QEats Performance Testing

Overview

QEats is a food delivery application, and the purpose of this project is to monitor the performance of the application under high load and ensure it meets expected benchmark metrics.

Project Highlights:

• Wrote and executed JMeter tests to validate the happy flow of the application under various load conditions.
• Tested the application under different load scenarios to evaluate business metrics and application performance.
• Used SigNoz for monitoring the host machine's metrics where the test-bed was set up, ensuring comprehensive visibility of the system’s health during tests.
• Focused on performance validation for critical application functionalities, ensuring they could handle expected user traffic without degradation in performance.
• Provided insights into system behavior under load, identifying areas for performance optimization.

Tools and Technologies Used:

• JMeter
• SigNoz
• Postman

Project Structure:

The project is organized into the following modules:

1. JMeter Test Scripts:

   • Contains the test scripts that simulate various user activities and actions on the QEats application to test its performance under load.
   • Includes tests for user login, placing orders, browsing menu items, and checking order statuses.

2. Performance Metrics:

   • In-depth performance metrics collected from SigNoz to track resource utilization and system health during the tests.
   • Host machine metrics (CPU, memory, network) monitored throughout the test runs to provide a full picture of performance.

3. Test Execution Reports:

   • Contains the results of each test, providing detailed logs, performance graphs, and insights into how the application performed under various conditions.

4. Postman Collections:

   • A collection of Postman API tests used for validating backend API performance and response times, providing a comprehensive view of the application's behavior under load.

Features Implemented:

• Load Testing:
  • Simulated different load scenarios with varying numbers of concurrent users to measure response times, throughput, and system stability.
• Performance Monitoring:
  • Integrated with SigNoz to monitor system health and resource usage during the tests.
• Business Metrics Testing:
  • Validated critical business metrics like order placement time, browsing response time, and transaction processing under load.
• Test Results Reporting:
  • Detailed test execution reports, including response times, throughput, and error rates under various load conditions.
• Scalability Validation:
  • Ensured the system could scale under increasing traffic loads and meet expected service levels.

How to Run:

1. Clone the repository:

   git clone https://github.com/kvishalrj/QEats_Performance_Testing.git

2. Ensure JMeter and SigNoz are installed and configured.
3. Import the JMeter test plan into JMeter for executing the load tests.
4. Run the tests and observe performance metrics in SigNoz.

Running Tests:

Using JMeter:

• Open the JMeter test plan (.jmx file) in JMeter.
• Configure the number of threads (virtual users) to simulate various load scenarios.
• Run the tests and analyze the results in the Summary Report and Graphs.

Monitoring Performance with SigNoz:

• Access the SigNoz dashboard to monitor host machine metrics in real time while tests are running.
• Review the metrics to identify potential bottlenecks and areas of improvement.

Reporting:

After the test run, detailed reports are generated:

• Performance Graphs: Found in the reports/ directory, showing metrics like response times, throughput, and errors.
• Test Logs: Detailed logs for each test, including timestamps and actions performed.
• System Metrics: Collected data on CPU, memory, and network usage during test execution, available through SigNoz.

Future Scope:

• Enhance test coverage to include stress and spike testing scenarios.
• Integrate performance tests with CI/CD pipelines to continuously validate performance with every code change.
• Optimize performance based on identified bottlenecks and resource consumption patterns.