

A How the API performs under many simultaneous requests

B If the API returns correct data

C The security of the API

D The API's user interface design

Load testing focuses on performance under heavy use, not correctness or security.

Which of these is a common metric measured during load testing?

A Response time

B Number of API endpoints

C API documentation length

D User login count

Response time shows how fast the API answers requests under load.

What does simulating 'concurrent users' mean in load testing?

A Sending requests one after another slowly

B Checking user passwords

C Sending many requests at the same time

D Testing only one user repeatedly

Concurrent users means multiple requests happening simultaneously.

If an API crashes during load testing, what does it indicate?

A The API is secure

B The API is perfectly optimized

C The API has no users

D The API cannot handle the current load

Crashing means the API fails under heavy use and needs improvement.

Which tool is commonly used for load testing REST APIs?

A Photoshop

B Apache JMeter

C Excel

D Slack

Apache JMeter is designed for performance and load testing.

Explain what load testing is and why it is important for REST APIs.

Think about how many users might use an app at the same time.

Describe how you would simulate multiple users sending requests to a REST API during load testing.

Imagine many friends calling you at once to see if you can answer all.

Practice

1. What is the main purpose of load testing in REST APIs?

easy

A. To check how many users the API can handle at once

B. To find syntax errors in the API code

C. To improve the API's user interface design

D. To add new features to the API

Solution

1. Step 1: Understand load testing goal

   Load testing is done to see how many users or requests an API can handle simultaneously without failing.

2. Step 2: Compare options with load testing purpose

   Only To check how many users the API can handle at once talks about handling many users at once, which matches load testing's main goal.

3. Final Answer:

   To check how many users the API can handle at once -> Option A

4. Quick Check:

   Load testing = check user load [OK]

Hint: Load testing measures user capacity, not code errors [OK]

Common Mistakes:

• Confusing load testing with debugging
• Thinking load testing improves UI
• Assuming load testing adds features

2. Which of the following is the correct way to start a basic Locust load test script?

easy

A. from locust import User

class MyUser(User):
def run():
get('/')

B. import locust

class MyUser:
def task():
client.get('/')

C. from locust import HttpUser, task

class MyUser(HttpUser):
@task
def load(self):
self.client.get('/')

D. from locust import HttpUser

class MyUser(HttpUser):
def test():
self.get('/')

Solution

1. Step 1: Identify correct Locust import and class

   The Locust library uses HttpUser and @task decorator to define user behavior.

2. Step 2: Check method and client usage

   from locust import HttpUser, task class MyUser(HttpUser): @task def load(self): self.client.get('/') correctly uses HttpUser, @task, and self.client.get('/'). Other options miss correct imports or method calls.

3. Final Answer:

   from locust import HttpUser, task class MyUser(HttpUser): @task def load(self): self.client.get('/') -> Option C

4. Quick Check:

   Use HttpUser and @task decorator [OK]

Hint: Look for HttpUser and @task decorator to spot correct syntax [OK]

Common Mistakes:

• Missing @task decorator
• Using wrong class name like User instead of HttpUser
• Calling get() without self.client

3. What will be the output when running this Locust test snippet?

from locust import HttpUser, task

class MyUser(HttpUser):
    @task
    def index(self):
        self.client.get("/home")

# Simulate 10 users making requests concurrently

medium

A. No requests will be sent because task is missing

B. 10 users will send GET requests to /home concurrently

C. Syntax error due to missing main function

D. Only 1 user will send a GET request to /home

Solution

1. Step 1: Understand Locust user simulation

   Locust simulates multiple users running the tasks concurrently when configured to do so.

2. Step 2: Analyze the code behavior

   The class defines a task sending GET requests to /home. If run with 10 users, all will send requests concurrently.

3. Final Answer:

   10 users will send GET requests to /home concurrently -> Option B

4. Quick Check:

   Locust simulates multiple users running tasks [OK]

Hint: Tasks run concurrently for all simulated users [OK]

Common Mistakes:

• Thinking only one user runs the task
• Assuming missing main causes syntax error
• Believing tasks won't run without explicit start

4. Identify the error in this Locust test code:

from locust import HttpUser, task

class MyUser(HttpUser):
    def index(self):
        self.client.get("/")

medium

A. Class name should be lowercase

B. HttpUser class should be imported from requests

C. self.client.get should be self.get

D. Missing @task decorator before index method

Solution

1. Step 1: Check for task decorator usage

   Locust requires the @task decorator on methods to mark them as tasks to run.

2. Step 2: Verify other parts of the code

   HttpUser is correctly imported, self.client.get is correct, and class names are capitalized by convention.

3. Final Answer:

   Missing @task decorator before index method -> Option D

4. Quick Check:

   Tasks need @task decorator [OK]

Hint: Always add @task decorator to methods to run as tasks [OK]

Common Mistakes:

• Forgetting @task decorator
• Importing HttpUser from wrong module
• Using self.get instead of self.client.get

5. You want to test your REST API's login endpoint under heavy load using Locust. Which approach best simulates realistic user behavior?

hard

A. Create a task that sends POST requests with different user credentials and add wait times between requests

B. Send continuous GET requests to the login endpoint without any delay

C. Run a single user sending POST requests in a tight loop

D. Only test the API with one user to avoid server overload

Solution

1. Step 1: Understand realistic load testing

   Realistic load testing simulates many users with varied data and natural pauses between actions.

2. Step 2: Evaluate options for realism

   Create a task that sends POST requests with different user credentials and add wait times between requests uses different credentials and wait times, mimicking real user behavior. Others either overload or under-test the API.

3. Final Answer:

   Create a task that sends POST requests with different user credentials and add wait times between requests -> Option A

4. Quick Check:

   Use varied data and wait times for realism [OK]

Hint: Add wait times and varied data for realistic load [OK]

Common Mistakes:

• Sending requests nonstop without delay
• Testing with only one user
• Using only GET requests for login