elemes/test/LOAD_TESTING.md

# Load Testing Documentation for C Programming Learning Management System

## Overview
This document describes how to perform load testing on the C Programming Learning Management System to ensure it can handle at least 50 concurrent students accessing the application.

## Requirements
- Python 3.7+ (for local testing)
- Locust (installed via pip)
- Podman (for containerized testing)
- The LMS application running on your local machine or a test server

## Setup

### Option 1: Local Installation
#### 1. Install Locust
```bash
pip install locust
```

Alternatively, install from the requirements file in the test directory:
```bash
cd /path/to/lms-c/test
pip install -r requirements.txt
```

#### 2. Ensure the LMS Application is Running
Start your LMS application:
```bash
cd /path/to/lms-c
./start.sh
```
Or if running directly:
```bash
python app.py
```

By default, the application runs on `http://localhost:5000`.

### Option 2: Containerized Setup with Podman
The load testing can also be run in a containerized environment using Podman. This ensures consistency across different environments and simplifies setup.

#### 1. Using the provided podman-compose file
A podman-compose file is provided in the test directory that sets up both the LMS application and the load testing service:

```bash
cd /path/to/lms-c
podman-compose -f test/podman-compose.yml up --build
```

This will start both the LMS application and the Locust load testing service. The Locust web interface will be available at `http://localhost:8089`.

#### 2. Running only the load test container against an existing LMS
If you already have the LMS running, you can run just the load test container:

```bash
cd /path/to/lms-c
podman build -f test/Dockerfile -t lms-c-load-test .
podman run -p 8089:8089 --network=host lms-c-load-test
```

Note: Using `--network=host` allows the container to access the LMS running on localhost.

## Running the Load Test

### Local Load Test
#### Basic Load Test
To run a load test simulating 50 users with a hatch rate of 2 users per second:
```bash
cd /path/to/lms-c/test
locust -f load_test.py --host=http://localhost:5000
```

Then open your browser and go to `http://localhost:8089` to configure the load test.

#### Command Line Load Test
To run the load test directly from the command line without the web interface:
```bash
locust -f load_test.py --host=http://localhost:5000 --users=50 --spawn-rate=2 --run-time=5m --headless
```

This command will:
- Simulate 50 users (`--users=50`)
- Spawn 2 users per second (`--spawn-rate=2`)
- Run for 5 minutes (`--run-time=5m`)
- Run in headless mode without the web interface (`--headless`)

### Containerized Load Test
#### Using Podman Compose
To run both the LMS and load testing services together:
```bash
cd /path/to/lms-c
podman-compose -f test/podman-compose.yml up --build
```

Then access the Locust web interface at `http://localhost:8089` to configure and start the load test.

#### Running Load Test Only
To run just the load test against an existing LMS service:
```bash
cd /path/to/lms-c
podman build -f test/Dockerfile -t lms-c-load-test .
podman run -p 8089:8089 --network=host lms-c-load-test
```

#### Command Line Load Test in Container
To run the load test directly from the command line in headless mode:
```bash
cd /path/to/lms-c
podman build -f test/Dockerfile -t lms-c-load-test .
podman run --network=host lms-c-load-test locust -f /app/test/load_test.py --host=http://localhost:5000 --users=50 --spawn-rate=2 --run-time=5m --headless
```

## Test Scenarios

The load test script simulates the following student behaviors:

1. **Viewing the homepage** (30% of requests) - Students browsing available lessons
2. **Viewing lesson pages** (40% of requests) - Students reading lesson content
3. **Compiling code** (20% of requests) - Students submitting C code for compilation
4. **Logging in** (10% of requests) - Students authenticating with tokens
5. **Validating tokens** (10% of requests) - Students validating their access tokens
6. **Tracking progress** (10% of requests) - Students saving their lesson progress

## Monitoring and Metrics

During the load test, Locust provides the following metrics:

- **Users**: Number of concurrent users
- **Failures**: Number of failed requests
- **Requests per second**: Average request rate
- **Average response time**: Mean response time across all requests
- **95th percentile response time**: Response time below which 95% of requests complete
- **99th percentile response time**: Response time below which 99% of requests complete
- **Total requests**: Total number of requests made during the test

## Performance Benchmarks

For the application to be considered capable of handling 50 students simultaneously, it should meet these benchmarks:

- Less than 1% failure rate
- Average response time under 2 seconds
- 95th percentile response time under 5 seconds
- No significant degradation in performance as user count increases

## Customizing the Load Test

You can modify the `load_test.py` file to adjust:

- The number of users simulated
- The distribution of different types of requests
- The wait time between requests
- The sample code used for compilation tests
- The tokens used for authentication

## Troubleshooting

### Common Issues:

1. **Connection Refused**: Ensure the LMS application is running on the specified host/port
2. **High Failure Rate**: Check application logs for errors during the load test
3. **Memory Issues**: Monitor system resources during the test
4. **GCC Compilation Errors**: The application compiles C code, which may consume resources

### Resource Monitoring:
Consider monitoring system resources (CPU, memory, disk I/O) during the test using tools like `htop`, `iostat`, or `vmstat`.

## Conclusion

This load testing setup allows you to verify that the C Programming Learning Management System can handle at least 50 concurrent students. Regular load testing should be performed, especially after major updates, to ensure the application continues to meet performance requirements.

Both local and containerized options are available to suit different deployment scenarios. The containerized approach using Podman ensures consistency across different environments and simplifies the setup process.